Turnaround

turnaround

has been nominated for a 2020 #Noonie in Pierre-Antoine Mill from France Software Development category. So, we asked our Noonie Nominees to tell us. Here’s what had to share. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. Pierre 1.  Which 2020 Noonie have you been nominated for? TypeScript 2. Tell us a bit about yourself. I <3 TypeScript, React, and Functional Programming. I type it all. OSS contributor. Sport & life addict. I'm interested in sustainable development and environment/climate. 3. Tell us about the things you make / write / manage / build. I write blog posts, courses, and libs for the TypeScript ecosystem. 4. What are you most excited about right now? The future. 5. What are you worried about right now? The state of our society and the environment. 6.   What's the most useful advice you've ever given somebody? Change your inner intent and your life will turn around. 7. How has the pandemic changed your life and/or career? In some way. It has accelerated the choices I have to make. It made stuff clearer. 8. If we gave you $10 million to invest in one thing right now, where would you put it? In sustainable, automated, organic, technological agriculture. 9. What's an opinion you have that most people don't agree with? The human being is essentially wrong. 10. Which apps can't you live without? I create apps but I don't use them much. I'm free from my phone. 11. What are you currently learning? React Native For much the same reasons Hacker Noon decided not to put good ideas behind pop-ups or paywalls — nor abuse your personal data to target you with creepy ads — we also decided that you don’t have to be a #thinkfluencer or have 50k followers on Twitter to earn the recognition that comes with a 2020 Noonie Nomination. Make somebody’s day and nominate them to be recognized in the internet’s most independent and community-driven awards: . NOONIES.TECH

"Change Your Inner Intent And Your Life Will Turn Around", Interview with Pierre-Antoine Mill

I’m seeing the first full quarter of appreciation for many alt-coins. Appreciation is low but a turn around from what largely has been consistent declines for the life of alt-coins. This assumes this trend holds through the end of this month. Bitcoin will also appreciate this quarter if the same holds true. A few select Tokens: ETH moves directly with the volume of ICOs. . The new IEO (initial exchange offering) movement being touted as the second coming of ICOs may prove to do the same for ETH There is a high level of unsatisfied demand for IEOs. The gambling culture of crypto will fuel this. The wave will be nowhere near that of ICO’s. Because exchanges are pre-vetting the IEO projects on top of the hard lessons learned from poor quality companies behind all too many ICOs we’ll have increased quality in the IEO as an investment opportunity. Q2 is going to be exciting in crypto. This is my first-hand experience and information. I’m buying more bitcoin. I’m still wary of putting my own money on non-reputable crypto exchanges. Too many posts of people having trouble getting their money off of the exchanges likely due more to technical bugs more than intentional activities. Rapidly constructed, the technologies behind these exchanges is young and brittle while improving fast. Exchange support teams are having trouble keeping up with support requests. If you are a blockchain company this may be an optimal time to list your token on an exchange. My company Efficient Frontier an algo trading company in crypto provides market making services and strategic planning can help. If you are a listed ICO you should also contact us. chris@efrontier.io This is not investment advice. Do your own research. Thanks for reading! ;-) If you like this article you may be interested in this one as well Here is the blog maintained by the company I work for an algo trading market-making firm in blockchain/crypto Efficient Frontier If you would like an email alert for my next post sign up here.

Crypto markets show signs of a turnaround

Business Growth Won’t Last Forever Business growth should never be taken for granted. Even when things look promising, your business could still be at risk for falling into a slump, or worse, taking a dive. It’s impossible to deny that the market is volatile and unpredictable. Competition is abundant, and all businesses lose market share at some point. When revenue hits a speed bump, it’s common for leaders to anxiously ride it out. But when business growth really slows down, denial isn’t going to get you out of the rut. Leadership may react with a mad scramble to redirect the sinking ship: new products, new markets, or new acquisitions can transpire overnight. But if these changes occur in a C-suite silo there’s a high probability your employees and core customers will feel left in the dark. And employees with varying ideas about the future and customers who no longer feel connected to your business can easily derail a growth strategy. Misalignment only leaves more opportunity for your competitors to encroach on even more of your space. And when that happens, employee morale can sink deeper, recruiting becomes challenging, and share prices may plummet as investors abandon ship. At this point your options are clear: execute a thoughtful, agile turnaround strategy or . risk becoming obsolete Bringing Your Brand Along For the Ride Some of the most common approaches to a turnaround strategy include increasing growth through new market penetration, tapping into new markets, pursuing alternate sales channels, developing new products, or expanding your customer base. Regardless of which avenue you follow, transforming your business without preparing your brand to adapt alongside it can jeopardize your plans for growth. Consider the recent news of . With Tesla shares dropping 16% over the last year, Tesla turned to the acquisition as a business growth strategy. They could no longer afford to burn through the $2.2 billion dollars of the last 4 quarters. The acquisition was an obvious attempt to turnaround Tesla’s business. The problem was that Tesla took the leap into solar energy without fully considering the consequences of the brand, and more specifically the people the brand matters to. And even further since the deal announcement. What’s the cause? Apparently, investors aren’t on board with the idea of buying solar panels. Tesla misunderstood its target audience and will now have to backtrack to renew brand loyalty. Although this example shows long-term vision, business growth without a parallel brand strategy may have insurmountable long-term consequences for Tesla and SolarCity. Tesla Motor’s offer to acquire SolarCity shares have plummeted Map Brand to Business Aligning your brand to your business as it undergoes a transformation is the best way to create a road map that is tailored for business growth. The vision for the business needs to guide all business decisions. By bringing your brand and business together, you’ll ensure that your business stays true to who you are, what you do, and why you matter. As you position your business to grow, respecting the underlying vision of the company will ensure that the growth is in line with what the brand stands for. Brand strategy will address your key target audiences, your value proposition, your positioning, your narrative, and your go to market strategy. With these elements adjusted to reflect the business transformation, your brand will be prepared to roll out a strategic marketing plan. To really change the tide of business growth, the strategy needs to incorporate long-term goals with a short-term action plan. It needs to be measurable, too. Identify the key indicators of business growth and hold the business accountable as it transforms. A turnaround that shows improvement to employee engagement, brand perception, NPS, and customer loyalty will ultimately affect sales. And increasing top line revenue increases market share and for some, stock price. But that’s just the plan. Executing the brand strategy as the business shifts requires agility in the changing market. Too often, companies take a year to develop a strategy and by the time they’re ready to implement it, the market has shifted. Testing and iterating in real time will allow your business and brand to adapt quickly so that it remains relevant. It’s critical to develop a flexible strategy with participation from your sales and marketing team. Sales and marketing will provide necessary, immediate feedback from the people on the front line of your business. Their buy-in and shared responsibility creates alignment from the top-down. As the business turnaround happens, the sales and marketing team drive the messaging to make sure your target audience is on board too. Achieving Sustainable Growth are never achieved without a brand that is strong enough to weather the tides of change. Whether you’re looking to grow through market penetration, market development, alternative channels, product development, or expanding your customer base, your brand needs to lead the way. And change doesn’t happen in a vacuum. An agile, measurable approach that stays true to the long-term vision is key to turning around your company’s trajectory. Grow your brand and grow your business. Growth strategies Emotive Brand is a San Francisco brand strategy agency.

Turnaround Strategy: Include Your Brand

How Digital Platforms Undo Paperwork In Real Estate

Automated solutions have been around for over a decade in the cybersecurity space. However, their use has been confined to very surface-level areas—it’s time this changed. With the recent advancements in generative AI, the technology has proven to have a higher ceiling for application, including time-consuming yet vital processes like cyber discovery. AI In fact, organizations that automate cyber defense mechanisms will experience a in security breaches by 2026. So, it’s evident that automation will become the norm for many cybersecurity processes. This is especially important given the more targeted and aggressive attacks, such as advanced persistent threats (APTs), that have been launched in the past year. 66% reduction APTs make cybersecurity much murkier. If a company experiences an attack of this nature, they’re as good as gone; the sophistication of these threats can penetrate the most robust systems when mildly overlooked. One of many examples from last year is the highly secure USBs, gaining access to government data via APTs by subtly infecting and taking over every device they connected them to. malicious actors that targeted Here’s where more advanced automation comes in to keep APTs at bay. Let’s see how adopting these new practices can highly benefit your security team and business. Faster and more controlled threat intelligence turnarounds The APT protection market is set to grow by a staggering annually until 2032 when it will be worth $51.5 billion. The increasing severity of these attacks is what’s driving CISOs to spend more of their budget protecting their company’s attack surfaces — starting with proper threat intelligence and asset discovery methods. 18% Automation can speed up asset discovery, and time is of the essence when it comes to data security. A typical discovery process for a company of around 10,000 employees would take a security team a couple of days to finish. By automating this process, the timeline plummets to just one day. Instead of outsourcing this process to cybersecurity companies, in-house security teams can cut the middleman and use threat intelligence tools to automate the discovery process for them. This is possible with SaaS programs that use open source and proprietary codes to create a workflow, allowing companies to run tailored discovery as often as they see fit. The time difference between manual and automated can be a costly APT attack or a safe system. More precise and accurate discovery to bridge the skills gap You wouldn’t begin securing your systems without knowing exactly what needs to be done. It would be a waste of both time and money. This is why discovery is so crucial to threat intelligence and successful cybersecurity practices — it’s the foundation for protecting attack surfaces. And for discovery to be worthwhile, it must be done right. However, the cybersecurity talent shortage is making it harder for companies to properly protect their systems, especially when are being affected by the lack of professionals globally. Automation is helping bridge this gap and giving companies peace of mind for their foundational cybersecurity strategies. 71% of organizations By using threat intelligence SaaS tools that automate discovery, security teams can forego a discovery expert while knowing this crucial piece of the puzzle is still being handled with precision and accuracy by an AI workflow. APTs don’t let up, and minimal human error could open the gates for malicious actors to break in and set up shop. Increased human power in other specialized activities CISOs that human error is their organization’s biggest cyber vulnerability. Discovery can be a very time-consuming task, and human processes can become faulty at any point in the job. Now that threat intelligence automation is a more accessible and developed practice, security leaders shouldn’t have to place their human power in these intensive and often flawed activities. highly agree After adopting SaaS tools for discovery, security teams will rest assured their discovery needs will be handled by software that takes less time and significantly reduces errors in threat detection. What will this translate to? More time applying threat intelligence data into more robust security protocols like vulnerability prioritization and remediation, meeting data security compliance requirements, and other activities that require human input. Keeping APTs away from your company encompasses several steps in your cybersecurity practices. Automation is making it easier for security teams to spend their time on valuable activities while others run on AI workflows with less margin for errors. In this new age of AI and automation, these tools are becoming more affordable and user-friendly to address skills shortages and the increase in complex cyber attacks. Ultimately, automating certain tasks is a win-win for security teams, budgeting, and cybersecurity measures — bringing in convenience, precision, and affordability. , co-founder, Trickest Nenad Zaric

Ensuring Cyber Resilience: The Imperative of Automating the APT Discovery Process

