We try the Framework Desktop, the first desktop PC from the modular repairable computer company.

Framework Desktop hands-on: a possible new direction for gaming desktops | The Verge

Warner Bros. Discovery is canceling its Wonder Woman game and shutting down Monolith Productions, Player First Games, and Warner Bros. Games San Diego.

Warner Bros. Games is canceling its Wonder Woman game and shutting down three studios | The Verge

Chinese AI startup DeepSeek claims its open-source AI models outperform rivals at a fraction of the cost, affecting stock prices for companies like Nvidia.

DeepSeek: all the news about the startup that’s shaking up AI stocks | The Verge

We took the Nvidia RTX 5090, the world’s fastest GPU, and routed its power to a gaming handheld over an Oculink cable.

I plugged an Nvidia RTX 5090 into a gaming handheld | The Verge

Once Acer announced its jumbo-sized Nitro Blaze 11 handheld, I thought what if I add controllers to a 13-inch Asus ROG Flow Z13 handheld? This could be the future.

Maybe giant gaming handhelds are where it’s at - The Verge

Jason Ronald, Microsoft’s VP of “Next Generation,” tells The Verge that we should expect to see the Windows handheld gaming experience change within this calendar year.

Microsoft is combining ‘the best of Xbox and Windows together’ for handhelds - The Verge

The Verge

theverge

The Verge: A Path To Making Ethereum Verifiable And Sustainable

Technology research company Gartner predicts that there will be 20.8 billion connected things in the world by the year 2020, which would equal almost triple the number of humans on our planet. Clearly, the Internet of Things is on the verge of radically changing the core fabric of our communities and the manner in which people live. Although IoT has the potential to make our lives much more efficient and enjoyable in a multitude of ways, many industries are going to have to weather the storm as the disruption takes hold, and there may be sectors that are unable to remain relevant within the rapidly changing landscape. Virtually every industry will feel the effects of the proliferation of IoT in our communities, but there are five sectors that are expected to be hit the hardest by these changes in the short-term. Health Most experts agree that the healthcare industry stands to benefit the most from adopting IoT. However, to date, it has proven to be one of the most delayed industries to put connected technologies into practice. At the same time, the increased prevalence of IoT will be impossible to avoid for much longer, and it is expected that new methods of data sharing will expand significantly over the next few years, including patient-reported health outcomes and data collected from medical devices or wearable hardware. Energy and Utilities Sensor-based communication has been a central component of this sector for years, including smart-meters, which allow companies to read meters via sensors to access real-time usage information. The next logical step will be a “smart-grid” concept, providing customers with the ability to switch between customer-owned renewable energy systems and traditional energy supplies. IoT technology will be critical in developing a smart grid that is adaptable to a variety of energy sources, which is essential in order for the sector to survive the evolving landscape. Retail The incredible growth of e-commerce and online shopping has posed a threat to traditional retailers for approximately a decade. Conversely, the application of recent IoT innovations to traditional brick-and-mortar assets provides the potential for a radical transformation of the retail industry. Combining real-time analytics with RFID and beacon-based technologies carries a wealth of possibilities for the retail sector, such as allowing companies to send push notifications to customers as they walk by the store to tell them about current sales, promotions and special deals, or even make automated checkouts when they leave the store. Transportation There is no question that efficient transportation of both goods and people is a key aspect of the smooth running of any economy or society, with a massive impact on productivity, health and environmental preservation. IoT is poised to disrupt this sector through the creation of transportation systems that are able to sense and respond to changes in real-time, thereby broadening operational efficiencies, improving public safety, and increasing fuel efficiency through capacity analytics. Government IoT technology provides the foundations for the concept of “smart cities,” a term used to describe ways in which technology can be used to create more efficient solutions for communities of people. Fujisawa in Japan is an excellent example of this concept being put into practice. The brainchild of Panasonic, Fujisawa has deployed IoT to use sensors to monitor buses, garbage bins, and automate transport. It is expected that a growing number of governments will start integrating IoT into the core services of cities, thereby utilizing the technology in an increasing percentage of city-run business. Virtually every industry either already has been or is about to be disrupted in one form or the other by the proliferation of IoT in all aspects of our communities, leading to an increasingly efficient and safe world for the future. Written by Igor Ilunin, head of IoT at DataArt .

Top 5 Industries on the Verge of IoT Disruption

Every day, every minute, and every second, AI is digging deeper into data analytics. It automates routine tasks, writes SQL queries, cleans messy datasets, builds charts — and, at first glance, even seems to understand what it's doing. Well... almost. This raises a very real, slightly unsettling question: Will AI replace data analysts? Or at least those junior analysts still wrangling spreadsheets in Excel? On the surface, it makes sense. Analytics is about spotting patterns, finding correlations, and building scripts. Can AI find correlations? Can it structure data? It can. Put it all together, and AI starts to look like less of an assistant — and more like a real contender on the analytics team. But there’s a catch. Several, in fact. AI in Analytics: Between Expectations and Reality AI in Analytics: Between Expectations and Reality If you take a closer look at what’s being written about AI in data analytics today, you’ll notice a clear divide in perspectives. On one side — there are the overly optimistic takes, usually aimed at beginners. Here, AI is positioned as a universal solution: it quickly finds patterns, cleans data, writes SQL queries, and even draws conclusions. After reading a few of these, you might start to believe analysts are on the verge of becoming obsolete. On the other side — there are experienced professionals raising very valid concerns. These articles focus on AI’s weaknesses: how it struggles with complex cases, generates questionable insights, and doesn’t always grasp what it’s dealing with. The takeaway often sounds like: “AI isn’t a replacement for humans — it’s just a lightweight helper.” But both sides are missing the complete picture. The first tends to overestimate what AI can do. The second risks underestimating the real value it already brings to day-to-day analytical work. So, what are we left to do? We either buy into the hype or ignore a tool that’s already capable of saving hours of effort. The goal of this article is to offer a third perspective: a practical take from those already using AI in real workflows. Let’s see where it helps, where it fails, and how to actually make it work without expecting miracles. a practical take from those already using AI in real workflows. We’ll walk through real-world use cases with ChatGPT, taking a clear look at how AI can complement the analyst’s work and why thoughtful integration is no longer just a trend but a new layer of professional reality. Simple Technical Tasks: How Good Is AI, Really? Simple Technical Tasks: How Good Is AI, Really? Before we dive into the examples, here’s a bit of context. I’m the founder and former CTO of Infolek (until 2022) and currently the CTO of OMNI Digital. I'm focused on building innovative solutions for automating and optimizing business processes in pharmacies and pharmaceutical distribution, including in Kazakhstan. The examples below come from real-world challenges we’ve encountered — drawn directly from actual projects. Example 1: Parsing Kazakhstan Addresses into Segments Example 1: Parsing Kazakhstan Addresses into Segments Imagine a dataset with 30,000 addresses from Kazakhstan. The task is to split each address into separate components: region, district, locality, street, and building number. A straightforward prompt like “split the address into these parts” doesn’t quite work — the structure of the data is too complex. There are many nuances. For instance, if you try to split by delimiter, the number of segments can vary widely — from 3 (e.g., Almaty, Baizakov Street, bld.7) to 8 or more (e.g., Kazakhstan, Aktobe Region, Aktobe, Almaty District, Kargaly Residential Area, Yeset Batyr Microdistrict, 1st Microdistrict, bld.34). Some addresses even contain multiple nested localities. In short — the rules aren’t consistent. Almaty, Baizakov Street, bld.7 Kazakhstan, Aktobe Region, Aktobe, Almaty District, Kargaly Residential Area, Yeset Batyr Microdistrict, 1st Microdistrict, bld.34 The solution? Iteration. Prompt → result → refinement → another prompt. Rinse and repeat — until it works. Solving this manually takes a lot more time. But here’s where AI shines: Given a precise prompt, it can quickly test hypotheses, generate scripts or regular expressions, and adapt to complex conditions. It easily picks out specific subtasks when they’re clearly formulated — like this one: precise prompt, “Create a new column adr1_hc2 that contains a substring composed of the final address segments, joined by commas, that were not previously classified. Extract the digits starting from the first encountered number, up to the first non-numeric character…” adr1_hc2 With clear instructions, AI becomes a powerful helper — fast, consistent, and surprisingly accurate in handling technical logic. With clear instructions, AI becomes a powerful helper — fast, consistent, and surprisingly accurate in handling technical logic. Bulk Data Validation: Where AI Hits Its Limits (and Then Pushes Past Them) Bulk Data Validation: Where AI Hits Its Limits (and Then Pushes Past Them) Example 2: Validating Kazakh Rural Districts Example 2: Validating Kazakh Rural Districts Let’s say you have a spreadsheet with 1,000 rural districts in Kazakhstan — and a list of assumed matches to the administrative regions they belong to. The problem? There’s no reliable open-source table you can just plug into your model. A human would have to search for each entry manually — one by one. ChatGPT handles single-row validation pretty well. Give it one district, and it can cross-check, reason, and suggest corrections. But try to scale that to 1,000 rows in the standard interface, and things get tricky. Fast. But ChatGPT will politely (and sometimes not so politely) refuse. It starts throwing limitations and safety warnings or just says, "I’m sorry, I can’t help with that." The workaround? Use the API. By switching to the API and automating the prompt flow, the task became totally manageable — and was completed successfully. What took hours of manual effort could now be done consistently at scale. The key takeaway: AI can absolutely assist with bulk data validation — but it needs the right infrastructure. For anything beyond small-scale checks, you’ll need to go beyond the chat window and tap into automation. AI can absolutely assist with bulk data validation — but it needs the right infrastructure. For anything beyond small-scale checks, you’ll need to go beyond the chat window and tap into automation. Advanced Analytics: When AI Starts to Struggle Advanced Analytics: When AI Starts to Struggle Example 3: Investigating Data Discrepancies in Pharmaceutical Reports Example 3: Investigating Data Discrepancies in Pharmaceutical Reports A pharmaceutical distributor sends monthly reports. One month, something’s off — 95% of customer names and addresses no longer match any previous reports. It’s not just a formatting issue — it looks like a completely different dataset. A quick manual review showed that about 25% of the records can be partially matched using semi-automated methods. The rest? They needed full-on human review. So, we decided to put AI to the test. First Attempt: Let AI figure it out on its own. We uploaded both files and asked ChatGPT to identify discrepancies and explain what was happening. This resulted in a complete failure. The model doesn't even come close to identifying the issue — it just highlights the obvious mismatches without meaningful analysis.


