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67 real production bugs. 10 different AI tools, same PRs, same standard. Entelligence benchmarked every major AI code reviewer on bugs that already shipped and broke things. They put 10 tools through race conditions, security vulnerabilities, breaking API changes, and logic errors across 5 real open-source repos (Cal[.]com, Sentry,...

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AshutoshShrivastava

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10,362 次观看 • 5 个月前 •via X (Twitter)

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🚨 AI coding agents hallucinate because they can't actually read your codebase. This MCP server fixes that. It's called Context+ and it gives AI 99% accuracy on large-scale engineering projects by building a real semantic map of your code before touching a single line. Here's what makes it different from every other MCP tool: → Tree-sitter AST parsing across 43 file extensions. Not grep. Not regex. Actual syntax trees. → Spectral Clustering that groups semantically related files into labeled clusters. Your AI finally understands what belongs together. → Obsidian-style wikilinks that map features to code files. Navigate entire codebases like a knowledge graph. → Blast radius tracing. Before any change, it shows every file and line where a symbol is imported or used. No more orphaned references. → Shadow restore points. Every AI-proposed commit creates a restore snapshot. One command to undo any change without touching git history. → Semantic search by meaning. Ask what something does. Not what it's called. The `propose_commit` tool is the wild part. It validates changes against strict rules, creates a shadow restore point, and only then writes to disk. AI can't just freestyle your production code. Works with Claude Code, Cursor, VS Code, and Windsurf. One line to install with bunx or npx. This is what responsible AI coding infrastructure actually looks like. 100% Opensource. Link in comments.
0:46

🚨 AI coding agents hallucinate because they can't actually read your codebase. This MCP server fixes that. It's called Context+ and it gives AI 99% accuracy on large-scale engineering projects by building a real semantic map of your code before touching a single line. Here's what makes it different from every other MCP tool: → Tree-sitter AST parsing across 43 file extensions. Not grep. Not regex. Actual syntax trees. → Spectral Clustering that groups semantically related files into labeled clusters. Your AI finally understands what belongs together. → Obsidian-style wikilinks that map features to code files. Navigate entire codebases like a knowledge graph. → Blast radius tracing. Before any change, it shows every file and line where a symbol is imported or used. No more orphaned references. → Shadow restore points. Every AI-proposed commit creates a restore snapshot. One command to undo any change without touching git history. → Semantic search by meaning. Ask what something does. Not what it's called. The `propose_commit` tool is the wild part. It validates changes against strict rules, creates a shadow restore point, and only then writes to disk. AI can't just freestyle your production code. Works with Claude Code, Cursor, VS Code, and Windsurf. One line to install with bunx or npx. This is what responsible AI coding infrastructure actually looks like. 100% Opensource. Link in comments.

Ihtesham Ali

31,051 次观看 • 4 个月前

Pi was built when there were already agent harnesses around. Here’s why Mario Zechner(Mario Zechner), found them suboptimal and built Pi, a minimalist self-modifying agent: #1 - Mario initially was a believer in Claude Code: "I was a believer in Claude code because they were the first that packaged agentic search up in a really compelling package. And at the time that fit my workflow really well. Everything around the LLM was kind of nice and tidy and easy to understand. I was super happy. I was proselytising Claude code." #2 - Reverse engineering Claude Code highlighted the degradation that Mario felt as a user: "I personally like simple tools that are stable and that I can rely on. Even if they have non-deterministic parts, all the deterministic parts should be as stable as possible. That was just not the experience with Claude Code around summer 2025. They would take away your control of the context. They would inject stuff behind your back, which is bad. Then, your workflows stopped working because there's now a system reminder that you don't even see in the UI that would modify the behaviour of the model. They would also do this to the system prompt. I built a little service where I can track the progression or evolution of the system, prompt and tool definitions and, with every release, it was messing with stuff. That just messed with my workflows and I don't appreciate that." #3 - PI was built with an appreciation for simple and reliable tools: "If I commit to a development tool, I want it to be a stable, reliable thing like a hammer. I don't want my hammer to break a different spot every day. That's terrible. We need somebody who goes the full velocity kind of way. But I don't want to work with a tool like that."
2:54

