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WHOEVER BUILT THESE AI HACKING TOOLS WAS NOT PLAYING And the crazy part? They don’t just scan for vulnerabilities. They can actually simulate attacks—> chain attack paths—> validate what they find. Here are the 3 tools: → Decepticon runs 16 autonomous red team processes inside a hardened Kali Linux... show more

MAX

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17,739 次观看 • 20 天前 •via X (Twitter)

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GOODBYE TO CYBERSECURITY! Someone just open-sourced an arsenal of AI hacking tools. Not one. Not ten. Hundreds. Inside the repository: • Jailbreak frameworks for LLMs • Prompt injection testing tools • AI red team agents • Model extraction utilities • Supply chain attack demos • Automated AI pentesting frameworks These are the same categories of tools security researchers use to find vulnerabilities before attackers do. Now anyone can study them. That's both exciting... and terrifying. The biggest threat to AI isn't smarter models. It's insecure ones. If you're building with LLMs and you're not actively testing your prompts, agents, and infrastructure— You're probably shipping vulnerabilities you don't even know exist. Open source is accelerating AI. It's also accelerating AI attacks. Repository link in the comments ↓

GOODBYE TO CYBERSECURITY! Someone just open-sourced an arsenal of AI hacking tools. Not one. Not ten. Hundreds. Inside the repository: • Jailbreak frameworks for LLMs • Prompt injection testing tools • AI red team agents • Model extraction utilities • Supply chain attack demos • Automated AI pentesting frameworks These are the same categories of tools security researchers use to find vulnerabilities before attackers do. Now anyone can study them. That's both exciting... and terrifying. The biggest threat to AI isn't smarter models. It's insecure ones. If you're building with LLMs and you're not actively testing your prompts, agents, and infrastructure— You're probably shipping vulnerabilities you don't even know exist. Open source is accelerating AI. It's also accelerating AI attacks. Repository link in the comments ↓

Shruti Codes

126,325 次观看 • 1 个月前

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.

Thomas Kurian

198,394 次观看 • 3 个月前

Holy shit... Keygraph just built an AI that hacks your web app before hackers do. It's called Shannon and it's a fully autonomous AI pentester that finds REAL exploits, not just alerts. 96.15% success rate on the hint-free XBOW Benchmark. Your team ships code every day with Claude Code and Cursor. Your pentest? Once a year. That's 364 days of shipping vulnerabilities to production. Shannon closes that gap. What it actually does: → Autonomously hunts attack vectors in your source code → Uses a built-in browser to execute real exploits → Handles 2FA/TOTP logins with zero intervention → Delivers copy-paste Proof-of-Concepts (no false positives) → Runs Nmap, Subfinder, WhatWeb, Schemathesis under the hood Real results on OWASP Juice Shop in a single run: → 20+ high-impact vulnerabilities found → Complete auth bypass + full database exfiltration → Privilege escalation to admin via registration bypass → SSRF enabling internal network recon → Systemic IDOR across user data The architecture is what makes it work. 4 phases: Recon → Vuln Analysis → Exploitation → Reporting Specialized agents run in parallel for Injection, XSS, SSRF, and Broken Auth. Strict "No Exploit, No Report" policy kills false positives at the source. Covers the critical OWASP classes: - Injection - XSS - SSRF - Broken Authentication & Authorization One command. ~1 hour runtime. ~$50 per full pentest with Claude Sonnet. Every Claude (coder) deserves their Shannon. The Red Team to your vibe-coding Blue Team. 100% Opensource (AGPL-3.0). 10.6k stars already. Repo in reply ↓

Holy shit... Keygraph just built an AI that hacks your web app before hackers do. It's called Shannon and it's a fully autonomous AI pentester that finds REAL exploits, not just alerts. 96.15% success rate on the hint-free XBOW Benchmark. Your team ships code every day with Claude Code and Cursor. Your pentest? Once a year. That's 364 days of shipping vulnerabilities to production. Shannon closes that gap. What it actually does: → Autonomously hunts attack vectors in your source code → Uses a built-in browser to execute real exploits → Handles 2FA/TOTP logins with zero intervention → Delivers copy-paste Proof-of-Concepts (no false positives) → Runs Nmap, Subfinder, WhatWeb, Schemathesis under the hood Real results on OWASP Juice Shop in a single run: → 20+ high-impact vulnerabilities found → Complete auth bypass + full database exfiltration → Privilege escalation to admin via registration bypass → SSRF enabling internal network recon → Systemic IDOR across user data The architecture is what makes it work. 4 phases: Recon → Vuln Analysis → Exploitation → Reporting Specialized agents run in parallel for Injection, XSS, SSRF, and Broken Auth. Strict "No Exploit, No Report" policy kills false positives at the source. Covers the critical OWASP classes: - Injection - XSS - SSRF - Broken Authentication & Authorization One command. ~1 hour runtime. ~$50 per full pentest with Claude Sonnet. Every Claude (coder) deserves their Shannon. The Red Team to your vibe-coding Blue Team. 100% Opensource (AGPL-3.0). 10.6k stars already. Repo in reply ↓

