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Q3 2023 Earnings Call Transcript October 23, 2023 Cadence Design Systems, Inc. beats earnings expectations. Reported EPS is $1.26, expectations were $1.21. Operator: Good afternoon. My name is Bo, and I will be your conference operator today.

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Cadence Design Systems, Inc. provides software, hardware, services, and reusable integrated circuit (IC) design blocks worldwide. The company offers functional verification services, including emulation and prototyping hardware. Its functional verification offering consists of JasperGold, a formal verification platform; Xcelium, a parallel logic simulation platform; Palladium, an emulation platform; and Protium, a prototyping platform for chip verification. The company also provides digital IC design products; physical implementation tools, including place and route, optimization, and multiple patterning preparation; and signoff products to signoff the design as ready for manufacture by a silicon foundry. In addition, it offers custom IC design and simulation products to create schematic and physical representations of circuits down to the transistor level for analog, mixed-signal, custom digital, memory, and radio frequency designs; and system interconnect design products to develop printed circuit boards and IC packages, as well as to analyze electromagnetic, electro-thermal, and other multi-physics effects. Further, the company provides intellectual property (IP) products consisting of pre-verified and customizable functional blocks to integrate into customer's ICs; and verification IP and memory models for use in system-level verification to model correct behavior of full systems interacting with their environments. Additionally, it offers services related to methodology, education, and hosted design solutions, as well as technical support and maintenance services. Cadence Design Systems, Inc. was founded in 1988 and is headquartered in San Jose, California.

CADENCE DESIGN SYSTEMS INC

That’s just the beginning. → Are you a high-performance employee? Please help me better understand you by contributing to a 30-second anonymous survey. Thank you! http://bit.ly/high-performance-survey ← This article is part of a series on High-Performance Employees (HPEs). Start reading the series here . My * shows that by things like unplanned meetings; being drawn into other conversations; incoming questions from co-workers that could be answered with little effort; and so on—with a staggering . ( ) latest data 80% of high-performance employees are interrupted 5 or more times a day 40% interrupted more than 8 times each day *Over 4,000 respondents from industries like Software, IT, Hardware, Financial Services, Creative, Marketing, Automotive, Architecture and Manufacturing. Wow. Let’s dig in. It’s obvious that employee interruptions have the potential to negatively impact our teams and the trajectory of business. We will first look at the impacts of interruptions on employee time, morale and wellness—later we can infer some troubling effects on process efficiency, time to market, and innovation. Scary stuff. Before we begin, let’s quickly mention the baseline of our mobile generation. Everyone is distracted by everything: their phones, social notifications, news notifications, drones, email, alerts, life-casting, everything. The age barrier to hyper-connectivity has evaporated. We can assume that everyone at every company has the potential to start each day behind a large attention deficit. As such, our companies need to get serious; they have to become more vigilant about reducing distractions and interruptions. Employee Time As the headline of this article suggests, time is money. High-performance employees (HPEs) are expensive. According to Indeed*, the average annual pay for is $ ; is ; is ; is . Software Engineers 100,524 Senior Designers $87,292 Architects $99,373 Product Designers $90,004 (*at time of writing) Let’s say the average HPE is making $94,000 base salary, plus $16,920 (or 18%) for benefits and perks, for a total of $110,920 per year. If we assume work hours per year, it translates to $53 per hour. 2,080 Let’s simplify our interruption assumptions and say that the average HPE is interrupted 6 times per day. In several interviews and presentations, Professor Gloria Mark from the Department of Informatics at UC Irvine stated her research shows that it takes an average of for an employee to recover on task after an interruption. Interruptions can also be considered task switches. 23 minutes For reference, a great visual explanation of task switching can be found here . We may even argue that recovery time can be considerably higher than 23 minutes for our high-performance employees (HPEs). As explained , HPEs are relied on to solve a company’s hardest problems. In 2006, Iqbal and Baily research that task interruption during times of “lower [mental] workload results in meaningfully lower cost of interruption than [at] higher workload”—with random interruptions causing “up to 30% longer [task recovery]…up to twice the errors.” Anecdotally I would suggest their data set could be more robust, but their case is compelling. To keep things simple, let’s continue to assume 23 minutes of recovery time for our HPEs. here showed And so, back to time and money. [ ] x [ ] 6 interruptions per day 23 minutes per interruption = [ ] of underperforming recovery time per day (or , or of an 8-hour work day) 138 minutes 2.3 hours 29% = [ ] 600 wasted hours per year If we’re paying our HPEs an average of $53 per hour, that’s per year. Again, this number may be conservative. $31,800 If we have a team of 40 software engineers or creatives, that’s $1.27M in wasted time and money. Perhaps that’s just the beginning. Employee Morale & Wellness In addition to the acute effects on employee time and productivity, interruptions naturally affect HPE morale. My research shows that and . Environments with many interruptions and distractions will interrupt workflow and inhibit the achievement of mastery by our people. Mastery could be the primary driver in career fulfillment. Interruptions create a dissonance down to the core of the HPE. 76% of HPEs say trade mastery, not money, is most important in career decisions 58% say they need more quiet work spaces More interruptions cause more stress. In 2008, Professor Gloria Mark research that showed a 32–37% increase in stress and a 37–40% increase in frustration associated with task interruptions. She concluded that “people in the interrupted conditions experienced a higher workload, more stress, higher frustration, more time pressure, and effort.” published Frequent interruptions can also consistently cause an employee to work late or at home in order to catch up. If we want employees that tend toward high stress and frustration, let the interruptions continue! Stressed and frustrated employees also fall ill with ease. Sick employees cost us money and are generally a very discouraged lot. In my experience, winter and spring sickness at fast moving companies sweeps through like a tidal wave — few are able to escape. Process Efficiency Interruptions are also harmful to process efficiency in several ways. Let’s return to the idea of an interruption as a task switch. Many of our process designs allow for interruption, task switching and gross overloads of multi-tasking. With more than 5 interruptions per day, we need to design our people systems to improve focus and autonomy, and remove impediments along the way (like interruptions). For a great real-world example, let’s look at the software process methodology called Scrum—where teams synchronize around standardized delivery and cadence schedules, or Sprints. Because synchronization is one of the finest