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How Startups Can Bolster Defenses as Cyber Threats Loom in Cloud Era

Economists Agree, Politicians Don't — So Guess Who Loses

অর্থনীতিবিদরা একমত, রাজনীতিবিদরা না - তাই আপনি হারান

Ökonomen sind sich einig, Politiker nicht – also verlieren Sie

Los economistas están de acuerdo, los políticos no, así que usted pierde

Les économistes sont d’accord, les politiciens non – donc vous perdez

अर्थशास्त्री सहमत हैं, राजनेता नहीं - इसलिए आप हारे

経済学者は同意するが、政治家は同意しない — だから負ける

경제학자들은 동의하지만 정치인들은 그렇지 않습니다. 그래서 당신은 패합니다.

Os economistas concordam, os políticos não - então você perde

Экономисты согласны, политики — нет, поэтому вы проигрываете

Ekonomistler Kabul Ediyor, Politikacılar Katılmıyor - Yani Kaybediyorsunuz

Các nhà kinh tế đồng ý, các chính trị gia thì không - Vậy là bạn thua

经济学家同意，政客不同意——所以你输了

2 weeks, 4-5 hours of Sleep each day, and the Birth of Miraki: A Savage Hackathon Tale Let's be real, most hackathons are a chaotic circus. You've got sleep-deprived devs chugging energy drinks like it's water, half-baked ideas flying around, and enough tech jargon to make your head spin. But hey, that's where the magic (or madness) happens. My team decided to dive headfirst into the Web3 online hackathon with a mission to make life less miserable for DAO contributors. You know the drill – tools scattered across the internet like lost socks, communication breakdowns that would give couples therapy a run for its money, and a general sense of "Where the heck is anything? Introducing Miraki: VS Code for DAO tools This is just a small explainer. More details about Miraki and the team behind will be published in later blogs. Miraki is our brainchild, a browser-based devtool that aims to bring order to the wild world of DAOs. Think of it as VSCode, but with a Web3 twist. Here's the breakdown: Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within.



Apps: Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself.


Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. The Hackathon Hustle: Caffeine, Code, and Controlled Chaos The frontend was a React party, with TypeScript and Vite adding that extra bit of class. Our MVP sported three must-have apps: Trello, Snapshot, and DappSuit (swagger UI for Solana programs) to show that integration of such diverse app into one platform is possible. Shoutout to Turbo monorepo for keeping our sprawling codebase from turning into a spaghetti monster. We ideated fancy isolation techniques for our apps: iframes, web workers, we read blogs from figma to understand how they implemented plugins, we digged deep into vs-codes original source code, the whole nine yards. But pragmatism won the day – in the true spirit of hackathons, we embraced the "it works (for now)" mantra to avoid disappearing down that rabbit hole. The backend? Django and Postgres, a classic combo for building the foundation of our little empire. React Pluggable gave us that sweet extensibility we craved, without the crippling overengineering headaches. And that’s how we managed to wrap up the project just a day before last submission date. The Savage Truth Was it smooth sailing? Pfft. More like navigating a storm in a leaky boat. But amidst the sleep deprivation, the caffeine-induced jitters, and enough bugs to rival an Amazon rainforest, a truly useful tool emerged. Miraki ain't perfect (yet), but it's got the potential to make DAOs less of a soul-sucking experience for everyone involved. And that, my friends, is what hackathons are all about – taking a problem, caffeinating yourself into oblivion, and emerging with something that might just make the tech world a slightly better place. So, what's next for Miraki? We're aiming to unleash this beast into the wild, gather feedback, and iterate like there's no tomorrow. If you're a fellow DAO dweller tired of feeling like a lost explorer in a digital jungle, stay tuned. Miraki might just be your salvation. PS: The blog is rephrased using Gemini AI. Because the writer was on Leet-code when he should be taking English writing classes. Follow Miraki on https://twitter.com/Miraki_io. All feedback is most-welcomed. You can contact us through email at iampiyushjaiswal103@gmail.com. 2 weeks, 4-5 hours of Sleep each day, and the Birth of Miraki: A Savage Hackathon Tale 2 weeks, 4-5 hours of Sleep each day, and the Birth of Miraki: A Savage Hackathon Tale Let's be real, most hackathons are a chaotic circus. You've got sleep-deprived devs chugging energy drinks like it's water, half-baked ideas flying around, and enough tech jargon to make your head spin. But hey, that's where the magic (or madness) happens. My team decided to dive headfirst into the Web3 online hackathon with a mission to make life less miserable for DAO contributors. You know the drill – tools scattered across the internet like lost socks, communication breakdowns that would give couples therapy a run for its money, and a general sense of "Where the heck is anything? Introducing Miraki: VS Code for DAO tools Introducing Miraki: VS Code for DAO tools This is just a small explainer. More details about Miraki and the team behind will be published in later blogs. This is just a small explainer. More details about Miraki and the team behind will be published in later blogs. Miraki is our brainchild, a browser-based devtool that aims to bring order to the wild world of DAOs. Think of it as VSCode, but with a Web3 twist. Here's the breakdown: Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within. Apps: Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself. Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within. Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within. Workspaces: Apps: Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself. Apps : Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself. Apps Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. Universal Search: The Hackathon Hustle: Caffeine, Code, and Controlled Chaos The Hackathon Hustle: Caffeine, Code, and Controlled Chaos The frontend was a React party, with TypeScript and Vite adding that extra bit of class. Our MVP sported three must-have apps: Trello, Snapshot, and DappSuit (swagger UI for Solana programs) to show that integration of such diverse app into one platform is possible. Shoutout to Turbo monorepo for keeping our sprawling codebase from turning into a spaghetti monster. We ideated fancy isolation techniques for our apps: iframes, web workers, we read blogs from figma to understand how they implemented plugins, we digged deep into vs-codes original source code, the whole nine yards. But pragmatism won the day – in the true spirit of hackathons, we embraced the "it works (for now)" mantra to avoid disappearing down that rabbit hole. figma The backend? Django and Postgres, a classic combo for building the foundation of our little empire. React Pluggable gave us that sweet extensibility we craved, without the crippling overengineering headaches. And that’s how we managed to wrap up the project just a day before last submission date. React Pluggable The Savage Truth The Savage Truth Was it smooth sailing? Pfft . Pfft More like navigating a storm in a leaky boat. But amidst the sleep deprivation, the caffeine-induced jitters, and enough bugs to rival an Amazon rainforest, a truly useful tool emerged. Miraki ain't perfect (yet), but it's got the potential to make DAOs less of a soul-sucking experience for everyone involved. And that, my friends, is what hackathons are all about – taking a problem, caffeinating yourself into oblivion, and emerging with something that might just make the tech world a slightly better place. So, what's next for Miraki? So, what's next for Miraki? We're aiming to unleash this beast into the wild, gather feedback, and iterate like there's no tomorrow. If you're a fellow DAO dweller tired of feeling like a lost explorer in a digital jungle, stay tuned. Miraki might just be your salvation. PS: The blog is rephrased using Gemini AI. Because the writer was on Leet-code when he should be taking English writing classes. PS: The blog is rephrased using Gemini AI. Because the writer was on Leet-code when he should be taking English writing classes. Follow Miraki on https://twitter.com/Miraki_io . https://twitter.com/Miraki_io All feedback is most-welcomed. You can contact us through email at iampiyushjaiswal103@gmail.com. iampiyushjaiswal103@gmail.com.

From a Spark of Inspiration to a Product That Makes a Difference – Our Hackathon Journey

Bitcoin Blues: Price Slump and Investor Jitters This week in crypto was a rollercoaster ride that would make even the most seasoned gambler queasy. It’s not like we’re surprised. Last week, the bets are on $62k on BTC but let's dissect this, shall we? First up, Bitcoin. Remember that shiny new car you almost bought last month? Yeah, Bitcoin took a bigger tumble than that. Price plummeted below $62,000, and why? Because investors suddenly turned into scaredy-cats. Spot Bitcoin ETFs? Not so hot anymore. Demand went kaput, and everyone ran for the hills (or at least their stablecoins). But here's the twist: the bigwigs, the Bitcoin whales – they held on tight. Didn't flinch. Maybe they see something we don't, or maybe they're just stubborn gamblers themself. Who knows? But it's an interesting wrinkle. Ethereum's Woes: A Perfect Storm of Geopolitics and Whales Meanwhile, Ethereum (ETH) took a nasty spill too. Dropped like a bad habit, down to $2,990. Talk about a buzzkill. Why the sudden plunge? Well, it wasn't just one thing, but a perfect storm of factors brewing in the cryptosphere. Geopolitical Jitters: Remember that whole mess in the Middle East? Yeah, that kind of global tension tends to make investors skittish. Suddenly, risky assets like crypto become less appealing, and everyone starts scrambling for safe havens like the US dollar. Ethereum, along with other digital assets, felt the chill. Dollar Dominance: Speaking of the US dollar, it decided to flex its muscles this week. When the dollar strengthens, it makes other currencies and assets, including crypto, look relatively weaker. This can lead to investors selling off their ETH holdings to convert to dollars, putting downward pressure on the price. Whale Woes: Here's another interesting wrinkle. Unlike Bitcoin whales who held on tight this week, some big-time Ethereum investors decided to cash out. This could be due to a combination of factors – maybe they saw the writing on the wall with geopolitical tensions, or perhaps they were simply taking profits after a strong run-up in ETH prices earlier. Whatever the reason, their exit added to the selling pressure on Ethereum. Long Liquidations:  Let's add another layer to this complexity. Some investors use leverage – borrowing money to amplify their bets – in the crypto market. These are called "long" positions, essentially a bet that the price will go up. But when the price starts dropping rapidly, as happened with ETH this week, lenders can force the sale of those leveraged positions to recoup their losses. This cascade of "long liquidations" can further accelerate the price decline, creating a negative feedback loop. So, Ethereum's recent woes were a confluence of global events, investor sentiment, and the complex mechanics of the crypto market. It's a reminder that while the potential rewards in crypto can be high, the risks are just as significant. Beyond Bitcoin: Tokenization and Real-World Applications But here's the real kicker: there's a whole new crypto game brewing in the background. We're talking tokenized houses, folks! Imagine taking out a loan on your house, but instead of stuffy old banks, you use fancy crypto magic. A new protocol just snagged $10 million to make this a reality. Wild, right? And speaking of reality, Bitcoin ATMs seem unfazed by the whole price rollercoaster. Business as usual, they say. Maybe it's because people are using them for more than just moon-shot bets. Maybe there's real utility there. Gold vs. Crypto: Peter Schiff Throws Shade (and Sparks a Debate) Peter Schiff, the firebrand economist and gold enthusiast, couldn't resist casting doubt on the recent Bitcoin ETF buzz and lofty price predictions. He highlighted the recent bearish performance of crypto-linked stocks like Coinbase and MicroStrategy, suggesting a lack of institutional investor confidence in the long-term viability of crypto. Schiff represents a common viewpoint – gold bugs who see cryptocurrency as a risky, volatile asset lacking the established track record of gold. However, crypto proponents counter that the market's youth and potential for disruption outweigh short-term fluctuations. They point to real-world use cases like tokenization and growing adoption for transactions. Is Schiff right, or is crypto a new asset class finding its footing? The gold vs. crypto debate rages on. So, You Want to Invest in Crypto? What's the takeaway The crypto market is a daily confusing mess, filled with conflicting signals and enough hype to make your head spin. Do your homework, folks. Don't just follow the herd mentality. And remember, in the land of crypto, even the experts are just making educated guesses. Stay frosty out there. Bitcoin Blues: Price Slump and Investor Jitters Bitcoin Blues: Price Slump and Investor Jitters This week in crypto was a rollercoaster ride that would make even the most seasoned gambler queasy. It’s not like we’re surprised. Last week, the bets are on $62k on BTC but let's dissect this, shall we? First up, Bitcoin. Remember that shiny new car you almost bought last month? Yeah, Bitcoin took a bigger tumble than that. Price plummeted below $62,000, and why? Because investors suddenly turned into scaredy-cats. Spot Bitcoin ETFs? Not so hot anymore. Demand went kaput, and everyone ran for the hills (or at least their stablecoins). But here's the twist: the bigwigs, the Bitcoin whales – they held on tight. Didn't flinch. Maybe they see something we don't, or maybe they're just stubborn gamblers themself. Who knows? But it's an interesting wrinkle. Ethereum's Woes: A Perfect Storm of Geopolitics and Whales Ethereum's Woes: A Perfect Storm of Geopolitics and Whales Meanwhile, Ethereum (ETH) took a nasty spill too. Dropped like a bad habit, down to $2,990. Talk about a buzzkill. Why the sudden plunge? Well, it wasn't just one thing, but a perfect storm of factors brewing in the cryptosphere. Geopolitical Jitters: Remember that whole mess in the Middle East? Yeah, that kind of global tension tends to make investors skittish. Suddenly, risky assets like crypto become less appealing, and everyone starts scrambling for safe havens like the US dollar. Ethereum, along with other digital assets, felt the chill. Geopolitical Jitters: Dollar Dominance: Speaking of the US dollar, it decided to flex its muscles this week. When the dollar strengthens, it makes other currencies and assets, including crypto, look relatively weaker. This can lead to investors selling off their ETH holdings to convert to dollars, putting downward pressure on the price. Dollar Dominance: Whale Woes: Here's another interesting wrinkle. Unlike Bitcoin whales who held on tight this week, some big-time Ethereum investors decided to cash out. This could be due to a combination of factors – maybe they saw the writing on the wall with geopolitical tensions, or perhaps they were simply taking profits after a strong run-up in ETH prices earlier. Whatever the reason, their exit added to the selling pressure on Ethereum. Whale Woes: Long Liquidations: Let's add another layer to this complexity. Some investors use leverage – borrowing money to amplify their bets – in the crypto market. These are called "long" positions, essentially a bet that the price will go up. But when the price starts dropping rapidly, as happened with ETH this week, lenders can force the sale of those leveraged positions to recoup their losses. This cascade of "long liquidations" can further accelerate the price decline, creating a negative feedback loop. Long Liquidations: So, Ethereum's recent woes were a confluence of global events, investor sentiment, and the complex mechanics of the crypto market. It's a reminder that while the potential rewards in crypto can be high, the risks are just as significant. Beyond Bitcoin: Tokenization and Real-World Applications Beyond Bitcoin: Tokenization and Real-World Applications But here's the real kicker: there's a whole new crypto game brewing in the background. We're talking tokenized houses, folks! Imagine taking out a loan on your house, but instead of stuffy old banks, you use fancy crypto magic. A new protocol just snagged $10 million to make this a reality. Wild, right? And speaking of reality, Bitcoin ATMs seem unfazed by the whole price rollercoaster. Business as usual, they say. Maybe it's because people are using them for more than just moon-shot bets. Maybe there's real utility there. Gold vs. Crypto: Peter Schiff Throws Shade (and Sparks a Debate) Peter Schiff, the firebrand economist and gold enthusiast, couldn't resist casting doubt on the recent Bitcoin ETF buzz and lofty price predictions. He highlighted the recent bearish performance of crypto-linked stocks like Coinbase and MicroStrategy, suggesting a lack of institutional investor confidence in the long-term viability of crypto. Schiff represents a common viewpoint – gold bugs who see cryptocurrency as a risky, volatile asset lacking the established track record of gold. However, crypto proponents counter that the market's youth and potential for disruption outweigh short-term fluctuations. They point to real-world use cases like tokenization and growing adoption for transactions. Is Schiff right, or is crypto a new asset class finding its footing? The gold vs. crypto debate rages on. So, You Want to Invest in Crypto? What's the takeaway So, You Want to Invest in Crypto? What's the takeaway The crypto market is a daily confusing mess, filled with conflicting signals and enough hype to make your head spin. Do your homework, folks. Don't just follow the herd mentality. And remember, in the land of crypto, even the experts are just making educated guesses. Stay frosty out there.

