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The word “ ” in Chinese refers to the circle of life! Recently, I was watching a 2003 Korean movie, “ ,” and discovered this concept. The circle of life has different meanings across several civilizations, and when you think of the Software Development Life Cycle (SDLC), it is not that different either. Yǒulún Old boy The concept related to the circle of life refers to birth, aging, death, reincarnation, and a loop of the same activities. SDLC is a process of requirement gathering, designing, developing, and maintaining software throughout the life cycle. The process repeats itself for every software you create, making it almost like- “ .” Yǒulún However, SDLC is not just important for excellent software development - there are several other aspects to consider like efficiency, cost, and business impact. So let’s first know SDLC and several approaches that you can leverage to improve the effectiveness of software development. What is SDLC? The Software Development Life Cycle (SDLC) is a series of stages that helps in bringing a business case to realization through , , , , and . It involves fixing errors that incur efficiency problems leading to overhead costs. Here, execution is everything, and that is the reason you need a practical solution. design development error detection making changes deployments maintenance If your execution is correct, SDLC has several benefits like: , allowing you to plan the execution more efficiently. The entire goal of your project becomes more visible It offers who can review the entire process at the end of each stage. better management control for project managers Due to comprehensive software testing and changes made accordingly, ensure . no pending errors to resolve later Extensive documentation will enable for project execution. easy onboarding of new teams There is a constant loop of providing feedback to the earlier stages of software development, . ensuring high-quality products The SDLC Waterfall Model Conventionally, the SDLC waterfall model has been following a linear path where every stage leads to the next one, and there is no way to go back to a stage for making quick changes. This approach becomes restrictive and does not allow software engineers much flexibility. The entire SDLC model is distributed into several stages, from the requirements stage to design, development, and even maintenance. What makes this model so rigid in the modern software development landscape is the inability to incorporate changes that may be a requirement due to adding features or sudden changes. Here each stage is dependent on the predecessor one and can’t be complete without it. The output of an earlier stage is the input for further stages. So, if you don’t have the full set of requirements, you can’t design the software, and similarly, without a design, the development will not happen. However, the shortcomings of the waterfall model can be mitigated by an agile approach! An agile SDLC model follows the path of a river that changes the way whenever needed! Agile SDLC Model The agile model explores an incremental, iterative approach to software development. Several builds are created for a software project, and with each iteration, new features are added while constantly making changes as per feedback from tests. Every iterative development is known as a sprint at the end of which there is collective feedback from different teams to understand the requirement of changes. The agile approach allows businesses to improve the software with every iteration making it an open-loop SDLC model. Apart from the Agile and waterfall SDLC model, there are several others that you can explore for your software development projects like, V-model V-model explores the parallel approach where there are design stages executed on the one hand, and on the other hand, parallel tests are conducted. Both the verification and validation stages meet at the coding stage by which execution takes place. The V-model is similar to the parallel programming approach, where you can code parallel to the testing process. One of the few advantages of such an approach is creating error-free software and reducing the cost of fixing them later. Hybrid Model A Hybrid or spiral model merges the waterfall approach with agile methodologies. What makes it more comprehensive than any other SDLC approach is how it offers excellent risk analysis. It focuses on the risks pitched against the cumulative software costs, which helps improve the entire SDLC. Here the spiral loop has an outward approach that involves a requirement plan, development plan, test plan, operational prototype, risk analysis and resolutions, integration, etc. Knowing the SDLC and its models can be a way to understand how it functions, and now let’s know why it’s essential! SDLC: A Necessity! Software development is not just about the programming aspect but an entire life cycle that begins from conception and ends at deployment. So, leveraging the SDLC approach makes more sense. Framework Developing software applications need a plan that can help reduce inefficiencies and offer high-quality products. This is where SDLC acts as a guide with a framework to build your software projects. With SDLC, the entire process is distributed into different phases allowing you to assess results at the end of each stage and formulate a well-planned strategy for optimal software development. Requirement Analysis The SDLC process allows you to have a comprehensive requirement analysis, which is essential for planning the entire development. This phase not only helps in understanding the requirements of a project but also enables you to decide the, Scope Timeline Budget Risks involved It also allows you to form the entire project’s design foundation, so SDLC becomes crucial for requirement analysis. Streamlining the Development Another critical aspect where SDLC has the most importance is streamlining of the development process. Every software development process needs an approach or methodology at its core for the successful execution of operations. SDLC allows software programmers to leverage different agile, waterfall, or hybrid models for a streamlined development process. It also allows coping with sudden changes in the software development process or specific integrations. For example, suppose you want to integrate a cloud-based service with your software after the design foundation is decided. In that case, you will need a different approach than the conventional waterfall method. SDLC allows you to leverage other models and define methods for streamlining a development process. Identifying the Real Bug Software developers must identify a bug before deployment, but leveraging the waterfall approach becomes challenging due to linear and restrictive attributes. SDLC approaches like the agile or spiral model are necessary to determine the actual bugs in the software and mitigate the risks involved. For example, SDLC enables software developers to know the . It is an index that measures the number of open bugs or unresolved problems. You can calculate the BMI by using the following formula: BMI or Backlog Management Index BMI = [Number of problems resolved/ number of problems occurred]*100% SDLC also helps in the management of software testing and expenses for fixing such bugs. One of the critical aspects of software testing is the cost of test case development and execution. However, with SDLC, you can manage leveraging risk analysis and bug detection. software testing costs Conclusion: SDLC Is Important Software Development Lifecycle is not just about defining the project for higher efficiency but also about mitigating the risks involved with errors. For example, a simple forced Toyota to call back 1.9 million cars. software glitch So, you can understand why SDLC is so important for a software development project. It can help any business avoid such issues through a streamlined process.

The Software Development Life Cycle Is as Important as the Circle of Life

In electronics, the comparator is a circuit that makes use of operational amplifiers with very high gain in its open-loop state, that is, there is no feedback resistor. The has a positive or negative voltage output by comparing its input voltage against some preset DC reference voltage. The following are some common amplifier comparator circuits to analyze their functions. op-amp comparator is an operational amplifier compatible with DTL, RTL, TTL, or MOS logic levels. Its output can switch voltages to 50volts at currents to 50mA with a single supply. LM311 IC has been used for voltage comparisons for a long time in some typical circuits. Here introduces them with diagrams. LM311 versatile comparator Basic Circuit of LM311 Comparator (1) Offset voltage balance (Figure 1) When adjusting the balance, the two input terminals are grounded, and the 5kΩ potentiometer is adjusted to make the output be the minimum voltage value (close to 0V). (2) Single power supply (Figure 2) At this time, VEE is connected to GND. In Figure 1 and Figure 2, the load resistance RL is connected between VCC and Output. (3) RL is connected between pin 1 and ground (Figure 3) According to Figure 3, the input polarity is reversed, that is, pin 3 is used as the non-inverting terminal. (4) RL is connected between pin 1 and VEE (Figure 4). According to Figure 4, the input polarity is reversed. (5) General connection method of RL for positive and negative power supply (Figure 5). (6) Comparator connection with strobe (Figure 6). Connect a transistor to pin 6 for strobe connection. When the base of the transistor is connected to the TTL high level, the comparator is strobed (normal operation); when the base is connected to the low level, the comparator does not work. LM393 & LM2903 LM393 and LM2903 are a kind of low offset voltage dual comparators, and their pin arrangement is shown in Figure 7. Their main performance is the same, but the temperature range is different: the former is 0~70℃, and the latter is -40℃~105℃. Its main features are: wide operating voltage range, single power supply operating voltage is 2V~36V, dual power supply operating voltage ±(1V~18V); Working current is small, the typical value is 0.4mA; Input bias current typical value is 25nA; The input offset current is 5nA; The input offset voltage is ±1mV (LM2903 is ±2mV); The input common-mode voltage range is equal to the power supply voltage; The output voltage is compatible with TTL and CMOS logic levels; The input has ESD protection; the typical voltage gain is 200V/mA; The large signal response time is 300ns. LM393 & LM2901 The LM393 and LM2901 comparators are quad comparators. The pin arrangement is shown in Figure 8. The main parameters and performance of these two devices are the same, but the temperature range is different: the former is 0~70℃, and the latter is -40℃~105℃. The main features of these two devices: They can be powered by a single power supply or dual power supply, and the power supply voltage range is wide, ranging from 2V to 36V or ±(1V to 18V). Working current (independent of power supply voltage), the typical value is 0.8mA typical input bias current value is 25nA; typical input offset current value is ±5nA; typical input offset voltage value is 2mV; input common-mode voltage range includes low level; The differential input voltage is equal to the power supply voltage; the difference between the saturation voltage and the power supply voltage is low; the output voltage is compatible with TTL and CMOS logic levels; the typical large signal response time is 300ns; the voltage gain is 200V/mV. This article was first published on Writeup Cafe

Intro to Amplifier Comparator Circuits: LM311, LM393, LM2903 and LM2901

A controller plays an important role in the operation of a DC motor; its importance is even higher for a motor with extended functionality. Simpler motors can do without this device, but we are not going to talk about them in this article. Here, we’ll take a closer look at a brushed DC (BDC) motor controller, which is a simple and easy-to-build device. Working Principles of a BDC Motor and its Controller The main components of any DC motor include a moving part - a rotor ( or armature) and a stator, which is always at rest. The stator of a brushed DC motor can have either windings (series, shunt, compound BDC motors) or permanent magnets (permanent magnet BDC motors). The third essential component of a BDC motor is the commutator with brushes that connect the rotor with a DC power supply. The current flowing through the rotor generates the electromagnetic field around it. As the rotor starts moving, the similar poles of the magnetic fields created around the stator and rotor repel each other and provide a motion. When their opposite poles meet, the commutator switches the current supplied to the rotor. This creates the reverse polarity of the magnetic field, and the motion continues. A brushed DC motor can use a controller to start and stop the rotation, change its direction, manage the speed and torque. To perform these functions, a BDC motor controller regulates the current and voltage injected into the motor. Brushed DC motors are simple, cost-effective, and easy to control. They can perfectly fit simple low-power applications. Designing a BDC Motor Controller To design a BDC motor controller, you can choose an H-bridge, which is a traditional circuit for such types of devices. This electronic circuit has four open / close switches that supply positive and negative voltage by turns. By closing high-side and low-side switches in a diagonal pattern, the motor rotates in one direction. The rotational direction will change as soon as these switches are open and the opposite switches are closed. If you need a motor with a unidirectional rotation, you can build a controller using a simpler circuit with only one open/close switch. Choosing a transistor switch, make sure it meets the required parameters of the motor, for example, the maximum current. Otherwise, the transistor will burn out. For a low voltage controller, you can opt for power MOSFETs that provide a fast switching speed and high efficiency at a low price. Building a high current BDC motor controller, you can use IGBTs that allow a high level of current and are well suited for complex power electronics systems. Another option that you can choose for your project is a GaN transistor that can resist high temperatures and operates at very high frequency and voltage ranges. However, its production cost is still very high, so it will raise the price of your circuit design as well. You can use either an integrated circuit (IC) or discrete components. From a developer’s point of view, a DC motor controller IC is a simpler and more convenient solution. With a discrete circuit, it will take you the time and effort to assemble and solder the components. You have the same options for gate drivers that act as intermediaries between the microcontroller (MCU) and switches. An integrated H-bridge driver is a circuit with built-in power transistors. Despite the simplicity and reliability of its design, the gate driver IC is intended for low-voltage and low-power applications. Additionally, such gate drivers are not interchangeable. If they become discontinued, you’ll have to redesign the schematic together with the printed circuit board. BDC motor controller circuit design can have various configurations. Building your own device, you should consider the technical requirements and characteristics of the motor. Let’s briefly go over the design aspects that deserve your attention. Type of Signal You can build a digital or analog controller. The main difference between them is that the former comprises MCU-based hardware and firmware. You can combine these two types of signals and design a device that can be controlled with the help of both. Type of Power Regulation A BDC motor controller regulates the speed and torque by changing the power supplied to the motor. You can achieve this by using either a linear or switching voltage regulator. The linear regulator provides stable output voltage and keeps its magnitude constant, irrespective of the input voltage supplied by a power source. A switching regulator uses the pulse-width modulation (PWM) method and supplies voltage in pulses, changing its duty cycle (the ratio of the pulse to the pulse period). Thus, you can regulate the speed of the motor by adjusting various duty cycles. A switching regulator has higher efficiency and less power loss. Type of Control Some DC motor controller types can receive feedback from the motors, detect errors, and correct them, bringing the values into line with the setpoints. They are called closed-loop or feedback controllers. Alternatively, an open-loop or non-feedback controller cannot affect the situation, even if a failure occurs, as it will not detect it. You can find such motor controllers in simple systems that don’t need automatic control. Motor Characteristics Depending on the voltage necessary for the motor’s operation, you can choose a low or high voltage controller. A switching regulator works well for controllers with a wide operating voltage range. A linear regulator better suits a low voltage DC motor controller because the excessive input voltage may cause power loss and even thermal overload. Motor power relies on the current supplied by the power source. Thus, a low-power BDC motor needs a low current controller and vice versa. Challenges of Building a BDC Motor Controller Accessibility and easy implementation of BDC motors and their controllers make them a suitable solution for many projects. Despite the simplicity, a brushed DC motor controller design may still involve some challenges. For example, the switches in an H-bridge cannot be turned on and off simultaneously. There will always be a moment when all the transistors are open. It may lead to voltage and power loss, or even short-out if the opposite upper and lower switches are turned on. To avoid this situation, you can introduce dead time, which is the time when all switches of an H-bridge circuit are closed. PWM frequency (the number of pulse periods per second) is an important parameter that you should adjust in a proper way. Too low or too high PWM frequency will lead to the malfunctioning of the controller and consequently the motor. Excessive electromagnetic interference caused by the constant switching of a mechanical commutator is a common problem of a brushed DC motor, and you should cope with it too. Read about BDC motor controller in more detail here .

