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IDA Pro into an AI-Reverse engineering workstation: - Instant function explanations + security analysis - Semantic knowledge graph of the entire binary - Autonomous ReAct agent, Extended thinking levels - Full MCP tool integration - Smart rename suggestions - Interactive Q&A with tool-calling - RAG over your own docs... show more

Md Ismail Šojal 🕷️

57,822 subscribers

34,551 Aufrufe • vor 10 Tagen •via X (Twitter)

Nachrichten & Politik Bildung Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

7 Kommentare

Profilbild von banzaan

banzaanvor 9 Tagen

the binary now explains itself

Profilbild von Cryptoartmaster

Cryptoartmastervor 9 Tagen

instant function explanations with a semantic graph is the real edge for binary analysis.

Profilbild von Nicholas Michael Kloster

Nicholas Michael Klostervor 9 Tagen

Ablation you don't need IDA PRO. Fully autonomous reverse engineering with Claude or Codex

Profilbild von Medo

Medovor 9 Tagen

deobfuscation?

Profilbild von tau-home.com

tau-home.comvor 9 Tagen

good

Profilbild von 🌧️💙 Neo the Kobold

🌧️💙 Neo the Koboldvor 9 Tagen

Genuine question- why use IDA pro, a paid thing, when Ghidra exists? This has been frustrating as I look at other decomps files and try to contribute to them, but I don’t own IDA pro, but it seems like Ghidra can do the same things. I’m very new to reverse engineering b nice 😊

Profilbild von Tushar kumar

Tushar kumarvor 9 Tagen

Before installing this open-source tool, you may want to verify that your reverse engineering platform is updated to version 9.0 or higher.

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YanXbt

32,680 Aufrufe • vor 4 Monaten

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142,256 Aufrufe • vor 1 Jahr

New short course with Mistral AI ! Mistral's open-source Mixtral 8x7B model uses a "mixture of experts" (MoE) architecture. Unlike a standard transformer, an MoE model has multiple expert feed-forward networks (8 in this case), with a gating network selecting two experts at inference time. This enables MoE to match the performance of a large model but faster inference. Mixtral 8x7B has 46.7B parameters but activates only 12.9B at inference to predict the next token. In Getting Started with Mistral, you’ll learn from Mistral’s Sophia Yang to: - Explore Mistral's open-source models (Mistral 7B, Mixtral 8x7B) and commercial models via API calls and Mistral AI's Le Chat website - Implement JSON mode to generate structured outputs to integrate directly into larger software systems. - Use function calling for Tool Use, such as calling custom Python code that queries tabular data - Ground your LLM's response with external knowledge sources using RAG - Build a Mistral-powered chat interface that can reference external documents This course will help deepen your prompt engineering skills. Please sign up here:

New short course with Mistral AI ! Mistral's open-source Mixtral 8x7B model uses a "mixture of experts" (MoE) architecture. Unlike a standard transformer, an MoE model has multiple expert feed-forward networks (8 in this case), with a gating network selecting two experts at inference time. This enables MoE to match the performance of a large model but faster inference. Mixtral 8x7B has 46.7B parameters but activates only 12.9B at inference to predict the next token. In Getting Started with Mistral, you’ll learn from Mistral’s Sophia Yang to: - Explore Mistral's open-source models (Mistral 7B, Mixtral 8x7B) and commercial models via API calls and Mistral AI's Le Chat website - Implement JSON mode to generate structured outputs to integrate directly into larger software systems. - Use function calling for Tool Use, such as calling custom Python code that queries tabular data - Ground your LLM's response with external knowledge sources using RAG - Build a Mistral-powered chat interface that can reference external documents This course will help deepen your prompt engineering skills. Please sign up here:

Andrew Ng

387,454 Aufrufe • vor 2 Jahren

🚨 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.

🚨 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

395,396 Aufrufe • vor 6 Monaten

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,884 Aufrufe • vor 2 Jahren

After 2.5 months in beta, and over 1000 organic users, and a small discord community, I am finally annoucing the launch of The Open Binary Project The Free and Opensource collaborative security research and reverse engineering platform. Weather you're into malware analysis, or vulnerability research, you can now create and access your projects anywhere completely for free, share projects with friends and fellow researchers, and publish your analysis reports or vulnerability research publicly with the community! You can also peer review publicly posted research, fork and make your own revisions, and build your public profile, all with fully build in LLM access to get AI assistance with your work in openbin. This platform was build to address the gap in the security research community where we did not have a collaborative space to work on things together like how developers have github, and github would probably ban you anyways, we don't! Join us today, a growing ecosystem of enthusiastic, passionate, and collaborative security researchers, completely for free!

