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Introducing OpenReview An AI code review system I'm building to learn how real systems work. No noise. No generic comments. Just reviews that actually understand your code. Open source. Currently in build. Everything I'm exploring: • AI PR reviews with real context • Full repo indexing (embeddings + vector...

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11,249 görüntüleme • 3 ay önce •via X (Twitter)

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Today, I'm excited to launch Entelligence AI DeepReviews, the next generation AI-powered code reviews. Vibe coded PRs are not production ready often creating more subtle bugs throughout the codebase than before. Entelligence fixes this by understanding the entire codebase and flags bugs, regressions, and breakages across files before you ship. But it goes beyond review comments: ✅ Auto-generated docs with architecture diagrams ✅ Team Insights to improve process, not just code ✅ Real time code reviews within your editor If you're building or leading a dev team, this is what smarter, context-aware reviews look like. Try it out in the link below 👇
1:13

Today, I'm excited to launch Entelligence AI DeepReviews, the next generation AI-powered code reviews. Vibe coded PRs are not production ready often creating more subtle bugs throughout the codebase than before. Entelligence fixes this by understanding the entire codebase and flags bugs, regressions, and breakages across files before you ship. But it goes beyond review comments: ✅ Auto-generated docs with architecture diagrams ✅ Team Insights to improve process, not just code ✅ Real time code reviews within your editor If you're building or leading a dev team, this is what smarter, context-aware reviews look like. Try it out in the link below 👇

Aiswarya Sankar

95,962 görüntüleme • 1 yıl önce

Introducing "codebase to course", a skill that turns any codebase into an interactive coding course So that you can learn coding through your own projects, complete with visualization, plain-English code translations, metaphors, even quizzes... I vibe code a lot but have no idea how the code works under the hood. This is how I think "learning to code" should be in the AI age: Build first, learn later Link below
2:22

Introducing "codebase to course", a skill that turns any codebase into an interactive coding course So that you can learn coding through your own projects, complete with visualization, plain-English code translations, metaphors, even quizzes... I vibe code a lot but have no idea how the code works under the hood. This is how I think "learning to code" should be in the AI age: Build first, learn later Link below

Zara Zhang

204,314 görüntüleme • 3 ay önce

INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7
0:57

INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7

Factory

327,596 görüntüleme • 1 yıl önce

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 görüntüleme • 7 ay önce

Introducing Claude Code Analytics Dashboard 🙌 Open source project to monitor your Claude Code agents in real-time as they work on your projects. Just run: npx claude-code-templates@latest --analytics Dashboard opens at localhost:3333 showing: - All active Claude Code sessions - Token usage & conversation stats - Real-time activity monitoring - Message history with export (CSV/JSON) Perfect for devs running multiple Claude Code agents - finally see what they're actually doing! The conversation view shows full chat history, token breakdowns, and current status. Finally understand your AI workflow patterns. Best part... Everything runs locally with total privacy! Sharing repo in first comment if you want to star or contribute 👇
0:40

Introducing Claude Code Analytics Dashboard 🙌 Open source project to monitor your Claude Code agents in real-time as they work on your projects. Just run: npx claude-code-templates@latest --analytics Dashboard opens at localhost:3333 showing: - All active Claude Code sessions - Token usage & conversation stats - Real-time activity monitoring - Message history with export (CSV/JSON) Perfect for devs running multiple Claude Code agents - finally see what they're actually doing! The conversation view shows full chat history, token breakdowns, and current status. Finally understand your AI workflow patterns. Best part... Everything runs locally with total privacy! Sharing repo in first comment if you want to star or contribute 👇

Daniel San

62,057 görüntüleme • 1 yıl önce

We’re applying to the Pump.fun Build in Public Hackathon. We're building Clovr. An AI tool that turns ideas into real Next.js frontends. No more boilerplate. No more blank files. Just clean, ready-to-use, beautiful code you can export or open straight in a GitHub repo. We’re building the future of how frontends are built.
0:38

We’re applying to the Pump.fun Build in Public Hackathon. We're building Clovr. An AI tool that turns ideas into real Next.js frontends. No more boilerplate. No more blank files. Just clean, ready-to-use, beautiful code you can export or open straight in a GitHub repo. We’re building the future of how frontends are built.