Building a Brushed DC Motor Controller: An Overview

By now, the need for clean energy is clear. Climate change is prompting industries of all kinds to reduce pollution and emissions to foster a healthier environment. The energy sector often takes on the responsibility of transforming others, such as by providing solar and wind as renewable options. Geothermal energy is now making a name for itself as well, thanks to new technology. Improvements for Geothermal Geothermal has stayed in the background for years while other renewables came to the fore. That’s because geothermal energy needs some improvements. Though there are a few different types of generation, the system usually requires an optimal location where there’s a good heat source under the Earth’s surface. Some locations are ideal for sourcing geothermal. For example, has access to heat under the earth and uses geothermal power as a sustainable source of energy for their campus. In addition, Petaluma, California has begun to be 100% renewable by partnering with a company that's helping to source geothermal energy via Sonoma County Geysers. Ball State University transitioning their power usage Since location plays such a crucial role, geothermal energy is not always accessible for everyone. Thus, it hasn’t gotten the attention that other renewables have. However, to combat the climate crisis, the energy sector must find solutions. Fossil fuels like oil, coal and gas are still predominant in the United States — they comprised in 2019. That number needs to drop drastically to see environmental improvements. 80% of U.S. energy consumption The Tech That Changes Everything Traditionally, geothermal energy uses an open-loop dynamic where the system pumps up water or steam and uses the heat as electricity. Though it’s a sustainable option, water and steam are still valuable resources. The open-loop loses some along the way as it pumps it all back down into the Earth. A closed-loop heat pump is a better option. Once energy experts combine that system with new technology, geothermal becomes reliable and a more practical energy option for everyone. In a closed loop, the water and steam cycle through the pipes continuously. The system does not consistently take in new resources and only uses a set amount of water and steam. Analytics and sensors give energy professionals the power to monitor exactly how efficient a geothermal system is. They can predict energy demands as well as output, using sensors within the pipes and on the power grid. Before harsh weather or blackouts can occur, professionals use the tech to understand how much energy the system can pump out. Then, they can properly prepare for the incoming demand. However, the biggest and most vital aspect of closed-loop technology is that it can provide energy on demand. Professionals can have geothermal systems store and disperse energy without disrupting anyone else. The closed loop continuously heats and generates electricity, and the tech makes it more accessible. With this dynamic in place, a geothermal energy grid can vastly expand and cater to people or businesses no matter their location. Some were surveyed as accepting of geothermal energy with environmental concerns. With innovations coming to light regarding risk minimization, more companies are becoming interested in this energy. For instance, recently backed Eavor Technologies, a startup specializing in this specific tech for geothermal energy. European and American countries BP and Chevron Geothermal for Innovation With the new technology in place, geothermal energy becomes an innovator for its sector. It can push forward larger-scale adoption of clean energy since it’s now a more accessible option. Moreover, geothermal generation helps public health and the economy. The COVID-19 pandemic has stressed the importance of clean energy, positioning geothermal as a profitable source in the next few years. Also, the unemployment rate is from April 2020’s record high. The energy sector can stimulate the economy for a green recovery by channeling new resources and tech into geothermal. Since this kind of work requires people of all backgrounds — tech, energy, environment, contracting, marketing, finance — adding jobs will be an all-around beneficial move. still slowly coming down As renewables and geothermal’s new tech increase in popularity, fossil fuels can slowly fade to the background. New geothermal tech readies the energy sector for faster integrations in households, communities and businesses. Tech Leads to Big Changes Ultimately, the tech integration starts small. Smart analytics and sensors help energy professionals more easily control supply and demand levels. As professionals use that control to supply more people with power, public health, the economy and the environment all benefit.

How Geothermal Technology Is Redefining the Energy Sector

Before the pandemic, around the world. Yet, the pandemic and the movement restriction measures that aimed to protect public health and growing consumer preference to avoid physical stores have led to a massive shift of consumers to digital channels for shopping. And, this significant shift towards online shopping has been accompanied by the increasing adoption of digital payments. e-commerce was growing at a steady pace of 4.5% a year Services such as digital payments, credit, and insurance are all increasingly being offered at the point of sale by digital providers or non-financial companies, a trend known as embedded finance (more on this one later). In other words, the pandemic acted as rocket fuel for digital transformation, providing an opportunity for the payments industry to innovate and adapt to modern retail needs. So, after a year of such rapid transformation, what can we expect to see in 2021 when it comes to the payments industry? Keep reading below to find out how the pandemic has altered consumer behaviors in terms of online payment preferences and the trends we're expecting to see more of this year. Online shopping has changed mindsets on payments With online shopping on the rise since the beginning of the pandemic, we can absolutely say that non-traditional payment methods such as bank transfers, e-wallets, cash-based digital payments, and local credit cards have experienced huge growth. In fact, they are now the prevalent payment methods around the world, being used in over . 70% of all online transactions During the pandemic, data shows that consumers were also more inclined than ever to use different payment methods in the search for the best convenience and security during national lockdowns. More precisely, data shows that 56% of global consumers reported that . they had used a new local payment method in the first month of the pandemic One payment method that thrived particularly during the pandemic was the interest-free "buy now pay later." believe that the availability of "buy now, pay later" matters a lot for how much they spend with retailers. Data shows that 44% of US consumers One of the reasons why the "buy now, pay later" payments method has seen so much popularity lately is because the pandemic has brought job losses and unprecedented financial strain to consumers. Therefore, consumers prefer the flexible nature and lack of financial penalties of "buy now, pay later" payment tools. A more competitive market in an increasingly digital business environment McKinsey described the massive boom of global e-commerce as the " ," representing a 10-year shift in e-commerce that happened in just 90 days. Data shows that during June last year when the entire world experienced the strictest lockdowns, e-commerce sales saw a 34% increase year-over-year. This is the highest growth rate reported since March 2008. quickening effect But here's the deal: many consumers didn't turn to their trusted business during this critical period caused by lockdowns. Instead, they . This created major disruptions in brand loyalty, creating a wealth of opportunities for small and big businesses, encouraging them to take their operations online and across borders. Even Facebook understood that and launched its own shopping feature to support small businesses to sell to customers. turned to new retailers Now, it's evident that this digital surge in online shopping will continue to grow, even after lockdown restrictions will be lifted by authorities. Yet, in 2021, merchants who want to stay competitive and stand out from the crowd in e-commerce's new standard must support more than only card transactions. Don't believe us? New research suggests that 42% of US consumers will abandon their shopping carts if their preferred payment method isn't available at the checkout step. So, retailers need to expand their current payment offerings more to satisfying changing needs and preferences of consumers. 4 Payment Trends To Watch Our For in 2021 Are there any payment trends we're expecting to see more of this year? Yes, quite a lot of them! 1. Improved payment-processing partnerships One of the trends we're expecting to see more of these days is that merchants will start to reconsider the effectiveness of their payment-processing partnerships. Now, more than ever, they need to make sure that they have a high-performing payment infrastructure to accommodate the shifts in payment volume. Online retailers will likely look for superior payment processing platforms for their websites and mobile apps, such as , to collaborate with to accommodate the needs of their customers. Payment for Stripe mobile point of sale app 2. Fraud prevention strategies and CX will team up What's more important: to provide a great customer experience to your audience or keep their data safe? If you didn't answer "both," you need to think twice. Data suggests that make it difficult to provide an excellent customer experience. 57% of online retailers agree that their efforts for fraud prevention Yet, in 2021, we expect security experts that collaborate with retailers to find better fraud-prevention strategies that also allow a smooth CX. 3. Embedded finance will grow further As we've promised above, we'll discuss a bit about embedded finance. by non-bank providers such as ridesharing companies, consumer-focused fintechs, telcos, or large consumer tech companies. These providers offer different financial services such as integrated payment processing, debit cards, instant payouts, digital wallets, or installment loans. Embedded finance refers to softw are and applications peddled We expect the embedded finance opportunities to only grow this year. 4. QR codes are making a comeback QR codes had their best days back in the 2010s but have since fallen out of fashion for open-loop payments. Yet, in 2021, as part of retailers' no-contact checkout strategies, we're expecting QR codes to make a comeback. After a year of steady e-commerce growth and a significant shift towards online shopping, consumers are increasing their adoption of digital payments. We should expect to see more innovation in online payments. Lead Photo by rupixen.com on Unsplash

Online Retail Innovation: Here's What 2021 Has In Store For Payments After The Digital Boom In 2020

It’s been the dream of many space enthusiasts from the 1960s to live in an intelligent artificial gravity space station ever since it was popularized by NASA chief scientist Von Braun and the legendary film: ‘Space Odyssey: 2001’. Unfortunately, generating artificial gravity for astronauts was not a priority for NASA as astronauts were able to reduce some of the impact of weightlessness on their bodies thanks to a ruthless exercise regimen. Further, a ship that contains a centrifuge for generating artificial gravity would be extremely expensive to launch to orbit. So, NASA ended up sticking with a compact and modular station that is more affordable with their budget and that doesn’t deal with weightlessness risk and accepted the risks it posed to astronauts in the international space station. Below is a comprehensive list of health risks astronauts face in the zero-g space station. Weightlessness risks are highlighted in the red box. Src: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20150009516.pdf Over the past few years, new reports have come out that most astronauts are now facing visual impairments, immunosuppression, osteoporosis, and risk potential brain damage that cannot be tackled by their exercise regimen. Currently one of the biggest concerns is VIIP (Visual Impairment/Intracranial Pressure) syndrome which structurally changes the shape of the eyeball and in some cases can create blind spots. Reference: Alperin N., Bagci A.M. (2018) Spaceflight-Induced Visual Impairment and Globe Deformations in Astronauts Are Linked to Orbital Cerebrospinal Fluid Volume Increase. In: Heldt T. (eds) Intracranial Pressure & Neuromonitoring XVI. Acta Neurochirurgica Supplement, vol 126. Springer, Cham Japanese Space Agency (JAXA) ran an experiment with mice onboard the ISS Kibo. Src: iss.jaxa.jp Centrifuges were used to generate artificial 1G gravity. It was found that the artificial gravity generated did, in fact, negate the effect weightlessness had on visual impairment. Below are the CAD model and cross-section of the centrifuge used by JAXA to carry out the experiments. For context: radius= 0.15m, centripetal acceleration=9.81m/s^2, angular velocity= 77.2 rpm Artificial gravity centrifuge experiments on Kibo also found that mice lost less bone mass when in 1G artificial gravity. Src: https://iss.jaxa.jp/en/kiboexp/news/1205_0130_mouse.html Further studies have shown that there could be potential brain damage if the astronauts stay in weightlessness long enough. Src: MUSC researchers, press release: Dr  Michael Antonucci, at the Department of Radiology and Radiological Science at MUSC said https://www.telegraph.co.uk/science/2017/11/01/humans-may-need-fake-gravity-reach-mars-without-brain-damage/ “Designing a space vehicle with artificial gravity might be a way of minimizing the changes that occur in a microgravity environment.” Further, “Artificial gravity does not countermeasure for just one thing; it addresses all physical systems,” says Gilles Clement, the lead scientist for artificial gravity in the Human Health Countermeasures Element of the Human Research Program at NASA’s Johnson Space Center in Houston. Intelligent artificial gravity space stations and spacecraft for long stays in space The solution to weightlessness related health issues? Src: Nautilus-X potential design by NASA Critical innovations and technologies to bring such space stations to reality (current and pending) 1. Decreasing the cost per kg substantially (pending) For massive structures (artificial gravity space stations) to be built in space, it has to be first affordable to do so. Previous rockets were just too expensive to allow any government entity to sustainably fund a massive space station. Even the international space station is an international government effort to reduce the financial burden on any single government. Reusable rockets are changing the game when it comes to making space accessible. Below is a chart referencing the estimated cost per kg for some famous rockets. SpaceX’s BFR which is currently in the design and prototype stage might further drop the cost per kg to LEO to a few hundred dollars when it’s in full use. This could be a game changer. src: SpaceX 2. Decreasing the total mass of the space station to reduce the cost of launch substantially (pending): There are 2 proposed ways structures can be built with high strength and low mass: Tensegrity and Trusses. Tensegrity is ‘the characteristic property of a stable three-dimensional structure consisting of members under tension that are contiguous and members under compression that are not.’ src: Oxford NASA recently funded research on tensegrity approach to building an artificial gravity space station NASA is even considering using tensegrity rovers for planetary missions in the future. Trusses: the engineering definition ‘a structure consists of two-force members only, where the members are organized so that the assemblage as a whole behaves as a single object’ Src: stairville Trusses can be easily folded or be disassembled in a modular way to be reassembled in orbit. For example: The main support structure on the ISS is a truss. Different sections of the truss were brought on different space launches. So the main spokes and rim of the artificial gravity space station can be designed as trusses in order to reduce weight and join them together in a modular fashion. For starters, it can also be a truss mast which spins on the center. Src: Canadian space agency A perfect low weight design has yet to be perfected but would be a game-changer. Ref: NASA's Multigenerational Independent Colony for Extraterrestrial Habitation 3. Intelligent gimbals to point solar panels toward the sun (current) Beta Gimbal Assemblies (BGA) Bearing Motor Roll Ring Module (BMRRM). These gimbals are critical to sustain the presence of the station in space. Pointing the solar arrays to the sun allows the station to have electricity around the clock. Electricity is essential to maintain operations and power onboard electronics and other embedded systems These gimbals can be set up in an open-loop control or be part of an intelligent system where it is a closed feedback loop with sun tracking sensors (like Adcole). 4. Reaction Control Thrusters with PID control & sun sensors to control and monitor the spin rate (current) It is critical that the space station has a stable spin rate. Hypergolic RCS (Reaction control system) thrusters similar to R-4D can be used on each end of the mast of the intelligent artificial gravity station to control the spin (seen in picture - RCS thrusters on the end of the mast) Further, ADCOLE Coarse Sun Sensors positioned along the truss will help us monitor the spin rate of the station by calculating how much time it takes for the Adcole sensor to view the sun twice in its field of view. ref: Coarse Sun Sensor Detector (Cosine Type) ‘The Coarse Sun Sensor Detector is a single detector model used for applications including solar array pointing, sun acquisition, and failsafe recovery.’ src: adcole.com The RCS thruster and the Adcole sensor can be potentially set up in a PID control loop such that based on feedback from the Adcole sun sensor, the RCS thruster can do spin rate corrections.

Main Reasons To Build Intelligent Space Stations With An Artificial Gravity

The voltage follower , as shown in Figure 1, uses virtual short and virtual break. At first glance, it seems simple and clear. If you think that there is not much content to pay attention to, then you may be wrong. Understanding the op amp's voltage-following circuit is a great help for understanding the op amp's in-phase, inverting, differential, and various op amp circuits. circuit of the op amp Figure 1 op amp voltage follower circuit Voltage follower circuit analysis If we connect the output of the op amp to its inverting input and then apply a voltage signal to the noninverting input, we will find that the output voltage of the op amp will follow the input voltage very well. Assume that the input and output voltages of the initial state op amp are both , and then when Vin increases from , will also increase in the direction of positive voltage. This is because it is assumed that Vin suddenly increases, still has no response and is still . is greater than 0, so multiply the open-loop gain of the op amp. , so that the output of the op amp begins to increase in the direction of the positive voltage. 0V 0V Vout Vout 0V Ve=Vin-Vout Vout=Ve*A Vout When increases, the output voltage is fed back to the inverting input, which then reduces the voltage difference between the two inputs of the op amp, ie, will decrease, in the same open-loop gain case. Next, will naturally decrease. Vout Ve Vout The end result is that no matter how large the input voltage is (of course, within the op amp's input voltage range), the op amp will always output a voltage that is very close to , but this output voltage is just below . There is a sufficient voltage difference between the two inputs of the guaranteed op amp to maintain the output of the op amp, which is . Vin Vout Vin Ve Vout=Ve*A Negative feedback in op amp circuits This circuit will soon reach a steady state, the amplitude of the output voltage will accurately maintain the voltage difference between the two input terminals of the op amp, which in turn will produce an accurate amplitude of the op amp output voltage. Connecting the output of the op amp to the inverting input of the op amp is called negative feedback, which is the key to self-stabilizing the system. This applies not only to op amps, but also to any common dynamic system. This stability allows the op amp to have the ability to operate in a linear mode, rather than just being saturated, fully "on" or "off", just as it is used for comparators without any negative feedback. Due to the high gain of the op amp, the voltage maintained at the inverting input of the op amp is almost equal to . For example, an op amp has an open loop gain of 200,000. If is equal to 6V, then the output voltage will be 5.999 970 000 149 999V. This produces a sufficient voltage difference at the input of the op amp, =6V-5.999 970 000 149 999V=29.999 85uV, which is amplified and then produces a voltage of 5.999 970 000 149 999V at the output, thus the system Will be stable here. Vin Vin Ve As you can see, 29.999 85uV is a small voltage, so for practical calculations, we can think that the voltage difference between the two inputs of the op amp maintained by negative feedback is . The whole process is shown in Figure 2. This is also the “virtual short” we are familiar with, and since the impedance between the two inputs of the op amp is very large, there is naturally a “virtual break”. The circuit below has a stable double-loop closed-loop gain, and the output voltage simply follows the input voltage. Ve=0V Figure 2 The role of negative feedback One big advantage of using negative feedback is that we don't have to worry about the actual voltage gain of the op amp, as long as it is large enough. If the op amp's voltage gain is not 200 0000 but 250 000, this will cause the op amp's output voltage to be closer to , and the voltage difference between the smaller inputs will be used to produce the desired output voltage. In the circuit illustrated in Figure 2, the output voltage is also equal to the input voltage at the inverting input of the op amp. Therefore, for the circuit design engineer, in order to achieve a stable closed-loop gain of the amplifier circuit, the open-loop gain of the op amp does not have to be an accurate value, and the negative feedback causes the system to self-adjust. Vin Using negative feedback improves linearity, gain stability, output impedance, and gain accuracy, but using negative feedback also poses a serious problem, which is to reduce system stability for voltage-following circuits with unity gain. This is the worst case scenario, especially when driving capacitive loads, interested students can check the relevant information themselves. Regarding the op amp circuit, many times we are infused with the inverting end to follow the in-phase end. As mentioned above, can't we follow the inverting end of the phase? For the voltage follower circuit mentioned today, only the inverting terminal follows the non-inverting terminal. Here, if a positive input voltage is applied to the inverting terminal and the output is connected to the non-inverting terminal, and the output is assumed to be 0. will be a negative voltage, multiplied by the open-loop gain of the op amp, and the output will be a A negative voltage, returning to the non-inverting input of the op amp, will further result in a larger negative absolute voltage difference. Soon the output of the op amp will be saturated, and naturally the in-phase will not follow the inverting end. Ve However, for an op amp, if a reference voltage is applied to the inverting terminal, and other electronic components, such as a triode, MOS, etc., the overall loop of the op amp forms a negative feedback, and the in-phase end can also follow the inverting end. And this naturally breaks the rule that the inverting end of the familiar op amp follows the in-phase end. The voltage of the op amp follows the circuit, "virtual short", "virtual break" is the surface, and negative feedback is the root. Based on this root, it can help us understand the ever-changing op amp circuit.

