Most developers are sleeping on this AI dev stack that quietly 10x’d my output. I stopped opening 7 tabs, 3 docs, and 12 StackOverflow threads per task. Instead, I wired 3 “under-the-radar” tools into my daily workflow: - **Continue.dev** → VS Code/Cursor-style inline AI without sending your whole codebase to the cloud. - **smol-developer** → auto-generates small, focused codebases from specs (great for boring boilerplate). - **Codspeed** → AI-powered benchmark runner that actually tells you *where* your Python is slow. How I use it in practice: 1️⃣ Draft feature spec in Markdown. 2️⃣ Use smol-developer to generate the boring scaffolding. 3️⃣ Refactor + implement logic with Continue.dev in-editor. 4️⃣ Run Codspeed to hunt the real bottlenecks instead of guessing. This combo feels illegal because it removes 80% of the “grunt work” we’ve been gaslit into thinking is “real engineering.” Hot take: if you’re still doing everything manually “for learning,” you’re optimizing for ego, not impact. Which underrated dev tool changed the way *you* code? Drop it below so we can all steal it. Follow @flazetech for more. #Developers #AItools #Python #VSCode #Productivity #DevTools #Programming

I built and deployed my AI coding assistant: CodeMate AI CodeMate AI is a Streamlit-based web app that helps users understand Python code, fix errors, upload files, and get clear AI-generated explanations. What it can do: • Explain Python code in simple language for beginners specially. • Help fix common coding errors. • Upload Python/Text files. • Support light and dark mode. • Keep output saved after theme/language changes. • Deployed live using Streamlit Community Cloud. Tech Stack: Python | Streamlit | Google Gemini API | GitHub | Streamlit Cloud This project helped me practice real world AI app development, API integration, deployment, Git/GitHub workflow, and UI improvements. Live App: https://lnkd.in/dYE9nD2V GitHub Repo: https://lnkd.in/dG8-FhyW #Python #Streamlit #ArtificialIntelligence #MachineLearning #GoogleGemini #GitHub #DataScience #AIProjects

84% of students already use AI tools. Only 18% feel prepared to use them professionally. This course closes that gap. 260+ lessons. 20 phases. ~290 hours. From linear algebra to autonomous agent swarms. Python, TypeScript, Rust, Julia. Every lesson produces something reusable -- prompts, skills, agents, MCP servers. You don't just learn AI. You learn AI with AI. Then you build real things. Then you ship tools others can use.

🚀 Built a Simple AI Code Debugger I recently created a small project using Streamlit where users can upload screenshots of their code and get help to debug it. 🔍 Features: * Upload up to 3 code screenshots * Get step-by-step hints (without code) * Or get full solution with corrected code * Clean and simple UI ⚙️ Tech Used: * Python * Streamlit * Google Gemini API 💡 Idea: Sometimes beginners struggle to understand errors. This tool helps them by giving hints or solutions directly from their code screenshots. 🌐 Live Demo: https://lnkd.in/gwedCaYA 🔗 GitHub: https://lnkd.in/g-FFETRh I am still improving this project and planning to add more features soon. Feedback is always welcome 🙌 #Python #Streamlit #AI #MachineLearning #WebApp #BeginnerProject #Coding #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

I built a Smart Atomic Habit Tracker using Python and Streamlit. And it changed how I think about discipline. Most people don’t fail because they lack motivation. They fail because they don’t track consistency. So I built something simple — but powerful: 🧠 Atomic Habit Tracker App 💡 What it does: • Add daily habits in seconds • Track streaks automatically 📈 • Visual progress insights • AI-based improvement suggestions 🤖 • Clean, distraction-free UI ⚙️ Tech Stack: Python • Streamlit • Logic-based tracking • Deployed on cloud 📌 What I learned: • Building real-world logic with Python • Turning ideas into usable apps • Debugging like a developer • Deploying end-to-end projects 🔥 This project reminded me: “Small habits are easy to ignore, but powerful when tracked consistently.” 🚀 Still improving it with more AI features and smarter insights. 🔗 GitHub: https://lnkd.in/dsq8zDba 🔗 Live App: https://lnkd.in/dkzc6b6a If you’re building in public too — keep going. Consistency wins. #Python #AI #Streamlit #MachineLearning #BuildInPublic #Coding #Projects #DeveloperJourney

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

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 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

Excited to Share My Latest Project! I’m proud to present SmartCodeFixer – AI-Based Code Error Detection & Fixing System 💻 This project is designed to help developers automatically detect coding errors and provide intelligent suggestions to fix them, improving efficiency and reducing debugging time. 🔹 Tech Stack: • Python • Machine Learning / AI • Flask / Backend Integration • HTML, CSS, JavaScript (Frontend) 🔹 Key Features: • Automatic code error detection • Smart suggestions for bug fixing • Clean and user-friendly interface • Faster debugging workflow 🔹 What I Learned: • Applying AI concepts to real-world problems • Building full-stack applications • Improving problem-solving and debugging skills 🔗 GitHub Repository: https://lnkd.in/gmjfqJ2v #ArtificialIntelligence #MachineLearning #Python #WebDevelopment #Innovation