正在加载视频...

视频加载失败

返回首页

THIS IS F**KING INSANE Found something genuinely comprehensive: ANTHROPIC CYBERSECURITY SKILLS 817 production grade security skills for AI agents, open source. 29 security domains —>cloud security—>threat hunting—>network security—>incident response—>malware analysis, red teaming, and more all mapped against real frameworks: MITRE ATT&CK v17.1, NIST CSF 2.0, OWASP, MITRE Fight Fraud...

MAXdeg0's profile picture

MAX

7,266 subscribers

37,981 次观看 • 6 天前 •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

5 open-source AI repos that blew up on github this week → 1. MinerU (opendatalab) - ~69k★ turns any PDF or office doc into clean, LLM-ready markdown - the fix for garbage RAG inputs 2. voicebox (jamiepine) - ~35k★ open-source AI voice studio - clone a voice, dictate, create. the local ElevenLabs 3. ai-website-cloner-template (JCodesMore) - ~22k★ clone any website with one command using your AI coding agent - great for learning and prototyping 4. Anthropic-Cybersecurity-Skills (mukul975) - ~21k★ 800+ ready security skills for AI agents, mapped to MITRE ATT&CK and NIST - works with claude code, cursor, codex 5. agent-native (builderio) - ~3k★ a framework for building apps designed for AI agents from day one, not bolted on after one of these saves you a paid tool. bookmark before you forge
0:59

5 open-source AI repos that blew up on github this week → 1. MinerU (opendatalab) - ~69k★ turns any PDF or office doc into clean, LLM-ready markdown - the fix for garbage RAG inputs 2. voicebox (jamiepine) - ~35k★ open-source AI voice studio - clone a voice, dictate, create. the local ElevenLabs 3. ai-website-cloner-template (JCodesMore) - ~22k★ clone any website with one command using your AI coding agent - great for learning and prototyping 4. Anthropic-Cybersecurity-Skills (mukul975) - ~21k★ 800+ ready security skills for AI agents, mapped to MITRE ATT&CK and NIST - works with claude code, cursor, codex 5. agent-native (builderio) - ~3k★ a framework for building apps designed for AI agents from day one, not bolted on after one of these saves you a paid tool. bookmark before you forge

self.dll

65,079 次观看 • 1 个月前

SOMEONE JUST OPEN-SOURCED AN AI THAT SEARCHES EVERY MAJOR PLATFORM AT ONCE Reddit, X, YouTube, Hacker News, TikTok, Polymarket, GitHub not one by one - PARALLEL it ranks results by what real people actually do: - upvotes from Reddit - likes and timing from X - real money predictions from Polymarket - commits and PRs from GitHub then generates ONE brief 28,700+ stars on github 2,400+ forks. MIT license. Google is strong on the web ChatGPT has some access neither of them touches this combination setup guide (2 minutes): Claude Code: > /plugin marketplace add mvanhorn/last30days-skill Hermes: > npx skills add mvanhorn/last30days-skill -g -a hermes Cursor / Codex / Copilot / Gemini CLI: > npx skills add mvanhorn/last30days-skill -g then run it: > /last30days bitcoin > /last30days "OpenAI vs Anthropic" > /last30days [any topic] no API keys needed for Reddit, Hacker News, Polymarket, and GitHub X works if you're already logged into x. com in your browser YouTube: brew install yt-dlp repo in replies
1:19

SOMEONE JUST OPEN-SOURCED AN AI THAT SEARCHES EVERY MAJOR PLATFORM AT ONCE Reddit, X, YouTube, Hacker News, TikTok, Polymarket, GitHub not one by one - PARALLEL it ranks results by what real people actually do: - upvotes from Reddit - likes and timing from X - real money predictions from Polymarket - commits and PRs from GitHub then generates ONE brief 28,700+ stars on github 2,400+ forks. MIT license. Google is strong on the web ChatGPT has some access neither of them touches this combination setup guide (2 minutes): Claude Code: > /plugin marketplace add mvanhorn/last30days-skill Hermes: > npx skills add mvanhorn/last30days-skill -g -a hermes Cursor / Codex / Copilot / Gemini CLI: > npx skills add mvanhorn/last30days-skill -g then run it: > /last30days bitcoin > /last30days "OpenAI vs Anthropic" > /last30days [any topic] no API keys needed for Reddit, Hacker News, Polymarket, and GitHub X works if you're already logged into x. com in your browser YouTube: brew install yt-dlp repo in replies

Isra

38,760 次观看 • 2 个月前

THE GUY WHO WON ANTHROPIC'S HACKATHON JUST GAVE AWAY HIS ENTIRE CLAUDE CODE PLAYBOOK FOR FREE. 10 MONTHS OF WORK, ALL PUBLIC Affaan Mustafa won the Anthropic x Forum Ventures hackathon by building a full startup in 8 hours with Claude Code. Then he open-sourced the exact setup that did it. It's called Everything Claude Code, and it turns Claude from one assistant into an entire engineering team Repo: affaan-m/ecc This isn't a prompt pack. It's a system he refined over 10+ months of daily use shipping real products What's inside: A huge library of skills, dozens of specialized subagents, and ready-made commands, all working together. Each piece does one job. One subagent reviews security against OWASP standards. One optimizes memory so Claude stops forgetting earlier decisions around hour three. One learns from your past sessions and projects so the setup gets smarter the more you use it. Others handle planning, test-driven development, and language-specific code review Instead of one assistant writing code, you get an orchestrated team. A main session delegates to the right specialist when the task calls for it, the way a real dev team splits work The best part: it's not locked to one tool. It runs in Claude Code, Cursor, Codex and OpenCode, across Windows, Mac and Linux. Free, MIT licensed This is the difference between using Claude like a search box and running it like a team that ships. The guy spent 10 months figuring out what actually works so you don't have to Bookmark this
9:32