Guri Singh

20,591 次观看 • 5 个月前

AI IS NO LONGER JUST WRITING CODE IT iS STARTING TO MOVE THINGS IN THE REAL WORLD. Someone just built a pizza delivery system where the drone does the driving No delivery car No traffic No driver sitting behind the wheel Just: -> Order comes in -> Drone picks up the pizza -> Flies directly to the destination -> Delivers it -> Returns And this is where the AI story gets interesting For years, the AI boom was mostly digital: > Chatbots > Coding agents > Image generation > AI music > AI video But the next phase is different AI is getting a body The same technology stack that started with models and GPUs is now moving into the physical world NVIDIA built the compute layer Researchers built the models Companies like Zoox are building autonomous vehicles And now we're seeing AI powered machines actually move through the real world The crazy part? We designed entire cities around the assumption that humans have to physically drive everything AI doesn't have that limitation Why send a pizza through 5 km of traffic when a machine can simply fly over it? The AI boom isn't just about replacing human work It's about removing constraints humans had to design around The next big AI companies might not live inside your browser They might be flying above your house Bookmark this so you wont miss the next delivery

AI IS NO LONGER JUST WRITING CODE IT iS STARTING TO MOVE THINGS IN THE REAL WORLD. Someone just built a pizza delivery system where the drone does the driving No delivery car No traffic No driver sitting behind the wheel Just: -> Order comes in -> Drone picks up the pizza -> Flies directly to the destination -> Delivers it -> Returns And this is where the AI story gets interesting For years, the AI boom was mostly digital: > Chatbots > Coding agents > Image generation > AI music > AI video But the next phase is different AI is getting a body The same technology stack that started with models and GPUs is now moving into the physical world NVIDIA built the compute layer Researchers built the models Companies like Zoox are building autonomous vehicles And now we're seeing AI powered machines actually move through the real world The crazy part? We designed entire cities around the assumption that humans have to physically drive everything AI doesn't have that limitation Why send a pizza through 5 km of traffic when a machine can simply fly over it? The AI boom isn't just about replacing human work It's about removing constraints humans had to design around The next big AI companies might not live inside your browser They might be flying above your house Bookmark this so you wont miss the next delivery

0xSlyth

14,149 次观看 • 1 个月前

let me explain what Anthropic just did they built an AI model so good at finding security vulnerabilities that they have refused to release it meet Claude Mythos → it’s Anthropic’s newest frontier model and it’s not available to the public. not because it’s not ready. because it’s too dangerous → Mythos found tens of thousands of zero day vulnerabilities across every major operating system and web browser… many of them 1 to 2 decades old. for context… Opus 4.6 found about 500. Mythos found tens of thousands → it found vulnerabilities in the Linux kernel. a 27 year old vulnerability in OpenBSD. a 16 year old vulnerability in FFmpeg → it doesn’t just find bugs. it writes the exploits too. that’s the part that scared them → so instead of releasing it… Anthropic has created Project Glasswing. a cybersecurity initiative where they hand picked 40+ companies to use Mythos for defense only → the partner list reads like a who’s who of tech… Amazon, Apple, Microsoft, Google, Nvidia, Broadcom, Cisco, CrowdStrike, Palo Alto Networks, JPMorgan, the Linux Foundation → Anthropic is giving up to $100 million in usage credits to these partners and $4 million to open source security organizations → they’re briefing CISA and the Commerce Department on how to handle this → the benchmarks are truly insane… Mythos hit 77.8% on SWE-bench Pro where Opus 4.6 scored 53.4%. hit 93.9% on SWE-bench Verified where Opus 4.6 scored 80.8% → Anthropic’s head of frontier red team said this is “the first time a model is this good that we decided to approach release in a very different way” this is the first time an AI company has held back a model because it was too capable not too expensive. not too slow. too dangerous and instead of locking it in a vault they weaponized it for defense and gave it to the companies that run the internet that’s either the most responsible thing an AI company has ever done… or the scariest only time will tell