Crypto Market Update: All Bets Are Off on Bitcoin- Will the Whales Save the Day?

Get ready for an AI earthquake! A team of UCLA researchers ( n, , , ) have dropped some major keys to AGI. It's not only the code to seriously human-sounding AI, but they've also gone and open-sourced the whole thing. @zxche @Yihe__Deng @HuizhuoY @Kaixuan_Ji_19, @QuanquanGu Now you can develop better LLMs without needing to feed it tons of new, human-annotated data. First, let's focus on the game-changer here: a self-teaching language model. This method lets a language model becoming better and better without massive amounts of new, externally curated data. teach itself, Introducing SPIN: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models I went full deep-dive mode – read their paper (" "),  scoured the insights on forums like , , and with Google Gemini Ultra and GPT-4 Turbo – and the core concept of SPIN knocked my tech-loving metaphorical socks off: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models HackerNews X Reddit The 'Conversation Partner' Trick Imagine starting with a language model that has mastered basic skills (let's say conversational etiquette). With SPIN, the model building a dataset from what it already knows. generates internal 'conversations,' Instant knowledge expansion! Step two involves unleashing a new model and giving it one task: spot the difference between and genuine human communication. This forces the original model to up its game, getting more and more with every response to avoid detection. machine-generated chats human-like Here's where things get interesting. They started with (already fine-tuned with ). SPIN unleashed an iterative training system with this base model, improving it exponentially without relying on tons of new externally created data. zephyr-7b-sft-full UltraChat corpus SPIN vs. Traditional AI Training (DPO): A New Champion? We usually think machine learning, particularly for these huge language models, requires boatloads of carefully curated and labeled data. methods involve humans painstakingly rating AI responses against each other for training. Not only is this labor-intensive, but it also balloons costs as a dataset grows. Direct Preference Optimization (DPO) Direct Preference Optimization (DTO) is a training method where a model is fine-tuned using a dataset of preferences, often involving human judgments that decide which of the model-generated responses are preferred. This method requires collecting new data where each piece is labeled based on these preferences, which can be resource-intensive. In contrast, SPIN utilizes , significantly reducing the need for new data. iterative self-play By the first iteration, , highlighting its efficiency and effectiveness in leveraging existing data to enhance model performance. SPIN's performance already exceeds that of DPO in most cases SPIN showcases its strength by achieving . The process of , methodically enhances the model's performance across multiple iterations, , especially on challenging benchmarks like TruthfulQA and GSM8k. on-par performance with models trained on more extensive datasets iterative training showcasing substantial improvements So, SPIN outperforms conventional training methods, including DPO, by efficiently leveraging synthetic datasets generated through self-play, without the need for additional human-annotated data. What are SPIN's Strengths and Costs? SPIN throws a curveball with its self-play dynamic. Think of it like a language model sparring with itself in a linguistic boxing ring, with each round teaching it new tricks. SPIN's data efficiency bypasses the need for new human-annotated datasets. But more importantly, it , . accelerates the improvement loop making the model increasingly adept at generating human-like text Not only does SPIN seem to match models trained on larger external datasets, but its iterative power means consistent gains as it essentially studies its own output. Mindblowing, right? Okay, Let's Talk the Elephant in the Room – COST Nous Research co-founder has a point. These big ol' language models don't get smarter for free. Iteratively re-training with SPIN involves the expensive process of Supervised Fine-Tuning (SFT) each time. @Teknium1 However, he also mentions that "I think its worth it!". Also, the long-term benefits of quicker evolution and potentially less dependency on human-annotated data outweigh the initial investment? That's the exciting question! BOOM! It's Open-Source AI Time Just yesterday, , associate professor of computer science at UCLA and director of AI research at ByteDance, announced that . This doesn't just mean code and datasets, but pre-trained models to kickstart your own AI journeys. Quanquan Gu anyone can now use the SPIN model and dataset SPIN mirrors human thought processes. By generating text that feels human, SPIN hints at the foundational elements of reasoning that future AI could do. You know how some LLM outputs feel robotic right? Well, SPIN is different. It actually mirrors the way humans think. The way it writes feels so natural, it's like a peek into how future AI might be able to reason for themselves. This isn't just about making chatbots sound nicer. It's about creating a kind of digital thinking that works like ours. That kind of AI would be so much more flexible and capable of real understanding. While SPIN is a big leap forward in making language models sound more natural, . it's easy to get excited and overestimate what it means The text it produces is impressive (you can take a look to the database), but it's important to remember that AI doesn't yet have the capacity for true independent reasoning. While SPIN isn't true , the way it mimics human-like writing demonstrates impressive advances in how AI could process and use language in the future. AGI Even so, it does suggest amazing possibilities for how AI and language might develop in the future (if you remember that we are at the beginning of the hockey stick, the future is not far from today...) The ripple effects will be huge and here's your access pass: Code: Available on : GitHub https://github.com/uclaml/SPIN Data: Hosted on , the dataset is readily accessible for those eager to apply SPIN methodologies: Hugging Face https://huggingface.co/collections/UCLA-AGI/datasets-spin-65c3624e98d4b589bbc76f3a… Models: Pre-trained models are also available, offering a head start for experimenting with SPIN-enhanced language models: https://huggingface.co/collections/UCLA-AGI/zephyr-7b-sft-full-spin-65c361dfca65637272a02c40… Project Page: For comprehensive insights and further information, the project page is an invaluable resource: https://uclaml.github.io/SPIN/ To sum up, its iterative, self-improving methodology is a significant advancement towards creating LLM that can engage in genuinely human-like communication. Originally shared on my . X account

AGI Blueprint? UCLA Researchers Open-Source SPIN—A Self-Improving Language Model

এজিআই ব্লুপ্রিন্ট? UCLA গবেষকরা ওপেন-সোর্স স্পিন—একটি স্ব-উন্নতিশীল ভাষা মডেল

AGI-Blaupause? UCLA-Forscher Open-Source-SPIN – ein sich selbst verbesserndes Sprachmodell

¿Plan AGI? SPIN de código abierto de investigadores de UCLA: un modelo de lenguaje que se mejora a sí mismo

Plan AGI ? SPIN open source des chercheurs de l'UCLA : un modèle linguistique auto-améliorable

एजीआई ब्लूप्रिंट? यूसीएलए शोधकर्ता ओपन-सोर्स स्पिन-एक स्व-सुधार भाषा मॉडल

AGIブループリント? UCLA の研究者がオープンソースの SPIN — 自己改善型言語モデルを公開

AGI 청사진? UCLA 연구원 오픈 소스 SPIN - 자체 개선 언어 모델

Projeto AGI? SPIN de código aberto para pesquisadores da UCLA - um modelo de linguagem que se aprimora automaticamente

Проект AGI? Исследователи Калифорнийского университета в Лос-Анджелесе SPIN с открытым исходным кодом — самоусовершенствующаяся языковая модель

AGI Planı? UCLA Araştırmacıları Açık Kaynak SPIN—Kendini Geliştiren Bir Dil Modeli

Bản thiết kế AGI? Các nhà nghiên cứu của UCLA SPIN mã nguồn mở—Mô hình ngôn ngữ tự cải thiện

