Sajidahmed Reshmi’s Post

Recently explored an interesting open-source project: code-review-graph It builds a local knowledge graph of your codebase, mapping functions, classes, and dependencies — so AI tools only read what actually matters instead of scanning entire repos. Result:  ▶ Faster code reviews  ▶ Better context understanding  ▶ Significant reduction in token usage What stood out is its ability to track changes incrementally and quickly identify impacted areas, making large codebases much easier to navigate. Feels like a step towards solving one of the biggest problems in AI coding: context, not capability. 🌐 https://lnkd.in/dRKnbqVj 👏 Kudos to Tirth Kanani for building this! #AI #CodeReview #OpenSource #DeveloperTools #LLM

Stop burning AI credits on context AI doesn't need. If you use Claude or Cursor, you've felt this: You ask one question. AI reads your entire codebase. The result? → Credits gone → Slow answers → Still average output You try writing .md files to help it. AI still misses what actually matters. The real problem isn't the AI. It's the input. AI doesn't know what to focus on so it reads everything. That's where two tools are changing the game: 1.Graphify Maps your entire codebase so AI understands connections, not just files. Link : https://lnkd.in/d2wg9xsb 2. Code Review Graph (officially contributed to by Claude) Filters your code so AI reads only what's relevant to your question. Link : https://lnkd.in/d8W77tEa The shift happening right now in AI-assisted coding: ❌ AI reads everything ✅ AI reads only what matters Less noise. Faster answers. Better output. Simple way to think: Use Graphify → to understand the system Use Code Review Graph → to work faster with AI Once you start using this approach you will see a big difference in output and credit usage

I'm sharing with you this very interesting AI code assistant project called Nexus (link below) it analyses your entire project written in any of 30 languages and outputs basically an optimized AST (abstract syntax tree). but it doesn't sit there doing nothing: it provides your AI a skill to navigate that tree with a query language. It has the huge advantage of your AI not guessing anymore from the few files it reads and "imagines the rest" the AI has the real situation accessible in a token saving manner. meaning you can use your context window to do what you are paying for: write correct code. It starts making you productive in new discussions immediately, sparing you the overhead of discovery on each discussion start. Additionally, it and your git history to detect the hot spots you're focused on. I'm sharing my journey writing a unique multidimensional multi-tenant platform with multiple innovations. It is materialized in Svelter(.me) as a proof of concept playground. A platform dedicated to NIS2 is in the oven and will hopefully bring huge value. Follow me for more feedback from real world AI use. #AI #SKILLS #Claude https://lnkd.in/dTWEUm_B

Andrej Karpathy posted his LLM Knowledge Base workflow. 48 hours later, someone shipped the exact tool he described. It's called Graphify. One command. Any folder. Full knowledge graph. No vector database, no config files, no setup. Point it at a codebase or research folder and you get: → A navigable knowledge graph of everything in that folder → An Obsidian vault with backlinked articles → Plain English Q&A over your entire project The number that stopped me: 71.5x fewer tokens per query compared to reading raw files. Haven't tested it yet, but if that holds up, this isn't an incremental improvement. It's a different paradigm for how AI agents reason over large codebases. Open source. Free. This is the leverage that makes a solo builder dangerous in 2026. 🔗 https://lnkd.in/gwnaJ6Qb P.S. They named it Graphify. Functional. Gets the job done. As someone who agonizes over product names for weeks - genuinely respect the 48-hour ship-first-name-later energy. #DeveloperVelocity #AI #BuildInPublic #ClaudeCode

🚨 Everyone is talking about Claude leaks… But here’s something actually useful 👇 I found this GitHub repo: 👉 https://lnkd.in/g8GimQMz Instead of exploiting anything, this project does something smarter: It rebuilds the idea of an AI “agent system” in a clean, legal, and understandable way. So what’s the real value here? 💡 For engineers • A simple reference on how to structure AI agents • How tools, commands, and workflows connect together • Python-based → easy to read, modify, and extend 🧪 For AI builders & tinkerers • A playground to experiment with agent workflows • Learn how systems track tools, tasks, and behavior • Understand how to compare (audit) different AI systems ⚖️ For people thinking about ethics • Shows how to learn from closed systems without copying them • A great example of “build inspired, not duplicated” 🌍 For the AI community • Real-world example of an agent framework • Easy way to understand how modern AI systems are structured The biggest takeaway? 👉 AI is not just about models anymore 👉 It’s about how you design the system around them Most people will chase leaks. Smart builders will learn from them. #AI #Github #Claude #PromptEngineering #Developers #BuildInPublic

#Anthropic on Tuesday confirmed that internal code for its popular #artificialintelligence (#AI) #coding assistant, #ClaudeCode, had been inadvertently released due to a #humanerror. https://lnkd.in/eBwEkUGz

Andrej Karpathy is right that AI-maintained knowledge stores are powerful - for research docs and text-heavy workflows. But I keep seeing people apply the same idea to code, and it's backwards. Here's the thing: code isn't text. Your codebase already has structure. It has a call graph. Functions know their callers and callees. Files know their dependencies. That graph is already there. You just need something that reads it. So when I saw yet another "AI-powered code context" tool drop last week, I went the other direction and built kosha (कोश) - Sanskrit for sheath or layers. No LLMs. ```python k = Kosha() k.sync() # parses your repo + env packages via BFS results = k.context('render a toast notification', limit=10) # → ranked snippets with callers, callees, pagerank ``` Filter inline like a pro: "stripe webhook path:payments/ type:FunctionDef" One call writes a SKILL.md to your repo — commit it and every contributor's AI assistant (Claude Code, Cursor, codex) picks up the same context automatically. The insight isn't "use AI for everything." It's knowing when your problem already has a structure and just surfacing it. 📚 Docs: https://lnkd.in/gJhHe4WE 🐙 Repo: https://lnkd.in/g7FxM8J7

Reduce your Claude AI code token usage by 6.8× fewer tokens on reviews and up to 49× on daily coding tasks with a single command. AI coding tools often re-read your entire codebase for every task. That means unnecessary token consumption, slower responses, and higher costs. code-review-graph solves this. It builds a structural map of your code using Tree-sitter, tracks changes incrementally, and feeds your AI assistant only the relevant context via MCP. The result? ⚡ Faster responses 💸 Lower token usage 🎯 More precise code understanding 🔗 GitHub: https://lnkd.in/gbeCdG8z Commands: pip install code-review-graph code-review-graph install code-review-graph build #tree #claude

🚀 This repo changed how I think about RAG: 👉 Graphify Andrej Karpathy’s shared personal knowledge base went viral, and within 48 hours, someone had already delivered a fully built version right to users. https://lnkd.in/gHW-8dn3 Turn any folder (code, docs, images) into a queryable knowledge graph — in one command. 💡 Why it matters: We’ve been over-optimizing for: • embeddings • vector DBs Graphify shows: structure > embeddings • Extracts structure (AST for code = zero token cost) • Uses LLM only where needed • Builds persistent relationships • No vector DB required 🔥 Big idea: • The future of RAG isn’t better retrieval • It’s better representation If you’re building: • AI agents • dev tools • knowledge systems If this resonates: • Like 👍 • Repost 🔁 • Or drop your thoughts — especially if you're building in this space Curious how others are thinking about: 👉 Graph vs Vector vs Hybrid approaches #AI #LLM #GraphRAG #AgenticAI #OpenSource #KnowledgeGraph #DeveloperTools #Andrej #Karpathy