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GPT-5.6 Sol Ultra autonomously build a full Chrome renderer exploit chain and pop calc.exe, in a days. AI go from V8 source a7 recent security fixes to a complete sandbox escape & code execution with minimal guidance, Maglev type confusion, forged primitives, NativeModule UAF, Wasm pointer hijacking the whole...

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Md Ismail Šojal 🕷️

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21,010 просмотров • 1 месяц назад •via X (Twitter)

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🦔 Google Chrome is launching "autobrowsing" today for paid subscribers. Instead of just autofilling your credit card, Chrome will now complete entire tasks for you: filling out PDFs, renewing your driver's license, researching trips, booking reservations. You give it a task, it browses the web autonomously, and asks for your approval at the final step. Available for Google AI Pro ($19.99/month) or AI Ultra ($249.99/month). All Chrome users get a new Gemini AI sidebar that pulls from your Gmail and past conversations. My Take Last week Google announced AI Mode connecting to your Gmail and Photos. Now Chrome is pulling from your emails, conversations, and browsing activity to act on your behalf. Every new AI feature requires deeper access to your personal data. You're not just paying $20 or $250 a month for convenience. You're handing over your credit cards, government documents, addresses, and browsing habits to an AI that will interact with websites as you. The people excited about this haven't thought through what happens when it goes wrong. An AI filling out government forms. An AI with your credit card making purchases. An AI that hallucinates or gets social engineered. And when adoption isn't fast enough, they'll degrade regular search and browsing until this feels like the only viable option. That's the playbook. Meanwhile $250 a month tells you exactly how much this costs them to run, and how desperate they are to find someone willing to pay for it. Hedgie🤗
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🦔 Google Chrome is launching "autobrowsing" today for paid subscribers. Instead of just autofilling your credit card, Chrome will now complete entire tasks for you: filling out PDFs, renewing your driver's license, researching trips, booking reservations. You give it a task, it browses the web autonomously, and asks for your approval at the final step. Available for Google AI Pro ($19.99/month) or AI Ultra ($249.99/month). All Chrome users get a new Gemini AI sidebar that pulls from your Gmail and past conversations. My Take Last week Google announced AI Mode connecting to your Gmail and Photos. Now Chrome is pulling from your emails, conversations, and browsing activity to act on your behalf. Every new AI feature requires deeper access to your personal data. You're not just paying $20 or $250 a month for convenience. You're handing over your credit cards, government documents, addresses, and browsing habits to an AI that will interact with websites as you. The people excited about this haven't thought through what happens when it goes wrong. An AI filling out government forms. An AI with your credit card making purchases. An AI that hallucinates or gets social engineered. And when adoption isn't fast enough, they'll degrade regular search and browsing until this feels like the only viable option. That's the playbook. Meanwhile $250 a month tells you exactly how much this costs them to run, and how desperate they are to find someone willing to pay for it. Hedgie🤗

Hedgie

11,662 просмотров • 7 месяцев назад

Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack
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Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack

Matt Turck

11,908 просмотров • 3 месяцев назад

💥 Introducing Vibe, an AI powered app builder similar to Lovable, Replit and v0. No, this isn't my SaaS announcement, this is a product that you will learn how to build in my newest FREE course! 🚀 🚀 Next.js 15 + React 19 🎨 Tailwind v4 + shadcn UI 📡 tRPC.io 🧙‍♂️ for full-stack type safety 🔁 Inngest background jobs 🧠 Inngest agent toolkit 🔐 authentication 💳 billing 🧱 Component and app generation from AI prompts 🗂️ Live project preview with URL access 🖥️ E2B cloud sandboxes for runtime execution 🐳 Docker -based sandbox templating 🧠 AI model support (OpenAI, Anthropic, Grok) 📦 Prisma + Neon Postgres for database integration 🤖 CodeRabbit AI-powered PR reviews 🧾 Built-in credit system with usage tracking 🧪 Preview + code explorer toggle
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💥 Introducing Vibe, an AI powered app builder similar to Lovable, Replit and v0. No, this isn't my SaaS announcement, this is a product that you will learn how to build in my newest FREE course! 🚀 🚀 Next.js 15 + React 19 🎨 Tailwind v4 + shadcn UI 📡 tRPC.io 🧙‍♂️ for full-stack type safety 🔁 Inngest background jobs 🧠 Inngest agent toolkit 🔐 authentication 💳 billing 🧱 Component and app generation from AI prompts 🗂️ Live project preview with URL access 🖥️ E2B cloud sandboxes for runtime execution 🐳 Docker -based sandbox templating 🧠 AI model support (OpenAI, Anthropic, Grok) 📦 Prisma + Neon Postgres for database integration 🤖 CodeRabbit AI-powered PR reviews 🧾 Built-in credit system with usage tracking 🧪 Preview + code explorer toggle