BytecodeAssassin (Husam)

27,975 Aufrufe • vor 1 Monat

Nobody is talking about this. There's an open-source AI platform with 17.3K stars that connects to 40+ of your internal tools and runs completely on your own servers. It's called Onyx. Most companies using ChatGPT Enterprise are feeding sensitive internal data to OpenAI's cloud every single day. Onyx flips that. You self-host it. You control the LLM. Your documents, Slack messages, Notion pages, and Jira tickets stay on your infrastructure. The connectors are the killer feature. Onyx pulls live knowledge from Slack, Notion, Google Drive, Confluence, GitHub, Jira, and 35+ more then indexes everything with hybrid search and a knowledge graph that stays accurate at tens of millions of documents. Document permissioning mirrors your existing access controls automatically. If someone doesn't have access to a Confluence page, the AI won't surface it to them in answers. No extra config. No permission rebuilds. Everything else: → Custom agents with unique instructions and MCP tool access → Deep Research for complex multi-step questions → Code interpreter, image generation, web search → SSO, RBAC, full enterprise security stack → Docker, Kubernetes, Terraform full deployment flexibility MIT License on Community Edition. 17.3K stars. 2.3K forks. 192 contributors.

Nobody is talking about this. There's an open-source AI platform with 17.3K stars that connects to 40+ of your internal tools and runs completely on your own servers. It's called Onyx. Most companies using ChatGPT Enterprise are feeding sensitive internal data to OpenAI's cloud every single day. Onyx flips that. You self-host it. You control the LLM. Your documents, Slack messages, Notion pages, and Jira tickets stay on your infrastructure. The connectors are the killer feature. Onyx pulls live knowledge from Slack, Notion, Google Drive, Confluence, GitHub, Jira, and 35+ more then indexes everything with hybrid search and a knowledge graph that stays accurate at tens of millions of documents. Document permissioning mirrors your existing access controls automatically. If someone doesn't have access to a Confluence page, the AI won't surface it to them in answers. No extra config. No permission rebuilds. Everything else: → Custom agents with unique instructions and MCP tool access → Deep Research for complex multi-step questions → Code interpreter, image generation, web search → SSO, RBAC, full enterprise security stack → Docker, Kubernetes, Terraform full deployment flexibility MIT License on Community Edition. 17.3K stars. 2.3K forks. 192 contributors.

Hasan Toor

11,638 Aufrufe • vor 6 Monaten

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

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

Katyayani Shukla

16,964 Aufrufe • vor 5 Monaten

Graph Engineering became the default way every serious team builds agents now, and here's what people have already shipped with it if you want to actually draw the graph instead of guessing, copy this: LangGraph - the framework behind Uber, Replit, LinkedIn, and GitLab's own production agents. models every workflow as nodes and edges with conditional branching, the exact vocabulary that makes fake edges impossible to hide CrewAI - role-based multi-agent teams with async execution. 47,000 stars, 5.2 million monthly downloads, no LangChain dependency since version 1.14 crewAI-examples - the self-evaluation loop flow example is the one to clone first: a working verifier pattern you can read end to end before you build your own and get it wrong the first time open-multi-agent - TypeScript-native, three runtime dependencies, nothing else. one runTeam() call decomposes a goal into a task DAG, resolves every dependency, and runs the independent pieces in parallel without you drawing a single node AutoGen - Microsoft's event-driven framework, merged with Semantic Kernel for production. the conversation-based model that taught half the industry exactly what a shared-context failure looks like, the expensive way Google ADK - modular agent dev kit with native Gemini and Vertex AI integration, built for teams that refuse to run the graph anywhere they don't already trust the infrastructure Mastra - the TypeScript entrant carving out ground the bigger frameworks ignored, built specifically for graph control without Python's weight sitting on top of it none of these frameworks fix a bad graph for you. they only make it visible the moment your worker and your verifier have been sharing the same context the entire time full build in the article, then run the fake-edge test before you wire up an eighth