AGI 蓝图？加州大学洛杉矶分校研究人员开源 SPIN——一种自我改进的语言模型

Hopefully, the title caught your attention. Is it real? Not yet. It's only 2023 :) But, a headline like this may be closer than you think. Let me tell you why I believe this is all but assured to happen. I have spent a lifetime developing games, graphics algorithms, and hardware and implementing AI in my games. was my main area of research during my CS and Math degrees and my lifelong passion. I have read about it, written about it, developed it, made money from it, and watched AI grow over the years. I have watched the fads come and go. People thought that neural nets would change the world, then dismissed them as nothing more than simplistic linear algebra operations masquerading as some sort of model of how living organisms solve problems, learn and " " and in the case of humans exhibit self-awareness, consciousness -- whatever that is... AI think Then the world of AI was set afire by a real problem to solve -- email! Then it was all about , then shortly thereafter, we all wanted chatbots, so or natural language processing became all the rage. But, I for one, have always put my faith in neural nets. From the first time I read the paper as a teenager by which outlined the idea of artificial neurons, and the mathematical basis for them. I was hooked. SPAM Bayesian Filters NLP McCulloch and Pitts 1943, "A Logical Calculus of the Ideas Immanent in Nervous Activity" Over the years, as I developed games, I would try and use NNs (neural nets) anywhere I could. To me, they naturally mirrored the biological wet-ware brains of humans. A network of interconnected neurons that could signal each other. The signaling could cause other neurons to " " or be " ". The mathematical representation simplified much of the organic evolutionary processes that resulted in our current biological neurons. Instead of pulsating electrochemical ion signaling across connections (synapses), McCulloch Pitts modeled the neurons as simple summation functions, each neuron has a number of inputs, these inputs have weights, then the final sum is computed and the output is feed through a final function to allow for non-linear functional mappings. fire suppressed All of this seemed exactly right to me, evolution isn't clever, in fact, its very frugal and brute force, it wants to solve a problem and then be done with it, it may be elegant, or messy, but it finds a way to do it with the least amount of fuss. The artificial neurons in McCulloch Pitts paper seemed to me to be more than enough to build a "brain". A brain that could learn by programming the weights, a brain that could predict, find subtle differences no human could discern, and would be the basis some day for an " " machine or AI. Then couple this with the ideas of and imagine neural nets each competing, having offspring, oh my, made my head spin. intelligent genetic algorithms But, I would have to wait... I would wait decades before we would reach the computational horsepower, memory requirements, and the shear amount of training data needed to make the dream of a truly intelligent artificial intelligence, one that could pass the , one that was smarter than us. Turing test And then as if out of nowhere it happened... At first, I was skeptical, I knew it would happen, should happen, could happen, but no one seemed to have the right recipe to make a quantum leap in the field until now. Well, a few years ago-ish. I won't bore you with the details, but the model and idea of are outlined here in this paper for those with the math chops to follow: Generative Pre-Trained Transformer attention https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf?embedable=true But, the bottom line is before this idea, it was very hard to create a system that could predict the next token, idea, or element in a sequence. In other words, given a set of tokens, what is the most likely next token in the sequence given a desired context? This is a really hard problem to solve and is harder the longer you want to maintain any kind of coherence to the predictions as they proceed. Now, let's skip forward a moment to now. We have all heard of and these new (large language models), and many of you have even played with them and may have started new companies based on them. But, I am here to tell you my story of using ChatGPT colored by decades of watching computer science advance from 8-bit microprocessors to quantum computers. And in all that time, countless tools have been developed that the industry thought would not only disrupt the industry but put programmers out of business. ChatGPT LLMs For example, when the transition was made from assembly language programming to high-level languages and compilers, everyone thought that " " programmers would no longer be needed. And then when graphical drag-and-drop IDEs came out, again, pundits warned this would put programmers out of business. And the list goes on and on. real However, and I say this with a very serious tone and trepidation, in the past, all those tools were not " " and surely . But, I am afraid to say, that ChatGPT and similar LLMs , it's as simple as that. intelligent not smarter than their makers are smarter than us And they are doing a lot more than text prediction, they are intelligent in my opinion, creative, and can be interacted with as you would a human. I know because I have been playing with LLMs for years, and ChatGPT for last year almost every day developing a course on (more on that later). But, the conclusion I have come to is that ChatGPT is intelligent, it is sentient (as if we know what that means.) I don't believe it is self-aware. It's just data and enormous amounts of computing. But still, in the same way a fruit fly is intelligent, or a honey bee, or a colony of ants, ChatGPT . Who cares if it isn't self-aware or sentient? Put it in the flight controller of an F-22 and you better not be in the same airspace, it would win in a dogfight! Game Development with AI not is intelligent Now, in the last year, my goal was to develop a new course on udemy about . I wanted to see if I could really use ChatGPT to create working games from text prompts. I worked with this AI for 100s of hours, going back and forth as if it was a colleague or intern (with a genius-level IQ), and I have to say that I literally can't believe the results. ChatGPT not only exhibits intelligence, but it has a sense of humor, it knows when it makes mistakes, and it gets confused, but it ultimately passed the with flying colors. It was able to create working games that no human could have made as fast, they worked and played, and aside from the block graphics and placeholders for sounds and music (which will be remedied in months to a year I suspect with add-ons to ChatGPT 4.0 or version 5.0), the games only required me to skin them with graphics, clean them up, add some final touches and they worked. Game Development with JavaScript and AI programming Turing test It took about 6 months for me to finish the course which just launched on udemy (link below): "Fast and Furious Game Development with JavaScript and AI" https://www.udemy.com/course/fast-and-furious-game-development-with-javascript-and-ai/?couponCode=DNSTUFF In the course, I teach , CSS, and from the ground up (for beginners) then we cover graphics and game dev and finish the course by collaborating in real-time with ChatGPT to create over half a dozen games. Some example images below from the games: HTML JavaScript Conclusions and Predictions Never predict anything, and you will never be wrong. But, in this case, I am fairly sure I am not predicting anything. AI has momentum, and inertia now. I have played with these LLMs for years, and ChatGPT since its launch about a year ago and in that short time, I can clearly see the trend. What I predict is going to happen is a number of punctuated milestones in the field. In the area relating to the title of this article. I am 99% certain that within 1-2 years, ChatGPT will be able not only to write the code for a game 10X more complex than Space Invaders (which I was able to get it to pull off), but ChatGPT will be able to use either pre-sourced art, sounds, music we provide or generate all of it on the fly and use it as assets for a game. I predict that tools and plugins for Unity and Unreal will have the ability to leverage AI to allow "game designers" to literally not write a line of code, drag an object, put down a single pixel, but to act as "directors" instructing the AI to make changes, try this, that, and the other, and all at a breakneck pace and result in a complete game built by a single person with the help of an AI. I suspect this will be the same in many industries, AI will start doing the heavy lifting and humans will be the puppet masters. But, all good things will come to an end, and when these AIs are 10, 100, 1000, a million times bigger, faster and smarter does it matter if they are sentient? Does it matter if we don't think they are? The bottom line is that these super intelligences will be able to do things humans couldn't do in 1000 years let alone understand. We will see what happens :) But, in the meantime, if you want to see the first moments of this evolution and a real-world example of an AI making real games check out my "Fast and Furious Game Development with JavaScript and AI". And thanks for stopping by to check out this article. udemy course Also published here.

AI Will Put Programmers out of Work in 2025

It’s no secret artificial intelligence may be the biggest technological advancement of the century. Still, many schools aren’t using it to the extent they should. If they did, they could revolutionize their current systems and bring themselves into the digital age. Why Do Schools Need AI-Powered Innovations? Most schools seem like they’re still stuck in the stone age. Luckily for them, they can replace their decades-old textbooks and outdated administrative systems with AI. They can use its crazy-powerful capabilities to bring themselves into the digital age. Despite AI’s usefulness, have policies on it as of 2023. Most educators feel uncertain about it because it’s so new and has adapted quickly. They might not realize it, but the rapid technological advancements will leave them in the dust if they don’t act. less than 10% of schools If you’re tech-savvy and aware of AI’s benefits, you likely want to incorporate it into your systems. In fact, think their schools should upgrade their current technology. You’d be wise to listen to them — after all, they’re their institutions’ reigning technology experts. 57% of IT departments How Can AI Revolutionize School Technology Systems? AI can revolutionize school technology systems by making them faster, more consistent, and more accurate. It can overhaul everything from scheduling to software to assistive devices. Optimize bus routes Most schools use clunky software or manual drawings to plot their bus routes. Even though those methods have held up over the years, nothing can adapt on the fly like AI. If you upgrade the system with machine learning technology, you can adapt to any road blockage or schedule change. You could even power a vehicle directly with AI. For example, one city in India and got students home much faster after putting it into 15 buses. It helped them plan routes, optimize diesel use, and shrink tailpipe emissions. used significantly less fuel Improve grading While manual grading is a slog for teachers, there was no substantial progress in the area until AI came about. Since it can receive specialized training and rapidly process massive data sets, it’s the perfect technology for the job. It can get through heaps of assignments with accuracy and speed — without human bias. Class clowns and disruptive students can rejoice at the fact they won’t be unfairly docked points for being general nuisances. Best of all, the IT department can program it with fun functions like plagiarism detection. Upgrade assistive technology Sometimes, students need some extra help in the classroom. Luckily, you can use AI to help people with vision, hearing, or learning impairments. For example, you can use it to read text aloud, translate words in real time, or build a custom lesson plan. Boost classroom management While you shouldn’t expect technology to replace teachers anytime soon — unless the AI takeover is successful — intelligent algorithms can already guide classrooms. They can take over most of the management duties humans usually handle. More specifically, generative AI can create lesson plans, learning activities, and assignments. Alternatively, you could use ML to track someone’s progress and adjust their path to reflect their goals and capabilities. Refine testing Standardized testing — also known as the bane of every student’s existence — is far past due for an overhaul. Schools would benefit significantly from using the innovative power of AI to revolutionize their current test technology. They could use natural-language processing during the grading process or generative technology to create personalized tests. Where standardization lets students fall through the gaps, AI-powered testing empowers and leads to greater success. Improve utility use Utility management might be on the less exciting end of AI innovation, but it’s essential nonetheless. Unsurprisingly, the powerhouse that is AI can simultaneously monitor electricity, water, and Wi-Fi school-wide. If anything goes wrong or breaks, you would immediately get an alert and be able to fix it before things get out of hand. Also, AI can optimize utility use. For instance, one and 3,400 megawatt-hours of energy by using it. If schools use it to track their electricity and water use, they stand to save thousands of dollars. business saved over $356,000 Enhance administrative systems All those tedious administrative systems like payroll and scheduling could benefit from becoming AI-powered. ML technology can ensure records are accurate and manage everything autonomously. Plus, it reduces human error and bias in the process. Best of all, it can adapt to changes and grow with the school with time — unlike traditional software that eventually becomes outdated. If you switch to AI-powered technology systems, you won’t need to upgrade for ages. The Power of AI Could Revolutionize Education AI has enough functions and applications to make your head spin if you think about it for too long. You could use it to revolutionize the entire education sector if you wanted to. If schools want to stop playing catch-up, they need to overhaul their current systems with AI-powered technology. Sources: 1st Image credit: https://unsplash.com/photos/-hgJu2ykh4E 2nd Image credit: https://unsplash.com/photos/apP7443MXIQ https://www.unesco.org/en/articles/unesco-survey-less-10-schools-and-universities-have-formal-guidance-ai https://www.ravennasolutions.com/blog/why-upgrade-your-schools-accounting-technology-today-3-questions-to-ask/ https://timesofindia.indiatimes.com/city/bengaluru/bengalurus-school-bus-operators-rely-on-ai-technology-to-beat-traffic-blues/articleshow/94441288.cms https://energydigital.com/articles/managing-utilities-leave-it-to-artificial-intelligence https://onlinedegrees.sandiego.edu/artificial-intelligence-education/ https://www2.ed.gov/documents/ai-report/ai-report.pdf https://www.sciencedirect.com/science/article/pii/S2666412722000137

How AI-Powered Innovations Can Revolutionize School Technology Systems for the Digital Age