let me explain what Anthropic just did they built an AI model so good at finding security vulnerabilities that they have refused to release it meet Claude Mythos → it’s Anthropic’s newest frontier model and it’s not available to the public. not because it’s not ready. because it’s too dangerous → Mythos found tens of thousands of zero day vulnerabilities across every major operating system and web browser… many of them 1 to 2 decades old. for context… Opus 4.6 found about 500. Mythos found tens of thousands → it found vulnerabilities in the Linux kernel. a 27 year old vulnerability in OpenBSD. a 16 year old vulnerability in FFmpeg → it doesn’t just find bugs. it writes the exploits too. that’s the part that scared them → so instead of releasing it… Anthropic has created Project Glasswing. a cybersecurity initiative where they hand picked 40+ companies to use Mythos for defense only → the partner list reads like a who’s who of tech… Amazon, Apple, Microsoft, Google, Nvidia, Broadcom, Cisco, CrowdStrike, Palo Alto Networks, JPMorgan, the Linux Foundation → Anthropic is giving up to $100 million in usage credits to these partners and $4 million to open source security organizations → they’re briefing CISA and the Commerce Department on how to handle this → the benchmarks are truly insane… Mythos hit 77.8% on SWE-bench Pro where Opus 4.6 scored 53.4%. hit 93.9% on SWE-bench Verified where Opus 4.6 scored 80.8% → Anthropic’s head of frontier red team said this is “the first time a model is this good that we decided to approach release in a very different way” this is the first time an AI company has held back a model because it was too capable not too expensive. not too slow. too dangerous and instead of locking it in a vault they weaponized it for defense and gave it to the companies that run the internet that’s either the most responsible thing an AI company has ever done… or the scariest only time will tell

klöss

21,270 次观看 • 5 个月前

⏱️ THE WAIT IS FINALLY OVER In just 2 days you will see what FAR Labs have been building. We are about to reveal the foundation of FAR AI, built on new compute, new biometric signals, new science models, and new forms of intelligent play. Each part is designed to help you run faster, produce more, and open fresh revenue streams across AI, science, and gaming. You will see how these layers connect and how they create a system built for real users, real ownership, and real outcomes. Be the first to use FAR AI and unlock new revenue. Follow these steps: 1. Click the link below. 2. Enter your email in the box. 3. Confirm your signup and you are in. Here's the link 👉 PS Spots for early access will move fast, act now.

⏱️ THE WAIT IS FINALLY OVER In just 2 days you will see what FAR Labs have been building. We are about to reveal the foundation of FAR AI, built on new compute, new biometric signals, new science models, and new forms of intelligent play. Each part is designed to help you run faster, produce more, and open fresh revenue streams across AI, science, and gaming. You will see how these layers connect and how they create a system built for real users, real ownership, and real outcomes. Be the first to use FAR AI and unlock new revenue. Follow these steps: 1. Click the link below. 2. Enter your email in the box. 3. Confirm your signup and you are in. Here's the link 👉 PS Spots for early access will move fast, act now.

FAR Labs

31,500 次观看 • 9 个月前

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 次观看 • 2 个月前

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

281,328 次观看 • 3 个月前

Postman's AI-readiness Playbook is one of the most important documents you can read today as a developer! We are headed into an era where every website must be "Agent-ready". - Agents will make purchases, not humans. - Agents will find the best options, not humans. - Agents will fill out job applications, not humans. The same applies to APIs. While human devs can hustle through poor docs and broken endpoints, most Agents can’t (yet). They need: - Predictable structures - Machine-readable metadata - Standardized behavior Postman's 90-day AI readiness playbook details how to turn your APIs into reliable, AI-ready tools. My two biggest takeaways from the Playbook: 1) Automatic documentation (Week 3): Once you standardize your API format, Postman’s Spec Hub automatically generates and validates API docs for both humans and AI agents without any manual work. 2) Seamless AI tooling (Week 9): Turn your validated specs into hosted, function-style endpoints, letting AI agents invoke your APIs like native commands. Find the link to the Playbook in the comments. Thanks to the Postman team for partnering on today's post!