This is paper is available on arxiv under CC 4.0 DEED license.
Authors:
(1) Dhruv Shah, UC Berkeley and he contributed equally;
(2)  Michael Equi, UC Berkeley and he contributed equally;
(3) Blazej Osinski, University of Warsaw;
(4) Fei Xia, Google DeepMind;
(5) Brian Ichter, Google DeepMind;
(6) Sergey Levine, UC Berkeley and Google DeepMind. Table of Links Abstract & Introduction
Related Work
Problem Formulation and Overview
LFG: Scoring Subgoals by Polling LLMs
LLM Heuristics for Goal-Directed Exploration
System Evaluation
Discussion and References
A. Implementation Details
B. Prompts A Implementation Details A.1 Hyperparameters A.2 Computational Resources A.3 Real World Results Generating Prompts: For both topological and geometric maps we use hand engineered methods for clustering objects in ways that the LLM can efficiently reason over. For geometric maps we implement two functions: parseObjects and clusterObjects. In our implementation, parseObejcts filters the geometric map and identifies the cluster centers among each class. clusterObjects takes the cluster centers and performs agglomerative clustering with a threshold of 6 meters, which is roughly the size of one section of a standard house. For topological maps we rely on the configuration of the four cameras to automatically perform parsing and clustering. In our implementation all the objects detected in each frame from either the front, left, right, or rear facing cameras is considered a single cluster. Perception: For the hardware, we use a locobot base with a four HD logitech web cameras that are positioned at 90 degrees relative to each other. At each step of LFG each of four cameras is recorded and frames are semantically annotated. LFG directly uses these frames to determine if the robot should continue to move forward, turn left, turn right, or turn around a full 180 degrees. To improve the performance of our system we choose to whitelist a subset of the 20,000 classes. This reduces the size of the API calls to the language models and helps steer the LLM to focus on more useful information. Following is the complete whitelist used in our experiments: • toaster • projector • chair • kitchen table • sink • kitchen sink • water faucet • faucet • microwave oven • toaster oven • oven • coffee table • coffee maker • coffeepot • dining table • table • bathtub • bath towel • urinal • toilet • toilet tissue • refrigerator • automatic washer • washbasin • dishwasher • television set • sofa • sofa bed • bed • chandelier • ottoman • dresser • curtain • shower curtain • trash can • garbage • cabinet • file cabinet • monitor (computer equipment) • computer monitor • computer keyboard • laptop computer • desk • stool • hand towel • shampoo • soap • drawer • pillow Low-level Policy: The low-level policy running on the robot is the NoMaD goal-conditioned diffusion policy trained to avoid obstacles during exploration and determine which frontiers can be explored further [41]. High-level Planning: For real-world experiments, we follow the setup of ViKiNG [34], where the agent runs a simple frontier-based exploration algorithm and incorporates the LLM scores as goaldirected heuristics to pick the best subgoal frontier. For simulation experiments, we use a geometric map coupled with frontier-based exploration, following the setup of Chaplot et al. [38]. Algorithms 3 and 4 summarize the high-level planning module in both cases. A.4 More Experiment Rollouts Figure 6 shows an example where the negative scoring is essential to LFG’s success. Figures 7 and 8 show examples of LFG deployed in a previously unseen apartment and an office building, successfully exploring the environments to find an oven and a kitchen sink. This is paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Dhruv Shah, UC Berkeley and he contributed equally; (2)  Michael Equi, UC Berkeley and he contributed equally; (3) Blazej Osinski, University of Warsaw; (4) Fei Xia, Google DeepMind; (5) Brian Ichter, Google DeepMind; (6) Sergey Levine, UC Berkeley and Google DeepMind. This is paper is available on arxiv under CC 4.0 DEED license. Authors: Authors: (1) Dhruv Shah, UC Berkeley and he contributed equally; (2)  Michael Equi, UC Berkeley and he contributed equally; (3) Blazej Osinski, University of Warsaw; (4) Fei Xia, Google DeepMind; (5) Brian Ichter, Google DeepMind; (6) Sergey Levine, UC Berkeley and Google DeepMind. Table of Links Abstract & Introduction Related Work Problem Formulation and Overview LFG: Scoring Subgoals by Polling LLMs LLM Heuristics for Goal-Directed Exploration System Evaluation Discussion and References A. Implementation Details B. Prompts Abstract & Introduction Abstract & Introduction Related Work Related Work Problem Formulation and Overview Problem Formulation and Overview LFG: Scoring Subgoals by Polling LLMs LFG: Scoring Subgoals by Polling LLMs LLM Heuristics for Goal-Directed Exploration LLM Heuristics for Goal-Directed Exploration System Evaluation System Evaluation Discussion and References Discussion and References A. Implementation Details A. Implementation Details B. Prompts B. Prompts A Implementation Details A.1 Hyperparameters A.2 Computational Resources A.3 Real World Results Generating Prompts: For both topological and geometric maps we use hand engineered methods for clustering objects in ways that the LLM can efficiently reason over. For geometric maps we implement two functions: parseObjects and clusterObjects. In our implementation, parseObejcts filters the geometric map and identifies the cluster centers among each class. clusterObjects takes the cluster centers and performs agglomerative clustering with a threshold of 6 meters, which is roughly the size of one section of a standard house. For topological maps we rely on the configuration of the four cameras to automatically perform parsing and clustering. In our implementation all the objects detected in each frame from either the front, left, right, or rear facing cameras is considered a single cluster. Generating Prompts: Perception: For the hardware, we use a locobot base with a four HD logitech web cameras that are positioned at 90 degrees relative to each other. At each step of LFG each of four cameras is recorded and frames are semantically annotated. LFG directly uses these frames to determine if the robot should continue to move forward, turn left, turn right, or turn around a full 180 degrees. To improve the performance of our system we choose to whitelist a subset of the 20,000 classes. This reduces the size of the API calls to the language models and helps steer the LLM to focus on more useful information. Following is the complete whitelist used in our experiments: Perception: • toaster • projector • chair • kitchen table • sink • kitchen sink • water faucet • faucet • microwave oven • toaster oven • oven • coffee table • coffee maker • coffeepot • dining table • table • bathtub • bath towel • urinal • toilet • toilet tissue • refrigerator • automatic washer • washbasin • dishwasher • television set • sofa • sofa bed • bed • chandelier • ottoman • dresser • curtain • shower curtain • trash can • garbage • cabinet • file cabinet • monitor (computer equipment) • computer monitor • computer keyboard • laptop computer • desk • stool • hand towel • shampoo • soap • drawer • pillow Low-level Policy: The low-level policy running on the robot is the NoMaD goal-conditioned diffusion policy trained to avoid obstacles during exploration and determine which frontiers can be explored further [41]. Low-level Policy: High-level Planning: For real-world experiments, we follow the setup of ViKiNG [34], where the agent runs a simple frontier-based exploration algorithm and incorporates the LLM scores as goaldirected heuristics to pick the best subgoal frontier. For simulation experiments, we use a geometric map coupled with frontier-based exploration, following the setup of Chaplot et al. [38]. Algorithms 3 and 4 summarize the high-level planning module in both cases. High-level Planning: A.4 More Experiment Rollouts Figure 6 shows an example where the negative scoring is essential to LFG’s success. Figures 7 and 8 show examples of LFG deployed in a previously unseen apartment and an office building, successfully exploring the environments to find an oven and a kitchen sink.

Navigation with Large Language Models: Implementation Details

HackerNoon launches our long-awaited offering: Technology Press Releases with HackerNoon! You can now leverage HackerNoon’s distribution engine to share your press releases! Leveraging prominent onsite and newsletter distribution, owned and paid social media, traditional media press release pickup, and machine learning-generated translations, HackerNoon now averages 5k readers per press release. Visit our Technology Press Releases home page to learn more. The introductory prices start at $750 per release, and bulk discounts are available. There is no expiration date for your Press Release credits. Avail the Introductory Pricing for Your Press Release Now. Why Publish Your Company’s Technology Press Releases with HackerNoon? Great question. Here are not one but 8 reasons why: Technical Achievements Are Best Hosted with a Technical Audience HackerNoon is the go-to tech news and learning platform for millions of technologists. Our library of 100k+ technology stories attracts 3M+ monthly visitors. See below the data gathered from 1k+ job titles entered via HackerNoon.com in Q1 2024. Accessibility: Translated into 12 Languages, 2 Audio Files, and Backed Up on the Blockchain HackerNoon makes content more accessible to increase audience size. Via machine learning models from OpenAI, Google, & others, we translate your story into 12+ languages, 2 audio files and back it up on the Arweave blockchain for good measure. Distribution Across HackerNoon and Around the Web Via owned and paid social media, traditional media like press releases, machine learning generated translations, and the creation and distribution of the audio story, HackerNoon maximizes technology readership. Creating an Average of 5k Readers Per Press Release We’ve used the traditional press release services, and they usually get just a couple hundred readers per story. Sometimes even less. When you publish your technology press release on HackerNoon, you should expect ~5k readers. Republished on the Wire to 100+ Traditional Media Sites Just like the traditional press release wire services, we put your story on the wire. You should expect your technology press release to be re-published to ~100 more sites, such as Benzinga, Digital Journal, Yahoo Finance, and The AP. Prominent Distribution Across the HackerNoon Site, App, and Newsletter Technology Press Releases on HackerNoon are featured on the Homepage, the Category Page, 8 Tagged Pages, 12 Translation Homepages, the Mobile App, the Terminal App, the Lite Version, and the Noonification Newsletters (250k+ subscribers). Easy Content Uploading and Formatting with Staff Support We hate how difficult it is to upload content to archaic press release distribution services. The HackerNoon uploader and story editor are smooth AF. We also accept your submissions via unlisted link like Google doc or even a URL like your pressroom. 1 Business Day Turnaround Time (TAT) and Professional Editorial Support Technology press releases are timely content. Our editorial and support staff guarantee one business day turnaround time. HackerNoon is here to make sure your story gets distributed on time. Book a meeting with us to learn how Octopus Network, KucCoin Community Chain, Avalanche, ICON Foundation, and other tech companies publish their press releases with HackerNoon. HackerNoon launches our long-awaited offering: Technology Press Releases with HackerNoon! You can now leverage HackerNoon’s distribution engine to share your press releases! Leveraging prominent onsite and newsletter distribution, owned and paid social media, traditional media press release pickup, and machine learning-generated translations, HackerNoon now averages 5k readers per press release. Visit our Technology Press Releases home page to learn more. Technology Press Releases home page The introductory prices start at $750 per release , and bulk discounts are available. There is no expiration date for your Press Release credits. $750 per release Avail the Introductory Pricing for Your Press Release Now. Avail the Introductory Pricing for Your Press Release Now. Avail the Introductory Pricing for Your Press Release Now. Why Publish Your Company’s Technology Press Releases with HackerNoon? Great question. Here are not one but 8 reasons why: Technical Achievements Are Best Hosted with a Technical Audience HackerNoon is the go-to tech news and learning platform for millions of technologists. Our library of 100k+ technology stories attracts 3M+ monthly visitors. See below the data gathered from 1k+ job titles entered via HackerNoon.com in Q1 2024. HackerNoon.com Accessibility: Translated into 12 Languages, 2 Audio Files, and Backed Up on the Blockchain HackerNoon makes content more accessible to increase audience size. Via machine learning models from OpenAI, Google, & others, we translate your story into 12+ languages, 2 audio files and back it up on the Arweave blockchain for good measure. Distribution Across HackerNoon and Around the Web Via owned and paid social media, traditional media like press releases, machine learning generated translations, and the creation and distribution of the audio story, HackerNoon maximizes technology readership. Creating an Average of 5k Readers Per Press Release We’ve used the traditional press release services, and they usually get just a couple hundred readers per story. Sometimes even less. When you publish your technology press release on HackerNoon, you should expect ~5k readers. Republished on the Wire to 100+ Traditional Media Sites Just like the traditional press release wire services, we put your story on the wire. You should expect your technology press release to be re-published to ~100 more sites, such as Benzinga, Digital Journal, Yahoo Finance, and The AP. Prominent Distribution Across the HackerNoon Site, App, and Newsletter Technology Press Releases on HackerNoon are featured on the Homepage, the Category Page, 8 Tagged Pages, 12 Translation Homepages, the Mobile App, the Terminal App, the Lite Version, and the Noonification Newsletters (250k+ subscribers). Easy Content Uploading and Formatting with Staff Support We hate how difficult it is to upload content to archaic press release distribution services. The HackerNoon uploader and story editor are smooth AF. We also accept your submissions via unlisted link like Google doc or even a URL like your pressroom. 1 Business Day Turnaround Time (TAT) and Professional Editorial Support Technology press releases are timely content. Our editorial and support staff guarantee one business day turnaround time. HackerNoon is here to make sure your story gets distributed on time. Book a meeting with us to learn how Octopus Network , KucCoin Community Chain , Avalanche, ICON Foundation , and other tech companies publish their press releases with HackerNoon. Book a meeting with us Book a meeting with us Octopus Network Octopus Network KucCoin Community Chain KucCoin Community Chain ICON Foundation ICON Foundation