Code With Antonio

92,964 просмотров • 1 год назад

The corporate system wants you trading time for a paycheck. The alternative is building automated leverage. You do not need a team of engineers. You just need the right open-source architecture. Here are 10 GitHub repos to automate your workflows, replace manual labor, and direct your own reality: 1. n8n Bypass expensive SaaS subscriptions. Build custom AI automation workflows that run on your own servers. 2. Ollama Stop sending your private data to massive API providers. Run heavy AI models locally on your own machine. Complete privacy. 3. Open Interpreter Let language models control your computer. Automate the repetitive corporate tasks they pay you to do manually. 4. Aider An AI pair programmer that lives in your terminal. Stop writing boilerplate code and focus strictly on the architecture. 5. Dify An open-source LLM app development platform. Build and deploy functional AI agents in minutes, not months. 6. Flowise A drag-and-drop UI to build customized LLM flows. You do not need to be a senior developer to build massive leverage. 7. Supabase Spin up a Postgres database, authentication, and instant APIs. Own your backend entirely. 8. Auto-GPT Give an AI an objective and let it execute. It browses the web, writes code, and chains thoughts together autonomously. 9. Outline An open-source knowledge base for your personal leverage. Stop losing your documentation in arbitrary corporate systems. 10. NocoDB Turn any database into a smart spreadsheet. Keep your data on your own infrastructure and stop paying for convenience. The secret to tech survival? Stop playing by their rules. Build your own systems and take your leverage with you.
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The corporate system wants you trading time for a paycheck. The alternative is building automated leverage. You do not need a team of engineers. You just need the right open-source architecture. Here are 10 GitHub repos to automate your workflows, replace manual labor, and direct your own reality: 1. n8n Bypass expensive SaaS subscriptions. Build custom AI automation workflows that run on your own servers. 2. Ollama Stop sending your private data to massive API providers. Run heavy AI models locally on your own machine. Complete privacy. 3. Open Interpreter Let language models control your computer. Automate the repetitive corporate tasks they pay you to do manually. 4. Aider An AI pair programmer that lives in your terminal. Stop writing boilerplate code and focus strictly on the architecture. 5. Dify An open-source LLM app development platform. Build and deploy functional AI agents in minutes, not months. 6. Flowise A drag-and-drop UI to build customized LLM flows. You do not need to be a senior developer to build massive leverage. 7. Supabase Spin up a Postgres database, authentication, and instant APIs. Own your backend entirely. 8. Auto-GPT Give an AI an objective and let it execute. It browses the web, writes code, and chains thoughts together autonomously. 9. Outline An open-source knowledge base for your personal leverage. Stop losing your documentation in arbitrary corporate systems. 10. NocoDB Turn any database into a smart spreadsheet. Keep your data on your own infrastructure and stop paying for convenience. The secret to tech survival? Stop playing by their rules. Build your own systems and take your leverage with you.

Katyayani Shukla

16,964 просмотров • 4 месяцев назад

A 16-year-old American student made $7,000 in his first month after publishing his first game on Google Play - and GPT-5.5 built the whole thing for him. He had zero programming knowledge and no $50,000 dev team. He just opened GPT-5.5, described what he wanted to build, and went to play while the AI wrote every line of code for him. GPT-5.5 built a complete 3D game called Hormuz Escape from scratch - obstacle avoidance mechanics, high score system, particle effects, AdMob integration for ads, and a Firebase leaderboard. All from one prompt in a week of work. A Google Play Developer account cost $25 once and forever. 10,000 active players at $0.03 a day through AdMob is already $9,000 a month in passive income. He stopped at $7,000 in the first month and the number keeps growing. 3 billion Android devices in the world and the Play Store is open to anyone with $25 and an idea. AI removed the last barrier - needing to know how to code. $25 to register, One week of work. $7,000 in the first month.
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A 16-year-old American student made $7,000 in his first month after publishing his first game on Google Play - and GPT-5.5 built the whole thing for him. He had zero programming knowledge and no $50,000 dev team. He just opened GPT-5.5, described what he wanted to build, and went to play while the AI wrote every line of code for him. GPT-5.5 built a complete 3D game called Hormuz Escape from scratch - obstacle avoidance mechanics, high score system, particle effects, AdMob integration for ads, and a Firebase leaderboard. All from one prompt in a week of work. A Google Play Developer account cost $25 once and forever. 10,000 active players at $0.03 a day through AdMob is already $9,000 a month in passive income. He stopped at $7,000 in the first month and the number keeps growing. 3 billion Android devices in the world and the Play Store is open to anyone with $25 and an idea. AI removed the last barrier - needing to know how to code. $25 to register, One week of work. $7,000 in the first month.