Second Attempt: Help it along. We gave ChatGPT detailed context: data structure, business logic, reporting rules, possible scenarios, and hypotheses to test. We guided it step by step. Still no luck. Here, the model either offers vague generalizations or latches on to irrelevant details. No progress.


Final Attempt: Use AI as a tool, not a brain. A human analyst takes over. Using ChatGPT for routine checks, hypothesis scripting, and basic structuring, the analyst tested several theories in about 30–40 minutes. Eventually, a pattern emerges: the issue lies in the distributor’s reporting system — confirmed later by their tech support. First Attempt: Let AI figure it out on its own. We uploaded both files and asked ChatGPT to identify discrepancies and explain what was happening. This resulted in a complete failure. The model doesn't even come close to identifying the issue — it just highlights the obvious mismatches without meaningful analysis. First Attempt: Let AI figure it out on its own. We uploaded both files and asked ChatGPT to identify discrepancies and explain what was happening. This resulted in a complete failure. The model doesn't even come close to identifying the issue — it just highlights the obvious mismatches without meaningful analysis. Second Attempt: Help it along. We gave ChatGPT detailed context: data structure, business logic, reporting rules, possible scenarios, and hypotheses to test. We guided it step by step. Still no luck. Here, the model either offers vague generalizations or latches on to irrelevant details. No progress. Second Attempt: Help it along. We gave ChatGPT detailed context: data structure, business logic, reporting rules, possible scenarios, and hypotheses to test. We guided it step by step. Still no luck. Here, the model either offers vague generalizations or latches on to irrelevant details. No progress. Final Attempt: Use AI as a tool, not a brain. A human analyst takes over. Using ChatGPT for routine checks, hypothesis scripting, and basic structuring, the analyst tested several theories in about 30–40 minutes. Eventually, a pattern emerges: the issue lies in the distributor’s reporting system — confirmed later by their tech support. Final Attempt: Use AI as a tool, not a brain. A human analyst takes over. Using ChatGPT for routine checks, hypothesis scripting, and basic structuring, the analyst tested several theories in about 30–40 minutes. Eventually, a pattern emerges: the issue lies in the distributor’s reporting system — confirmed later by their tech support. The takeaway: The takeaway: When it’s about structuring or visualizing known data, AI does well. But when the task requires identifying hidden patterns, dealing with non-linear relationships, or forming new hypotheses — AI hits its current limits. When it’s about structuring or visualizing known data, AI does well. But when the task requires identifying hidden patterns, dealing with non-linear relationships, or forming new hypotheses — AI hits its current limits. It’s not that it’s terrible. It’s just not a replacement for experience, context, and human intuition — at least not yet. AI is great at the boring stuff. It handles repetitive tasks like text parsing, pattern recognition, and basic structuring and even writes scripts and regex faster than most junior analysts. It can even run simple forecasts — but let’s be clear: the quality of those forecasts still depends on a human analyst setting the direction, checking the assumptions, and approving the results. AI can also help visualize data — as long as the metrics are well-defined. But when it comes to identifying non-standard KPIs, it often misses the point entirely. Why? Because novelty isn’t its strength — it's still pattern-matching inside known boundaries. Need to scale? That’s where APIs come in. With the right setup, you can automate large-scale tasks that would normally eat up hours (or days) of manual work. Bulk validation, mass transformations, structured prompting — all of it becomes doable. But there’s a catch. AI still throws out false correlations and misleading conclusions — especially when your data is messy, incomplete, or when the problem isn’t clearly defined. That’s when a human needs to step in. Not just to review the output but to ask the right questions in the first place. The hard truth? You still need a human brain for anything nuanced, nonlinear, or business-specific. Analysts know how to adapt methods to context, create custom metrics, and see patterns no model would notice. Humans bring intuition. AI brings speed. And right now, that’s the real power combo.

AI is Neither the Magical Replacement for Human Analysts Nor a Useless Gimmick

Twitter/X had a massive outage on Monday morning EST, with Downdetector spikes surging; Downdetector In a post, Elon Musk has claimed this was due to a “massive cyberattack” against X. massive cyberattack Mashable seem skeptical of Elon, citing the lack of evidence in Musk’s reporting, alongside his prior claim that Trump suffered an attack in August, 2024 – which X employees said was untrue, corroborated via The Verge. Mashable seem skeptical claim that Trump suffered an attack The Verge Though we’re unsure if ‘skeptical’ is fair, given Elon is Twitter’s biggest misinformation spreader according to both Reuters and his own AI, Grok; Reuters Grok This could be an attack squarely designed to rustle Elon’s jimmies considering the recent spate of vandalism against Musk’s Tesla. (The Guardian, CNN, News.com, The Washington Post), or it could have some other malicious intent. The Guardian CNN News.com The Washington Post Given the recent DOGE site vulnerabilities, and his evident de-prioritization of transparency reporting at Twitter/X – Musk is developing a reputation for poor digital hygiene, and relaxed security practices. DOGE site vulnerabilities evident de-prioritization of transparency reporting Did The Twitter / X Outage Leak Data? Given DOGE’s digital track record, cost-cutting focus, and lack of transparency – this cyberattack should post considerable concerns for anyone on Twitter / X. Lets understand why; Twitter/X is vulnerable to cyberattacks; it is likely bad actors will exploit the platform’s poor security for malicious purposes in future.
Potential for data breaches; No data leak has been reported this time; though Elon has not shared the root cause of the attack. There is a real potential that future attacks will involve capturing user data.
Elon is controversial; As a billionaire-politician, Musk is a hugely polarizing figure; hacktivist groups may target X specifically due to Elon’s ownership of the platform.
Twitter had a data breach in 2023; the probability of this happening again will only increase as more groups and individuals target X. 200 million email addresses were leaked. Twitter/X is vulnerable to cyberattacks; it is likely bad actors will exploit the platform’s poor security for malicious purposes in future. Twitter/X is vulnerable to cyberattacks Potential for data breaches; No data leak has been reported this time; though Elon has not shared the root cause of the attack. There is a real potential that future attacks will involve capturing user data. Potential for data breaches; Elon is controversial; As a billionaire-politician, Musk is a hugely polarizing figure; hacktivist groups may target X specifically due to Elon’s ownership of the platform. Elon is controversial; Twitter had a data breach in 2023; the probability of this happening again will only increase as more groups and individuals target X. 200 million email addresses were leaked. Twitter had a data breach in 2023 had a data breach in 2023 While we hope you are not impacted directly by the security practices at Twitter – we wouldn’t recommend risking your data in future attacks and data breaches. You can secure your Twitter profile easily, and for free in a few basic steps. How to Secure Your Twitter/X Profile The most secure Twitter profile is probably the one that doesn’t exist… but, if you need to be on the platform, we recommend; Review your account history, and delete all old content, or content with any personal information. You can set up targeted mass deletion with our app – just download Redact and follow the steps here.
We recommend requesting your data from Twitter, so you have a local copy.
Alias as much personal information attached to your account as possible, and set a totally unique password; in the event of a data breach, you don’t want important emails or passwords leaked.
Consider deleting your account altogether. We’d recommend working through the steps above before you do this. Review your account history, and delete all old content, or content with any personal information. You can set up targeted mass deletion with our app – just download Redact and follow the steps here. download Redact the steps here We recommend requesting your data from Twitter, so you have a local copy. requesting your data Alias as much personal information attached to your account as possible, and set a totally unique password; in the event of a data breach, you don’t want important emails or passwords leaked. Alias as much personal information attached to your account as possible Consider deleting your account altogether. We’d recommend working through the steps above before you do this. before While Twitter / X cyberattacks are worrying, if you take the proper steps you can mitigate the potential damage this does to you.

X Outage Exposes Musk's Poor Digital Hygiene

انقطاع خدمة X يكشف عن ضعف نظافة ماسك الرقمية

এক্স বিভ্রাট মাস্কের দুর্বল ডিজিটাল স্বাস্থ্যবিধি প্রকাশ করে

La interrupción de X expone la mala higiene digital de Musk

قطعی X بهداشت ضعیف دیجیتال ماسک را آشکار می کند

X-katkos paljastaa Muskin huonon digitaalisen hygienian

X անջատումը բացահայտում է Մասկի վատ թվային հիգիենան

Gangguan X Menunjukkan Buruknya Kebersihan Digital Musk

Xの障害でマスク氏のデジタル衛生管理の悪さが露呈

X pārtraukums atklāj Muska slikto digitālo higiēnu

Ny fahatapahan'ny X dia mampiharihary ny fahadiovana nomerika ratsy an'i Musk

X Kgaotšo ya go Kgaotša e Pepentšha Bohlweki bjo bo Fokolago bja Dijithale bja Musk

X öçürilmegi, Muskyň sanly arassaçylygyny paş edýär

The Current State of Plain English Coding in Feb, 2025 Introduction Imagine instructing your computer, “Build me an application,” and watching it materialize—no arcane syntax or advanced training required. This is natural language coding (NLC): artificial intelligence translates everyday English into functional code. By February 2025, GitHub Copilot leads the charge, offering a free tier with 2,000 monthly code completions, accessible to its estimated 2 million developers—a leap from 1.5 million in 2024.[GitHub Copilot - Wikipedia] New tools like Cursor, Windsurf, Lovable, Bolt, and Cline have joined, each pushing the boundaries of accessibility and efficiency. Yet, despite this momentum, NLC remains out of reach for many. Having tested these platforms myself, I’ve encountered their strengths and shortcomings firsthand. This article examines NLC’s current capabilities, its persistent barriers, and the path ahead. What Is Natural Language Coding in 2025? Natural language coding enables users to issue commands in plain language—“sort this list”—and receive executable code instantly. GitHub Copilot, now free for basic use and $10/month for its Pro tier, anchors the ecosystem, seamlessly integrated into Visual Studio Code. Its adoption has likely exceeded 2 million developers by early 2025, building on its 55% productivity boost documented in prior years.[Microsoft has over a million paying Github Copilot users | ZDNET] Emerging tools enhance the landscape: Cursor: $20/month, generates multi-file projects from prompts like “create a todo app.”


