GitHub repo Graphify gains 14k+ stars in 3 days

Quick Quiz: What’s the biggest 'red flag' on a junior dev's GitHub right now? 🚩 Is it: A) Too many forks? B) No README? C) Portfolio full of browser-made "sandboxes"? D) No green squares? The answer is C. Recruiters can tell when you haven't worked in a professional local environment. On KodeMaster AI, you don't code in a browser-based playground. You use your favorite editor, push to Git, and get instant test feedback: just like you would at a Top-Tier tech company. Let’s clean up that GitHub with real projects. #CodingTips #JuniorDev #GitHub #SoftwareEngineering #CareerGrowth #KodeMasterAI

GitHub Copilot is switching to usage-based billing starting June 1. 🚨 Instead of a flat monthly fee, you now get a monthly credit allotment tied to token consumption. Chat, agentic sessions, model choice - all of it running on a meter. 📊 For years, companies poured billions into making AI tools cheap, fast, and deeply integrated into how developers work. GitHub Copilot, ChatGPT, Cursor - the goal was never just adoption. It was habituation. Make the tool so embedded in your daily workflow that removing it feels like losing a limb. 🧠 That phase is over. Now that AI is infrastructure - woven into how engineers write code, review PRs, and run agentic tasks across entire repos - the pricing model can evolve. Because switching costs are real, and everyone knows it. 💸 GitHub's reasoning isn't wrong: a one-liner completion and a multi-hour autonomous coding session costing the same flat rate was never sustainable. But the timing is telling. The price change comes exactly when these tools have become hardest to walk away from. GitHub is offering a preview billing experience in early May. Worth checking before June 1 surprises your budget. 📅 For sure, this won't be the last tool to make this move, others will follow the same path. #GitHubCopilot #AI #DeveloperTools #SoftwareEngineering #TechTrends

I like #GitHub. Love the way it is built. And recently, I came across GitHub Trending for the first time. I had no idea this page existed. Turns out, there is a page that shows which repositories are gaining the most stars right now. With so many contributions happening every day in AI, automation, and developer tooling, it is a great pulse check on what the community actually cares about. So I thought, what better way to showcase a new tool I built than to let it demo GitHub Trending itself? The attached video is a 1-minute 43-second walkthrough. Two AI speakers have a natural conversation, exploring trending repos, filtering by Python, and clicking into a top project. Every word, every click, every scroll — generated from a single JSON file. No screen recording. No video editor. No voiceover session. How it works: - Write a scenes.json — define your speakers, dialogue, and browser actions - Run one command — it generates voice audio, drives a real Chromium browser, and assembles the final MP4 - Done. Polished video, zero editing. Tech stack: - Playwright — drives real Chromium, records the screen - macOS TTS — native voices for narration (Tara + Aman in this demo) - ffmpeg — audio-video sync and final assembly - Node.js + Bash — single command runs the full pipeline What I learned about making AI voices sound natural: - Short sentences. 10-15 words max. TTS handles them much better. - Commas create natural breathing pauses. - Spell out abbreviations. "A-I" not "AI". "A-P-I" not "API". - Punctuation drives everything. Question marks give rising tone. Periods force stops. - Sweet spot is 165-190 words per minute. Where teams can use this: - Customer demos and sprint showcases - Onboarding videos that stay in sync with the actual UI - Pre-sales walkthroughs tailored per customer - QA documentation as video evidence Pro tip: Do not write the scenes.json by hand. Let your AI agent write it. Describe your app and the flow you want. It generates the full script in seconds. Give it a try and share what you build. Working on open sourcing this. Will share the repo link here once it is available. #AIAgents #OpenSource #BuildInPublic #GitHub #Saama

now with my agent swarm and repository of 300+ skills and pre/post edit/commit hooks, the barrier to coding is literally ZERO! guys- the barrier to coding was pretty much always zero. and the funniest part is if you spent half the time you do on tweaking your md files, you’d be a decent coder.