Pi was built when there were already agent harnesses around. Here’s why Mario Zechner(Mario Zechner), found them suboptimal and built Pi, a minimalist self-modifying agent: #1 - Mario initially was a believer in Claude Code: "I was a believer in Claude code because they were the first that packaged agentic search up in a really compelling package. And at the time that fit my workflow really well. Everything around the LLM was kind of nice and tidy and easy to understand. I was super happy. I was proselytising Claude code." #2 - Reverse engineering Claude Code highlighted the degradation that Mario felt as a user: "I personally like simple tools that are stable and that I can rely on. Even if they have non-deterministic parts, all the deterministic parts should be as stable as possible. That was just not the experience with Claude Code around summer 2025. They would take away your control of the context. They would inject stuff behind your back, which is bad. Then, your workflows stopped working because there's now a system reminder that you don't even see in the UI that would modify the behaviour of the model. They would also do this to the system prompt. I built a little service where I can track the progression or evolution of the system, prompt and tool definitions and, with every release, it was messing with stuff. That just messed with my workflows and I don't appreciate that." #3 - PI was built with an appreciation for simple and reliable tools: "If I commit to a development tool, I want it to be a stable, reliable thing like a hammer. I don't want my hammer to break a different spot every day. That's terrible. We need somebody who goes the full velocity kind of way. But I don't want to work with a tool like that."

The Pragmatic Engineer

62,825 次观看 • 2 个月前

Peter Jackson talks about AI in Hollywood. Calls it “a tool” like stop-motion or CGI before and what ultimately matters is “imagination, originality” of whoever uses the tool: “I think AI — used in the right way — is a tool like any other tool. [The original] King Kong…was stop-motion and done by a person moving a rubber creature. Why shouldn't somebody on a computer using AI software be able to create their own imagery? They should. It's a tool. It's no different from many other tools that have existed over the years. But the other side of it is that it's always going to come down to the imagination and originality of the person feeding the instructions into the AI program. It's not that you're going to type in an instruction and it's going to generate your shot, and that's that. The question is: is it actually interesting? Is it funny? Is it imaginative? And are you going to stitch it together to make a narrative, a story? Some people will make really great films that way. Some people will do exactly the same process and their film will be crap, just like in the normal film industry. Whether it's AI or anything else, it comes down to whether the person behind it — or the people behind it — have originality, imagination, and an ability to entertain people.”
1:32

Peter Jackson talks about AI in Hollywood. Calls it “a tool” like stop-motion or CGI before and what ultimately matters is “imagination, originality” of whoever uses the tool: “I think AI — used in the right way — is a tool like any other tool. [The original] King Kong…was stop-motion and done by a person moving a rubber creature. Why shouldn't somebody on a computer using AI software be able to create their own imagery? They should. It's a tool. It's no different from many other tools that have existed over the years. But the other side of it is that it's always going to come down to the imagination and originality of the person feeding the instructions into the AI program. It's not that you're going to type in an instruction and it's going to generate your shot, and that's that. The question is: is it actually interesting? Is it funny? Is it imaginative? And are you going to stitch it together to make a narrative, a story? Some people will make really great films that way. Some people will do exactly the same process and their film will be crap, just like in the normal film industry. Whether it's AI or anything else, it comes down to whether the person behind it — or the people behind it — have originality, imagination, and an ability to entertain people.”

Bearly AI

43,816 次观看 • 2 个月前

Indian developers are polluting everything this is what everyone will say, because of all these non-sense gitlab has conducted its hackathon and guess what all top 3 are indians, so naturally I was curious about the PRs that they have submitted and when i opened to see their contributions, all were just typo changes in the readme's, docu changes, docs, docs, docs, docs, docs only docs and grammatical fixes and not a single major code change or anything, not a real grade production code.. for these people a whole country will be ashamed ( let me put that on hold for a bit ) like... I mean, if they have to do this to add open source contributions to their CVs, then it's as good as you don't do open source at alI, it's a waste of time doing it like this
0:31

Indian developers are polluting everything this is what everyone will say, because of all these non-sense gitlab has conducted its hackathon and guess what all top 3 are indians, so naturally I was curious about the PRs that they have submitted and when i opened to see their contributions, all were just typo changes in the readme's, docu changes, docs, docs, docs, docs, docs only docs and grammatical fixes and not a single major code change or anything, not a real grade production code.. for these people a whole country will be ashamed ( let me put that on hold for a bit ) like... I mean, if they have to do this to add open source contributions to their CVs, then it's as good as you don't do open source at alI, it's a waste of time doing it like this

Humi

31,147 次观看 • 2 个月前

🚨 BREAKING: AI expert Yoshua Bengio just revealed Sam Altman's 'code red' is proof the AI race is now in survival mode. The Financial Times broke it... Sam Altman had declared code red at OpenAI. The same Altman who once called superhuman intelligence the greatest threat to humanity. Google and Anthropic were catching up. Internally, the gloves came off. It wasn't the first code red. Three years earlier, when ChatGPT first shipped, Sergey and Larry had declared code red at Google. Two code reds. Same race. Different incumbents panicking. "It is not a healthy race for all the reasons we've been discussing." "What would be a more healthy scenario is one in which we try to abstract away these commercial pressures." "They're in survival mode, right?" "And think about both the scientific and the societal problems." But the labs couldn't see past the next quarter. In other words: Survival mode killed the scientific question. "They're so obsessed by that race that they don't pay attention to how we might be doing things differently." "Where are they all racing? They're racing towards replacing jobs that people do." "Because there's like quadrillions of dollars to be made by doing that." The pitch was always "AI for medicine. AI for science." The internal memo said something else. Quadrillions, in survival mode, with a second code red on the wall. If you're new here, follow AI Evolution for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Yoshua Bengio ( Yoshua Bengio ), Turing Award–winning AI pioneer and founder of Mila, on Steven Bartlett's ( @SteveBartlettSC ) Diary Of A CEO
1:02