Windsurf: Free from Codeium, predicts coding patterns with precision.


Lovable: $15/month, simplifies app creation for novices—“design a quiz” yields results fast.


Bolt: Free, browser-based, delivers full-stack prototypes in moments.


Cline: $5/month VS Code extension, refines vague inputs into scripts.

Powered by advanced AI models, these tools fuel ambitions of universal coding access—yet significant hurdles remain. Persistent Barriers to Universal Adoption Even with these innovations, NLC poses challenges: Ambiguity in Language Natural language’s nuances often mislead AI. When I asked Cursor to “update my application,” it altered an irrelevant module, requiring manual fixes. A 2023 Stanford study found that roughly 40% of AI-generated code contains subtle bugs when prompts lack precision—a challenge still evident in 2025.[Natural Language Programming - GeeksforGeeks] Seasoned developers can adjust such outputs, but novices lack the insight to proceed effectively. Prerequisite Knowledge Required NLC tools demand a foundational grasp of coding concepts to fully utilize their outputs. When I prompted Lovable with “generate a basic calculator” it produced a fully functional web-based calculator application, complete with a user interface featuring buttons for operations like addition, subtraction, multiplication, and division, as shown below. While impressive, the app handled basic arithmetic seamlessly, but it lacked error handling—say, for non-numeric inputs like letters or special characters—which could crash the interface or produce unexpected results. Recognizing these gaps required understanding TypeScript, React, and state management. Without knowledge of “variables,” “functions,” or “components,” beginners struggle to assess, debug, or enhance such code, limiting their ability to adapt Lovable’s output effectively. Complexity and Debugging Challenges Complex tasks reveal NLC’s limitations, even with advanced tools. When I prompted Bolt to “create a REST API endpoint to fetch user data in Python,” it generated a functional Flask-based web application with two endpoints: one to retrieve all users (/api/users) and another for a specific user (/api/user/<user_id>). The output included sample user data, JSON responses, and basic error handling, as shown below. However, the application encountered an issue: Bolt failed to generate or configure a requirements.txt file, marked with an error (red “X”), leaving dependencies like Flask undefined. This oversight requires manual intervention—installing Flask (pip install flask) and creating the file—to make the API runnable. Experienced developers can resolve this quickly, but novices, lacking knowledge of dependency management or Flask setup, face an insurmountable barrier. This gap underscores NLC’s reliance on underlying technical expertise, even when tools appear to deliver complete solutions. Experienced developers find themselves frustrated by such oversights, while novices face an insurmountable debugging barrier. Stack Overflow’s 2024 Developer Survey revealed that 45% of professional developers believe AI tools are inadequate or very inadequate at handling complex tasks, a trend I’ve observed persisting into 2025, particularly with tools like Bolt and Cline.[Stack Overflow 2024 Developer Survey] Developer Advantages and Ethical Considerations For seasoned developers, NLC streamlines repetitive tasks—Windsurf’s predictive features accelerate testing, Bolt’s prototyping saves time. Yet, limitations linger. My experience with Cline yielded a script that failed silently, forcing a manual rewrite—a recurring frustration. Ethical concerns also loom large. GitHub Copilot’s early reliance on public repositories sparked intellectual property debates.[Copilot IP Controversy - The Verge] In 2025, Bolt counters this by scoping outputs to avoid replication, while Lovable uses curated datasets to ensure originality. These measures mitigate risks, though broader legal clarity on code ownership remains pending, a critical factor for enterprise adoption. The Path Forward in 2025 Current NLC tools signal progress: Enhanced Precision: Windsurf’s Flow feature clarifies vague inputs with follow-up questions.
Simplified Access: Lovable’s app-based interface bypasses complex IDEs.
Specialized Support: Cursor’s bug-detection flags issues proactively.While these advancements narrow the divide, universal adoption hinges on further refinement and user education. Conclusion and Call to Action Natural language coding thrives in 2025—millions of developers leverage its efficiency—yet it falls short of universal accessibility. Ambiguity, requisite knowledge, and debugging complexities exclude many, while ethical frameworks evolve. Developers must integrate tools like Cursor or Bolt to enhance productivity, refining skills to bridge AI’s gaps. Beginners face a choice: acquire foundational knowledge or await further simplification. "The question isn’t whether AI will change coding—it already has. The real challenge? Ensuring we shape its future rather than being shaped by it." References GitHub Copilot - Wikipedia – 2025 adoption estimated from 2024 trends.
Microsoft has over a million paying Github Copilot users | ZDNET – 55% productivity stat.
Natural Language Programming - GeeksforGeeks – 40% bug rate, AI training context.
Stack Overflow 2024 Developer Survey – 45% complex task difficulty.
Copilot IP Controversy - The Verge – Ethical context. About  the Author: I’m Jay Thakur, a Senior Software Engineer at Microsoft,  exploring the transformative potential of AI Agents. With over 8 years  of experience building and scaling AI solutions at Amazon, Accenture  Labs, and now Microsoft, combined with my studies at Stanford GSB, I  bring a unique perspective to the intersection of tech and business. I’m  dedicated to making AI accessible to all — from beginners to experts —  with a focus on building impactful products. As a speaker and aspiring  startup advisor, I share insights on AI Agents, GenAI, LLMs, SMLs,  responsible AI, and the evolving AI landscape. Connect with me on Linkedin The Current State of Plain English Coding in Feb, 2025 The Current State of Plain English Coding in Feb, 2025 Introduction Imagine instructing your computer, “Build me an application,” and watching it materialize—no arcane syntax or advanced training required. This is natural language coding (NLC): artificial intelligence translates everyday English into functional code. By February 2025, GitHub Copilot leads the charge, offering a free tier with 2,000 monthly code completions, accessible to its estimated 2 million developers—a leap from 1.5 million in 2024.[ GitHub Copilot - Wikipedia ] New tools like Cursor, Windsurf, Lovable, Bolt, and Cline have joined, each pushing the boundaries of accessibility and efficiency. Yet, despite this momentum, NLC remains out of reach for many. Having tested these platforms myself, I’ve encountered their strengths and shortcomings firsthand. This article examines NLC’s current capabilities, its persistent barriers, and the path ahead. GitHub Copilot - Wikipedia What Is Natural Language Coding in 2025? Natural language coding enables users to issue commands in plain language—“sort this list”—and receive executable code instantly. GitHub Copilot, now free for basic use and $10/month for its Pro tier, anchors the ecosystem, seamlessly integrated into Visual Studio Code. Its adoption has likely exceeded 2 million developers by early 2025, building on its 55% productivity boost documented in prior years.[ Microsoft has over a million paying Github Copilot users | ZDNET ] Emerging tools enhance the landscape: Microsoft has over a million paying Github Copilot users | ZDNET Cursor: $20/month, generates multi-file projects from prompts like “create a todo app.” Windsurf: Free from Codeium, predicts coding patterns with precision. Lovable: $15/month, simplifies app creation for novices—“design a quiz” yields results fast. Bolt: Free, browser-based, delivers full-stack prototypes in moments. Cline: $5/month VS Code extension, refines vague inputs into scripts.