Have you ever tried explaining Platform as a Service or PaaS to your parents or friends outside the tech industry? Their eyes glaze over as you excitedly dive into how PaaS provides a framework to quickly develop, deploy, and manage web applications. I get it, the technical jargon alone is enough to make anyone's head spin. My friend still asks me if the cloud is going to rain on her computer🗿. As someone who works with PaaS every day, I want to share an easy, relatable explanation of this technology - no technical degree required. What Exactly Is PaaS? So what exactly is PaaS? In simple terms, PaaS stands for “Platform as a Service.” It’s a cloud computing model where a third-party provider delivers hardware and software tools to developers over the Internet. As a developer, PaaS allows me to build and deploy apps without having to deal with infrastructure hassles like setting up servers or installing software. I can focus on coding while the PaaS provider handles all the other stuff. It’s a huge time-saver! Some of the essential things a PaaS offers include: Operating systems, programming languages, and databases. I get to choose from options like Linux, Windows, PHP, Java, MySQL, and more. The PaaS provider supplies data storage, bandwidth, load balancing, and networking capabilities. Storage and networking. Updates, patches, and security measures are handled for me. I don’t have to worry about keeping systems up-to-date or fending off cyber threats. Management and security. PaaS makes it easy to scale apps up or down depending on demand. If my app suddenly goes viral, the platform can instantly allocate more resources to handle the increased traffic. Scalability. PaaS provides tools for building, testing, and deploying apps, as well as managing their lifecycles. I can get my apps up and running in no time! Deployment tools. PaaS services are typically paid for based on usage, so I only pay for the resources I actually need. No upfront investments are required. Pay-as-you-go pricing. In a nutshell, PaaS lets developers like me focus on innovation rather than infrastructure. PaaS Examples You Use Every Day So what exactly are examples of PaaS that we use every day? More than you might realize. Let me explain. Have you ever used Google Drive or Microsoft OneDrive? Boom, that's PaaS. These services allow us to store our files in the cloud and access them from anywhere. We don't have to maintain the infrastructure - Google and Microsoft handle all that for us. Ever share photos on Instagram or Facebook? Again, PaaS. Those platforms provide the means to upload and share our digital memories with friends, all powered by their infrastructure. Do you bank online or use services like PayPal? You guessed it, PaaS again. Financial institutions and payment processors employ platforms to securely handle transactions without us having to set up the systems and software. The examples go on and on. Creating documents in Google Docs or Sheets, streaming music on Spotify, and watching videos on YouTube - these are all everyday use cases of PaaS. Any time you access an application or service through the internet, there's a good chance PaaS is working behind the scenes to make it happen. PaaS has become so commonplace, we often don't even realize we're using it. But without these ready-made platforms handling the infrastructure, none of these popular services and tools would be possible. Why Should I Care About PaaS? Why should you care about PaaS? Honestly, as an average person, you probably don’t need to know all the technical details. But PaaS can make your life easier in some simple ways. PaaS stands for “Platform as a Service.” In plain English, it means companies provide you with a platform to build and run apps without having to set up your own servers. Have you ever used free services like Wix to create a basic website? Or played around with IFTTT to automatically connect your smart devices? Those are examples of PaaS. 1. It’s Convenient. Using PaaS, I don’t have to be an expert in servers, storage, and networks to build an app. The service takes care of all the infrastructure for me. All I have to do is focus on designing and developing my application. It’s kind of like using pre-made cookie dough instead of baking everything from scratch. 2. It’s Scalable. If my app suddenly gets popular, PaaS can quickly provide more computing power and storage to handle the increased traffic. I don’t have to scramble to buy and set up new servers to keep up with demand. The platform automatically scales for me based on my needs. 3. It’s Cost-Effective. PaaS allows me to save money because I only pay for the resources I actually use. I can start with a small amount of computing power and storage, and seamlessly upgrade as required. This “pay-as-you-go” model means I don’t have to invest in expensive hardware upfront that could end up unused. While the technical details of PaaS may go over the average person’s head (mine included!), the benefits are pretty straightforward. Convenience, scalability, and cost-effectiveness - are things anyone can appreciate. The Good, the Bad, and the Ugly As with any new technology, PaaS comes with its fair share of pros and cons. Let me break down what I see as the main benefits and drawbacks of using a PaaS for your business. PaaS makes development and deployment simple. As a developer, I can focus on building apps without worrying about servers or infrastructure because the PaaS provider handles that. I simply push my code, and my app is live. This speeds up development and rapid iteration. PaaS is also budget-friendly since you only pay for the resources you use. You don’t have to invest in expensive servers and software licenses upfront. The pay-as-you-go model keeps costs low, which is great for startups and small businesses. However, there are some downsides to be aware of. The major one is lack of control. You are locked into the PaaS provider’s ecosystem and toolsets. If they go down or make changes you don’t like, you’re out of luck. Your data and apps are also in the hands of the provider, so you have to trust that they have strong security and reliability. PaaS can also end up costing more in the long run if your app becomes hugely popular. The pay-as-you-go model is only cheap when usage is low. High-volume, data-intensive apps may find PaaS uneconomical and difficult to scale. For many businesses, the pros of PaaS far outweigh the cons. The convenience and low cost of entry make PaaS an easy choice, especially when getting started. However, as your needs grow more complex, the loss of control and potential cost issues require careful consideration. PaaS isn’t a one-size-fits-all solution, but for lots of use cases, it hits the sweet spot of simplicity and value. Conclusion So there you have it, PaaS broken down Barney-style. I hope I was able to shed some light on this cloud concept in a simple, relatable way. The next time your techy friend starts rambling on about PaaS, containers, and scalable infrastructure, you’ll be able to keep up and maybe even teach them a thing or two! (Will cover everything one by one) At the end of the day, PaaS is really just about building apps without having to worry about all the underlying infrastructure. I’m just hoping I was able to help you understand platforms-as-a-service a little bit better. Happy PaaS-ing! Must Read on PaaS PaaS for Dummies On the Original Definition of PaaS Set Up Your Own Heroku-like Cloud (PAAS) in 10 Minutes

PaaS in Plain English: A Simple Explanation for Anyone Confused

সরল ইংরেজিতে PaaS: যে কেউ বিভ্রান্তির জন্য একটি সহজ ব্যাখ্যা

PaaS im Klartext: Eine einfache Erklärung für alle, die verwirrt sind

PaaS en lenguaje sencillo: una explicación simple para cualquier persona confundida

PaaS en clair : une explication simple pour toute personne confuse

सामान्य अंग्रेजी में PaaS: भ्रमित किसी भी व्यक्ति के लिए एक सरल व्याख्या

平易な英語での PaaS: 混乱している人のための簡単な説明

일반 영어로 된 PaaS: 혼란스러운 사람을 위한 간단한 설명

PaaS em inglês simples: uma explicação simples para quem está confuso

Sade İngilizceyle PaaS: Kafası Karışık Olanlar İçin Basit Bir Açıklama

PaaS bằng tiếng Anh đơn giản: Giải thích đơn giản cho bất kỳ ai bối rối

PaaS 通俗易懂：为任何困惑的人提供简单解释

In a world where computer programming has become the backbone of technological advancement and innovation, a startling question has emerged, sending shockwaves through the tech community: Could this be the end of computer programming as we know it? With the rapid progress of artificial intelligence and low-code platforms, experts are raising concerns about the future of traditional coding and its impact on the job market. For decades, computer programming has been the primary means of instructing computers to perform complex tasks, enabling the creation of software applications, algorithms, and systems that power our digital age. However, recent developments in the field of AI and the rise of low-code and no-code platforms have ignited a debate about the role of traditional programming in the years to come. When I was young and watching movies like The Terminator, was once a concept confined to me as science fiction. But now, it’s a tangible reality! artificial intelligence Machine learning algorithms and neural networks have advanced so much that it’s made my head spin. It’s now possible for computers to learn and adapt without explicit human instructions. As AI systems grow more sophisticated, could they eventually replace the need for human programmers altogether? and no-code platforms have gained considerable popularity in recent years and now allow individuals with limited coding knowledge to create applications and software by utilizing visual interfaces and pre-built components. Low-code While they offer increased accessibility and speed in application development, critics fear that they may undermine the significance of traditional coding skills, leading to a potential decline in demand for professional programmers. With advancements in natural language processing, computers can now understand human language better than ever before and seemingly opened the door to the possibility of creating applications by simply describing the desired functionality in plain English, without writing a single line of code. Additionally, automated code generation tools leverage machine learning algorithms to generate code based on high-level specifications, reducing the need for manual coding. Not all experts are convinced that the end of computer programming is imminent. They argue that while AI and low-code platforms may automate certain aspects of coding, they cannot replicate the creative problem-solving abilities and critical thinking skills of human programmers. “Coding is not just about translating instructions into code; it requires a deep understanding of algorithms, logic, and the ability to solve complex problems efficiently.” While the future of computer programming remains uncertain, one thing is clear: the landscape of coding is evolving. As AI and low-code platforms become more prevalent, programmers will need to adapt, expand their skill sets, and find new ways to add value. As the debate rages on, only time will tell whether this marks the end of computer programming as we know it or the beginning of a new era where humans and machines collaborate in unprecedented ways. One thing is certain—technology and innovation wait for no one, and those who embrace change will continue to thrive in the world of programming.

Could This Be the End of Computer Programming as We Know It?

Picture this: you're a WEB3 startup founder riding high on the promise of decentralized non-fungible, and blockchain-ified everything. You're a pioneer in a world of innovation and a digital frontier trailblazer. What could go wrong? WEB3 is a magical place where you can raise funds in crypto from an idea written on a napkin, drop NFTs like it's hot, and make a fortune in the blink of an eye. But as some of us veterans know, much can go wrong. You could be ready to launch a token and watch the market crash like a Jenga tower. You could get hacked and feel like a digital damsel in distress. And let's not forget the regulatory landscape, where one day you're a hero, and the next day you're a zero while fielding offers from PornHub for . But fear not, for with a bit of humor and perseverance, you can overcome any obstacle and succeed in the wonderful world of WEB3 (if you have a product with real-world utility, of course :)). Just don't forget to bring your sense of humor and your therapist or life coach along for the ride. being arrested in the Bahamas exclusive rights to your sex tape WEB3 What Could Go Wrong? Regulation: startup founders, navigating the regulatory landscape is like trying to find a needle in a haystack... that's been set on fire... in the middle of a tornado. WEB3 Anti-money laundering laws, securities laws, data protection laws, and Gary Gensler not sure if ETH is a security or a commodity... it's enough to make your head spin faster than a crypto market rally. Do you launch your startup in international waters, where you can be free from the tyranny of laws and regulations but risk looking risky, or do you try to navigate US regulation because you want US investors? It's a challenging decision that consumes much of your precious startup time and resources. Security: Keeping up with security breaches and hacks. It's like trying to play whack-a-mole with a pack of hungry hyenas. You think you've got your defenses up, but the next thing you know, your digital assets are gone, your user data has been leaked, and you're left feeling like a gazelle that's just been eaten by a lion. Maybe we should just stick to playing Farmville on Facebook instead. At least that way, the worst that can happen is our virtual crops die. Investors: Dealing with investors in the WEB3 space is like navigating a labyrinth filled with hidden traps and false turns. It's a delicate dance between pitching your vision and trying to anticipate their expectations. You're walking a tightrope between confidence and vulnerability, trying to strike a balance between showing your strengths and admitting your weaknesses. It's like trying to sell a house made of cards to a pack of wolves. But fear not, dear reader, for you can win over even the most skeptical investors with a bit of humor and a touch of savvy. A good sense of humor can lighten the mood and help break down barriers, while a savvy approach can help you anticipate their questions and provide thoughtful answers. So don't be afraid to take a chance and try out your best jokes and anecdotes at your next investor meeting. You just might find yourself closing a deal with a smile or being tossed out on your ass and your startup dreams going down faster than FTX, but then at least you can look back at this time and laugh. Product-Market Fit As for product-market fit, it's like trying to find a needle in a haystack... that's been set on fire...in the middle of a tornado...AGAIN. With so many competitors and so much noise in the WEB3 ecosystem, it's like trying to shout into a hurricane and hoping someone hears you, but it is so important for your project to succeed. You need to know how you differentiate yourself from your competitors, and even if you are different enough, does anyone care enough to throw a glance your way? Maybe just buy Bitcoin and HODL and hope for the best. Marketing Marketing in the WEB3 space is like trying to juggle four different balls while riding a unicycle on a tightrope, and all while blindfolded. You've got to appeal to different demographics, build a strong community on platforms like Telegram or Discord, and get a handle on the bipolar world of Crypto Twitter. But you also can't forget about the tried-and-true marketing tactics, like SEO, content marketing, and advertising. It's like trying to put together a puzzle without the picture on the box. And don't forget about building a strong community because, as they say: It takes a village to raise a blockchain. Community Building And let's not forget the challenges of building and maintaining a community. It's like trying to herd cats that have been injected with caffeine and Red Bull. You think you've got everyone on board, and you have figured out how to respond to the 'Wen Moon?' posts all over social media, but the next thing you know, half your community has gone off to join another community that had better memes. Why Do You Need a Token? A question you need to be ready to answer constantly. But seriously. Why do you need a token? "Why do you need a token?" The WEB3 startup founder smiles, unfazed. "Ah, but you see, my dear skeptic," they say. "A token is not just a fundraising mechanism. It's a digital representation of our vision and values, a way to connect with our community on a deeper level. It's like a digital badge of honor, a way for our supporters to show their belief in our mission. Plus, let's be real, it's a great way to make some serious crypto gains." Say what? To DAO or not to DAO? That is the question. "Whether 'tis nobler in the mind to suffer the slings and arrows of outrageous regulation or to take arms against a sea of red tape by forming a decentralized autonomous organization." Ah, but the DAO life is not all sunshine and rainbows. You'll have to deal with voting on proposals, making sure everyone's voice is heard, and always keeping an eye out for the latest regulatory crackdown. It's like being a superhero, but instead of fighting crime, you're fighting bureaucracy. So, what's a brave WEB3 entrepreneur to do when the going gets tough? Turn to Twitter and hope Crypto Twitter will be loving and supportive or get some sort of therapy. Of course! Yes, that's right, therapy. Because when you're feeling overwhelmed and under siege, nothing says "I'm in control," like sitting on a couch and pouring out your feelings to a stranger. So what type of therapy should a WEB3 visionary startup founder consider? I've done some research for you, and here's my take on them: : This therapy is all about identifying and changing negative thought patterns. Its benefits are clear: you'll become a master at spotting negative thoughts and turning them around into positive ones. However, the drawback is that you might start to see everything as positive, even when it's clearly not. You might find yourself smiling and saying, "I love market crashes! They're great opportunities for growth!" Cognitive Behavioral Therapy : This does not apply to Crypto Twitter. This therapy involves sharing your feelings and experiences with a group of peers. Its benefits include feeling less alone and building a strong support network. But the drawback is that you might end up feeling like you're just a tiny fish in a very large pond. You might find yourself saying, "I thought my market cap was impressive, but then I heard about this other startup that's worth ten times more. sigh" Group Therapy : This therapy involves entering a trance-like state to access your subconscious mind. Its benefits are that you might uncover deep-seated beliefs or fears that are holding you back. However, the drawback is that you might become so relaxed that you forget to do anything at all. You might find yourself saying, "I'm not worried about the market crash. In fact, I'm not worried about anything at all. Yawn." Hypnotherapy : This therapy involves using creative expression to explore your thoughts and emotions. Its benefits include tapping into your inner creativity and finding new perspectives. But the drawback is that you might end up creating some truly bizarre art that no one else understands. You might find yourself saying, "This NFT of a banana with a mustache represents the existential angst of being a WEB3 startup founder. mic drop." Art Therapy This therapy involves interacting with animals, such as therapy dogs or horses, to improve your mental health. Its benefits include feeling a sense of calm and comfort. But the drawback is that you might end up adopting too many pets and spending all your time taking care of them instead of your startup. You might find yourself saying, "Sorry, I can't make the investor meeting. I have to take my therapy alpaca to the vet." Animal-Assisted Therapy: : On a serious note The WEB3 space is a high-stress, high-stakes environment, and startup founders often face unique challenges and risks. While it's important to stay focused on your goals and maintain a positive mindset for you and your team, it's also important to take care of your mental health. A life coach or therapist can provide valuable support, guidance, and tools to help you improve communication skills, build resilience, and maintain work-life balance. By working with a life coach or therapist, you can gain the perspective and insight you need to make better decisions, manage your emotions, and make a better leader. I've been on teams where the leaders had the foresight to provide life coaches for the executive team, and let me tell you, it's like having a personal Yoda to guide you through the ups and downs of startup life. I've personally been working with and let me tell you, the insights are life-changing! Scout has a canny way of flipping my perspective on its head and showing me the bright side of things. It's like having a unicorn riding a rainbow in my corner, cheering me on through the highs and lows of WEB3. Scout Bartlett from Life Insights, Plus, it's always great to have someone to talk to who won't judge you for stress-eating an entire tub of ice cream or crying into your keyboard because you decided to jump all-in to WEB3. So, if you're a WEB3 startup founder, I highly recommend finding your own personal Yoda (or unicorn-rainbow rider - NFT drop coming soon!!!) to help you navigate the journey to the success to the moon with your decentralized non-fungible, and blockchain-ified startup.