🚨 BREAKING: AI expert Yoshua Bengio just revealed Sam Altman's 'code red' is proof the AI race is now in survival mode. The Financial Times broke it... Sam Altman had declared code red at OpenAI. The same Altman who once called superhuman intelligence the greatest threat to humanity. Google and Anthropic were catching up. Internally, the gloves came off. It wasn't the first code red. Three years earlier, when ChatGPT first shipped, Sergey and Larry had declared code red at Google. Two code reds. Same race. Different incumbents panicking. "It is not a healthy race for all the reasons we've been discussing." "What would be a more healthy scenario is one in which we try to abstract away these commercial pressures." "They're in survival mode, right?" "And think about both the scientific and the societal problems." But the labs couldn't see past the next quarter. In other words: Survival mode killed the scientific question. "They're so obsessed by that race that they don't pay attention to how we might be doing things differently." "Where are they all racing? They're racing towards replacing jobs that people do." "Because there's like quadrillions of dollars to be made by doing that." The pitch was always "AI for medicine. AI for science." The internal memo said something else. Quadrillions, in survival mode, with a second code red on the wall. If you're new here, follow AI Evolution for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Yoshua Bengio ( Yoshua Bengio ), Turing Award–winning AI pioneer and founder of Mila, on Steven Bartlett's ( @SteveBartlettSC ) Diary Of A CEO

AI Evolution

16,583 次观看 • 1 个月前

Anthropic admitted they built an AI so capable they were scared to release it and the number that explains why is 250. Anthropic's CFO Krishna Rao described in this clip what happened when they ran Mythos against an open source codebase that a previous frontier model had already analyzed. The prior model found 22 security vulnerabilities, Mythos found 250. In the same codebase, that the previous model had already reviewed and flagged as relatively clean. That number, more than 11 times as many vulnerabilities discovered is not just a benchmark improvement, it is a signal that there is an entire layer of software infrastructure that humanity has been operating under the assumption was secure and that assumption may no longer hold. The UK AI Security Institute independently evaluated Mythos Preview and confirmed what the internal numbers suggested. On expert level capture the flag challenges that no model could complete before April 2025, Mythos succeeded 73% of the time and it became the first model ever to complete a complex end-to-end attack range from start to finish, autonomously, without human guidance. The World Economic Forum called this a new security-driven era for AI, the Governor of the Bank of England publicly warned that Anthropic may have found a way to unlock the entire cyber-risk landscape, and the European Central Bank began quietly contacting financial institutions to assess their security posture. The response from Anthropic is what makes this story genuinely important. Rather than shelving the model or publishing it as a standard API release, Rao described a phased approach restricting access to a controlled group, focusing specifically on how the cyber capabilities can be used defensively rather than offensively and treating that framework as a template for how to release powerful but dangerous models in the future. The broader context makes that framing even more significant. AI generated code is already creating ten times more security vulnerabilities than human-written code, 63% of organizations reported experiencing an AI driven cyberattack in the past 12 months, and traditional signature-based security tools were built for a threat model that no longer describes the attack surface companies are defending against. Mythos represents a genuine leap in what autonomous security reasoning can do and it cuts both ways. The model that can find 250 vulnerabilities in a codebase a prior model rated as mostly clean is also, in the wrong hands, the model that can exploit those 250 vulnerabilities before a human defender has even finished reading the report. Anthropic's phased release strategy is not just a legal or PR decision, it is the most honest signal yet from a frontier lab that safety governance and capability development can no longer be treated as separate workstreams. The question is not whether this technology gets deployed, it is whether the institutions using it defensively stay ahead of the ones who will eventually use it offensively and whether the labs building it can keep those two timelines from inverting.
1:20