Powered by advanced AI models, these tools fuel ambitions of universal coding access—yet significant hurdles remain. Cursor: $20/month, generates multi-file projects from prompts like “create a todo app.” Cursor : $20/month, generates multi-file projects from prompts like “create a todo app.” Cursor Cursor Windsurf: Free from Codeium, predicts coding patterns with precision. Windsurf : Free from Codeium, predicts coding patterns with precision. Windsurf Windsurf Lovable: $15/month, simplifies app creation for novices—“design a quiz” yields results fast. Lovable : $15/month, simplifies app creation for novices—“design a quiz” yields results fast. Lovable Lovable Bolt: Free, browser-based, delivers full-stack prototypes in moments. Bolt : Free, browser-based, delivers full-stack prototypes in moments. Bolt Bolt Cline: $5/month VS Code extension, refines vague inputs into scripts. Powered by advanced AI models, these tools fuel ambitions of universal coding access—yet significant hurdles remain. Cline : $5/month VS Code extension, refines vague inputs into scripts. Cline Cline Powered by advanced AI models, these tools fuel ambitions of universal coding access—yet significant hurdles remain. Powered by advanced AI models, these tools fuel ambitions of universal coding access—yet significant hurdles remain. Powered by advanced AI models, these tools fuel ambitions of universal coding access—yet significant hurdles remain. Persistent Barriers to Universal Adoption Even with these innovations, NLC poses challenges: Ambiguity in Language Natural language’s nuances often mislead AI. When I asked Cursor to “update my application,” it altered an irrelevant module, requiring manual fixes. A 2023 Stanford study found that roughly 40% of AI-generated code contains subtle bugs when prompts lack precision—a challenge still evident in 2025.[ Natural Language Programming - GeeksforGeeks ] Seasoned developers can adjust such outputs, but novices lack the insight to proceed effectively. Natural Language Programming - GeeksforGeeks Prerequisite Knowledge Required NLC tools demand a foundational grasp of coding concepts to fully utilize their outputs. When I prompted Lovable with “generate a basic calculator” it produced a fully functional web-based calculator application, complete with a user interface featuring buttons for operations like addition, subtraction, multiplication, and division, as shown below. While impressive, the app handled basic arithmetic seamlessly, but it lacked error handling—say, for non-numeric inputs like letters or special characters—which could crash the interface or produce unexpected results. Recognizing these gaps required understanding TypeScript, React, and state management. Without knowledge of “variables,” “functions,” or “components,” beginners struggle to assess, debug, or enhance such code, limiting their ability to adapt Lovable’s output effectively. Complexity and Debugging Challenges Complex tasks reveal NLC’s limitations, even with advanced tools. When I prompted Bolt to “create a REST API endpoint to fetch user data in Python,” it generated a functional Flask-based web application with two endpoints: one to retrieve all users ( /api/users ) and another for a specific user ( /api/user/<user_id> ). The output included sample user data, JSON responses, and basic error handling, as shown below. /api/users /api/user/<user_id> However, the application encountered an issue: Bolt failed to generate or configure a requirements.txt file, marked with an error (red “X”), leaving dependencies like Flask undefined. This oversight requires manual intervention—installing Flask ( pip install flask ) and creating the file—to make the API runnable. Experienced developers can resolve this quickly, but novices, lacking knowledge of dependency management or Flask setup, face an insurmountable barrier. This gap underscores NLC’s reliance on underlying technical expertise, even when tools appear to deliver complete solutions. requirements.txt pip install flask Experienced developers find themselves frustrated by such oversights, while novices face an insurmountable debugging barrier. Stack Overflow’s 2024 Developer Survey revealed that 45% of professional developers believe AI tools are inadequate or very inadequate at handling complex tasks, a trend I’ve observed persisting into 2025, particularly with tools like Bolt and Cline.[ Stack Overflow 2024 Developer Survey ] Stack Overflow 2024 Developer Survey Developer Advantages and Ethical Considerations For seasoned developers, NLC streamlines repetitive tasks—Windsurf’s predictive features accelerate testing, Bolt’s prototyping saves time. Yet, limitations linger. My experience with Cline yielded a script that failed silently, forcing a manual rewrite—a recurring frustration. Ethical concerns also loom large. GitHub Copilot’s early reliance on public repositories sparked intellectual property debates.[ Copilot IP Controversy - The Verge ] In 2025, Bolt counters this by scoping outputs to avoid replication, while Lovable uses curated datasets to ensure originality. These measures mitigate risks, though broader legal clarity on code ownership remains pending, a critical factor for enterprise adoption. Copilot IP Controversy - The Verge The Path Forward in 2025 Current NLC tools signal progress: Enhanced Precision: Windsurf’s Flow feature clarifies vague inputs with follow-up questions. Simplified Access: Lovable’s app-based interface bypasses complex IDEs. Specialized Support: Cursor’s bug-detection flags issues proactively.While these advancements narrow the divide, universal adoption hinges on further refinement and user education. Enhanced Precision : Windsurf’s Flow feature clarifies vague inputs with follow-up questions. Enhanced Precision Simplified Access : Lovable’s app-based interface bypasses complex IDEs. Simplified Access Specialized Support : Cursor’s bug-detection flags issues proactively. While these advancements narrow the divide, universal adoption hinges on further refinement and user education. Specialized Support Conclusion and Call to Action Natural language coding thrives in 2025—millions of developers leverage its efficiency—yet it falls short of universal accessibility. Ambiguity, requisite knowledge, and debugging complexities exclude many, while ethical frameworks evolve. Developers must integrate tools like Cursor or Bolt to enhance productivity, refining skills to bridge AI’s gaps. Beginners face a choice: acquire foundational knowledge or await further simplification. "The question isn’t whether AI will change coding—it already has. The real challenge? Ensuring we shape its future rather than being shaped by it." "The question isn’t whether AI will change coding—it already has. The real challenge? Ensuring we shape its future rather than being shaped by it." References GitHub Copilot - Wikipedia – 2025 adoption estimated from 2024 trends. Microsoft has over a million paying Github Copilot users | ZDNET – 55% productivity stat. Natural Language Programming - GeeksforGeeks – 40% bug rate, AI training context. Stack Overflow 2024 Developer Survey – 45% complex task difficulty. Copilot IP Controversy - The Verge – Ethical context. GitHub Copilot - Wikipedia – 2025 adoption estimated from 2024 trends. GitHub Copilot - Wikipedia Microsoft has over a million paying Github Copilot users | ZDNET – 55% productivity stat. Microsoft has over a million paying Github Copilot users | ZDNET Natural Language Programming - GeeksforGeeks – 40% bug rate, AI training context. Natural Language Programming - GeeksforGeeks Stack Overflow 2024 Developer Survey – 45% complex task difficulty. Stack Overflow 2024 Developer Survey Copilot IP Controversy - The Verge – Ethical context. Copilot IP Controversy - The Verge About  the Author: I’m Jay Thakur, a Senior Software Engineer at Microsoft,  exploring the transformative potential of AI Agents. With over 8 years  of experience building and scaling AI solutions at Amazon, Accenture  Labs, and now Microsoft, combined with my studies at Stanford GSB, I  bring a unique perspective to the intersection of tech and business. I’m  dedicated to making AI accessible to all — from beginners to experts —  with a focus on building impactful products. As a speaker and aspiring  startup advisor, I share insights on AI Agents, GenAI, LLMs, SMLs,  responsible AI, and the evolving AI landscape. Connect with me on Linkedin About  the Author: I’m Jay Thakur, a Senior Software Engineer at Microsoft,  exploring the transformative potential of AI Agents. With over 8 years  of experience building and scaling AI solutions at Amazon, Accenture  Labs, and now Microsoft, combined with my studies at Stanford GSB, I  bring a unique perspective to the intersection of tech and business. I’m  dedicated to making AI accessible to all — from beginners to experts —  with a focus on building impactful products. As a speaker and aspiring  startup advisor, I share insights on AI Agents, GenAI, LLMs, SMLs,  responsible AI, and the evolving AI landscape. Connect with me on Linkedin Linkedin

Why Natural Language Coding Isn’t for Everyone—Yet

Waarom natuurlike taalkodering nie vir almal is nie—nog

Niyə təbii dil kodlaşdırması hər kəs üçün deyil - hələ

Hvorfor naturlig sprogkodning ikke er for alle – endnu

Por qué la codificación en lenguaje natural no es para todos (aún)

מדוע קידוד שפה טבעית לא מתאים לכולם - עדיין

自然言語コーディングがまだすべての人に適しているわけではない理由

ولې د طبیعي ژبې کوډ کول د هرچا لپاره ندي — بیا هم

De ce codarea limbajului natural nu este pentru toată lumea — încă

Varför naturlig språkkodning inte är för alla – ännu

Bakit Hindi Para sa Lahat ang Likas na Language Coding—Pa

Ha Yini Ku Khoda Ririmi Ra Ntumbuluko Ku Nga Ri Ka Vanhu Hinkwavo—Kambe

Nima uchun tabiiy tilni kodlash hamma uchun emas - hali

Nvidia's recent unveiling of the GeForce RTX 50-series graphics cards has generated significant buzz in the tech community. The lineup, introduced at CES 2025, includes the RTX 5070, 5070 Ti, 5080, and the flagship 5090, with prices ranging from $549 to $1,999. These GPUs are built on Nvidia's new Blackwell architecture and feature GDDR7 memory, promising substantial performance improvements over the previous generation. Anticipation for the RTX 50-series was high, fueled by Nvidia CEO Jensen Huang's keynote at CES 2025. Huang emphasized the integration of advanced AI technologies into the new GPUs, particularly the DLSS 4 with Multi Frame Generation, which leverages AI to enhance frame rates and visual quality. This announcement positioned the RTX 50-series as a significant leap forward in both hardware capabilities and AI-driven gaming enhancements. However, early reviews present a mixed picture. The Verge's assessment of the RTX 5090 highlights its impressive 4K gaming performance and the benefits of DLSS 4, noting frame rate improvements of up to eight times in certain titles. However, the review also points out that the most significant gains are observed in DLSS-supported games, suggesting that raw performance improvements may be less pronounced in other scenarios. Similarly, Wired's review of the RTX 5080 acknowledges its strong 4K gaming capabilities and AI-powered enhancements but questions whether the performance uplift justifies the $1,000 price tag, especially when compared to previous models. From a business perspective, the RTX 50-series launch underscores Nvidia's strategy to maintain its leadership in the GPU market by integrating cutting-edge AI technologies. The emphasis on AI-driven features like DLSS 4 reflects the company's broader vision of AI as a "multitrillion-dollar opportunity," as articulated by Huang. However, the launch has not been without challenges. Investor reactions have been cautious, with Nvidia's stock experiencing a 6% drop following the CES announcement. This suggests that while the technological advancements are noteworthy, there are concerns regarding immediate returns and broader market conditions. Additionally, supply constraints and pricing discrepancies have marred the initial rollout, leading to frustration among consumers and potential impacts on Nvidia's brand reputation. The company will need to address these issues promptly to capitalize fully on the RTX 50-series' technological innovations. Nvidia's trajectory has consistently been marked by a push toward integrating AI into its hardware offerings. The RTX 50-series represents a culmination of this approach, embedding AI not just as a supplementary feature but as a core component of the gaming experience. This aligns with Nvidia's broader business strategy of positioning itself at the nexus of AI and high-performance computing, a move that has significant implications for industries ranging from gaming to autonomous vehicles. While the technological advancements are impressive, the company faces challenges related to pricing, availability, and market reception that will need to be navigated carefully in the coming months. Nvidia's recent unveiling of the GeForce RTX 50-series graphics cards has generated significant buzz in the tech community. The lineup, introduced at CES 2025, includes the RTX 5070, 5070 Ti, 5080, and the flagship 5090, with prices ranging from $549 to $1,999. These GPUs are built on Nvidia's new Blackwell architecture and feature GDDR7 memory, promising substantial performance improvements over the previous generation. introduced Anticipation for the RTX 50-series was high, fueled by Nvidia CEO Jensen Huang's keynote at CES 2025. Huang emphasized the integration of advanced AI technologies into the new GPUs, particularly the DLSS 4 with Multi Frame Generation, which leverages AI to enhance frame rates and visual quality. This announcement positioned the RTX 50-series as a significant leap forward in both hardware capabilities and AI-driven gaming enhancements. keynote However, early reviews present a mixed picture. The Verge's assessment of the RTX 5090 highlights its impressive 4K gaming performance and the benefits of DLSS 4, noting frame rate improvements of up to eight times in certain titles. However, the review also points out that the most significant gains are observed in DLSS-supported games, suggesting that raw performance improvements may be less pronounced in other scenarios. assessment Similarly, Wired's review of the RTX 5080 acknowledges its strong 4K gaming capabilities and AI-powered enhancements but questions whether the performance uplift justifies the $1,000 price tag, especially when compared to previous models. review From a business perspective, the RTX 50-series launch underscores Nvidia's strategy to maintain its leadership in the GPU market by integrating cutting-edge AI technologies. The emphasis on AI-driven features like DLSS 4 reflects the company's broader vision of AI as a "multitrillion-dollar opportunity," as articulated by Huang. underscores However, the launch has not been without challenges. Investor reactions have been cautious, with Nvidia's stock experiencing a 6% drop following the CES announcement. This suggests that while the technological advancements are noteworthy, there are concerns regarding immediate returns and broader market conditions. experiencing Additionally, supply constraints and pricing discrepancies have marred the initial rollout, leading to frustration among consumers and potential impacts on Nvidia's brand reputation. The company will need to address these issues promptly to capitalize fully on the RTX 50-series' technological innovations. Nvidia's trajectory has consistently been marked by a push toward integrating AI into its hardware offerings. The RTX 50-series represents a culmination of this approach, embedding AI not just as a supplementary feature but as a core component of the gaming experience. This aligns with Nvidia's broader business strategy of positioning itself at the nexus of AI and high-performance computing, a move that has significant implications for industries ranging from gaming to autonomous vehicles. While the technological advancements are impressive, the company faces challenges related to pricing, availability, and market reception that will need to be navigated carefully in the coming months.