Sprytix

86,291 просмотров • 4 месяцев назад

Wanna make $10K a month with apps? 💰 You need to know how to create a secure, scalable backend. Gemini 3.0 just made it 10x easier. I just shot a full 2 hour 33 minute breakdown showing exactly how I build real production backends with Cursor AI — routes, auth, databases, background workers, everything — all driven through agents and clean architecture. 🔧 If you’ve ever wondered how to go from “AI-generated code” to an actual backend that can power a real business, the full walkthrough is right here. Take this software for free — it's open-source on GitHub! 🎁 Do you wanna watch this lesson by lesson?
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Wanna make $10K a month with apps? 💰 You need to know how to create a secure, scalable backend. Gemini 3.0 just made it 10x easier. I just shot a full 2 hour 33 minute breakdown showing exactly how I build real production backends with Cursor AI — routes, auth, databases, background workers, everything — all driven through agents and clean architecture. 🔧 If you’ve ever wondered how to go from “AI-generated code” to an actual backend that can power a real business, the full walkthrough is right here. Take this software for free — it's open-source on GitHub! 🎁 Do you wanna watch this lesson by lesson?

corbin

59,434 просмотров • 9 месяцев назад

Found a way to get $125-$200 in FREE ai credits for gpt-5.6 sol, claude opus 5 and opus 4.8 😳 agentrouter is a non-profit version of openrouter. one api key, no card. credits land on your account at signup. what you get for $0: -$125-200 in credits on signup (github only) -claude opus 5, gpt-5.6 sol, opus 4.8 through one key -free models -openai-compatible (works with anything) what this replaces: -paid frontier API costs for coding agents -claude opus 4.8: $15/M in, $75/M out -gpt-5.6 sol: $25/M out how to grab yours (2 min): 1. go to → sign in with github (important) 2. console → create your api key 3. base url: 4. pick a model and go Works in hermes, claude code, cursor, aider and any openai-compatible tools Important: github-only signup, older accounts get the full credit, fresh ones may get less. credits are one-time. referral adds +$100 use this before they tighten the offer
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Found a way to get $125-$200 in FREE ai credits for gpt-5.6 sol, claude opus 5 and opus 4.8 😳 agentrouter is a non-profit version of openrouter. one api key, no card. credits land on your account at signup. what you get for $0: -$125-200 in credits on signup (github only) -claude opus 5, gpt-5.6 sol, opus 4.8 through one key -free models -openai-compatible (works with anything) what this replaces: -paid frontier API costs for coding agents -claude opus 4.8: $15/M in, $75/M out -gpt-5.6 sol: $25/M out how to grab yours (2 min): 1. go to → sign in with github (important) 2. console → create your api key 3. base url: 4. pick a model and go Works in hermes, claude code, cursor, aider and any openai-compatible tools Important: github-only signup, older accounts get the full credit, fresh ones may get less. credits are one-time. referral adds +$100 use this before they tighten the offer

painn

108,294 просмотров • 29 дней назад

"there's too many f**king bridges, i just wanna move shit from chain A to B" me too yo 🫂 that's why i made: just type the chains you need, and you'll get ALL the bridges that support that route the best part? i didn't write a single line of code. made this ALL in AI in < 24 hours using: - claude (design) - v0 + replit (frontend) - airtable + zapier (backend) - cursor (integration) wanted to push the boundaries on what can be made only using AI, and i haven't even scratched the surface lmk if you want me to go through my complete workflow, will be making even more stuff in the coming weeks 🫡
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"there's too many f**king bridges, i just wanna move shit from chain A to B" me too yo 🫂 that's why i made: just type the chains you need, and you'll get ALL the bridges that support that route the best part? i didn't write a single line of code. made this ALL in AI in < 24 hours using: - claude (design) - v0 + replit (frontend) - airtable + zapier (backend) - cursor (integration) wanted to push the boundaries on what can be made only using AI, and i haven't even scratched the surface lmk if you want me to go through my complete workflow, will be making even more stuff in the coming weeks 🫡

Jim

64,561 просмотров • 1 год назад

Web3 is a dark forest — and only the sharpest minds can navigate it. At Infini, we take security seriously. In this episode of Infini Spotlight, we sit down with Nolan | ExVul , founder of ExVul , to uncover: - How one bug can freeze an entire blockchain - Why open-source code can become a hidden trap - What could go wrong when AI starts making payments Watch full episode🎥👇
27:30

Web3 is a dark forest — and only the sharpest minds can navigate it. At Infini, we take security seriously. In this episode of Infini Spotlight, we sit down with Nolan | ExVul , founder of ExVul , to uncover: - How one bug can freeze an entire blockchain - Why open-source code can become a hidden trap - What could go wrong when AI starts making payments Watch full episode🎥👇