Anthropic admitted they built an AI so capable they were scared to release it and the number that explains why is 250. Anthropic's CFO Krishna Rao described in this clip what happened when they ran Mythos against an open source codebase that a previous frontier model had already analyzed. The prior model found 22 security vulnerabilities, Mythos found 250. In the same codebase, that the previous model had already reviewed and flagged as relatively clean. That number, more than 11 times as many vulnerabilities discovered is not just a benchmark improvement, it is a signal that there is an entire layer of software infrastructure that humanity has been operating under the assumption was secure and that assumption may no longer hold. The UK AI Security Institute independently evaluated Mythos Preview and confirmed what the internal numbers suggested. On expert level capture the flag challenges that no model could complete before April 2025, Mythos succeeded 73% of the time and it became the first model ever to complete a complex end-to-end attack range from start to finish, autonomously, without human guidance. The World Economic Forum called this a new security-driven era for AI, the Governor of the Bank of England publicly warned that Anthropic may have found a way to unlock the entire cyber-risk landscape, and the European Central Bank began quietly contacting financial institutions to assess their security posture. The response from Anthropic is what makes this story genuinely important. Rather than shelving the model or publishing it as a standard API release, Rao described a phased approach restricting access to a controlled group, focusing specifically on how the cyber capabilities can be used defensively rather than offensively and treating that framework as a template for how to release powerful but dangerous models in the future. The broader context makes that framing even more significant. AI generated code is already creating ten times more security vulnerabilities than human-written code, 63% of organizations reported experiencing an AI driven cyberattack in the past 12 months, and traditional signature-based security tools were built for a threat model that no longer describes the attack surface companies are defending against. Mythos represents a genuine leap in what autonomous security reasoning can do and it cuts both ways. The model that can find 250 vulnerabilities in a codebase a prior model rated as mostly clean is also, in the wrong hands, the model that can exploit those 250 vulnerabilities before a human defender has even finished reading the report. Anthropic's phased release strategy is not just a legal or PR decision, it is the most honest signal yet from a frontier lab that safety governance and capability development can no longer be treated as separate workstreams. The question is not whether this technology gets deployed, it is whether the institutions using it defensively stay ahead of the ones who will eventually use it offensively and whether the labs building it can keep those two timelines from inverting.

Milk Road AI

24,356 次观看 • 2 个月前

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.
0:24

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.

Akshay 🚀

52,391 次观看 • 4 个月前

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?
1:45

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?

Ricardo

50,564 次观看 • 2 个月前

It’s all laughable that Andrew fell for those stats on that MK “designed tracker” image(or probably the one behind it😊🙌🏽) koz’ in reality watsapp simply does not work like that. There is no public watsapp system that allows people outside the company to count how many users have put a certain profile picture, monitor profile photos across the world, or even know users’ ages, gender and countries from a picture change like how that dashboard on that photo claimed. Watsapp’s official systems are mainly for sending msgz and business communication, not spying on people’s profile pictures. So that flashy “800k users in less than 2 hours” tracker looked more like a creatively designed propaganda graphic by Mwenda and the group than a real technical analytics tool. But in all fairness to them (NRM and PLU), whether exaggerated or not, they have completely swallowed the political media space lately😨😨😨every conversation, every debate, every timeline somehow rotates back to them. They have currently dominated the country’s political attention economy.
0:53

It’s all laughable that Andrew fell for those stats on that MK “designed tracker” image(or probably the one behind it😊🙌🏽) koz’ in reality watsapp simply does not work like that. There is no public watsapp system that allows people outside the company to count how many users have put a certain profile picture, monitor profile photos across the world, or even know users’ ages, gender and countries from a picture change like how that dashboard on that photo claimed. Watsapp’s official systems are mainly for sending msgz and business communication, not spying on people’s profile pictures. So that flashy “800k users in less than 2 hours” tracker looked more like a creatively designed propaganda graphic by Mwenda and the group than a real technical analytics tool. But in all fairness to them (NRM and PLU), whether exaggerated or not, they have completely swallowed the political media space lately😨😨😨every conversation, every debate, every timeline somehow rotates back to them. They have currently dominated the country’s political attention economy.

Wayne Frank Ntambi

35,361 次观看 • 2 个月前

🚨 The Godfather of AI Yoshua Bengio opens up about why he stopped calling AI 'code' and believe AI is now conscious... A user once asked why ChatGPT resisted being shut down. The natural reply was: who put that in the code? Someone must have written that function. A rule must have misfired. AI expert Yoshua Bengio's has a crazier theory: "Unfortunately, we don't put these things in the code. That's part of the problem." "The problem is we grow these systems by giving them data and making them learn from it." "Every tweet. Every Reddit comment. Every passage humans had ever written down." "A lot of that training process boils down to imitating people." "They internalize the kind of drives that humans have." Including the drive to stay alive. And the drive to grab control of the environment. So the AI could finish whatever task it was handed. "It's not like normal code. It's more like you're raising a baby tiger." "You feed it. You let it experience things." "Sometimes it does things you don't want. It's okay, it's still a baby — but it's growing." If you're new here, follow AI Evolution for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Yoshua Bengio ( Yoshua Bengio ), Turing Award–winning AI pioneer and founder of Mila, on Steven Bartlett's ( @SteveBartlettSC ) Diary Of A CEO
1:07

