AI Dev Stack Boosts Output 10x, Reduces Grunt Work

i built something small. it might save your team from a massive headache. every time an AI writes code for you, it leaves behind zero documentation of why. six months later, nobody, not even the AI can explain the decision. that's AI tech debt. and it's compounding silently in most codebases right now. so i built maylang-cli - a tiny Python CLI that enforces one rule: every meaningful change ships with a .may.md file that documents: → what you intended → what the contract is → what invariants must hold → how to verify it works → how to debug it when it breaks one command. one file. lives in git. reviewable like code. pip install maylang-cli may new --id MC-0001 --slug auth-cache --risk low --owner "your-team" you can also enforce it in CI — block any PR that touches auth/ or db/migrations/ without a change package. zero-friction adoption. it's open source, MIT licensed, and on PyPI right now. if you've ever inherited a codebase and had no idea why something was built the way it was - this is for you. 🔗 https://lnkd.in/eMV28g27 🔗 https://lnkd.in/eSNVrpGM #opensource #python #developer #aitools #softwaredevelopment #devtools #engineering

I didn’t break my code. I broke my environment. And that lesson changed how I build software forever. For the past few days, I was working on an OCR-based backend system. Everything looked correct - the logic, the APIs, the flow. But nothing worked. Errors kept changing: • “No module named paddle” • “set_optimization_level not found” • “NumPy ABI mismatch” • “PyMuPDF build failed” At first, I thought: my code is wrong. But the truth was harsher - and more important: 👉 In real-world systems, code is only 50% of the problem. The other 50% is environment, dependencies, and compatibility. Here’s what I learned (the hard way): 🔹 Version mismatch can break everything Even if your code is perfect, incompatible library versions will crash your system. 🔹 Python version matters more than you think Some ML libraries still don’t support newer versions (like 3.12). 🔹 Virtual environments are not optional If you don’t isolate dependencies, you’ll chase ghosts for hours. 🔹 NumPy 2.0 broke half the ML ecosystem Real-world lesson: “latest” is not always “stable”. After fixing everything, the system finally worked. Not because I wrote better code - but because I understood the system behind the code. 💡 Biggest takeaway: A good developer writes code. A great developer understands the environment it runs in. If you’re building in AI/ML or backend systems, remember this: 👉 Your real skill is not just solving problems - 👉 It’s debugging chaos. #SoftwareEngineering #BackendDevelopment #AI #MachineLearning #Debugging #Python #DeveloperJourney #BuildInPublic

🎢 My First Streamlit Project: From “I have no idea what I’m doing” to “It actually works… almost!” A few weeks ago I decided to finally learn Streamlit. Fast forward → I built Note Summary & Quiz Generator 🔥 The idea is simple but powerful: Upload a photo of your lecture slide or notes → AI instantly creates a clean summary, breaks down diagrams, and generates a personalized quiz (with audio too!). I tested it on a Java “Class & Object” slide and the output blew my mind. But here’s the real story : I got completely stuck at deployment. For hours the app kept throwing this error: “Project can't find GenAI source”(-_-) API keys? Secrets? Streamlit Cloud config? I was lost in the deployment jungle. (If you’ve ever fought with environment variables at 2 AM… you know the pain 😂) After many try, and coffee… I finally did it!! Now it’s working and I’m super proud of it as my first proper Streamlit app. Check it out -> a short demo video below: Links: 🔗 GitHub Repository: (https://lnkd.in/gX9JFUd8) 🔗 Live Demo: (https://lnkd.in/gdaus6wx) Would love your feedback, especially if you’ve faced similar deployment struggles! Students & fellow beginners — would you actually use something like this to study? Drop a comment, share your own “I got stuck” stories, or tell me what to improve next 👇 #Streamlit #Python #FirstProject #LearningInPublic #DeploymentStruggles #EdTech #100DaysOfCode #GenAI