Alby Churven

24,523 görüntüleme • 4 ay önce

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.
0:31

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

Akshay 🚀

30,806 görüntüleme • 1 yıl önce

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:
4:42

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Santiago

296,363 görüntüleme • 9 ay önce

Wanna make $10K a month with apps? 💰 You need to know how to create a secure, scalable backend. Gemini 3.0 just made it 10x easier. I just shot a full 2 hour 33 minute breakdown showing exactly how I build real production backends with Cursor AI — routes, auth, databases, background workers, everything — all driven through agents and clean architecture. 🔧 If you’ve ever wondered how to go from “AI-generated code” to an actual backend that can power a real business, the full walkthrough is right here. Take this software for free — it's open-source on GitHub! 🎁 Do you wanna watch this lesson by lesson?
2:33:49

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

corbin

59,434 görüntüleme • 7 ay önce

AI didn't make code cheap. it made bad code lethal. Matt Pocock: "Software fundamentals matter more than ever" AI in a good codebase ships gold. AI in a bad codebase ships garbage. framework mastery doesn't compound. fundamentals do. if you're starting with AI agents, build these 5 patterns before anything else: > Tool Use Design Pattern. tool calling is the core of every useful agent. > Agentic RAG. grounded answers over hallucination. maps to real products. > Planning Design Pattern. agents that break a goal into steps before acting. > Multi-Agent Design Pattern. orchestrator-subagent is the only shape that ships in production. > Building Trustworthy AI Agents. reliability, safety, knowing when not to use one. this article covers what to learn, build, and skip in AI agents in 2026. bookmark and read this today. repo with 500 more in comments
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AI didn't make code cheap. it made bad code lethal. Matt Pocock: "Software fundamentals matter more than ever" AI in a good codebase ships gold. AI in a bad codebase ships garbage. framework mastery doesn't compound. fundamentals do. if you're starting with AI agents, build these 5 patterns before anything else: > Tool Use Design Pattern. tool calling is the core of every useful agent. > Agentic RAG. grounded answers over hallucination. maps to real products. > Planning Design Pattern. agents that break a goal into steps before acting. > Multi-Agent Design Pattern. orchestrator-subagent is the only shape that ships in production. > Building Trustworthy AI Agents. reliability, safety, knowing when not to use one. this article covers what to learn, build, and skip in AI agents in 2026. bookmark and read this today. repo with 500 more in comments

Rohit

26,371 görüntüleme • 2 ay önce

WHAT?!? 🤯 I asked Claude to build me an all in one AI Operating System for working with Claude Code. An hour later here we are... → Full ability to chat with Claude Code → Markdown file browser/editor built right in I've been using VS Code but there is a lot that I just don't need. I'm a knowledge worker, not a developer. So I guess I just built an IDE for knowledge workers? Now we're building: → Voice Mode (ability to switch to voice conversations) → Cron Job Management - so I can manage all the automated processes we have running → Ability to switch to different agents (currently I just have one... my AI Cos, Chad)
0:22

WHAT?!? 🤯 I asked Claude to build me an all in one AI Operating System for working with Claude Code. An hour later here we are... → Full ability to chat with Claude Code → Markdown file browser/editor built right in I've been using VS Code but there is a lot that I just don't need. I'm a knowledge worker, not a developer. So I guess I just built an IDE for knowledge workers? Now we're building: → Voice Mode (ability to switch to voice conversations) → Cron Job Management - so I can manage all the automated processes we have running → Ability to switch to different agents (currently I just have one... my AI Cos, Chad)

⚡️ Ev Chapman 🚢 | Creative Entrepreneur

38,577 görüntüleme • 3 ay önce

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.
0:24

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.

Akshay 🚀

52,377 görüntüleme • 3 ay önce

Claw Code just embarrassed Anthropic. A packaging mistake exposed 512,000 lines of Claude Code source. Within 48 hours, a developer rebuilt the architecture, the repo exploded past 100,000 stars, and GitHub had a full-blown open source war on its hands. This is bigger than “another coding tool.” It shows how fast closed AI products can get disrupted when the architecture leaks, the community moves faster than legal, and demand is already there. The real story is not the repo. The real story is that AI companies used open ecosystems to grow, then tightened access the second those ecosystems became expensive to support. That shift is happening everywhere now. If you run a business with AI, pay attention. Your workflow is only as safe as the company policy behind it.
13:03

Claw Code just embarrassed Anthropic. A packaging mistake exposed 512,000 lines of Claude Code source. Within 48 hours, a developer rebuilt the architecture, the repo exploded past 100,000 stars, and GitHub had a full-blown open source war on its hands. This is bigger than “another coding tool.” It shows how fast closed AI products can get disrupted when the architecture leaks, the community moves faster than legal, and demand is already there. The real story is not the repo. The real story is that AI companies used open ecosystems to grow, then tightened access the second those ecosystems became expensive to support. That shift is happening everywhere now. If you run a business with AI, pay attention. Your workflow is only as safe as the company policy behind it.