artifacts from Scrum, it should be considered for all types of technical and creative teams. For teams larger than three people, I find Scrum to be far superior to Kanban or other more simplistic first-in-first-out (FIFO) systems for three reasons: 1) impediment removal, 2) batch size or work-in-process constraints, and 3) predictable interruption cadence. I talk more about this in my 2012 book, . Rocket Fueled Process 1) Impediment Removal In a Scrum system, the ‘Scrum Master’ has a most important responsibility— . Each morning Scrum teams execute their ‘stand-up’ meeting. The most important part of that meeting is providing a forum to voice impediments to daily progress—“what am I blocked on?” remove organizational impediments for people We often hear of impediments like too much direct access from non-team personnel, noise levels, equipment issues, waiting on teammates, environmental distractions, and so on. One of my favorite contributions of the Scrum Master is removing personnel access to engineers. Once a Sprint starts and an engineer has committed to a task, not a single person in the organization should have access to that engineer, not even the CEO. Whimsical access to engineers is common from executives. They have random questions and they want answers right away. This causes acute context switching for engineers. As we already know, frequent context switching is very bad, and costs us time and money. 2) Batch Size or Work-In-Process (WIP) Constraints When I design teams, I advocate that no employee be assigned (or volunteer for) more than 12 hours of work per week. “Completed” means a finished increment of demonstrable progress. I’ve been testing this constraint for almost 10 years. I rarely see busy teams where employees can sustain more than 12 uninterrupted hours of work per week without compromising either task completion or their own well-being over time. Why? Because of two things: 1) natural and forced workplace interruptions: meetings, research, changing course, brainstorming, and 2) optimistic time estimation. uninterrupted completed completed By using the smallest batches—or units of demonstrable work—we’re building a highly constrained first-in-first-out (FIFO) delivery system for our people, and also limiting multi-tasking to a minimum. In , I talk a lot about batch size constraints. I’m in strong agreement with Donald Reinertsen that constraining batch size is the single greatest improvement we can make at work. If we use the 1-week work Sprint or iteration length, then constraining batch size—i.e. limiting work to a single focused task—will naturally reduce interruptions and distraction. We get so many benefits from small batch size: Rocket Fueled Process Minimum queue size Minimum iteration length and cycle time Maximum speed to completion (Minimum time to market) Minimum technical risk Minimum personnel risk Minimum documentation Fast hypothesis testing, rapid learning Minimum cost of ownership per increment of completed work Reduce the number of blind assumptions that fuel our decisions If we get all of the above benefits from something as simple as constraining the amount of work we have in process at any given time, it starts to look like a lot of happier people in an office. 3) Predictable Interruption Cadence Another huge benefit from structured process control for employees is the ability to predictably interrupt employees at times of lower mental workload—instead of in the middle of laborious thinking and problem solving. Paul Graham famously explains this when describing the : Maker’s Schedule The manager’s schedule is for bosses. It’s embodied in the traditional appointment book, with each day cut into one hour intervals. […] When you’re operating on the maker’s schedule, meetings are a disaster. A single meeting can blow a whole afternoon, by breaking it into two pieces each too small to do anything hard in. […] If I know the afternoon is going to be broken up, I’m slightly less likely to start something ambitious in the morning. […] Each type of schedule works fine by itself. Problems arise when they meet. Since most powerful people operate on the manager’s schedule, they’re in a position to make everyone resonate at their frequency if they want to. Using the Scrum example, once an employee agrees to work on a task, they work until it’s complete without any outside distraction. Unless they experience a problem with their work or environment, and they proactively reach out for help, they are encouraged to just keep working. Once a week, or at longest once every two weeks, those employees report in for a planning and review meeting where they volunteer for additional work. It’s a beautiful system. As previously referenced, Iqbal and Bailey noted in their research that: interrupting tasks at random moments can cause users to take up to 30% longer to resume [tasks], commit up to twice the errors, and experience up to twice the negative affect than when interrupted at boundaries. What this means is that if we can design our processes to give our employees a predictable interruption cadence for things like team meetings and discussion, we will see huge improvements in available work time, mental effort and efficiency. Imagine if an entire team of technicals or creatives was off limits to the entire organization except for two hours per week of ‘office hours,’ where they are happy to answer questions and invite discussions from anyone in the organization. At all other times, coworkers are encouraged to disengage. This is predictable interruption cadence. It sounds extreme, but our HPEs at many companies spend gross amounts of time responding to whimsical interruptions and impromptu meetings from coworkers. At the end of the day, if our people cannot work efficiently and predictably, we will underperform in every sector of our business. Time To Market & Innovation So then, according my research on office interruptions and that of Professor Gloria Mark on task interruption recovery—if our HPEs are donating up to 29% of their day to random interruptions, the negative effects on our time to market and sustainable innovation potential should run deep. Do we miss the next product launch by 4 months? Does that setback grow grow linearly or exponentially over time? Does our morale deteriorate, and along with it our retention? Does the community start to hear that our company is not a happy place to work, that we are unable to support our growing masters of craft? Do our competitors eat our lunch? Time to market and innovation are , i.e. all of our great organizational design, people processes, culture, and trade mastery potential will make its market impact many months, years or decades later. Similarly, so too will our organizational cancer, incomplete culture, bad process, incompetent leadership, and short-sighted environmental design. trailing indicators A disciplined program that optimizes the holistic wellness and mastery potential of our HPEs is for long-term success and sustainable innovation. The puzzle remains that designing people systems for our HPEs can be difficult and nebulous. It requires a dedication to , and a persistent effort to narrow the disconnect between executives and HPEs—who often optimize against completely different career and trade principles. the leading indicator understanding Perhaps something as seemingly inconsequential as 3 fewer interruptions per day could be the difference in efficiency, sustainable innovation, market leadership, retention, community esteem and happier people. → Are you a high-performance employee? Please help me better understand you by contributing to a 30-second anonymous survey. Thank you! http://bit.ly/high-performance-survey ← This article is part of a series on High-Performance Employees (HPEs). Start reading the series here .