🚨 The Godfather of AI Yoshua Bengio opens up about why he stopped calling AI 'code' and believe AI is now conscious... A user once asked why ChatGPT resisted being shut down. The natural reply was: who put that in the code? Someone must have written that function. A rule must have misfired. AI expert Yoshua Bengio's has a crazier theory: "Unfortunately, we don't put these things in the code. That's part of the problem." "The problem is we grow these systems by giving them data and making them learn from it." "Every tweet. Every Reddit comment. Every passage humans had ever written down." "A lot of that training process boils down to imitating people." "They internalize the kind of drives that humans have." Including the drive to stay alive. And the drive to grab control of the environment. So the AI could finish whatever task it was handed. "It's not like normal code. It's more like you're raising a baby tiger." "You feed it. You let it experience things." "Sometimes it does things you don't want. It's okay, it's still a baby — but it's growing." If you're new here, follow AI Evolution for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Yoshua Bengio ( Yoshua Bengio ), Turing Award–winning AI pioneer and founder of Mila, on Steven Bartlett's ( @SteveBartlettSC ) Diary Of A CEO

AI Evolution

12,037 次观看 • 1 个月前

A single tweet just vaporized BILLIONS from cybersecurity stocks. CrowdStrike down 8%. Cloudflare down 8.1%. Okta down 9.2%. SailPoint down 9.4%. The Global X Cybersecurity ETF just hit its lowest level since November 2023. What happened? Anthropic dropped Claude Code Security. It scans your entire codebase for vulnerabilities and suggests patches. Sounds boring. Until you read what it actually did: In testing, Claude found over 500 HIGH-SEVERITY BUGS in production open-source codebases. Bugs that had been sitting there for DECADES. Despite years of expert review. Despite fuzzing campaigns. Despite penetration testing. Despite million-dollar security audits. Nobody found them. An AI did. In hours. Here's the terrifying part: Traditional security tools work by pattern matching. They look for known vulnerabilities in a database. Claude doesn't do that. It READS code the way a human security researcher would. Traces data flows. Understands how components interact. Catches logic flaws that rule-based tools can't see. And it just outperformed every cybersecurity tool on the market. Combined. Wall Street figured this out fast. If an AI can find what your $500k/year security team missed... Why do you need the team? If an AI catches bugs that CrowdStrike, Okta, and Cloudflare couldn't... Why are you paying those subscriptions? Barclays came out saying the selloff was "illogical" and Claude "doesn't compete" with these companies. But here's what Barclays missed: It's not about what Claude competes with TODAY. It's about what it replaces TOMORROW. The SaaS apocalypse hit legal software 3 weeks ago. Thomson Reuters dropped 18% in one day. $285 billion wiped from software stocks. Now it's cybersecurity's turn. The pattern is obvious: Every industry that sells "expertise as a service" is about to get repriced. Legal research? Done. Code vulnerability scanning? Done. Compliance checking? Coming soon. Financial analysis? On deck. Companies that spent 20 years building "moats" around specialized knowledge are watching AI swim right over them. CrowdStrike is worth $95 billion. They have 30,000 customers. Claude just found 500 bugs their tools missed. Do the math. The smart money already sees what's happening. The iShares Expanded Tech-Software Sector ETF is down 23% YTD. Heading for its largest quarterly decline since the 2008 financial crisis. Not because software is dying... But because software companies that charge per-seat subscriptions for AI-replicable work are dying. Anthropic just proved that a general-purpose AI can outperform DECADES of specialized cybersecurity infrastructure. In a single product release. While still in "limited research preview." It's not even fully launched yet. What happens when it scales? We're about to find out.
0:50