Postman's AI-readiness Playbook is one of the most important documents you can read today as a developer! We are headed into an era where every website must be "Agent-ready". - Agents will make purchases, not humans. - Agents will find the best options, not humans. - Agents will fill out job applications, not humans. The same applies to APIs. While human devs can hustle through poor docs and broken endpoints, most Agents can’t (yet). They need: - Predictable structures - Machine-readable metadata - Standardized behavior Postman's 90-day AI readiness playbook details how to turn your APIs into reliable, AI-ready tools. My two biggest takeaways from the Playbook: 1) Automatic documentation (Week 3): Once you standardize your API format, Postman’s Spec Hub automatically generates and validates API docs for both humans and AI agents without any manual work. 2) Seamless AI tooling (Week 9): Turn your validated specs into hosted, function-style endpoints, letting AI agents invoke your APIs like native commands. Find the link to the Playbook in the comments. Thanks to the Postman team for partnering on today's post!

Avi Chawla

22,830 次观看 • 1 年前

"Europe has already lost the AI race." I hear this bashing almost every single week on LinkedIn and X. Not so much when I talk to the teams who are actually working on it. They are trying to do something about it. Take Feyer. They are not building another generic wrapper application. Feyer is developing AI systems that autonomously design novel industrial hardware. Their neural explorers are coupled directly with differentiable physics simulations, allowing them to search enormous design spaces and discover hardware that humans might never come up with themselves. That could accelerate innovation across everything from lasers and quantum technology to microchip production. On September 9, Cyber Valley celebrates its 10th anniversary here in Tübingen. And I want to show and talk about companies like Feyer that are sitting here. They are a pretty good example of what can happen when world-class research turns into an ambitious company. They are building right here in Tübingen and just secured €3 million in the SPRIND, Federal Agency for Breakthrough Innovation - Bundesagentur für Sprunginnovationen Next Frontier AI Challenge. I spent some time on a call with their CTO Sören Arlt last week, and the level of technical ambition there is exactly what this ecosystem needs right now. And Feyer is part of a much bigger bet. SPRIND, Federal Agency for Breakthrough Innovation is deploying €125 million over 24 months to build three internationally competitive European frontier AI labs. Ten teams start with up to €3 million each, six can advance with another €8 million, and the final three can receive another €15.5 million each. Up to €26.5 million per winning team. And this is not a research thesis invented for a startup pitch. The work builds on years of research by Sören Arlt, Mario Krenn and their collaborators into machine-driven scientific discovery. I am going to share a lot more about what Sören Arlt, Jonathan Klimesch, Mario Krenn and the rest of the Feyer team are building very soon. If you want to see what European frontier AI can actually look like, keep an eye on them. Follow for more insights into AI and robotics. {Quick animation by me for now. We’ll have much better visuals to share over the next few weeks ;)}

"Europe has already lost the AI race." I hear this bashing almost every single week on LinkedIn and X. Not so much when I talk to the teams who are actually working on it. They are trying to do something about it. Take Feyer. They are not building another generic wrapper application. Feyer is developing AI systems that autonomously design novel industrial hardware. Their neural explorers are coupled directly with differentiable physics simulations, allowing them to search enormous design spaces and discover hardware that humans might never come up with themselves. That could accelerate innovation across everything from lasers and quantum technology to microchip production. On September 9, Cyber Valley celebrates its 10th anniversary here in Tübingen. And I want to show and talk about companies like Feyer that are sitting here. They are a pretty good example of what can happen when world-class research turns into an ambitious company. They are building right here in Tübingen and just secured €3 million in the SPRIND, Federal Agency for Breakthrough Innovation - Bundesagentur für Sprunginnovationen Next Frontier AI Challenge. I spent some time on a call with their CTO Sören Arlt last week, and the level of technical ambition there is exactly what this ecosystem needs right now. And Feyer is part of a much bigger bet. SPRIND, Federal Agency for Breakthrough Innovation is deploying €125 million over 24 months to build three internationally competitive European frontier AI labs. Ten teams start with up to €3 million each, six can advance with another €8 million, and the final three can receive another €15.5 million each. Up to €26.5 million per winning team. And this is not a research thesis invented for a startup pitch. The work builds on years of research by Sören Arlt, Mario Krenn and their collaborators into machine-driven scientific discovery. I am going to share a lot more about what Sören Arlt, Jonathan Klimesch, Mario Krenn and the rest of the Feyer team are building very soon. If you want to see what European frontier AI can actually look like, keep an eye on them. Follow for more insights into AI and robotics. {Quick animation by me for now. We’ll have much better visuals to share over the next few weeks ;)}