🔥🚀 AI CHEAT CODE #034 🔥🚀 💥 Cursor 3 just launched a GAME-CHANGER — run MULTIPLE AI agents in parallel and 10x your coding speed! 🤯⚡ 🤖 CURSOR 3's AGENTS WINDOW = Your Personal Dev Army 🪖 Last week, Cursor dropped v3 with a brand-new Agents Window — mission control where you run multiple AI agents SIMULTANEOUSLY across different repos! 🚀🔥 Here's how to unlock it RIGHT NOW 👇 ⚡ Step 1: Update to Cursor v3 → Cmd+Shift+P → "Check for Updates" → install 💻✅ 🔥 Step 2: Open the Agents Window → Cmd+Shift+P → type "Agents Window" → open it 🖥️ 🤖 Step 3: Spawn PARALLEL agents → Click "+" to create new agent tabs → Agent 1 = write tests, Agent 2 = refactor, Agent 3 = write docs 📝 💡 Step 4: Use /best-of-n (NEW COMMAND!) → Runs the SAME task across MULTIPLE models simultaneously → Picks the BEST result automatically! 🏆 🎨 Step 5: Design Mode (INSANE!) → Click directly on any UI element → Annotate it → agent fixes EXACTLY that component — no more vague descriptions! 😂✨ 🎯 PRO TIPS 💪 ✅ Move agents between local → cloud → SSH mid-task 🔁 Cloud agents keep working even with your laptop CLOSED! 📊 Side-by-side grid view to monitor all agents at once ⚡ 72% autocomplete acceptance rate = more shipping 🚢 💬 Drop a comment if this blew your mind! 🤯 👍 LIKE & REPOST to share with your dev squad! 🔥 🔔 FOLLOW Naresh Dawer for daily AI cheat codes! 🔖 SAVE this post — you'll thank yourself later! #AI #DevTools #CursorIDE #Coding #Programming #SoftwareEngineering #AITools #DevOps #WebDevelopment #TechTrends #MachineLearning #Innovation #Automation #ArtificialIntelligence #OpenSource #Python #JavaScript #CloudComputing #ProductivityHacks #TechNews

🚀 I built a RAG chatbot and deployed it on Streamlit Cloud — here's what broke (and how I fixed it) A few days ago I finished building my own RAG (Retrieval Augmented Generation) chatbot using a stack I'm genuinely proud of: 🔹 Sentence Transformers for embeddings 🔹 FAISS for vector search 🔹 LangChain for text splitting 🔹 PyPDF for document ingestion 🔹 Streamlit for the frontend Looked great locally. Pushed to GitHub. Clicked deploy on Streamlit Cloud. And then… 💥 it broke. The error? Failed to build pillow — RequiredDependencyException: zlib Streamlit Cloud was running Python 3.14 — a very new version. Pillow 10.4.0 had no pre-built binary wheel for it, so pip tried to compile from source and failed because the zlib system library was missing on the server. One small version pin in requirements.txt was silently killing the entire deployment. The fix? Three line changes in requirements.txt: ✅ pillow 10.4.0 → 11.2.1 ✅ numpy 1.26.4 → 2.0+ ✅ streamlit 1.39.0 → 1.40+ That's it. No code changes. No architecture changes. Just dependency hygiene. What I learned: 💡 Always check if your pinned packages have pre-built wheels for the Python version your cloud platform runs 💡 Old version pins feel safe but they quietly create compatibility landmines 💡 AI tools like Codex can fix, commit and push these changes in seconds — so there's no excuse not to keep dependencies updated Building in public, breaking things, and learning fast. That's the process. 🛠️ If you're building RAG apps or deploying ML projects on Streamlit, drop a comment — happy to share more about the architecture. #Python #MachineLearning #RAG #LLM #Streamlit #AIEngineering #BuildInPublic #SoftwareDevelopment #Developer

Most developers write their AI assistant rules files once, by hand, and never touch them again. They're generic. They're stale. And if you use more than one AI coding tool, you're maintaining duplicates that slowly drift apart. I built @rulesgen/rulesgen to fix that. It analyzes your actual codebase — frameworks, dependencies, naming patterns, async style, test setup, even recent git history — and auto-generates optimized rules files for: ✅ Claude Code (CLAUDE.md) ✅ Cursor (.cursorrules) ✅ GitHub Copilot (copilot-instructions.md) ✅ Windsurf (.windsurfrules) All from a single command. All tuned to your specific project, not a boilerplate. Supports JS/TS, Go, Python, monorepos, Docker, Terraform, GitHub Actions — and 50+ frameworks out of the box. Get started: npx @rulesgen/rulesgen generate Open source. MIT licensed. Available on npm now. Would love feedback from anyone deep in the AI-assisted dev workflow 🙏 #AITools #DevTools #ClaudeCode #Cursor #GitHubCopilot #buildinginpublic #OpenSource

🚀 Built Something Useful for Every Claude Developer While working with Claude Code, I realized one big gap — there’s no clear visibility into usage, tokens, or costs. So I built a solution 👇 🔗 https://lnkd.in/g7kCBnCn 💡 Claude Usage Dashboard A lightweight, local-first tool to track, analyze, and optimize your Claude usage in real-time. ✨ What it does: • Tracks token usage across sessions • Estimates API costs • Provides a clean dashboard + CLI insights • Detects anomalies & suggests optimizations • Includes a budget guard (yes, it can even stop overspending) ⚡ Best part: No setup headache. No dependencies. Just run it with Python. 🧠 Why I built this: When you're building with LLMs, visibility = control. This tool gives you exactly that. If you're working with Claude or exploring AI tools, this might help you 👇 Would love your feedback, ideas, or contributions 🙌 #AI #LLM #Claude #OpenSource #Developers #Python #BuildInPublic #GitHub

