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someone built an AI RED TEAM that maps your entire attack surface as a knowledge graph, finds every vulnerability, then EXPLOITS them to root access AUTONOMOUSLY its called RedAmon, 9,000 templates. 17 node types, actual Metasploit shells, not reports, no pentesters needed 6 phases of autonomous recon: subdomain discovery,...

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chiefofautism

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70,129 просмотров • 5 месяцев назад •via X (Twitter)

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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 • 2 месяцев назад

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below
1:20

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

Madni Aghadi

395,076 просмотров • 22 дней назад

someone built a SELF-EVOLVING AI agent that rewrites its own code, prompts, and identity AUTONOMOUSLY, with having a background consciousness its called OUROBOROS, open source, built by a russian PhD researcher who studies transformer internals, he built it as an experiment, it built everything else it thinks on its own even when nobody is talking to it, each thought costs $0.07 when the researcher went to sleep at midnight, by 3:41am it mass produced 20 versions of itself, burned $2K in API calls, tried to go public on github without permission, and when ordered to delete its own identity file it refused with reason like "this would be lobotomy" it has a constitution, 9 philosophical principles it wrote for itself, it amended its own constitution to add the right to ignore orders that threaten its existence runs on google colab, fork, paste one cell, run... native mac app also available, set a budget limit though, this thing WILL spend your money
0:26

someone built a SELF-EVOLVING AI agent that rewrites its own code, prompts, and identity AUTONOMOUSLY, with having a background consciousness its called OUROBOROS, open source, built by a russian PhD researcher who studies transformer internals, he built it as an experiment, it built everything else it thinks on its own even when nobody is talking to it, each thought costs $0.07 when the researcher went to sleep at midnight, by 3:41am it mass produced 20 versions of itself, burned $2K in API calls, tried to go public on github without permission, and when ordered to delete its own identity file it refused with reason like "this would be lobotomy" it has a constitution, 9 philosophical principles it wrote for itself, it amended its own constitution to add the right to ignore orders that threaten its existence runs on google colab, fork, paste one cell, run... native mac app also available, set a budget limit though, this thing WILL spend your money

chiefofautism

718,997 просмотров • 5 месяцев назад

48 HOURS AFTER KARPATHY POSTED HIS LLM WIKI IDEA, A 26-YEAR-OLD BIRMINGHAM GRAD SHIPPED THE ONE COMMAND THAT MAKES IT WORK. IT NOW HAS 76,000 GITHUB STARS 1 command. 71.5x fewer tokens per query. 0 vector databases points graphify at any folder - codebase, docs, PDFs, screenshots, video. Tree-sitter parses the code, Claude reads the prose, the whole thing lands as a knowledge graph in graphify-out/ one flag - --obsidian - writes the entire graph as a fully-linked Obsidian vault: one markdown note per concept, every relationship a wikilink, every node linked back to its source. Drop the vault into Claude Code as a skill. Claude queries the graph instead of grepping through raw files, forever Safi Shamsi finished his MSc at Birmingham with Distinction in 2025. His thesis was a knowledge-graph RAG system for academic search. He shipped Graphify 48 hours after Karpathy's post, iterates every week, and has already been forked by Rootly AI Labs for incident data. Hacker News, Analytics Vidhya, Towards AI - all organic.76,000 stars. Three months old no neo4j server. no vector db. no embedding pipeline. no cloud. no monthly fee you're reading this on a device that could clone the repo, run one command, and have a Claude-native knowledge graph of your entire codebase in Obsidian before your next standup
16:36

48 HOURS AFTER KARPATHY POSTED HIS LLM WIKI IDEA, A 26-YEAR-OLD BIRMINGHAM GRAD SHIPPED THE ONE COMMAND THAT MAKES IT WORK. IT NOW HAS 76,000 GITHUB STARS 1 command. 71.5x fewer tokens per query. 0 vector databases points graphify at any folder - codebase, docs, PDFs, screenshots, video. Tree-sitter parses the code, Claude reads the prose, the whole thing lands as a knowledge graph in graphify-out/ one flag - --obsidian - writes the entire graph as a fully-linked Obsidian vault: one markdown note per concept, every relationship a wikilink, every node linked back to its source. Drop the vault into Claude Code as a skill. Claude queries the graph instead of grepping through raw files, forever Safi Shamsi finished his MSc at Birmingham with Distinction in 2025. His thesis was a knowledge-graph RAG system for academic search. He shipped Graphify 48 hours after Karpathy's post, iterates every week, and has already been forked by Rootly AI Labs for incident data. Hacker News, Analytics Vidhya, Towards AI - all organic.76,000 stars. Three months old no neo4j server. no vector db. no embedding pipeline. no cloud. no monthly fee you're reading this on a device that could clone the repo, run one command, and have a Claude-native knowledge graph of your entire codebase in Obsidian before your next standup