Julian Goldie SEO

14,771 görüntüleme • 3 ay önce

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:
0:44

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Femke Plantinga

140,618 görüntüleme • 2 yıl önce

Introducing Instant Agents. Your AI team for Shopify that actually does the work: “Build me a PDP for this product.” “Turn this Figma design into a live page.” “Test 3 variants and pick a winner.” “Create better product images.” Done. Instant Agents are there for you 24/7. We’ve trained AI to understand our product. This way you get the true power of AI in combination with the visual control of our no-code builder. Real layouts, real components. Published to Shopify in 1 click.
1:07

Introducing Instant Agents. Your AI team for Shopify that actually does the work: “Build me a PDP for this product.” “Turn this Figma design into a live page.” “Test 3 variants and pick a winner.” “Create better product images.” Done. Instant Agents are there for you 24/7. We’ve trained AI to understand our product. This way you get the true power of AI in combination with the visual control of our no-code builder. Real layouts, real components. Published to Shopify in 1 click.

Instant

11,932 görüntüleme • 3 ay önce

🚨 AI coding agents hallucinate because they can't actually read your codebase. This MCP server fixes that. It's called Context+ and it gives AI 99% accuracy on large-scale engineering projects by building a real semantic map of your code before touching a single line. Here's what makes it different from every other MCP tool: → Tree-sitter AST parsing across 43 file extensions. Not grep. Not regex. Actual syntax trees. → Spectral Clustering that groups semantically related files into labeled clusters. Your AI finally understands what belongs together. → Obsidian-style wikilinks that map features to code files. Navigate entire codebases like a knowledge graph. → Blast radius tracing. Before any change, it shows every file and line where a symbol is imported or used. No more orphaned references. → Shadow restore points. Every AI-proposed commit creates a restore snapshot. One command to undo any change without touching git history. → Semantic search by meaning. Ask what something does. Not what it's called. The `propose_commit` tool is the wild part. It validates changes against strict rules, creates a shadow restore point, and only then writes to disk. AI can't just freestyle your production code. Works with Claude Code, Cursor, VS Code, and Windsurf. One line to install with bunx or npx. This is what responsible AI coding infrastructure actually looks like. 100% Opensource. Link in comments.
0:46

🚨 AI coding agents hallucinate because they can't actually read your codebase. This MCP server fixes that. It's called Context+ and it gives AI 99% accuracy on large-scale engineering projects by building a real semantic map of your code before touching a single line. Here's what makes it different from every other MCP tool: → Tree-sitter AST parsing across 43 file extensions. Not grep. Not regex. Actual syntax trees. → Spectral Clustering that groups semantically related files into labeled clusters. Your AI finally understands what belongs together. → Obsidian-style wikilinks that map features to code files. Navigate entire codebases like a knowledge graph. → Blast radius tracing. Before any change, it shows every file and line where a symbol is imported or used. No more orphaned references. → Shadow restore points. Every AI-proposed commit creates a restore snapshot. One command to undo any change without touching git history. → Semantic search by meaning. Ask what something does. Not what it's called. The `propose_commit` tool is the wild part. It validates changes against strict rules, creates a shadow restore point, and only then writes to disk. AI can't just freestyle your production code. Works with Claude Code, Cursor, VS Code, and Windsurf. One line to install with bunx or npx. This is what responsible AI coding infrastructure actually looks like. 100% Opensource. Link in comments.

Ihtesham Ali

29,781 görüntüleme • 3 ay önce

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

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

Andrew Ng

142,010 görüntüleme • 1 yıl önce

📣 Part 2/2 of my newest course is out! Ready to continue building your SaaS with Real-Time AI Agents? 🤯 In this part, you'll learn how to: 📝 Create searchable transcripts from meetings 💬 Build an AI Chat that understands meeting context 💰 Create Free Trials and Subscriptions using Polar 🚀 Deploy our app to production on Vercel In Part 1, we developed: 🔐 Auth using Better Auth 🗃️ Database using Neon - Serverless Postgres and Drizzle ORM 🤖 Real-time AI Video calls with Stream 💼 Background jobs using Inngest and OpenAI 🔍 Full Git workflow with branching, pull request and code reviews using CodeRabbit Check it out below ⬇️
1:24

📣 Part 2/2 of my newest course is out! Ready to continue building your SaaS with Real-Time AI Agents? 🤯 In this part, you'll learn how to: 📝 Create searchable transcripts from meetings 💬 Build an AI Chat that understands meeting context 💰 Create Free Trials and Subscriptions using Polar 🚀 Deploy our app to production on Vercel In Part 1, we developed: 🔐 Auth using Better Auth 🗃️ Database using Neon - Serverless Postgres and Drizzle ORM 🤖 Real-time AI Video calls with Stream 💼 Background jobs using Inngest and OpenAI 🔍 Full Git workflow with branching, pull request and code reviews using CodeRabbit Check it out below ⬇️

Code With Antonio

11,638 görüntüleme • 1 yıl önce