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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... show more

OPM

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11,249 views • 3 months ago •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 👇

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 views • 1 year ago

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

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

206,251 views • 4 months ago

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

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

327,877 views • 1 year ago

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.

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 views • 8 months ago

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 👇

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 views • 1 year ago

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.

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 views • 5 months ago

Holy sh*t this is gold THE FULL GRAPH ENGINEERING PIPELINE, GROUNDED IN REAL, OPEN SOURCE WORK. Representation. Ontology. Entity extraction. Relations. Events. Quality gate. Fusion. Embeddings. Memory your agent can actually traverse. Real repos already cover this end to end. Strwythura walks through the full ontology pipeline with entity resolution and embedding-enhanced GraphRAG. llm2kg covers entity and relationship extraction against an ontology, Neo4j storage with vector embeddings, and a ReAct agent that queries the graph directly. No PhD required. Real, working code you can run against your own data today.

Holy sh*t this is gold THE FULL GRAPH ENGINEERING PIPELINE, GROUNDED IN REAL, OPEN SOURCE WORK. Representation. Ontology. Entity extraction. Relations. Events. Quality gate. Fusion. Embeddings. Memory your agent can actually traverse. Real repos already cover this end to end. Strwythura walks through the full ontology pipeline with entity resolution and embedding-enhanced GraphRAG. llm2kg covers entity and relationship extraction against an ontology, Neo4j storage with vector embeddings, and a ReAct agent that queries the graph directly. No PhD required. Real, working code you can run against your own data today.

CyrilXBT

48,495 views • 6 days ago

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.

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 🚀

30,848 views • 1 year ago

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

77,102 views • 5 days ago

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:

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

296,570 views • 10 months ago

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?

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 views • 8 months ago

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

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

28,332 views • 2 months ago

This guy literally built an AI job-search system for Claude Code that analyzed 740+ job listings, and it actually landed him a Head of Applied AI role. Even better: it is now open-source. The system scans career pages, evaluates your fit, and generates ATS-optimized PDFs automatically. The project includes: → 14 specialized agent modes → A Go-based terminal dashboard → ATS-optimized PDF generation with Playwright → 45+ companies pre-configured (Anthropic, OpenAI, Stripe) This is a perfect example of AI eliminating the grind of job hunting instead of replacing people. 100% free and open-source. Link to the repo below ↓

This guy literally built an AI job-search system for Claude Code that analyzed 740+ job listings, and it actually landed him a Head of Applied AI role. Even better: it is now open-source. The system scans career pages, evaluates your fit, and generates ATS-optimized PDFs automatically. The project includes: → 14 specialized agent modes → A Go-based terminal dashboard → ATS-optimized PDF generation with Playwright → 45+ companies pre-configured (Anthropic, OpenAI, Stripe) This is a perfect example of AI eliminating the grind of job hunting instead of replacing people. 100% free and open-source. Link to the repo below ↓

Charly Wargnier

13,012 views • 1 month ago

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)

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 views • 4 months ago

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.

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 🚀

52,399 views • 4 months ago

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:

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 views • 2 years ago

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.

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 views • 3 months ago

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.

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

12,342 views • 3 months ago

🚨 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.

🚨 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

31,051 views • 4 months ago