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At some point, too many people will have lost too much Bitcoin that they will meet to talk about it. Later, they will opt to do something about it. There can only be a (decreasing number) of bitcoins Lost cryptocurrency is a unique beast, quite different from lost information for 2 reasons: 1. We can continue maintaining its value. Right? Is it really still valuable? 2. Other people know it is lost. They simply check the blockchain. Whereas with lost information, nobody else can tell except those who have lost that intel. Take Bitcoin as an example. We still say 'There can only be 21 million'. But if this includes even lost bitcoins, then we are counting our losses as assets! That is very strange. Imagine if you were an accountant for a major company. The company deals in all sorts of assets, but sometimes writes some physical assets off as lost either due to theft, fire outbreak, or irreparable damage. But to your shock, the manager insists that not only do you keep counting those assets, but also, count them as fully functional assets whose value is going up every year. "But they are damaged", you might say. "Yeah, but they are still our assets" "What, how?" "There is hope we can recover them exactly as they were before they were lost. And then some". It’s hard to fathom. It indeed feels like Alice in Wonderland, where cards talk, a piece of cake can make one bigger, or smaller, and a cat can disappear leaving only its grin behind. Bitcoin and most crypto are the same. The question is thus; Is real value riding on all coins? Even when some are lost? Certainly, supply and demand economics might make all the coins feel equally valuable hence we would say that if Bitcoin is at $70,000 then the market cap is $1.47 trillion. But taking the figures as per this reddit post on lost bitcoins, if 6 million are lost then only 15 million coins are existing which are backed by a market cap of $1.05 trillion. Less by $420 billion. This does deflate our dreams and begs the question --> are we kidding ourselves? The road to Zero bitcoins By some estimates, 4% of 21,000,000 bitcoins continue to be lost every year. How many shall be lost, going forward? Let's assume this 4% holds true. 4% × 21,000,000 = 840,000 bitcoins. This is very steep. Well then, in 15 years, we shall lose 840,000 * 15 = 12,600,000 Add 6 million to this batch and circa 2039, 'There can only be 2,400,000 bitcoins' When will they be finished? Now, a little hope is in order. The 4% cannot hold steady forever, so it would be wise to say it too will be a declining figure as we progress. Because as Bitcoins get more valuable, people will be more careful with their keys. It is inevitable. Taking it at face value though, we shall run out of bitcoins to trade within the next 18 years. So Circa 2042. I hope the naysayers will be back to tell us they told us so, or to plead guilty of doomism and explain why there are still many millions of Bitcoins being traded daily. However, it certainly looks like way before the year 2137 when the last satoshi will be mined, we shall hit a big wall as per the coins lost vs the coins in the available supply. When enough is enough, for lost Bitcoins Assume at the time this shocking reality hits the Bitcoin community, we have only 4 million coins tradable around the world. Assume even Michael Saylor shall have lost some of his keys/coins. What then? Well, the price of a single coin, feeling so much demand, will spike to about $10 million, and then it will dawn on everybody. Bitcoin has a $40 trillion market capitalization and not a $210 trillion market capitalization. Right now, this is an elephant 🐘 in the Bitcoin room, but it is still a baby and we are all happy to pet it and feed it with careless abandon. Soon, however, it will become a giant. A big fat frustration. At $10 trillion per coin, the fees to move even a few sats will likely be thousands of dollars because; a) the Bitcoin reward is smaller. b) miners have also lost their coins. c) losing coins to hackers will be most likely (hackers will be sniffing all over every wallet, waiting for the SSL to get some slight holes) Everybody will be losing coins. It will be wild. But before I proceed? Why do people lose keys hence coins? Well, if you are using the key every time, sometimes changing it this way and that way to maintain your security (because AI powered cameras may be watching), it so happens that you are bound to make a mistake and lose the key. Or else, be hacked (since you cannot fathom changing the key all the time). Like smart people must have said, meat brains are not good at holding random numbers or random sequences of words. Luke Dash Jnr, a seasoned cypherpunk, was hacked last year. He lost 200 bitcoins. Elon Musk may have lost his Bitcon keys back in '20. So, shit happens. To hard fork those lost coins back, or not. That is the question So here's the thing. We wake up and Bitcoin is seizing up. Crashing into its mass and threatening to take our financial dreams with it when it turns into a black hole of lost keys / burned coins. We also wake up and realize that the debt spiral is still sucking us down with it, even as we hodl Bitcoin. Climate is now a real issue and oh, AI is giving us headaches. Too many addictive cat videos. Too many coins are lost, the blocks are always full so confirmations take decades, the core software is too large, the fees are too high. So we sit down, like AA group members, and decide that continuing to preach financial freedom yet the currency is fast tending towards technological obsolescence, is like being high on alcohol and thinking that one is blissful for other reasons. We need to quit the 'lost coins are good for all' alcohol. In short, it will be time for a hard fork. The Hard Fork --> Bitcoin ver. 2.0 Innovation comes full circle. After factoring in the reality of available supply and not merely supply, we could let Satoshi Nakamoto's 2 million or so coins be our target practice and our saving grace. One shot and bull’s eye. Trillions of dollars pour down from the Bitcoin piñata. Like manna from the heavens. Right? Alas, There shall only be 4 million bitcoins and the value of lost coins, if "re-supplied" to the blockchain, will quickly deflate the value of the 4 million coins existing. Overall, lost coins would have helped the remaining coins get valuable to that point. Say $10 million per BTC. If you un-lose them by the most brute force of them all -- a hard fork -- you shock the demand economics and viola, have worth-less bitcoins than you started with. Now whoever is holding their previously un-lost bitcoins cannot be happy about that. It begs the question, what would they do? Refuse the hard fork outright. Obviously. Indeed, even if the value of the bitcoins would overall increase the market cap of the available supply, we can be damn sure it would reduce the value of each individual coin. Supply and Demand. Economics 101. The Big Question: Is this a financial revolution for the better, or for the worse? For example, let's say we had 4 million coins left. We then redeem the coins in the wallets of those proven deceased without any heir. Say Satoshi Finney is that first choice. At $10 million per coin, 4 million coins give up a market cap of 40 trillion USD. Add 2 million coins to the available supply but have the price shocked from 10 million to only 7 million. Total availability supply = 6 million. 6 million x 7 million USD = 42 trillion dollars. It is a good financial explosion. But to whom? Who gets the money?? Who makes the ultimate sacrifice of accepting to end up with less money than before? If rational and educated human beings cannot agree on simple (obvious) things like "Are fossil fuels necessary for economic progress or not?", how can they agree on how to share this pie? It is a financial explosion, except the majority will not want it. Satoshi may have solved the Byzantine General's problem. Somebody needs to solve the "Goodness of the community over the Goodness of the individual" problem. Of course, with my limited brains, I know most people are wary of a Marxist dystopia sliding in during this very crucial process. E.g. The coins could instead be coded to go to a handful of powerful individuals, whereas before it seemed like they would go to people who deserved a financial turnaround. Especially in the wake of what we see today with government debt ballooning, government operatives hoping to snatch some of this reclaimed BTC so as to help the government pay its gigantic debt is not an impossibility. Talk about being stuck between a rock and a hard place. Not the best kind of sandwich. At some point, too many people will have lost too much Bitcoin that they will meet to talk about it. Bitcoin Later, they will opt to do something about it. There can only be a (decreasing number) of bitcoins L ost cryptocurrency is a unique beast, quite different from lost information for 2 reasons: ost cryptocurrency is a unique beast, quite different from lost information for 2 reasons: 1. We can continue maintaining its value. Right? Is it really still valuable? Is it really still valuable? really 2. Other people know it is lost. They simply check the blockchain. Whereas with lost information, nobody else can tell except those who have lost that intel. Take Bitcoin as an example. We still say 'There can only be 21 million'. But if this includes even lost bitcoins, then we are counting our losses as assets! That is very strange. Imagine if you were an accountant for a major company. The company deals in all sorts of assets, but sometimes writes some physical assets off as lost either due to theft, fire outbreak, or irreparable damage. But to your shock, the manager insists that not only do you keep counting those assets, but also, count them as fully functional assets whose value is going up every year. " But they are damaged ", you might say. But they are damaged " Yeah, but they are still our assets " Yeah, but they are still our assets " What, how? " What, how? " There is hope we can recover them exactly as they were before they were lost. There is hope we can recover them exactly as they were before they were lost. And then some ". And then some It’s hard to fathom. It indeed feels like Alice in Wonderland, where cards talk, a piece of cake can make one bigger, or smaller, and a cat can disappear leaving only its grin behind. Bitcoin and most crypto are the same. The question is thus; Is real value riding on all coins? Even when some are lost? Certainly, supply and demand economics might make all the coins feel equally valuable hence we would say that if Bitcoin is at $70,000 then the market cap is $1.47 trillion. But taking the figures as per this reddit post on lost bitcoins , if 6 million are lost then only 15 million coins are existing which are backed by a market cap of $1.05 trillion. reddit post on lost bitcoins Less by $420 billion. This does deflate our dreams and begs the question --> are we kidding ourselves? The road to Zero bitcoins By some estimates , 4% of 21,000,000 bitcoins continue to be lost every year. some estimates How many shall be lost, going forward? Let's assume this 4% holds true. 4% × 21,000,000 = 840,000 bitcoins. This is very steep. Well then, in 15 years, we shall lose 840,000 * 15 = 12,600,000 Add 6 million to this batch and circa 2039, 'There can only be 2,400,000 bitcoins' When will they be finished? Now, a little hope is in order. The 4% cannot hold steady forever, so it would be wise to say it too will be a declining figure as we progress. Because as Bitcoins get more valuable, people will be more careful with their keys. It is inevitable. Taking it at face value though, we shall run out of bitcoins to trade within the next 18 years. So Circa 2042. I hope the naysayers will be back to tell us they told us so, or to plead guilty of doomism and explain why there are still many millions of Bitcoins being traded daily. However, it certainly looks like way before the year 2137 when the last satoshi will be mined, we shall hit a big wall as per the coins lost vs the coins in the available supply. When enough is enough, for lost Bitcoins Assume at the time this shocking reality hits the Bitcoin community, we have only 4 million coins tradable around the world. Assume even Michael Saylor shall have lost some of his keys/coins. What then? Well, the price of a single coin, feeling so much demand, will spike to about $10 million, and then it will dawn on everybody. Bitcoin has a $40 trillion market capitalization and not a $210 trillion market capitalization. Right now, this is an elephant 🐘 in the Bitcoin room, but it is still a baby and we are all happy to pet it and feed it with careless abandon. Soon, however, it will become a giant. A big fat frustration. At $10 trillion per coin, the fees to move even a few sats will likely be thousands of dollars because; a) the Bitcoin reward is smaller. b) miners have also lost their coins. c) losing coins to hackers will be most likely (hackers will be sniffing all over every wallet, waiting for the SSL to get some slight holes) Everybody will be losing coins. It will be wild. But before I proceed? Why do people lose keys hence coins? Well, if you are using the key every time, sometimes changing it this way and that way to maintain your security (because AI powered cameras may be watching), it so happens that you are bound to make a mistake and lose the key. Or else, be hacked (since you cannot fathom changing the key all the time). Like smart people must have said, meat brains are not good at holding random numbers or random sequences of words. Luke Dash Jnr, a seasoned cypherpunk, was hacked last year. He lost 200 bitcoins. Elon Musk may have lost his Bitcon keys back in '20. So, shit happens. To hard fork those lost coins back, or not. That is the question So here's the thing. We wake up and Bitcoin is seizing up. Crashing into its mass and threatening to take our financial dreams with it when it turns into a black hole of lost keys / burned coins. We also wake up and realize that the debt spiral is still sucking us down with it, even as we hodl Bitcoin. Climate is now a real issue and oh, AI is giving us headaches. Too many addictive cat videos. Too many coins are lost, the blocks are always full so confirmations take decades, the core software is too large, the fees are too high. So we sit down, like AA group members, and decide that continuing to preach financial freedom yet the currency is fast tending towards technological obsolescence, is like being high on alcohol and thinking that one is blissful for other reasons. We need to quit the 'lost coins are good for all' alcohol. In short, it will be time for a hard fork. The Hard Fork --> Bitcoin ver. 2.0 Innovation comes full circle. Innovation comes full circle. After factoring in the reality of available supply and not merely supply, we could let Satoshi Nakamoto's 2 million or so coins be our target practice and our saving grace. One shot and bull’s eye. Trillions of dollars pour down from the Bitcoin piñata. Like manna from the heavens. Right? Alas, There shall only be 4 million bitcoins and the value of lost coins, if "re-supplied" to the blockchain, will quickly deflate the value of the 4 million coins existing. Overall, lost coins would have helped the remaining coins get valuable to that point. Say $10 million per BTC. If you un-lose them by the most brute force of them all -- a hard fork -- you shock the demand economics and viola, have worth-less bitcoins than you started with. Now whoever is holding their previously un-lost bitcoins cannot be happy about that. It begs the question, what would they do? Refuse the hard fork outright. Obviously. Indeed, even if the value of the bitcoins would overall increase the market cap of the available supply, we can be damn sure it would reduce the value of each individual coin. Supply and Demand. Economics 101. The Big Question: Is this a financial revolution for the better, or for the worse? For example, let's say we had 4 million coins left. We then redeem the coins in the wallets of those proven deceased without any heir. Say Satoshi Finney is that first choice. At $10 million per coin, 4 million coins give up a market cap of 40 trillion USD. Add 2 million coins to the available supply but have the price shocked from 10 million to only 7 million. Total availability supply = 6 million. 6 million x 7 million USD = 42 trillion dollars. It is a good financial explosion. But to whom? Who gets the money?? Who makes the ultimate sacrifice of accepting to end up with less money than before? If rational and educated human beings cannot agree on simple (obvious) things like "Are fossil fuels necessary for economic progress or not?" , how can they agree on how to share this pie? "Are fossil fuels necessary for economic progress or not?" It is a financial explosion, except the majority will not want it. Satoshi may have solved the Byzantine General's problem. Somebody needs to solve the "Goodness of the community over the Goodness of the individual" problem. Of course, with my limited brains, I know most people are wary of a Marxist dystopia sliding in during this very crucial process. E.g. The coins could instead be coded to go to a handful of powerful individuals, whereas before it seemed like they would go to people who deserved a financial turnaround. Especially in the wake of what we see today with government debt ballooning, government operatives hoping to snatch some of this reclaimed BTC so as to help the government pay its gigantic debt is not an impossibility. Talk about being stuck between a rock and a hard place. Not the best kind of sandwich.

Is Lost Cryptocurrency Still Valuable, and Can We Reclaim It? 