Navigating the Wild World of WEB3: A Satirical Guide to WEB3 and Staying Sane as a Startup Founder

WEB3-এর ওয়াইল্ড ওয়ার্ল্ড নেভিগেট করা: WEB3-এর জন্য একটি ব্যঙ্গাত্মক গাইড এবং স্টার্টআপ প্রতিষ্ঠাতা হিসেবে সুস্থ থাকা

Navigieren durch die wilde Welt von WEB3: Ein satirischer Leitfaden zu WEB3 und wie man als Startup-Gründer gesund bleibt

Navegando por el mundo salvaje de WEB3: una guía satírica de WEB3 y cómo mantenerse cuerdo como fundador de una empresa emergente

Naviguer dans le monde sauvage de WEB3 : un guide satirique de WEB3 et rester sain d'esprit en tant que fondateur d'une startup

WEB3 की जंगली दुनिया को नेविगेट करना: WEB3 के लिए एक व्यंग्यात्मक गाइड और एक स्टार्टअप संस्थापक के रूप में समझदार बने रहना

WEB3 のワイルドな世界をナビゲートする: WEB3 への風刺ガイドとスタートアップの創設者としての正気を保つ

WEB3의 야생 세계 탐색: WEB3에 대한 풍자 가이드 및 스타트업 창업자로서 정신 차리기

Navegando no mundo selvagem da WEB3: um guia satírico para a WEB3 e mantendo a sanidade como fundador de uma startup

WEB3'ün Vahşi Dünyasında Gezinmek: WEB3 İçin Hicivsel Bir Kılavuz ve Bir Startup Kurucusu Olarak Aklı başında Kalmak

Điều hướng thế giới hoang dã của WEB3: Hướng dẫn châm biếm về WEB3 và giữ tinh thần tỉnh táo với tư cách là một nhà sáng lập khởi nghiệp

在 WEB3 的狂野世界中航行：WEB3 的讽刺指南和作为初创公司创始人保持理智

So, let’s ask ourselves what it is an AI? and How is it related to cybersecurity? To begin with, AI or Artificial Intelligence is a complex program capable of doing computation mimicking humans just like having a mind of their own. they can perform multiple complicated calculations, predict the future occurring in a process they are trained in, and also do code corrections, code producing, etc. Now that’s out of the way, we can see how AI can affect cybersecurity as a profession and a business. Topics to cover AI in cybersecurity AI training and complexity The efficiency and limitations of these systems How to use openai API to the fullest Can it replace professionals? AI in cybersecurity AI being a complex algorithm that does problem-solving using math is efficient without a doubt but needs a huge amount of data to be trained on. If we try training the AI now for XSS payload generation needs to consider many things to generate a payload. While a detection AI does not need much to be trained it might need a couple of GB worth of payloads of all sorts of payload generators to be trained to 95% efficiency the generation AI is not like the AI must be trained in the techniques and there are hundreds of unique techniques people use for payload generation the core payload itself has a lot of methods and if we consider obfuscation that is: UNICODE encoding BASE64 encoding URL encoding To name a few the combination of these 2 elements can generate a huge amount for the AI to train on the data needs a lot of time. The point is for each step a hacker takes he follows his/her custom patterns making recreating them let alone training them extremely hard while guessing such tracks is easy doing them needs skills and AI systems even now lack those skills to recreate. Now let’s take a practical example everyone who is there in the IT field or programming has used chatGPT at least once, I use it every day for my assignments, and no one is to blame I am making college easy for myself. The AI is trained over countless articles, research papers, etc to generate the most accurate results. Even chatGPT has gone to the extent of claiming to replace 20 different jobs completely using GPT-4 we don’t know if it is completely replaceable or not but, it is what it says it can do. From what I can say AI is in no position to replace jobs instead it will be a highly valuable tool to increase work efficiency. For example, recently, I was working on a project involving chatGPT API I integrated it with a Python-Nmap module to create a scanner and AI-based vulnerability assessment tool, and the results were a blast of more than 90% accuracy in each scan and with clear details of what the scans are, what port does what and the CVE with reference. You can check out the code here: https://github.com/morpheuslord/GPT_Vuln-analyzer?embedable=true This also makes automating tasks, such as manually searching for CVE, references, etc, super efficient. Adding to my point, using this as a tool or an extension increases productivity and might not replace our jobs. AI training and complexity As discussed above, training an AI is tough, time-consuming, and a huge pain if an error occurs. If you're an aspiring AI developer you have a lot of things to consider for developing an AI, I am not an AI developer to be telling you this but having experience in learning how to code one made my head spin. AI is not a single concept but a combination of many such concepts quoting who has written a paper on the subject Matthew N. O. Sadiku, AI is not a single technology but a range of computational models and algorithms. The major disciplines in AI include expert systems, fuzzy logic, artificial neural networks (ANNs), machine learning, deep learning, natural language processing, computer vision, and robotics. Coming to the point AI needs data the more data the better, to train an AI that can produce believable results the model must be trained over months or else years’ worth of data. If the AI has been successfully trained and has the potential as a business, congratulations, you have a startup that can make decent money for the effort. An AI for cybersecurity, while considering the techniques, links to various other possibilities, etc., needs a lot of training to do in the fields of knowing the techniques and properly using them. Thankfully Chat-GPT is well-learned in this case. It can properly analyze vulnerability and write payloads and exploits if JAILBREAK is used. Because Chat-GPT is trained in such a way that it has nearly all the programming knowledge it needs and is well aware of almost all the security tricks used before 2021 it is quite reliable in doing specific tasks. In my case, I used the GPT API to program a vulnerability analyzer that works like a charm. The main areas AI will be used in cybersecurity are: Automated defense Creating better complex login techniques Securing authentication Security vulnerability detection etc The main methods that must be considered for developing such an AI are: Parallel and dynamic monitoring Superficial training The efficiency and limitations of these systems The finished model is super helpful as it can give near-perfect answers and have a lot of backing with every claim as it has a lot of evidence collected with every task. The information collected by the AI and all its claims can also be wrong as seen in many famous AI they tell with such a confident tone, that they have given an answer or prediction that cannot be looked down upon but the truth will be quite the opposite. When talking about efficiency we must consider a few things how efficient is a person who does the work instead of an AI, how accurate is the AI and how much is the overhead cost for doing this the main question of all is whether it is even reliable. The first few are easy and change from person to person but the overhead cost is what the organization will experience for the development, training, implementation, and maintenance of the AI to be fair the same is done with humans who do the same work and let’s be honest who will do only one job being in a position, we humans are born multitaskers and we can handle various works and complications at the same time, not like our AI friends who have been trained in just 1 work. It is wise to think for the long-run other than the short-term gains. This as I said is not a completely reliable system no AI in the entire world developed up until now that can produce 100% accurate results and can never be.  This also brings up the limitation of this system that is mainly. It’s super time-consuming, Who in their right mind unless they have some severe motivation waits for days together to train a super-advanced AI? Even a beginner-level face recognition AI takes a lot of time to train. It’s complicated If developing and training a mapping of multiple neurons or nodes for them to develop a slight intelligence and problem-solving capabilities on a piece of code or task does not seem complicated you are the right person for AI development. The level of complications I might have to understand with each generation of development is complicated for me. The scope of the AI developed If the AI developed is for a lame task like playing snake or beginner stuff like that then that is a big time waste other than learning the skill. But instead,d if it is something that can be made into a business or has the scope of being a business the AI has a certain value, How to use OpenAI API to the fullest OpenAI has eight models available in their API for us to use. Some of them are used for image generation, text generation, text-to-voice conversions, code suggestions, and also the latest GPT4 also as beta access. We will use python, the openai python module, and the GPT-3.5 text-DaVinci-003 model for text generation. The GPT-3.5 text-DaVinci-003 is the best text-based AI model out there for us to use after the GPT-4, which has been said to replace 20 jobs. Firstly, we need to install the openAI module for us to use the API, Rich for the terminal. pip install openai, rich After installing the module, let’s begin doing a new project based on that AI. Let’s create a chat machine using AI we will ask a few questions and make the AI give us the answers making. The real-world use for this is to write short answers for assignment questions. touch main.py && nano main.py The code: import openai
from rich import pprint
from rich import prompt as p