Ilir Aliu

10,745 次观看 • 27 天前

We are building the home of the doge economy Projects across every major vertical are already gearing up to launch on DogeOS. Builders are cooking. Here's what the community should be excited to explore.... Much Thoughts ✍️ Very 🐕 So Soon... 💱 DeFi Swaps, lending, yield, memes all running on DogeOS with Dogecoin at the heart of it all. This means the most beloved and recognized asset in crypto finally gets a real financial system built around it. Doge stops being just a vibe and starts being productive. The community has been asking for this for years. 🎮 Gaming On-chain games built on DogeOS: play, compete, unlock with Dogecoin. Doge was born from meme and gaming culture. Bringing games on-chain is just Doge coming home. Play to unlock, play to grow, play because it's fun. Gaming is one of the strongest onramps in all of crypto. It's how you turn curious onlookers into active participants. This is a chance to bring the fun and the love of this community to a whole new wave of users, and remind the old ones why they fell in love with Doge in the first place. 🤖 AI Builders are already positioned to ship AI powered dApps, agents, and tools natively on DogeOS. Think automated trading agents, AI assistants that manage your wallet, dApp builders, and solutions that get smarter the more the ecosystem grows. Agents benefit from fast and cheap transaction environments, and that's exactly what DogeOS is built for. DogeOS becomes the rails for the next wave of on-chain AI, with Doge as the fuel. 📣 Community and Social Apps built for the culture. Social tools, creator platforms, community coordination, all on-chain and all for the Doge economy. Dogecoin has always had the strongest community in crypto. Now that community gets its own social layer instead of living as a guest in another chain's world. Dogecoin should be the focal point, not just another asset being served. 🐕 All your desires. One ecosystem. We are building toward the future this community has dreamt of, making Dogecoin the central piece of a real on-chain economy. This is just the start. Much building. Prepare your paws.

We are building the home of the doge economy Projects across every major vertical are already gearing up to launch on DogeOS. Builders are cooking. Here's what the community should be excited to explore.... Much Thoughts ✍️ Very 🐕 So Soon... 💱 DeFi Swaps, lending, yield, memes all running on DogeOS with Dogecoin at the heart of it all. This means the most beloved and recognized asset in crypto finally gets a real financial system built around it. Doge stops being just a vibe and starts being productive. The community has been asking for this for years. 🎮 Gaming On-chain games built on DogeOS: play, compete, unlock with Dogecoin. Doge was born from meme and gaming culture. Bringing games on-chain is just Doge coming home. Play to unlock, play to grow, play because it's fun. Gaming is one of the strongest onramps in all of crypto. It's how you turn curious onlookers into active participants. This is a chance to bring the fun and the love of this community to a whole new wave of users, and remind the old ones why they fell in love with Doge in the first place. 🤖 AI Builders are already positioned to ship AI powered dApps, agents, and tools natively on DogeOS. Think automated trading agents, AI assistants that manage your wallet, dApp builders, and solutions that get smarter the more the ecosystem grows. Agents benefit from fast and cheap transaction environments, and that's exactly what DogeOS is built for. DogeOS becomes the rails for the next wave of on-chain AI, with Doge as the fuel. 📣 Community and Social Apps built for the culture. Social tools, creator platforms, community coordination, all on-chain and all for the Doge economy. Dogecoin has always had the strongest community in crypto. Now that community gets its own social layer instead of living as a guest in another chain's world. Dogecoin should be the focal point, not just another asset being served. 🐕 All your desires. One ecosystem. We are building toward the future this community has dreamt of, making Dogecoin the central piece of a real on-chain economy. This is just the start. Much building. Prepare your paws.

DogeOS

42,738 次观看 • 11 天前

What happens when you stop guessing and let machine learning read the market for you? $2.2M in 4 months. ilovecircle built something different on Polymarket. Not a speed bot. Not a spread farmer. An AI system that actually thinks. 1,347 predictions. 74% win rate. Biggest single hit: $258.4K. Current positions: basically zero he extracted everything. The setup: 10 machine learning models running in parallel, each trained on news feeds and social media data. They don't predict events they predict when the crowd is wrong about probabilities. Market prices an outcome at 50 cents. His ensemble says the real odds are 60%. That gap is the trade. Every week the models retrain themselves on fresh data. The edge evolves because the system never stops learning. Most traders react to headlines. This wallet front runs the market's understanding of what headlines actually mean. 51K people watching now. Most still think AI trading is a scam until they see a curve like this. → Following wallets that run AI-powered probability models is simpler with PMX.