Reviewers Can't Decide What to Make of Nvidia's 50-Series Launch

Intel Corporation, a technological stalwart in the semiconductor industry, has recently undergone a significant leadership change with the departure of CEO Pat Gelsinger (Reuters). This move comes amid a period of declining stock performance, with shares dropping approximately 60% over the past three years, and missed opportunities in the rapidly evolving AI sector. While leadership transitions can introduce uncertainty, historical precedents suggest that such changes can rejuvenate a company's strategic direction and enhance shareholder value (HackerNoon). Intel's Historical Context and Recent Challenges Founded in 1968, Intel has been a pioneer in developing microprocessors, playing a pivotal role in the personal computer revolution. However, in recent years, the company has faced intensified competition from rivals like NVIDIA and AMD, particularly in the AI and data center markets. Under Gelsinger's tenure, which began in February 2021, when the stock price was approximately $55, Intel aimed to regain its technological edge. Despite initiatives such as the introduction of AI chips like Gaudi3 and plans to expand manufacturing capabilities, the company struggled to keep pace with competitors. Notably, Intel's stock price declined by approximately 60% during Gelsinger's leadership, reflecting investor concerns over the company's strategic direction and execution (Tom's Hardware, CNBC). Missed Opportunities in the AI Boom The AI industry has experienced exponential growth, with companies like NVIDIA leading the charge through advanced GPU technologies essential for AI computations. Intel's delay in capitalizing on this trend, coupled with its decision to forgo early investments in AI innovators like OpenAI, positioned the company at a disadvantage. In contrast, Microsoft's strategic investment in OpenAI has yielded significant returns, underscoring the importance of timely engagement in emerging technologies (The Verge). Parallels with Other Technology Companies After a  Notable CEO Departs Several notable examples illustrate how leadership changes have revitalized companies: Apple Inc.: Steve Jobs' return to Apple in 1997 marked a turning point for the company. At the time of his departure in 1985, shares were approximately $0.07 (adjusted for stock splits). By late 1998, a year after his return, shares had risen to $0.31, signaling renewed investor confidence (Investopedia).
Starbucks: In 2024, Starbucks experienced a challenging year with declining sales and stock value. The appointment of former Chipotle CEO Brian Niccol brought a strategic shift aimed at simplifying operations and revitalizing the brand. This leadership change boosted investor confidence, resulting in a 24% stock surge (Barron's).
Under Armour: The return of founder Kevin Plank as CEO led to a significant stock surge of 20% following stronger-than-expected earnings, indicating early success from leadership changes (Barron's).
Google (Alphabet): When Sundar Pichai took over as CEO from co-founders Larry Page and Sergey Brin in 2019, Alphabet underwent a major transformation. Under his leadership, the company expanded into AI and cloud computing, driving stock performance to new highs (CNBC).
IBM: When Ginni Rometty stepped down as CEO and was replaced by Arvind Krishna in 2020, IBM refocused on cloud computing and AI, leading to a significant stock resurgence (Forbes).
Microsoft: The transition from Steve Ballmer to Satya Nadella in 2014 transformed Microsoft into a cloud-first company, significantly boosting its market value and stock performance (The Verge).
Tesla: When Elon Musk assumed full control of Tesla as CEO, his leadership propelled the company into new markets, securing dominance in electric vehicles and renewable energy solutions, driving stock gains (Bloomberg).
Yahoo: The appointment of Marissa Mayer as CEO in 2012 led to a brief resurgence in Yahoo's stock price, though long-term struggles persisted. Her leadership underscored the impact a high-profile CEO change can have on investor confidence (The Guardian).
Adobe: The transition from Bruce Chizen to Shantanu Narayen in 2007 set the stage for Adobe's successful pivot to a subscription-based business model, fueling long-term stock growth (Business Insider).
Netflix: Reed Hastings' transition to an executive chairman role in 2023 allowed Ted Sarandos and Greg Peters to take over as co-CEOs, maintaining Netflix's industry dominance and stabilizing stock performance (Hollywood Reporter). Intel's recent leadership change, prompted by a period of underperformance and missed opportunities in the AI sector, marks a decisive inflection point for the company. Historical precedents from companies like Apple, Microsoft, and Adobe demonstrate that such transitions can drive revitalization and substantial stock appreciation when paired with aggressive strategic realignment. Intel's deep-rooted expertise in semiconductor innovation, coupled with its formidable manufacturing infrastructure, provides a strong foundation for resurgence. If the company can successfully leverage AI advancements, expand foundry operations, and capitalize on emerging market demands, it stands to reclaim dominance in a rapidly evolving industry. The leadership shift signals a commitment to transformation, with the potential to position Intel as a frontrunner in next-generation computing and long-term value creation for investors. Additional Resource About the Intel CEO Reboot: Intel CEO Gelsinger's removal raises doubts over turnaround plan
HackerNoon Analysis: Why Intel’s CEO Change Matters
CNBC: Intel Stock Reaction to CEO Departure
The Verge: Microsoft and OpenAI Investment
Bloomberg: Intel CEO Exit and Market Impact
Level Fields AI: Stock Trends Post CEO Departures
Financial Times: Intel’s Future After Leadership Shakeup
Forbes: CEO Turnover and Market Performance
MarketWatch: How New Leadership Could Boost Intel’s AI Game
WSJ: The Broader Implications of Intel’s CEO Transition Intel Corporation, a technological stalwart in the semiconductor industry, has recently undergone a significant leadership change with the departure of CEO Pat Gelsinger ( Reuters ). This move comes amid a period of declining stock performance, with shares dropping approximately 60% over the past three years, and missed opportunities in the rapidly evolving AI sector. While leadership transitions can introduce uncertainty, historical precedents suggest that such changes can rejuvenate a company's strategic direction and enhance shareholder value ( HackerNoon ). Reuters Reuters HackerNoon HackerNoon Intel's Historical Context and Recent Challenges Founded in 1968, Intel has been a pioneer in developing microprocessors, playing a pivotal role in the personal computer revolution. However, in recent years, the company has faced intensified competition from rivals like NVIDIA and AMD, particularly in the AI and data center markets. Under Gelsinger's tenure, which began in February 2021, when the stock price was approximately $55, Intel aimed to regain its technological edge. Despite initiatives such as the introduction of AI chips like Gaudi3 and plans to expand manufacturing capabilities, the company struggled to keep pace with competitors. Notably, Intel's stock price declined by approximately 60% during Gelsinger's leadership, reflecting investor concerns over the company's strategic direction and execution ( Tom's Hardware , CNBC ). Tom's Hardware Tom's Hardware CNBC CNBC Missed Opportunities in the AI Boom The AI industry has experienced exponential growth, with companies like NVIDIA leading the charge through advanced GPU technologies essential for AI computations. Intel's delay in capitalizing on this trend, coupled with its decision to forgo early investments in AI innovators like OpenAI, positioned the company at a disadvantage. In contrast, Microsoft's strategic investment in OpenAI has yielded significant returns, underscoring the importance of timely engagement in emerging technologies ( The Verge ). The Verge The Verge Parallels with Other Technology Companies After a  Notable CEO Departs Parallels with