THE GUY WHO WON ANTHROPIC'S HACKATHON JUST GAVE AWAY HIS ENTIRE CLAUDE CODE PLAYBOOK FOR FREE. 10 MONTHS OF WORK, ALL PUBLIC Affaan Mustafa won the Anthropic x Forum Ventures hackathon by building a full startup in 8 hours with Claude Code. Then he open-sourced the exact setup that did it. It's called Everything Claude Code, and it turns Claude from one assistant into an entire engineering team Repo: affaan-m/ecc This isn't a prompt pack. It's a system he refined over 10+ months of daily use shipping real products What's inside: A huge library of skills, dozens of specialized subagents, and ready-made commands, all working together. Each piece does one job. One subagent reviews security against OWASP standards. One optimizes memory so Claude stops forgetting earlier decisions around hour three. One learns from your past sessions and projects so the setup gets smarter the more you use it. Others handle planning, test-driven development, and language-specific code review Instead of one assistant writing code, you get an orchestrated team. A main session delegates to the right specialist when the task calls for it, the way a real dev team splits work The best part: it's not locked to one tool. It runs in Claude Code, Cursor, Codex and OpenCode, across Windows, Mac and Linux. Free, MIT licensed This is the difference between using Claude like a search box and running it like a team that ships. The guy spent 10 months figuring out what actually works so you don't have to Bookmark this

Yarchi

814,905 次观看 • 2 个月前

I found a skill that turns Claude into an AI ad creative maker. It’s called Goose Ads by Gooseworks, and it works directly inside Claude, Claude Code, Cowork, or Codex. You can now create ad creatives for your brand with one command. How it works: → Install Gooseworks npx gooseworks install --all → Run this prompt /goose-ads create ads for my brand → Optional Pick templates you like on the platform Goose Ads finds creatives that companies are already spending money on, studies what is working, and generates new ad creatives for your brand. It also keeps everything aligned with your website, messaging, logo, assets, and positioning, so the final output feels brand-ready instead of generic. And this is bigger than one ad skill. Gooseworks has 100+ open-source growth skills for ads, content, competitor research, GTM, SEO, lead generation, and more. Claude is slowly becoming more than a chatbot. It is becoming a growth operator. Comment 'Goose' and I’ll DM you the full open-source skill library.
0:28

I found a skill that turns Claude into an AI ad creative maker. It’s called Goose Ads by Gooseworks, and it works directly inside Claude, Claude Code, Cowork, or Codex. You can now create ad creatives for your brand with one command. How it works: → Install Gooseworks npx gooseworks install --all → Run this prompt /goose-ads create ads for my brand → Optional Pick templates you like on the platform Goose Ads finds creatives that companies are already spending money on, studies what is working, and generates new ad creatives for your brand. It also keeps everything aligned with your website, messaging, logo, assets, and positioning, so the final output feels brand-ready instead of generic. And this is bigger than one ad skill. Gooseworks has 100+ open-source growth skills for ads, content, competitor research, GTM, SEO, lead generation, and more. Claude is slowly becoming more than a chatbot. It is becoming a growth operator. Comment 'Goose' and I’ll DM you the full open-source skill library.

Jaynit Makwana

53,943 次观看 • 1 个月前

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.
1:49

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

Nav Toor

393,720 次观看 • 4 个月前

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!
0:53

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Akshay 🚀

86,515 次观看 • 3 个月前

XBOW raised $117M to build AI hacking agents. Now someone just open-sourced it for FREE. Strix deploys autonomous AI agents that act like real hackers - they run your code dynamically, find vulnerabilities, and validate them through actual proof-of-concepts. Why it matters: The biggest problem with traditional security testing is that it doesn't keep up with development speed. Strix solves this by integrating directly into your workflow: ↳ Run it in CI/CD to catch vulnerabilities before production ↳ Get real proof-of-concepts, not false positives from static analysis ↳ Test everything: injection attacks, access control, business logic flaws The best part? You don't need to be a security expert. Strix includes a complete hacker toolkit - HTTP proxy, browser automation, and Python runtime for exploit development. It's like having a security team that works at the speed of your CI/CD pipeline. The best part is that the tool runs locally in Docker containers, so your code never leaves your environment. Getting started is simple: - pipx install strix-agent - Point it at your codebase (app, repo, or directory) Everything is 100% open-source! I've shared link to the GitHub repo in the replies!
0:23

XBOW raised $117M to build AI hacking agents. Now someone just open-sourced it for FREE. Strix deploys autonomous AI agents that act like real hackers - they run your code dynamically, find vulnerabilities, and validate them through actual proof-of-concepts. Why it matters: The biggest problem with traditional security testing is that it doesn't keep up with development speed. Strix solves this by integrating directly into your workflow: ↳ Run it in CI/CD to catch vulnerabilities before production ↳ Get real proof-of-concepts, not false positives from static analysis ↳ Test everything: injection attacks, access control, business logic flaws The best part? You don't need to be a security expert. Strix includes a complete hacker toolkit - HTTP proxy, browser automation, and Python runtime for exploit development. It's like having a security team that works at the speed of your CI/CD pipeline. The best part is that the tool runs locally in Docker containers, so your code never leaves your environment. Getting started is simple: - pipx install strix-agent - Point it at your codebase (app, repo, or directory) Everything is 100% open-source! I've shared link to the GitHub repo in the replies!