What happens when you stop guessing and let machine learning read the market for you? $2.2M in 4 months. ilovecircle built something different on Polymarket. Not a speed bot. Not a spread farmer. An AI system that actually thinks. 1,347 predictions. 74% win rate. Biggest single hit: $258.4K. Current positions: basically zero he extracted everything. The setup: 10 machine learning models running in parallel, each trained on news feeds and social media data. They don't predict events they predict when the crowd is wrong about probabilities. Market prices an outcome at 50 cents. His ensemble says the real odds are 60%. That gap is the trade. Every week the models retrain themselves on fresh data. The edge evolves because the system never stops learning. Most traders react to headlines. This wallet front runs the market's understanding of what headlines actually mean. 51K people watching now. Most still think AI trading is a scam until they see a curve like this. → Following wallets that run AI-powered probability models is simpler with PMX.

Carver

13,072 次观看 • 7 个月前

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

Alex Prompter

7,168,724 次观看 • 2 个月前

Got reached out by Wellex and they offered me a spot in their ambassadorship and band testing program I have seen projects try to gamify health for years and most of them do not get past the token launch This one actually feels different Real wearable band -> AI coach that does not just show you numbers -> it gives clear daily direction on what to actually do with them And on top of that your real health performance directly impacts yield on staked USDT through their Live to Earn model Proper utility -> no fluff -> no complicated mechanics that do not make sense They are still moving quiet right now -> working with KOLs before the big wave hits I got my spot in the early testing batch and I am genuinely hyped to get the band and share everything as it lands If health x Web3 is your thing -> go follow Wellex and stay tuned -> they are planning something exciting for early users This one is worth watching

Got reached out by Wellex and they offered me a spot in their ambassadorship and band testing program I have seen projects try to gamify health for years and most of them do not get past the token launch This one actually feels different Real wearable band -> AI coach that does not just show you numbers -> it gives clear daily direction on what to actually do with them And on top of that your real health performance directly impacts yield on staked USDT through their Live to Earn model Proper utility -> no fluff -> no complicated mechanics that do not make sense They are still moving quiet right now -> working with KOLs before the big wave hits I got my spot in the early testing batch and I am genuinely hyped to get the band and share everything as it lands If health x Web3 is your thing -> go follow Wellex and stay tuned -> they are planning something exciting for early users This one is worth watching

Shahzada

13,270 次观看 • 3 个月前

Google dropped another banger! They just released a comprehensive white-paper on AgentOps - the missing piece between building AI agents and actually shipping them to production. Here's the reality: Building an AI agent takes minutes. Making it production-ready? That's where 80% of the real work begins. Google's "Prototype to Production" guide tackles this exact problem. The framework has three core pillars: 1. Evaluation-Gated Deployment: No agent reaches users without passing tests. Build a "golden dataset" that validates behavior, not just functionality. This catches what unit tests miss - agents choosing wrong tools or hallucinating responses. 2. Automated CI/CD for Agents: Test in stages: pre-merge checks for fast feedback, staging for load testing, then gated production. Version everything: prompts, tools, configs, evaluation datasets. 3. Observe → Act → Evolve Loop Production isn't the finish line. Monitor through logs, traces, and metrics. Act with circuit breakers and human escalation. Evolve by turning production failures into test cases. The best part? They released the Agent Starter Pack - a template with CI/CD, Terraform deployment, and built-in observability. Helps you spin up an evaluation pipeline in minutes. The guide also talks about the two major protocols and how they can work together. ↳ MCP for tool integration ↳ A2A for agent collaboration If you're shipping agents to production, you should read this. I've shared the full white-paper in the next tweet!

Google dropped another banger! They just released a comprehensive white-paper on AgentOps - the missing piece between building AI agents and actually shipping them to production. Here's the reality: Building an AI agent takes minutes. Making it production-ready? That's where 80% of the real work begins. Google's "Prototype to Production" guide tackles this exact problem. The framework has three core pillars: 1. Evaluation-Gated Deployment: No agent reaches users without passing tests. Build a "golden dataset" that validates behavior, not just functionality. This catches what unit tests miss - agents choosing wrong tools or hallucinating responses. 2. Automated CI/CD for Agents: Test in stages: pre-merge checks for fast feedback, staging for load testing, then gated production. Version everything: prompts, tools, configs, evaluation datasets. 3. Observe → Act → Evolve Loop Production isn't the finish line. Monitor through logs, traces, and metrics. Act with circuit breakers and human escalation. Evolve by turning production failures into test cases. The best part? They released the Agent Starter Pack - a template with CI/CD, Terraform deployment, and built-in observability. Helps you spin up an evaluation pipeline in minutes. The guide also talks about the two major protocols and how they can work together. ↳ MCP for tool integration ↳ A2A for agent collaboration If you're shipping agents to production, you should read this. I've shared the full white-paper in the next tweet!