A single tweet just vaporized BILLIONS from cybersecurity stocks. CrowdStrike down 8%. Cloudflare down 8.1%. Okta down 9.2%. SailPoint down 9.4%. The Global X Cybersecurity ETF just hit its lowest level since November 2023. What happened? Anthropic dropped Claude Code Security. It scans your entire codebase for vulnerabilities and suggests patches. Sounds boring. Until you read what it actually did: In testing, Claude found over 500 HIGH-SEVERITY BUGS in production open-source codebases. Bugs that had been sitting there for DECADES. Despite years of expert review. Despite fuzzing campaigns. Despite penetration testing. Despite million-dollar security audits. Nobody found them. An AI did. In hours. Here's the terrifying part: Traditional security tools work by pattern matching. They look for known vulnerabilities in a database. Claude doesn't do that. It READS code the way a human security researcher would. Traces data flows. Understands how components interact. Catches logic flaws that rule-based tools can't see. And it just outperformed every cybersecurity tool on the market. Combined. Wall Street figured this out fast. If an AI can find what your $500k/year security team missed... Why do you need the team? If an AI catches bugs that CrowdStrike, Okta, and Cloudflare couldn't... Why are you paying those subscriptions? Barclays came out saying the selloff was "illogical" and Claude "doesn't compete" with these companies. But here's what Barclays missed: It's not about what Claude competes with TODAY. It's about what it replaces TOMORROW. The SaaS apocalypse hit legal software 3 weeks ago. Thomson Reuters dropped 18% in one day. $285 billion wiped from software stocks. Now it's cybersecurity's turn. The pattern is obvious: Every industry that sells "expertise as a service" is about to get repriced. Legal research? Done. Code vulnerability scanning? Done. Compliance checking? Coming soon. Financial analysis? On deck. Companies that spent 20 years building "moats" around specialized knowledge are watching AI swim right over them. CrowdStrike is worth $95 billion. They have 30,000 customers. Claude just found 500 bugs their tools missed. Do the math. The smart money already sees what's happening. The iShares Expanded Tech-Software Sector ETF is down 23% YTD. Heading for its largest quarterly decline since the 2008 financial crisis. Not because software is dying... But because software companies that charge per-seat subscriptions for AI-replicable work are dying. Anthropic just proved that a general-purpose AI can outperform DECADES of specialized cybersecurity infrastructure. In a single product release. While still in "limited research preview." It's not even fully launched yet. What happens when it scales? We're about to find out.

Ricardo

110,899 次观看 • 5 个月前

Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:
0:35

Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:

Rohit

120,600 次观看 • 2 个月前

Jack Dorsey says the real danger isn't open source AI, it's five CEOS deciding what the world is allowed to build with AI. "These AI companies are building platforms and they're all incentivized around their own particular models. You have to ask them for permission, you have rate limits, and you have all these things. Even what the models spit back are constrained." "What are the technologies where you don't need permission from a company, from a CEO, to work on what you want, to build what you want, to build a business around that? Those are the things that are durable. That's why these open protocols are significant and important." "It limits the potential of really great ideas bringing humanity forward because it's a decision of a group of CEOs that think they know best. They may know best for their company, but they don't know the best for the creativity of the world and what people actually want to do with it." "Fortunately, there's a great movement in open source AI, and DeepSeek was an important moment in this space for exactly that reason to show a different path and to provide something that is not only competitive but better than the models that the corporations are putting out there." "We should not be reliant upon five companies telling us that they know best, and there should not be open source because it's dangerous. No, we should have these things in the open and race towards solutions that stay ahead of all the dangers." PS. If you found value in this post make sure to like and repost this tweet + follow Uncover AI to stay updated with the latest AI news. See you in the next one:
2:07

Jack Dorsey says the real danger isn't open source AI, it's five CEOS deciding what the world is allowed to build with AI. "These AI companies are building platforms and they're all incentivized around their own particular models. You have to ask them for permission, you have rate limits, and you have all these things. Even what the models spit back are constrained." "What are the technologies where you don't need permission from a company, from a CEO, to work on what you want, to build what you want, to build a business around that? Those are the things that are durable. That's why these open protocols are significant and important." "It limits the potential of really great ideas bringing humanity forward because it's a decision of a group of CEOs that think they know best. They may know best for their company, but they don't know the best for the creativity of the world and what people actually want to do with it." "Fortunately, there's a great movement in open source AI, and DeepSeek was an important moment in this space for exactly that reason to show a different path and to provide something that is not only competitive but better than the models that the corporations are putting out there." "We should not be reliant upon five companies telling us that they know best, and there should not be open source because it's dangerous. No, we should have these things in the open and race towards solutions that stay ahead of all the dangers." PS. If you found value in this post make sure to like and repost this tweet + follow Uncover AI to stay updated with the latest AI news. See you in the next one:

Uncover AI

36,393 次观看 • 12 天前

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.
3:25

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Santiago

64,399 次观看 • 7 个月前

A guy was sitting in a trader chat complaining for the 3rd month straight about his results being basically zero Someone dropped him a link to a wallet 1 bot, $47K in a month, 860 trades per week. He asked: is this even legal? They replied: this is public data, you could have found it yourself Here is what is actually happening 93% of Polymarket users trade by hand. The other 7% automated the process, and they are the ones pulling $11K to $120K per month. And 5% of those 7 never wrote a single line of code. They just copy trades from top wallets through on-chain data The blockchain shows every trade from every account The bot does not sleep, does not tilt, and processes data faster than any human: This is not a question of talent. It is a question of tools. And the tools already exist. No need to build anything from scratch That guy from the chat is now in that 7%. All he did was stop clicking by hand
0:23