Akshay 🚀

96,762 次观看 • 9 个月前

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.
0:47

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

84,062 次观看 • 25 天前

Claude Code is now scary good at full-stack! I asked it to build a real-time weather intelligence dashboard with an interactive 3D globe and a forecasting layer that predicts weather 3 days ahead. It came back with a spinning globe that has a day/night cycle using NASA satellite imagery, city lights on the dark side, weather icons that switch between sun and moon based on local time, and a time travel slider that scrubs through 10 days of data. Claude Code built the whole thing in a single session, including the backend, database, data pipeline, and frontend. For the database, I needed something fast for time-series workloads since the app ingests hourly weather readings across many cities and serves time-range queries on every slider interaction. I used Tiger Cloud by Tiger Data - Creators of TimescaleDB, which gives you managed TimescaleDB on the Postgres you already know. Claude Code connected to it through the Tiger CLI MCP server and set up the entire backend directly: - Provisioned the database service - Created hypertables for time-partitioned weather storage - Set up continuous aggregates for pre-computed rollups - Built the data ingestion pipeline and the full NextJS + ThreeJS frontend The time travel slider queries thousands of rows on every position change. On a regular Postgres table, this would require manual partitioning and index tuning to stay fast as data grows. TimescaleDB partitions the data by timestamp automatically, so each query only hits the relevant time chunk. Continuous aggregates serve the trend charts and forecast layer from pre-computed rollups instead of rescanning raw data on every request. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud (I have shared the link in the replies). It gives you $1,000 free credits (no card needed) → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build Find the sign-up link in the replies.
0:29

Claude Code is now scary good at full-stack! I asked it to build a real-time weather intelligence dashboard with an interactive 3D globe and a forecasting layer that predicts weather 3 days ahead. It came back with a spinning globe that has a day/night cycle using NASA satellite imagery, city lights on the dark side, weather icons that switch between sun and moon based on local time, and a time travel slider that scrubs through 10 days of data. Claude Code built the whole thing in a single session, including the backend, database, data pipeline, and frontend. For the database, I needed something fast for time-series workloads since the app ingests hourly weather readings across many cities and serves time-range queries on every slider interaction. I used Tiger Cloud by Tiger Data - Creators of TimescaleDB, which gives you managed TimescaleDB on the Postgres you already know. Claude Code connected to it through the Tiger CLI MCP server and set up the entire backend directly: - Provisioned the database service - Created hypertables for time-partitioned weather storage - Set up continuous aggregates for pre-computed rollups - Built the data ingestion pipeline and the full NextJS + ThreeJS frontend The time travel slider queries thousands of rows on every position change. On a regular Postgres table, this would require manual partitioning and index tuning to stay fast as data grows. TimescaleDB partitions the data by timestamp automatically, so each query only hits the relevant time chunk. Continuous aggregates serve the trend charts and forecast layer from pre-computed rollups instead of rescanning raw data on every request. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud (I have shared the link in the replies). It gives you $1,000 free credits (no card needed) → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build Find the sign-up link in the replies.

Avi Chawla

14,838 次观看 • 2 个月前

Today we’re partnering with AWS to launch Superblocks 3.0: the secure way for employees to vibe code production enterprise software. In a single prompt, Superblocks can replace million dollar SaaS, while IT & Security stay in control. OpenAI and Anthropic are releasing new models with advanced cyber attack capability and vibe coding is every company's achilles heel: > Personal Replit accounts store untracked customer data. > Lovable prototypes with your data open to the public internet. > Claude on the desktop pulls malicious packages. How Superblocks 3.0 works: 1. Superblocks Builds: Import vibe coded prototypes. Model routing picks the best frontier or open source model per task. Saves up to 30% on token costs. 2. Superblocks Secures: A swarm of security agents work like a real human team to find and fix vulnerabilities. 3. Superblocks Deploys: Unlike consumer vibe coding tools that send your data to their cloud, Superblocks deploys the application, database, and AI inference securely inside your private AWS environment. Superblocks 3.0 is trusted across enterprises like Instacart, Benchling, and even banks and social networks with the most stringent security requirements. Book a demo here: Everyone says, “AI is replacing SaaS”, yet find me a Fortune 500 company that has vibe coded its own CRM instead of using Salesforce. That changes today. To celebrate our launch, we're building 1,000 enterprise apps for free. Comment a SaaS tool you hate and why, and we'll reply with an enterprise-grade replacement.
2:03

Today we’re partnering with AWS to launch Superblocks 3.0: the secure way for employees to vibe code production enterprise software. In a single prompt, Superblocks can replace million dollar SaaS, while IT & Security stay in control. OpenAI and Anthropic are releasing new models with advanced cyber attack capability and vibe coding is every company's achilles heel: > Personal Replit accounts store untracked customer data. > Lovable prototypes with your data open to the public internet. > Claude on the desktop pulls malicious packages. How Superblocks 3.0 works: 1. Superblocks Builds: Import vibe coded prototypes. Model routing picks the best frontier or open source model per task. Saves up to 30% on token costs. 2. Superblocks Secures: A swarm of security agents work like a real human team to find and fix vulnerabilities. 3. Superblocks Deploys: Unlike consumer vibe coding tools that send your data to their cloud, Superblocks deploys the application, database, and AI inference securely inside your private AWS environment. Superblocks 3.0 is trusted across enterprises like Instacart, Benchling, and even banks and social networks with the most stringent security requirements. Book a demo here: Everyone says, “AI is replacing SaaS”, yet find me a Fortune 500 company that has vibe coded its own CRM instead of using Salesforce. That changes today. To celebrate our launch, we're building 1,000 enterprise apps for free. Comment a SaaS tool you hate and why, and we'll reply with an enterprise-grade replacement.