Akshay 🚀

37,187 次观看 • 10 个月前

AI is not accelerating nearly fast enough to cure cancer. That is the problem. And it literally cannot without actually seeing what’s happening inside the cell. There’s this new project claiming their models will “solve” cancer by predicting drug responses at scale. Cool story. Except their entire approach still treats the cell like a black box. They’re optimizing for binding affinity while having zero idea what the drug actually does once it’s inside a real, living cell. Meanwhile we’re over here building the tools to watch the cell in real time. No staining or sequencing. Just direct observation of mechanism. You can simulate all you want. But if you can’t see the actual cellular response, you’re just making very confident guesses about something you’ve never actually looked at. We’ve spent two years proving this. The data is there. The difference is night and day. AI is a tool. It’s not the microscope. If your model can’t tell you why a drug is killing the wrong cells or why resistance is forming in real time… it’s not ready to cure cancer. It’s ready to waste another billion dollars. We’re not competing with AI. We’re giving it eyes. Goodbye.

AI is not accelerating nearly fast enough to cure cancer. That is the problem. And it literally cannot without actually seeing what’s happening inside the cell. There’s this new project claiming their models will “solve” cancer by predicting drug responses at scale. Cool story. Except their entire approach still treats the cell like a black box. They’re optimizing for binding affinity while having zero idea what the drug actually does once it’s inside a real, living cell. Meanwhile we’re over here building the tools to watch the cell in real time. No staining or sequencing. Just direct observation of mechanism. You can simulate all you want. But if you can’t see the actual cellular response, you’re just making very confident guesses about something you’ve never actually looked at. We’ve spent two years proving this. The data is there. The difference is night and day. AI is a tool. It’s not the microscope. If your model can’t tell you why a drug is killing the wrong cells or why resistance is forming in real time… it’s not ready to cure cancer. It’s ready to waste another billion dollars. We’re not competing with AI. We’re giving it eyes. Goodbye.

Parmita Mishra

48,203 次观看 • 2 个月前

M E S S I E R | P2P Partner We are excited to have teamed up with Chainpal as our new P2P partner. They have opened a swap pool for their native token, $CPAL, on our #P2P exchange. The token listing allows users to buy or even sell their own tokens without slippage, token tax, or MEV losses: Chainpal 🤖🧠 is a Telegram-based trading bot for cryptocurrency traders, supporting multiple blockchains like #Ethereum, Binance Smart Chain, Solana, Base, and TON. It offers AI-powered trade automation, advanced security with 2FA and AES256 encryption, real-time charting with indicators, sniper and trading bots for token launches, and tools for copy trading. Users can also earn through a multi-level referral program. Chainpal combines efficiency, security, and automation for optimized trading strategies.

M E S S I E R | P2P Partner We are excited to have teamed up with Chainpal as our new P2P partner. They have opened a swap pool for their native token, $CPAL, on our #P2P exchange. The token listing allows users to buy or even sell their own tokens without slippage, token tax, or MEV losses: Chainpal 🤖🧠 is a Telegram-based trading bot for cryptocurrency traders, supporting multiple blockchains like #Ethereum, Binance Smart Chain, Solana, Base, and TON. It offers AI-powered trade automation, advanced security with 2FA and AES256 encryption, real-time charting with indicators, sniper and trading bots for token launches, and tools for copy trading. Users can also earn through a multi-level referral program. Chainpal combines efficiency, security, and automation for optimized trading strategies.

MESSIER | M87

117,873 次观看 • 1 年前

🚨 A FAKE “HOLLYWOOD SIGN” JUST APPEARED OVER A BUSY L.A. FREEWAY — AND DRIVERS CAN’T BELIEVE WHAT IT’S ADVERTISING Drivers in Los Angeles are doing double takes at highway speeds after a massive sign showed up above the freeway… designed to look almost identical to the real Hollywood sign. And this thing isn’t small. • Roughly 230 feet wide • About 30 feet tall • Giant white letters stretched across the hillside • Positioned right above heavy traffic But here’s the part that’s setting people off. It’s not promoting a movie. It’s not promoting Hollywood. It’s promoting Fiverr’s new AI video tools… where companies can generate ads, content, and campaigns without hiring real creatives. So now you’ve got: • A fake Hollywood sign • Sitting over one of the busiest roads in the city • Advertising AI replacing the very industry Hollywood was built on It’s just similar enough to the real thing to make drivers look twice… exactly where they shouldn’t be. So is this just a clever ad… or are they quietly telling you what happens to Hollywood next?