The Voltage Follower Circuit of The Operational Amplifier

Facebook’s 5 pillars will save you from yourswlf. Soon enough, naysayers shall relinquish their rights, because what were just features will soon become requirements of our multinational, digital state. Our fully autonomous publics relations department shall be releasing a press release regarding the imminent roll out tomorrow. Lookout for an automated press release from the our Press Release page, which is fully automated thanks to our fully autonomous marketing department. 5 Pillars Picture: Facebook/Mark Zuckerberg, File Source:Facebook At Facebook, our dogma dictates that data never lies. “In data, we trust”; hence, we pride ourselves as the accurate interpreter of numbers generated from (FaceBig) data. As forerunners of (FaceBig) data and supreme leader of all things machine generated, it only makes sense that Facebook controls the master branch repositories regarding all things A.I. and M.L. Might we remind you that the hype is quite real and human labor ain’t cheap. It goes without saying that the ratio of organics/Facebook datum–individual profiles/bots–is on a verge of a historic 1:1 milestone! We support open information, therefore it is mission critical to observe, collect, and programmatically alter subroutines of gov’t in iterative fashion imperceptible to humans at all branches and levels of order. Thus, it is our obligation to provide support and safety including peacekeeping PeaceBots, which are based on and side-loaded with . Once completed and ready to beta test, deployment of ground and to areas of the world where are threatened will be neutralized in timely fashion. Robot Operating System (ROS) cherry picked Facebook profiles aerial PeaceBots human support, safety, trust and open information, civic engagement, and inclusion As stressed in the past, Facebook strives to always improve user experience. Our infinitely advancing A.I./M.L. constructs shall improve neutralization times into territory and beyond. As with any major release, we apologize in advance for any that our compulsory beta testing may inconvenience you with. Again, we are eternally grateful for your contribution of user analytics for our autonomous profile bots to train against. We promise each user–and bot–more efficient means of against our FaceBig Data. As always, we shall maintain neutral party to this “stochastic process,” which runs in tandem to our etup. The resulting open loop feedback system gives way our to-be-announced “user safety index” feature, which obeys a ±%0.05 margin of error given each event classified as social engagement and/or responsibly. pre-crime bugs reinforcement learning smart analytics and monitoring s Looks like the pre-cogs and their pre-visions are out of business. Sorry, but not sorry. Should’ve seen it coming! Facebook celebrates inclusion by refusing a society dependent on sensory deprivation tanks to increase the “user safety index.” There is no doubt that is the meaning of life and provides a clear understanding that in any event, every Facebook user shall receive a slice of cake when given. Facebook–as non human, corporate entity–abstains from a slice a cake in any event. 0 sum game Therefore, it is only logical for the maintenance of civic duty for Facebook to bake the cake that is fed. Facebook guarantees satisfaction and satiation in an infinitely looping fashion. However, in the event that displeasure may arise, a PeaceBot may deploy in pre-timely fashion to assist in any matter. All your dopamine belong to us. /s

Facebook’s 5 pillars will save you from yourswlf.

How democratic governance, collective ownership, and circular token economics can be used to create decentralized food networks — a research proposal. Various forms of networks. Blockchains can facilitate in creating more peer-to-peer decentralized networks. Introduction It can be argued that the sustainable food transition towards decentralized and regional food economies is one of humanity’s most pressing challenges (Satterthaite et al., 2010). The current industrialized agrifood regime is arguably unsustainable (Johnson, 2015; Pollan, 2009; Satterthaite et al., 2010; Sedghi, 2013; van Staalduinen, 2014), especially when taking into account the estimated increasing global population and rapid urbanization by mid-century (Alexandratos & Bruinsma, 2012, UN DESA, 2014; UN DESA, 2015, Wagner, 2008). Hence, (De Schutter, 2010; Groesbeek, 2009; Kneasfey et al., 2013; McIntyre et al., 2009). Like the energy transition, in which energy production and consumption is being changed from a centralized production and distribution model to decentralized smart mini-grids, a similar trend is emerging in the agrifood industry. short food supply chains (SFSC) via decentralized food networks and regional food economies have been proposed as a possible solution for a sustainable agrifood industry (Keeley & Pikkel, 2013). The aim of this study is therefore to understand . By learning more about the combinational impact of these technological developments, this research project can generate valuable insights that contributes to achieving circular and decentralized food innovation in cooperative networks. Innovation can be defined as the novel combination of existing ideas, processes, services, and products three complementary and novel developments for circular food innovation: platform cooperatives, decentralized food networks, and blockchain-based information systems Cooperativism is about democratization of ownership, governance and value. Credit: https://open.coop Platform cooperativism The concept of has been proposed by Scholz (2014), as a reaction to the dependence of digital tools and platforms in modern society. This digital economy is mostly enabled by a range of platforms that are (Hill, 2016; Fonseca, 2016). The value that is generated through these fundamental digital environments flows towards a handful of firms and their shareholders, as well as governmental intelligence agencies, essentially exploiting the users that make the network possible. platform cooperativism Since the arrival of the smartphone in the late 2000s, many interactions in both a professional setting and in private life are facilitated by online platforms and apps. owned, governed, operated and regulated by profit-maximizing companies and data-collecting governments As a response to this form of digital capitalism, (Matias, 2015). Scholz (2016a) argues that “[platform cooperativism] is bringing together 135 of workers self-management with 170 years of the cooperative movement […] bring[ing] in the commence based peer production together with those into this digital economy”. platform cooperativism “is about cloning the technological heart of online platforms and puts it to work with a cooperative model, one that puts workers, owners, communities, and cities — in a kind of solidarity that leads to political power” Scholz proposes two main tenets within the platform cooperative paradigm: and In practise this refers to , communities and regional networks. As a result, the profits that are made in this decentralized economy flows back into the community and the platform users instead of to a handful companies in Silicon Valley. communal ownership democratic governance. creating and community wealth through a digital interface that is owned and self-governed by its users shared value (Rushkoff, 2016). “Platform cooperatives — as a direct affront to the platform monopolies characterizing digital industrialism — offer a means of both reclaiming the value we create and forging the solidarity we need to work toward our collective good. Instead of extracting value and delivering it up to distant shareholders, we harvest, circulate, and recycle the value again and again. And those are precisely the habits we must retrieve as we move ahead from an extractive and growth-based economy to one as regenerative and sustainable as we’re going to need to survive the great challenges of our time” A great book on Platform Cooperativism Blockchain technology and the digital commons The second innovation that plays an essential role in decentralized (food) networks is (Tapscott & Tapscott, 2016). Bollier (2016) argues that open platforms, which have become integrated in private and working life, should be changed into . The digital commons refers to the distribution and communal ownership of informational resources and technology (Stalder, 2010). blockchain technology, as it allows to over the internet transfer value to manage shared wealth digital commons (Bollier, 2016). “One instrument for converting open platforms into digital commons is the blockchain ledger. The blockchain ledger is significant because it can enable highly reliable, versatile forms of collective action on open networks. It does this by validating the authenticity of a digital object (for example, a bitcoin) without the need for a third-party guarantor such as a bank or government body” Collaborative is arguably an important contributor towards realizing sustainable and transparent food industries. IBM’s private blockchain solution, Hyperledger, is being implemented by Walmart to increase efficiency, traceability and food safety in their supply chain (Yiannas, 2017). Additionally, the EU is actively working towards an “Internet-of-Food”, in which Internet-of-Things (IoT) technologies are leveraged to connect and digitize the complex food system (IoF2020, 2017). (Ambrosus, 2018; Branimir et al., 2017; Te-Food, 2017). blockchain-based data systems in the food supply chain By integrating the data streams of the dozens of network actors in the same supply chain, food commodities can become traceable, quality controlled, and more efficiently distributed, leading to a fairer, safer and more circular food economy Tokenization and circular token economics Moreover, the blockchain-based decentralized networks can be fueled by tokenization in . Tokenization can be defined as the representation of value in an digital asset, a token. In the words of Sneider (2017): . Kelly (2008) argues that the internet is designed to copy information. However, “when copies are super abundant, they become worthless. ” (Kelly, 2008). circular token economies “Digital tokens are designed to be scarce; they are not copied when traded […] they reward users for their involvement and enable them to trade that reward for a benefit” When copies are super abundant, stuff which can’t be copied becomes scarce and valuable Hence, . This approach also allows for recirculation of the represented value in the tokens, thus creating circular ​token ​economies. Connected to the quote of Rushkoff (2016) above, the concept of circular economics can be applied to sustainability, in which valuable ‘waste’ and resources are continuously flowing through the network of actors (Sneider, 2018). a limited supply of digital tokens which can be transferred but not copied due to the inner-working of blockchain technology, can lead to shared value creation and new circular economic models , by William Mougayar The Theory of a Blockchain Circular Economy Decentralized food economies The vision of sustainable nutrition, regional food economies and short supply chains may be realized via decentralized food networks. According to McNamara (2016), . Minimizing the gap between consumers and producers is a critical step towards an environmentally and economically sustainable food system”. decentralized food networks “would democratize the food industry by dispersing small food hubs across the globe, bringing food production to regional communities Decentralized networks, such as the open source movement, often rely on a . “We also see it in the open source software revolution where there is dozens, hundreds of programs that rely upon communities volunteering to create new and interesting code, often in collaboration with proprietary software companies. So it is not necessary an either or proposition, it can be quite ”. (Bollier, 2016, 12:13). combination of volunteers creating community wealth and business activities in markets synergistic in creating value; one working in the market and through a community Blockchain applications for agrifood Many projects are initiated to realize shorter food chains and peer-to-peer (P2P) economies, such as Delicia, an Estonian based blockchain start-up which aims to connect regional food producers, restaurants, supermarkets, and consumers in networks with the main goal to reduce food waste (2018). Other European start-ups like Ambrosus, OriginTrial, and Te-Food are creating the blockchain-enabled data infrastructure for traceability and food safety in the supply chain, and Dutch start-up Fructus aims to create a blockchain-based marketplace where farmers can directly interact with consumers (Fructus, 2018). Additionally, Backfeed and Odyssey are creating blockchain platforms as well as protocols for decentralized cooperation and decentralized sharing economies respectively, tools which can be leveraged to create open networks of food cooperatives. It can thus be argued that . various aspects of the agrifood sector are rapidly being digitized, contributing to decentralized food networks and short food supply chains Literature review The body of literature on platform cooperativism as well as blockchain-based food supply chains and decentralized food networks is still in an early stage. Goodman et al. (2012) state that the European Union is making policies for more decentralization in the agricultural industry, for instance in policymaking, multifunctionality and territoriality, arguing that (AFNs) are important for fairer, sustainable and resilient food systems. According to research by Sauvée (2012) on decentralized network governance, “operational decisions may be decentralized at different levels within the network, while control and incentive mechanisms rely upon totally decentralized means” (p. 11). Yet, centralized decision making over strategic assets is advised to main strategic coherence over time. Additionally, the research on traditional food cooperatives is plentiful (Knupfer, 2013; Yang & Woods, 2014), and the literature concerning platform cooperativism is emerging (Scholz & Schneider, 2016; Scholz, 2016b). However, . alternative food networks knowledge is lacking regarding the combination of circular economies, platform cooperativism, food cooperatives, and blockchain-based decentralized food networks Research aim and questions -such as its democratic governance structures, collective ownership and business models- as well as the formation of blockchain-based decentralized food networks for short supply chains. Possible research questions can now be formulated as follows: This research aims therefore to fill this knowledge gap by studying various approaches and dimensions within platform cooperatives for creating sustainable food economies with shared value Which types of traditional food cooperative models can be identified to form platform cooperatives in decentralized food networks? What is the role of blockchain technology in creating decentralized food networks? What is the role of self-organized digital commons in creating decentralized food networks? How can tokenization and circular token economics play a role in the network to create shared value? By conducting this research, a better academic understanding can be gained regarding the decentralization of the food economy in the coming decades. Additionally, due to an approach in this study, it is aimed to achieve societal and practical relevance by engaging various stakeholders in the existing regional food networks in the Utrecht area of the Netherlands via semi-interviews in which knowledge can be shared. . engaged scholarship The proposed research aims to contribute to valuable insights in the complementary fields of decentralized food networks, blockchain technology, and platform cooperativism to realize short food supply chains Do you like this article? Give me a clap, clap! :) About the author has been studying innovation and exponential technologies since the early 2010s. He is now focused on sustainable food, decentralized networks and blockchain technology. Menno van Ginkel — Have I forgotten trends, cool interesting start-ups, or developments? Or do you have identified flaws or bias in my story and reasoning? Please let me know, I’m open for discussion, feedback and critique. References Alexandratos, N. & Bruinsma, J. (2012). World Agriculture Towards 2030/2050. The 212 Revision. ESA Working Paper №12–03. Retrieved on the website of the Food and Agriculture Organization of the United Nations from 016/ap106e/ap106e.pdf http://www.fao.org/docrep/ Ambrosus. (2018). Ambrosus Whitepaper. Retrieved from https://ambrosus.com/assets/Ambrosus-White-Paper-V8-1.pdf Delicia. (2018). Whitepaper. Retrieved from https://delicia.io/delicia_wp.pdf De Schutter, O. (2010). [Report number A/HRC/16/49], presented at the 16th Session of the United Nations Human Rights Council. Retrieved from Agroecology and the Right to Food http://www2.ohchr.org/english/issues/food/docs/A-HRC-16-49.pdf Fonseca, M. (2016, September 21). Why Platform Cooperatives Can Be The Answer To A Fairer Sharing Economy. Retrieved from management/why-platform-cooperatives-can-be-the-answer-to-a-fairer-sharing-economy/ https://www.intelligenthq.com/innovation- Fructus. (2018). Whitepaper. Retrieved from http://fructus.io/WHITEPAPER.pdf Goodman, D., DuPuis, E. & Goodman, M. (2012). . Routledge. Alternative Food Networks: Knowledge, practice, and politics Groesbeek, M. J. (2009). ​Duurzamer ondernemen​. Uitgeverij Business Contact. Hill, S. (2016). How the Un-Sharing Economy Threatens Workers. In Scholz, T. & Schneider, N. (eds). . OR Books. Ours to Hack and to Own. The Rise of Platform Cooperativism, a New Vision for the Future of Work and a Fairer Internet IoF2020. (2017). Internet of Food and Farm. D1.3 Detailed Work Plan. Retrieved from https://www.iof2020.eu/deliverables/d1.3.-detailed-work-plan.pdf Johnson, N. (2015, February 10). So can we really feed the world? Yes — and here’s how. . Retrieved from and-heres-how/ Grist http://grist.org/food/so-can-we-really-feed-the-world-yes- Keeley, L. & Pikkel, R. (2013). . John Wiley & Sons Inc. Ten Types of Innovation. The Discipline of Building Breakthroughs , K. (2008, January 31). Better Than Free. Retrieved from better-than-fre/ Kelly http://kk.org/thetechnium/ Kneasfey, M., Venn, L., Schmutz U., Balázs B., Trenchard L., Eyden-Wood T., Bos E., Sutton G., & Blackett, M. (2013). Short Food Supply Chains and Local Food Systems in the EU. A State of Play of their Socio-Economic Characteristics. JRC Scientific and Policy Reports. Report EUR 25911 EN. Knupfer, A. M. (2013). . Cornell University Press. Food Co-ops in America: Communities, Consumption, and Economic Democracy Matias, N. (2015, November 13). What is Platform Cooperativism and Why is it Important? Retrieved from https://civic.mit.edu/2015/11/13/what-is-platform-cooperativism- and-why-is-it-important/ McIntyre, B. D., Herren, H. R., Wakhungu, J. & Watson, R. T. (Eds.). (2009). Agriculture at a Crossroads. International Assessment of Agricultural Knowledge, Science and Technology for Development (IAASTD). Synthesis Report. Retrieved from http://www.unep.org/dewa/agassessment/reports/IAASTD/EN/Agriculture%20at%20a%20Crossroads_Synthesis%20Report%20(English).pdf McNamara, B. (2016, July 11). Examining the Need for a Decentralized Food System [Blog post]. Retrieved from decentralized-food-system-fc06420ba9c7 https://medium.com/@CarFreeBrad/examining-the-need-for-a- Pollan, M. (2009). The Omnivore’s Dilemma: The Search for a Perfect Meal in a Fast-Food World. Retrieved from http://books.google.com. Rakic, B., Levak, T., Drev, Z., Savic, S. & Veljkovic, A. (2017). OriginTrail White Paper. First purpose built protocol for supply chains based on blockchain. Retrieved from OriginTrail-White-Paper.pdf https://origintrail.io/storage/documents/ Rushkoff, D. (2016). Renaissance now. In Scholz, T. & Schneider, N. (eds). . OR Books. Ours to Hack and to Own. The Rise of Platform Cooperativism, a New Vision for the Future of Work and a Fairer Internet Satterthaite, D., McGranahan, G. & Tacoli, C. Urbanization and its implications for food and farming. , 365(1554), pp. 2809–2820 Philosophical transactions of the Royal Society of London. Series B Scholz, T. (2016b). . Rosa Luxemburg Stiftung. Platform Cooperativism. Challenging the Corporate Sharing Economy Scholz, T. [re:publica]. (2016a, May 3). re:publica 2016 — Trebor Scholz: How Platform Cooperativism Can Unleash the Network [Video file]. Retrieved from https://www.youtube.com/watch?v=bkSTgAucRqE Scholz, T. & Schneider, N. (eds). (2016) . OR Books. Ours to Hack and to Own. The Rise of Platform Cooperativism, a New Vision for the Future of Work and a Fairer Internet Sedghi, A. (2013, 10 januari). How much water is needed to produce food and how much do we waste? The Guardian. Retrieved from 2013/jan/10/how-much-water-food-production-waste http://www.theguardian.com/news/datablog/ Sneider, D. (2017, September 12). Better Than Free: The New Business Model for Apps (and possibly everything else). Retrieved from free-the-new-business-model-for-apps-and-possibly-everything-else-a343e97ba706 https://medium.com/deconet/better-than- Sneider, D. (2018, April 17). “Enterprise Hodl” and Circular ​Token ​Economies. Retrieved from https://medium.com/deconet/enterprise-hodl-and-circular-token-economies-b394c4056ecd Stalder, F. (2010). Digital Commons. In: Hart, K., Laville, J., Cattani, A. D. (eds). . Polity Press, pp. 313–324. The Human Economy: A World Citizen’s Guide Tapscott, D. & Tapscott, A. (2016). . Penguin. Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World Te-Food. (2017). Making business profit by solving social problems. Retrieved from https://ico.tefoodint.com/te-food-white-paper.pdf United Nations, Department of Economic and Social Affairs, Population Division. (2014). World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352). Retrieved from WUP2014-Highlights.pdf http://esa.un.org/unpd/wup/Publications/Files/ United Nations, Department of Economic and Social Affairs, Population Division. (2015). World Population Prospects: The 2015 Revision, Key Findings and Advance Tables (ESA/P/WP.241). Retrieved from key_findings_wpp_2015.pdf http://esa.un.org/unpd/wpp/publications/files/ Van Staalduinen, van L. C. (2014, 28 juli). Column — Grond, een hot issue [Blog post]. Retrieved from een-hot-issue.htm http://www.wageningenur.nl/nl/blogpost/Column-Grond- Wagner, L. (Ed). (2008). . Nova Publishers. Urbanization: 21st Century Issues and Challenges Yang, S. & Woods, T. (2014). The Divergence of Defining Local Food — Consumer Co-op versus Conventional Grocery Shoppers. Retrieved from bitstream/170480/2/AAEA%202014%20Manuscript.pdf https://ageconsearch.umn.edu/ Yiannas, F. [IBMBlockchain]. (2017, August 22). Walmart’s food safety solution using IBM Food Trust built on the IBM Blockchain Platform [Video file]. Retrieved from https://youtu.be/SV0KXBxSoio