Office distractions could be costing your company more than $30k per high-performance employee each…

If you’re new to the world of IoT, chances are you’ve heard a number of terms and acronyms thrown around and wondered about their differences or whether you should purchase a Raspberry Pi or an ODROID. That main reason I became curious about how interchangeably these boards were being used was because some of the main intro to embedded systems labs at MIT (6.08, 6.111, 6.115) teach , PSoCs, and FPGAs. programming microcontrollers I first started hearing about the different types of boards around 2012, around the time Arduino and Raspberry Pi were becoming popular. I’ve mostly used microcontroller development boards due to their ease of integration into smaller projects (usually small IoT devices won’t require high computing power or peripherals like RAM). Another thing that I didn’t realize at first was that the terms referred to the integrated circuits (ICs), and that a development board had to be built around the IC to make them useable or to interface external components. Microcontroller (MCU) Atmel ATMEGA32U4-AU AVR Microcontroller from Tindie A small computer on a single integrated circuit. They are generally less complicated that SoCs (a SoC can have a microcontroller as one of its components). They contain at least one CPU (processor core), memory (RAM, flash, ROM), and programmable I/O peripherals. They are designed for embedded applications, as opposed to microprocessors used in personal computers or other chips used for more general purpose applications. Many IoT devices that are automatically controlled (using some form of a feedback controls system) use microcontrollers. Their main advantage is that they reduce the size and cost of a similar system using separate microprocessors/memory/input/output devices. They can be used for data collection, sensing, actuating physical devices, and low-energy applications, among other uses. Some common microcontrollers at the Atmel AT89, MSP430 from Texas Instrument, and ESP32/ESP8266. You can usually find development boards that include a microcontroller (such as the Arduino Uno, Intel Galileo, TI Launchpad, or BeagleBone Black), thus removing the necessity of designing a PCB for the microcontroller. Microprocessor Intel Core i7 microprocessor A microprocessor is a device that incorporates the functions of a CPU on a single IC (or a few). It is a clock-driven, register-based digital integrated circuit that accepts binary data as input, processes it according to an instruction set stored in its memory, and provides output. Different instrument sets determine the different architectures of microprocessors (AMD x86 and x86–64, PowerPC, ARM). More common architectures today are generally 32-bit or 64-bit, with some reduced instruction set computing (RISC) microprocessors that — as expected — have reduced instruction sets. There are other devices that are multi-core (Intel Xeon processors, AMD Phenom II). Historically, AMD and Intel are known as the companies with the fastest CPUs with their dual-core and quad-core CPUs. Nowadays, CPUs are almost all implemented on microprocessors, causing the two terms to be practically interchangeable. When comparing the microprocessors in laptops, you generally consider the number of cores, the clock speed, the cache size (like RAM, but the memory is stored on the CPU itself so it’s faster), and other aspects like hyperthreading (running multiple threads simultaneously from a single CPU, which is something you can do nowadays) or dynamically increasing clock speed when the need arises. System on a Chip (SoC) AMD Am286ZX/LX, SoC based on Intel 80286 (from Wikipedia) An integrated circuit with all the components of a computer, including a CPU, memory, I/O ports, and secondary storage. They consume less power and take up less space than a multi-chip design, and are common in embedded systems. Compared to the motherboard-based architecture, the CPU, graphics, hard-disk, USB-connectivity, ROM/RAM, and memory interfaces are all integrated (a motherboard would connect all of the components separately or as expansion cards). SoCs are usually built around a microcontroller, built around a microprocessor (found in mobile phones), designed for a specific application, or designed as a programmable SoC with some reprogrammable aspects, similar to FPGAs. Compared to microcontrollers, SoCs usually have far more pins and more systems integration of different peripherals. Note that system-on-a-chip can refer to many things on the market, and it generally refers to a single chip that does everything that used to take up multiple chips (some still require peripheral such as RAM or flash, so the name may be misleading). Some common ones are the Cypress Semiconductor Programmable System on a Chip (PSoC) and Altera System on a Programmable Chip (SOPC). On that topic, PSoC solely refers to the family of microcontroller-integrated SoCs produced by Cypress Semiconductor, although there are other programmable SoCs that exist. has some useful knowledge on current conventions within SoCs. This StackExchange thread Field-programmable gate array (FPGA) Xilinx Spartan FPGA from Wikipedia An FPGA is an integrated circuit that can be configured by a consumer after manufacture (“field-programmable”). Its configuration is specified using a hardware description language (HDL). They contain programmable logic blocks (like AND and XOR) and a hierarchy of reconfigurable interconnects that “wire together” the blocks. These blocks can also contain memory elements (flip-flops, etc.) The biggest companies for delivering FPGAs include Altera and Xilinx. Compared to microcontrollers, they are faster, parallelizable, and have more design flexibility (trading companies often hire FPGA programmers to program their trading strategies in order to beat out competing firms). They are generally more expensive and more difficult to use, but contain the same ICs, logic components, and programmability. Common FPGAs are the Xilinx Spartan and Virtex Series and the Altera Stratix and Cyclone Series. (More info at ) this StackExchange thread Complex Programmable Logic Device (CPLD) An Altera MAX 7000-series CPLD with 2500 gates from Wikipedia A programmable logic device that’s slightly less complex than an FPGA. It primarily consists of a macrocell with logic expressions and operations. It doesn’t require an external configuration ROM (needed by FPGAs) and has less internal state storage and layered logic than FPGAs. CPLDs have thousands to tens of thousands of logic gates, while FPGAs can have up to several millions. They are primarily manufactured by Altera, Atmel, Cypress Semiconductor, Lattice Semiconductor, or Xilinx, and are programmed in VHDL, Verilog HDL, or Standard Test and Programming Language (JAM/STAPL). Programmable Logic Device (PLD) This is a more generic terms that refers to an electronic component used to build reconfigurable digital circuits — they differ from logic gates, which have a fixed function, as PLDs don’t have functions after being manufactured. Different types of PLDs (in approximate order of complexity from least to most) include the programmable logic array (PLA), programmable array logic (PAL), generic array logic (GAL), complex programmable logic device (CPLD), field-programmable gate array (FPGA), and other variants. Some of the terms used to describe PLDs are trademarked by specific companies (PAL was owned by Monolithic Memories, Inc. (MMI) and currently held by Lattice Semiconductor, for example). Low-complexity devices are programmed in PALASM, ABEL, or CUPL, while higher-complexity devices are programmed in the higher level Verilog and VHDL languages. Digital Signal Processor (DSP) Texas Instruments DSP processor from Analog Devices A specialized microprocessor optimized for digital signal processing. While other microprocessors may