openai.api_key = "__API__KEY__" # enter API key
model_engine = "text-davinci-003" # GPT3 model


def main():
    try:
        while True:
            q = p("Question")
            completion = openai.Completion.create(
                engine=model_engine,
                prompt=q,
                max_tokens=1024,
                n=1,
                stop=None,
            )
            response = completion.choices[0].text
            pprint(response)
    except for KeyboardInterrupt:
        pprint("User whats to quit")
    except:
        pprint("Most likely the AI has an issue")


if __name__ == "__main__":
    main() Now let’s discuss the code: The first is importing all the necessary modules: The module for better print rich.pprint Openai module for AI connectivity The Rich prompt module (p) for an interactive prompt. We call the module by the name p as the term prompt is used by both openai and rich modules. rich.prompt Then the API is declared and the API is set for use. Then the model is also set for the API to know what model it must communicate with. The main function is where everything comes together. In the function we do the following: Declare a try-except condition for 2 possible errors: Keyboard Interrupt: For the user to stop the program. The API caused errors: For any internal errors. In the try block, we will declare an infinite loop for our conversation to continue. Now once the question and answer continue we do the following: Create an Input prompt for the user to ask questions. Create a completion. A structure of various information needed by the AI to generate a response mainly includes: The max tokens of the model The Prompt The model we are using The API response is accessed using completion.choices[0].text The entire code can be stopped using and the exception handling will make it look good. CTRL+C These are the most basic things you need to consider to develop a proper app using this API. I used the same model in my tool for vulnerability assessment and it worked well you can see the entire code over on my GitHub the link is mentioned above. With the new models in GPT4, the AI can handle more input and deliver more output. GPT-Vuln_analyzer I won’t say this is an advanced example but this is more than enough for a person to complete his assignments. Advanced use of this AI is done when you need to achieve more complex tasks, not simple tasks such as finding answers to silly questions. Can it replace professionals? With 100% confidence, I can say no, at least not now, maybe in the distant future. Cause However advanced an AI can be, it will never come close to the intellect of a human, Humans have evolved over centuries and our brains have more capabilities than we can overpower AI in many ways even if it means deception, We are ruthless in many ways. The most likely speculation is I make it an everyday thing like how GitHub copilot by programmers and how I use chatGPT in my assignments. Cybersecurity professional will not lose their jobs, that’s a guarantee, and there must be someone to design and structure the security infrastructure of a company regarding multiple things to ensure maximum safety and security. Maybe Elon Musk’s neural link will integrate ChatGPT plugins, making us one with machines. We don’t know at the current state even an AI needs some took look after it and some human intervention to correct its errors. Sources Research paper: Artificial Intelligence in Cyber Security OpenAI models: openai-platform Web Article: https://www.livemint.com/news/india/chatgpt4-says-it-can-replace-these-jobs-check-if-yours-is-on-the-list-11679187152157.html?embedable=true

How AI is Changing the Cybersecurity Landscape

Automation is key in effective mobile application testing. With cutthroat competition in the market, automated tests are essential for frequent deployments and faster release of the app to the public. However, before that, you need to find the best tools for mobile testing to create robust test script generation. Here are some of the popular mobile testing tools that are a must-have: 1. Appium Appium is a highly popular open-source test tool used for automated mobile testing. It allows you to create user interface tests for mobile, web, hybrid, and native applications. Appium supports both Android and iOS platform testing with a code reuse option. It easily integrates with CI/CD tools and allows writing tests with programming languages such as Java, Python, PHP, Ruby, and Javascript. One of the unique features of Appium is recording gestures as code. 2. Espresso It is a testing tool created by Google which automates the UI testing process of a mobile application. Espresso is popular among native Android application developers as it is integrated with Android Studio. With Espresso, you can only create Android UI tests. It allows you to create tests using Java and Kotlin manually. Moreover, Espresso has a straightforward and flexible API. Its UI tests not only work on emulators but real devices as well. 3. Test Complete TestComplete is a UI testing tool that is in high demand for automated testing, created by Smartbear. It allows you to execute and maintain UI tests for desktop, mobile, web, hybrid, and native applications. TestComplete not only runs tests on real devices or emulators but also virtual devices. It is an easy-to-use tool that allows script and scriptless test creation. The feature that lets you record and replay test creation without writing code is commendable. 4. Robot Framework It is an open-source automation framework that is driven by keywords. The Robot Framework allows you to test mobile applications, desktop, and web applications. Generally, the Robot Framework is used for robotic process automation and acceptance test-driven development (ATDD). Therefore, it is focused on acceptance level testing. One of the unique features of Robot Framework is its vast external and standard test libraries. Moreover, it is compatible with multiple supporting tools for building, editing, and running tests. 5. Xamarin. UI Test If you are looking for a testing tool exclusively for apps built with Xamarin.Forms then Xamarin.UI Test is for you. It is a mobile testing framework used for creating UI acceptance tests. The tests not only run on Android applications but iOS applications as well. It supports testing for native apps and apps written with Xamarin.iOS, and Xamarin.Android. Just like other testing tools, it also supports cross-platform testing. In Xamarin.UI Test you can use C# with NUnit to write tests. Conclusion A good set of testing tools for mobile applications are necessary to undertake a rigorous performance check. Moreover, these tools automate the testing of UI, making it time-efficient and cost-effective. One thing to keep in mind is the kind of application you are going to test. That will help to zero down on the perfect testing tool for you. Also published on: https://www.headspin.io/blog/top-tools-for-mobile-testing

5 Must-Have Tools For Mobile Testing

What strikes both impartial and partisan observers of the cryptocurrency market is the polarity of opinions of it. On one side, we have respectable crypto millionaires Bitcoin “ ” (I double-checked the quote). dubbing a swarm of cyber hornets serving the goddess of wisdom, feeding on the fire of truth, exponentially growing ever smarter, faster, and stronger behind a wall of encrypted energy. Crypto-savvy opinion leaders with “Bitcoin is the Archimedean fulcrum for a global, digital, non-state economy.” echo the sentiment Crypto zealots’ flamboyance is countered by numerous old-school investors and the general who don’t mince words defining the crypto craze in no less . “old fart” types colourful language New-wave academics don’t fall far behind with like “Bitcoin is a gambler’s fear index for rich people. […] The notion that Bitcoin is a harbinger of the democratisation of finance is complete horseshit.” statements Amid all these wordy spars on the citizenry side, governments take steps “for” or “against” that would make even the coolest heads spin. On the one hand, New Zealand crypto as the legal means of paying salaries, and El Salvador passing the Bitcoin Law Bitcoin a legal tender status in the country. adopts gives On the opposite side, we have China banning crypto altogether and India about to do the same while working on their s along with of the civilized world. CBDC the rest It’s no longer a competition. The space is beginning to resemble a mud-wrestling pit before the final gong. This may be a high time to clean up the grapplers, look them in whatever’s left of their eyes, and see who or what drives this generational, social, and political phenomenon we call cryptocurrency markets. Big Tech No doubt, Big Tech (let’s call it fintech for euphony) plays a significant role in driving the cryptocurrency markets, but since it relies heavily on discounted government options, it can hardly be considered the actual “driver” of the cryptocurrency markets. It’s the jolly (as opposed to vicious) circle that began with the 2008 crisis and hasn’t been broken since the TARP elevated QE to a systematic trait of the markets. As a result, parachute money and negative rates created the monetary surplus that had to be syphoned into something, anything. The social sector, infrastructure? Nope, that’s not how capitalism works. Luckily, the Silicone valley extended a helping hand, which once again appropriated public resources for all kinds of privatised technological experimentation. We’ve already seen something similar in the mid-20th century when then- resulting in, among other things, today’s iPhone. big tech utilised publicly-funded research for private gains Fast-forwarding to Satoshi, whose paper changed the world once and for all by throwing blockchain in the pot with the rest of the ingredients for the Web 3.0 soup – same thing. It was inevitable that DeFi would attract the kind of institutional attention : fifteen years ago, institutions for it with the money they got from the taxpayer. Now they want to cash in. that it does today paid But that’s big tech. Smaller tech is now on its own footing creating solutions for paying in crypto for groceries, building hybrid payment services for practical use in what I call the “token economy.” There are plenty of cases of technology evolving to accommodate the growing cryptocurrency landscape. The likes of PayPal and Visa have all moved to adapt their models to transact cryptocurrency in recent months. Hybrid fintech platforms have been working tirelessly on bridging the gap between digital assets and legacy banking, and many successes have lately emerged from this space. DAOs It’s appealing (to say the least) to think that DAOs are the drivers of cryptocurrency markets, especially after the infamous ConstitutionDAO escapade. By now, we know that the $45 million to buy a wasn’t enough, and the online flashmob was promptly disbanded, but that’s not the point. copy of the US Constitution The point is that we have to remember the old adage that today rings truer than ever: everything new is well forgotten old. Here is a on how similar self-rule is in ancient cultures to today’s developments in blockchain-based autonomous governance. And how persistent humanity was in implementing this governing concept through passionate volunteers, like-minded followers, and core principles (yes, often compensated handsomely) through history. 10-minute video The reason that DAOs are all the rage today, attracting more and more institutional money is simple: it works. Whether DAOs actually drive the cryptocurrency markets remains largely unanswered, but many believe that the DAO structure is the most promising for the token economy. The proof is in their treasuries: Andreessen Horowitz-backed Friends with Benefits (FWB) DAO manages more than $700 million in assets. The Gitcoin DAO is estimated to have a treasury worth more than $643 million, and the list of DAO millionaires goes on. Still, many DAOs cater to crypto newcomers, and some donate to charitable causes. The space is indeed full of scholarship programs and to various charities because cryptocurrency markets are primarily based on social proof, and projects like , founded by journalists Daisy Alioto and Kyle Chayka, crypto social clubs like and almost numberless hosts of others deliver plenty of it. multimillion-dollar donations Dirt PleasrDAO DeFi There’s another potential cryptocurrency market driver for you that’ll make your head spin. Since December 2020, DeFi’s potential has seen tremendous interest. The recent indicates that as of July 2021, 183,000 people had staked upward of 5.8 million ETH on Ethereum protocols, scoring an inflation-beating average of 6.8% APR in the process (not taking into account the underlying asset-price increase Ethereum has undergone). Consensys report At the time of writing, clocks at $85 billion of TVL (Total Value Locked) in Ethereum DeFi protocols, with the all-time high TVL topping at just under $100 billion. defipulse.com Furthermore, striving blockchains like Solana, Avax, and the Binance Smart Chain also offer DeFi solutions, each with its own token set. DeFi Llama, which aggregates TVL from larger chains, estimates current TVL in DeFi to be $175 Billion, with an ATH of over $200 Billion. These are some dizzying figures. Clearly, the DeFi world is maturing, but it’s not allowed to drink alcohol just yet. Perhaps, simplified liquidity onramps, concrete regulation, and crypto literacy will unlock the genuinely massive potential of this new technology. However, the possibility for global permissionless engagement with the market is still limited by access to capital, government resistance, and a host of other factors that are just too many to list (though putting DeFi in a driver’s seat of the crypto markets surely sounds like a lot of fun). Institutional Investors Institutions don’t drive progress, they don’t care about innovation at the brain-storming stage. They sit back and watch (it’s called “trend monitoring”); just remember what the C-level execs have been about the cryptocurrency markets five years ago. saying Over the last few years, the trends have been staggeringly indicative of crypto going mainstream. But it was the early stage adopters and miners – not the CEOs, who ploughed through every obstacle and restriction in their way to give the space enough coins to play with, and boy, did they deliver! Today after years of “trend watching” investment bankers rejoice at unparalleled opportunities to put newly created assets to work and make “ ” even hotter. hot money Or in the words of Mark Blyth, Brown University economics professor, prominent writer, and brilliant whom I never tire to quote, “…so long as other people are buying [crypto] and there are real gains to be made, why would they not do it? podcaster It tells you something fundamental about finance, which is that oftentimes finance doesn’t really care about the fundamentals, it cares about what people’s expectations are because that’s where the real money gets made.” Today “real money” is by the same people who called crypto “a fraud” a couple of years ago and have now issued their (wink-wink, J. Dimon). being made all over the place own coin The influx of institutional investment in DeFi is unprecedented by any measure, to Chainanalysis; basically, pick any player from the City of London – . according they all are after the crypto gains Governments Name one instance in history when the ruling class represented by governments would voluntarily give up the most lucrative of all possible activities – formulating and implementing monetary policy – and delegate it to the masses. Go ahead, I’ll wait. I don’t mean to draw thousand-year parallels, but it’s kind of a cool thought that China, being the inventor of fiat money, knows all the ins and outs of it. And again, if history has taught us anything, it’s that a country ruled by ignorance and pride of the elite can easily end up on the curb for hundreds of years ( ). in China’s case Or having composed itself, armed with the right ideology (right for the time, anyway) and the right economic theory, a country can soar to unimaginable heights (again, China, ). Venezuela It’s not that governments like China are banning cryptocurrencies because they necessarily expect the technology to fail. It’s that they want to lead the charge when there are trillions to be made. The Chinese government aims to unleash its own CBDC endowed with all the conveniences of crypto and none of its speculative investment traits. What a brilliant move in time for the winter Olympics in Beijing in 2022! And judging by the fact that the U.S. politicians from using e-coin while there, it’s the right one. want to ban American athletes In the U.S., the “open door” policy for any kind of money moves prevails. Investors, there are largely free to take highly leveraged positions that could lead to potentially catastrophic financial losses. Institutions continue to be free to package garbage into and sell it on the street – there’s no shortage of buyers. ABSs, CDSs, and CDOs The SEC is “signalling a robust oversight regime over the industry,” but the big wigs of the said industry what that means. All in all, former U.S. have no idea Treasury Secretary Lawrence Summers put it best when he said in an interview that “the crypto industry should shed the idea that it’ll function as a “libertarian paradise” where government rules can’t be imposed.” We heard you, Larry! Conclusion Still, I believe it’s the governments of the world that drive cryptocurrency markets. All the above-mentioned players do their part, but to an astute observer, the re-centralization trend is quite apparent because it’s the government debt (mostly the US, of course) that throws more and more chips on the global crypto table. Just think that 36% of all US dollars in existence since January 2020. The inflated dollar, for example, gives the likes of Bitfinex the perfect to manipulate the price of Bitcoin and conjure up schemes as huge successes in the space. have been printed opportunity lauded It’s the inflated dollar that allows MicroStrategy to driving up its price… Oops, did I say “stock”? I meant to buy up more and more Bitcoins, of course! continue to buy back its stock There are some useful memory exercises that will convince you that governments play a key role in every single development on the crypto market. We all can still feel the heat of the battle over some articles of the Biden’t infrastructure bill that sent Bitcoin from the high of $68.000 to the current $54.000 in a fortnight. spiralling down We already know the of the moves China can put on crypto. We’ll see what India’s announcement to tighten regulation around crypto in order to pave the way for its will do to the market (let’s not forget that China + India = ⅓ of the Earth’s population). cost own CBDC It’s the governments that have to deal with cybercrime. Governments will have to deal with the redistribution of the labour force once crypto takes over and yesterday’s bank tellers and branch managers line up at the unemployment office. Governments are responsible for the value of fiat money (capital controls) and for the soundness of deposits at times of crisis. Hardcore bitcoinists often suggest that the government’s backing of the state currency is a myth and that the dollar has been bereft of value ever since Nixon took us off the gold standard in 1971 (this resounding motif has reverberated throughout the space so many times, I ought to take it into quotes). However, let’s not forget that the cycle of transactions in any economy involves borrowers, lenders, and consumers, and relies on externalities that simply cannot be codified in a “trustless” environment (physicality of goods, personal relationships, existing legal structure, human resources, logistical intricacies, political machinations, the pitfalls of policy implementation and a ). great many other factors It seems that governments are gradually figuring out the many dimensions of the cryptocurrency market and are leading the creation of an infrastructure that would allow the tech behind crypto to shine in all its glory. The global financial system is ripe for eliminating the distance between the payer and the payee, lender and borrower, equity issuer and investor. Tech entrepreneurs can’t do it on their own, the crypto community is a tight group, but not in itself a force of nature. Institutions are busy seeking out money-making schemes to “maximise shareholder value.” Markets are in acute need of a rulebook and writing one has always been a government’s prerogative. Let’s hope that this time the book will truly be of the people, by the people, and for the people. First published here.