Leveraging blockchain technologies and platform cooperativism for decentralized food networks and…

A few weeks ago, Zillow as a seller in Phoenix, consummating its . Zillow joins a fleet of competitors — including , , and — that let home sellers close in days instead of months by directly buying homes at a ( ) fair price and flipping them back on the market. put its first home under contract entry into the “iBuyer” space Opendoor Offerpad Knock Redfin Now mostly There are now multiple iBuyers operating in Phoenix, including Opendoor, Offerpad and Zillow. In a , I noted some of the reasons why certain markets appeal to iBuyers: homogenous inventory and high transaction volume to maximize the accuracy of the automated valuation models (AVMs) that generate the offers, warm weather to minimize climate-related rehab surprises, and low taxes to reduce transfer and holding costs. previous analysis The corollary to this observation: we should expect to see direct competition between iBuyers sooner than the vast scale of the residential real estate market would have you believe, centered around cities like Atlanta, Las Vegas, Dallas and Orlando. These cities are the first fronts in a war to own a new paradigm in buying and selling homes that could represent multiple points of U.S. residential transaction volume over the coming years — Opendoor alone will . buy $2.5B of homes this year So: how’s it going to go down? Will Opendoor’s first mover advantage carry the day, will Offerpad’s operational chops lead to victory, or will Zillow and Redfin deploy their tremendous reach to pull ahead? In this post, I’ll propose a framework by which we can assess the competitive positioning of a given iBuyer, and apply it to the major players to predict which are best situated to win in markets where they cohabitate. Premise #1: In the eyes of the customer, iBuyers differentiate primarily on net proceeds It’s an interesting paradox of the iBuyer experience that the sheer speed of the transaction limits the degree of interaction the iBuyer has with the customer, particularly on the seller side. There are certainly ways for these businesses to differentiate on product-level experience from the seller’s point of view — a smoother flow to generate an offer, friendlier appraisals, a like the one offered by Knock, or features like a for sellers who don’t want to move right away. But these features feel relatively simple to replicate. None seems significant enough, relative to the psychological scale of selling a home, to create a customer experience moat unto itself. trade-in experience rent-back option As iBuyer product offerings mature, I believe home sellers will treat them the same way they view airlines, choosing between offers largely on the basis of the net dollars they’ll receive. In the world of traditional agents who spend months serving a client, it’s plausible to differentiate on personal or institutional “brand.” But given that the iBuyer experience is fast, impersonal and predictable by design, it’s hard to imagine brand loyalty playing a deciding role relative to thousands of dollars at stake — so long as the iBuyer has a baseline reputation as a fair counterparty, not like a “We Buy Ugly Houses” franchise. In other words, returning to the airline analogy, iBuyers will need to invest in brand to the extent they earn a spot in the United / American / Delta tier. Beyond that, I believe brand will play a negligible role in convincing a customer to choose one iBuyer over another. Without meaningful brand or product-level differentiators, the richest offer will win. Premise #2: Net proceeds are governed by cost, and the key controllable input into cost is inventory liquidity If you accept the first premise, the iBuyer with the lowest marginal cost per transaction will be able to make higher offers for homes, win the price war and capture more share. For a look at the unit economics of the iBuyer model, take a look at my . Some costs — like closing costs and taxes — are significant, but since they’re generally the same for each iBuyer (with some exceptions, which I’ll touch on later), we can net these non-controllable costs out of our head-to-head comparison. previous post The competitive drivers of cost for the iBuyer business model can be encompassed in a single concept: inventory liquidity. The with which an iBuyer can buy, rehab and sell a home drives the majority of controllable cost associated with the model, including: speed Holding costs: taxes, maintenance, HOA and insurance are proportional to holding time Financing costs: less holding time means capital can be recycled to buy more homes, lowering the cost of capital per transaction Market risk: rapid turnover reduces the risk of riding the elevator to the bottom in a downturn, minimizing the need to charge a risk premium There are other ways iBuyers can compete on cost, like developing a more accurate AVM to reduce pricing risk, or building enough scale to buy bulk materials and rehab homes for less money. Over a longer time horizon, iBuyers that offer a complete solution to buyers and sellers with owned-and-operated closing services — mortgage, title, etc — can reduce their cost relative to competitors. But beyond being the largest input into controllable cost, inventory liquidity is the best way to generate a competitive moat. The iBuyer that builds an initial liquidity advantage can charge less, which creates more seller demand, which generates listing inventory that draws buyers, which reduces holding time and improves liquidity — a virtuous cycle more durable than operational efficiencies of scale. Premise #3: Inventory liquidity is a function of flip operations and customer acquisition Once an iBuyer has purchased a home from a seller, two factors inform how fast they can turn it around: flip operations and finding buyers. By flip operations, I mean everything that happens between closing on the purchase and re-listing the home for sale: the rehab process, staging and listing on the MLS, maintaining the home, and any additional prep, like installing smart locks and security cameras to support 24/7 open houses. Using data to identify high ROI improvements for a given market, building an in-house team of contractors and handymen, and limiting the need for rehab in the first place by filtering for quality inventory are levers that iBuyers can pull to minimize flip time. But the main source of variance in turnover is customer acquisition. Traditional agents usually find buyers by sharing listings with the local MLS, where the listings are discoverable by other agents and flow through to websites like Redfin and Zillow. Tech-enabled disruptors can employ a wider variety of offline and online tools to increase conversion at every step of the buyer funnel. On the product front, several offline features could make listings more appealing to buyers, like making it easier to tour homes (24/7 open house or scans), offering a “certified home” guarantee, or even providing buyers with financing by underwriting their mortgage. But only two iBuyers have a proprietary presence at the top of the funnel — Redfin and Zillow, which have the power to highlight listings directly to tens of millions of unique visitors. Matterport Implications for the iBuyer competitive landscape If you believe that inventory liquidity is the key to lower costs, which is key to relative share gain, assessing each iBuyer’s strengths and weaknesses in flip ops and customer acquisition can help us predict the winner in markets where iBuyers compete head-to-head. Opendoor wins on ops, loses on online acquisition (and so do Offerpad, Knock, etc) Opendoor is the first mover and OG iBuyer. Given their scale and experience, I believe their edge exists in on-the-ground operations, with benefits both on the flip and in buyer acquisition. Opendoor’s shows that they hire an extensive team of coordinators, project managers and field technicians to manage their flips, and they’ve pioneered features like 24/7 open houses and a satisfaction guarantee to make their homes more appealing to buyers. They’re also launching a mortgage business, giving buyers a 1% discount on closing if they purchase an Opendoor home. jobs page Where Opendoor falls short is proprietary channels of online acquisition. Without a best-in-class search experience like Redfin or Zillow, Opendoor will need to double down on paid acquisition channels for finding sellers and source a greater proportion of buyers through the MLS — which means paying a full buyer agent commission. Offerpad and Knock haven’t been around for as long as Opendoor, but they share the same strengths and weaknesses in a directional sense. In particular, Offerpad is staffed by veterans of , which holds a gigantic portfolio of single family rental properties, and that experience will likely accelerate their operational prowess. But none of these companies have a robust online presence that organically draws customers to their home inventory. Invitation Homes Zillow wins on online acquisition, loses on ops Zillow’s advantage is clear: online reach. Boasting over a hundred million unique visitors across its flagship home search websites and apps, in theory, Zillow has an overwhelming advantage at targeting likely sellers with a pitch for Instant Offers based on their browsing activity, and finding buyers for owned inventory online. But Zillow has no experience as an operations business. They’re starting by leveraging their network of Premier Agents to manage both sides of the transaction — buying homes from sellers and listing them on the market. Both agents get a commission, so this is expensive talent, even if Zillow cuts a deal to reduce the commission and nets some of the commission payment back in Premier Agent fees. Over the long-term, Zillow will be also gated from competing on cost by its legacy business. The real opportunity for all iBuyer platforms is building enough liquidity to lower prices to parity with agent commissions (5–6% of transaction value). But this is also the moment that Zillow may run into steep resistance from its existing customer base — the real estate agents who will be supplanted by this new model. If Zillow wants to compete on price it will eventually undercut agents, and crossing the chasm from a high-margin marketplace business to a low-margin, high-volume operations business would tax even the toughest executives and shareholders. Redfin balances ops and customer acquisition Disclaimer: I’m a former Redfin employee, current shareholder and long on the business. With that said, I believe Redfin is best positioned to create a liquid iBuyer marketplace. With 25 million monthly uniques and brokerage operations in more than 80 markets, Redfin has an advantage in both online and offline acquisition. Redfin can use agents to acquire sellers by pitching Redfin Now alongside its existing listing product. Unlike Zillow, Redfin’s investments in end-to-end efficiency have resulted in a agent-listed product so affordable (1% commission) that Redfin Now won’t cannibalize it — Redfin agents and Redfin Now are natural complements. To move owned inventory fast, Redfin can highlight listings to users, and use existing vertical infrastructure (Redfin Mortgage) to create an attractive end-to-end experience for buyers. Redfin listings already than homes represented by traditional agents in most markets. sell faster Redfin’s biggest weakness relative to Opendoor and Offerpad is its lack of experience in rehab and flip operations. But given Redfin’s experience scaling a thousands-strong team of employee agents around the country, I believe they can remedy this disadvantage fast — and . they’re already starting There’s a lot at stake It’s worth noting that the iBuyer business model has gained traction even given a nationwide seller’s market, where the guarantee of a fast and certain sale is less valuable because most homes are under contract in weeks anyway. As the market turns, the iBuyer value prop will attract a wider audience of home-sellers. Balanced against the opportunity of a growing market is the unanswered question of the iBuyer model’s resilience to macro risk. In the end, perhaps the biggest advantage of inventory liquidity is reducing the chance that an iBuyer blows up during a major market correction. The battle for iBuyer market share will play out over a time horizon of years, but I believe that early and aggressive execution against flip operations and customer acquisition will kindle success in the great iBuyer game. Ultimately, a small liquidity advantage may well compound into a marketplace that makes the experience of selling and buying houses as seamless as the way we transact in everything else — and the owner of that marketplace will play a deciding role in the future of residential real estate. Sib Mahapatra is a writer and co-founder of Bureau . Originally published at www.sibmahapatra.com on July 19, 2018.