Technology is rapidly reshaping our everyday lives, providing us with greater access to information and allowing us near-seamless communication with anyone, anywhere, at any time. We tend to perceive the digital and technological drive towards transformation as a possible threat to our ordinary and seemingly nostalgic way of life. Yet, technology has enabled us to better understand our planet and those around us. It’s helped us create a better understanding of life and everything that makes us human. It’s allowed us to make faster, better, and more accurate data-driven decisions and mitigate the potential of long-term risks. For all of this to happen, continuous investment in innovation and the masterminds behind them ensures that humanity, in (AI) can reach further parts of the universe, come up with better solutions to social and inequality issues, and act as the pivotal moment that enables us to enter a new digital era. collaboration with machines and Artificial Intelligence Implementing Technology in Your Business With all of this technology, endless digital tools, reams of data, and countless resources, why wouldn’t you want to invest in the potential of technology and the future of your business? While an important question to ask, the more important thing to consider is what tools you have available and how they will help increase employee satisfaction, drive customer engagement, and improve overall productivity. Technology helps to reshape our way of thinking, interacting, working, and communicating. Already, businesses are seeing the potential of technology on a wider scale and leveraging new tools to help scale their business, increase market share, and put them ahead of their competitors. As a result, more than half of business owners - 56 percent - have recently reported that they have started increasing their investment in technology, by cloud-based accounting conglomerate . according to a report Xero Following this, a robust 40 percent have mentioned that they strongly intend to further build on their technological investing in the year ahead, purely as these tools are helping them reach new customers, increase their market presentation, and help scale their business. For any business owner, introducing a new strategy, concept, or idea often requires careful consideration. Understanding your organization’s strengths and weaknesses can help identify new opportunities and help you and others pay close attention to potential risks, including cybersecurity pitfalls and malicious cyber threats. just earlier this year published research on and found out how American adults lost more than $2.7 billion last year on social media scams and fraud. This alarming amount is only set to increase in the coming years as more people begin to use technology for everyday activities and companies and governments continue to digitize their infrastructure. ExpressVPN 2024 scams to be aware of Through the right tools and facilitating training sessions for employees, without having to disrupt day-to-day workflow or further increase budget expenditure, your company can develop a digital transformation strategy that increases employee output, boosts productivity, and promotes overall team engagement. How Does New Technology in the Workplace Benefit Employees? Whether you employ a handful of people or are in the process of rapidly growing your employee headcount, right off the bat, it’s important to consider how new workplace technology will affect, benefit, and influence the way staffers are working, communicating and engaging with their peers and your customers. Increased Workflow Your employees’ time is valuable, and often, the disconnection is that with technology, employees will have more work on their hands instead of alleviating their workloads - although this isn’t always the case. In one estimate, experts can help improve 90 percent of knowledge workers and help to further replace repetitive and time-consuming tasks. believe that automation in the workplace By implementing the right set of tools, you can help minimize potential barriers for your teams, whether it’s communication gaps or employees working in different time zones, help boost creativity, and minimize the need to complete time-consuming tasks in their day-to-day lives. Improved Productivity Thanks to technology, and the right sets of tools, your employees can now get more done in less time, ultimately helping to drive overall business output. In one report, businesses in software, telecommunications, and internet publishing have seen since 2005, well above the lackluster 1.4 percent U.S. average. productivity grow by 5.5 percent These companies have seen productivity propelled by the innovations and wide-scale introduction of internet services, software, and cloud computing. By enabling employees to have access to the right digital assets of your business, you’re not only investing in near-term successes but long-term scalability. Data Harvesting On the back end, data plays an important role in the entire business structure. With access to the right data and information, companies can better understand their consumers and help provide them with the services they need. However, as an employer, this provides an opportunity to find and retain highly skilled individuals, train new employees more effectively, and monitor performance. With data at the helm of things, owners can now ensure that employees receive training in areas where they may be lacking critical skills. They can further help to improve communication abilities among teams, as they now better understand their peers and their clients' needs. How Does Technology Benefit My Business? Next, you should be considering how technology, in the wider scope of things, will help benefit your business right now and in the future. Improved Customer Engagement Having your eyes and ears everywhere all the time can be challenging. Fortunately, tech enables you to increase your presence, not just physically, but in the way through which you interact, engage, and communicate with clients. Through technology, businesses can now provide around-the-clock customer service with things such as chatbots. They can follow trends, understand how customer demands are changing, make quick and accurate decisions, and, most importantly, enhance the customer experience by providing them with the answers they want. Provides Competitive Advantage Customers expect you to be where they want you to be, but it's a bit hard to get going when you’re only a handful of people running the business. With digital tools, owners can begin to connect the dots, seeing where their customers are and how they can stand out against their competitors. Making the switch over to technology will enable you to plan for changing market trends and provide you with the insights needed to gain a footing above other businesses. Delivery of high-value insights, such as consumer data and macro data, helps you better understand your positioning in the marketplace and how to navigate challenges. Importance of AI You’ve probably realized that you might need to introduce some form of artificial intelligence into your business. In fact, one of 1,000 small business owners found that 91 percent of those who had implemented AI witnessed an improvement in their success. AI is helping improve company efficiency, driving productivity, and helping boost employee workflow. survey More than this, AI is designed to provide personalization, allowing businesses to create content and ideas that are closely related to their business and their customers. AI enhances the customer experience through unique algorithms, helps to gather and sort data more efficiently, and provides customer support when no one is around. Artificial intelligence helps to fill the cracks and bridge the gaps, ensuring your business is always operating at its peak performance. What Are the Security Concerns Regarding More Tech in the Office? Cybersecurity plays an important role in a business that works, communicates, and engages mostly online. Potential Security Risks There is no such thing as “too small” of a business to experience a cyberattack. In 2023, more than 73 percent of small business owners and leaders reported a data breach or cyberattack. Cybercriminals come in all shapes and forms, often disguised as unsuspecting emails, a link on a social media post, or even a text message. Not only this, but the various types of threats a person may encounter can range from malware attacks, spyware injections, ransomware, or social engineering attacks. You always run the risk of coming face to face with some sort of suspicious cyber activity, and when that time does come around, you’ll need to be best prepared. Employee Cybersecurity Training While it’s important to provide employees with sufficient digital training, other aspects, such as cybersecurity training, can be just as important for any employer and their business. Knowing what a cyberattack might be or what it looks like can help employees use predetermined models to curb potential threats. By making sure that your employees are well-trained and know what to do in the event of a cyberattack, you can protect valuable information and reduce the risk of losing money or having your company’s reputation tarnished. What Are the Short-Term Considerations? Although it’s important to think about the long-term success of a tech transformation in your business, the near-term turnaround is perhaps more important at the moment. Employee Training Being ready for the future means preparing everyone right now, and that includes sufficient employee training. Your technology will only be as successful as the teams using it, and without adequate training, employees tend to lose interest or often feel demotivated to work in collaboration with technology and artificial intelligence. You want to hire a digital consultant to provide insight regarding training boot camps. You can talk to employees long before implementing any technology to better understand their opinions and level of expertise with certain programs and digital tools. Communication is key, and the more there is of this, the better, that’s because think they’re helping employees learn new skills, when in fact, only 38 percent of employees agree. 74 percent of employers Available Resources Unfortunately, implementing new technology in the workplace isn’t a simple plug-and-play scenario. From the first day, you will need to make sure that you have the capacity to handle such a big transformation and that you can manage all the various facets of a digital transformation. You’ll need to make sure that your team has the necessary skills to work with certain tools, and if not, how you can provide them with sufficient training. Additionally, you will need to develop a standard operating procedure that employees can use as a reference when they are unsure of something. Holding regular training sessions and making sure you have the necessary resources will help make the transition feel less unorganized. Running Costs Next is the cost of implementing the office tech and the initial operating costs that will follow. Typically, business owners only plan for the initial cost of implementing and starting up their new office tech; however, other unforeseen costs can arise at any given moment. Routine maintenance, upgrading tools, or even replacing some components are all normal things that come with having office tech. As a business owner, you should ensure that you have a budget and the necessary finances to cover the cost of new technology and all the . various components that come with such a transformation Final Thoughts The future of work is beginning to change, and business owners need to continuously deliver exceptional digital solutions to ensure they remain ahead of their competitors and perhaps more importantly, leverage technology as a way to grow and increase their market share. With the right set of tools and the right skills, your business can transform from a robust company to a seamlessly running enterprise that uses technology to promote communication and engagement among employees and customers and provide sufficient, timeless, and relevant data-driven solutions that can benefit the scalability of your business.

How to Choose The Best Office Tech For Your Business

This interview series celebrates all the phenomenal . Congratulations! Well-Deserved, Champion.The HackerNoon community is excited to learn more about you! Startups of the Year Winners share the link to your winning city award here [example: ] https://hackernoon.com/startups/north-america/north-america-toronto-canada What does it mean for you to win this title? To us, it highlights that people recognize the need for a VPN in the privacy space that takes privacy seriously - and that you can do this while still maintaining a sense of humor. We’ve been working tirelessly to ensure connectivity in many challenging regions lately. Be it politics, censorship, or dictatorial aggression – we will power through with smiles on our faces. What sets you apart from the competition? I briefly mentioned our sense of humor, and that really does have an impact on all aspects of Windscribe. I think that shows. We get a ton of feedback from people saying that they love our VPN not just because it works well; it’s because of how we communicate with our community. We have the largest VPN community at discord.gg/VPN. We get fan mail from people around the world saying how certain jokes made their day - it’s great, our user base is so awesome. It makes us more approachable and that allows us to cater to their needs better than anyone else. We genuinely love this business and what it does for others. What do you love about your team, and why are you the ones to solve this problem? Our team is awesome: super smart people, from around the world, that get along and are genuinely concerned with privacy. Obviously, there are a lot of VPN companies out there, but we don’t cut corners, we don’t waste money on advertising or affiliate marketing, and we have rigid ethical standards (sometimes to the detriment of our revenue, but to the relief of our consciousness). We have the largest physical network out of any of our competitors. We are entirely self-funded so no one can force us to abandon our ethics. We are open about our ownership and our location - basically, there is a face behind the company, instead of a series of hidden shell companies. We spend a lot on talent, infrastructure, and researching the best solutions to online privacy issues. We take our mission very seriously, but we don’t take ourselves seriously. If you weren’t building your startup, what would you be doing? Eating chili cheese dogs and letting shit slide? Enjoying having a good posture, unburdened by sitting and typing for 11 hours a day? Yelling at the moon for not being the sun? Teaching my dog how to drive? There is so much I could be doing. At the moment, how do you measure success? What are your core metrics? We have a ton of internal metrics. Speed, reliability, and ability for quick turnaround on new features. To roll out fixes, find solutions for censorship, and ensure we respond to any issue within hours. Tell us how your startup is changing the world. At the risk of sounding like we are pitching to a bunch of out-of-touch San Francisco VCs - we are changing the world by helping people who are isolated in countries with restricted internet the means to stay connected and informed about the world around them. We help journalists, protestors, and activists to champion their cause. We provide a means of communication between loved ones. We allow those in countries with only state-approved media a glimpse at the truth of issues that affect them from sources they may not traditionally have access to. We fight for an open internet and privacy for all. We’re also kind of funny, so there’s that, too. How do you or your company intend to embrace the responsibility of this title in 2024? I think if we’re able to maintain and improve on the level of service and reliability of our network, we’ll be able to make Windscribers proud. We often have cat-and-mouse-like antics with those who attempt to censor their citizens. They will often try to find a way to block us which requires us to then find ways to circumvent their censorship. Luckily our circumvent censorship feature, something we’re very proud of, works incredibly well to this extent. Our team knows what to look for, how to adapt, and how to overcome these challenges. What goals are you looking forward to accomplishing in 2024? We want to continue to grow both of our companies, Windscribe and Control D, internally and externally. It feels genuinely great knowing that we can help more and more people access information in traditionally “closed-off” parts of the world and manage their knowledge on their own terms - and do so with an international team of people who care about it just as much, and in some cases, even more than I can (some of our employees live in heavily censored countries). Pretty cliché answer, I know - we just want to keep growing. Which trend(s) are you most excited about in 2024? Share your reasons. Broccoli-style haircuts. Their rise in popularity has given us much to laugh at. But we will be much more excited once that trend dies like the man-bun trend before it. What will come after? Who knows. In the realm of technology, the list is really endless: rocketry and space exploration, AI, increasingly more useful and easier-to-access online educational content… there is a lot of interesting technology being worked on - so we are permanently excited to see what awaits the world. 2023 had been another crazy year, especially in tech, with layoffs and the Generative AI takeover! Which trend are you most concerned about? Be as brief or as detailed as you like. Honestly, AI has been really helpful to our teams. We have adopted it to help teams do more in less time — it eases the burden/annoyance of repetitive and lower-priority tasks, giving everyone more time to work on engaging problems. As for the layoffs, we haven’t had to lay anyone off, which is a blessing. We know it really sucks being let go from a company, but at the same time, this market has allowed us to scoop up a ton of really high-level talent. We have been hiring like crazy, and are still hiring more. Come and apply! Share your biggest success so far and/or your biggest failure so far. We have a company filled with very smart people who all get along to work on problems that affect people directly (censorship, access to information, etc). The team is our biggest success. We also are close to having 69 million users, which is numerologically important to us because funny sex number. My dog didn’t poop in the office this week either. Really, it’s a string of one success after another. Biggest failure? Can’t remember, overshadowed by too much success (see previous sentence). Any words of wisdom you’d like to share with us? At a time when the world is increasing its interconnectivity, it is becoming harder and harder to safeguard one’s own privacy. The main word of “wisdom” (I’ll leave it to you to decide whether it is wise), is that everyone should pay close attention to their online privacy and protect it fiercely, as it is becoming a commodity for companies, governments, and various groups to exploit citizens, and profit off of them both monetarily and psychologically. Take your privacy seriously because no one else will. Other than that… never judge a group of people by the way their government acts. Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. See our global winners announcement . here Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners will get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Meet Windscribe, Winner of the Startups of the Year in Toronto