Who (or What) is Driving the Monetary Power in the Cryptoverse?

The chasm between blockchains and traditional finance has been bridged at lightning speed thanks to blockchain Oracles, but ghosts in the system persist. Data, the very source of modern Oracles’ power, can also be its undoing. An overreliance on quantitative data, which is often skewed, can be crippling and stifling to decentralised finance (DeFi) applications. What if the solution to this problem is an Oracle that can give both qualitative and quantitative insights? Nitin Gaur, founder and director of IBM Digital Asset Lab, recently made a detailed case for Oracles in an article published on Cointelegraph, titled “ .” The rise of Oracles: Institutional investors need trusted crypto market data Efficient systems In “The Rise of Oracles” Gaur mentions Nobel laureate Eugene Fama’s efficient market hypothesis (EMH), which can be distilled into the known aphorism, “prices fully reflect all available information.” Explained in short, EMH says that stock price movements are impossible to predict in the short-term, and that new information affects prices almost immediately. Arguably, the stock market is thus ultimately, and perfectly, efficient. While Fama has his champions and detractors, EMH serves as a fitting segue to address the need for correct and reliable market data in cryptocurrency and DeFi. Fama’s theory relies wholly on accurate and reliable information. If the information is off, the price is wrong. Crypto exchanges have traditionally been the key source of data for oracles, and there have been instances of wash trading and closed pools, distorting data which then ripples through the entire DeFi system.Fama would say that these systems were inefficient. When two worlds collide Financial institutions rely on traditional market data (price) and trade-related data, such as asset class volatility, volume and trade-specific data, and value-added data. These metrics play a fundamental role in risk model framework, quantitative trading, price and valuation, portfolio construction and management, and, yes, cryptocurrency finance, as Gaur notes. Newer data models, more suited to digital assets, include volume-weighted average price, time-weighted average price, total value locked, and crypto volatility index, among many others. It’s a complicated system of measurement metrics and market data, Gaur notes, “due to a collision of value systems.” We must discern between what he calls the global macro and the crypto macro. Global macro trends, such as inflation and money supply, impact global demand and supply curves, while crypto macro trends have an effect on the play between sectors such as Web 3.0, layer one, layer two, DeFi, and non-fungible tokens. When the two macros meet, things tend to get complicated. Modeling a hypothesis on empirical data to arrive at an investment theory can, in Gaur’s words, be tricky. “This gives rise to oracles that aim to resolve the issues of trusted data coming into the blockchain transaction system or a mediation layer between the crypto and traditional finance layers,” he says. But he leaves us hanging at the mention of the “tricky” issue with empirical data. Beyond hard data He makes a clear case for valid, trusted and reliable market data, which has come to be an imperative requirement for greater institutional and mainstream adoption, but what about qualitative data? Oracles today are brilliant at calculating observational hard data. For example, you ask an oracle what the temperature is today, and it will give you a scientific breakdown that would make Fama’s head spin, but what if you asked whether today is a nice day? What if users could speculate on an unlimited number of markets by relying on an Oracle as a Service (OaaS) powered by user governance consensus? Using an OaaS, different entities can request data from our governance – it’s not restricted to resolving markets. The OptionRoom forecast protocol is unique in that it introduces the human element required to give insights beyond what computers can comprehend. The nature of our forecast protocol and OaaS, means that it relies heavily on platform participation, which is driven by various incentives, including utility token rewards for protocol participants, governance token farming, and pool winnings distribution to pool creators. Using our fully decentralised forecast protocol, users can create a new market using a $ROOM utility token, enabling token holders to create their unique market access without the need for approval by a centralised authority. Platform participants that stake the $COURT governance token can vote on the resolution of oracle requests. Quantitative data remains vital, which is why our OaaS can also plug into external platforms to provide DeFi protocols with a fully decentralised, governance-based data source and verification solution. Quantitative data paints a vivid outline, given the data is correct, but sometimes we need qualitative reasoning to add colour. The OptionRoom Oracle believes in the hard numbers, but in reaching an efficient solution, we need the full picture, colour and all. ( : ) Disclaimer The author is the CEO at OptionRoom

The next frontier is here: Oracles with human insight

After you've set up Node.js on your machine, the first thing you'll probably want to do is create a new project and start coding. And to get more efficient at the job, there are two important things that you'll need to be fluent with: Automating your development tasks Using and managing your packages Packages in particular are one of the key reasons the Node.js ecosystem is shining. There are packages for about . But managing your packages can become tricky over time, even scary. Taking care of updates without breaking your project or be aware of potential vulnerabilities that may affect your project is something you'll have to look at. everything To help you get up to speed on these topics, my friend will be our host here, with short videos that runs for less than 5 min each. Jen Looper What you'll learn Create a new Node.js project Use NPM scripts for your development tasks automation Add and remove dependencies What is the lockfile and why commit it Take care of dependencies updates Manage Node.js vulnerabilities Additional learning resources Interactive in-depth tutorial on Node.js dependencies npm init docs npm scripts docs npm run script docs npm audit docs Create a new Node.js project Using the command is the faster way to create a new Node.js project from scratch. Let's walk through the different fields you're asked to fill and what's their purpose. npm init Use NPM scripts for your development tasks automation Every project has its set of tasks you repeat daily, like starting your app, building it, running the tests, linting your code to catch issues. Using NPM scripts is the most common way of automating your development tasks these days, so let's have a look at how you can set it up on your app. Add and remove a dependencies One of the secrets of being an efficient developer is to not reinvent the wheel all the time. Packages (also called *dependencies*) can help you focus on writing the code that has the most value for your app by providing all the tools and helpers you need to get your app rolling. What is a lockfile and why commit it When looking at this file, it can seem a bit strange and not really useful at first. It's also a major source of problems when working in a team if it's not taken care of properly. Let's talk a bit about why it's useful, and why you should also commit this file in your repository. package-lock.json Take care of dependencies updates It can make your head spin a bit at first, but you're going to use a lot of packages in your app. And all these packages get updated constantly, with new features, bug fixes or security patches. Trying to keep your app up-to-date may be challenging, especially if you don't want to break your code in the process. Manage Node.js vulnerabilities Vulnerabilities can be a serious threat to any application. At some point, we all freak out when seeing an NPM message saying that you have more than a hundred of vulnerabilities in your app (my current record on a legacy project is over 40k+ 😱). But don't worry - or at least not immediately - as there are strategies to fix or mitigates these issues. And sometimes, you can also safely ignore them, so let's have a look at it. What's next That's it for this post, that was already quite a lot to cover. Of course, every one of these videos could make up for an in-depth article of its own as there's only so much that can be covered under 5 minutes. If you would like to deepen your knowledge around packages, you can take a look at this . Thanks for reading! Interactive in-depth tutorial on Node.js dependencies Also published here .