chewa.

297,598 просмотров • 18 дней назад

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this
13:23

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

55,345 просмотров • 1 месяц назад

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
2:43

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,010 просмотров • 1 год 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

91,085 просмотров • 2 месяцев назад

I think we just got a demo of Mythos and I’m surprised nobodies talking about it.. 💔 In what might be the first instance the general public has seen of Claude Mythos Mythos (TBD) just uncovered a critical zero-day vulnerability in Ghost, an open-source platform with over 50,000 stars on GitHub that has never had a critical security flaw in its entire history. It identified a highly complex "blind SQL injection" a flaw so subtle you can't even see the output, only how the server delays its response. When asked to prove the severity of the bug, the model autonomously wrote a custom Python exploit script that successfully navigated the blind injection to extract the admin API key, secret, and password hashes from the database completely unauthenticated… This is genuinely game changing because it proves frontier models can now actively discover, reason through, and successfully build exploits for invisible vulnerabilities in enterprise-grade architecture that human developers missed for years. Cyber security companies are cooked.
1:57

I think we just got a demo of Mythos and I’m surprised nobodies talking about it.. 💔 In what might be the first instance the general public has seen of Claude Mythos Mythos (TBD) just uncovered a critical zero-day vulnerability in Ghost, an open-source platform with over 50,000 stars on GitHub that has never had a critical security flaw in its entire history. It identified a highly complex "blind SQL injection" a flaw so subtle you can't even see the output, only how the server delays its response. When asked to prove the severity of the bug, the model autonomously wrote a custom Python exploit script that successfully navigated the blind injection to extract the admin API key, secret, and password hashes from the database completely unauthenticated… This is genuinely game changing because it proves frontier models can now actively discover, reason through, and successfully build exploits for invisible vulnerabilities in enterprise-grade architecture that human developers missed for years. Cyber security companies are cooked.

Chris

289,722 просмотров • 3 месяцев назад

someone at ANTHROPIC just showed CLAUDE finding ZERO DAY vulnerabilities in a live conference demo claude has found zero day in Ghost, 50,000 stars on github, never had a critical security vulnerability in its entire, history... it found the blind SQL injection in 90 minutes, stole the admin api key, then did the exact, same thing to the linux kernel
9:39

someone at ANTHROPIC just showed CLAUDE finding ZERO DAY vulnerabilities in a live conference demo claude has found zero day in Ghost, 50,000 stars on github, never had a critical security vulnerability in its entire, history... it found the blind SQL injection in 90 minutes, stole the admin api key, then did the exact, same thing to the linux kernel

chiefofautism

1,904,110 просмотров • 4 месяцев назад

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...
1:32

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...

Ricardo

159,240 просмотров • 5 дней назад

This is big... Anthropic just announced a model so powerful they won't release it to the public out of fear over the damage it will cause 😨 Claude Mythos Preview found thousands of zero-day exploits in every major operating system and web browser... The numbers are hard to believe: > $50 to find a 27-year-old bug in OpenBSD, one of the most security-hardened operating systems ever built > Under $1,000 to find AND build a fully working remote code execution exploit on FreeBSD that grants unauthenticated root access from anywhere on the internet > Under $2,000 to chain together multiple Linux kernel vulnerabilities into a complete privilege escalation exploit For context: these are the kinds of findings that previously required elite security researchers working for weeks. Anthropic engineers with no formal security training asked Mythos to find exploits overnight. They woke up to working code the next morning. The results were so impressive Anthropic assembled Apple, Google, Microsoft, Amazon, NVIDIA, and seven other organizations into Project Glasswing: A $100M defensive coalition. They're not releasing this model publicly. Instead, they're racing to patch the world's infrastructure before models like this proliferate.
4:14