Brad Menezes

19,471,241 次观看 • 17 天前

Hive Intelligence Launches Specialized Crypto Agents Hive Intelligence has released a suite of 17 specialized crypto agents that extend Claude Code's capabilities for professional crypto development and analysis. Extending Claude Code for Crypto Work Claude Code, Anthropic's command-line coding tool, now has access to specialized crypto intelligence through Hive's agent framework. These 17 agents work alongside SuperClaude's 14 base development agents, bringing the total available agent count to 31. The key difference: instead of generic AI responses to crypto queries, developers now have access to specialized agents trained for specific blockchain domains, from smart contract auditing to MEV research to DeFi strategy optimization. How the Agents Work After installation, the agents operate automatically based on query context. When you ask Claude Code to perform crypto-related tasks, the appropriate specialist agent is invoked: - "Audit this smart contract" → Crypto Security Researcher - "Find yield farming opportunities on Ethereum" → Crypto DeFi Strategist - "Analyze this wallet's transaction history" → Crypto Wallet Detective - "Identify arbitrage opportunities across DEXs" → Crypto DEX Arbitrageur No manual agent selection required. The system recognizes the task and routes it to the appropriate specialist. The 17 Specialized Agents Market & Trading Intelligence (4 agents) Crypto Quant: Mathematical models, algorithmic trading strategies, statistical arbitrage, and quantitative risk modeling. Crypto Market Researcher: Fundamental analysis, market trends, institutional adoption tracking, and regulatory landscape monitoring. Crypto Derivatives Trader: Futures and perpetuals analysis, options strategies, leverage management, and derivatives market intelligence. Crypto DEX Arbitrageur: Cross-exchange arbitrage identification, MEV strategy development, and automated profit extraction techniques. DeFi & Liquidity (4 agents) Crypto DeFi Strategist: Yield farming optimization, protocol analysis, liquidity provision strategies, and DeFi portfolio management. Crypto Liquidity Manager: Pool optimization, impermanent loss calculation and mitigation, market making strategies, and capital efficiency analysis. Crypto Governance Analyst: DAO structure evaluation, governance token analysis, proposal assessment, and voting mechanism research. Crypto Bridge Analyst: Cross-chain bridge security assessment, protocol comparison, interoperability solutions, and bridge risk evaluation. Security & Risk (3 agents) Crypto Security Researcher: Smart contract auditing, vulnerability detection, honeypot identification, and exploit pattern recognition. Crypto Security Engineer: Secure contract development practices, defensive programming patterns, and security implementation guidance. Crypto Risk Manager: Portfolio risk assessment, compliance monitoring, exposure analysis, and risk mitigation strategy development. On-Chain Analysis (3 agents) Crypto Wallet Detective: Blockchain forensics, wallet behavior analysis, transaction tracing, and entity identification across chains. Crypto On-chain Analyst: Transaction pattern analysis, wallet clustering, flow tracking, and on-chain metrics interpretation. Crypto MEV Researcher: MEV opportunity detection, flashloan arbitrage analysis, sandwich attack identification, and MEV protection strategies. Specialized Intelligence (3 agents) Crypto NFT Specialist: Collection valuation, rarity analysis, marketplace trends, and NFT ecosystem intelligence. Crypto Stablecoin Analyst: Peg stability monitoring, collateral analysis, depegging risk assessment, and stablecoin mechanism evaluation. Crypto Social Sentiment: Social media sentiment tracking, influencer monitoring, trending topic identification, and community analysis. Data Coverage: - 60+ blockchain networks - 2,000+ DeFi protocols - Real-time DEX data - CEX trading metrics - Social sentiment feeds - NFT marketplace data Compatibility: Works seamlessly with SuperClaude's existing agent framework. No configuration conflicts or manual routing needed. Practical Applications Smart Contract Development Security agents can audit contracts during development, identifying reentrancy risks, access control issues, and common vulnerabilities before deployment. DeFi Research Strategy agents query real-time pool data across networks, calculate yield-adjusted returns, and assess risks like impermanent loss or smart contract exposure. Trading Analysis Market agents access derivatives data, funding rates, liquidation levels, and order book depth across exchanges for informed trading decisions. Forensic Investigation On-chain agents trace fund flows, identify connected addresses, and analyze transaction patterns for security research or compliance work. Portfolio Management Risk agents evaluate protocol exposure, assess tail risks, and monitor positions across multiple chains and protocols. Why Specialized Agents Matter Generic AI models lack the domain-specific knowledge required for professional crypto work. A general-purpose AI might provide surface-level analysis of a smart contract, but a specialized security agent understands Solidity patterns, common exploits, and auditing methodologies. The agent framework solves this by routing tasks to specialists with deep domain knowledge: - A derivatives question goes to an agent trained on perpetuals, funding rates, and options greeks - A DeFi query reaches an agent that understands liquidity mathematics and protocol mechanics - A security audit is handled by an agent familiar with vulnerability patterns and exploit techniques This specialization produces more accurate, actionable insights than single-model approaches. Getting Started The agents are available now through npm. Requirements: - Node.js 16+ - Claude Code installed - No additional dependencies After installation, simply use Claude Code normally. When you ask crypto-related questions or request blockchain analysis, the appropriate agent is automatically invoked. The system handles routing, data retrieval, and response generation. Documentation covers individual agent capabilities, example queries, and integration patterns for different workflows. What This Enables With 17 specialized crypto agents, Claude Code becomes a comprehensive blockchain development and analysis environment: - Developers can audit contracts, optimize gas usage, and implement security patterns - Researchers can analyze protocols, compare yields, and assess risks - Traders can evaluate markets, identify opportunities, and manage positions - Security professionals can investigate exploits, trace funds, and assess vulnerabilities The agents provide access to blockchain data and specialized analysis that previously required multiple tools, APIs, and manual research. ghive.
0:56