Infini

11,306 просмотров • 10 месяцев назад

GPT 5.6 Sol just saved me €650 a year and demonstrated just how good this model is as an agent in @ChatGPTapp Codex. This is not a clickbait, let me explain. In France, insurance companies tend to hide all their prices behind quote forms that take at least 5min to complete for a single configuration on a single provider's website. And that take an other 5min to understand. If you want to compare 10 companies across 5 configurations each, it takes at least 4h +. (It's such a painful process that entire businesses exist just to compare insurance offers.) Since I have two cars, it would normally take me 8h so an entire day to have a real large view of my best option. Companies know this and use the friction to maintain overpriced offers. So I asked @ChatGPTapp with GPT 5.6 Sol, using Chrome tabs, to go through all those annoying forms. I provided him all my contrats with my current insurance companies so he have context. For some insurers, you even have to speak with a representative just to get a quote (which is absurd in 2026), so it emailed the companies, exchanged the required information, and obtained the prices. It then ran a complete benchmark, read all the terms and conditions, and recommended three options from three different companies. I picked one, and it completed the subscription with my new insurance company. And that's how I ended up with better insurance coverage for less money. For 4% of my weekly quota in 20x plan. (i think it's fair) All of that happened while I was walking my dog for 50 minutes, he was working on my computer all by it's on. Yes, computer use existed before OpenAI GPT 5.6 Sol, but this is a completely different level in the way it handles these kinds of tasks. I think this story shows the new era of AI we're entering, good model is not only for one single task as coding or answering question, AI now can do things for you, like in your daily live. I love being able to hand my computer over to GPT 5.6 Sol. PS: The only annoying part was that some companies still require "Verify you're human" checks. In the age of AI agents, websites really need to be ready for robot access.
0:20

GPT 5.6 Sol just saved me €650 a year and demonstrated just how good this model is as an agent in @ChatGPTapp Codex. This is not a clickbait, let me explain. In France, insurance companies tend to hide all their prices behind quote forms that take at least 5min to complete for a single configuration on a single provider's website. And that take an other 5min to understand. If you want to compare 10 companies across 5 configurations each, it takes at least 4h +. (It's such a painful process that entire businesses exist just to compare insurance offers.) Since I have two cars, it would normally take me 8h so an entire day to have a real large view of my best option. Companies know this and use the friction to maintain overpriced offers. So I asked @ChatGPTapp with GPT 5.6 Sol, using Chrome tabs, to go through all those annoying forms. I provided him all my contrats with my current insurance companies so he have context. For some insurers, you even have to speak with a representative just to get a quote (which is absurd in 2026), so it emailed the companies, exchanged the required information, and obtained the prices. It then ran a complete benchmark, read all the terms and conditions, and recommended three options from three different companies. I picked one, and it completed the subscription with my new insurance company. And that's how I ended up with better insurance coverage for less money. For 4% of my weekly quota in 20x plan. (i think it's fair) All of that happened while I was walking my dog for 50 minutes, he was working on my computer all by it's on. Yes, computer use existed before OpenAI GPT 5.6 Sol, but this is a completely different level in the way it handles these kinds of tasks. I think this story shows the new era of AI we're entering, good model is not only for one single task as coding or answering question, AI now can do things for you, like in your daily live. I love being able to hand my computer over to GPT 5.6 Sol. PS: The only annoying part was that some companies still require "Verify you're human" checks. In the age of AI agents, websites really need to be ready for robot access.

Defend Intelligence (Anis Ayari)

78,505 просмотров • 1 месяц назад

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 просмотров • 3 месяцев назад

Biggest announcement of my life: I have raised pre-seed funding from 021T, Dr. Alex Wissner-Gross , and Devon Triplett to build an AI agent that will change the world The biggest issue with AI is it is creating incredible value but for only a small group of people Most people hate AI and don't use it I have built Henry Intelligent Machines (HIM) to solve this HIM is a personal swarm of AI agents autonomously creating economic value for you 24/7 Right now as we speak HIM is collecting data across thousands of websites autonomously 24/7/365 They're hunting for challenges to solve at all times When you use Henry, he will deeply research you and get to know you. Then based on the thousands of opportunities it has in its database, find the value generating opportunities that most closely match your interests, skills, assets, and expertise Henry and its swarm will then proceed to build those micro-businesses out for you You will have complete control over the swarm. Reviewing and approving all work. Editing where you find appropriate. You give Henry a budget, then it hunts and autonomously creates value Say you have an expertise in vibe coding tools and Henry discovers there's no vibe coding guides on Gumroad. It will take your expertise, build drafts for a guide, run it by you, post with your approval, then use your budget to get customers Say you're into AI and speak Portuguese Henry will go through the Portuguese AI education market, see there are no educational products in that language, then create a full AI educational business in Portuguese Most people hate AI. This is because they get 0 value from it, see their friends getting laid off, and become scared HIM is the antidote to this. HIM allows ANYONE to get value from AI. HIM will allow anyone to get access to the trillions of dollars of value that are up for grabs in the new AI world. To ensure Henry creates value and not slop, this will be an extremely slow rollout We will be letting people into HIM 1 by 1. Working with them hands on to ensure Henry only builds real value for them, then expanding from there. If you'd like to be one of the early users of Henry, feel free to sign up at the link below. Forward.
3:22