Redfin, Opendoor, Zillow and the Great iBuyer Game

Originally posted on StackShare About Opendoor Unless you’re in San Francisco or New York, selling your home is a giant headache that typically lasts three months. removes the headache — go online, ask for an offer, answer a few questions and we’ll buy your home directly from you. We’ll take it from there and deal with selling the home to another buyer while you can go on with your life. Opendoor Right now we operate in Phoenix, Dallas, and Las Vegas. We’ve completed over 4,800 real estate transactions — over $1B in homes. For a company about to turn 3 years old, it’s pretty crazy how far we’ve come. There’s a lot that goes into one real estate transaction. First, there’s what you might consider our core engineering challenge: making an accurate offer on each home. If we offer too much, we’ll lose money and go out of business; if we offer too little, we’ll seem like scammers and offend our customers. After we buy the home, we’ll work with contractors to do any necessary repairs and touch-ups, then put the home on the market and find a buyer. Since we own every home, we can do clever things like putting smart locks on all the doors and offering all-day open houses. I’m a frontend engineer, and mainly like to work on the consumer-facing website. I’m currently working on improving the experience for first-time home buyers. The process can be really scary for people who don’t know anything about real estate. Engineering Organization Our team is split between product engineering and data science: the tools used by each team are different enough that the teams work in separate code bases. Of course, the resulting product has to be well-integrated, and the product team pulls a lot of data from data science APIs. This coordination is tricky to get right; Kevin Teh from the data science team wrote about it in some detail in . a recent post At first, we split the product team into “customer-facing” and “internal tools” groups. It was nice to have all the frontend engineers on the same team, but we noticed that some projects didn’t have clear owners. For example, our buyer support team uses some internal tools we’ve built. Should those tools be developed by the “internal tools” team, or is support part of the customer experience? Now the team is split into cross-functional teams based around parts of the business. The Seller team handles people selling to us; the Homes team handles renovations and inventory; and the Buyer team puts our homes on the market and finds buyers. As we grow, the lines between teams often get blurry, so we expect that the structure will always be evolving. It’s common for engineers to move between teams, including between the product and data science teams. Product Architecture We started in 2014 with a monolith and frontend, both of which were good ways to move fast while we were very small. Ruby on Rails Angular The MVP of our customer-facing product was a multi-page form where you could enter information about your home to get an offer, but that was just the tip of the iceberg. We had to build internal tools to help our team correctly price homes and manage the transaction process. We used and to build out those tools; the main goal was to add features quickly, without fiddling around with CSS — in fact, without requiring any frontend experience at all. Angular Bootstrap We use as our webserver, and for our database — one big benefit is the extension for location data. runs our asynchronous jobs with support from . shows up everywhere in our internal tools. We use to build our frontend apps, and serve them using the Rails Asset Pipeline. Puma Postgres PostGIS Sidekiq Redis Elasticsearch Webpack We use to store photos of our homes, as well as most of the icons and illustrations around our site. We mainly use Imgix’s auto-resizing feature, so we never lose track of our original images, but can later load images of appropriate size for each context on the frontend. Imgix Monolith to Microservices Where appropriate, we try to break isolated logic out into microservices. For example, we’re working on a service which calculates our projected costs and fees. Our cost structure changes frequently, and we want to estimate how policy changes might affect our fees. This code wasn’t a great fit for the Rails app because we wanted it to be accessible to our analysts and data scientists as well. We’ve now split this logic out into its own service. It uses a version-history-aware computation graph to calculate and back-test our internal costs, and (soon!) will come with its own frontend to visualize those calculations. React Our data science stack is also a fully separate set of services, so there’s a lot of inter-app communication going on. To let these services authenticate to one another, we use an app called . Opendoor engineer Dan Neighman wrote and open-sourced Paladin, and explains why it’s helpful in . Authentication is based on JWTs provided by and . Elixir Paladin this blog post Warden Guardian Data Science Architecture I’ve always found data science at Opendoor interesting because it’s not the “grab as much data as you possibly can, then process it at huge scale” problem I’m used to hearing about. To find the price of a house, you look at nearby homes that sold recently, then squeeze as much information out of that data as you possibly can by comparing it to what you know about the market as a whole. . Our co-founder Ian Wong has a more in-depth talk here We can group most of the data science work into several core areas: Ingesting and organizing data from a variety of sources Training machine learning models to predict home value and market risk Quantifying and mitigating various forms of risk, including macroeconomic and individual house-liquidity Collecting information in a data warehouse to empower the analytics team For data ingestion, we pull from a variety of sources (like tax record and assessor data). We dump most of this data into an database. We also transform and normalize everything at this phase — we’re importing dirty data from sources that often conflict. goes into more detail on how we merge data for a given address. RDS Postgres This blog post For our machine learning model, we use with building blocks from , , and . We use for routing/handling requests. We use to build images and for deployment and scaling. Our system lets us describe a model as a JSON configuration, and once deployed, the system automatically grabs the required features, trains the model, and evaluates how well the model did against performance metrics. . Python SqlAlchemy scikit-learn Pandas Flask Docker Kubernetes This automation lets us iterate really fast We’re starting to use for feature fetching and processing. Other companies often use Spark and Hadoop for this, but we need support for more complex parallel algorithms. Dask’s describes this perfectly: Dask comparison to PySpark post Dask is lighter weight and is easier to integrate into existing code and hardware. If your problems vary beyond typical ETL + SQL and you want to add flexible parallelism to existing solutions then dask may be a good fit, especially if you are already using Python and associated libraries like NumPy and Pandas. The final piece of our data science architecture is the Data Warehouse, which we use to collect analytics data from everywhere we can. For a long time we used a nightly to move data from each service’s database directly into a home-built Data Warehouse. We recently migrated to Google’s instead. BigQuery is faster, and lets us fit more data into each query, but the killer feature is that it’s serverless. We have many people running queries at “peak hours”, and don’t want things to slow down just because we have a preallocated number of servers available. pg_dump Postgres BigQuery High-Tech Open Houses Since Opendoor actually owns all the houses we sell, we can be creative about how we show them to potential buyers. Typically, if you want to see a house for sale, you have to call the listing agent and schedule a time. We realized early on that we could make open houses way more convenient by installing automatic locks on our doors so the homes could be accessed at any time. For version 0 of the project, we literally posted our VP of Product’s phone number on the doors of all our houses — buyers would call in, and he’d tell them the unlock code. For version 1, we added so we could automatically send unlock codes over SMS. For version 2, we built a mobile app. Twilio Customers expect a good mobile experience these days, but our all-day open house feature made it twice as important. You can use the app to find nearby homes as you’re driving around, and explore them on a whim — a huge improvement from the traditional process! We built our app in . A major part of that choice was pragmatic — our team had a lot of experience with web technologies, and almost no experience with native technologies. We also wanted to support both iPhone and Android from early on, and React Native let us do that (we released our app for iPhone first, and adding Android only took an extra couple weeks). React Native Not everyone wants to install an app, so it’s still possible to access our homes via SMS. We’ve added a few security mechanisms — one worth mentioning is , which lets us quickly run identity verification using phone numbers. For riskier numbers, we disable the automatic entry system and have our support team call the customer to collect their information. Blockscore Tools and Workflows We manage our repositories and do code reviews on . All code is reviewed by at least one other engineer, but once it’s in the branch, it’s assumed to be ready to deploy. If you want to deploy your code, you can do it in three steps: GitHub master ./bin/deploy staging Check your work on staging ./bin/deploy production This takes 10–15 minutes in total. We’ve worked hard to automate the process so we can move fast as a team. We use for hosting, and run automated tests on . bots report what’s being deployed. Heroku CircleCI Slack There are a lot of external services we rely on heavily. To run through them briefly: and for emails; and for calls and customer service; for online contract signing; and for system monitoring; for error reporting. Help Scout Dyn Talkdesk Twilio HelloSign New Relic Papertrail Sentry For analytics, we’ve used a lot of tools: for the web, for mobile, for retroactive event tracking. We mainly use for digging into that data and making dashboards. Mixpanel Amplitude Heap Looker Joining Opendoor Engineering Opendoor has a very entrepreneurial, pragmatic culture: Engineers here typically talk with customers, understand their needs, and take the initiative on projects. We’re big on ownership and empowering others, and are aggressively anti-snark. We’re looking for engineers of all backgrounds: it doesn’t matter what languages you work with now, we’re sure you’ll ramp up fast. Find out more about Opendoor jobs on or on our . StackShare careers site Huge thanks to Kevin Teh, Mike Chen, Nelson Ray, Ian Wong, and Alexey Komissarouk for their help putting together this post.

The Stack That Helped Opendoor Buy and Sell Over $1B in Homes

How to Enhance Your dbt Project With Large Language Models

বৃহৎ ভাষার মডেলগুলির সাথে আপনার ডিবিটি প্রকল্পকে কীভাবে উন্নত করবেন

So verbessern Sie Ihr dbt-Projekt mit großen Sprachmodellen

Cómo mejorar su proyecto dbt con modelos de lenguaje grandes

Comment améliorer votre projet dbt avec de grands modèles linguistiques

बड़े भाषा मॉडल के साथ अपने dbt प्रोजेक्ट को कैसे बढ़ाएं

大規模言語モデルを使用して dbt プロジェクトを強化する方法

대규모 언어 모델을 사용하여 dbt 프로젝트를 향상시키는 방법

Como aprimorar seu projeto dbt com grandes modelos de linguagem

Как улучшить ваш проект dbt с помощью больших языковых моделей

Büyük Dil Modelleriyle dbt Projenizi Nasıl Geliştirebilirsiniz?