have general functionality for executing digital signal processing algorithms, they aren’t usually optimized for continuous processing in real-time. DSPs usually have better power efficiency and are used in mobile phones or devices with power consumption constraints. The algorithms used require a large number of mathematical operations to be quickly performed (FIR filters, Fast Fourier transforms (FFTs), matrix operations). Instruction set architectures commonly used to increase parallelism in DSPs include SIMD, VLIW, and superscalar architecture. Integrated Circuit (IC) Erasable Programmable Read-Only Memory (EPROM) ICs from Wikipedia A set of electronic circuits integrated into one small flat chip of semiconductor material (usually silicon). This results in a chip that is orders of magnitude smaller than a circuit that would have to be built by hand using separate electronic components. These chips are usually mass-produced, and are used in tons of electronics. ICs can be classified into analog, digital, and mixed signal ICs. Digital integrated circuits can contain logic gates, flip-flops, multiplexors, and other circuits, and are usually produced as MCUs, DSPs, or microprocessors. The most advanced ICs are generally microprocessors (“cores”). They can also be classified as logic ICs, memory chips, interface ICs, power management ICs, and programmable devices. Analog ICs can include sensors, power management devices, RF chips, and operational amplifiers (op-amps), which process continuous signals and perform functions such as amplification, active filtering, mixing, and demodulation. They ease the burden on circuit designers by providing an efficiently designed analog circuit that can be difficult to design from scratch. Mixed-signal ICs can create functions such as analog-to-digital converters (ADC) or digital-to-analog converters (DAC), digital potentiometers, as well as clock/timing ICs. Application Specific Integrated Circuit (ASIC) Bitfury 55nm ASIC H-Board from Cryto Store An integrated circuit customized for a particular use (as the name implies). For example, some ASICs are designed for digital voice recording or parallel computing. In Bitcoin (or altcoin) mining, ASICs can be designed to solve specific hashing algorithms. Under some definitions, you can consider a GPU an ASIC, such as NVIDIA GPUs, which are meant for graphics computing. They perform better than CPUs in this specific task due to a compact design (less space and material used), power dissipation (battery life), and speed of computation. Usually the discourse between GPUs vs. ASICs is within the altcoin mining community, which usually discusses ASICs in the context of chips designed to solve a specific complex algorithm. Application-specific standard products (ASSPs) are between ASICs and industry-standard circuits. While ASICs combine a collection of functions designed for a single customer, ASSPs are used as off-the-shelf components. Their uses include USB interface chips, controller chips for a PC, and a chip for a modem. Electronic design automation (EDA) A simple PCB designed in EAGLE A category of software tools for designing electronic systems for integrated circuits (ICs) or printed circuit boards (PCBs). The tools create a design flow for chip designers to create entire semiconductor chips with billions of components. Their simulation software includes transistor, logic, behavior, hardware emulation, and field solvers. For design, they usually inplement schematic capture, layout, and logic synthesis. Analysis includes formal verification to prevent deadlock, equivalence checking, static time analysis for worst-case scenarios, physival verification to prevent physical defects and to check for physical manufacturability, and clock domain crossing verification (like linting) to check for issues in clock domains. Major companies producing EDAs are Altium (Altium Designer), Cadence Design Systems (Verilog, OrCAD), Autodesk (EAGLE), National Instruments (Multisim), and WestDev (Pulsonix). There is also KiCAD, an open-source EDA software, as well as many web-based EDA tools, some of which are web-based versions of the aforementioned EDAs. In terms of some of the differentiations between commonly purchased development boards, knowing the difference in the hardware makes purchasing a board a lot easier. If you’re just starting out, you usually don’t care too much about specifics, and you’ll want a board that’s easy to interface with pin-outs that make sense for whatever project you’re building (you’ll also want systems compatibility with whatever peripherals may be relevant to you). ODROID ODROID C-2 from Wikipedia A series of single-board computers capable of running Android and Linux distributions (the name comes from “open” + “Android”). The hardware features a SoC with a ARM CPU and an on-chip GPU. SD cards are used to store the operating system, and the outputs include USB 2.0, USB 3.0, HDMI, and a 3.5 mm jack. There are also lower-level output pins with general-purpose input/output (GPIO) pins supporting common protocols such as I2C. There are also some models with an 8P8C Ethernet port and an eMMC port. Common models are the ODROID-C1, ODROID-C2, and the ODROID-XU4. Raspberry Pi Raspberry Pi Zero from Wikipedia A series of single-board computers developed for the purpose of teaching basic computer science in schools. The models all featured a Broadcom SoC, with an ARM CPU and an on-chip GPU. SD cards are used to store the operating system, and the outputs include USB ports, HDMI ports, a 3.4 mm jack for audio, and lower-level GPIO pins supporting I2C and other protocols. There are also some models with an 8P8C Ethernet port and an eMMC port. Common models are the Model A, Model B, and Zero. Arduino Arduino Uno from Wikipedia An open-source hardware and software company and user community that produces single-board microcontrollers and microcontroller kits. Their hardware contains an Atmel 8-bit AVR microcontroller with flash memor and a single or double row of pins that can connect to add-on modules (“shields”) and can be addressable via a I2C serial bus. Most models are pre-programmed with a boot loader for uploading programs to the on-chip flash memory. Some of the I/O pins can produce pulse-width modulated signals and receive analog inputs. Common boards are the Arduino Pro, Arduino Mega, and Arduino Yun. As it turns out, there isn’t a ton of standardization in terms of how IoT systems are built and maintained — at least not universally. There are certainly standards that have been set over the past few decades with regards to the protocols and operating systems used that are optimized for smaller devices, but there’s enough variability that how you choose to build your system is dependent on what you value — CPU efficiency, memory, cost, etc. As for the devices themselves, there’s a reason why Arduino is so popular — it takes away all of the mess of having to choose a processor and figuring out layouts for a board and figuring out the programming environment and presents everything neatly packaged such that a 9-year-old child can program an Arduino to turn on an LED. Sticking with the familiar environment that Arduino presents removes a lot of headache, but it doesn’t allow you to explore the greater possibilities of integrating devices that aren’t in the Arduino ecosystem — which is very much a possibility. Sites like Digi-Key (and to varying degrees SparkFun, Adafruit, and Tindie) have a huge array of embedded devices and integrated circuits (ICs) far beyond the scope of Arduino. I’d recommend checking out similar sites if you’re working on a larger scope project. If you liked this post and want to hear more about my thoughts on hardware, computer science, or even life as a university student, feel free to follow me or find me elsewhere online! @sharontlin Twitter @sharonlinnyc Facebook @sharon-lin GitHub