In the fast-paced world of startups, time is not just a resource; it's the currency of innovation and growth. Yet, many entrepreneurs find themselves bogged down by a myriad of operational tasks that, while crucial, can devour this precious currency at an alarming rate. From managing intricate HR processes to navigating the complex maze of compliance, these necessary but time-consuming activities often divert focus from a startup's core mission: to innovate and grow. Enter the realm of smart outsourcing – a strategic paradigm shift that is redefining the startup success story. Smart outsourcing is not just about delegating tasks; it's about unlocking a treasure trove of time and opportunity. Imagine reclaiming up to three months of focused, strategic effort – time that could be channeled into perfecting your product, enhancing customer experiences, or breaking into new markets. This is where come into play, offering a pathway to unlock this potential. smart outsourcing strategies But how significant is this impact? Consider this: a by Deloitte revealed that companies leveraging outsourcing could see a 31% reduction in operational costs. Moreover, outsourcing allows startups to tap into a pool of specialized skills and expertise, which might be too costly or time-consuming to develop in-house. This approach isn't just a lifeline; it's a catapult to accelerated growth and innovation. study The Burden of Operational Tasks in Startups As we delve deeper into the world of smart outsourcing, we will explore how this approach is not just a stop-gap measure but a strategic tool in the arsenal of modern startups. It's about making every second count, ensuring that every minute spent by a startup propels it toward its ultimate goal. So, if you're ready to unlock the full potential of your startup, let's embark on this journey of transformation together. In the adrenaline-fueled race to innovate and disrupt, startups often encounter an unexpected adversary: the sheer volume and complexity of operational tasks. These tasks, ranging from human resources to financial management, form the backbone of any business. However, for startups focused on rapid growth and development, they can represent a significant burden. The Multifaceted Nature of Operational Challenges Startups, especially in their early stages, typically operate with lean teams. Each member is often stretched across multiple roles, juggling core business activities with a plethora of operational responsibilities. A by McKinsey & Company highlights that small businesses can spend up to 40% of their working hours on tasks that do not generate income, such as hiring, HR tasks, and managing payroll. report The Cost Implication The cost of handling these operations in-house can be staggering. For instance, consider the financial implications of setting up an HR department – recruitment, training, payroll management, compliance with labor laws, and employee benefits management. A by the Society for Human Resource Management (SHRM) estimates that the average annual cost per employee for HR alone can be as high as $2,296. For a startup operating on a tight budget, these costs can divert crucial funds from core business development. Exploring can be a game-changer in this scenario. study cost-effective operational solutions The Time Factor Beyond the financial aspect, there's the critical factor of time. Operational tasks are often repetitive and time-consuming. For example, a by Score found that small business owners spend an average of 120 working hours a year on accounting and bookkeeping tasks. survey This is time that could be better spent on activities that directly contribute to business growth, such as product development, market research, and customer engagement. Impact on Agility and Innovation In the startup ecosystem, agility and the ability to innovate rapidly are key competitive advantages. However, the burden of operational tasks can significantly slow down these processes. It creates a scenario where the startup's agility is hampered, and its ability to innovate at speed is compromised. This is particularly detrimental in industries where the pace of change is rapid and the window of opportunity is narrow. So, while operational tasks are indispensable to the functioning of any business, for startups, they can pose a significant challenge – financially, in terms of time, and in maintaining competitive agility. This sets the stage for exploring how smart outsourcing can be a game-changer in addressing these challenges, a topic we will delve into in the next section. Understanding Smart Outsourcing As startups grapple with the operational challenges outlined earlier, smart outsourcing emerges not just as a solution but as a strategic enabler. But what exactly is smart outsourcing, and how does it differ from traditional outsourcing models? This section delves into the nuances of smart outsourcing and its role in transforming startup operations. Defining Smart Outsourcing Smart outsourcing goes beyond the conventional approach of simply hiring external agencies to handle non-core tasks. It involves strategic partnerships with firms like , which understand the unique needs of startups. It's a strategic decision involving a careful analysis of which operations can be outsourced for maximum efficiency and impact. This approach focuses on partnering with experts who not only take over routine tasks but also add value through their specialized skills and knowledge. levy Choosing the Right Tasks to Outsource The key to smart outsourcing is identifying the right tasks to delegate. These are typically non-core activities that are essential but don't necessarily contribute directly to the startup's unique value proposition. For instance, tasks like payroll processing, accounting, and administrative duties are prime candidates. According to a by Deloitte, 57% of companies outsource to focus on their core business. survey Finding the Right Outsourcing Partner Equally important is choosing the right partner. A good outsourcing partner should understand the startup's vision and culture. They should offer not just services but solutions tailored to the startup's specific needs. This involves a blend of reliability, expertise in their domain, and the ability to scale services as the startup grows. Benefits of Smart Outsourcing The benefits of smart outsourcing are multifaceted: : By outsourcing, startups can convert fixed costs into variable costs, releasing capital for investment elsewhere in their business. This also eliminates the need for significant investment in infrastructure and technology. Cost Efficiency : Outsourcing operational tasks allows the startup team to focus on developing their core products or services, market expansion, and other strategic areas. Focus on Core Business : Startups gain access to world-class capabilities and experience without the need to hire and train specialists. Access to Specialized Skills : Outsourcing partners can quickly scale services up or down depending on the startup's requirements, providing flexibility that is crucial in the dynamic startup environment. Scalability Smart Outsourcing in Action To illustrate smart outsourcing in action, consider a tech startup that outsources its customer support to a specialized firm. This not only ensures professional handling of customer queries but also frees up the core team to focus on product development and innovation. In a way, smart outsourcing is about making informed choices on what to outsource, who to partner with, and how to integrate these services into the overall business strategy. It's a step beyond traditional outsourcing, offering a pathway to greater efficiency, innovation, and strategic focus for startups. The Time-Saving Impact of Outsourcing In the quest for startup success, time is an asset as valuable as capital. Smart outsourcing, when executed strategically, can unlock significant time savings, a crucial factor for startups aiming to stay agile and competitive. This section explores how outsourcing operational tasks can lead to substantial time efficiencies, ultimately contributing to faster growth and innovation. Quantifying Time Savings The immediate benefit of outsourcing is the liberation of time. Tasks that previously consumed hours of a startup team's day can be efficiently managed by specialized external partners. For instance, a by the National Small Business Association (NSBA) indicates that 40% of small business owners spend over 80 hours a year on tax preparation alone. Leveraging can significantly reduce this burden. Outsourcing such financial tasks can reclaim these hours for core business activities. study expert financial outsourcing services Refocusing on Core Competencies By freeing up time through outsourcing, startup founders and their teams can refocus their energies on areas where they excel and which directly contribute to business growth. This shift from operational management to strategic development can be transformative. It allows startups to accelerate product development, enhance customer engagement, and pursue new market opportunities. The Ripple Effect of Time Savings The time saved through outsourcing has a ripple effect across the startup. It leads to quicker decision-making, faster turnaround times for product updates, and more time for customer-focused innovation. This agility is often what gives startups a competitive edge in fast-paced industries. Consider the case of a startup that outsourced its digital marketing efforts. By entrusting this aspect to experts, the startup not only benefited from professional marketing strategies but also gained valuable time to focus on product refinement and user experience, leading to increased customer satisfaction and growth. Similarly, a fintech startup outsourcing its compliance and regulatory tasks to a specialized firm could navigate the complex financial landscape more efficiently, ensuring compliance while dedicating more time to innovation and customer service. The Strategic Value of Time In the startup world, time is not just about hours saved; it's about strategic value gained. The time reclaimed through outsourcing can be strategically invested in areas that offer the highest return in terms of business growth and innovation. This strategic reallocation of time resources is a critical component of a startup's journey to success. The time-saving impact of outsourcing is a game-changer for startups. It's not merely about offloading tasks; it's about strategically reallocating one of the most precious resources – time – towards activities that drive growth, innovation, and competitive advantage. Beyond Time Savings: The Strategic Advantages While the time-saving aspect of outsourcing is undoubtedly significant, its strategic benefits extend far beyond just freeing up hours in a day. Smart outsourcing can be a catalyst for several key advantages that propel startups into a trajectory of sustainable growth and competitive edge. This section delves into these strategic advantages, illustrating how outsourcing transcends mere efficiency and becomes a powerful tool for business transformation. Enhanced Focus on Innovation and Growth Outsourcing operational tasks allows startups to channel their creativity and resources towards innovation. With the operational load handled by experts, startup teams can dedicate more time to research and development, brainstorming new ideas, and exploring growth opportunities. This heightened focus on innovation can lead to breakthrough products and services, setting the startup apart in the market. Access to World-Class Expertise and Technology Outsourcing opens the door to a world of expertise and advanced technology that might otherwise be inaccessible for startups, especially those with limited resources. By partnering with specialized service providers, startups benefit from the latest tools, technologies, and industry insights. This access can improve product quality, enhance customer experiences, and streamline business processes, driving overall business performance. Scalability and Flexibility Startups need to be agile and scalable, ready to grow or pivot in response to market demands. Outsourcing provides this scalability, allowing startups to easily ramp up or scale down operations without the constraints of in-house resources. This flexibility is crucial in navigating the unpredictable waters of the startup ecosystem and responding effectively to growth opportunities or challenges. Risk Mitigation and Compliance Startups operate in a landscape filled with regulatory, financial, and operational risks. Outsourcing to specialized firms can significantly mitigate these risks, especially in areas like legal compliance, financial auditing, and data security. These firms bring in expertise and processes that ensure compliance with regulations, reducing the risk of costly legal or financial penalties. Consider a tech startup that outsourced its customer service to a firm with advanced AI-driven support systems. This move not only enhanced customer satisfaction through prompt and efficient service but also provided the startup with valuable customer insights, driving product improvements and targeted marketing strategies. Another example is a health-tech startup that outsourced its regulatory compliance to a firm specializing in healthcare laws. This partnership ensured that the startup remained compliant with complex healthcare regulations, avoiding potential legal hurdles and building trust with its customer base. The strategic advantages of outsourcing extend well beyond time savings. They encompass access to expertise, technological advancement, scalability, and risk mitigation – all crucial elements for a startup's success. By embracing smart outsourcing, startups can not only optimize their operations but also position themselves for innovation, growth, and long-term success in the competitive business landscape. Implementing Smart Outsourcing in Your Startup Implementing smart outsourcing is a strategic move that requires careful planning and execution. For startups looking to harness the full potential of outsourcing, it's crucial to approach this process methodically. This section provides a roadmap for startups to effectively integrate smart outsourcing into their business model, ensuring they reap the maximum benefits. Identifying What to Outsource The first step in smart outsourcing is to identify which tasks or processes should be outsourced. Startups should focus on non-core activities that are essential but don't necessarily require in-house expertise. These can include functions like accounting, HR, customer service, or IT support. The key is to retain core competencies in-house while outsourcing the tasks that are routine yet critical. Choosing the Right Outsourcing Partner Once the tasks to be outsourced are identified, the next step is selecting the right partner. This decision should be based on several factors: : Look for partners with a proven track record in your specific area of need. Expertise and Experience : Ensure the partner understands your startup's vision and goals. Alignment with Business Goals : Choose a partner who can scale their services as your startup grows. Scalability and Flexibility : Consider the quality of service, including communication, reliability, and feedback from other clients. Quality of Service Integrating Outsourced Operations After selecting an outsourcing partner, the next challenge is integrating their services into your existing operations. This integration should be seamless, ensuring that the outsourced services align with your business processes. Effective communication and a clear understanding of roles and expectations are crucial. Regular meetings and updates can help maintain alignment and address any issues promptly. Measuring the Impact To evaluate the effectiveness of outsourcing, startups should establish clear metrics and KPIs. These could include cost savings, time saved, improvement in service quality, or increased customer satisfaction. Regularly reviewing these metrics will help assess whether the outsourcing arrangement is meeting its objectives and where adjustments may be needed. Building a Collaborative Relationship Finally, successful outsourcing is about building a collaborative relationship with your partner. Treat them as an extension of your team, involving them in relevant discussions and decision-making processes. A collaborative approach ensures that both parties are aligned and working towards the common goal of growing your startup. Implementing smart outsourcing in a startup is not just about delegating tasks; it's about strategically enhancing your business's efficiency and capacity for growth. By carefully selecting tasks to outsource, choosing the right partners, seamlessly integrating their services, measuring impact, and fostering collaboration, startups can turn outsourcing into a powerful tool for success. Conclusion: As we've navigated through the intricacies of smart outsourcing, it's clear that this approach is more than a mere operational tactic; it's a strategic cornerstone for startup growth and efficiency. Smart outsourcing, when implemented thoughtfully, can be a transformative force, unlocking new levels of innovation, agility, and focus for startups. The journey from recognizing the burden of operational tasks to understanding and implementing smart outsourcing underscores a fundamental shift in how startups can optimize their resources. By reallocating time and energy from routine tasks to core business functions, startups can not only enhance their operational efficiency but also foster a culture of innovation and strategic growth. The key takeaway is that smart outsourcing isn't just about cutting costs or saving time; it's about empowering startups to focus on what they do best – innovating and growing their business. It's about building partnerships that extend beyond mere service agreements, creating synergies that propel startups toward their goals. As startups continue to navigate the ever-evolving business landscape, smart outsourcing stands out as a vital tool in their arsenal – a tool that offers not just operational solutions but strategic advantages. By embracing this approach, startups can position themselves to thrive in a competitive environment, turning challenges into opportunities for growth and success. To conclude, smart outsourcing is not just a pathway to efficiency; it's a gateway to unlocking the full potential of your startup. It's an invitation to rethink how you operate, to focus on your strengths, and to catapult your business towards unprecedented growth.

Smart Outsourcing: Unlocking 3 Months of Focus and Redefining Startup Success

স্মার্ট আউটসোর্সিং: 3 মাসের ফোকাস আনলক করা এবং স্টার্টআপ সাফল্যকে পুনরায় সংজ্ঞায়িত করা

Intelligentes Outsourcing: 3 Monate Fokus freisetzen und Startup-Erfolg neu definieren

Subcontratación inteligente: desbloquear 3 meses de enfoque y redefinir el éxito de una startup

Externalisation intelligente : débloquer 3 mois de concentration et redéfinir le succès des startups

स्मार्ट आउटसोर्सिंग: 3 महीने के फोकस को अनलॉक करना और स्टार्टअप की सफलता को फिर से परिभाषित करना

スマート アウトソーシング: 3 か月の集中力を解き放ち、スタートアップの成功を再定義する

스마트 아웃소싱: 3개월 간의 집중 활성화 및 스타트업 성공 재정의

Terceirização inteligente: liberando 3 meses de foco e redefinindo o sucesso das startups

Умный аутсорсинг: 3 месяца сосредоточенности и новое определение успеха стартапа

Akıllı Dış Kaynak Kullanımı: 3 Aylık Odaklanmanın Kilidini Açmak ve Startup Başarısını Yeniden Tanımlamak

Gia công phần mềm thông minh: Mở khóa 3 tháng tập trung và xác định lại thành công của khởi nghiệp