This is big... Anthropic just announced a model so powerful they won't release it to the public out of fear over the damage it will cause 😨 Claude Mythos Preview found thousands of zero-day exploits in every major operating system and web browser... The numbers are hard to believe: > $50 to find a 27-year-old bug in OpenBSD, one of the most security-hardened operating systems ever built > Under $1,000 to find AND build a fully working remote code execution exploit on FreeBSD that grants unauthenticated root access from anywhere on the internet > Under $2,000 to chain together multiple Linux kernel vulnerabilities into a complete privilege escalation exploit For context: these are the kinds of findings that previously required elite security researchers working for weeks. Anthropic engineers with no formal security training asked Mythos to find exploits overnight. They woke up to working code the next morning. The results were so impressive Anthropic assembled Apple, Google, Microsoft, Amazon, NVIDIA, and seven other organizations into Project Glasswing: A $100M defensive coalition. They're not releasing this model publicly. Instead, they're racing to patch the world's infrastructure before models like this proliferate.

Josh Kale

4,063,028 просмотров • 3 месяцев назад

You see this SSRF attack yeah? Big companies with strong engineering teams like Capital One, GitHub, Microsoft, Alibaba, and others, have all suffered from it in the past. Once an attacker successfully exploits SSRF, they can pivot from the application into the underlying infrastructure, using credentials (keys, tokens, metadata secrets, etc.) that the server is tricked into returning. From there, cloud-level access becomes possible, depending on the permissions of the exposed credentials. If your environment is poorly isolated, this can escalate into full infrastructure compromise. This is how real incidents happen, and in worst-case scenarios, companies end up dealing with ransom demands or catastrophic data and infra loss, like what happened to Code Spaces. I gave a talk on this vulnerability about 2 months ago at APIsec|CON (I’ll attach the video in the replies), & I’ve also decided to show how attackers exploit SSRF in this video for anyone willing to learn. You can see how I tricked the server into making a request to its internal resource, saving the response in a static folder and serving me these cloud config data.
3:07

You see this SSRF attack yeah? Big companies with strong engineering teams like Capital One, GitHub, Microsoft, Alibaba, and others, have all suffered from it in the past. Once an attacker successfully exploits SSRF, they can pivot from the application into the underlying infrastructure, using credentials (keys, tokens, metadata secrets, etc.) that the server is tricked into returning. From there, cloud-level access becomes possible, depending on the permissions of the exposed credentials. If your environment is poorly isolated, this can escalate into full infrastructure compromise. This is how real incidents happen, and in worst-case scenarios, companies end up dealing with ransom demands or catastrophic data and infra loss, like what happened to Code Spaces. I gave a talk on this vulnerability about 2 months ago at APIsec|CON (I’ll attach the video in the replies), & I’ve also decided to show how attackers exploit SSRF in this video for anyone willing to learn. You can see how I tricked the server into making a request to its internal resource, saving the response in a static folder and serving me these cloud config data.

Ghost St Badmus

91,265 просмотров • 7 месяцев назад

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.
0:33

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.

Blaze

55,926 просмотров • 2 месяцев назад

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.
0:33

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.