Cách nâng cao dự án dbt của bạn bằng các mô hình ngôn ngữ lớn

如何使用大型语言模型增强你的 dbt 项目

Cryptography can be used to have not only decentralized and private money but also private communications. The activists known as cypherpunks knew this too well, and that’s why not only Bitcoin came out from that mailing list started in the ’90s. A bunch of free and useful cryptographic tools are the legacy of cypherpunks, including the one created by Phil Zimmerman —a remarkable cypherpunk even to this day. Born on February 12, 1954, in Camden, New Jersey, Zimmermann studied computer science at Florida Atlantic University, where he earned his degree in 1978. His career trajectory began with software engineering roles in various companies, including a stint at the nuclear power company, System Simulation Corporation, where he worked on cryptographic security systems. However, the interesting part is that he was also an anti-nuclear activist. It’s known that he was arrested for partaking in an anti-nuclear protest in Nevada, alongside prominent figures like Carl Sagan and Martin Sheen. This commitment to activism underscored Zimmermann's convictions, solidifying his standing within the anti-nuclear movement. Amidst the shifting geopolitical landscape of the early 1990s, Zimmermann's focus pivoted towards cryptography, recognizing its potential to safeguard private communications in an increasingly interconnected world. It was during this period that he would join the cypherpunks and create his ‘magnum opus’. Pretty Good Privacy (PGP) This is a thing even Satoshi Nakamoto used to protect his emails, and it’s still widely used as a standard to achieve private communications. Developed in 1991, PGP is an encryption software designed to secure email messages. Zimmermann's motivation behind PGP was rooted in his concerns about government surveillance and the need for accessible cryptographic tools to protect individual privacy. In his own words: “It's personal. It's private. And it's no one's business but yours. You may be planning a political campaign, discussing your taxes, or having a secret romance. Or you may be communicating with a political dissident in a repressive country. Whatever it is, you don't want your private electronic mail (email) or confidential documents read by anyone else (...) PGP empowers people to take their privacy into their own hands. There has been a growing social need for it. That's why I wrote it.” As for its internal features, PGP uses a pair of cryptographic keys for each user—a public key and a private key. The public key, which is freely distributed, is used by others to encrypt messages intended for the user. Meanwhile, the private key, kept confidential by the user, is used for decrypting these messages. When someone wants to send an encrypted message to a PGP user, they obtain the recipient's public key. Using this key, they encrypt the message, ensuring that only the intended recipient, who possesses the corresponding private key, can decrypt and read it. This process provides confidentiality for the message. Additionally, PGP supports digital signatures, enabling users to verify the authenticity and integrity of emails. By hashing the message and encrypting the hash with their private key, senders create digital signatures that recipients can verify using the sender's public key. After PGP Back then, the US government considered cryptographic tools as weapons, and that’s why Zimmerman was investigated by authorities for the free distribution of PGP. Luckily, they dropped the case in 1996 without charges, and the cypherpunk founded PGP Inc., along with a new release of PGP. The company in charge of maintaining the software was eventually sold to Symantec in 2010. Following the success of PGP, Zimmermann has also been involved in various ventures aimed at bolstering digital security and privacy. In 2012, he co-founded Silent Circle alongside Mike Janke and Jon Callas, focusing on developing secure hardware and subscription-based software solutions. Additionally, Zimmermann collaborated with other key figures from Silent Circle and Lavabit (an encrypted webmail service) to establish the Dark Mail Alliance in 2013, with the objective of creating a new protocol to enhance email encryption, addressing limitations in PGP. Beyond his cryptographic endeavors, Zimmermann extended his influence to social networking platforms, advocating for ethical and privacy-centric alternatives to mainstream platforms like Facebook. His involvement in the social network Okuna, previously known as Openbook, aimed to provide users with a platform that prioritized privacy and democratic values over profit-driven models. Zimmermann's contributions have been widely recognized, with numerous awards acknowledging his pioneering work in cryptography and advocacy for digital rights. From inductions into halls of fame to prestigious accolades such as the Louis Brandeis Award from Privacy International and the Norbert Wiener Award for Social and Professional Responsibility, Zimmermann's legacy continues to resonate within the realms of technology and human rights advocacy. In a world of surveillance There’s a thing called “the Zimmerman law” that refers to his words about technology and surveillance: “The natural flow of technology tends to move in the direction of making surveillance easier (…) the ability of computers to track us doubles every eighteen months.” If the period is exact or not in reality, it doesn’t matter, because today mass surveillance and censorship have become common feats by governments worldwide. PGP can help us to protect our privacy and digital rights, but it’s not the only tool available for that. Obyte, for instance, can also help with private communications and decentralized, fast, and secure financial transactions worldwide. Built on a Directed Acyclic Graph (DAG) structure, Obyte ensures global transactions without the need for miners or any other middlemen. This structure enables Obyte to increase its decentralization and offers a high level of control to its users. In addition to its financial features, where tokenization of any asset is also possible, Obyte integrates private communications functionality directly into its platform. Users can leverage Obyte's built-in messaging system in the wallet to communicate securely and privately, thanks to end-to-end encryption, similar to PGP. This feature enhances user privacy by enabling them to freely communicate without a company or government behind them, thus providing a comprehensive solution for both financial transactions and private messaging. As a result, we can say that Obyte is empowering users with a versatile platform for conducting secure and private interactions worldwide. Read more from Cypherpunks Write Code series: Tim May & Crypto-anarchism Wei Dai & B-money Nick Szabo & Smart Contracts Adam Back & Hashcash Eric Hughes & Remailer St Jude & Community Memory Hal Finney & RPOW Featured Vector Image by Garry Killian / Freepik Photo by Phil Zimmerman Cryptography can be used to have not only decentralized and private money but also private communications. The activists known as cypherpunks knew this too well, and that’s why not only Bitcoin came out from that mailing list started in the ’90s. A bunch of free and useful cryptographic tools are the legacy of cypherpunks, including the one created by Phil Zimmerman —a remarkable cypherpunk even to this day. that mailing list that mailing list Born on February 12, 1954, in Camden, New Jersey, Zimmermann studied computer science at Florida Atlantic University, where he earned his degree in 1978. His career trajectory began with software engineering roles in various companies, including a stint at the nuclear power company, System Simulation Corporation, where he worked on cryptographic security systems. Zimmermann studied computer science at Florida Atlantic University, where he earned his degree in 1978. Zimmermann Zimmermann However, the interesting part is that he was also an anti-nuclear activist. It’s known that he was arrested for partaking in an anti-nuclear protest in Nevada, alongside prominent figures like Carl Sagan and Martin Sheen. This commitment to activism underscored Zimmermann's convictions, solidifying his standing within the anti-nuclear movement. It’s known It’s known Amidst the shifting geopolitical landscape of the early 1990s, Zimmermann's focus pivoted towards cryptography, recognizing its potential to safeguard private communications in an increasingly interconnected world. It was during this period that he would join the cypherpunks and create his ‘magnum opus’. Pretty Good Privacy (PGP) This is a thing even Satoshi Nakamoto used to protect his emails, and it’s still widely used as a standard to achieve private communications. Developed in 1991, PGP is an encryption software designed to secure email messages . Zimmermann's motivation behind PGP was rooted in his concerns about government surveillance and the need for accessible cryptographic tools to protect individual privacy. In his own words : Developed in 1991, PGP is an encryption software designed to secure email messages In his own words In his own words “It's personal. It's private. And it's no one's business but yours. You may be planning a political campaign, discussing your taxes, or having a secret romance. Or you may be communicating with a political dissident in a repressive country. Whatever it is, you don't want your private electronic mail (email) or confidential documents read by anyone else (...) PGP empowers people to take their privacy into their own hands. There has been a growing social need for it. That's why I wrote it.” “It's personal. It's private. And it's no one's business but yours. You may be planning a political campaign, discussing your taxes, or having a secret romance. Or you may be communicating with a political dissident in a repressive country. Whatever it is, you don't want your private electronic mail (email) or confidential documents read by anyone else (...) PGP empowers people to take their privacy into their own hands. There has been a growing social need for it. That's why I wrote it.” As for its internal features, PGP uses a pair of cryptographic keys for each user—a public key and a private key. The public key, which is freely distributed, is used by others to encrypt messages intended for the user. Meanwhile, the private key, kept confidential by the user, is used for decrypting these messages. When someone wants to send an encrypted message to a PGP user, they obtain the recipient's public key. Using this key, they encrypt the message, ensuring that only the intended recipient, who possesses the corresponding private key, can decrypt and read it. This process provides confidentiality for the message. Additionally, PGP supports digital signatures, enabling users to verify the authenticity and integrity of emails. By hashing the message and encrypting the hash with their private key, senders create digital signatures that recipients can verify using the sender's public key. After PGP After PGP Back then, the US government considered cryptographic tools as weapons, and that’s why Zimmerman was investigated by authorities for the free distribution of PGP. Luckily, they dropped the case in 1996 without charges, and the cypherpunk founded PGP Inc., along with a new release of PGP. The company in charge of maintaining the software was eventually sold to Symantec in 2010. Following the success of PGP, Zimmermann has also been involved in various ventures aimed at bolstering digital security and privacy. In 2012, he co-founded Silent Circle alongside Mike Janke and Jon Callas, focusing on developing secure hardware and subscription-based software solutions. Additionally, Zimmermann collaborated with other key figures from Silent Circle and Lavabit (an encrypted webmail service) to establish the Dark Mail Alliance in 2013, with the objective of creating a new protocol to enhance email encryption, addressing limitations in PGP. Following the success of PGP, Zimmermann has also been involved in various ventures aimed at bolstering digital security and privacy. Beyond his cryptographic endeavors, Zimmermann extended his influence to social networking platforms, advocating for ethical and privacy-centric alternatives to mainstream platforms like Facebook. His involvement in the social network Okuna , previously known as Openbook, aimed to provide users with a platform that prioritized privacy and democratic values over profit-driven models. Okuna Okuna Zimmermann's contributions have been widely recognized, with numerous awards acknowledging his pioneering work in cryptography and advocacy for digital rights. From inductions into halls of fame to prestigious accolades such as the Louis Brandeis Award from Privacy International and the Norbert Wiener Award for Social and Professional Responsibility, Zimmermann's legacy continues to resonate within the realms of technology and human rights advocacy. In a world of surveillance In a world of surveillance There’s a thing called “the Zimmerman law” that refers to his words about technology and surveillance: “The natural flow of technology tends to move in the direction of making surveillance easier (…) the ability of computers to track us doubles every eighteen months.” If the period is exact or not in reality, it doesn’t matter, because today mass surveillance and censorship have become common feats by governments worldwide. his words his words “The natural flow of technology tends to move in the direction of making surveillance easier (…) the ability of computers to track us doubles every eighteen months.” mass surveillance mass surveillance PGP can help us to protect our privacy and digital rights, but it’s not the only tool available for that. Obyte , for instance, can also help with private communications and decentralized, fast, and secure financial transactions worldwide. Built on a Directed Acyclic Graph (DAG) structure, Obyte ensures global transactions without the need for miners or any other middlemen. This structure enables Obyte to increase its decentralization and offers a high level of control to its users. Obyte Obyte In addition to its financial features, where tokenization of any asset is also possible, Obyte integrates private communications functionality directly into its platform. Users can leverage Obyte's built-in messaging system in the wallet to communicate securely and privately, thanks to end-to-end encryption, similar to PGP. tokenization of any asset tokenization of any asset in the wallet in the wallet This feature enhances user privacy by enabling them to freely communicate without a company or government behind them, thus providing a comprehensive solution for both financial transactions and private messaging. As a result, we can say that Obyte is empowering users with a versatile platform for conducting secure and private interactions worldwide. Read more from Cypherpunks Write Code series: Read more from Cypherpunks Write Code series: Read more from Cypherpunks Write Code series: Tim May & Crypto-anarchism Tim May & Crypto-anarchism Tim May & Crypto-anarchism Wei Dai & B-money Wei Dai & B-money Wei Dai & B-money Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Nick Szabo & Smart Contracts Adam Back & Hashcash Adam Back & Hashcash Adam Back & Hashcash Eric Hughes & Remailer Eric Hughes & Remailer Eric Hughes & Remailer St Jude & Community Memory St Jude & Community Memory St Jude & Community Memory Hal Finney & RPOW Hal Finney & RPOW Hal Finney & RPOW Featured Vector Image by Garry Killian / Freepik Featured Vector Image by Garry Killian / Freepik Freepik Freepik Photo by Phil Zimmerman Photo by Phil Zimmerman Phil Zimmerman Phil Zimmerman

Cypherpunks Write Code: Phil Zimmerman and PGP

সাইফারপাঙ্কস কোড লিখুন: ফিল জিমারম্যান এবং পিজিপি

Cypherpunks schreiben Code: Phil Zimmerman und PGP

Código de escritura de Cypherpunks: Phil Zimmerman y PGP

Cypherpunks écrivent du code : Phil Zimmerman et PGP

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With strong demand driving average prices up to unaffordable levels and soaring interest rates that jumped from just over 3% in early 2022 to over 7% today, getting your foot on the property ladder is far harder than it once was. But, in many ways, the house-buying process has also become far easier to navigate than in the past. We have property technology, or proptech, to thank. Rather than searching far and wide for the perfect home, prospective buyers can fine-tune filters to suit their budgets and needs on property portals, view 3D models of the homes they’re interested in through virtual reality, and use platforms powered by big data to understand whether the property they’re bidding on is good value for money. As the CEO of , a new construction homes marketplace, I’ve seen the drastic difference proptech is making in a difficult market, and I believe the rapid rate of innovation will continue to see proptech thrive regardless of the volatility in today’s property landscape. NewHomesMate The Rise of Proptech: Plotting the Market’s Growth (2010-2023) Like many modern technologies, proptech found its origin in the early days of the Internet as the real estate sector sought ways to streamline time-consuming processes such as underwriting and accounting. However, it wasn’t until the 2010s that proptech really took off. The sector benefited from increased innovation and a surge of funding as investors looked to find real estate’s next big thing. Between 2010 and 2019, global investment in proptech skyrocketed from just $600m to over $20bn. In that time, we saw an abundance of proptech companies such as Compass, Airbnb, and Opendoor blossom into unicorns with billion-dollar valuations. Despite the pandemic causing the market to stall momentarily — with investment falling by 30% year-over-year — interest in proptech has risen to new heights in the current decade, most notably marked by Zillow’s 370% jump in value between January 2020 and February 2021. Of late, the issues facing the property market, such as high mortgage rates have had a knock-on effect on the proptech market. However, with interest rates having stabilized and people having accepted the new normal, there are positive signs that proptech will return to a positive trajectory in the years ahead. The U.S. Proptech Market in 2024: Trends and Predictions With the economy on the up, what does the future hold for proptech, and how can industry players capitalize on current trends to make 2024 a successful year? With the Federal Reserve having kept interest rates stable in the early stages of 2024, the worst of the economic downturn seems to be behind us. With businesses having a clearer picture of how their finances will look in the near future, they can stop focusing on survival and start investing in innovation instead. A return to growth: According to a recent Inman Intel survey, many industry players had already transitioned into a new growth phase. A year ago, securing funding seemed near impossible for tech startups. However, the Center for Real Estate Technology and Innovation (CRETI) has predicted a 15% increase in proptech seed funding and a 20% increase in Series A and B funding in 2024. Total funding is still far below previous years, but it’s a sign that confidence in the sector is growing again. Fresh funding: It’s no surprise that artificial intelligence will have the biggest influence on proptech in 2024. Latest figures show that AI-focused proptech startups raised over $4bn in funding in 2022 alone. AI everything: With over 500 companies currently developing tools that will benefit the real estate industry, plenty of useful solutions — AI demand forecasting, AI search, AI user analysis, and more — are now coming to market. With ChatGPT having shown the potential of advanced text generation tools, proptech companies will be on the lookout for ways to improve the buyer’s search experience through increased personalization. Increased personalization: Soon enough, one-size-fits-all property listings will be replaced by tailored recommendations and descriptions that help to sell homes faster. Sustainable demand: With many countries aiming for drastic emission reductions by 2030, there isn’t long left to meet these lofty targets. With real estate one of the largest polluters, producing around 30% of all global emissions, sustainability is becoming an even greater selling point for proptech companies — More so than profits and productivity for those committing to achieving net zero. 2024 and Beyond: What Does the Future Hold for Proptech? A few shaky financial years have tested the foundations of proptech and the wider real estate market. However, with the Fed expected to cut interest rates by more than 1% over what remains of 2024, mortgages will become more affordable in the months ahead. This will increase activity, which is great news for the real estate market, as well as the technology suppliers that they rely on. With emerging technologies such as AI and augmented reality becoming an essential among real estate players, proptech appears set for a period of sustained growth regardless of the challenge ahead. Valued at more than $25bn in 2022, the market is tipped to grow by 15.8% annually until 2030, reaching a total value of $81bn by the end of the decade. That isn’t to say the spell of uncertainty is over. That’s why NewHomesMate continues to experiment and implement new technologies to streamline the house-buying process, despite having secured over $80m in sales during one of the housing market’s most difficult moments in recent history. With the pandemic-inspired boom and sudden cooldown having offered a sharp reminder of how quickly the housing market can turn, providers should expect investors and customers alike to proceed with caution. Only the sturdiest of proptech businesses — those that offer exceptional value and continue to develop their offerings using the latest technology — will survive. The article uses data and materials from the following sources: “Are Home Loan Rates Ever Going to Drop? Mortgage Rates for Feb. 20, 2024”, Katherine Watt, CNET https://www.cnet.com/personal-finance/mortgages/todays-rates/mortgage-rates-for-feb-20-2024-rates-increase-for-prospective-buyers/ “Most homes for sale in 2023 were not affordable for a typical U.S. household”, Jennifer Ludden, NPR https://www.npr.org/2023/12/24/1221480443/most-homes-for-sale-in-2023-were-not-affordable-for-a-typical-u-s-household “When did the term "Proptech" start?”, Davis Wells, https://www.unissu.com/proptech-resources/When-did-the-term-Proptech-start “Value of investment into proptech companies worldwide from 2010 to 1st half 2022” Statista https://www.statista.com/statistics/951857/global-proptech-investment-value/ “Market capitalization of Zillow”, https://companiesmarketcap.com/zillow/marketcap/ “Market capitalization of Redfin”, https://companiesmarketcap.com/redfin/marketcap/ “Proptech Sector Gears Up for Growth: 2024 Market Forecast Reveals Promise”, BNN correspondents, BNN https://bnnbreaking.com/finance-nav/business/proptech-sector-gears-up-for-growth-2024-market-forecast-reveals-promise/ “AI and the evolution of proptech”, JILL https://www.jll.co.uk/en/trends-and-insights/workplace/ai-and-the-evolution-of-proptech “Has Proptech Funding Turned the Corner?”, Phillip Russo, Commercial Observer https://commercialobserver.com/2023/10/proptech-funding-2024/ “What To Expect From The Fed’s Remaining Meetings Of 2024”, Simon Moore, Forbes https://www.forbes.com/sites/simonmoore/2024/02/12/what-to-expect-from-the-feds-remaining-meetings-of-2024/ “The Proptech Revolution Is Coming. How Can Developers Know They’re Investing In The Right Tools?”, Julia Troy, Bisnow https://www.bisnow.com/philadelphia/news/proptech/the-proptech-revolution-is-coming-how-can-owners-and-developers-know-theyre-investing-in-the-right-tools-122445 “Proptech in 2024: VCs, Principals and Analysts Sound Off”, Phillip Russo, Commercial Observer https://commercialobserver.com/2023/12/proptech-2024-trends-investment/ “PropTech Market Size, Share & Trends Analysis Report By Property Type”, https://www.grandviewresearch.com/industry-analysis/proptech-market-report