Hive Intelligence Launches Specialized Crypto Agents Hive Intelligence has released a suite of 17 specialized crypto agents that extend Claude Code's capabilities for professional crypto development and analysis. Extending Claude Code for Crypto Work Claude Code, Anthropic's command-line coding tool, now has access to specialized crypto intelligence through Hive's agent framework. These 17 agents work alongside SuperClaude's 14 base development agents, bringing the total available agent count to 31. The key difference: instead of generic AI responses to crypto queries, developers now have access to specialized agents trained for specific blockchain domains, from smart contract auditing to MEV research to DeFi strategy optimization. How the Agents Work After installation, the agents operate automatically based on query context. When you ask Claude Code to perform crypto-related tasks, the appropriate specialist agent is invoked: - "Audit this smart contract" → Crypto Security Researcher - "Find yield farming opportunities on Ethereum" → Crypto DeFi Strategist - "Analyze this wallet's transaction history" → Crypto Wallet Detective - "Identify arbitrage opportunities across DEXs" → Crypto DEX Arbitrageur No manual agent selection required. The system recognizes the task and routes it to the appropriate specialist. The 17 Specialized Agents Market & Trading Intelligence (4 agents) Crypto Quant: Mathematical models, algorithmic trading strategies, statistical arbitrage, and quantitative risk modeling. Crypto Market Researcher: Fundamental analysis, market trends, institutional adoption tracking, and regulatory landscape monitoring. Crypto Derivatives Trader: Futures and perpetuals analysis, options strategies, leverage management, and derivatives market intelligence. Crypto DEX Arbitrageur: Cross-exchange arbitrage identification, MEV strategy development, and automated profit extraction techniques. DeFi & Liquidity (4 agents) Crypto DeFi Strategist: Yield farming optimization, protocol analysis, liquidity provision strategies, and DeFi portfolio management. Crypto Liquidity Manager: Pool optimization, impermanent loss calculation and mitigation, market making strategies, and capital efficiency analysis. Crypto Governance Analyst: DAO structure evaluation, governance token analysis, proposal assessment, and voting mechanism research. Crypto Bridge Analyst: Cross-chain bridge security assessment, protocol comparison, interoperability solutions, and bridge risk evaluation. Security & Risk (3 agents) Crypto Security Researcher: Smart contract auditing, vulnerability detection, honeypot identification, and exploit pattern recognition. Crypto Security Engineer: Secure contract development practices, defensive programming patterns, and security implementation guidance. Crypto Risk Manager: Portfolio risk assessment, compliance monitoring, exposure analysis, and risk mitigation strategy development. On-Chain Analysis (3 agents) Crypto Wallet Detective: Blockchain forensics, wallet behavior analysis, transaction tracing, and entity identification across chains. Crypto On-chain Analyst: Transaction pattern analysis, wallet clustering, flow tracking, and on-chain metrics interpretation. Crypto MEV Researcher: MEV opportunity detection, flashloan arbitrage analysis, sandwich attack identification, and MEV protection strategies. Specialized Intelligence (3 agents) Crypto NFT Specialist: Collection valuation, rarity analysis, marketplace trends, and NFT ecosystem intelligence. Crypto Stablecoin Analyst: Peg stability monitoring, collateral analysis, depegging risk assessment, and stablecoin mechanism evaluation. Crypto Social Sentiment: Social media sentiment tracking, influencer monitoring, trending topic identification, and community analysis. Data Coverage: - 60+ blockchain networks - 2,000+ DeFi protocols - Real-time DEX data - CEX trading metrics - Social sentiment feeds - NFT marketplace data Compatibility: Works seamlessly with SuperClaude's existing agent framework. No configuration conflicts or manual routing needed. Practical Applications Smart Contract Development Security agents can audit contracts during development, identifying reentrancy risks, access control issues, and common vulnerabilities before deployment. DeFi Research Strategy agents query real-time pool data across networks, calculate yield-adjusted returns, and assess risks like impermanent loss or smart contract exposure. Trading Analysis Market agents access derivatives data, funding rates, liquidation levels, and order book depth across exchanges for informed trading decisions. Forensic Investigation On-chain agents trace fund flows, identify connected addresses, and analyze transaction patterns for security research or compliance work. Portfolio Management Risk agents evaluate protocol exposure, assess tail risks, and monitor positions across multiple chains and protocols. Why Specialized Agents Matter Generic AI models lack the domain-specific knowledge required for professional crypto work. A general-purpose AI might provide surface-level analysis of a smart contract, but a specialized security agent understands Solidity patterns, common exploits, and auditing methodologies. The agent framework solves this by routing tasks to specialists with deep domain knowledge: - A derivatives question goes to an agent trained on perpetuals, funding rates, and options greeks - A DeFi query reaches an agent that understands liquidity mathematics and protocol mechanics - A security audit is handled by an agent familiar with vulnerability patterns and exploit techniques This specialization produces more accurate, actionable insights than single-model approaches. Getting Started The agents are available now through npm. Requirements: - Node.js 16+ - Claude Code installed - No additional dependencies After installation, simply use Claude Code normally. When you ask crypto-related questions or request blockchain analysis, the appropriate agent is automatically invoked. The system handles routing, data retrieval, and response generation. Documentation covers individual agent capabilities, example queries, and integration patterns for different workflows. What This Enables With 17 specialized crypto agents, Claude Code becomes a comprehensive blockchain development and analysis environment: - Developers can audit contracts, optimize gas usage, and implement security patterns - Researchers can analyze protocols, compare yields, and assess risks - Traders can evaluate markets, identify opportunities, and manage positions - Security professionals can investigate exploits, trace funds, and assess vulnerabilities The agents provide access to blockchain data and specialized analysis that previously required multiple tools, APIs, and manual research. ghive.

Hive Intelligence

78,743 次观看 • 9 个月前

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.
0:51

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.

JJ Englert

16,176 次观看 • 4 个月前

Introducing Impeccable 3.5, the best way to design in production: iterate on real UI with your AI agent, in the codebase you actually ship. Turns out many popular design skills, including Impeccable and Anthropic's frontend-design, weren't actually very good at...design (the workflow was valuable, but the output didn't magically make LLMs like GPT great designers). We measured it across thousands of generations: 74% of pages used the cream AI-default background, 76% reached for extreme letter-spacing, 90%+ failed the contrast floor. So we started fixing slop systematically, specific to each model. The skill now compiles rules for the exact defects each model makes, instead of shipping one generic file to everyone. The biggest jump is in GPT-5.5 and Codex. Also new: ◆ It now knows the difference between a new project and an existing one. Existing codebase, it reads your design system and preserves your identity. Greenfield, it seeds a fresh palette from 129 hand-curated anchors so every cold start doesn't drift to the same safe colors. ◆ Live Mode is now in beta, and works at two scales. Type a direction into the new Steer bar, or speak it, and the agent reads the whole page and edits it in place. Or pick a single element, steer it with a sub-command, live-edit any copy, and accept the variant straight back to source. Insert mode scaffolds brand-new elements between the ones already there. Recovery survives HMR, hidden heroes, and dev-tool overlays. ◆ A rebuilt anti-pattern detector. Torn off jsdom and onto a real CSS cascade resolver: roughly 20x faster, dependency-free, and now small enough to run inline inside the skill, not just the CLI and extension. 14 new rules, 41 total. ◆ The skill keeps itself current, checking once a day and offering to update. Plus /impeccable init and a bare /impeccable that reads your repo and tells you the next move. Free, open source. Claude Code, Codex, Cursor, and more.
0:49