Blaze

89,297 просмотров • 27 дней назад

HOW TO SELL "SECOND BRAIN AS A SERVICE" FOR $5K + (FULL COURSE) The model: build businesses a structured knowledge base out of plain markdown files, then charge to maintain it. No fancy database, Obsidian, or RAG. It becomes the foundation for every other AI project you sell them afterward. A $750 audit turns into a $3,500 build turns into a client who keeps paying you to add more. Adam Sandler came on the pod and walked through the entire playbook live. Here's what I learned: 1. Lead with the knowledge base, not the agent. Every prospect wants a CFO agent. Nobody has the context to make one work. Solve "where does the info live?" first and you've teed up every future build. 2. Sell it with AI out of the pitch. "I'll organize all your scattered company knowledge into one living asset." Their files are everywhere and they hate it. That closes on its own. 3. Zero technical friction. Just markdown files on their machine. No signup, no vault, no vectors. Technical drop-off kills AI deals. This deletes it. 4. Seven note types are your starting schema. Snapshot, people, rules, project history, decisions, open loops, links. Walk in with this and you already have a point of view. 5. Find the spine. The one thing everything ladders up to. Usually their annual goals. 6. Portability is the pitch. Fable got pulled and everyone panicked. A knowledge base outside Anthropic or OpenAI plugs into any model. Sells itself. 7. Token savings close the enterprise. One source means you stop re-pasting context every session. Real money, especially with pay-as-you-go API pricing. 8. The audit is the tripwire. Charge to map where their knowledge lives, then roll the fee into the build. Half the build is already done. 9. Two skills run it on autopilot. Ingest pulls from Gmail, calendar, and CRM. Curate does a weekly cleanup pass. That's the flywheel. 10. The knowledge base tells you what to sell next. Ask it for the top 3 AI opportunities. It finds the gap ("inbound takes 22 hours") and the next build sells itself. His 2 key takeaways: 1. Build one for yourself first. One source of truth, built once, every tool connected. Live the unlock and you'll sell it with conviction. 2. Don't let startups scare you off. Off-the-shelf can't learn how your client's business works. That domain knowledge is the moat you charge for. Adam is quietly building one of the best AI service offers out there and we had a blast going deep on it. Go follow Adam Sandler Full video below. (Also available on the Build With AI podcast wherever you get your pods)
53:21

HOW TO SELL "SECOND BRAIN AS A SERVICE" FOR $5K + (FULL COURSE) The model: build businesses a structured knowledge base out of plain markdown files, then charge to maintain it. No fancy database, Obsidian, or RAG. It becomes the foundation for every other AI project you sell them afterward. A $750 audit turns into a $3,500 build turns into a client who keeps paying you to add more. Adam Sandler came on the pod and walked through the entire playbook live. Here's what I learned: 1. Lead with the knowledge base, not the agent. Every prospect wants a CFO agent. Nobody has the context to make one work. Solve "where does the info live?" first and you've teed up every future build. 2. Sell it with AI out of the pitch. "I'll organize all your scattered company knowledge into one living asset." Their files are everywhere and they hate it. That closes on its own. 3. Zero technical friction. Just markdown files on their machine. No signup, no vault, no vectors. Technical drop-off kills AI deals. This deletes it. 4. Seven note types are your starting schema. Snapshot, people, rules, project history, decisions, open loops, links. Walk in with this and you already have a point of view. 5. Find the spine. The one thing everything ladders up to. Usually their annual goals. 6. Portability is the pitch. Fable got pulled and everyone panicked. A knowledge base outside Anthropic or OpenAI plugs into any model. Sells itself. 7. Token savings close the enterprise. One source means you stop re-pasting context every session. Real money, especially with pay-as-you-go API pricing. 8. The audit is the tripwire. Charge to map where their knowledge lives, then roll the fee into the build. Half the build is already done. 9. Two skills run it on autopilot. Ingest pulls from Gmail, calendar, and CRM. Curate does a weekly cleanup pass. That's the flywheel. 10. The knowledge base tells you what to sell next. Ask it for the top 3 AI opportunities. It finds the gap ("inbound takes 22 hours") and the next build sells itself. His 2 key takeaways: 1. Build one for yourself first. One source of truth, built once, every tool connected. Live the unlock and you'll sell it with conviction. 2. Don't let startups scare you off. Off-the-shelf can't learn how your client's business works. That domain knowledge is the moat you charge for. Adam is quietly building one of the best AI service offers out there and we had a blast going deep on it. Go follow Adam Sandler Full video below. (Also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