Graph Engineering became the default way every serious team builds agents now, and here's what people have already shipped with it if you want to actually draw the graph instead of guessing, copy this: LangGraph - the framework behind Uber, Replit, LinkedIn, and GitLab's own production agents. models every workflow as nodes and edges with conditional branching, the exact vocabulary that makes fake edges impossible to hide CrewAI - role-based multi-agent teams with async execution. 47,000 stars, 5.2 million monthly downloads, no LangChain dependency since version 1.14 crewAI-examples - the self-evaluation loop flow example is the one to clone first: a working verifier pattern you can read end to end before you build your own and get it wrong the first time open-multi-agent - TypeScript-native, three runtime dependencies, nothing else. one runTeam() call decomposes a goal into a task DAG, resolves every dependency, and runs the independent pieces in parallel without you drawing a single node AutoGen - Microsoft's event-driven framework, merged with Semantic Kernel for production. the conversation-based model that taught half the industry exactly what a shared-context failure looks like, the expensive way Google ADK - modular agent dev kit with native Gemini and Vertex AI integration, built for teams that refuse to run the graph anywhere they don't already trust the infrastructure Mastra - the TypeScript entrant carving out ground the bigger frameworks ignored, built specifically for graph control without Python's weight sitting on top of it none of these frameworks fix a bad graph for you. they only make it visible the moment your worker and your verifier have been sharing the same context the entire time full build in the article, then run the fake-edge test before you wire up an eighth

Ryven

54,861 Aufrufe • vor 23 Tagen

OpenAI, Anthropic and Microsoft all back the same standard for running apps inside AI chats. Booking .com, Figma, and Canva are already using it. Still, most developers have never heard of it. It's called MCP Apps. So, why does it matter? The next time someone buys from your website, they may never visit it. They'll ask ChatGPT or Claude, and the model decides how your product shows up. Usually as plain text, sometimes wrong, with none of the experience you built. That's the next shift in how people find you: - SEO was about Google finding your page. - AEO was about AI citing you. - MCP Apps are about your actual product showing up inside the chat. An MCP server returns data. An MCP App returns data plus an interactive UI, like hotel cards or an add-to-cart flow, right inside the chat. The model even knows what the user clicked. In this episode, we build an MCP app from start to finish with Julien, the lead maintainer of Skybridge, an open-source framework that lets you turn any React app into an MCP app. Chapters: 0:00 - Intro 2:44 - MCP servers vs MCP Apps 5:12 - Live demo: Booking .com in ChatGPT 9:24 - How MCP Apps work: context and shared state 15:43 - Skybridge and its devtools 17:48 - Live demo: an e-commerce app in ChatGPT 24:49 - Code walkthrough: MCP tools to React views 28:10 - Syncing UI state: "What's in my cart?" 32:16 - Agent skill to turn any React app → MCP App 37:52 - Outro Link to Skybridge GitHub repo: MCP Apps bring your website into the chat. WebMCP does the reverse and lets AI agents use your website through tools it exposes. I've also written a full article on it, quoted below.

OpenAI, Anthropic and Microsoft all back the same standard for running apps inside AI chats. Booking .com, Figma, and Canva are already using it. Still, most developers have never heard of it. It's called MCP Apps. So, why does it matter? The next time someone buys from your website, they may never visit it. They'll ask ChatGPT or Claude, and the model decides how your product shows up. Usually as plain text, sometimes wrong, with none of the experience you built. That's the next shift in how people find you: - SEO was about Google finding your page. - AEO was about AI citing you. - MCP Apps are about your actual product showing up inside the chat. An MCP server returns data. An MCP App returns data plus an interactive UI, like hotel cards or an add-to-cart flow, right inside the chat. The model even knows what the user clicked. In this episode, we build an MCP app from start to finish with Julien, the lead maintainer of Skybridge, an open-source framework that lets you turn any React app into an MCP app. Chapters: 0:00 - Intro 2:44 - MCP servers vs MCP Apps 5:12 - Live demo: Booking .com in ChatGPT 9:24 - How MCP Apps work: context and shared state 15:43 - Skybridge and its devtools 17:48 - Live demo: an e-commerce app in ChatGPT 24:49 - Code walkthrough: MCP tools to React views 28:10 - Syncing UI state: "What's in my cart?" 32:16 - Agent skill to turn any React app → MCP App 37:52 - Outro Link to Skybridge GitHub repo: MCP Apps bring your website into the chat. WebMCP does the reverse and lets AI agents use your website through tools it exposes. I've also written a full article on it, quoted below.

Akshay 🚀

18,064 Aufrufe • vor 16 Tagen