Other Technology Companies After a  Notable CEO Departs Several notable examples illustrate how leadership changes have revitalized companies: Apple Inc.: Steve Jobs' return to Apple in 1997 marked a turning point for the company. At the time of his departure in 1985, shares were approximately $0.07 (adjusted for stock splits). By late 1998, a year after his return, shares had risen to $0.31, signaling renewed investor confidence (Investopedia). Starbucks: In 2024, Starbucks experienced a challenging year with declining sales and stock value. The appointment of former Chipotle CEO Brian Niccol brought a strategic shift aimed at simplifying operations and revitalizing the brand. This leadership change boosted investor confidence, resulting in a 24% stock surge (Barron's). Under Armour: The return of founder Kevin Plank as CEO led to a significant stock surge of 20% following stronger-than-expected earnings, indicating early success from leadership changes (Barron's). Google (Alphabet): When Sundar Pichai took over as CEO from co-founders Larry Page and Sergey Brin in 2019, Alphabet underwent a major transformation. Under his leadership, the company expanded into AI and cloud computing, driving stock performance to new highs (CNBC). IBM: When Ginni Rometty stepped down as CEO and was replaced by Arvind Krishna in 2020, IBM refocused on cloud computing and AI, leading to a significant stock resurgence (Forbes). Microsoft: The transition from Steve Ballmer to Satya Nadella in 2014 transformed Microsoft into a cloud-first company, significantly boosting its market value and stock performance (The Verge). Tesla: When Elon Musk assumed full control of Tesla as CEO, his leadership propelled the company into new markets, securing dominance in electric vehicles and renewable energy solutions, driving stock gains (Bloomberg). Yahoo: The appointment of Marissa Mayer as CEO in 2012 led to a brief resurgence in Yahoo's stock price, though long-term struggles persisted. Her leadership underscored the impact a high-profile CEO change can have on investor confidence (The Guardian). Adobe: The transition from Bruce Chizen to Shantanu Narayen in 2007 set the stage for Adobe's successful pivot to a subscription-based business model, fueling long-term stock growth (Business Insider). Netflix: Reed Hastings' transition to an executive chairman role in 2023 allowed Ted Sarandos and Greg Peters to take over as co-CEOs, maintaining Netflix's industry dominance and stabilizing stock performance (Hollywood Reporter). Apple Inc.: Steve Jobs' return to Apple in 1997 marked a turning point for the company. At the time of his departure in 1985, shares were approximately $0.07 (adjusted for stock splits). By late 1998, a year after his return, shares had risen to $0.31, signaling renewed investor confidence ( Investopedia ). Investopedia Investopedia Starbucks: In 2024, Starbucks experienced a challenging year with declining sales and stock value. The appointment of former Chipotle CEO Brian Niccol brought a strategic shift aimed at simplifying operations and revitalizing the brand. This leadership change boosted investor confidence, resulting in a 24% stock surge ( Barron's ). Barron's Barron's Under Armour: The return of founder Kevin Plank as CEO led to a significant stock surge of 20% following stronger-than-expected earnings, indicating early success from leadership changes ( Barron's ). Barron's Barron's Google (Alphabet): When Sundar Pichai took over as CEO from co-founders Larry Page and Sergey Brin in 2019, Alphabet underwent a major transformation. Under his leadership, the company expanded into AI and cloud computing, driving stock performance to new highs ( CNBC ). CNBC CNBC IBM: When Ginni Rometty stepped down as CEO and was replaced by Arvind Krishna in 2020, IBM refocused on cloud computing and AI, leading to a significant stock resurgence ( Forbes ). Forbes Forbes Microsoft: The transition from Steve Ballmer to Satya Nadella in 2014 transformed Microsoft into a cloud-first company, significantly boosting its market value and stock performance ( The Verge ). The Verge The Verge Tesla: When Elon Musk assumed full control of Tesla as CEO, his leadership propelled the company into new markets, securing dominance in electric vehicles and renewable energy solutions, driving stock gains ( Bloomberg ). Bloomberg Bloomberg Yahoo: The appointment of Marissa Mayer as CEO in 2012 led to a brief resurgence in Yahoo's stock price, though long-term struggles persisted. Her leadership underscored the impact a high-profile CEO change can have on investor confidence ( The Guardian ). The Guardian The Guardian Adobe: The transition from Bruce Chizen to Shantanu Narayen in 2007 set the stage for Adobe's successful pivot to a subscription-based business model, fueling long-term stock growth ( Business Insider ). Business Insider Business Insider Netflix: Reed Hastings' transition to an executive chairman role in 2023 allowed Ted Sarandos and Greg Peters to take over as co-CEOs, maintaining Netflix's industry dominance and stabilizing stock performance ( Hollywood Reporter ). Hollywood Reporter Hollywood Reporter Intel's recent leadership change, prompted by a period of underperformance and missed opportunities in the AI sector, marks a decisive inflection point for the company. Historical precedents from companies like Apple, Microsoft, and Adobe demonstrate that such transitions can drive revitalization and substantial stock appreciation when paired with aggressive strategic realignment. Intel's deep-rooted expertise in semiconductor innovation, coupled with its formidable manufacturing infrastructure, provides a strong foundation for resurgence. If the company can successfully leverage AI advancements, expand foundry operations, and capitalize on emerging market demands, it stands to reclaim dominance in a rapidly evolving industry. The leadership shift signals a commitment to transformation, with the potential to position Intel as a frontrunner in next-generation computing and long-term value creation for investors. Additional Resource About the Intel CEO Reboot: Intel CEO Gelsinger's removal raises doubts over turnaround plan HackerNoon Analysis: Why Intel’s CEO Change Matters CNBC: Intel Stock Reaction to CEO Departure The Verge: Microsoft and OpenAI Investment Bloomberg: Intel CEO Exit and Market Impact Level Fields AI: Stock Trends Post CEO Departures Financial Times: Intel’s Future After Leadership Shakeup Forbes: CEO Turnover and Market Performance MarketWatch: How New Leadership Could Boost Intel’s AI Game WSJ: The Broader Implications of Intel’s CEO Transition Intel CEO Gelsinger's removal raises doubts over turnaround plan Intel CEO Gelsinger's removal raises doubts over turnaround plan Intel CEO Gelsinger's removal raises doubts over turnaround plan HackerNoon Analysis: Why Intel’s CEO Change Matters HackerNoon Analysis: Why Intel’s CEO Change Matters HackerNoon Analysis: Why Intel’s CEO Change Matters CNBC: Intel Stock Reaction to CEO Departure CNBC: Intel Stock Reaction to CEO Departure CNBC: Intel Stock Reaction to CEO Departure The Verge: Microsoft and OpenAI Investment The Verge: Microsoft and OpenAI Investment The Verge: Microsoft and OpenAI Investment Bloomberg: Intel CEO Exit and Market Impact Bloomberg: Intel CEO Exit and Market Impact Bloomberg: Intel CEO Exit and Market Impact Level Fields AI: Stock Trends Post CEO Departures Level Fields AI: Stock Trends Post CEO Departures Level Fields AI: Stock Trends Post CEO Departures Financial Times: Intel’s Future After Leadership Shakeup Financial Times: Intel’s Future After Leadership Shakeup Financial Times: Intel’s Future After Leadership Shakeup Forbes: CEO Turnover and Market Performance Forbes: CEO Turnover and Market Performance Forbes: CEO Turnover and Market Performance MarketWatch: How New Leadership Could Boost Intel’s AI Game MarketWatch: How New Leadership Could Boost Intel’s AI Game MarketWatch: How New Leadership Could Boost Intel’s AI Game WSJ: The Broader Implications of Intel’s CEO Transition WSJ: The Broader Implications of Intel’s CEO Transition WSJ: The Broader Implications of Intel’s CEO Transition