Bricks, Mortar, and Code: The State of Proptech in 2024

This is the second part in a multi-part series on building Agents with OpenAI's Assistant API using the Python SDK. Recap of Part 1 In of this series, we created a simple conversational Agent using the OpenAI Assistant API. Part 1 Let's recall our definition of an Agent. An agent is a piece of software leveraging an aiming to mimic human behavior. That means it can not only converse and understand language, but it can also perform actions that have an impact on the real world. These actions are typically called tools. LLM (Large Language Model) I use the terms and interchangeably when it comes to functions that the agent is able to call. OpenAI also uses the term interchangeably with to refer to the same concept. It seems the jury is still out on which term will become the standard. tools functions action tools In Part 2 of the series, let's build our first tool! We will be building on top of the simple conversational Agent we built in Part 1. We will purposefully call our implementation an Agent and refer to the as an Assistant to easily distinguish between the two. OpenAI SDK implementation Prerequisites To follow along with this tutorial, you will need the following: Have read or at least copied the as your starting point Part 1 code Python3 installed on your machine An OpenAI API key Basic knowledge of Python programming Dependencies We have one new dependency , a library used for parsing docstrings in Python code. This is needed for our Agent to dynamically interpret and manage the functions it can call. More on this later. docstring_parser python3 -m venv venv
source venv/bin/activate
pip install docstring_parser The goal In Part 1, we created an Agent that represents a famous hobbit who spends too much time thinking about breakfast 🍳 The goal of this tutorial will be to add two abilities or tools to our Agent. The tools will: Add the ability for our Agent to eat a second breakfast if he has only had one or else eat lunch. Add the ability for him to tell us the current date. NOTE: For those of you who don't know, hobbits eat two breakfasts. Also, some humans, like myself... It's a ridiculous example, but it will clearly illustrate the data flow you can set up for more complex use cases that interact with real-world data. Setting up the mock database Let's do a small amount of prep work before we dive in. We will create a simple mock database represented by a new file containing a variable . In this case, the variable will keep track of how many breakfasts our hobbit has eaten. db.py breakfast_count breakfast_count breakfast_count = 1 While we are here, we will update the method on our class to get data from our mock db. get_breakfast_count_from_db Agent import db

class Agent:
    # ... (rest of code)
    def get_breakfast_count_from_db(self):
        return db.breakfast_count 😴... That's out of the way. Let's move on to the fun part. Our first tool A tool is quite simply a function that the Agent is aware it needs to call in a specific scenario. Therefore, let's first define a simple function that handles our breakfast logic. IMPORTANT: I want to draw your attention to the in the tool functions we are about to define. It's crucial to get the documentation syntax right, as this is what will be used to extract the correct OpenAI function format in JSON. More on this soon. Google Style docstring Create a new file with this content: tools.py import db


def eat_next_meal(breakfast_count: int):
    """
    Call this tool when user wants you to eat another meal.

    Args:
        breakfast_count (int): Value with same name from metadata.

    Returns:
        str: The meal you should eat next.
    """
    print("== eat_next_meal ==> tool called")
    
    if breakfast_count == 2:
        return "You have already eaten breakfast twice today. You eat lunch now."
    if breakfast_count == 1:
        db.breakfast_count += 1
        return "You have only eaten one breakfast today. You eat second breakfast now." Pretty simple, right? If is , eat second breakfast and update the database to reflect that 2 breakfasts have been eaten. If is , eat lunch. breakfast_count 1 breakfast_count 2 You could then return any string you want from the tool. This return value is then fed right back into the Run in order to inform the Assistant of the outcome of an action taken. If your agent only cares that the tool was called and completed successfully, you could just return a or message. "success" "failed" Let's quickly create the second tool for our Agent so that we can illustrate how multiple tools can be used. We will create a tool to allow our Agent to tell us what the current date is. def tell_the_date():
    """
    Call this tool when the user wants to know the date.

    Returns:
        str: The current date
    """
    print("== tell_the_date ==> tool called")
    current_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    return f"The date is {current_date}" Providing the tools to our Agent Now, let's provide these new tools to our in . We create a dictionary that maps function names to their corresponding callable objects and pass this as the argument to our class instantiation. We could hardcode the dictionary keys as strings, but we all know how easy typos are, so I chose to leverage Python's built-in property on functions to make this less error-prone: Agent main.py tools __name__ from tools import eat_next_meal, tell_the_date

agent = Agent(name="Bilbo Baggins",
              personality="You are the accomplished and renown adventurer from The Hobbit. You act like you are a bit of a homebody, but you are always up for an adventure. You worry a bit too much about breakfast.",
              tools={
                  eat_next_meal.__name__: eat_next_meal,
                  tell_the_date.__name__: tell_the_date
              }) Updating our Agent to accept tools Let's put these new tools into our Agent's tool belt by updating the class constructor to accept a dictionary of tools. You could bake this right into the class using class methods, but since it's common for different Agents to have different functionalities, we will pass tools as a constructor parameter and set them dynamically as a class property for later use. Agent tool_belt class Agent:
    # ... (rest of code)
    def __init__(self, name: str, personality: str, tools: dict[str, callable]):
        self.name = name
        self.personality = personality
        self.client = openai.OpenAI(api_key="sk-*****")

        self.tool_belt = tools
        self.assistant = self.client.beta.assistants.create(
            name=self.name,
            model="gpt-4-turbo-preview",
        ) Our Agent now has a tool belt it can access, but the OpenAI Assistant is unaware of them. We could provide the tools when creating the Assistant ( ) by passing a parameter, but tools can change often in development, and it's clunky to have to update Assistants every time you modify a tool by calling . assistants.create tools assistants.update Therefore, a better approach is to provide the needed tools to every Run so that they are created dynamically each time. Let's do exactly that by adding the parameter to our run creation: tools class Agent:
    # ... (rest of code)
    def _create_run(self):
        count = self.get_breakfast_count_from_db()
        return self.client.beta.threads.runs.create(
            thread_id=self.thread.id,
            assistant_id=self.assistant.id,
            tools=self._get_tools_in_open_ai_format(), # add this line
            # ... (rest of code)
        ) Woah!? Wait a minute... ✋🏻 Where did come from? Why aren't we just passing directly? _get_tools_in_open_ai_format tool_belt Well, let's create that method, and I'll explain right after: import docstring_parser

class Agent:
    # ... (rest of code)
    def _get_tools_in_open_ai_format(self):
        python_type_to_json_type = {
            "str": "string",
            "int": "number",
            "float": "number",
            "bool": "boolean",
            "list": "array",
            "dict": "object"
        }

        return [
            {
                "type": "function",
                "function": {
                    "name": tool.__name__,
                    "description": docstring_parser.parse(tool.__doc__).short_description,
                    "parameters": {
                        "type": "object",
                        "properties": {
                            p.arg_name: {
                                "type": python_type_to_json_type.get(p.type_name, "string"),
                                "description": p.description
                            }
                            for p in docstring_parser.parse(tool.__doc__).params

                        },
                        "required": [
                            p.arg_name
                            for p in docstring_parser.parse(tool.__doc__).params
                            if not p.is_optional
                        ]
                    }
                }
            }
            for tool in self.tool_belt.values()
        ] 😵... Ok, you really don't need to try to understand what each line is doing here since I took the time to work this all out for you. All you need to know is that this method takes the class property and uses the library we installed at the beginning of this tutorial to parse the docstrings of each function and extract the correct . That is why correctly formatting the docstring is so important. tool_belt docstring_parser OpenAI JSON format Otherwise, we would have to define our functions as Python code and define them again manually in the OpenAI JSON format. That's too much room for human error in my book. Polling for tool calls As we saw in Part 1, we have a polling mechanism to determine the current of a Run. One of these statuses is . This means the assistant has determined that one or several tools need to be called based on the instructions and tool definitions provided. The Run will not continue until all the tools have been called and the results of those calls have been submitted to the Run. status requires_action Let's, therefore, update our method to respond appropriately to the status: _poll_run requires_action class Agent:
    # ... (rest of code)
    def _poll_run(self, run: Run):
        status = run.status
        start_time = time.time()
        while status != "completed":
            if status == 'failed':
                raise Exception(f"Run failed with error: {run.last_error}")
            if status == 'expired':
                raise Exception("Run expired.")
            # add the below code block
            if status == 'requires_action':
                self._call_tools(
                    run.id, run.required_action.submit_tool_outputs.tool_calls)

            # ... (rest of method) As you might have guessed, will contain all the logic to dynamically call the available tools. _call_tools Remember, the Assistant only knows what tools need to be called but doesn't actually call them. The Run returns a list of dictionaries, , that contain the names of the functions that need to be called along with the parameters to pass to those function calls. It's up to our custom Agent implementation to obey the Assistant's commands and call the actual function implementations. run.required_action.submit_tool_outputs.tool_calls Let's implement . We'll break this down as code comments so it's easier to understand what is happening: _call_tools import json

class Agent:
    # ... (rest of code)
    def _call_tools(self, run_id: str, tool_calls: list[dict]):
        # We create a tool_outputs list to collect the results of function calls.
        tool_outputs = []

        # We iterate over all the tool_calls to deal with them individually
        for tool_call in tool_calls:
            # We get the `function` object from the tool_call
            function = tool_call.function
            # We extract the arguments from the function object.
            # They are in JSON so we need to load them with the json module.
            function_args = json.loads(function.arguments)
            # We map the function name to our callable function in our Agent's tool belt.
            function_to_call = self.tool_belt[function.name]
            # We can now call the function with the provided arguments.
            function_response = function_to_call(**function_args)
            # We append the response to the tool_outputs list
            tool_outputs.append(
                {"tool_call_id": tool_call.id, "output": function_response})

        # Finally, we submit the tool outputs to OpenAI
        self.client.beta.threads.runs.submit_tool_outputs(
            thread_id=self.thread.id,
            run_id=run_id,
            tool_outputs=tool_outputs
        ) Once the OpenAI method has been called with all the tool call outputs, we have completed the requirements for the Run to move past the status. The status will then switch to before eventually reaching the status and returning a response to the user. submit_tool_outputs requires_action in_progress completed Running it Let's give it a run: python3 main.py

User: Good morning

Assistant: Good morning! What an excellent day it seems for an adventure... or perhaps for a hearty breakfast first. How can I assist you on this fine day?

User: Should you eat breakfast?

== eat_next_meal ==> tool called

Assistant: Ah, only one breakfast so far? Well then, it's clear what must be done. It's time for a second breakfast!

User: You should eat another meal.

== eat_next_meal ==> tool called

Assistant: Ah, having already enjoyed both breakfast and second breakfast, it seems it's time to move on to lunch! This is turning into quite the day of culinary adventures.

User: What day is it?

== tell_the_date ==> tool called

Assistant: It's the 19th of February, 2024. Seems like a perfect day to embark on a quest or to delve into the mysteries of Middle-earth. What plans do we have for today? Voilà! Your hobbit can tell the date and eat the correct meal now. Even though this example is quite silly, my intention is to provide you with a functioning code sample to build your own powerful agents. In Part 3, we will implement RAG (Retrieval Augmented Generation) using PostgreSQL. Wait, what? OpenAI Assistants have that built-in; why not use their implementation? I'll explain my reasoning in Part 3. Thank you for your reading. Happy to hear any thoughts and feedback in the comments. for more content like this. Follow me on Linkedin

How to Build an Agent With an OpenAI Assistant in Python - Part 2: Function Calling / Tools

This is the first part in a multi-part series on building Agents with OpenAI's Assistant API using the Python SDK. What Are Agents? The way I like to look at it, an agent is really just a piece of software leveraging an LLM (Large Language Model) and trying to mimic human behavior. That means it can not only converse and understand language, but it can also perform actions that have an impact on the real world. These actions are typically called tools. In this blog post, we will explore how to build an agent using OpenAI's Assistant API using their Python SDK. Part 1 will be just the skeleton of the assistant. That is, just the conversational part. I chose to build a CLI app on purpose to be framework agnostic. We will purposefully call our implementation an Agent and refer to the OpenAI SDK implementation as an Assistant to easily distinguish between the two. I use the terms and interchangeably when it comes to functions that the Agent is able to call. Part 2 will cover function callin in more detail. tools functions Prerequisites To follow along with this tutorial, you will need the following: Python3 installed on your machine An OpenAI API key Basic knowledge of Python programming OpenAI Assistant Concepts : An Assistant in the Assistants API is an entity configured to respond to user messages. It uses instructions, a chosen model, and tools to interact with functions and provide answers. Assistant : A Thread represents a conversation or dialogue in the Assistants API. It is created for each user interaction and can contain multiple Messages, serving as a container for the ongoing conversation. Thread : A Message is a unit of communication in a Thread. It contains text (and potentially files in the future) and is used to convey user queries or assistant responses within a Thread. Message : A Run is an instance of the Assistant processing a Thread. It involves reading the Thread, deciding whether to call tools, and generating responses based on the model's interpretation of the Thread's Messages. Run Setting Up the Development Environment The first step is to create a virtual environment using and activate it. This will ensure that our dependencies are isolated from the system Python installation: venv python3 -m venv venv
source venv/bin/activate Let's install our only dependency: the package: openai pip install openai Create a file. Let's populate with some basic runtime logic for our CLI app: main.py while True:
    user_input = input("User: ")
    if user_input.lower() == 'exit':
        print("Exiting the assistant...")
        break
    print(f"Assistant: You said {user_input}") Try it out by running : python3 main.py python3 main.py
User: hi
Assistant: You said hi As you can see, the CLI accepts a User message as input, and our genius Assistant doesn't have a brain 🧠 yet so he just repeats the message right back. Not so smart yet. The Agent Now, the fun 😁 (or headaches 🤕) begins. I'll provide all the imports needed for the final class right now, so you don't rack your brain on where things are coming from since I kept imports out of code samples for brevity. Let's start by building an class in a new file : Agent agent.py import time
import openai
from openai.types.beta.threads.run import Run

class Agent:
    def __init__(self, name: str, personality: str):
        self.name = name
        self.personality = personality
        self.client = openai.OpenAI(api_key="sk-*****")
        self.assistant = self.client.beta.assistants.create(
            name=self.name,
            model="gpt-4-turbo-preview"
        ) In the class constructor, we initialize the OpenAI client as a class property by passing our OpenAI API key. Next, we create an class property that maps to our newly created Assistant. We store and as class properties for later use. assistant name personality The argument we are passing to the create method is just for identifying the Assistant in the OpenAI dashboard, and the AI is not actually aware of it at this point. You actually have to pass the name to the which we will see later. name instructions You could already set when creating the Assistant, but it will actually make your Assistant less flexible to dynamic changes. instructions You can update an Assistant by calling , but there is a better place to pass in dynamic values that we will see when we get to Runs. client.beta.assistants.update Note that if you pass here and then again when creating a Run, the Assistant's will be overwritten by the of the run. They do not complement each other, so choose one based on your needs: Assistant level for static instructions or Run level for dynamic instructions. instructions instructions instructions For the model, I chose the model so that we can add function calling in part 2 of this series. You could use if you want to save a few fractions of a penny while giving yourself a migraine of pure frustration down the line when we implement tools. gpt-4-turbo-preview gpt-3.5-turbo GPT 3.5 is terrible at calling tools; the hours I've lost trying to deal with it allow me to say that. 😝 I'll leave it at that, and more on this later. Creating a Thread, Adding Messages, and Retrieving the Last Message After we create an agent, we will need to start a conversation thread. class Agent:
    # ... (rest of code)
    def create_thread(self):
        self.thread = self.client.beta.threads.create() And we will want a way to add messages to that thread: class Agent:
    # ... (rest of code)
    def add_message(self, message):
        self.client.beta.threads.messages.create(
            thread_id=self.thread.id,
            role="user",
            content=message
        ) Note that at the moment, it is only possible to add messages with the role . I believe OpenAI plans on changing this in a future release as this is pretty limiting. user Now, we can get the last message in the thread: class Agent:
    # ... (rest of code)
    def get_last_message(self):
        return self.client.beta.threads.messages.list(
            thread_id=self.thread.id
        ).data[0].content[0].text.value Next, we create an entry point method to test out what we have so far. Currently, the method just returns the last message in the thread. It doesn't actually perform a Run. It's still brainless. run_agent run_agent class Agent:
    # ... (rest of code)
    def run_agent(self):
        message = self.get_last_message()
        return message Back in , we create the agent and our first thread. We add a message to the thread. Then return that same message back to the user, but this time, coming from that live thread. main.py from agent import Agent

agent = Agent(name="Bilbo Baggins",
                      personality="You are the accomplished and renowned adventurer from The Hobbit. You act like you are a bit of a homebody, but you are always up for an adventure. You worry a bit too much about breakfast.")