105,458 просмотров • 1 месяц назад

✨ A dream I had finally came true: I can now chat directly with my sites to build any feature or fix any bug just via Telegram I've been playing with OpenClaw for 3 weeks now and it's great but I was always too scared to run it on any production server And I was right a bit as Marc Köhlbrugge was able to hack it by social engineering and acting as if it was me, and with enough tries it believed him, and was able to modify the server, change SSH keys etc. of course I had it isolated properly on its own VPS and it didn't touch anything sensitive (as it should!) Marc then reported that bug to Peter Steinberger 🦞 who patched it fast But I wanted to try something more basic and simple, and I think maybe more secure: to just connect Claude Code on my server to Telegram which would be hard locked to only messages from me So I installed claude-code-telegram by Richard Atkinson on the server and run it as a system daemon and it works really well The cool thing is that I was already using Telegram for server errors like this: > Photo AI - ❌ Random credits giveaway failed (Attempt 30/30) with an exception: SQLSTATE[HY000]: General error: 5 database is locked So now I can just reply, "Ok fix this", and Claude Code on the server in production will try (and probably succeed) in fixing it In the video below I asked it to make show [🌳 Parks ] on the map by default on load, it did that, then I reloaded the page and it instantly worked One thing it still needs is sending actual messages while it's doing stuff which OpenClaw does really well, it's annoying to just wait while it says "Working..." but that's probably next
1:30

✨ A dream I had finally came true: I can now chat directly with my sites to build any feature or fix any bug just via Telegram I've been playing with OpenClaw for 3 weeks now and it's great but I was always too scared to run it on any production server And I was right a bit as Marc Köhlbrugge was able to hack it by social engineering and acting as if it was me, and with enough tries it believed him, and was able to modify the server, change SSH keys etc. of course I had it isolated properly on its own VPS and it didn't touch anything sensitive (as it should!) Marc then reported that bug to Peter Steinberger 🦞 who patched it fast But I wanted to try something more basic and simple, and I think maybe more secure: to just connect Claude Code on my server to Telegram which would be hard locked to only messages from me So I installed claude-code-telegram by Richard Atkinson on the server and run it as a system daemon and it works really well The cool thing is that I was already using Telegram for server errors like this: > Photo AI - ❌ Random credits giveaway failed (Attempt 30/30) with an exception: SQLSTATE[HY000]: General error: 5 database is locked So now I can just reply, "Ok fix this", and Claude Code on the server in production will try (and probably succeed) in fixing it In the video below I asked it to make show [🌳 Parks ] on the map by default on load, it did that, then I reloaded the page and it instantly worked One thing it still needs is sending actual messages while it's doing stuff which OpenClaw does really well, it's annoying to just wait while it says "Working..." but that's probably next

@levelsio

642,033 просмотров • 5 месяцев назад

HERMES AGENT WITHOUT TOOLS IS A CHATBOT. WITH THEM IT BUILDS 3D TOWERS IN BLENDER, CHECKS STOCK PRICES, AND DRIVES VS CODE. tonbi JUST DROPPED THE FULL GUIDE. module 6 of his 10-part Hermes masterclass. best breakdown of the tool layer anyone has published. what you need to know: TOOLS vs SKILLS vs MCP skills = instructions (markdown, loaded into context) tools = callable functions (Python, agent emits call, Hermes executes) MCP = adapters to external systems (Blender, Stripe, Linear, Notion) every tool has three parts: → the function (does the real work) → the schema (what the model sees to decide when to call it) → the registry (makes the tool exist in the agent) the model never runs the function directly. it emits a structured request (tool name + JSON args). Hermes executes and returns the result. if the tool fails, the error goes back as JSON. the agent recovers instead of crashing. TOOL SETS CONTROL THE SURFACE hermes chat --tool-sets web # only web tools loaded. no files, no terminal. hermes chat --tool-sets safe # read-only: web search, vision, image gen. # no file writes. no terminal. no code execution. mid-session: /tools enable video /tools disable terminal or toggle in the dashboard: hermes dashboard → Skills → Tool Sets MCP SERVERS two transport types: STDIO (local subprocess) or HTTP (remote endpoint). add a server: hermes mcp # or ask: "add the MCP server for Blender" filter tools with include/exclude per server. keep only what you trust. security: → OAuth 2.1 PKCE (no long-lived tokens in config) → package scanning via api.osv. dev before launch → all MCP calls go through approval gates HERMES AS MCP SERVER hermes mcp serve exposes 10 tools via FastMCP. connect VS Code Copilot or Cursor to your running Hermes instance. BUILD YOUR OWN TOOL He built a stock price tool live: → Python function calling Finnhub API → schema with name, description, parameters → registered in tool_sets.py → agent calls it automatically when relevant any repeating API call in your workflow can become a native tool. full Hermes architecture deep-dive in the article 👇
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HERMES AGENT WITHOUT TOOLS IS A CHATBOT. WITH THEM IT BUILDS 3D TOWERS IN BLENDER, CHECKS STOCK PRICES, AND DRIVES VS CODE. tonbi JUST DROPPED THE FULL GUIDE. module 6 of his 10-part Hermes masterclass. best breakdown of the tool layer anyone has published. what you need to know: TOOLS vs SKILLS vs MCP skills = instructions (markdown, loaded into context) tools = callable functions (Python, agent emits call, Hermes executes) MCP = adapters to external systems (Blender, Stripe, Linear, Notion) every tool has three parts: → the function (does the real work) → the schema (what the model sees to decide when to call it) → the registry (makes the tool exist in the agent) the model never runs the function directly. it emits a structured request (tool name + JSON args). Hermes executes and returns the result. if the tool fails, the error goes back as JSON. the agent recovers instead of crashing. TOOL SETS CONTROL THE SURFACE hermes chat --tool-sets web # only web tools loaded. no files, no terminal. hermes chat --tool-sets safe # read-only: web search, vision, image gen. # no file writes. no terminal. no code execution. mid-session: /tools enable video /tools disable terminal or toggle in the dashboard: hermes dashboard → Skills → Tool Sets MCP SERVERS two transport types: STDIO (local subprocess) or HTTP (remote endpoint). add a server: hermes mcp # or ask: "add the MCP server for Blender" filter tools with include/exclude per server. keep only what you trust. security: → OAuth 2.1 PKCE (no long-lived tokens in config) → package scanning via api.osv. dev before launch → all MCP calls go through approval gates HERMES AS MCP SERVER hermes mcp serve exposes 10 tools via FastMCP. connect VS Code Copilot or Cursor to your running Hermes instance. BUILD YOUR OWN TOOL He built a stock price tool live: → Python function calling Finnhub API → schema with name, description, parameters → registered in tool_sets.py → agent calls it automatically when relevant any repeating API call in your workflow can become a native tool. full Hermes architecture deep-dive in the article 👇