Introducing Impeccable 3.5, the best way to design in production: iterate on real UI with your AI agent, in the codebase you actually ship. Turns out many popular design skills, including Impeccable and Anthropic's frontend-design, weren't actually very good at...design (the workflow was valuable, but the output didn't magically make LLMs like GPT great designers). We measured it across thousands of generations: 74% of pages used the cream AI-default background, 76% reached for extreme letter-spacing, 90%+ failed the contrast floor. So we started fixing slop systematically, specific to each model. The skill now compiles rules for the exact defects each model makes, instead of shipping one generic file to everyone. The biggest jump is in GPT-5.5 and Codex. Also new: ◆ It now knows the difference between a new project and an existing one. Existing codebase, it reads your design system and preserves your identity. Greenfield, it seeds a fresh palette from 129 hand-curated anchors so every cold start doesn't drift to the same safe colors. ◆ Live Mode is now in beta, and works at two scales. Type a direction into the new Steer bar, or speak it, and the agent reads the whole page and edits it in place. Or pick a single element, steer it with a sub-command, live-edit any copy, and accept the variant straight back to source. Insert mode scaffolds brand-new elements between the ones already there. Recovery survives HMR, hidden heroes, and dev-tool overlays. ◆ A rebuilt anti-pattern detector. Torn off jsdom and onto a real CSS cascade resolver: roughly 20x faster, dependency-free, and now small enough to run inline inside the skill, not just the CLI and extension. 14 new rules, 41 total. ◆ The skill keeps itself current, checking once a day and offering to update. Plus /impeccable init and a bare /impeccable that reads your repo and tells you the next move. Free, open source. Claude Code, Codex, Cursor, and more.

Paul Bakaus

103,002 次观看 • 2 个月前

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.
0:34

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.

Akshay 🚀

33,358 次观看 • 8 个月前

How Cloudflare cut query times by 35x without leaving Postgres: Their Postgres tables hit billions of rows, and every time-range query started getting slower. Plain Postgres scans the entire table on every query, regardless of the time window. They tried the manual route by building precomputed aggregates with cron jobs and evaluating ClickHouse, which needed a full ingestion pipeline just to handle their write pattern. Here's where most teams get stuck at exactly this point. They utilize manual partitioning, splitting data into child tables by day or month. Then they wire up cron jobs to refresh aggregate tables. Every schema change after that requires updating the cron logic and coordinating across teams. The infrastructure becomes the project. Tiger Cloud is managed TimescaleDB by Tiger Data - Creators of TimescaleDB on the Postgres you already know, with automatic time-based partitioning, continuous aggregates, and compression built in from the start. Cloudflare moved to TimescaleDB after exhausting the manual route and saw 5-35x query performance improvement on the same data. Here is how it works: → Hypertables partition data by timestamp automatically. Every time-range query hits only the relevant chunk, not the full table. → Continuous aggregates refresh incrementally in the background, with no cron jobs to maintain. To show what this looks like in practice, I built a real-time earthquake intelligence dashboard on a 3D globe using Claude Code and Tiger Cloud in a single session. The USGS earthquake catalog has 400,000+ events since 1900, each a timestamped row. The dashboard plots them as ripple animations sized by magnitude and colored by depth, with a time slider that scrubs through 120 years of seismic history. Every slider position fires a live query against the hypertable, and the side panel pulls from continuous aggregates. Claude Code connected to Tiger Cloud through the Tiger CLI MCP server, provisioned the database, pulled the USGS catalog, and assembled the full Next.js and Three.js frontend without leaving the session. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud here: New accounts get $1,000 in free credits, no credit card required. → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build. I also wrote a full walkthrough on how you can turn any coding agent into a production-grade data engineer that can manage over a billion-row Postgres workloads. It covers everything from the database setup to the final build. Read it below.
0:55

How Cloudflare cut query times by 35x without leaving Postgres: Their Postgres tables hit billions of rows, and every time-range query started getting slower. Plain Postgres scans the entire table on every query, regardless of the time window. They tried the manual route by building precomputed aggregates with cron jobs and evaluating ClickHouse, which needed a full ingestion pipeline just to handle their write pattern. Here's where most teams get stuck at exactly this point. They utilize manual partitioning, splitting data into child tables by day or month. Then they wire up cron jobs to refresh aggregate tables. Every schema change after that requires updating the cron logic and coordinating across teams. The infrastructure becomes the project. Tiger Cloud is managed TimescaleDB by Tiger Data - Creators of TimescaleDB on the Postgres you already know, with automatic time-based partitioning, continuous aggregates, and compression built in from the start. Cloudflare moved to TimescaleDB after exhausting the manual route and saw 5-35x query performance improvement on the same data. Here is how it works: → Hypertables partition data by timestamp automatically. Every time-range query hits only the relevant chunk, not the full table. → Continuous aggregates refresh incrementally in the background, with no cron jobs to maintain. To show what this looks like in practice, I built a real-time earthquake intelligence dashboard on a 3D globe using Claude Code and Tiger Cloud in a single session. The USGS earthquake catalog has 400,000+ events since 1900, each a timestamped row. The dashboard plots them as ripple animations sized by magnitude and colored by depth, with a time slider that scrubs through 120 years of seismic history. Every slider position fires a live query against the hypertable, and the side panel pulls from continuous aggregates. Claude Code connected to Tiger Cloud through the Tiger CLI MCP server, provisioned the database, pulled the USGS catalog, and assembled the full Next.js and Three.js frontend without leaving the session. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud here: New accounts get $1,000 in free credits, no credit card required. → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build. I also wrote a full walkthrough on how you can turn any coding agent into a production-grade data engineer that can manage over a billion-row Postgres workloads. It covers everything from the database setup to the final build. Read it below.