智能外包：释放 3 个月的专注力并重新定义初创公司的成功

Composability in blockchains refers to a framework that permits the interaction and interconnection of discrete elements, especially smart contracts. This kind of modular ecosystem is important as it helps programmers utilize existing components to build advanced systems very quickly. In this article, we’ll look at composability — what it means, how it works on blockchain, some of its benefits and opportunities, and how it powers some of the innovations behind the Flow blockchain. What is composability? Composability is an age-old programming concept that saves developers (who are working within an ecosystem) from writing redundant code. This is achieved by abstracting out programming logic into logically independent units called functions. For example, let’s say you’re building an application that needs to provide functionality to convert USD to INR on several pages. You could either write the same logic repeatedly on different pages or define it in a function and just call that function everywhere. In essence, this concept resembles constructing a monument using LEGO bricks, where the bricks are the composable functions. Composability is like the compound interest of programming. Once someone solves a problem and publishes it in a modular way for others to use, the problem is solved for everyone.Benefits of composability Benefits of composability Composability has several benefits. Saves developer hours: If developers use pre-existing functions written by other devs, it saves them a lot of time compared to if they implemented their own logic. This could be anywhere from three lines of code to 1,000 lines. Reduces probability of errors: This happens for two reasons. If every developer implemented logic on their own, there would be an increased risk of one of them making an error. Secondly, as the same logic gets used in multiple places, it creates several testers who are incentivized to make sure the logic works. Readability: Replacing hundreds of lines of code with a logical function name definitely enhances the readability of code for future developers. Easier to make changes: If a certain flaw is found or an update in logic is necessary, it can be done in one place instead of making the change in several places. Composability in Blockchains In blockchain, composability denotes the smooth intercommunication among distinct constituents, like smart contracts and dApps, within a network. Any smart contracts you write on the blockchain can use functionalities from other smart contracts and dApps. One way blockchains like Ethereum have achieved this is via the creation of infrastructure-level composable units in the form of Ethereum Request for Comment (ERC), which are essentially proposals. These proposals, when approved by the Ethereum community, turn into smart contracts deployed and publicly available on-chain. For example, ERC-721 is a smart contract standard for creating NFTs. It’s one of the most used composable units among NFT projects in the Ethereum ecosystem. These NFT projects just import and extend functions from ERC-721 to give their contracts powerful functionality right away. Flash loans in decentralized finance (DeFi) are also a good example of composability among several platforms. To begin, you first create a set of predesignated operations on providers like Aave. Then you run transactions on different swap protocols, like MakerDAO and Uniswap. Eventually, you repay the flash loan and mint your profit. Composability in Flow blockchain is a permissionless layer-1 blockchain that was built to support the high-scale use cases of games, virtual worlds, and the digital assets that power them. It excels in high-throughput settings such as decentralized gaming and NFT markets — situations that have a large transaction volume and high user engagement. Flow The Flow blockchain was built with composability in mind every step of the way. Let’s look at how. Sharding versus multi-node architecture There are two mechanisms commonly used in blockchains to achieve scalability: sharding for layer-1 solutions and rollups for layer-2 solutions. Ethereum, for example, has been transitioning recently from its single-node architecture to sharding. However, this has its own limitations. In sharding, the state of the blockchain is split up into multiple smaller chunks and distributed across nodes. This makes it much harder to achieve composability, as a component that a smart contract might be referring to may be in a completely different shard chain. Flow solves this problem in an innovative way, while also achieving scalability, through a process called pipe-lining. In Flow, four types of nodes work together to make a transaction happen. Collection nodes collect well-formed transactions in the transaction pool to pass on to the consensus node. Consensus nodes then form and propose blocks to the blockchain. Then execution nodes actually execute the computations of a block and finally the verification nodes verify the accuracy of computations performed by the execution node. You can read more about each of these nodes here. Below is an image that shows the sequential steps. The benefit is that Flow is able to maintain a single shared state for the entire chain as it achieves scalability without breaking the state into shards. This is very powerful and ultimately maintains composability in Flow. Composable resources in Cadence In Cadence, the language used to write smart contracts on Flow, resources can be created, deployed, and moved by other resources. This is most commonly used in NFTs, where an NFT collection is defined as a resource. It then owns NFTs inside it, which are also resources. These NFTs defined inside the collection can be accessed and used by other contracts; only the access control is defined by the parent resource. The contracts can also be written in a way that is generic and can be easily extended by other contracts. Compatibility with EVM and Ethereum The Flow blockchain ecosystem has recently announced compatibility with EVM. The benefits of this are twofold: Popular Ethereum applications like Aave, MetaMask, Uniswap, and so on will be fully available out of the box on the Flow blockchain and do not need any porting. This will encourage developers to onboard the Flow blockchain. Developers can enhance their existing Solidity smart contracts using Cadence on Flow. This encourages a new level of composability across chains. You can read more about the announcement . This was launched recently. here Business opportunities enabled by composability on Flow Gaming and NFTs Games might have several assets, like player accessories and skins, which are highly desirable among gamers. More often than not, these assets are obtained by the gamer after spending a lot of time on the game. If these assets are modeled as NFTs, the concepts of composability on Flow can be utilized to make them available across games. This would mean: Increased gamer engagement: Gamers will now know their assets are valuable even if their game shuts down. This will result in better financial outcomes for all. Lower onboarding barrier: Every time a player starts a new game, they will not be starting from scratch as they already have some assets. This makes it very easy for games to acquire new players. Increased “GDP”: Composability and interoperability will create a secondary market for assets that increases the overall GDP of the gaming ecosystem. Publishing Content, like written posts, can be modeled as NFTs and used across platforms. Recently, a partnership was forged between Zora, an NFT marketplace, and Mirror, a decentralized blogging platform. This partnership allowed writers on Mirror to package their popular articles as NFTs and auction them on Zora. This resulted in increased income for these writers. Similar models are possible on the Flow blockchain. Writers can then use these funds to fuel their future projects. DeFi As previously mentioned, flash loans are constructed on the fundamentals of composability and prove very beneficial in market-making. Even normal tokens on Flow can be used across protocols and smart contracts owing to the tenets of composability. Every token carries its own identity and code that defines its behavior. So every time a token exchanges hands, all the “composable” functions come with it. Cross-chain projects As mentioned earlier, it is now very easy for Ethereum projects to launch themselves on Flow and vice versa. This requires no new programming and can be done in a very short turnaround time. This means popular projects on Ethereum can now tap the Flow ecosystem and its users. This also means that existing Flow projects will gain traction from existing Ethereum protocol users, who will now discover Flow. This is a big win-win for both ecosystems and will boost traction overall in the world of blockchain. Conclusion The Flow blockchain has powerful built-in support for composability. This empowers Flow developers to build on top of existing infrastructure without having to write it on their own again. This also has a lot of added business advantages, as composability reduces time to market and increases interoperability. It will be interesting to see how compatibility between Flow and Ethereum evolves. You can on multi-node architecture and composability to learn more. I also recommend that you get your hands dirty by going through . refer to the official docs page Flow Docs Also appears . here

Composability in Flow: Exploring Technical and Business Opportunities 

AI is revolutionising software development, making it more efficient, productive, and innovative. In this article, we explore the impact of AI in software development, delving into the insights gained from our investigation of GitHub Copilot performed by the ELEKS R&D team. As the world of software development continues to evolve, the role of has become increasingly important. With benefits like the ability to reduce the time and effort required for coding and more, it's no wonder AI in software development is a hot topic nowadays. AI 92% of developers based in the U.S. are already using AI coding tools both at work and outside of it. - GitHub 70% of developers believe that AI coding tools will give them a distinct edge in their professional endeavours, including improved code quality, reduced completion time, and enhanced incident resolution capabilities. - GitHub 4 out of 5 developers expect AI coding tools to make their team more collaborative. - GitHub Although AI hasn't reached a stage where it can independently create, test, and launch software products without human input, compared to the past, it has greatly speeded up the overall turnaround time. Many AI-driven tools are available that can assist in developing custom applications by generating and autocompleting code, as well as detecting and fixing bugs. The ELEKS Research & Development team thoroughly assessed one such tool - GitHub Copilot - to evaluate its influence on developers' tasks, completion duration, and quality standards for recommendations provided. Let's take a closer look. What is GitHub Copilot? GitHub Copilot is a tool for code autocompletion and generation. It is built based on OpenAI Codex and trained on public GitHub repositories. As a result, it suggests code for any programming language. However, the quality of proposals depends on the number of public repositories that are based on a specific programming language and framework. GitHub Copilot works with program code using Integrated Development Environment (IDE) plugins. Only the following IDEs are currently supported: Visual Studio Code Visual Studio Neovim all IDEs from JetBrains Overview of ELEKS' GitHub Copilot investigation This investigation aimed to explore the impact of using Copilot on both development speed and quality. To conduct this study, a small pet project was initiated as a testing ground for various assumptions. With a team comprising four skilled middle developers armed with expertise in React, Redux, TypeScript, Jest, Vite, PHP, Symfony, and Codeception, we started the investigation and defined our goals and approaches as follows. Goal Approach Evaluate the dependency between the developer’s competency and performance when using Copilot We selected the Symfony framework for back-end development. The back-end team had no substantial experience with this framework. To assess the quality of the back-end solution, a Symfony expert was involved in a code review. The expert also developed a basic project structure that went beyond the typical one and was used in a large commercial project. Investigate the impact on typical developer tasks Before the project started, we created a WBS that covered the typical work of a developer on a commercial project. Also, we conducted a task estimation session. Investigate the impact on programming languages and frameworks by popularity We developed a web application with test coverage. Evaluate the impact on task completion time We made a preliminary estimation of individual tasks on the project. Enhance the research effectiveness We conducted knowledge-sharing sessions within the team during daily syncs. Explore the quality of suggestions We tested how Copilot deals with a non-classical and more complex project structure. Key findings from ELEKS' GitHub Copilot investigation ELEKS' investigation of GitHub Copilot yielded several key findings that shed light on the tool's capabilities and benefits. Here is a more detailed description: Please note that the investigation outcomes are based on the team's subjective feedback, observations of their work, and code reviews of their solutions. The GitHub Copilot has two modes of operation: Code auto-completion mode – almost always positively affects performance. Code generation mode based on a comment with a prompt – works well only for typical code structures. It can be counterproductive when generating business logic or non-typical solutions because analysing the proposed code variants takes a lot of time. The quality of GitHub Copilot code suggestions depends on the following: Popularity of the selected tech stack in public repositories. The more popular the technology or language, the higher the quality of GitHub Copilot suggestions. Code structure and application architecture. The more typical your solution is, the more effective suggestions GitHub Copilot provides. This is especially noticeable at the start of the project development. Other advantages of GitHub Copilot: Over time, the quality of code suggestions increases. GitHub Copilot learns and adapts to the project structure, code style, and typical approaches to technical solutions on the project. Positively influences developer job satisfaction. GitHub Copilot takes on a portion of routine, typical, and boring tasks. The ratio between developing interesting and routine tasks changes, which increases developers’ comfort. General team findings: The tool showed no impact on the code quality—neither positive nor negative. We think that for now, it cannot replace the code review process, manual code testing done by the developer, running autotests, security testing, and so on. The effectiveness depends on the developer's competence in the given tech stack. The better the developer's skills, the more effective this tool proves to be. The value of GitHub Copilot for learning new technologies is very doubtful. The team formed an impression that code suggestions become better if all code-related files are open in the IDE. Like any other tool, GitHub Copilot requires certain skills. The more the team practiced, the more positive feedback was received. The feedback regarding the increase in performance varies depending on the tech stack, the specialist’s competency level, and subjective reasons. 5–10% the average increase in productivity of team members. - ELEKS R&D team On a side note, during the project, the team encountered several bugs in GitHub Copilot. For example, Copilot proposed code suggestions based on the code from another project open in another IDE window. It offered syntactically incorrect code. Copilot conflicts with the features of some plugins (built-in or added by a third party) that also have a code auto-completion functionality. Conclusions GitHub Copilot is a powerful tool that positively impacts development speed and developer job satisfaction. Like any other development tool, it requires time to master it. At this point, an individual monthly license costs only $10, while the business plan is priced at $19 per month. GitHub Copilot will undoubtedly be financially beneficial for use on the projects, especially for the specialists of middle competency level and higher. The team predicts that with the growth of experience using GitHub Copilot, the development speed can increase by 5–25%, depending on the technology's popularity and the developer's competency level. GitHub Copilot is guaranteed not to slow down development because developers will gradually gain the expertise and understanding of when it is beneficial to use the tool and when not. In conclusion, the impact of AI in software development, exemplified by tools like GitHub Copilot, is undeniable. Overall, such tools as GitHub Copilot cannot replace a developer but serve as valuable tools to enhance developers' comfort and productivity, contributing to the evolution of software development in an increasingly AI-driven world. Unlock the path to an efficient software development process and successful outcomes, . contact ELEKS experts Also published . here

AI in Software Development: Exploring GitHub Copilot with Insights from the ELEKS Team

সফটওয়্যার ডেভেলপমেন্টে AI: ELEKS R&D টিমের অন্তর্দৃষ্টি সহ GitHub কপাইলট অন্বেষণ করা

KI in der Softwareentwicklung: Erkunden Sie GitHub Copilot mit Erkenntnissen des ELEKS-Forschungs- und Entwicklungsteams

IA en el desarrollo de software: exploración de GitHub Copilot con información del equipo de I+D de ELEKS

L'IA dans le développement de logiciels : exploration de GitHub Copilot avec les informations de l'équipe R&D d'ELEKS

सॉफ्टवेयर विकास में एआई: ईएलईकेएस आर एंड डी टीम की अंतर्दृष्टि के साथ गिटहब कोपायलट की खोज

ソフトウェア開発における AI: ELEKS R&D チームからの洞察で GitHub Copilot を探索する

소프트웨어 개발의 AI: ELEKS R&D 팀의 통찰력으로 GitHub Copilot 살펴보기

IA no desenvolvimento de software: explorando o GitHub Copilot com insights da equipe de P&D da ELEKS

ИИ в разработке программного обеспечения: изучение GitHub Copilot с помощью идей команды исследований и разработок ELEKS

Yazılım Geliştirmede Yapay Zeka: ELEKS Ar-Ge Ekibinin İçgörüleriyle GitHub Copilot'u Keşfetmek

AI trong phát triển phần mềm: Khám phá GitHub Copilot với những hiểu biết sâu sắc từ Nhóm R&D ELEKS