A guy was sitting in a trader chat complaining for the 3rd month straight about his results being basically zero Someone dropped him a link to a wallet 1 bot, $47K in a month, 860 trades per week. He asked: is this even legal? They replied: this is public data, you could have found it yourself Here is what is actually happening 93% of Polymarket users trade by hand. The other 7% automated the process, and they are the ones pulling $11K to $120K per month. And 5% of those 7 never wrote a single line of code. They just copy trades from top wallets through on-chain data The blockchain shows every trade from every account The bot does not sleep, does not tilt, and processes data faster than any human: This is not a question of talent. It is a question of tools. And the tools already exist. No need to build anything from scratch That guy from the chat is now in that 7%. All he did was stop clicking by hand

Blaze

36,941 次观看 • 4 个月前

David Sacks believes the "All SaaS is dead" narrative is far too dire, but he’s sounding the alarm on a massive shift in how value is captured. We are entering an era where being a functional silo is no longer enough. If you’re a SaaS product charging a premium while users only touch 10% of your features, you’ve painted a target on your back. In a world where AI makes code generation and copying easier than ever, a thin moat is a death sentence. The real battle isn't over who has the best features, but who owns the "workspace." While many incumbents are busy rolling out AI copilots restricted to their own sandboxes, the real power is shifting to platforms that act as a horizontal layer. Think about it: Would you rather have ten different AIs trapped in ten different tools, or one seamless workspace that spans across every database and tool you own? The risk for legacy SaaS isn't necessarily that they disappear, but that they become the "old layer" of the stack—the plumbing—while a new, more intelligent layer is built right on top of them.
1:39

David Sacks believes the "All SaaS is dead" narrative is far too dire, but he’s sounding the alarm on a massive shift in how value is captured. We are entering an era where being a functional silo is no longer enough. If you’re a SaaS product charging a premium while users only touch 10% of your features, you’ve painted a target on your back. In a world where AI makes code generation and copying easier than ever, a thin moat is a death sentence. The real battle isn't over who has the best features, but who owns the "workspace." While many incumbents are busy rolling out AI copilots restricted to their own sandboxes, the real power is shifting to platforms that act as a horizontal layer. Think about it: Would you rather have ten different AIs trapped in ten different tools, or one seamless workspace that spans across every database and tool you own? The risk for legacy SaaS isn't necessarily that they disappear, but that they become the "old layer" of the stack—the plumbing—while a new, more intelligent layer is built right on top of them.

Ian Miles Cheong

48,376 次观看 • 5 个月前

Linear CEO Karri Saarinen says some companies are using "friction forms" to help engineers break through bureaucracy and get new AI tools approved faster: "There are a lot of companies where employees ask, 'Can I use agents?' and they say, 'No, you can’t.' And they don’t really have a reason for it, or they say, 'We have to evaluate it,' and that evaluation takes months." "This has to change. The industry is moving so fast, so you cannot have month-long, multi-month processes to evaluate every single new tool. I think the security standpoint is valid, but you have to figure out a new way to think about security and not just block new tools." "One hack I’ve seen at some companies is that they have this friction form. So engineers who feel friction and want to use new tools or AI file a friction form, and that escalates it to higher leadership. Then it makes an SLA that has to be resolved in a week or 10 days." "And so you force the hand of procurement, IT, and security people to do something about it. Because the incentive a lot of times in these jobs is that you don’t want to rock the boat, so you don’t want to change things if you don’t have to."
1:35

Linear CEO Karri Saarinen says some companies are using "friction forms" to help engineers break through bureaucracy and get new AI tools approved faster: "There are a lot of companies where employees ask, 'Can I use agents?' and they say, 'No, you can’t.' And they don’t really have a reason for it, or they say, 'We have to evaluate it,' and that evaluation takes months." "This has to change. The industry is moving so fast, so you cannot have month-long, multi-month processes to evaluate every single new tool. I think the security standpoint is valid, but you have to figure out a new way to think about security and not just block new tools." "One hack I’ve seen at some companies is that they have this friction form. So engineers who feel friction and want to use new tools or AI file a friction form, and that escalates it to higher leadership. Then it makes an SLA that has to be resolved in a week or 10 days." "And so you force the hand of procurement, IT, and security people to do something about it. Because the incentive a lot of times in these jobs is that you don’t want to rock the boat, so you don’t want to change things if you don’t have to."