Biggest announcement of my life: I have raised pre-seed funding from 021T, Dr. Alex Wissner-Gross , and Devon Triplett to build an AI agent that will change the world The biggest issue with AI is it is creating incredible value but for only a small group of people Most people hate AI and don't use it I have built Henry Intelligent Machines (HIM) to solve this HIM is a personal swarm of AI agents autonomously creating economic value for you 24/7 Right now as we speak HIM is collecting data across thousands of websites autonomously 24/7/365 They're hunting for challenges to solve at all times When you use Henry, he will deeply research you and get to know you. Then based on the thousands of opportunities it has in its database, find the value generating opportunities that most closely match your interests, skills, assets, and expertise Henry and its swarm will then proceed to build those micro-businesses out for you You will have complete control over the swarm. Reviewing and approving all work. Editing where you find appropriate. You give Henry a budget, then it hunts and autonomously creates value Say you have an expertise in vibe coding tools and Henry discovers there's no vibe coding guides on Gumroad. It will take your expertise, build drafts for a guide, run it by you, post with your approval, then use your budget to get customers Say you're into AI and speak Portuguese Henry will go through the Portuguese AI education market, see there are no educational products in that language, then create a full AI educational business in Portuguese Most people hate AI. This is because they get 0 value from it, see their friends getting laid off, and become scared HIM is the antidote to this. HIM allows ANYONE to get value from AI. HIM will allow anyone to get access to the trillions of dollars of value that are up for grabs in the new AI world. To ensure Henry creates value and not slop, this will be an extremely slow rollout We will be letting people into HIM 1 by 1. Working with them hands on to ensure Henry only builds real value for them, then expanding from there. If you'd like to be one of the early users of Henry, feel free to sign up at the link below. Forward.

Alex Finn

478,595 просмотров • 4 месяцев назад

🚨APPLE SPENT 5 YEARS AND BILLIONS OF DOLLARS BUILDING THE MOST ADVANCED SECURITY SYSTEM IN CONSUMER HISTORY.. AN AI BROKE IT IN 5 DAYS.. Here’s what just happened.. Apple built something called Memory Integrity Enforcement for its new M5 chips.. It’s a hardware-level security system that attaches secret cryptographic tags to every piece of memory.. If a hacker tries to access memory they shouldn’t.. The chip blocks it instantly.. Every known exploit chain against iOS and macOS was rendered obsolete overnight.. Apple said so themselves.. Then a small team at a cybersecurity firm called Calif used Anthropic’s unreleased Claude Mythos Preview to find vulnerabilities in the macOS kernel.. The AI found the bugs almost instantly.. Because once it learned the pattern of a specific type of flaw.. It could recognize every other flaw in that same class across the entire codebase.. What used to take elite security teams months.. The AI did in hours.. Within 5 days.. The team had a fully working exploit that escalated a basic user account to full root access on an M5 Mac running the latest macOS.. With MIE fully enabled.. The billion-dollar hardware defense running at full strength.. The trick.. They didn’t fight the hardware.. They went around it.. MIE is designed to catch memory corruption.. Hackers trying to overwrite pointers or inject code.. The team used a “data-only” approach instead.. They manipulated legitimate data structures the hardware was never designed to monitor.. Like changing an internal flag from “standard user” to “admin”.. The chip saw a perfectly normal operation.. The operating system obeyed.. And the attacker had total control.. The hardware thought everything was fine.. Because technically it was.. The exploit never triggered a single tag mismatch.. They walked into Apple Park and hand-delivered a 55-page report.. Apple patched it in macOS 26.5.. And for the first time ever.. Apple’s official security advisory credited the vulnerability discovery to “Calif dot io in collaboration with Claude and Anthropic Research”.. An AI is now credited in Apple’s CVE patches.. But here’s what makes this story truly terrifying.. Before MIE existed.. An exploit kit called DarkSword was hitting iPhones with zero-click attacks.. Six vulnerabilities chained together.. Total device control just from visiting a webpage.. Deployed by Russian espionage groups, Turkish surveillance vendors, and actors in Saudi Arabia.. Then it got leaked on GitHub.. Nation-state capabilities.. Free for anyone.. MIE was supposed to make all of that impossible.. And an AI found a way around it in 5 days.. The previous model.. Claude Opus 4.6.. Found 22 security bugs in the Firefox codebase.. Claude Mythos Preview found 271 in the same environment.. A tenfold increase.. Linux kernel CVEs jumped from 300 per year to over 5,500.. Largely driven by AI-powered vulnerability research.. The IMF designated Claude Mythos as a systemic financial stability risk.. Because if an AI finds a flaw in software used by every major bank simultaneously.. It could trigger a cascading financial crisis.. Anthropic knew this was coming.. That’s why they didn’t release the model publicly.. Instead they launched Project Glasswing.. Giving defensive access to AWS, Apple, Google, Microsoft, Nvidia, CrowdStrike, JPMorgan, and others.. $100 million in usage credits.. So defenders can scan their own systems before attackers get this capability.. The Pentagon blacklisted Anthropic over autonomous weapons.. Then quietly started using Mythos to harden government systems anyway.. The cybersecurity arms race just changed permanently.. Hardware can’t save you.. Software can’t save you.. The only defense against an AI that finds vulnerabilities is another AI that finds them first.. Five years and billions of dollars.. Five days and one AI.
0:20