软件开发中的人工智能：借助 ELEKS 研发团队的见解探索 GitHub Copilot

Obviously, no job family is going to disappear overnight; change takes time. But looking ahead helps orient everyone’s energy and steer careers away from the doom and gloom of job decline. Reading AI job impact reports can give you vertigo: 85 million jobs will be displaced globally due to AI ( ) Oliver Wyman Forum There are predictions that software developers ?! might be extinct by 2040 : “In the next five years, 83 million jobs will be lost and 69 million are projected to be created” World Economic Forum 40% of the global workforce - 1.4 billion people - will need to reskill. ( ) IBM study As a product manager, I ask the question of whether there’s a future for my career in the post-AI world. naturally These numbers sound bad enough to throw in the towel and say “Well, I’ll just give up I guess, look the other way, and see what happens”. But is that: can my whole claim disruptive trends are visible far in advance if you’re looking the right way and can sift through some noise, getting to a level of operational expertise in any emerging tech is relatively simple. There are jobs that will probably, within the next several years, be displaced almost completely (my one bet is on frontline customer support), and then there are jobs that will go through a boom cycle. For those, the traditional market won’t be able to provide enough supply, and we will see similar qualifications rebranding as we did with coding boot camps over the last 10-15 years. Most of us in tech will likely land somewhere in the middle: even if the changes come quickly, we have the choice and the luxury of iterating towards new skills (and opportunities) rather than needing a complete pivot. Designers, Devs, and PMs: AI Will Kill Some & Create Some By 2027 ( ): World Economic Forum Demand for designers will increase some; Depending on the type of developer, the jobs might see up to a 25% increase (blockchain developers being one of the leaders of the pack!) Roughly ~10% of PM jobs are going to die, and roughly ~10% will be added, so net zero. Product management is actually . But the experience of an AI PM vs. a random PM is not fungible, so the key advantage is to be as close to the growing tech (whether it’s AI or other) as possible. one of the top AI talent demands Macro Trends That Will Influence Our Individual Experience: Engel’s Pause & Solow’s Paradox It will take more than just job opportunities that will determine we experience progress, our own relevance, and careers of the next decade or two; it will be influenced by macro trends too. how The Mid-Term Impact Might Be Just Bad in General, but Hopefully, We’ll Live to See the Turnaround: Engels’ Pause Engels’ Pause describes the British industrial revolution in the early 1800s when working-class wages stagnated while GDP grew quickly. There are several theories, the bottom line being that the owners and investors of the booming businesses pocketed the gains and kept wages relatively flat. If it sounds familiar - argues that advanced economies are now in a new, “post-1980 Computer Revolution” Engels' Pause. Carl Benedikt Frey He explains that early labor-replacing technologies tend to decrease wages; once more sophisticated labor-augmenting technologies show up, wages and demand for talent tend to increase: "If technology replaces labor in existing tasks, wages and the share of national income accruing to labor may fall. If, in contrast, technological change is augmenting labor, it will make workers more productive in existing tasks or create entirely new labor-intensive activities, thereby increasing the demand for labor. (…) The growing capital share of income meant that the gains from technological progress were very unequally distributed: corporate profits were captured by industrialists, who reinvested them in factories and machines.” This is an optimistic sign! And aligned with what the enterprises expect: 87% of executives surveyed believe employees are more likely to be augmented than replaced by generative AI. IBM From : OWF “As some experts look at how employeesʹ current tasks will be affected by generative AI and automation, they divide the work into three types: transactional, relational, and expertise-related. Much of our transactional work will be substituted by a combination of robotic process automation, machine learning, and generative AI, while work requiring deep expertise or human collaboration will increasingly be augmented.” Waiting for the Immediate Signs of AI Revolution Might Be a Snoozefest: Solow’s Paradox and the Productivity J-Curve Solow’s Paradox and talk about a similar phenomenon: that as we (we, as in economies, companies, CEOs, and investors) pour money into a “groundbreaking” technology, the effects of it don’t seem to show up for a while: Erik Brynjolffsen’s Productivity J-Curve A similar question was raised in 1987, when economist Robert Solow observed a disconnect between the burgeoning computer age and the anticipated productivity gains — an observation now known as “Solow’s paradox.” Businesses and governments poured money into IT infrastructure, yet the expected leap in productivity was conspicuously absent. This paradox left many wondering if the problem lay not with the capacity of the technology but rather with the manner of its implementation. ( ) OWF Erik Brynjolffsen explains why that might be happening: General purpose technologies enable and require significant complementary investments, including co-invention of new processes, products, business models and human capital. These complementary investments are often intangible and poorly measured in the national accounts, even when they create valuable assets for the firm. He , and why it took a while for the electric motors’ revolutionary effects to materialize: the first generation of factory managers took out the steam engine - out of a factory that was and organized around the limitations of the steam engine - and plopped the electric engine in its place. gives an example of the steam engine being replaced with electric motors specifically built for It wasn’t until the new wave of managers redesigned the systems and workflows for the affordances of the electric engine that the expected innovation and productivity could be seen. Nobody Should Count on Employers to be the Drivers of Effective Re-Skilling One might say “OK, so if AI is so important, and there is not enough talent out there to hire, then surely the companies will invest money to train people.” And sure, they will; there are plenty of statistics that confirm that. But there’s already a significant and growing mismatch between what (and how quickly) companies are able to provide and what people need for their jobs - let alone what employees will need to be competitive in the job market vs. just slightly better at their existing, narrowly defined jobs. From : OWF Ok, So What’s the Recipe for Staying Afloat Here? On an individual level, there’s a discouraging disconnect between: The vague promises of what AI is supposed to be able to do, or how it be revolutionizing our work, versus what the existing products can do in practice right now; should between what the demand for AI talent is looking for, and where the supply market is (i.e., where we, the employees, are) today. But both of these trends are moving pieces; the key is to be one of the pieces moving along, not staying behind. The antidote is to use something, build something, and learn something (which I cover in my next post) until, inevitably, each of us starts to notice the applications and opportunities that actually bring progress: PS. If you liked this post - you might like my future takes at , where I write pragmatic takes on disruptive tech & its opportunities, with a sprinkle of deep dives into data and market insights. hypegeist.substack.com Also published here

Product Managers, Designers, and Devs: What Does Their Future Look Like in a World Filled With AI?

প্রোডাক্ট ম্যানেজার, ডিজাইনার এবং ডেভস: এআই দিয়ে ভরা বিশ্বে তাদের ভবিষ্যত কেমন দেখায়?

Produktmanager, Designer und Entwickler: Wie sieht ihre Zukunft in einer Welt voller KI aus?

Gerentes de producto, diseñadores y desarrolladores: ¿cómo será su futuro en un mundo lleno de inteligencia artificial?

Chefs de produit, concepteurs et développeurs : à quoi ressemble leur avenir dans un monde rempli d'IA ?

उत्पाद प्रबंधक, डिज़ाइनर और डेवलपर: AI से भरी दुनिया में उनका भविष्य कैसा दिखता है?

プロダクト マネージャー、デザイナー、開発者: AI で満たされた世界における彼らの未来はどのようなものになるでしょうか?

제품 관리자, 디자이너, 개발자: AI로 가득 찬 세상에서 그들의 미래는 어떤 모습일까요?

Gerentes de produto, designers e desenvolvedores: como será o futuro deles em um mundo repleto de IA?

Менеджеры по продуктам, дизайнеры и разработчики: как выглядит их будущее в мире, наполненном искусственным интеллектом?

Ürün Yöneticileri, Tasarımcılar ve Geliştiriciler: Yapay Zekayla Dolu Bir Dünyada Gelecekleri Nasıl Görünüyor?

Giám đốc sản phẩm, nhà thiết kế và nhà phát triển: Tương lai của họ sẽ như thế nào trong một thế giới tràn ngập AI?

产品经理、设计师和开发人员：在充满人工智能的世界中，他们的未来是什么样子？

This story was originally published on by . ProPublica Catherine Rentz An alleged serial rapist was arrested last month with help from evidence saved by a Baltimore County doctor nearly half a century ago. ProPublica highlighted this rare trove of hospital microscope slides in its and inspired a new Maryland law to protect the evidence. Cold Justice series In this case, the evidence was collected from five women who visited the Greater Baltimore Medical Center for rape exams between 1978 and 1986. All said a man had violated them after breaking into their first-floor apartments in complexes within the same mile radius. Police at the time had not yet started saving standardized rape kits. But a prescient doctor, Rudiger Breitenecker, anticipated that one day, science might advance enough to make use of the specimens. Most of the evidence collected by Breitenecker between 1975 and 1997 sat untouched for decades, until a new generation of cops and slowly began to test it. By 2022, Baltimore County police knew four of the cases shared the same perpetrator’s DNA. recognized its value (Initial testing didn’t yield a profile from the fifth, according to prosecutors.) But the suspect’s identity was still a mystery. Enter Detective M. Lane, one of two detectives in the relatively new Special Victims Unit cold case division, who decided to read all of the police reports connected to those slides and link them to other cases. (Lane uses only her first initial on court documents.) She found a reported attack that wasn’t one of the four DNA-linked cases, but which happened just a short walk away from a rape connected to DNA evidence, and just two months later. The report contained an almost unbelievable lead: A woman attacked on Dec. 3, 1978, told police that the suspect told her his first and middle names: James William. He wore a bracelet with “Jim” on it and said that day was his 26th birthday. This June, Lane plugged those clues into a police database and found James William Shipe Sr., a man who had been arrested and charged for a separate attempted rape in 1979. Prosecutors at the time had dropped the case; Baltimore County State’s Attorney Scott Shellenberger said it is unclear why. The victim in this later case also reported that her attacker was wearing a bracelet with “Jim” on it. Lane had Shipe’s fingerprints from that case compared to the one pulled from the 1978 crime scene. They matched. Shipe had arrest reports for charges often associated with rapists: trespass and Peeping Tom, for which he served probation. Mugshots from James William Shipe Sr.’s 1972, 1979 and 1989 arrests Credit: Baltimore County Police Department The detective had enough information to request a warrant for Shipe’s DNA. The 70-year-old was living with his wife less than 10 miles away from the scenes of the five attacks. On July 22, Lane got the results: Shipe’s DNA matched those on the doctor’s rape evidence slides, according to court and police reports. Police had now linked Shipe to five cases by DNA or fingerprints. Shipe was arrested 10 days later and is now in jail, facing charges in three of the five rape cases; the two other women who were assaulted have since died, and prosecutors are not pursuing charges in those. ProPublica tried to reach Shipe, who is in a Baltimore County jail awaiting his next court hearing, but could not get through to him by phone. Reached by phone at her home, Cynthia Shipe said her husband has not yet been assigned a public defender. She said the charges against her husband are “totally bogus” and she believes that he is innocent. She said she did not know about the DNA evidence, but that she’s known him for 37 years and that he’s a “wonderful man” who just sent her a dozen red roses for her birthday and that he “doesn’t have a violent bone in his body.” Cynthia Shipe said her husband was a trucker until three years ago, when he had to retire due to heart problems, and she worries about his health now. She said he has not been given the proper medication in jail and she’s seen his health deteriorate. The Maryland Office of the Public Defender said they could not comment on the case at this time. “I am a new person, I feel totally different,” said Linda Shinault, who reported a rape to police in September 1986. She said someone broke into her apartment, severed her phone cord and was waiting for her in the bathroom after she got out of the shower. She said the attack and not knowing who did it had put a “heaviness” on her. When she got a call from a detective nearly 37 years later telling her a suspect had been arrested, she said she was screaming into the phone, she was so happy. She then told everyone she knew. She considers the doctor’s saving of evidence to be a “miracle.” “I feel the doctor is looking over us,” she said. Breitenecker passed away in September 2021. Linda said she wanted to tell her story to help other survivors and questioned why law enforcement was waiting “even another week” to test more evidence if there might be more victims whose cases could be solved. For the Street family, the answer came too late. Dennis Street said his late wife Patricia would have appreciated the answer. “I'm glad the guy was caught,” Street said. “And I know my wife would be happy, too, after all these years. Thank God for DNA testing. But I don’t know what took so long.” Patricia Street reported a rape in September 1978 that police say is the earliest case linked to Shipe by DNA so far. She died in 2021 after two decades of health complications. Baltimore County police say they are continuing to process cold case evidence and investigate whether more cases are tied to Shipe. Most of the rapists who have been caught with the historic hospital DNA have been found to have a long list of other criminal charges on their records, such as other rapes, theft, burglary and murders. Shipe does not have such charges on his record, according to the Maryland Judiciary online records. ProPublica , a 21-year-old college student, by studying perpetrator patterns. Alphonso W. Hill’s DNA has matched 11 hospital slide cases so far, including DNA from the assault of a Goucher student in the same location where Carter’s body was found. helped solve the 1983 murder of Alicia Carter After ProPublica’s investigation and police inquiry, he confessed in 2021 to raping and murdering her. As these cases show, finding suspects who have eluded police for decades often requires more than DNA tests. “You can have all the computers, all the DNA, all these things that you want, but it still takes good old-fashioned police work,” said Shellenberger. Shipe’s is the first arrest from the new effort to test the hospital slide cases, announced by Baltimore County officials in October 2019. Police have been delayed in part due to a backlog inside their forensics lab. Like many other agencies, the Baltimore County police face significant staffing shortages. The department has only two dedicated cold case detectives in its Special Victims Unit, though that is more than a previous cold case effort in the first decade of the century, which had no dedicated detectives. The unit often gets pushed down the priority list for testing even though rape cases notoriously involve serial perpetrators. As of January 2023, police had about 1,300 hospital slide cases. At the rate of testing so far, it could take another couple of decades to finish processing them. However, police have made a recent move that should help expedite testing. The remaining evidence, still at the hospital, is moving to police headquarters where it will be logged and sent for testing at a private lab. They are skipping a pre-screening step in their own lab and shipping the slides directly to a private DNA testing company. A police spokesperson wrote to ProPublica that "The Department looks forward to receiving all available evidence and is doing everything possible to expedite the analysis and investigation of these important cases." Part of that is driven by a new state law passed earlier this year in response to by ProPublica’s investigation. The law classifies the hospital slides as official rape evidence and requires the police department to count them among its rape kit backlog and retain the slides for at least 75 years after they were collected. issues raised Baltimore County police have released Shipe’s mugshots and are encouraging anyone with information on additional crimes to reach out at 410-307-2020. Survivors who would prefer to speak with the department’s victim advocate may call 443-345-7587 or email . aharkins@turnaroundinc.org Survivors can find additional resources at and Maryland Coalition Against Sexual Assault TurnAround, Inc. Photo by on Warren Umoh Unsplash