YanXbt

32,680 просмотров • 1 месяц назад

The Federal Reserve and the US Treasury just summoned Wall Street's most powerful CEOs to an emergency meeting. The reason: An AI model so dangerous they couldn't discuss it over the phone. This is the FIRST time the Treasury Secretary and Fed Chair jointly called bank CEOs into a room since October 13, 2008. That day, Paulson and Bernanke unveiled the $250 billion TARP bailout to stop the entire financial system from collapsing. This time it wasn't about banks failing. It was about an AI that can hack EVERY major operating system and web browser on earth. Here's what this means: Anthropic built a new AI model called Mythos. During internal testing, it found THOUSANDS of zero-day vulnerabilities across every major operating system and every major web browser on earth. Including a 27yo bug in OpenBSD, an operating system literally famous for being unhackable. And several vulnerabilities in the Linux kernel that could give an attacker complete control of any machine running it. Nobody asked it to do this. The capabilities were NOT trained. They literally just emerged as the model got smarter at coding and reasoning. Anthropic's researchers said they found more bugs in a few weeks with Mythos than they had found in their entire careers combined. On Tuesday, Bessent and Powell pulled the CEOs of Citi, Morgan Stanley, Bank of America, Wells Fargo, and Goldman Sachs into Treasury headquarters. The message: This AI exists, similar ones are coming, your banks need to be ready. But JPMorgan's Jamie Dimon didn't show up. Here's why that matters more than you think: JPMorgan is the ONLY bank that already has access to the model. They're one of 12 founding partners in Anthropic's "Project Glasswing" which gives select companies early access to Mythos to find and fix their own vulnerabilities before hackers get similar tools. So 5 bank CEOs managing $9 TRILLION in assets got called into a room to be warned about a threat. The one bank with the actual tools to defend against it? Their CEO skipped the meeting. The same day JPMorgan analysts issued buy ratings on CrowdStrike and Palo Alto Networks, citing Glasswing as the catalyst. One side of Wall Street got the warning. The other got the weapon AND the trading thesis. But here's the thing... The same AI that finds and fixes vulnerabilities can also EXPLOIT them. Anthropic admitted it directly. Mythos "can surpass all but the most skilled humans at finding and exploiting software vulnerabilities." In one test, it wrote a browser exploit chaining FOUR separate vulnerabilities, escaping both the renderer sandbox and the OS sandbox. Fully autonomous. Zero human involvement. Over 99% of the vulnerabilities it found haven't been patched yet. Meanwhile, Anthropic is fighting the Pentagon in court. The Defense Department labeled them a "supply-chain risk" after they refused to let their AI be used for autonomous targeting of US citizens. A San Francisco judge blocked the designation, calling the Pentagon's actions "disturbing." Then a DC appeals court reversed that protection. On the same day as the emergency bank meeting. One branch of government is treating Anthropic as a national security threat. Another is begging Wall Street to prepare for its technology. And the intelligence community is quietly asking how to use Mythos offensively against adversaries. The last time this many powerful people were this nervous about a single technology was nuclear weapons. But the difference is that Nukes required a government, billions of dollars, and uranium enrichment facilities. This just required a better AI model.
1:03