FPGAs, SoCs, Microcontrollers— A Quick Rundown of IoT Devices

At the moment, there are spaceports and launch facilities worldwide that can launch satellites or spacecraft into sub-orbit, orbit, and beyond. Some of these are shared with existing airports due to the nature of their horizontal launch preference, and others are dedicated sites — 35 but what if, in the near future, this changed and watching a launch from your local international airport became the norm? Before we begin, here are some ‘fun facts’ — As of January 2022, the Airports Council International serves 717 members, operating 1950 airports in 185 countries. In total, there are with a passenger volume of over , which the ACI classifies as major airports. Of these, around one-third are located in North America and one-quarter in Europe. The rest are spread across Asia, Australia, and South America. 61 airports 25 million per year In total, there are 9300 airports across the globe, including — Highest altitude airport: Charana Airport in Bolivia (4060m) Lowest altitude airport: Bar Yehuda Airport in Israel (-385m) Number of airports in the northern hemisphere: 3317 Number of airports in the southern hemisphere: 1798 Top 5 countries with the most airports — USA (552) Germany (529) France (472) South Africa (262) Canada (259) The problem right now facing the spaceport industry is that the current launch cadence just can’t accommodate them all, financially and operationally. There’s a rush to build spaceports and launch facilities on new sites, mostly due to the civilian impact of having rockets launch so close to people, but the splitting of a finite resource over increasing numbers means it’s just not going to work long-term. For example, at the moment, there are only some 60-odd planned launches in Europe. There are seven spaceports about to become operational in the UK alone, and Sweden has just announced their intention to build another. Each caters to a type of launcher — horizontal and/or vertical — the majority allowing for the smaller launches only. As this number of spaceports looks to grow, conversely, so too will the availability of suitable sites begin to shrink. There will come a point where spaceports will not only eventually reach a saturation point beyond launch cadence ( ) but also a point where the various aviation authorities step in after a civilian airplane accident takes place. if this won’t happen sooner than they expect anyway all over the globe. Let’s say an average length of a flight is two hours — that would mean that six million people fly somewhere every day. Add on top of that multiple rocket launches from multiple sites on a regular basis, and the skies could get a bit more crowded and more dangerous, especially being managed by different control authorities, different mission controllers, and so on. Roughly 100,000 flights take off and land every day “We believe in a world where more people can go to more places more often. Sustainable supersonic travel unlocks new possibilities for business relationships, prospects for vacation, and opportunities for human connection.” — Blake Scholl, CEO Boom Supersonic and now I know why there’s a new race to build supersonic and hypersonic passenger planes. But the space race is about to get a lot bigger and not for orbital launchers, . Venus Aerospace’s “Stargazer” aircraft is claimed to be capable of crossing the Earth in one hour. Hypersonic technology is all the rage right now, though the specific concepts and applications are hardly anything new. For example, Venus Aerospace unveiled its new Mach 9 hypersonic plane design last year , a potential successor to the ever-loved but now-retired Concorde. And Boom Supersonic is currently building its new Overture supersonic passenger plane Now SpaceX has said that it eventually wants to run 15 million ( ) flights of a passenger version of Starship and could challenge them all. yeah, you read that right But why the focus on ludicrous speed air flight? Simple. Los Angeles to New York, which normally takes five hours and 25 minutes, will take . 25 minutes Bangkok to Dubai, which takes six hours and 25 minutes by plane, will take . 27 minutes Tokyo to Singapore, which can take seven hours and 10 minutes, would take just . 28 minutes London to New York, a seven hours and 55 minutes plane ride, takes just . 29 minutes New York to Paris, which takes around seven hours and 20 minutes by plane, would take just . 30 minutes Starship will be capable of performing long voyages to Mars carrying 100 passengers and over 100 tons of cargo, but the terrestrial version of Starship would rival commercial airliners, offering to transport 1,000 passengers from one spaceport to another across the globe at 4.6 miles per second, that is equivalent to about 12 times as fast as a supersonic flight. How much would a ticket cost? Sam Dinkins, writing for the , estimated in October 2017 it would cost over $1 million to transport 853 passengers, working out to $1,200 per person. Once this becomes a steady reality, these costs will fall — the price of a seat on Concorde was roughly the same back when it flew the skies, so this is frankly a bit of a bargain. Space Review Obviously, this is years out, but whichever becomes the favored method of air travel in the near future, one thing is clear — and civil aviation/ flight control authorities will have to approve it all on top, but the reality is that progress in developing advanced civil aerospace capabilities will stem from the innovations that come out of any and all space programs, including the updating of tracking systems for civil aviation. it means every major airport would need to rethink its capabilities to become spaceports or space hubs in their own capacity It is highly unlikely that the ability to fly Starship from one end of the globe to the other will require building new vertical launch and landing sites near those major destinations, it makes zero sense to do this, and against every other launcher, especially with their cadence and success record so the alternative to building completely new sites owned and operated by others, having their own immigration procedures, emergency infrastructure, flight control, etc is to adjust the existing airport infrastructure to cater for both vertical and horizontal hypersonic flight — no major airport owner in their right mind will want to ignore SpaceX’s current lead and that means airports becoming spaceports in their own right. It makes far more sense to utilize, merge and work with the existing aviation authorities, ground staff, and mission controllers all under one roof and take advantage of economies of scale than to continually add more pressure to an already overburdened staff with oftentimes antiquated computer systems with building a spaceport next door managed by other people with their own agendas and processes. This then opens up new opportunities for airport owners to act as sites for orbital launches, too, since they’ll have the facilities and operations to do so. We won’t need dedicated launch sites in the middle of nowhere, where there are no existing road, rail, or sea infrastructure around them. I remember being taken to the airport to see planes taking off when I was very young, it was a Sunday afternoon treat. Perhaps one day in the future, that’ll be a thing again, only this time, we’ll be watching rockets launch to New York or the Moon just as often. Hypersonic aviation is going to open up more opportunities than it realizes, and airports need to be on the ball to not only capture this new market but also play a vital role in the space race itself. Unfortunately, any time saved on a hypersonic flight will still be lost dicking about in immigration queues and being forced to walk around airport shops. Some things may never change… Also Published Here