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In the real world, we often have very poor inputs to automate a process. For example, we want to automate a process that is now completely manual and people make a lot of mistakes in it. Moreover, there is a long delay in correcting errors and because of this, the current data contains a lot of misleading answers. However, if we have a lot of data, quantity inevitably turns into quality (hello, emergentism!). In this article, I will share a real case study of how we automated the transaction categorization process in bookkeeping. As a result, we managed to save the company $20,000+ in direct operational costs, while also reducing the average turnaround time from 24 to 2 hours and  increasing the average quality of categorization from ~80% to 95%+. In this article, I'll touch on a lot of practical aspects of how you can automate almost any repetitive manual process. You don't need to build your rocket science models for that - you just need to choose the right architecture from the directly making the final decision, a set of additional algorithms to process the data, and autonomous agents to collect the information (yes, we'll be using ChatGPT!). ML model The article will contain a certain amount of technical details to make it interesting for senior-level ML specialists, but I will also try to immerse the reader in the context of the business problem and explain the logic of making certain decisions in simple language. So even if you don't create ML models manually, the gained knowledge probably will give you many useful ideas for your project. Let's go! Content Overview Choosing an ML model architecture for a business problem The infrastructural and technical stages of the project Challenges in the model training The benefits of deep knowledge of a business process and most effective features Using LLM for data enrichment Anomaly search out of the box and global optimization Lyrical digression: why our model, on average, performs better than humans Instead of the bottom line Choosing an ML model architecture for a business problem Accounting categorization is a simple enough process to explain even to a 5-year-old child. You have some business transactions and you need to categorize them from an accounting perspective. For small companies, there's not a lot of nuance. You spend money, you record it as an expense. You get the money - put it in revenue. But the bigger the business, the more difficult it is to categorize its operations. Besides, many non-standard forms of documents appear, the description of transactions becomes more and more exotic, and some are not described in almost any way. The question appears: how do bookkeepers do their job under these conditions? We interviewed many of the agents doing categorization in our Bookkeeping Factory and watched dozens of hours of recordings of their work to understand exactly how they make decisions. It turned out that they were often making a single keyword choice, while also looking at secondary factors like counterparty or transaction amount. It quickly became clear that we don't need neural networks (at the beginning of the project we were considering specifically FCNN as it is often used for similar tasks), and we can solve the problem with a simpler model because we will very rarely categorize full-fledged sentences, and the keywords themselves can be conveniently turned into separate near-categorical features. Important: try to use as simple solutions as possible. Don't overengineer the solution architecture at the start and avoid more complex solutions in the distance. Very often you don't get the coverage / quality due to misunderstanding of the business context and, as a consequence, missing important features. With this in mind, Gradient Boosting proved to be the most optimal algorithm to train on our data. Here are several reasons why gradient-boosting algorithms excel in our scenario: Gradient Boosting algorithms can naturally handle mixed data types, including both scalar and categorical features, without requiring extensive preprocessing. Many other algorithms might struggle with categorical features, which often need encoding or transformation. Handling Mixed Data Types: Imbalanced class distributions are common in multi-class classification. Gradient Boosting algorithms allow you to assign different weights to different classes, which can help address class imbalance issues. Effective Handling of Imbalanced Data: Gradient Boosting algorithms can automatically detect and utilize feature interactions, which is essential for complex datasets with a mix of scalar and categorical features. Automatic Feature Interaction Detection: Gradient Boosting algorithms are known for their high predictive accuracy, making them well-suited for multi-class classification tasks where you want to maximize classification performance. (Most frequent winner algorithm in kaggle for multi classifications) High Predictive Accuracy: Gradient Boosting models are generally robust to noisy or incomplete data, which is often encountered when dealing with mixed feature types. Robust to Noisy Data: We were also using 2 different libraries for gradient boosting in parallel - XGBoost and CatBoost. XGBoost is the gold standard in the industry and needs no introduction. As for CatBoost, it is the only implementation of the Gradient Boosting algorithm that works with text features without any additional preprocessing. Also, CatBoost is specifically designed to handle categorical features seamlessly without the need for one-hot encoding or label encoding. In our case, CatBoost has shown better results. The infrastructural and technical stages of the project The first thing we did was to build a playground to quickly test all the new features. We couldn't predict ahead of time all the features and approaches we would eventually come up with and which of them would show the best results. However, we realized that we wanted to make experiments as fast and easy as possible when testing new hypotheses. We called this playground: 1-command run dataset preparation (download the actual dataset from an analytical database, split it into train/validation/test, and prepare it for use) Convenient configuration of experiments: when you work on a project for a long time, you accumulate a lot of new features and hyperparameters and their best combination with each other may change over time. You want to tune many parameters really fast and better to create the config file for that. Splitting the code into layers of data aggregation, preprocessing (e.g., text correction), enrichment (to add important features), model training, calculation and visualization of the experiment result. The layers themselves are also divided into separate domain-specific services. For example, in our case additional categorization of transaction counterparties is important. We use autonomous agents on LLM models (specifically ChatGPT) for that and, thus, we build a separate service with counterparty information retrieval. Without the existence of such a playground, the speed of new experiments will be many times slower. So I advise you to build such an environment from the beginning. When it comes to training, the implementation was done based on Azure cloud solution, since at that moment we had a grant from Microsoft, but a similar approach could be utilized with any cloud platform. The training process consists of the following steps: Run the needed instance*. First of all, check the number of kernels/threads available for instance, since the Gradient Boosting algorithm can be parallelized quite well; Copy the project to a remote server; Install the dependencies; Launch the experiment. Also pay attention to the instance type, because some of them might have limited work time with a full load, which can increase the training time. A good approach would be to create a docker and copy it instead. We could also have an overview of the instances, but it’s a bit of a different topic. Challenges in the model training By the time we started working on the model our dataset consisted of 3,000,000 manually categorized line items (this is a single line from a document, e.g. invoices often consist of multiple line items). Of these, only about half were reviewed by accountation: this is due to a peculiarity of the way our bookkeeping factory works. To build real-time bookkeeping, we immediately send all documents to our Kuala Lumpur-based bookkeeper department for categorization. They can escalate complex cases to accountants, but do most of the categorization themselves. That said, the accountants make sure to review all categorizations on their own before submitting the accounting period as they are responsible to the government for this. On average, a bookkeeper-level categorization contains about 20% errors of different severity. It’s important to note, most of the errors are not as critical from a financial cost analysis perspective. For example, bookkeepers often confuse revenue from core and non-core sales. From a tax perspective, this doesn't affect the bottom line, but from a potential audit perspective, it shouldn't be confused. These nuances created additional challenges in training, but we were able to solve them safely. Apart from feature engineering, the following actions significantly contribute to the model performance: 2 test datasets: Considering that the final accuracy can be only calculated on the submitted data (which can take up to several months), but coverage of the model should be estimated on the most recent data, we used two different datasets to get the model accuracy/coverage. For accuracy, we used the last 10% of the records that were submitted. And for the coverage we used 10% of the most recent data. Balancing class weights: since the classes are not balanced, we should adjust the weights of the rare account codes for training calculated as following: n_samples / (n_classes * np.bincount(y)). Catboost provides a specific “class_weights” parameter for passing an array with weights. Adjusting classes weights based on error likelihood: We also added additional weights for each record based on the frequency of human error while creating an account code (this is relevant for the model training on the not fully validated data) Feature with thresholds: it’s also possible to let the model decide on the thresholds of the features, where it’s applicable The benefits of deep knowledge of a business process and most effective features The most contributing features are often born at the intersection of domain and technical knowledge. In our case, the “strongest” feature that added the top 30% of coverage turned out to be the probability of keyword categorization in different dimensions. How it works. Based on the results of shadowing, it became clear that very often bookkeepers look at how similar cases have been categorized in the past to make decisions. At the same time, they determine similarity by matching keywords. We decided to formalize this process by engineering. For that, we record the keywords in all categorization cases. Then for each keyword, based on our dataset, we calculate the probability that a particular ledger account will be selected. Since there is often more than one keyword in a one-line item, we need to assign weights to them somehow. This can be done either as a simple weighted average - i.e., give the same weight to each keyword encountered in a line item - or it can be done in a similar way to tf-idf - i.e., give more weight to rarer keywords. Important: From our practice, the second approach (tf-idf like) works better, but we ended up adding both approaches as 2 separate features - one counts by the first approach and the other by the second. In doing so, both features essentially pick values from the same set (I'll talk about this a bit below). This approach, as a rule, does not lead to a drop in accuracy, but allows you to add an extra percentage of coverage, which is always nice. Let me point out why this works: with proper training, the algorithm will realize that one of the features has a higher correlation with the correct answer and therefore it will have a higher feature importance. In the case of a conflict between two features, the one with the higher feature importance will be prioritized. So the next time you have several hypotheses for how to calculate a particular feature, ask yourself whether to add all of them as separate features. Next, measure the error contribution of all the features. If it is negative, then feel free to leave all the features. Next, we extract keywords from the new line item that we want to categorize and calculate the products of the probabilities of selecting the bookkeeping accounts for these keywords. You'll end up with something like this: Account A - 80% Account B - 15% The rest - 5%. In the feature itself, we will write only the class that will pass the threshold specified in the config parameters. At the same time, you can add several such features with different threshold levels, for example, 75%, 95%, 99%. If some account has 99% probability, then all such features will have the same value - this account. If an account has a probability of 76%, then the first feature will have the value of the account, and the remaining two will have the value of null. Why does this work? Read the previous "important" section above. Using LLM for data enrichment LLM is a great technology for a lot of textual tasks. But categorizing small word combinations, often with no unambiguous meaning, is not the most appropriate task for it. Nevertheless, we can use it to search and summarize additional information and add the result of its work as separate features. In our case, we want to understand whether the counterparty of some transaction is the same counterparty that was encountered in a previous transaction. If yes, it very much increases the probability of categorization by the same account. If it is a new counterparty, we want to get additional useful information on it. To do this, we first want to make sure that the difference in names is not a typo/error at the OCR stage, and count the distance between counterparty names The Damerau–Levenshtein distance algorithm. If it does not exceed a threshold, then we run our autonomous agent on ChatGPT to crawl local company registers (e.g. for Singapore it is Acra Register) with the name of that counterparty. Thanks to this we can make sure that the company name is spelled correctly and also find out the main field of activity of the counterparty. We do a similar operation when we encounter obscure words - we Google the word and ask Chatgpt to make some conclusion about what the word is. For example, "M1133" may turn out to be the name of a specific phone model, or it may remain an incomprehensible set of letters and then we have to ask our client what it means. Important: use any technologies for those tasks for which they are created/show the best results. In our case, we are learning to predict the probability of categorization based on a statistical distribution of many factors whose weight of significance we do not know, and there is no formalized list of rules for that. In such circumstances, we are better served by simpler models that construct some statistical distribution of values depending on the input data. This is something that gradient boosting can do very well. And something that the Transformer architecture, on which all current LLM models are built, can easily dumb down. Anomaly search out of the box and global optimization One of the nice bonuses of ML model building turned out to be the following observation. Since the gradient boosting returns class probabilities, we can treat them as the confidence of the response. An obvious application of the confidence level is to define a threshold at which we use the model response. If the threshold is not reached (the model is not confident enough), then we send it out for human classification. The less obvious thing is that very low confidence can correlate with really complex cases that require additional human verification. That was exactly our situation! Most cases where the most likely class had a very low probability tend to have a higher error rate in human process. If such cases are sent through a standard process where they are categorized by bookkeepers, they are much more likely to exhibit an incorrect answer. From a product perspective, it’s more beneficial to send these cases straight to accountants or clients. That's why we put up a second threshold at the bottom: when it is breached, the document is sent to a separate flow. Although this approach leads to local costs, it generally contributes to global optimization of the process: less data errors -> more trust in real-time accounting (higher NPS) => less time for accountants to check (optimization of operating costs). Lyrical digression: why our model, on average, performs better than humans In addition to the ML model itself, which makes the final categorization decision, we built 2 important supporting components for it: A much more informative "interface": as a feature, we give as input the statistical distribution of past categorizations in different slices: at the level of company, counterparty, company activity type, company region, etc. (see Section “The benefits of deep knowledge of a business process and most effective features”) Autocollection of additional information (see section “Using LLM for data enrichment”). Without these two components, the result of the model would be many times worse. So we can't say that our ML model magically started to be smarter than humans. It's just that we added additional features that allow the model to make a more informed decision, and we did quality training. Important: in real life, the existing business process we are working on is often far from ideal. To avoid being blocked by product development, consider gathering important information on the backend side. By cutting off the visual interface that people need but ML models don't, you'll save a significant amount of resources. And with LLM technologies, you can implement many routine information gathering and summarization processes much faster, especially if they don't require high accuracy. Instead of the bottom line The problem we solved is both unique and standard at the same time. On the one hand, it is unique because every domain and business process has its nuances that need to be analyzed, comprehended, and taken into account when building an ML model. In this article, I have detailed a few such nuances in the context of building a bookkeeping classifier. But, of course, there are many more. On the other hand, the approaches outlined in this article can be widely reused to automate many other business processes, and not necessarily those where you need to classify something explicitly. After all, in essence, any business process is an ensemble of classifiers: you choose one of the possible solutions at stage 1, then make one of the possible solutions at stage 2, and so on. Combining LLM technologies with simpler ML models and depth of understanding of the business process one can achieve amazing results. In our case, we were able to automate 90%+ of the operations and achieve a much better quality (98%+) of responses than the people whose work we automated. In addition, we prevented a lot of errors and saved labor in the manual process by sending anomalies to a separate stage. Good luck with your ML experiments!) by , Peter Potapov special thanks to Andrey Mescheryakov

How We Built an Efficient ML Model With Dirty Data and Insufficient Information

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