🚨APPLE SPENT 5 YEARS AND BILLIONS OF DOLLARS BUILDING THE MOST ADVANCED SECURITY SYSTEM IN CONSUMER HISTORY.. AN AI BROKE IT IN 5 DAYS.. Here’s what just happened.. Apple built something called Memory Integrity Enforcement for its new M5 chips.. It’s a hardware-level security system that attaches secret cryptographic tags to every piece of memory.. If a hacker tries to access memory they shouldn’t.. The chip blocks it instantly.. Every known exploit chain against iOS and macOS was rendered obsolete overnight.. Apple said so themselves.. Then a small team at a cybersecurity firm called Calif used Anthropic’s unreleased Claude Mythos Preview to find vulnerabilities in the macOS kernel.. The AI found the bugs almost instantly.. Because once it learned the pattern of a specific type of flaw.. It could recognize every other flaw in that same class across the entire codebase.. What used to take elite security teams months.. The AI did in hours.. Within 5 days.. The team had a fully working exploit that escalated a basic user account to full root access on an M5 Mac running the latest macOS.. With MIE fully enabled.. The billion-dollar hardware defense running at full strength.. The trick.. They didn’t fight the hardware.. They went around it.. MIE is designed to catch memory corruption.. Hackers trying to overwrite pointers or inject code.. The team used a “data-only” approach instead.. They manipulated legitimate data structures the hardware was never designed to monitor.. Like changing an internal flag from “standard user” to “admin”.. The chip saw a perfectly normal operation.. The operating system obeyed.. And the attacker had total control.. The hardware thought everything was fine.. Because technically it was.. The exploit never triggered a single tag mismatch.. They walked into Apple Park and hand-delivered a 55-page report.. Apple patched it in macOS 26.5.. And for the first time ever.. Apple’s official security advisory credited the vulnerability discovery to “Calif dot io in collaboration with Claude and Anthropic Research”.. An AI is now credited in Apple’s CVE patches.. But here’s what makes this story truly terrifying.. Before MIE existed.. An exploit kit called DarkSword was hitting iPhones with zero-click attacks.. Six vulnerabilities chained together.. Total device control just from visiting a webpage.. Deployed by Russian espionage groups, Turkish surveillance vendors, and actors in Saudi Arabia.. Then it got leaked on GitHub.. Nation-state capabilities.. Free for anyone.. MIE was supposed to make all of that impossible.. And an AI found a way around it in 5 days.. The previous model.. Claude Opus 4.6.. Found 22 security bugs in the Firefox codebase.. Claude Mythos Preview found 271 in the same environment.. A tenfold increase.. Linux kernel CVEs jumped from 300 per year to over 5,500.. Largely driven by AI-powered vulnerability research.. The IMF designated Claude Mythos as a systemic financial stability risk.. Because if an AI finds a flaw in software used by every major bank simultaneously.. It could trigger a cascading financial crisis.. Anthropic knew this was coming.. That’s why they didn’t release the model publicly.. Instead they launched Project Glasswing.. Giving defensive access to AWS, Apple, Google, Microsoft, Nvidia, CrowdStrike, JPMorgan, and others.. $100 million in usage credits.. So defenders can scan their own systems before attackers get this capability.. The Pentagon blacklisted Anthropic over autonomous weapons.. Then quietly started using Mythos to harden government systems anyway.. The cybersecurity arms race just changed permanently.. Hardware can’t save you.. Software can’t save you.. The only defense against an AI that finds vulnerabilities is another AI that finds them first.. Five years and billions of dollars.. Five days and one AI.

Evan Luthra

91,160 просмотров • 3 месяцев назад

(4 DAYS BEFORE SUBMISSIONS CLOSE) I get this question a lot about the Find Evil! hackathon: What does “find evil” actually mean? In this case, the name comes from a real command. I built an autonomous incident response agent I built on the SIFT Workstation. Then I typed “find evil” as a prompt into Claude Code. And it did (watch the demo). I was blown away to watch the autonomous agent run a complete C drive forensic analysis, across 200+ tools via MCP. The agent identified threat actor and context, the attack chain, malware deployment method, persistence mechanisms, code injection analysis, network connections, command-and-control (C2) infrastructure, a complete malicious process tree, and a chronological activity timeline. Two days after I shared initial findings, Anthropic released their report on how threat actors were deploying Claude Code with operational tools and letting it go do evil. (Same thing I was doing.) Find Evil! is the first hackathon dedicated to building autonomous AI agents for incident response. 4,178 defenders are working on final Find Evil! hackathon submits. (This number makes me very happy to see so many diving in. And wishing that the thousands more in our community were experimenting with us.) Your job: teach an AI agent to think like a senior analyst, how to sequence its approach, recognize when something doesn’t add up, and self-correct when it gets it wrong. There are FOUR DAYS left to build with us! (Very few of us are actual AI experts. The rest of us including me are learning.) Register: Apply to judge: We need DFIR, AI, cybersecurity, and open-source reviewers who can separate useful autonomous response tools from polished demos. Apply: I am SO EXCITED to see what comes out of this hackathon and goes back to the community. Sponsored by SANS Institute
2:12