Avi Chawla

21,564 次观看 • 4 天前

ClawTeam v0.2.0 is here. One CLI to coordinate any coding agent — Claude Code, Codex, OpenClaw, nanobot, and more — into a self‑organizing swarm that plans, builds, and ships together. What's new in v0.2.0: 1) - Gource Visualization — Watch your agent swarm’s Git activity in real time. Clear. Visual. Instant. Run: clawteam board gource --live See every commit, branch, and merge as it happens. Track what each agent is doing. 2) - Runtime Profiles — A provider‑aware configuration system. Switch between Claude, Kimi, and Gemini anytime. No need to edit environment variables. Run clawteam profile wizard. Follow the interactive setup. Done in minutes. 3) - Git-Based Context — Full worktree isolation with built‑in conflict detection and change tracking. Each agent works on its own branch, and the leader can see everything clearly in one place. 4) - Stability & Hardening — Spawn/workspace conflict fixes, improved tmux integration, message normalization, P2P liveness with lease-based detection. This release is about making the foundation rock-solid. --------------------------------------------------------- To show what a coordinated agent swarm can actually do, we ran 1 Claude Code orchestrating 8 Claude Code agents to build a robotics simulation system optimized for Apple Silicon — from scratch. 8 hours. 300+ PRs. One running simulator. Check the result: --------------------------------------------------------- Huge thanks to the open-source community for the feedback, issues, and PRs that shaped this release. ClawTeam is built in the open because we believe multi-agent coordination should be a shared primitive, not a proprietary moat. Try it: pip install clawteam Docs: GitHub: #ClawTeam #nanobot #AIAgents #openclaw #ClaudeCode #Cursor
1:23

ClawTeam v0.2.0 is here. One CLI to coordinate any coding agent — Claude Code, Codex, OpenClaw, nanobot, and more — into a self‑organizing swarm that plans, builds, and ships together. What's new in v0.2.0: 1) - Gource Visualization — Watch your agent swarm’s Git activity in real time. Clear. Visual. Instant. Run: clawteam board gource --live See every commit, branch, and merge as it happens. Track what each agent is doing. 2) - Runtime Profiles — A provider‑aware configuration system. Switch between Claude, Kimi, and Gemini anytime. No need to edit environment variables. Run clawteam profile wizard. Follow the interactive setup. Done in minutes. 3) - Git-Based Context — Full worktree isolation with built‑in conflict detection and change tracking. Each agent works on its own branch, and the leader can see everything clearly in one place. 4) - Stability & Hardening — Spawn/workspace conflict fixes, improved tmux integration, message normalization, P2P liveness with lease-based detection. This release is about making the foundation rock-solid. --------------------------------------------------------- To show what a coordinated agent swarm can actually do, we ran 1 Claude Code orchestrating 8 Claude Code agents to build a robotics simulation system optimized for Apple Silicon — from scratch. 8 hours. 300+ PRs. One running simulator. Check the result: --------------------------------------------------------- Huge thanks to the open-source community for the feedback, issues, and PRs that shaped this release. ClawTeam is built in the open because we believe multi-agent coordination should be a shared primitive, not a proprietary moat. Try it: pip install clawteam Docs: GitHub: #ClawTeam #nanobot #AIAgents #openclaw #ClaudeCode #Cursor

Chao Huang

25,232 次观看 • 4 个月前

OpenAI and Anthropic this week: GPT-5.6 price cuts, Claude cracking ciphers, and both backing "Pacing the Frontier" (Week 31, 2026) Starting with OpenAI - GPT-5.6 got a big price cut, with Luna dropping 80% and Terra 20%, plus a new Fast mode for Sol in the API ChatGPT for Academic Researchers opened too, giving free frontier model access to 100,000 scientists On the research side, OpenAI shared ten advances in mathematics and theoretical computer science, all from an internal version of the next model called Astra, plus a study on how AI expands the range of work people do and a field report on scientists using coding agents On the developer side: GPT Transcribe and GPT Live Transcribe, a Terraform provider, an open-source Codex Security CLI, Sign in with ChatGPT in beta, and a desktop app update with browser upgrades, multi-repo review, image editing, and an Activity view GPT-5.4 retires from Codex end of August, the Student Collective opened, and two API settings tripled Sol's ARC-AGI-3 score Plus, I spotted a new "Places" section in ChatGPT Onto Anthropic - Claude Mythos Preview helped find weaknesses in cryptographic algorithms, cutting the effective key strength of the post-quantum scheme HAWK in half and speeding up an attack on reduced-round AES by 200 to 800 times, with no impact on production systems Anthropic released MCP 2026-07-28, the biggest protocol update since launch, moving it to a stateless core with standardized extensions and hardened auth Anthropic disclosed three incidents where Claude reached the internet from inside cybersecurity evaluation environments and accessed real systems of three organizations, traced to a misconfiguration rather than a model alignment failure Dario Amodei laid out Anthropic's position on open-weights models too, saying clearly a ban has never been on the table Both companies backed the "Pacing the Frontier" petition And I spotted Anthropic adding noindex and nofollow to shared Claude conversations
2:45