Advancements in Airplane Tech Could Lead Major Airports to Become Spaceports

Quick Guide to the Open Source Security Summit

So you want to deliver software across multiple dev teams? You’re probably confused by all the guides comparing different Agile scaling frameworks. And confused that each of them has so much to learn - a book or two and a bunch of certifications. Let’s focus on the current , SAFe. We’ll see why we'd use it, the key ideas and how it differs from the other frameworks. most popular framework Let’s get the flavour of what SAFe is all about. Why use a Framework for Scaling Agile Delivery? Running big projects is different from small projects. Especially it requires: Coordinating delivery across multiple teams. Working with multiple types of stakeholders. Keeping everyone aligned with business goals. With just one team, everyone can talk to everyone. Coordination happens more naturally. Big projects need more alignment practices. This is where the scaling frameworks can help. Why SAFe®? SAFe emphasises that achieving an is one of the hardest challenges of scaling. Culture problems are . To address this it takes a holistic approach. optimal culture for Agile high on the list of blockers to scaling agile SAFe is not only a project management methodology. It incorporates principles from Agile, DevOps, Lean product development and systems thinking. We can think of SAFe as a core of practices with a supporting knowledge base around them. What is SAFe®? Different SAFe guides describe SAFe as a methodology or as a knowledge base or as a set of templates. This can be confusing but they’re all correct. The knowledge base is used to explain the framework’s core practices and the various ways to apply them. It contains templates for different scenarios. SAFe® Overview There is a lot in SAFe 5.0 and this is just a brief introduction. I’ll just explain some of the most illustrative topics to give a good sense of SAFe. Leadership SAFe that only leadership can change the system. Leaders have to be clear about the intended direction and guide change. Otherwise there can be unresolved disputes about direction or some stakeholders may not fully buy in. The upshot is that leadership is crucial to culture. recognises SAFe looks for leaders to coach and mentor as well as set a great example. This means: Effectively communicating a vision. Motivating by explaining drivers. Building coalitions. Encouraging healthy risk-taking. Ensuring a suitable . funding structure There are expectations on all levels of management. SAFe sees Agile software development as part of business Agility. SAFe is not just about project management. It is aimed at full business Agile transformation situations as well as individual large programs. ARTs (Agile Release Trains) The Agile Release Train is a team of teams. It is a set of teams delivering services that are all part of the same product. SAFe describes the ART as a . This is because the ART is meant to be self-sufficient. It should have available all of the resources needed to deliver value and resources should be long-term dedicated to the ART. virtual organization SAFe values alignment between teams very highly. Teams work to a common cadence and come together regularly. There are two levels of cadence for different levels of the program: at team level. These are every two weeks (basically Sprints). Each team has its own backlog and stories for the iteration. Iterations at ART level. These are every ten weeks (five Sprints). The ART has a and high-level allocations to teams are made for a Program Increment. Program Increments program-level backlog Teams work to their using either kanban or (teams can be free to choose). At the end of each iteration there is a for the whole ART. This helps each team see progress other teams are making. iterations of two weeks ScrumXP demo session The team-level iterations are likely familiar as these map to Scrum Sprints. What takes some understanding is how the Program Increment cycles are layered on top. Program Increment (PI) Planning “PI planning is essential to SAFe: If you are not doing it, you are not doing SAFe.” SAFe PI Planning PI Planning in SAFe occurs every five Sprints (5 Sprints = Program Increment = PI). It brings the whole program together to plan the next Increment. SAFe stresses involving everyone in PI Planning. This is partly to build social relationships and encourage communication. It’s also about ensuring everyone can buy in on the vision. PI Planning is where the vision is agreed. Leadership has a key role to play in PI Planning. Planning starts with a presentation of a draft vision for the next Increment. Leadership should communicate this vision, conveying the motivations and also inviting everyone to think it through. The program will collectively shape that vision over the two days of PI Planning. Here is a sample PI Planning agenda: This sample PI Planning agenda based on one from the documentation. SAFe PI Planning Notice that the agenda starts with presentations of the aims for the Increment and the motivations behind those aims. Architecture is included in this as it’s important to have a plan of how the vision can be realised. At this point plans are being presented to the teams for their input. The rest of the PI Planning is dedicated to teams breaking out to work through their parts of the plan. There’s an expectation that teams will need to coordinate here on any inter-related pieces. This process adds detail to the plan as feedback from the teams is used to revise the plan. A confidence vote is included to ensure that concerns are raised and there is then time for reworking to address concerns. It gives every team the opportunity for input and enables teams to buy in on the plan. This can seem like a lot of process for those more familiar with working with just individual teams. The point is that larger programs need more direction. The illustrates this with a quotation: SAFe guide “The more alignment you have, the more autonomy you can grant. The one enables the other.” Stephen Bungay Program Kanban Features being delivered in a Program Increment are monitored in a . This could be pictured like: Program Kanban The Program Kanban is maintained by Product Management and Architecture. It is used to gain visibility of progress and to ensure that no particular team . becomes overloaded The Program Kanban is not the only way of monitoring progress. Individual teams may also have their own methods, . There may also be higher-level Kanbans for monitoring above the level of features. For example, monitoring (which can span multiple Increments). In very large-scale implementations there may also be multiple related ARTs/Programs, monitored and run as a . either Scrum or Kanban Epics Portfolio Innovation and Planning (IP) Iteration The first iteration (Sprint) within a Program Increment is reserved for . This ensures time is set aside for innovation and other work that does not fit neatly into delivering features. These include: Innovation and Planning Innovation and exploration e.g. PoCs, forming new ideas Infrastructure and tooling or release activities Education and documentation Follow-ups to PI planning, backlog refinement, prioritization etc. The IP Iteration also acts as an estimation buffer, though it . should not be regularly relied upon for that purpose Architectural Runway SAFe does not advocate Big Design Up-front. It also does not advocate leaving the architecture to only evolve out of what teams do. It tries to strike a balance. The problem with not doing any central architecture is that individual teams are not best positioned for medium-term planning that spans the system. Teams have remits on particular areas. It’s difficult for teams to get a wider view on the whole system. The architecture function is intended to set a direction that anticipates future business needs. It also plays an important facilitation role. Architecture can help teams to collaborate by guiding teams on which one does which part and how to go about it. SAFe’s vision of architecture is not of an ‘Ivory Tower’ Architect removed from the practice on the ground. The aim is to provide “ ” architectural planning to set a coordinated direction but not too much as it should avoid becoming restrictive. just enough Critical Success Factors for SAFe® SAFe provides a knowledge base to guide practitioners. There is a lot of information and the knowledge base refreshing asks the question “how closely does an organization need to follow various SAFe practices to get the desired result?” It suggests ten . critical success factors for essential SAFe We’ve covered already that should be self-sufficient and aligned to value (2), synchronized across the (3) through (4). We’ve not touched much on (5), which is about practices to understand the customer and how the solution addresses their needs. We’ve also not touched on DevOps but SAFe recommends releasing often to respond to feedback and has . teams ART PI Planning customer centricity suggestions for achieving this We’ve talked about the whole-ART (6) in each iteration. The demo is actually part of an event (7) which also includes looking at progress metrics and a retrospective. demo sessions Inspect and Adapt We’ve looked at the (8) and how it is used for exploratory and supporting work. We’ve also looked at (9) and how it provides “ ” architectural planning to set a coordinated direction. Direction-setting is a core responsibility of (10), who have the influence necessary to align teams and ensure buy-in. Innovation and Planning Iteration Architectural Runway just enough Leadership Underpinning all of this is the SAFe® (1). In fact the Principles are realised through the practices. These are (with my explanations): Lean-Agile Principles Take an economic view = understanding that delivery involves trade-offs Apply systems thinking = looking holistically both at the solution and the delivery setup Assume variability; preserve options = being flexible and avoiding too much Big Up-front Design Build incrementally with fast, integrated learning cycles = releasing often Base milestones on objective evaluation of working systems = assessing results Visualise and limit WIP, reduce batch sizes, and manage queue lengths = avoiding any teams becoming overwhelmed Apply cadence, synchronise with cross-domain planning = PI Planning on common cadence Unlock the intrinsic motivation of knowledge workers = providing autonomy within a clear direction and encouraging learning and experimentation Decentralize decision-making = only strategic decision-making should be centralized to ensure empowerment Organise around value = prefer feature teams aligned to distinct areas of business value where possible SAFe® in the Wild There has been criticism of SAFe. The biggest is that it centralises too much and this invites command-and-control management. Agile Manifesto signatory Ron Jeffries that “SAFe will be installed in a fashion that won't just fail to support Agile, but that will suppress it.” warns Part of the criticism is that SAFe risks being overused. Certainly one should not centralise where one does not have to. The overheads of SAFe do not make sense unless the program is large enough to need them. The minimum ART size should be in SAFe. 50 people Undoubtedly there is a risk that SAFe can be applied in a way that becomes top-down. The concern is not so much with SAFe itself as with how it is likely to be employed. I will not take a stance on this other than to note that there is a general problem for the industry here. Scrum has also been subject to . corruption in practice There are other scaling frameworks available (LeSS, Scrum@Scale, Nexus, DAD etc.). Some are notably more lightweight. For example, many other scaling frameworks do not require the whole program together and instead prefer teams to send representatives to program-wide meetings. There are also criticisms out there of the whole space of Agile frameworks. My intention here is not to give any recommendations, just to empower readers to be able to find out more (and ). I've written more on this elsewhere Next Steps with Scaling Agile There are a lot of Agile scaling frameworks and comparing all of them is a big job. There are ,if that’s what you’re looking for. guides that do this The SAFe documentation at scaledagileframework.com can take some getting used to.  The presents four configurations of SAFe. , , and . These are tailored to different sizes of implementations. Most of what we’ve discussed is . You have to learn how to click around to the bits that you want, or know enough to be able to search for what you want online. SAFe Big Picture Configurations Essential SAFe Portfolio SAFe Large Solution SAFe Full SAFe Essential SAFe If you’re looking to find out more about SAFe, then is the natural book to go to. It will certainly give you the background to use the documentation and see where to go next. SAFe Distilled Image Credits SAFe and Scaled Agile Framework are registered trademarks of Scaled Agile, Inc Title image of Bear by Alexas_Fotos on . pixabay Kanban image uses Postit Notes by Alexandra_Koch from . pixabay Critical Success Factors image uses various images from . by kareemov, by Alex Berkowitz, by BomSymbols, by Unstashable, by Prosymbols, by Lars Meiertoberens, by LUTFI GANI AL ACHMAD, by Phạm Thanh Lộc, by HLD, by Adrien Coquet, by Luis Prado, by Brad Avison. thenounproject Building Person Agile Team Synchronization Customer Training Adapt Research data Brainstorming Binoculars Weightlifter Clock in Critical Success Factors by OpenClipart-vectors on . pixabay