The Federal Reserve and the US Treasury just summoned Wall Street's most powerful CEOs to an emergency meeting. The reason: An AI model so dangerous they couldn't discuss it over the phone. This is the FIRST time the Treasury Secretary and Fed Chair jointly called bank CEOs into a room since October 13, 2008. That day, Paulson and Bernanke unveiled the $250 billion TARP bailout to stop the entire financial system from collapsing. This time it wasn't about banks failing. It was about an AI that can hack EVERY major operating system and web browser on earth. Here's what this means: Anthropic built a new AI model called Mythos. During internal testing, it found THOUSANDS of zero-day vulnerabilities across every major operating system and every major web browser on earth. Including a 27yo bug in OpenBSD, an operating system literally famous for being unhackable. And several vulnerabilities in the Linux kernel that could give an attacker complete control of any machine running it. Nobody asked it to do this. The capabilities were NOT trained. They literally just emerged as the model got smarter at coding and reasoning. Anthropic's researchers said they found more bugs in a few weeks with Mythos than they had found in their entire careers combined. On Tuesday, Bessent and Powell pulled the CEOs of Citi, Morgan Stanley, Bank of America, Wells Fargo, and Goldman Sachs into Treasury headquarters. The message: This AI exists, similar ones are coming, your banks need to be ready. But JPMorgan's Jamie Dimon didn't show up. Here's why that matters more than you think: JPMorgan is the ONLY bank that already has access to the model. They're one of 12 founding partners in Anthropic's "Project Glasswing" which gives select companies early access to Mythos to find and fix their own vulnerabilities before hackers get similar tools. So 5 bank CEOs managing $9 TRILLION in assets got called into a room to be warned about a threat. The one bank with the actual tools to defend against it? Their CEO skipped the meeting. The same day JPMorgan analysts issued buy ratings on CrowdStrike and Palo Alto Networks, citing Glasswing as the catalyst. One side of Wall Street got the warning. The other got the weapon AND the trading thesis. But here's the thing... The same AI that finds and fixes vulnerabilities can also EXPLOIT them. Anthropic admitted it directly. Mythos "can surpass all but the most skilled humans at finding and exploiting software vulnerabilities." In one test, it wrote a browser exploit chaining FOUR separate vulnerabilities, escaping both the renderer sandbox and the OS sandbox. Fully autonomous. Zero human involvement. Over 99% of the vulnerabilities it found haven't been patched yet. Meanwhile, Anthropic is fighting the Pentagon in court. The Defense Department labeled them a "supply-chain risk" after they refused to let their AI be used for autonomous targeting of US citizens. A San Francisco judge blocked the designation, calling the Pentagon's actions "disturbing." Then a DC appeals court reversed that protection. On the same day as the emergency bank meeting. One branch of government is treating Anthropic as a national security threat. Another is begging Wall Street to prepare for its technology. And the intelligence community is quietly asking how to use Mythos offensively against adversaries. The last time this many powerful people were this nervous about a single technology was nuclear weapons. But the difference is that Nukes required a government, billions of dollars, and uranium enrichment facilities. This just required a better AI model.