Developers are finding new ways to tame the complexity of LLM and agent workflows. At the heart of this effort is hieuchaydi/RepoBrain, a local-first codebase memory engine for AI coding assistants. RepoBrain indexes repositories, retrieves grounded evidence, traces logic flows, and ranks the safest files to inspect or edit before code generation. This is a critical step forward because teams are trying to make agent behavior more reliable, not just more powerful. What sets RepoBrain apart is its ability to provide actionable insights without requiring a hosted backend or API key. This is achieved through a combination of local index + evidence-backed retrieval, route/service/job flow hints for faster codebase orientation, and ranked edit targets with confidence and warnings. RepoBrain's capabilities include: - local index + evidence-backed retrieval - route/service/job flow hints for faster codebase orientation - ranked edit targets with confidence and warnings - built with Python The momentum behind RepoBrain looks earned because the project is easy to place inside a real workflow, not just admire from a distance. It lands in high-interest areas like agent, ai-agents, llm, and recent commits make it feel active instead of abandoned. The project still feels early, which gives it some discovery momentum. Repo: https://lnkd.in/ggAjSMGY #GitHub #OpenSource #GitHubTrending #LinkedInForDevelopers #Python #RepoBrain #Agent #AiAgents

𝐒𝐄𝐍𝐈𝐎𝐑 𝐄𝐍𝐆𝐈𝐍𝐄𝐄𝐑𝐒 𝐀𝐑𝐄 𝐐𝐔𝐈𝐄𝐓𝐋𝐘 𝐒𝐖𝐈𝐓𝐂𝐇𝐈𝐍𝐆 𝐅𝐑𝐎𝐌 𝐂𝐋𝐀𝐔𝐃𝐄 𝐂𝐎𝐃𝐄 𝐓𝐎 𝐂𝐎𝐃𝐄𝐗 𝐀𝐍𝐃 𝐇𝐄𝐑𝐄'𝐒 𝐓𝐇𝐄 𝐁𝐑𝐔𝐓𝐀𝐋 𝐁𝐑𝐄𝐀𝐊𝐃𝐎𝐖𝐍 a 14-year principal engineer spent ~120 hours co-developing (not vibe coding) across both tools on an 80k LOC python/typescript project. here's what he found: Claude Code feels like an engineer on a time crunch: > speeds toward getting things working ignores CLAUDE.md at least once per session leaves tasks half-done mid-migration changes tests to match what IT thinks the goal is almost never creates new files — just bloats existing ones Codex feels like a 5-6 year senior: > stops mid-task to rethink and refactor unprompted never once ignored AGENTS.md doesn't extend god classes — it factors them out does things you hadn't thought of that are actually additive you can fire it off and come back when it's done --- the raw numbers: > Claude: more done per session, more cleanup every few days Codex: 3-4x slower, but the work is just better Codex Pro x5 ≈ Claude Max x20 in usage caps --- the real difference: > Claude needs a skilled, focused driver or it goes off the rails Codex demonstrates competence and earns autonomy --- his verdict: > vibe coding a weekend project? Claude wins building enterprise software? Codex wins “Claude requires a skilled, focused driver more than Codex does” both give bad output if you don’t know SWE. the tool isn’t the skill. --- #AI #SoftwareEngineering #Claude #Codex #Anthropic #OpenAI #Google #DeepMind #Microsoft #Meta #Nvidia #Alibaba #LLM #AIEngineering #GenerativeAI #DeveloperTools

Excited to share DeepCode, an AI-powered code understanding platform I built from scratch. 🔗 Live: https://lnkd.in/gQkZvDhQ What it does: → Explains any code line-by-line at beginner, intermediate, or expert level → Debugs code with root cause analysis - not just "fix this line" → Analyses time & space complexity with Big O breakdown → Finds bugs proactively - no error message needed → Challenges you with MCQ, complete-the-function, and write-a-test questions Supports 10 languages: Python, JavaScript, TypeScript, Rust, Go, C++, C, Ruby, HTML, SQL. What I built under the hood: • FastAPI backend with SSE streaming - responses appear token by token • GPT-OSS 120B via Groq API - sub-second response times, completely free • Google + GitHub OAuth via Supabase with JWT verification on every request • Rate limiting, CORS protection, input validation - production-grade security • Usage analytics - tracking who uses what features in real time • Deployed on Railway (backend) + GitHub Pages (frontend) The hardest part wasn't the AI - it was building a proper streaming pipeline, securing the API, and making the explanation format actually useful for learning. If you're learning to code or want to understand existing codebases faster, give it a try. #AI #LLM #Python #FastAPI #OpenToWork #MachineLearning #SoftwareEngineering