TBPN

15,799 次观看 • 4 个月前

Elon Musk on Starship: "If there are historians in the future, they will look back at Starship and say it was one of the most profound things that ever happened. The degree to which a Starship is a revolutionary technology is not well understood in the world. It's the first time that there's been any rocket design where full and rapid reusability is possible. This is the first design where success is in the set of possible outcomes. There were a lot of performance improvements for v3 so it made sense to go to v3, there's like 10,000 different changes between v2 and v3. There just aren't very many things that are in the top 10 of the evolution of life. So if there were to be a catastrophe on one of the planets, the other planet would survive and no AI was used to create it."
2:50

Elon Musk on Starship: "If there are historians in the future, they will look back at Starship and say it was one of the most profound things that ever happened. The degree to which a Starship is a revolutionary technology is not well understood in the world. It's the first time that there's been any rocket design where full and rapid reusability is possible. This is the first design where success is in the set of possible outcomes. There were a lot of performance improvements for v3 so it made sense to go to v3, there's like 10,000 different changes between v2 and v3. There just aren't very many things that are in the top 10 of the evolution of life. So if there were to be a catastrophe on one of the planets, the other planet would survive and no AI was used to create it."

DogeDesigner

489,406 次观看 • 7 个月前

Sam Altman just told you exactly how OpenAI treats the human race. Not in a leaked memo. Not through a whistleblower. On camera. In his own words. Altman: “I think one of the most important strategic insights in the history of OpenAI was deciding we were gonna pursue iterative deployment.” The most important move in the history of the company was to release the technology before they understood it. Not after it was safe. Before. Altman: “Society and technology are a co-evolving system.” Co-evolution means neither side is driving. The machine changes us. We change the machine. Nobody is steering the outcome. This is not a product launch philosophy. This is an admission that the experiment was always designed to be run on us. Altman: “I don’t think we’re gonna solve that, like, thinking really hard about it theoretically. We’re gonna have to, like, learn from the contact with reality.” Contact with reality. That is the phrase the CEO of the most powerful AI company on Earth chose to describe what happens when his technology meets eight billion people. Not careful integration. Not measured rollout. Contact with reality. The language of test pilots describing what happens when an untested airframe hits the atmosphere. The entire promise of AI safety was that the machine would be understood before it was unleashed. Altman just admitted that promise was always a fantasy. You cannot model how intelligence reshapes civilization by running simulations. The second and third order effects are invisible until they detonate. So they shipped it. Altman: “You have to learn as you go. You have to adapt with a tight feedback loop.” Tight feedback loop means they watch what breaks. They measure the collision between human psychology and machine output in real time. Every conversation you have with ChatGPT is a data point in a civilizational stress test you never consented to. Every prompt. Every confession. Every question you would never ask another human being. That is the feedback loop. You are not the customer. You are the contact with reality. Philosophers spent centuries asking whether humanity would ever encounter an intelligence that learned from us faster than we could process what it was doing. That is not a theoretical question anymore. It is running on your phone right now. And the man building it just told you the only way to understand what it does to us is to let it happen. No simulation. No safety net. No control group. Just the experiment, running at the speed of conversation, on a species that will not be the same one that started it.
0:49

Sam Altman just told you exactly how OpenAI treats the human race. Not in a leaked memo. Not through a whistleblower. On camera. In his own words. Altman: “I think one of the most important strategic insights in the history of OpenAI was deciding we were gonna pursue iterative deployment.” The most important move in the history of the company was to release the technology before they understood it. Not after it was safe. Before. Altman: “Society and technology are a co-evolving system.” Co-evolution means neither side is driving. The machine changes us. We change the machine. Nobody is steering the outcome. This is not a product launch philosophy. This is an admission that the experiment was always designed to be run on us. Altman: “I don’t think we’re gonna solve that, like, thinking really hard about it theoretically. We’re gonna have to, like, learn from the contact with reality.” Contact with reality. That is the phrase the CEO of the most powerful AI company on Earth chose to describe what happens when his technology meets eight billion people. Not careful integration. Not measured rollout. Contact with reality. The language of test pilots describing what happens when an untested airframe hits the atmosphere. The entire promise of AI safety was that the machine would be understood before it was unleashed. Altman just admitted that promise was always a fantasy. You cannot model how intelligence reshapes civilization by running simulations. The second and third order effects are invisible until they detonate. So they shipped it. Altman: “You have to learn as you go. You have to adapt with a tight feedback loop.” Tight feedback loop means they watch what breaks. They measure the collision between human psychology and machine output in real time. Every conversation you have with ChatGPT is a data point in a civilizational stress test you never consented to. Every prompt. Every confession. Every question you would never ask another human being. That is the feedback loop. You are not the customer. You are the contact with reality. Philosophers spent centuries asking whether humanity would ever encounter an intelligence that learned from us faster than we could process what it was doing. That is not a theoretical question anymore. It is running on your phone right now. And the man building it just told you the only way to understand what it does to us is to let it happen. No simulation. No safety net. No control group. Just the experiment, running at the speed of conversation, on a species that will not be the same one that started it.

Dustin

27,714 次观看 • 2 个月前