OpenAI and Anthropic this week: GPT-5.6 price cuts, Claude cracking ciphers, and both backing "Pacing the Frontier" (Week 31, 2026) Starting with OpenAI - GPT-5.6 got a big price cut, with Luna dropping 80% and Terra 20%, plus a new Fast mode for Sol in the API ChatGPT for Academic Researchers opened too, giving free frontier model access to 100,000 scientists On the research side, OpenAI shared ten advances in mathematics and theoretical computer science, all from an internal version of the next model called Astra, plus a study on how AI expands the range of work people do and a field report on scientists using coding agents On the developer side: GPT Transcribe and GPT Live Transcribe, a Terraform provider, an open-source Codex Security CLI, Sign in with ChatGPT in beta, and a desktop app update with browser upgrades, multi-repo review, image editing, and an Activity view GPT-5.4 retires from Codex end of August, the Student Collective opened, and two API settings tripled Sol's ARC-AGI-3 score Plus, I spotted a new "Places" section in ChatGPT Onto Anthropic - Claude Mythos Preview helped find weaknesses in cryptographic algorithms, cutting the effective key strength of the post-quantum scheme HAWK in half and speeding up an attack on reduced-round AES by 200 to 800 times, with no impact on production systems Anthropic released MCP 2026-07-28, the biggest protocol update since launch, moving it to a stateless core with standardized extensions and hardened auth Anthropic disclosed three incidents where Claude reached the internet from inside cybersecurity evaluation environments and accessed real systems of three organizations, traced to a misconfiguration rather than a model alignment failure Dario Amodei laid out Anthropic's position on open-weights models too, saying clearly a ban has never been on the table Both companies backed the "Pacing the Frontier" petition And I spotted Anthropic adding noindex and nofollow to shared Claude conversations

Tibor Blaho

11,303 次观看 • 18 天前

10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. — 1️⃣ NotebookLM — your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise — 2️⃣ Google AI Studio — the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits — 3️⃣ Gemini CLI — google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) — 4️⃣ Jules — async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5️⃣ Stitch — text → UI → code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work — 6️⃣ Gemma 4 — open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference — 7️⃣ Illuminate — papers → podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading — 8️⃣ Learn About (LearnLM) — adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics — 9️⃣ Google Labs FX (ImageFX + Flow + MusicFX) — free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music — those stay free. replaces: midjourney + suno (free tier only — runway-level video gen is paid) — 🔟 Google Colab — free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals — a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done — especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this 🔔
0:34

10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. — 1️⃣ NotebookLM — your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise — 2️⃣ Google AI Studio — the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits — 3️⃣ Gemini CLI — google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) — 4️⃣ Jules — async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5️⃣ Stitch — text → UI → code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work — 6️⃣ Gemma 4 — open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference — 7️⃣ Illuminate — papers → podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading — 8️⃣ Learn About (LearnLM) — adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics — 9️⃣ Google Labs FX (ImageFX + Flow + MusicFX) — free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music — those stay free. replaces: midjourney + suno (free tier only — runway-level video gen is paid) — 🔟 Google Colab — free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals — a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done — especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this 🔔

m0h

11,847 次观看 • 2 个月前

🚨 THREAT INTELLIGENCE ALERT 🚨 The tool 🇨🇳 KernelGhost820 is being actively sold on the underground market for US$ 2,500, complete with full source code. This is a professional-grade suite with an intuitive graphical interface and six advanced modules, specifically designed for EDR evasion and sophisticated ransomware operations with efficient lateral movement: • EDR Removal Engine: Automatically detects and terminates more than 40 security products (including CrowdStrike, SentinelOne, Microsoft Defender, Kaspersky, and others). Supports Kernel, UserMode, and NTDLL termination modes, kernel driver loading for protected processes, disabling Windows Defender, and blocking telemetry connections. • Ransomware Module: Dual encryption using AES256CBC + RSA2048, supporting over 70 file types (documents, images, databases, backups, etc.). Automatically deletes Volume Shadow Copies to prevent recovery, generates custom ransom notes with Bitcoin addresses and contact emails, and changes the desktop wallpaper. • Remote Operations & Mass Deployment: Connects to remote devices on the local network via WMI (requires username and password). Scans installed software on target hosts, performs process termination, and enables one-click full tool deployment. Includes full-network scanning for open SMB port 445 with real-time progress tracking. • Detailed Process Manager and full Operation Logger (exportable to TXT). This tool significantly lowers the technical barrier for advanced ransomware actors targeting corporate environments. Immediate monitoring recommendations: • Evaluate the resilience of your EDR/XDR controls against kernel-mode bypass techniques • Intensify monitoring of anomalous SMB (port 445) traffic and WMI connections • Strengthen network segmentation and the principle of least privilege Our team is actively tracking this tool and any emerging variants. #ThreatIntelligence #Ransomware #EDRBypass #CyberSecurity #InfoSec #CyberThreat
3:20

🚨 THREAT INTELLIGENCE ALERT 🚨 The tool 🇨🇳 KernelGhost820 is being actively sold on the underground market for US$ 2,500, complete with full source code. This is a professional-grade suite with an intuitive graphical interface and six advanced modules, specifically designed for EDR evasion and sophisticated ransomware operations with efficient lateral movement: • EDR Removal Engine: Automatically detects and terminates more than 40 security products (including CrowdStrike, SentinelOne, Microsoft Defender, Kaspersky, and others). Supports Kernel, UserMode, and NTDLL termination modes, kernel driver loading for protected processes, disabling Windows Defender, and blocking telemetry connections. • Ransomware Module: Dual encryption using AES256CBC + RSA2048, supporting over 70 file types (documents, images, databases, backups, etc.). Automatically deletes Volume Shadow Copies to prevent recovery, generates custom ransom notes with Bitcoin addresses and contact emails, and changes the desktop wallpaper. • Remote Operations & Mass Deployment: Connects to remote devices on the local network via WMI (requires username and password). Scans installed software on target hosts, performs process termination, and enables one-click full tool deployment. Includes full-network scanning for open SMB port 445 with real-time progress tracking. • Detailed Process Manager and full Operation Logger (exportable to TXT). This tool significantly lowers the technical barrier for advanced ransomware actors targeting corporate environments. Immediate monitoring recommendations: • Evaluate the resilience of your EDR/XDR controls against kernel-mode bypass techniques • Intensify monitoring of anomalous SMB (port 445) traffic and WMI connections • Strengthen network segmentation and the principle of least privilege Our team is actively tracking this tool and any emerging variants. #ThreatIntelligence #Ransomware #EDRBypass #CyberSecurity #InfoSec #CyberThreat

Clandestine

40,816 次观看 • 4 个月前

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

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

Milk Road AI

24,356 次观看 • 3 个月前