With Gelsinger's Departure, Could Intel Be Headed Towards a Stock Surge?

Whisper Wars: Will AI Prompts Become the Secret Recipes of the Future?

Africa's crypto regulation landscape is diverse and dynamic with 54 countries, six major languages, and over 1 billion people. In this article, we explore the most significant events in crypto regulation on the African continent this year. Several countries made moves regarding crypto regulation in 2024, with details of their accomplishments. Nigeria Nigeria is one of the countries that has made major strides in crypto regulation in 2024. Initially, the country took a tough stance towards crypto, the positions of the two regulators differed - the SEC was in favour of legalisation and the Central Bank was in favour of prohibition. Eventually, regulation for the industry emerged in 2022 with SEC’s Digital Assets Rules. But due to high inflation, the country was on the verge of banning cryptocurrencies in the first half of 2024. Criminal cases against Binance employees and the company's withdrawal from the country were part of this campaign. However, crypto ban wasn’t imposed. On August 29, 2024, the country’s Securities and Exchange Commission announced it had granted licenses to two crypto exchanges, Quidax and Busha. Also Nigeria is going to provide a 2024 Tax Bill, which will probably provide regulation for crypto taxation. South Africa South Africa made a big move in crypto regulation this year after FAIS Act approval in 2023. It is a country with the most transparent licensing procedures for crypto entities in Africa and 248 companies licensed as CASPs. Only 9 applications from 430 sent to the local regulator (FSCA) were declined. Also SA has its crypto tax framework. In October 2024, the South African Revenue Service warned South Africans about failing to file their taxes on crypto assets. Mauritius The island nation of Mauritius has been at the forefront of crypto regulation in Africa. Following the passing of the Virtual Assets and Initial Token Offering Services (VAITOS) in 2022, it is the only country in Africa that complies with all forty recommendations of the Financial Action Task Force (FATF). Each year, the country continues to change its legislation to support the crypto sector. For instance, in the 2024/2025 budget speech, the government stated that it plans to introduce a 10-year expert Occupation Permit to attract in its crypto sector. Kenya Like most African countries, Kenya does not have explicit provisions for the crypto sector. However, crypto is not outright banned in the country. The country recently amended its Income Tax Act to include a digital services tax, which can be interpreted to mean a tax on crypto activity. However, there is still no VASP licensing procedure implemented in Kenya. Crypto enthusiasts represented by the Blockchain Association of Kenya (BAK) have sent its version of crypto bill to the local parliament this year. But it has not yet come to a vote in parliament. Seychelles In July 2024, Seychelles passed the Virtual Asset Service Providers Bill 2024, which regulates the activities of token issuers and virtual asset service providers in the country. This law created the first-ever framework for regulating cryptocurrency transactions and licensing VASPs. Cryptocurrency companies operating in Seychelles have until 31 December 2024 to apply for the relevant licence. Morocco During a recent speech at the Regional High-Level Symposium On Financial Stability in Rabat, Morocco, Mr. Abdellatif Jouahri, the Governor of Bank Al-Maghrib stated that the country plans to launch crypto regulation soon. He stated that they were working with the World Bank, and other stakeholders on the draft law. Tanzania In its Finance Act 2024, Tanzania introduced regulation for the crypto sector. Under the new law, non-resident operators of crypto platforms will have to pay a 3% tax when they make payments to Tanzanians. Rwanda A recent report by the Africa News Agency revealed that Rwanda plans to launch crypto regulation in the first quarter of 2025. The effort is being undertaken by Rwanda’s central bank in collaboration with the country’s Capital Market Authority (CMA). Previously, the Rwandan central bank released a research paper on a Central Bank Digital Currency (CBDC). The country’s National Payment System (RNPS) Strategy 2018 – 2024 envisions a cashless society with fiat-based cryptocurrency, and discusses policy measures to make it a reality. Ghana Ghana could be one of the latest entrants in the crypto regulation club of Africa. In August, the country’s central bank released a draft regulation for the crypto sector. The draft rules would allow Ghanaians to transact in crypto. However, the ban on banks from dealing with crypto that was passed in 2023 would remain in place. Ethiopia In June 2024, Ethiopia’s central bank announced that the country’s parliament had passed a new draft regulation. The regulation calls for the establishment of a central bank digital currency (CBDC). If passed, it would make Ethiopia the second African nation to adopt a CBDC after Nigeria. Regulation in Other African Countries Besides the above countries, various regulators, and regional organizations are working on coherent crypto regulation. For instance, Namibia and Botswana have already passed crypto regulations. Botswana passed the Virtual Assets Act, 2022, while Namibia passed the Virtual Assets Act, 2023. The Central African Republic attempted to pass a law to recognize Bitcoin as legal tender a while back. However, the move was thwarted by the COBAC, the regional banking regulator, and pressure from the IMF. Conclusion While several African countries have made huge progress in crypto regulation, a huge chunk of countries on the continent have no crypto regulation or even crypto ban. For instance, in Egypt, all crypto transactions are prohibited. In 2023, the country’s central bank issued its fourth warning regarding the prohibition of crypto activity in the country.  In Uganda, the government has issued a public statement stating that it does not recognize any type of crypto. Its statement added that the government has not regulated any crypto business in the country. Additionally, the government warned that anyone who engages in crypto trading is not protected by the consumer protection laws of the country. Various factors contribute to this problem, which includes political instability. Additionally, low internet access in some countries means that such regulation is not a priority. Besides that, high levels of inflation, and weak financial systems mean that regulators are not incentivized to allow crypto use, which could further destabilize their financial systems. Another factor contributing to the lack of regulation is a need to coordinate with international financial organizations, such as the IMF. A clear example is the Central African Republic, which had to abandon plans to recognize Bitcoin as legal tender. Despite these challenges, Africa has experienced major growth in crypto adoption. As the wave of crypto adoption continues to spread, it could force countries that have ignored the issue so far to take action. I prepared this article in collaboration with Kenyan crypto enthusiast Eric Maina. If you are interested in crypto regulation, please subscribe to my Telegram channel or check crypto regulation country ranking here. Africa's crypto regulation landscape is diverse and dynamic with 54 countries, six major languages, and over 1 billion people. In this article, we explore the most significant events in crypto regulation on the African continent this year. Several countries made moves regarding crypto regulation in 2024, with details of their accomplishments. Nigeria Nigeria is one of the countries that has made major strides in crypto regulation in 2024. Initially, the country took a tough stance towards crypto, the positions of the two regulators differed - the SEC was in favour of legalisation and the Central Bank was in favour of prohibition. Eventually, regulation for the industry emerged in 2022 with SEC’s Digital Assets Rules. But due to high inflation, the country was on the verge of banning cryptocurrencies in the first half of 2024. Criminal cases against Binance employees and the company's withdrawal from the country were part of this campaign. However, crypto ban wasn’t imposed. On August 29, 2024, the country’s Securities and Exchange Commission announced it had granted licenses to two crypto exchanges, Quidax and Busha. Also Nigeria is going to provide a 2024 Tax Bill , which will probably provide regulation for crypto taxation. announced announced 2024 Tax Bill South Africa South Africa made a big move in crypto regulation this year after FAIS Act approval in 2023. It is a country with the most transparent licensing procedures for crypto entities in Africa and 248 companies licensed as CASPs. Only 9 applications from 430 sent to the local regulator (FSCA) were declined. Also SA has its crypto tax framework. In October 2024, the South African Revenue Service warned South Africans about failing to file their taxes on crypto assets. warned warned Mauritius The island nation of Mauritius has been at the forefront of crypto regulation in Africa. Following the passing of the Virtual Assets and Initial Token Offering Services (VAITOS) in 2022, it is the only country in Africa that complies with all forty recommendations of the Financial Action Task Force (FATF). Each year, the country continues to change its legislation to support the crypto sector. For instance, in the 2024/2025 budget speech , the government stated that it plans to introduce a 10-year expert Occupation Permit to attract in its crypto sector. speech speech Kenya Like most African countries, Kenya does not have explicit provisions for the crypto sector. However, crypto is not outright banned in the country. The country recently amended its Income Tax Act to include a digital services tax, which can be interpreted to mean a tax on crypto activity. amended amended However, there is still no VASP licensing procedure implemented in Kenya. Crypto enthusiasts represented by the Blockchain Association of Kenya (BAK) have sent its version of crypto bill to the local parliament this year. But it has not yet come to a vote in parliament. Seychelles In July 2024, Seychelles passed the Virtual Asset Service Providers Bill 2024, which regulates the activities of token issuers and virtual asset service providers in the country. This law created the first-ever framework for regulating cryptocurrency transactions and licensing VASPs. Cryptocurrency companies operating in Seychelles have until 31 December 2024 to apply for the relevant licence. Morocco During a recent speech at the Regional High-Level Symposium On Financial Stability in Rabat, Morocco, Mr. Abdellatif Jouahri, the Governor of Bank Al-Maghrib stated that the country plans to launch crypto regulation soon. He stated that they were working with the World Bank, and other stakeholders on the draft law. speech speech Tanzania In its Finance Act 2024 , Tanzania introduced regulation for the crypto sector. Under the new law, non-resident operators of crypto platforms will have to pay a 3% tax when they make payments to Tanzanians. Finance Act 2024 Finance Act 2024 Rwanda A recent report by the Africa News Agency revealed that Rwanda plans to launch crypto regulation in the first quarter of 2025. The effort is being undertaken by Rwanda’s central bank in collaboration with the country’s Capital Market Authority (CMA). report report Previously, the Rwandan central bank released a research paper on a Central Bank Digital Currency (CBDC). The country’s National Payment System (RNPS) Strategy 2018 – 2024 envisions a cashless society with fiat-based cryptocurrency, and discusses policy measures to make it a reality. research paper research paper National Payment System (RNPS) Strategy 2018 – 2024 National Payment System (RNPS) Strategy 2018 – 2024 Ghana Ghana could be one of the latest entrants in the crypto regulation club of Africa. In August, the country’s central bank released a draft regulation for the crypto sector. The draft rules would allow Ghanaians to transact in crypto. However, the ban on banks from dealing with crypto that was passed in 2023 would remain in place. draft regulation draft regulation Ethiopia In June 2024, Ethiopia’s central bank announced that the country’s parliament had passed a new draft regulation. The regulation calls for the establishment of a central bank digital currency (CBDC). If passed, it would make Ethiopia the second African nation to adopt a CBDC after Nigeria. announced announced Regulation in Other African Countries Besides the above countries, various regulators, and regional organizations are working on coherent crypto regulation. For instance, Namibia and Botswana have already passed crypto regulations. Botswana passed the Virtual Assets Act, 2022 , while Namibia passed the Virtual Assets Act, 2023. The Central African Republic attempted to pass a law to recognize Bitcoin as legal tender a while back. However, the move was thwarted by the COBAC, the regional banking regulator, and pressure from the IMF . Virtual Assets Act, 2022 Virtual Assets Act, 2022 Virtual Assets Act, 2023. The Central African Republic attempted to pass a law to recognize Bitcoin as legal tender a while back. However, the move was thwarted by the COBAC, the regional banking regulator, and pressure from the Virtual Assets Act, 2023. IMF Conclusion While several African countries have made huge progress in crypto regulation, a huge chunk of countries on the continent have no crypto regulation or even crypto ban. For instance, in Egypt, all crypto transactions are prohibited. In 2023, the country’s central bank issued its fourth warning regarding the prohibition of crypto activity in the country.  In Uganda, the government has issued a public statement stating that it does not recognize any type of crypto. Its statement added that the government has not regulated any crypto business in the country. Additionally, the government warned that anyone who engages in crypto trading is not protected by the consumer protection laws of the country. warning warning statement statement Various factors contribute to this problem, which includes political instability. Additionally, low internet access in some countries means that such regulation is not a priority. Besides that, high levels of inflation, and weak financial systems mean that regulators are not incentivized to allow crypto use, which could further destabilize their financial systems. Another factor contributing to the lack of regulation is a need to coordinate with international financial organizations, such as the IMF. A clear example is the Central African Republic, which had to abandon plans to recognize Bitcoin as legal tender. Despite these challenges, Africa has experienced major growth in crypto adoption. As the wave of crypto adoption continues to spread, it could force countries that have ignored the issue so far to take action. I prepared this article in collaboration with Kenyan crypto enthusiast Eric Maina. If you are interested in crypto regulation, please subscribe to my Telegram channel or check crypto regulation country ranking here . Telegram channel Telegram channel here here

How Is Crypto Regulation Shaping Up In The 2nd Largest Continent in the World?