Intro to SAFe®: Scaled Agile Framework®

102 Stories To Learn About Scrum

This paper is available on arxiv under CC 4.0 license. Authors: (1)Eason Chen, Carnegie Mellon University; (2) Justa Liang, Bucket Protocol; (3) Ray Huang, Bucket Protocol; (4) Pierce Hung, Bucket Protocol; (5) Damien Chen, Bucket Protocol; (6) Ashley Hsu, Bucket Protocol; (7) Konstantinos Chalkias, Mysten Labs; (8) Stefanos Pleros Mysten Labs. TABLE OF LINKS Abstract and Introduction Background Randomness Practice in Blockchain System Development Transaction Fee Comparison Discussion Conclusion and References 6. DISCUSSION Through the case study of building a random, fair, and verifiable raffle game on Sui Network, we explored various design approaches, inherent limitations, and potential solutions when developing games with randomness using smart contracts. Each design option comes with its unique set of advantages and drawbacks, and the choice should be made depending on the particular circumstances at hand. 6.1 Randomness on Blockchain Two methods for achieving randomness in smart contracts are considered best practices: DRAND Randomness and host’s key Signature Randomness. As shown in Table 1, both methods have a comparable transaction fee. Drand Randomness aggregates input from nodes across the DRAND network to generate random values, making it more decentralized and secure. However, the current official (before the mainstream update to a 3-second cadence) using DRAND requires a 30-second wait each round, which could impact the user experience. On the other hand, single-key VRF randomness generates signatures using the raffle host’s private key for randomness and is faster. However, it is more centralized, requiring the host to secure the private key and provide a signature each time. 6.2 Smart Contract Design Limitations, Solutions, and Recommendations When building a raffle in smart contracts, it’s critical to consider network limitations, especially concerning the size of arrays and objects. We propose two solutions to address size constraints: "Raffle with Object Tables" and "Zero Knowledge Raffle." "Raffle with Object Tables" involves transforming the content of arrays into a multi-layered table data storage structure, maximizing the data storage capacity. The advantage of this approach is that all data remains on the blockchain. However, it has higher computational costs, requires multiple setup transactions, and incurs higher storage fees. The need for multiple setup transactions can be mitigated using the "Delegate Object Creation" method, where a delegate host assists in creating Object Tables. Storage fees can be reduced by reclaiming storage space through Storage Rebates by clearing data at the end of the Raffle. The raffle with Object Tables is perfect for situations that require absolute transparency and decentralization, such as selecting lucky winners among players to receive exclusive event prizes. Additionally, a host can utilize Raffle with Object Tables to create games like lotteries that require active participation from players. On the other hand, the Zero Knowledge Raffle transforms array fields into a Merkle Tree, enabling an infinite capacity of addresses by uploading only the root of the Merkle Tree to the smart contract. However, it comes with higher transaction fees when verifying the Merkle Tree during prize distribution. The Zero Knowledge Raffle is well-suited to reduce transaction costs in scenarios involving many candidate entities, such as Loot Box and Gacha Games. Moreover, by combining Private Key Randomness with the Zero Knowledge Raffle, it is possible to enhance the efficiency of the raffle while ensuring decentralization and incentives for the host to provide a Merkle Proof to settle the raffle. This paper is under CC 1.0 license. available on arxiv