🚨 A FAKE “HOLLYWOOD SIGN” JUST APPEARED OVER A BUSY L.A. FREEWAY — AND DRIVERS CAN’T BELIEVE WHAT IT’S ADVERTISING Drivers in Los Angeles are doing double takes at highway speeds after a massive sign showed up above the freeway… designed to look almost identical to the real Hollywood sign. And this thing isn’t small. • Roughly 230 feet wide • About 30 feet tall • Giant white letters stretched across the hillside • Positioned right above heavy traffic But here’s the part that’s setting people off. It’s not promoting a movie. It’s not promoting Hollywood. It’s promoting Fiverr’s new AI video tools… where companies can generate ads, content, and campaigns without hiring real creatives. So now you’ve got: • A fake Hollywood sign • Sitting over one of the busiest roads in the city • Advertising AI replacing the very industry Hollywood was built on It’s just similar enough to the real thing to make drivers look twice… exactly where they shouldn’t be. So is this just a clever ad… or are they quietly telling you what happens to Hollywood next?

HustleBitch

18,121 次观看 • 5 个月前

I was working through Steam Early Access requirements for VRUnderground and I had a really spooky realization. The whole conversation around training images and videos is not the important part. The media itself is just a bridge. What is actually being extracted is cognitive patterning. Models learn how people think, not just how they draw. The images are only the residue of the process. The real map is of the minds that created them. It reminds me of poker. A great poker player is not "stealing your cards". They are reading the patterns in your behavior and mapping your decision space. In that sense, training AI on human-created data is not about automating tasks. It is about mapping cognition through metadata and behavior trails. If that is true, then the internet starts looking a lot more like a poker table. And nobody wins a hand against someone who can read their mind.

I was working through Steam Early Access requirements for VRUnderground and I had a really spooky realization. The whole conversation around training images and videos is not the important part. The media itself is just a bridge. What is actually being extracted is cognitive patterning. Models learn how people think, not just how they draw. The images are only the residue of the process. The real map is of the minds that created them. It reminds me of poker. A great poker player is not "stealing your cards". They are reading the patterns in your behavior and mapping your decision space. In that sense, training AI on human-created data is not about automating tasks. It is about mapping cognition through metadata and behavior trails. If that is true, then the internet starts looking a lot more like a poker table. And nobody wins a hand against someone who can read their mind.

VRUnderground

26,437 次观看 • 9 个月前

🔐 Who We Are 🔐 At TRD Network, we’re building the foundation of secure AI infrastructure for the decentralized world. Our mission is simple: stop cyber threats before they start, with unmatched encryption, real-time monitoring, and decentralized protection. 🚀 What We Do 🚀 We combine military-grade encryption, AI-powered threat detection, and DePIN architecture to deliver enterprise-grade security across Web3. From crypto wallets to dApps, we guard your data and digital assets 24/7. 📍 How It Started 📍 Born from a vision to make Web3 truly secure, TRD was built by cybersecurity experts and blockchain engineers determined to end the rise of digital breaches. Today, we’re proud to protect protocols, platforms, and people across the globe. The future is decentralized, and it needs defense. That’s where we come in. Welcome to TRD Network! 💡

🔐 Who We Are 🔐 At TRD Network, we’re building the foundation of secure AI infrastructure for the decentralized world. Our mission is simple: stop cyber threats before they start, with unmatched encryption, real-time monitoring, and decentralized protection. 🚀 What We Do 🚀 We combine military-grade encryption, AI-powered threat detection, and DePIN architecture to deliver enterprise-grade security across Web3. From crypto wallets to dApps, we guard your data and digital assets 24/7. 📍 How It Started 📍 Born from a vision to make Web3 truly secure, TRD was built by cybersecurity experts and blockchain engineers determined to end the rise of digital breaches. Today, we’re proud to protect protocols, platforms, and people across the globe. The future is decentralized, and it needs defense. That’s where we come in. Welcome to TRD Network! 💡

T.R.D Network

133,527 次观看 • 1 年前