¿Cómo se perfila la regulación de las criptomonedas en el segundo continente más grande del mundo?

چگونه مقررات رمزنگاری در دومین قاره بزرگ جهان شکل می گیرد؟

दुनिया के दूसरे सबसे बड़े महाद्वीप में क्रिप्टो विनियमन कैसे आकार ले रहा है?

Bagaimana Regulasi Kripto Terbentuk di Benua Terbesar ke-2 di Dunia?

世界で 2 番目に大きい大陸で暗号通貨規制はどのように形成されつつあるのでしょうか?

Kā tiek veidota kriptovalūtu regulēšana 2. lielākajā kontinentā pasaulē?

Taolo ya Crypto e Bopa Bjang Kontinenteng ya Bo-2 e Kgolo Lefaseng?

Como a regulamentação das criptomoedas está se desenvolvendo no segundo maior continente do mundo?

ලොව 2 වන විශාලතම මහාද්වීපය තුළ ක්‍රිප්ටෝ නියාමනය හැඩගැසෙන්නේ කෙසේද?

กฎเกณฑ์ด้าน Crypto จะเป็นอย่างไรในทวีปที่ใหญ่เป็นอันดับ 2 ของโลก?

Quy định về tiền điện tử đang diễn ra như thế nào tại châu lục lớn thứ 2 thế giới?

世界第二大洲的加密监管情况如何？

Table of Links Abstract and 1. Introduction 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 4 The Future of GPT4All Limitations and References Limitations By enabling access to large language models, the GPT4All project also inherits many of the ethical concerns associated with generative models. Principal among these is the concern that unfiltered language models like GPT4All enable malicious users to generate content that could be harmful and dangerous (e.g., instructions on building bioweapons). While we recognize this risk, we also acknowledge the risk of concentrating this technology in the hands of a limited number of increasingly secretive research groups. We believe that the risk of focusing on the benefits of language model technology significantly outweighs the risk of misuse, and hence we prefer to make the technology as widely available as possible. Finally, we realize the challenge in assigning credit for large-scale open source initiatives. We make a first attempt at fair credit assignment by explicitly including the GPT4All open source developers as authors on this work, but recognize that this is insufficient fully characterize everyone involved in the GPT4All effort. Furthermore, we acknowledge the difficulty in citing open source works that do not necessarily have standardized citations, and do our best in this paper to provide URLs to projects whenever possible. We encourage further research in the area of open source credit assignment, and hope to be able to support some of this research ourselves in the future. References Nomic AI. 2023. Atlas. https://atlas.nomic.ai/. Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. Falcon-40B: an open large language model with state-of-the-art performance. Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt, and Andriy Mulyar. 2023. Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo. https://github.com/nomic-ai/gpt4all. BBC News. 2023. Chatgpt banned in italy over privacy concerns. BBC News. Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023. Pythia: A suite for analyzing large language models across training and scaling. Harrison Chase. 2022. langchain. https://github. com/langchain-ai/langchain. Mike Conover, Matt Hayes, Ankit Mathur, Xiangrui Meng, Jianwei Xie, Jun Wan, Ali Ghodsi, Patrick Wendell, and Matei Zaharia. 2023a. Hello dolly: Democratizing the magic of chatgpt with open models. Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023b. Free dolly: Introducing the world’s first truly open instructiontuned llm. Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023. Koala: A dialogue model for academic research. Blog post. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. imartinez. 2023. privategpt. https://github.com/ imartinez/privateGPT. Oscar Leo. 2023. GitHub: The Fastest Growing Repositories of All Time. Robert McMillan. 2023. A meta platforms leak put powerful ai in the hands of everyone. The Wall Street Journal. MindsDB. 2023. Mindsdb. https://github.com/ mindsdb/mindsdb. GitHub repository. MosaicML-Team. 2023. Introducing mpt-7b: A new standard for open-source, commercially usable llms. Accessed: 2023-08-07. Nous-Research. 2023a. gpt4-x-vicuna-13b. https://huggingface.co/NousResearch/ gpt4-x-vicuna-13b. Model on Hugging Face. Nous-Research. 2023b. Nous-hermes-13b. https://huggingface.co/NousResearch/ Nous-Hermes-13b. Model on Hugging Face. Nous-Research. 2023c. Nous-hermes-llama-2-7b. https://huggingface.co/NousResearch/ Nous-Hermes-llama-2-7b. Model on Hugging Face. Nous-Research. 2023d. Redmond-puffin-13b. https://huggingface.co/NousResearch/ Redmond-Puffin-13B. Model on Hugging Face. OpenAI. 2023. Gpt-4 technical report. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. 2021. Multitask prompted training enables zero-shot task generalization. Stability-AI. 2023. Stablelm. https://github.com/ Stability-AI/StableLM. GitHub repository. StanGirard. 2023. quivr. https://github.com/ StanGirard/quivr. GitHub repository. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https:// github.com/tatsu-lab/stanford_alpaca. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. The Verge. 2023. Meta’s powerful ai language model has leaked online — what happens now? The Verge. James Vincent. 2023. As an ai generated language model: The phrase that shows how ai is polluting the web. The Verge. Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/ mesh-transformer-jax. Eric J. Wang. 2023. alpaca-lora. https://github. com/tloen/alpaca-lora. GitHub repository. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. Self-instruct: Aligning language models with self-generated instructions. Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023. Wizardlm: Empowering large language models to follow complex instructions. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai;
(2) Zach Nussbaum, Nomic AI, zach@nomic.ai;
(3) Adam Treat, Nomic AI, adam@nomic.ai;
(4) Aaron Miller, Nomic AI, aaron@nomic.ai;
(5) Richard Guo, Nomic AI, richard@nomic.ai;
(6) Ben Schmidt, Nomic AI, ben@nomic.ai;
(7) GPT4All Community, Planet Earth;
(8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship;
(9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 The Original GPT4All Model 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 3.3 The Current State of GPT4All 4 The Future of GPT4All 4 The Future of GPT4All Limitations and References Limitations and References Limitations By enabling access to large language models, the GPT4All project also inherits many of the ethical concerns associated with generative models. Principal among these is the concern that unfiltered language models like GPT4All enable malicious users to generate content that could be harmful and dangerous (e.g., instructions on building bioweapons). While we recognize this risk, we also acknowledge the risk of concentrating this technology in the hands of a limited number of increasingly secretive research groups. We believe that the risk of focusing on the benefits of language model technology significantly outweighs the risk of misuse, and hence we prefer to make the technology as widely available as possible. Finally, we realize the challenge in assigning credit for large-scale open source initiatives. We make a first attempt at fair credit assignment by explicitly including the GPT4All open source developers as authors on this work, but recognize that this is insufficient fully characterize everyone involved in the GPT4All effort. Furthermore, we acknowledge the difficulty in citing open source works that do not necessarily have standardized citations, and do our best in this paper to provide URLs to projects whenever possible. We encourage further research in the area of open source credit assignment, and hope to be able to support some of this research ourselves in the future. References Nomic AI. 2023. Atlas. https://atlas.nomic.ai/. Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. Falcon-40B: an open large language model with state-of-the-art performance. Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt, and Andriy Mulyar. 2023. Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo. https://github.com/nomic-ai/gpt4all. BBC News. 2023. Chatgpt banned in italy over privacy concerns. BBC News. Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023. Pythia: A suite for analyzing large language models across training and scaling. Harrison Chase. 2022. langchain. https://github. com/langchain-ai/langchain. Mike Conover, Matt Hayes, Ankit Mathur, Xiangrui Meng, Jianwei Xie, Jun Wan, Ali Ghodsi, Patrick Wendell, and Matei Zaharia. 2023a. Hello dolly: Democratizing the magic of chatgpt with open models. Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023b. Free dolly: Introducing the world’s first truly open instructiontuned llm. Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023. Koala: A dialogue model for academic research. Blog post. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. imartinez. 2023. privategpt. https://github.com/ imartinez/privateGPT. Oscar Leo. 2023. GitHub: The Fastest Growing Repositories of All Time. Robert McMillan. 2023. A meta platforms leak put powerful ai in the hands of everyone. The Wall Street Journal. MindsDB. 2023. Mindsdb. https://github.com/ mindsdb/mindsdb. GitHub repository. MosaicML-Team. 2023. Introducing mpt-7b: A new standard for open-source, commercially usable llms. Accessed: 2023-08-07. Nous-Research. 2023a. gpt4-x-vicuna-13b. https://huggingface.co/NousResearch/ gpt4-x-vicuna-13b. Model on Hugging Face. Nous-Research. 2023b. Nous-hermes-13b. https://huggingface.co/NousResearch/ Nous-Hermes-13b. Model on Hugging Face. Nous-Research. 2023c. Nous-hermes-llama-2-7b. https://huggingface.co/NousResearch/ Nous-Hermes-llama-2-7b. Model on Hugging Face. Nous-Research. 2023d. Redmond-puffin-13b. https://huggingface.co/NousResearch/ Redmond-Puffin-13B. Model on Hugging Face. OpenAI. 2023. Gpt-4 technical report. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. 2021. Multitask prompted training enables zero-shot task generalization. Stability-AI. 2023. Stablelm. https://github.com/ Stability-AI/StableLM. GitHub repository. StanGirard. 2023. quivr. https://github.com/ StanGirard/quivr. GitHub repository. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https:// github.com/tatsu-lab/stanford_alpaca. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. The Verge. 2023. Meta’s powerful ai language model has leaked online — what happens now? The Verge. James Vincent. 2023. As an ai generated language model: The phrase that shows how ai is polluting the web. The Verge. Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/ mesh-transformer-jax. Eric J. Wang. 2023. alpaca-lora. https://github. com/tloen/alpaca-lora. GitHub repository. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. Self-instruct: Aligning language models with self-generated instructions. Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023. Wizardlm: Empowering large language models to follow complex instructions. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Authors: Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship.

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