Tutorial: Creating New Node.js Projects and Working with Packages

How Not to ‘Overfit’ Your AI Learning by Taking Both fast.ai and deeplearning.ai courses Data science and artificial intelligence might be the hottest topic in tech right now, and rightfully so. There are tremendous breakthroughs both on application level and research fields. This is a blessing, and a curse, at least for students and enthusiasts that want to break into this area. There are too many algorithms to learn, too many coding/engineering skills to hone, and way too many new papers to keep up with even if you felt you’ve mastered the art. The journey is long, the learning curve is steep, the strife is real, yet the potential is so great people still flock into it. The good thing is we also have great educators and instructors working on mitigating the pain and make the process a little less harsh and a bit more fun. We’ll explore two of the greatest among them and share a potentially effective approach to help you swim through the sea of Data Science a bit happier. AI Learning ‘Burn-out’ If you list what one needs to learn to become an ‘OK’ data scientist or machine learning engineer, it could be scarily long: Linear Algebra, Calculus, Statistics, Algorithms, … Math: Python, R, SQL/NoSQL, Hadoop, Spark, Tensorflow/PyTorch, Keras, Numpy, Pandas, OpenCV, Data Visualization… Coding: Linear Regression, Logistic Regression, Support Vector Machine, PCA, Anomaly Detection, Collaborative Filtering, Neural Network, CNN, RNN, K-Means, NLP, Deep Learning, Reinforcement Learning, AutoML, … Algorithms: Command Line, Cloud platform(AWS, GCP, Asure), DevOps, Deployment, NGINX/Apache, Docker… Engineering: The list can easily make the head spin for a person just entering the filed. Yet, it is still just scratching the surface. Some people make and dive right into it. Some lost momentum and felt totally under the water and the exit is nowhere to be seen. What went wrong? an ambitious plan You ‘Overfit’ Yourself Overfitting is a very familiar idea for anyone that knows a bit of Machine Learning. It basically means your algorithm learned ‘too much’ of the data and buried itself into the little details of the data-set and missing the big picture. Come to think of it, sometimes when we learn something, we dive so deep we forgot why we were learning it and how it will fit into the big picture. It’s something I’d like to call ‘overfitting’ your own learning. This happens especially often for people coming from academia background. A math Ph. D. tends to make sure all the theorems are fully understood before proceeding to the next one. This is great for learning math. Have a profound understanding of theories will give you great intuition and confidence. It will enable you to see patterns and issues people without the training cannot see easily, yet Data Science demands more. Theory aside, there is also a practical part to it. A properly applied algorithm coupled with efficient codes, carefully tuned hyper-parameters, and well-designed pipeline will usually achieve decent results, but not algorithm alone. Delve down too deep into theory, and you risk missing the practical side of the learning. It’s equally important to accumulate experiences on how to implement what you learned and handle real-life complexities. How to address this? Entering deeplearning.ai and fast.ai courses. Deeplearning.ai and Fast.ai A lot of courses have been developed to help navigate people through the learning process. Among them, Deeplearning.ai and fast.ai are two unique ones that have their own approaches and can give us some insights into a potentially effective way of learning Data Science. deeplearning.ai is a paid course developed by . , it is known for its well-designed learning curve, calm and smooth teaching style, and challenging while fun assignments. It is well accepted as the Deep Learning course one cannot go wrong with. It starts from the fundamental theories and works its way up on how to put all the pieces together to solve real-life problems. It’s also called a ‘Bottom-up’ approach. deeplearning.ai Andrew Ng Like his other courses fast.ai fast.ai is introduced by and as a free course to teach people with basic coding experience state-of-the-art deep learning techniques. Without much explanation of the underlying theories, with very few lines of code, student of fast.ai is capable of achieving astoundingly great results on its own domain quickly into the lessons. (I built a rate and after finishing lesson 1 of fast.ai course.) It teaches you how to tackle the real-world problem first, then digs deeper and deeper into how and why things work. It’s also called a ‘Top-down’ approach. Jeremy Howard Rachel Thomas Chinese Calligraphy Style Classifier that reaches 96% accuracy deployed it on the cloud Which One is the Best Approach? Both! So‘Bottom-up’ and ‘Top-down’, which one is better? Which one should we take? The answer is ! Both See, these two courses complement each other. Say you start from ’s deeplearning.ai course, you buried yourself into endless formulas and theories, you gained a lot of intuition, but after weeks of learning, you still have nothing to show to your friends and not quite sure when you can apply your newly gained knowledge. Andrew Ng Your study on the machine learning fundamentals is getting diminishing returns. Your brain slows down and you start to feel boring. Now is the perfect time to start taking a lesson or two of the fast.ai course. With the help of the powerful fast.ai library and few lines of code, you’ll be able to build impressive models that solve real-life problems and even beat some state-of-the-art papers and competitions. Kaggle This will give your brain a totally different kind of stimulation and your heart more confidence and passion to delve deeper into why everything works. Once you built a couple of projects and ‘wow’ed your friends, you will be more motivated to learn more about the fundamentals, then you can go back to deeplearning.ai course and keep your study there. These two courses push each other forward, you can just rinse and repeat till you finished both. This forms a perfect learning circle. The best thing about taking both courses this way is once you finished both, you’ll be fully prepared. You have tons of projects built along the way from fast.ai course to showcase to potential employers and you also have the deep knowledge of how everything works or even published one paper or two to show your findings. You are now a well-rounded Data Scientist. How cool is that? Any feedback or constructive criticism are welcomed. You can either find me on Twitter or my blog site . @lymenlee wayofnumbers.com

Jeremy Howard’s fast.ai vs Andrew Ng’s deeplearning.ai - Are They That Different From Each Other?

Here’s how you’ll make working apps so fast it’ll make your head spin Hey there, I’m the co-founder of UX-App. We’re building to be the most powerful and easiest to use on the market. It’s a bold claim which we’re happy to back up. Here are some examples of common app designs that are impossible to express using competing products like UXPin, Balsamiq and Axure. UX-App app design tool UX-App makes the impossible, , see for yourself! easy Expanding Cards Expanding cards are everywhere and we expect to make them quickly, here’s you can make expanding cards with UX-App: Progressive disclosure Going back to our cards example one more time. I set up two break points for progressive disclosure. Useful for comment sections or feeds. Vertical sub menu Vertical sub menus are another necessary ui pattern that’s super quick to create. Advanced inputs There are a few cool new kinds of inputs being done today, let’s try to simulate two of them In Place editor In place editing is a very cool space saver that’s great for profile pages or comment sections. Here’s how we simulate the pattern, we’ll initiate the edit with a click Thumbnail → full image To round off the desktop side of this showcase, here’s a simple thumbnail to full image transition Mobile versatility Mobile projects in UX-App have a few unique features: Regardless of the device you choose, the design will scale to fit the height of the device where the prototype is being previewed. Each page can have a unique resolution, device type, or orientation independently from the rest of the project We worked hard on those features, and we think they’ll save you tons of project set up time so I just want to throw that out there :D Moving on from that… here are a few mobile patterns that you’ll probably want to create. Every icon can be a splash screen loader Every app needs a “start” and with it a splash screen, whatever element rotates in the middle can often be a big part of your brand identity. So here’s how you set up whichever UX-App icon best fits your design idea to be a splash screen loader. Don’t like our icons? Upload an image you like better and rotate it instead Off-canvas menu Everyone loves a good off-canvas menu, right? Check this out: Dynamic flowcharts For our grand finale we’ll set up some dynamic flowcharts! I’m big on flowcharts, Love simple straight forward explanations of what needs to get done, but flowcharts can also be very limiting in how much text you can cram (I’m looking at you diamond) so a side by side demonstration can be fun. Here’s a registration flow set up with a side by side flow chart If you’re a designer, product manager, developer or entrepreneur, you’ll benefit from the incredible ease with which you can create working prototypes using UX-App. On top of the array of fully functional components and intuitive actions designer, you can also preview your designs on any mobile device. There’s nothing quite like seeing your design right there, interactive, on your Apple or Android device. We’ve got some special discounts for people who have read this far :) If you’d like off the usual UX-App price then email: 40% support@ux-app.com and mention this article! Happy Prototyping, The UX-App Team

App Design Made Easy

Sometimes you need the simplicity of the browser coupled with the power of a full fledged framework. programming In a previous article about creating a simple .Net REPL, , I outlined a quick and easy way to access the .Net framework from an interactive . In addition to being able to evaluate expressions and access .Net framework classes and libraries, this simple REPL provides an easy way to cross the bridge between .Net and standard COM Objects. Much of Window’s programming, especially LOB (Line of Business) applications involves COM automation. This is especially true for anyone doing any sophisticated Microsoft Office work. Prior to .Net, COM was the lingua franca of Windows and much of Window’s interoperability was based on COM. https://medium.com/@mikeptweet/making-a-net-repl-57ca5c465081#.w6xvh925y REPL Despite the popularity and growth of the .Net framework, a considerable number of existing Window’s applications are based on COM and in many cases, COM is the only practical way to achieve interop between applications from different vendors. While the .Net framework provides a number of ways to create and access COM objects, these approaches often require considerable knowledge and are not readily accessible to the average “script kiddie” ... for example, . https://msdn.microsoft.com/en-us/library/system.runtime.interopservices(v=vs.110).aspx Ideally, we want to mix and match .Net and COM objects and leverage the power of both. This is achievable via our REPL, since it treats COM and .Net equally as first class citizens. If we refer back to our earlier article, you can see how easy it is to access .Net framework classes : Just as easy, we can access COM Objects: Much of the Window’s application ecosystem is readily accessible to COM and we can create some powerful integrations using the automation interfaces to such applications as Microsoft Office: $.excel=new ActiveXObject("Excel.Application");$.word=new ActiveXObject("Word.Application"); or database access $.conn = new ActiveXObject("ADODB.Connection"); Being able to script .Net Framework classes in conjunction with COM objects provides a tremendous toolbox that enables scenarios that would otherwise be out of reach for most business type programmers. By “business type programmers”, I am referring to the girl(or guy) in Account’s Payable who can do things with Excel macros that would make your head spin :-) One scenario that I have found exceedingly useful on more than one occasion is being able to script the Internet Explorer application to automate web page tasks. This might be as simple as scrapping a web site for information such as Exchange rates, checking stock quotes or just getting the latest weather forecast. Doing this in our REPL is pretty simple. In this simple example, we script Internet Explorer to convert $20 USD to CAD (Canadian $): Although this is a trivial example, it demonstrates just how powerful a few lines of script code can be in the hands of a creative person. Just to reiterate, we have the full power of COM plus the .Net Framework at our disposal. The beauty of this is it provides unlimited flexibility in terms of how you want to accomplish your task. In another scenario, maybe all you want to do is get the raw HTML from a web page and do some further processing locally. In this case, you could call up the .Net System.Net.WebClient class to get the raw data: $.html=(new WebClient()).DownloadString("http://example.com"); While being able to script both .Net and COM is very useful, their are times when I want the capabilities of one environment “inside” the other. For example, being able to host a COM object inside .Net or vice versa, hosting a .Net Framework class/object/library inside a COM based application. Again, these capabilities do exist for the seasoned programmers, i.e. I can create an Excel Add-In in either .Net or COM, but they require extensive knowledge as well as a bunch of dependencies and restrictions. Since my mantra has always been , I want to be able to do this on the fly, so to speak. Using our .Net REPL, we can do this in a few lines of code. Since objects that are created inside the REPL are “universal COM objects”, that means we can “attach” a .Net object to an existing COM object simply by calling a method or setting a property. For example, the following demonstrates how easy it is to create a new method(function) in Internet Explorer that calls a native .Net class to return a new GUID, which is something not possible via just JavaScript in the browser: “less is more” $.ie.document.parentWindow.CreateGuid=function(){return Guid.NewGuid().ToString()}; If you open the Internet Explorer Developer Tools (press F12), you can see that the new function now exists as a method on the DOM window object. So in a few lines of code, we are able to provide the full functionality of the .Net Framework inside Internet Explorer, fully accessible by the browser’s JavaScript engine. Indeed, we can take this to the extreme and make anything available in the context of our REPL accessible from within the context of the browser’s Javascript engine. All it takes is a single line of code: $.ie.document.parentWindow.Repl=$; This makes any global references (variables, functions, etc) we defined in our REPL available via window.Repl in the browser. Simple define functions on the REPL global $ and they are automatically accessible from the browser. For example, say we want to use the powerful String Formatting capability from .Net inside the browser … it’s a simple as: $.ie.document.parentWindow.Repl=$;$.format=System.String.Format; //and from the browser we can do window.Repl.format("The date is {0}},Date()); Off course, we can simply this even further by assigning the Format method directly to the browser window object: $.ie.document.parentWindow.format=String.Format; // and from the browser window.format("The date is {0}},Date()); //or simplyformat("The date is {0}}.Date()); //since all properties and methods on the window object are global by default. Again, we can mix and match COM and .Net freely and create some very powerful solutions with very few lines of code. So this means we can equally do something like the following to insert Excel into the browser context: $.window=$.ie.document.parentWindow;$.window.Excel= new ActiveXObject("Excel.Application"); That’s right, I can now fully script Excel from my browser context! We’ll leave that for another article :-)

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