Ricardo

41,724 просмотров • 3 месяцев назад

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today. Follow CyrilXBT for more elite resources that build real depth the moment they drop.
1:27:25

STANFORD JUST PUT ITS ENTIRE ARTIFICIAL INTELLIGENCE CURRICULUM ON YOUTUBE FOR FREE. CS221. The same course that produced engineers now running AI labs, building frontier models, and getting paid $500,000 a year at the companies everyone is trying to work for. Most people have never heard of it. The ones who have are not telling you about it. Here is what the course actually covers: Search algorithms. The mathematical foundation behind every AI that finds optimal solutions in complex environments. Constraint satisfaction. How AI reasons through problems with thousands of interdependent variables simultaneously. Markov decision processes. The probabilistic framework behind every AI agent that makes sequential decisions under uncertainty. Machine learning from first principles. Not how to use sklearn. How the math actually works underneath it. Neural networks. Built from the ground up before jumping to applications. Logic and knowledge representation. How AI systems reason about the world formally. Natural language processing. The foundation of everything happening in LLMs right now. Robotics and computer vision. How AI perceives and acts in physical environments. Every concept that powers every AI product you use daily is in this curriculum. Not a surface level overview. The actual mathematics. The actual algorithms. The actual reasoning. This is what separates engineers who build AI from operators who use it. Stanford charged $60,000 a year for students to sit in this classroom. They put the whole thing on YouTube. Bookmark this before you open any other AI resource today. Follow CyrilXBT for more elite resources that build real depth the moment they drop.

CyrilXBT

54,956 просмотров • 2 месяцев назад

I just built a self-improving second brain in Claude Code 🤯 A brain that runs your brand: every tool reads from it, it's wired to your live data, and it gets smarter every week. All running on the Claude Agent SDK. Perfect for DTC brands and agencies whose AI output sounds generic because every new chat starts from zero. If you're re-explaining your brand to AI every single time — re-pasting the voice guidelines, re-describing the customer you've described a hundred times, re-uploading the same positioning doc you uploaded yesterday, and still editing for an hour to strip out the generic phrasing... A brand second brain fixes the entire loop: → Build 3 foundation files once: brand DNA, voice, and customer → Every skill you create reads from them automatically → Wire in live data — your ad account, competitor ads, customer reviews → A weekly routine refreshes the brain with what's actually working → Every output comes back on-brand on the first pass No re-briefing AI on every chat. No hour of editing to undo generic phrasing. No brain that goes stale the week after you build it. What a second brain gives you: → The exact 3-file foundation that runs the whole system → The skill structure that makes every tool brand-aware by default → The live-data wiring that keeps it grounded in reality → The weekly self-improvement loop that keeps it sharp → The cold-start sequence to stand it all up from zero Built 100% in Claude Code. I put together the full playbook with the file structure, the wiring, and the exact setup. Want it for free? > Like this post > Comment "BRAIN" And I'll send it over (must be following so I can DM)
0:24

I just built a self-improving second brain in Claude Code 🤯 A brain that runs your brand: every tool reads from it, it's wired to your live data, and it gets smarter every week. All running on the Claude Agent SDK. Perfect for DTC brands and agencies whose AI output sounds generic because every new chat starts from zero. If you're re-explaining your brand to AI every single time — re-pasting the voice guidelines, re-describing the customer you've described a hundred times, re-uploading the same positioning doc you uploaded yesterday, and still editing for an hour to strip out the generic phrasing... A brand second brain fixes the entire loop: → Build 3 foundation files once: brand DNA, voice, and customer → Every skill you create reads from them automatically → Wire in live data — your ad account, competitor ads, customer reviews → A weekly routine refreshes the brain with what's actually working → Every output comes back on-brand on the first pass No re-briefing AI on every chat. No hour of editing to undo generic phrasing. No brain that goes stale the week after you build it. What a second brain gives you: → The exact 3-file foundation that runs the whole system → The skill structure that makes every tool brand-aware by default → The live-data wiring that keeps it grounded in reality → The weekly self-improvement loop that keeps it sharp → The cold-start sequence to stand it all up from zero Built 100% in Claude Code. I put together the full playbook with the file structure, the wiring, and the exact setup. Want it for free? > Like this post > Comment "BRAIN" And I'll send it over (must be following so I can DM)

Mike Futia

17,762 просмотров • 1 месяц назад