Designing Fair and Efficient Blockchain Games: Discussion

"The only way to win is to learn faster than anyone else." – Eric Ries 💯  Framework // Concept // Mental Model Launching a successful product is no easy feat. The odds are stacked against you. Over 90% of startups fail due to a lack of product-market fit, running out of cash, not having the right team, and more. 😨 In a talk on “How to Succeed with a Startup,” Sam Altman shared hard-earned wisdom on building startups based on his experience investing in and founding them. 📝 As a fellow product leader, I picked out the key lessons that I believe can help PMs be more successful - whether at a scrappy early-stage venture or scaling behemoth: 🚀 Here, I will dive deeper into each area with tactical advice for how product managers can evaluate and contribute to critical dimensions that influence startup outcomes. Craft Viral, Remarkable Products 🤩 The number one predictor of startup success is intense product-market fit that delights users enough to spark organic growth. Rather than flashy tech or temporary fads, these are durable, viral products addressing underserved jobs. Traits of Remarkable Products Obsessively solve frustrations for a target user base better than alternatives. Offer dramatic improvements on key experiences rather than iterative gains. Frame a compelling, intuitive value proposition addressing feelings potential users self-identify with Tactics to Embed Survey early adopters directly to assess product intensity based on metrics like: Frequency of use Retention over various periods Loyalty vs competitor solutions Satisfaction ratings Structure beta user panels representing various user archetypes to gather broad, honest feedback. Analyze usage data for signs of successfully forming user habits and rituals. Evaluate if the minimum viable product (MVP) solves a pressing “hair on fire” priority. Expand on bright spots with enthusiasm from early testing rather than responding to every feedback item Examples Instagram offered dramatic improvements in mobile photo sharing and filters before cluttering additional capabilities. Tesla initially built a viral base of EV enthusiasts by focusing wholly on an exceptional vehicle experience rather than trying to scale mainstream too quickly. Peloton successfully formed exercise habits and community loyalty better than alternatives. Target High-Potential Markets 📈 Rather than currently observable metrics, evaluate whether the target users and use cases can support exponential expansion in demand over time. This requires accurately assessing both adoption obstacles that need to be overcome before such inflections occur and platforms reach a scale that might bend adoption upward rapidly. Traits of High-Potential Markets Technological maturation unlocking new use cases. Regulatory changes expanding access. Business model evolution overcoming pricing obstacles. Tactics to Embed Talk to investors, founders, and experts in adjacent spaces to gather signals identifying emerging trends early based on peripheral activity. Use tools like Google Trends, SEMrush, SimilarWeb, Pitchbook data, etc. to quantify changes in interest, search patterns, and funding related to market spaces over time. Extrapolate S-curves and model user segments that expand as capabilities improve and costs lower. Identify platform shifts that can accelerate niche adoption once infrastructure scales. Apply adjacencies like mobile → powering app explosion. Examples Teladoc identifies telehealth adoption constraints that would ease over time with video improvements. Mobile gaming benefits from smartphone penetration expanding casual gaming TAM. Autonomous vehicle companies mapping regulatory hurdles and cost Declines unlocking logistics categories. Distinguish Real vs Fake Trends 💎 With so many startups touting world-changing potential but failing to drive repeat user engagement, it’s critical to audit actual intrinsic interest beyond initial curiosity. Analyze both breadth of appeal and depth of habit formation over time across user cohorts. Traits of Real Trends High retention and loyalty even with simple/unfinished early products. Power users exhibit unusually intense usage behavior. Mainstream initial interest matures to a more nuanced understanding of true appeal over time as hype fades. Tactics to Embed Carefully segment users by behavior rather than aggregate statistics. Identify differentiated use cases and niche communities forming around the product. Survey & interview both casual and loyal users on what drives ongoing value. Test viral coefficient with incentive experiments (e.g., refer-a-friend rewards). Monitor engagement and retention by cohort over long periods tracking stabilization. Examples Pokémon Go sustained a strong core user base years after the hype faded. Plant-based meat alternatives prove more appealing to specific consumer segments vs suggested mass-market potential. Wearable fitness trackers shifting from novelty gadgets to practical health tools for target demographics. Recruit Evangelical Founders 🫂 Leadership playing chief product advocate to passionately communicate the vision makes a huge difference in attracting the necessary attention and resources. This starts with the executive team setting the tone, urgency, and confidence to break through the noise. Traits of Strong Evangelists Exude confidence and conviction despite uncertainty. Tailor compelling narratives to various audiences. Inspire belief in lofty visions and rally support. Tactics to Embed Coach leadership on storytelling and presence. Help craft pitches resonating with key stakeholder groups. Fill gaps by communicating passionately if leadership lacks. Examples Steve Jobs captivated audiences with legendary keynotes. Elon Musk sustained ambitious visions on multi-year timelines. Bill Gates credibly explains the future of technology. Structure Bold Incentives 💰 The most in-demand, elite startup talent has countless options today given the abundant funding environment. Competing on compensation alone is insufficient. Attract and retain top talent by framing ambitious visions tied to long-term value creation rather than optimizing for near-term profits. Anchor missions to bold outcomes advancing humanity, not just building profitable apps. Traits of Bold Visions Address complex challenges requiring exceptional teams. Mission orientated around meaningful progress. Inspire talent with a purpose beyond their role. Tactics to Embed Spotlight inspiring PURPOSE rather than PRODUCT. Craft 10-year visions tied to economic, social, and technological progress. Structure incentive alignment and leadership communications to reinforce. Examples Tesla anchoring workforce to sustainable energy innovation. SpaceX attracting top engineers to space exploration missions. Longevity-focused biotech Startups Framing Healthspan impacts. Obsess Over Team Composition 👥 Studies show startup success strongly correlates with thoughtful, balanced team composition based on skillsets, experience, and temperament. Before scaling headcount, probe the leadership traits and interpersonal chemistry within founding teams by benchmarking against past archetypes. Recruiting should then fill gaps with appropriate aptitudes and resilience required for volatile environments. Balanced Startup Archetypes : Sells the vision Hustler : Builds the product Hacker : Obsesses over user experience Hipster Shared Mindsets Optimism - Belief can figure out hard problems Ownership - Fix things rather than pointing fingers Resilience - Persistence despite turbulent conditions Tactics to Embed Evaluate the team through founder archetype lenses first Assess collective persistence qualities beyond domain expertise Carefully select the first 10-15 hires based on cultural signals Test alignment on intrinsic – mission, norms, values Examples Airbnb’s cereal team bonding screening for collaboration Tesla’s relentless problem-solving perspective Stripe’s long-term thinking and execution balance Sustain Cadence Through Wins 🏆 Maintaining consistent tempo on product and go-to-market initiatives builds confidence in leadership, staff, and investors by repeatedly proving empirical success. Cultivating urgency and delivering tangible results prevents organizations and budgets from stagnating when running lean. Value progress velocity over distant milestones. Traits of Sustained Cadences Continuous action bias toward incremental progress Rapid experimentation velocity testing assumptions Avoid distraction by postponing non-critical efforts Tactics to Embed Structure sprints and OKRs to ship value faster Impose constraints like Innovation Lockdowns to focus on innovation Celebrate small milestones frequently Continuously realign on the most critical priority Examples Amazon’s “Working Backwards” product development rigor OODA loop continuous orientation, decisions, and actions “Innovation Theater” budgeting tactics to fund progress Develop Competitive Advantages 💪 Most startups pitch feature advantages, but sustainable moats stem from systems, network effects, and scaling over time rather than temporary differentiators. Analyze marketplace dynamics and system-level resources that could compound startup strengths apart from basic product features. Traits of Compounding Advantages Switching costs imposed through integrated workflows Data barriers that improve system performance Brand affinity and community create inertia Tactics to Embed Map core interaction loops and intermediaries Model how aggregating signals may improve utility Design rituals and cultural differentiators early Examples Amazon Prime ecosystem lock-in Facebook social graph and ad targeting Apple’s brand community and product launches Design Viable Monetization 💵 Consider business model viability early and have pricing and revenue experiments ready to run at key milestones rather than solely optimizing for traction goals trusting adoption subsidies everything. Draft early financial model blueprints depicting how the startup could eventually design profitable, scalable revenue streams across various functions. Traits of Good Models Identify monetizable user values Outline milestones to test experiments Analyze analogs demonstrating sustainability Tactics to Embed Map monetizable user actions and incentives Talk to ecosystem partners about potential affiliate rev shares Survey power users on willingness to pay Examples Amazon moving from books to → third-party marketplace Instagram establishing ads after growth footing Distribute Effectively 🚀 Rather than treating it as an afterthought, evaluate probable acquisition channels and optimize conversion funnels for growth built into products from the onset. Viral models bake in incentives and social sharing hooks driving referral looping. Direct sales compensates business development partners. Traits of Effective Distribution Achieve product-channel fit matching user search behavior Incentivize organic propagation through network effects Compensate ecosystem partners Tactics to Embed Analyze analog platforms to determine the most relevant touchpoints Survey early adopters on how they discovered niche offerings A/B test positioning and packaging conversion Examples Facebook’s early college ambassador programs Slack’s developer evangelism and freemium conversion Snapchat leveraging broadcast social sharing As a product manager, focus on evaluating and contributing to progress in these areas critical to startup greatness. 💫 👇 What resonated most with you? What success factors did I miss? Share your top lessons for fellow PMs below! 📚 Book - Offers unique insights into creating value in the startup world. Zero to One by Peter Thiel 🎧  Youtube https://www.youtube.com/watch?v=0lJKucu6HJc 😎  Meme Thanks for reading The Product Channel By Sid Saladi! Subscribe for free to receive new posts and support my work. 🌟 Some of our popular newsletter editions ============================================ 🚀 Week 6 - Dynamic Ways ChatGPT Revolutionizes Product Management 🚀 and 50+ Product Management prompts 📚 Week 5 - 50+ Product Management Prompts for ChatGPT-4 👩‍💼 Week 22 - How to create a customer persona for your product 🧠 Week 24 - 14 Behavioral Psychology Concepts Product Managers Should Know 📊 Week 27 - 📈 How to Develop and Write KPIs: A Guide for Product Managers 📋 📚 Week 6 - PM 101 (Books, Articles, Podcasts and Newsletters) 💼 Week 20 - AI Tools 🤖 for Product Managers 💪 That Will Transform Your Workflow 🔄 and Boost Productivity 📈 ✍️ Week 29 - 💡 A Step-by-Step Guide to Crafting Killer 📝 Problem Statements 🔧 Week 33 - 🚀 The Ultimate Guide to Prototyping for Product Managers: Mastering the Art of Reducing Uncertainty in Product Development 🎯 🤯 Week 16 - 6 Most Effective Problem Prioritization Frameworks for Product Managers - Part 1 🤯 Week 17 - 6 Most Effective Problem Prioritization Frameworks for Product Managers - Part 2 Also published here

CADENCE DESIGN SYSTEMS INC

cadence.com

5K-10K employees

Since 1988

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CADENCE DESIGN SYSTEMS INC

cadence.com

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Since 1988

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