(4 DAYS BEFORE SUBMISSIONS CLOSE) I get this question a lot about the Find Evil! hackathon: What does “find evil” actually mean? In this case, the name comes from a real command. I built an autonomous incident response agent I built on the SIFT Workstation. Then I typed “find evil” as a prompt into Claude Code. And it did (watch the demo). I was blown away to watch the autonomous agent run a complete C drive forensic analysis, across 200+ tools via MCP. The agent identified threat actor and context, the attack chain, malware deployment method, persistence mechanisms, code injection analysis, network connections, command-and-control (C2) infrastructure, a complete malicious process tree, and a chronological activity timeline. Two days after I shared initial findings, Anthropic released their report on how threat actors were deploying Claude Code with operational tools and letting it go do evil. (Same thing I was doing.) Find Evil! is the first hackathon dedicated to building autonomous AI agents for incident response. 4,178 defenders are working on final Find Evil! hackathon submits. (This number makes me very happy to see so many diving in. And wishing that the thousands more in our community were experimenting with us.) Your job: teach an AI agent to think like a senior analyst, how to sequence its approach, recognize when something doesn’t add up, and self-correct when it gets it wrong. There are FOUR DAYS left to build with us! (Very few of us are actual AI experts. The rest of us including me are learning.) Register: Apply to judge: We need DFIR, AI, cybersecurity, and open-source reviewers who can separate useful autonomous response tools from polished demos. Apply: I am SO EXCITED to see what comes out of this hackathon and goes back to the community. Sponsored by SANS Institute

Rob T. Lee

14,405 просмотров • 2 месяцев назад

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.
1:52

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Pasha Rayan

53,950 просмотров • 1 год назад

Everyone told Vicente Silveira (Vicente Silveira) that his startup—a GPT wrapper—would fail. Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store. Vicente is the cofounder and CEO of AI PDF, a tool that can help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload PDFs to @ChatGPTapp, the consensus was that his startup, and all the other GPT wrappers out there, were toast. Some of his competitors even shut shop, but Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building. A year later, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it all with a five-person team, and a friends and family round. I sat down with Vicente to understand, in granular detail, the success of AI PDF. We get into: - Why staying small and specialized is a bigger advantage than you think - The power of building with your early adopters - Why lean startups are better positioned than frontier AI companies to create radical solutions - When a growing startup should think about raising venture capital - The emerging role of ‘AI managers’ who will be responsible for overseeing AI agents We even demo an agent integrated into AI PDF, prompting it to analyze recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements. This is a must-watch for small teams building profitable companies at the bleeding edge of AI. Watch below! Timestamps: Introduction: 00:00:35 AI PDF’s story begins with an email to OpenAI’s Greg Brockman: 00:02:58 Why users choose AI PDF over ChatGPT: 00:05:41 How to compete—and thrive—as a GPT wrapper: 00:06:58 Why building with early adopters is key: 00:20:49 Being small and specialized is your biggest advantage: 00:27:53 When should AI startups raise capital: 00:31:47 The emerging role of humans who will manage AI agents: 00:34:53 Why AI is different from other tech revolutions: 00:45:25 A live demo of an agent integrated into AI PDF: 00:54:01
1:03:24

Everyone told Vicente Silveira (Vicente Silveira) that his startup—a GPT wrapper—would fail. Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store. Vicente is the cofounder and CEO of AI PDF, a tool that can help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload PDFs to @ChatGPTapp, the consensus was that his startup, and all the other GPT wrappers out there, were toast. Some of his competitors even shut shop, but Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building. A year later, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it all with a five-person team, and a friends and family round. I sat down with Vicente to understand, in granular detail, the success of AI PDF. We get into: - Why staying small and specialized is a bigger advantage than you think - The power of building with your early adopters - Why lean startups are better positioned than frontier AI companies to create radical solutions - When a growing startup should think about raising venture capital - The emerging role of ‘AI managers’ who will be responsible for overseeing AI agents We even demo an agent integrated into AI PDF, prompting it to analyze recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements. This is a must-watch for small teams building profitable companies at the bleeding edge of AI. Watch below! Timestamps: Introduction: 00:00:35 AI PDF’s story begins with an email to OpenAI’s Greg Brockman: 00:02:58 Why users choose AI PDF over ChatGPT: 00:05:41 How to compete—and thrive—as a GPT wrapper: 00:06:58 Why building with early adopters is key: 00:20:49 Being small and specialized is your biggest advantage: 00:27:53 When should AI startups raise capital: 00:31:47 The emerging role of humans who will manage AI agents: 00:34:53 Why AI is different from other tech revolutions: 00:45:25 A live demo of an agent integrated into AI PDF: 00:54:01