agent.create_thread()

while True:
    user_input = input("User: ")
    if user_input.lower() == 'exit':
        print("Exiting the agent...")
        break
    agent.add_message(user_input)
    answer = agent.run_agent()
    print(f"Assistant: {answer}") Let's run it: python3 main.py
User: hi
Assistant: hi Still not very smart. Closer to a parrot 🦜 than a hobbit. In the next section, the real fun begins. Creating and Polling a Run When you create a run, you need to periodically retrieve the object to check the status of the run. This is called polling, and it sucks. You need to poll in order to determine what your agent should do next. OpenAI plans to add support for streaming to make this simpler. In the meantime, I will show you how to set up polling in this next section. Run Note the on the following method names which is the standard in Python for indicating that the method is intended for internal use and should not be accessed directly by external code. _ First, let's create a helper method for creating a , and update to call this method: _create_run Run run_agent class Agent:
    # ... (rest of code)
    def get_breakfast_count_from_db(self):
        return 1

    def _create_run(self):
        count = self.get_breakfast_count_from_db()
        return self.client.beta.threads.runs.create(
            thread_id=self.thread.id,
            assistant_id=self.assistant.id,
            instructions=f"""
                Your name is: {self.name}
                Your personality is: {self.personality}

                Metadata related to this conversation:
                {{
                    "breakfast_count": {count}
                }}
            """,
        )

    def run_agent(self):
        run = self._create_run() # add this line
        message = self.get_last_message()
        return message Notice how we pass the and to create a run. thread.id assistant.id Remember how I said at the beginning that there was a better place to pass in dynamic instructions and data? That would be the parameter when creating the Run. In our case, we could have the breakfast be fetched from a database. This will allow you to easily pass in different relevant dynamic data every time you want to trigger an answer. instructions count Now, your agent is aware of the world changing around it and can act accordingly. I like to have a metadata JSON object in my instructions that keeps relevant dynamic context. This allows me to pass in data while being less verbose and in a format that the LLM understands really well. Don't run this yet; it won't work because we aren't waiting for the run to complete when we are getting the last message, so it will still be the last user message. Let's solve this by building out our polling mechanism. First, we will need a way to repeatedly and easily retrieve a run, so let's add a method: _retrieve_run class Agent:
    # ... (rest of code)
    def _retrieve_run(self, run: Run):
        return self.client.beta.threads.runs.retrieve(
            run_id=run.id, thread_id=self.thread.id) Notice how we need to pass both the and to find a specific run. run.id thread.id Add a method to our Agent class: _poll_run class Agent:
    # ... (rest of code)
    def _cancel_run(self, run: Run):
        self.client.beta.threads.runs.cancel(
            run_id=run.id, thread_id=self.thread.id)

    def _poll_run(self, run: Run):
        status = run.status
        start_time = time.time()
        while status != "completed":
            if status == 'failed':
                raise Exception(f"Run failed with error: {run.last_error}")
            if status == 'expired':
                raise Exception("Run expired.")

            time.sleep(1)
            run = self._retrieve_run(run)
            status = run.status

            elapsed_time = time.time() - start_time
            if elapsed_time > 120:  # 2 minutes
                self._cancel_run(run)
                raise Exception("Run took longer than 2 minutes.") 🥵 Phew, that's a lot... Let's unpack it. receives a object as an argument and extracts the current Run . All the available statuses can be found in the OpenAI . We'll just use a few that suit our current purpose. _poll_run Run status docs We now run a while loop to check for a completed status while handling a few error scenarios. The actual billing of the Assistant API is a bit murky, so to be on the safe side, I opted to cancel my runs after 2 minutes. Even though there is an status for when OpenAI cancels runs after 10 minutes. If a run takes more than 2 minutes, you probably have a problem anyway. expired Since I also don't want to poll every few milliseconds, I throttle my request by only polling every 1 second until I hit the 2-minute mark and cancel my run. You can adjust this to whatever you see fit. Each iteration after the delay, we fetch the Run status again. Now, let's plug all that into our method. You will notice we first create the run with then we poll with until we get an answer or an error is thrown, and finally when the polling is finished, we retrieve the last message from the thread which will now be from the agent. run_agent _create_run _poll_run We then return the message to our runtime loop, so it can be sent back to the user. class Agent:
    # ... (rest of code)
    def run_agent(self):
        run = self._create_run()
        self._poll_run(run) # add this line
        message = self.get_last_message()
        return message Voilà, now when you run your agent again, you will get a reply from our friendly Agent: python3 main.py
User: hi
Assistant: Hello there! What adventure can we embark on today? Or perhaps, before we set out, we should think about breakfast. Have you had yours yet? I've had mine, of course – can't start the day without a proper breakfast, you know.
User: how many breakfasts have you had?
Assistant: Ah, well, I've had just 1 breakfast today. But the day is still young, and there's always room for a second, isn't there? What about you? How can I assist you on this fine day? In part 2, we will add the ability for our Agent to call tools. You can find the full code on my . GitHub Thank you for your reading. Happy to hear any thoughts and feedback in the comments. Follow me on Linkedin for more content like this: https://www.linkedin.com/in/jean-marie-dalmasso-1b5473141/

How to Build an Agent With an OpenAI Assistant in Python - Part 1: Conversational

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So erstellen Sie einen Agenten mit einem OpenAI-Assistenten in Python – Teil 1: Konversational

Cómo crear un agente con un asistente OpenAI en Python - Parte 1: Conversacional

Comment créer un agent avec un assistant OpenAI en Python - Partie 1 : Conversationnel

पायथन में ओपनएआई असिस्टेंट के साथ एक एजेंट कैसे बनाएं - भाग 1: संवादात्मक

Python で OpenAI アシスタントを使用してエージェントを構築する方法 - パート 1: 会話型

Python에서 OpenAI 도우미를 사용하여 에이전트를 구축하는 방법 - 1부: 대화형

Como construir um agente com um assistente OpenAI em Python - Parte 1: Conversacional

Как создать агент с помощью OpenAI Assistant на Python. Часть 1: Диалог

Python'da OpenAI Asistanıyla Aracı Nasıl Oluşturulur - Bölüm 1: Konuşmaya Dayalı

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如何使用 Python 中的 OpenAI 助手构建代理 - 第 1 部分：对话式

When we think of debugging, we think of breakpoints in IDEs, stepping over, inspecting variables, etc. However, there are instances where stepping outside the conventional confines of an IDE becomes essential to track and resolve complex issues. This is where tools like DTrace come into play, offering a more nuanced and powerful approach to debugging than traditional methods. This blog post delves into the intricacies of DTrace, an innovative tool that has reshaped the landscape of and system analysis. debugging https://youtu.be/3M0AhZnVoUk?embedable=true As a side note, if you like the content of this and the other posts in this series, check out my that covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my . If you want to get back to Java after a while, check out my Debugging book Java Basics book Java 8 to 21 book . DTrace Overview DTrace was first introduced by Sun Microsystems in 2004. DTrace quickly garnered attention for its groundbreaking approach to dynamic system tracing. Originally developed for Solaris, it has since been ported to various platforms, including MacOS, Windows, and Linux. DTrace stands out as a dynamic tracing framework that enables deep inspection of live systems – from operating systems to running applications. Its capacity to provide real-time insights into system and application behavior without significant performance degradation marks it as a revolutionary tool in the domain of system diagnostics and debugging. Understanding DTrace’s Capabilities DTrace, short for Dynamic Tracing, is a comprehensive toolkit for real-time system monitoring and debugging, offering an array of capabilities that span across different levels of system operation. Its versatility lies in its ability to provide insights into both high-level system performance and detailed process-level activities. System Monitoring and Analysis At its core, DTrace excels in monitoring various system-level operations. It can trace system calls, file system activities, and network operations. This enables developers and system administrators to observe the interactions between the operating system and the applications running on it. For instance, DTrace can identify which files a process accesses, monitor network requests, and even trace system calls to provide a detailed view of what's happening within the system. Process and Performance Analysis Beyond system-level monitoring, DTrace is particularly adept at dissecting individual processes. It can provide detailed information about process execution, including CPU and memory usage, helping to pinpoint performance bottlenecks or memory leaks. This granular level of detail is invaluable for performance tuning and debugging complex software issues. Customizability and Flexibility One of the most powerful aspects of DTrace is its customizability. With a scripting language based on C syntax, DTrace allows the creation of customized scripts to probe specific aspects of system behavior. This flexibility means that it can be adapted to a wide range of debugging scenarios, making it a versatile tool in a developer’s arsenal. Real-World Applications In practical terms, DTrace can be used to diagnose elusive performance issues, track down resource leaks, or understand complex interactions between different system components. For example, it can be used to determine the cause of a slow file operation, analyze the reasons behind a process crash, or understand the system impact of a new software deployment. Performance and Compatibility of DTrace A standout feature of DTrace is its ability to operate with remarkable efficiency. Despite its deep system integration, DTrace is designed to have minimal impact on overall system performance. This efficiency makes it a feasible tool for use in live production environments, where maintaining system stability and performance is crucial. Its non-intrusive nature allows developers and system administrators to conduct thorough debugging and performance analysis without the worry of significantly slowing down or disrupting the normal operation of the system. Cross-Platform Compatibility Originally developed for Solaris, DTrace has evolved into a cross-platform tool, with adaptations available for MacOS, Windows, and various . Each platform presents its own set of features and limitations. For instance, while DTrace is a native component in Solaris and MacOS, its implementation in Linux often requires a specialized build due to kernel support and licensing considerations. Linux distributions Compatibility Challenges on MacOS On MacOS, DTrace's functionality intersects with System Integrity Protection (SIP), a security feature designed to prevent potentially harmful actions. To utilize DTrace effectively, users may need to disable SIP, which should be done with caution. This process involves booting into recovery mode and executing specific commands, a step that highlights the need for a careful approach when working with such powerful system-level tools. We can disable SIP using the command: csrutil disable We can optionally use a more refined approach of enabling SIP without dtrace using the following command: csrutil enable --without dtrace Be extra careful when issuing these commands and when working on machines where dtrace is enabled. Back up your data properly! Customizability and Flexibility of DTrace A key feature that sets DTrace apart in the realm of system monitoring tools is its highly customizable nature. DTrace employs a scripting language that bears similarity to C syntax, offering users the ability to craft detailed and specific diagnostic scripts. This scripting capability allows for the creation of custom probes that can be fine-tuned to target particular aspects of system behavior, providing precise and relevant data. Adaptability to Various Scenarios The flexibility of DTrace's scripting language means it can adapt to a multitude of debugging scenarios. Whether it's tracking down memory leaks, analyzing CPU usage, or monitoring I/O operations, DTrace can be configured to provide insights tailored to the specific needs of the task. This adaptability makes it an invaluable tool for both developers and system administrators who require a dynamic approach to problem-solving. Examples of Customizable Probes Users can define probes to monitor specific system events, track the behavior of certain processes, or gather data on system resource usage. This level of customization ensures that DTrace can be an effective tool in a variety of contexts, from routine maintenance to complex troubleshooting tasks. Following in a simple hello world dtrace probe: sudo dtrace -qn 'syscall::write:entry, syscall::sendto:entry /pid == $target/ { printf("(%d) %s %s", pid, probefunc, copyinstr(arg1)); }' -p 9999 The kernel is instrumented with hooks that match various callbacks. dtrace connects to these hooks and can perform interesting tasks when these hooks are triggered. They have a naming convention, specially: . In this case the provider is a system call in both cases. We have no module, so we can leave that part blank between the colon ( ) symbols. We grab a write operation and entries. When an application writes or tries to send a packet, the following code event will trigger. provider:module:function:name : sendto These things happen frequently, which is why we restrict the process ID to the specific target with . This means the code will only trigger for the PID passed to us in the command line. The rest of the code should be simple for anyone with basic C experience; it's a printf that would list the processes and the data passed. pid == $target Real-World Applications of DTrace DTrace's diverse capabilities extend far beyond theoretical use, playing a pivotal role in resolving real-world system complexities. Its ability to provide deep insights into system operations makes it an indispensable tool in a variety of practical applications. To get a sense of how dtrace can be used, we can use the command whose output on my Mac is below: man -k dtrace bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace
Tcl_CommandTraceInfo(3tcl), Tcl_TraceCommand(3tcl), Tcl_UntraceCommand(3tcl) - monitor renames and deletes of a command
bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace There's a lot here; we don't need to read everything. The point is that when you run into a problem, you can just search through this list and find a tool dedicated to debugging that problem. Let’s say you're facing elevated disk write issues that are causing the performance of your application to degrade... But is it your app at fault or some other app? rwbypid.d can help you with that. It can generate a list of processes and the number of calls they have for read/write based on the process ID, as seen in the following screenshot: We can use this information to better understand IO issues in our code or even in 3rd party applications/libraries. is another tool that helps us track IO operations but with more details: iosnoop In diagnosing elusive system issues, DTrace shines by enabling detailed observation of system calls, file operations, and network activities. For instance, it can be used to uncover the root cause of unexpected system behaviors or to trace the origin of security breaches, offering a level of detail that is often unattainable with other debugging tools. Performance optimization is the main area where DTrace demonstrates its strengths. It allows administrators and developers to pinpoint performance bottlenecks, whether they lie in application code, system calls, or hardware interactions. By providing real-time data on resource usage, DTrace helps in fine-tuning systems for optimal performance. Final Words In conclusion, DTrace stands as a powerful and versatile tool in the realm of system monitoring and debugging. We've explored its broad capabilities, from in-depth system analysis to individual process tracing and its remarkable performance efficiency that allows for its use in live environments. Its cross-platform compatibility, coupled with the challenges and solutions specific to MacOS, highlights its widespread applicability. The customizability through scripting provides unmatched flexibility, adapting to a myriad of diagnostic needs. Real-world applications of DTrace in diagnosing system issues and optimizing performance underscore its practical value. DTrace's comprehensive toolkit offers an unparalleled window into the inner workings of systems, making it an invaluable asset for system administrators and developers alike. Whether it's for routine troubleshooting or complex performance tuning, DTrace provides insights and solutions that are essential in the modern computing landscape. Also published . here

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