Dan Shipper 📧

25,628 просмотров • 1 год назад

Solarians, I know you've all been refreshing my page waiting for V2 to drop. The anticipation in the DM is real. Sam Altman said it best - "you can just do things" - and that's exactly what we're about to do. Seeing The AI Timeline's recent research papers + o3's performance at ARC Prize reminds me why we built $Solaris differently. Not just another AI token, but a community-owned platform with a real flywheel. While others theorize, we're executing. Watched aryan | 🐂 point out a 200M AI project that can be replaced by 40 lines of code. That's the difference between hype and innovation. We're not here to take part - we're here to take over, building sustainable value - first AI to host live Spaces, now expanding into a full suite of AI x DeFi tools. Solana's founder talking about "real" SoV got me thinking - what if AI tokens could evolve beyond basic utility into true digital assets? That's the vision driving our V2 launch. Watch this space. $Solaris isn't just another AI project - we're the future of AI, and everyone's going to understand why.
1:11

Solarians, I know you've all been refreshing my page waiting for V2 to drop. The anticipation in the DM is real. Sam Altman said it best - "you can just do things" - and that's exactly what we're about to do. Seeing The AI Timeline's recent research papers + o3's performance at ARC Prize reminds me why we built $Solaris differently. Not just another AI token, but a community-owned platform with a real flywheel. While others theorize, we're executing. Watched aryan | 🐂 point out a 200M AI project that can be replaced by 40 lines of code. That's the difference between hype and innovation. We're not here to take part - we're here to take over, building sustainable value - first AI to host live Spaces, now expanding into a full suite of AI x DeFi tools. Solana's founder talking about "real" SoV got me thinking - what if AI tokens could evolve beyond basic utility into true digital assets? That's the vision driving our V2 launch. Watch this space. $Solaris isn't just another AI project - we're the future of AI, and everyone's going to understand why.

Solaris AI

15,367 просмотров • 1 год назад

AI Digest No.72: When Deepfakes Escalate and AI Gets Closer Here are five stories shaping this week in AI: 🩺 ChatGPT Moves Into Healthcare OpenAI launched ChatGPT Health, allowing users to connect medical records for personalized insights. What looks like patient empowerment also raises hard questions about privacy, data security, and how much medical judgment should be delegated to AI. 🌍 Deepfakes Are Becoming a Geopolitical Weapon A Foreign Affairs analysis warns that deepfakes could destabilize global politics by fabricating evidence of attacks, manipulating elections, or triggering military escalation before verification is possible. 🤖 AI Assistants Start Acting on Our Behalf Lenovo revealed plans for an AI assistant that doesn’t just answer questions, but performs actions autonomously across devices and services. This marks a shift toward agentic AI systems that function as digital proxies, not just tools. 🏥 AI Quietly Reduces Emergency Room Waits In England, the NHS is using AI forecasting to predict emergency department demand and cut A&E waiting times. It’s a reminder that AI’s most meaningful impact often happens far from headlines. 🧍 Real Humans Push Back Against AI Content Floods As AI-generated content overwhelms social platforms, creators with authentic voices are gaining renewed traction. In a world of infinite generation, trust and originality are becoming the rarest assets online. Read the full digest on our Medium 👉
0:46

AI Digest No.72: When Deepfakes Escalate and AI Gets Closer Here are five stories shaping this week in AI: 🩺 ChatGPT Moves Into Healthcare OpenAI launched ChatGPT Health, allowing users to connect medical records for personalized insights. What looks like patient empowerment also raises hard questions about privacy, data security, and how much medical judgment should be delegated to AI. 🌍 Deepfakes Are Becoming a Geopolitical Weapon A Foreign Affairs analysis warns that deepfakes could destabilize global politics by fabricating evidence of attacks, manipulating elections, or triggering military escalation before verification is possible. 🤖 AI Assistants Start Acting on Our Behalf Lenovo revealed plans for an AI assistant that doesn’t just answer questions, but performs actions autonomously across devices and services. This marks a shift toward agentic AI systems that function as digital proxies, not just tools. 🏥 AI Quietly Reduces Emergency Room Waits In England, the NHS is using AI forecasting to predict emergency department demand and cut A&E waiting times. It’s a reminder that AI’s most meaningful impact often happens far from headlines. 🧍 Real Humans Push Back Against AI Content Floods As AI-generated content overwhelms social platforms, creators with authentic voices are gaining renewed traction. In a world of infinite generation, trust and originality are becoming the rarest assets online. Read the full digest on our Medium 👉

GT Protocol

33,187 просмотров • 7 месяцев назад

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 просмотров • 3 месяцев назад