📂 Your finished app isn't enough anymore. Recruiters want to see your "Proof of Work." In 2026, a GitHub profile with a single "Final Commit" isn't impressive. What matters is the journey: ✅ Why did you choose that database? ✅ How did you handle that specific bug? ✅ What was your architectural trade-off? Your README and commit history should tell a story. At KodeMaster AI, our projects aren't just tutorial clones. They are industry-aligned challenges that force you to make these decisions, giving you a portfolio that actually speaks for your engineering maturity. Build something real: https://kodemaster.ai/ #GitHub #WebDev #EngineeringPortfolio #JobSearchTips #KodeMasterAI

Ever stared at a massive GitHub repository and wished you could just ask it how it works? I’ve been diving deep into local AI orchestration and wanted a better way to navigate new codebases. So, I built GitChat, A Retrieval-Augmented Generation (RAG) assistant that lets you chat directly with any public GitHub repository. It dynamically clones a repo, chunks the architecture, and streams context-aware answers back to a sleek, minimalist dark-mode interface. The best part? The LLM inference runs entirely locally. Features: - Dynamic Repo Ingestion: Automatically clones, filters out heavy/binary files, and chunks code for AI ingestion. - Zero-Hallucination Guardrails: Strict prompt engineering ensures the AI only answers using the provided codebase context. - Local LLM Orchestration: Runs models (like Qwen 2.5 Coder) locally, meaning zero API costs for inference. - Real-Time Streaming UI: Seamless, non-blocking message streaming with syntax-highlighted markdown rendering. - Dynamic Sandbox Controls: Real-time adjustments for LLM Temperature, K-value context retrieval, and model selection. The Tech Stack: - Frontend & API: Next.js (App Router), Tailwind CSS - AI Engine: Vercel AI SDK v6, LangChain - Database & Vector Search: MongoDB Atlas Vector Search - Local Models: Ollama (qwen2.5-coder, nomic-embed-text) I'm really proud of the resource management and the seamless integration between the backend data pipeline and the frontend UI. GitChat is fully open-source, and I am actively welcoming contributions! I would love for you to check out the architecture, test it out, or even open a PR if you have ideas for new features. Repository & Code: https://lnkd.in/dNuBfnzZ #Nextjs #LangChain #Ollama #MongoDB #SoftwareEngineering #AI #WebDevelopment

Last night I built something that replaced a $20/month AI coding agent. Not with another subscription. Not with a wrapper. With a Telegram bot and a free API key. I needed a system that generates production-grade code — 600+ lines — and pushes it directly to GitHub. Branch. Commit. PR. Merge. All from my phone. The "premium" tools? They want you locked in. Their UI. Their queue. Their pace. You type a prompt, wait in line, and hope the output is usable. I didn't want that. I wanted sovereignty. So I wired a Telegram bot to an open API. Dual-engine architecture — fast model for planning, heavy model for synthesis. A validation layer so it never commits garbage. A parser for structured tasks. GitHub API integration for direct commits and PRs. 6 critical bugs. Broken routing. Deprecated models. Token limits too low. Validator too aggressive. Local model intercepting every call. Pipeline generating meta-artifacts instead of actual code. Fixed them one by one. At 3 AM. From Kutch. The result? A 612-line production Python module — type hints, docstrings, TF-IDF scoring, batch processing, CLI — synthesized from a Telegram message and committed to GitHub in under 30 seconds. No subscription. No waitlist. No vendor lock-in. Some companies are building walled gardens around AI coding. They're not selling capability — they're selling dependency. The moment you stop paying, your workflow dies. That's not intelligence. That's a lease. I'm building the opposite. Tools I own. Pipelines I control. Models I can swap. Infrastructure that works on a laptop in a small city in western India. You don't need their permission to ship. You need an API key, a weekend, and the willingness to fight 6 bugs at 3 AM. Build sovereign. Ship sovereign. 🔥 *Building ViaDecide from Kutch, Bharat.* #BuildingInPublic #SoloFounder #AISovereignty #ViaDecide #BharatFirst #ShipIt

Google recently released Code Wiki -- and it basically turns any GitHub repo into living, auto-updating documentation. This feels like the first serious attempt at “docs that never fall out of sync with code.” What it does: - Makes onboarding dramatically faster -- first commit on Day 1 becomes realistic. - Cuts down hours of code digging, especially for large or unfamiliar repos. How it works - Watches your repo and regenerates docs on every commit (no manual updates). - Uses a Gemini-powered chat agent that understands your entire codebase contextually. - Every concept in the docs links to the exact file, class, or function. - Auto-generates architecture + sequence diagrams that always stay synced. - Works for any public GitHub repo today; private repo support coming via Gemini CLI. Fully free while in public preview. Feels like a big step toward instant code understanding especially for teams dealing with legacy systems or fast-moving codebases. ♻️ Share it with anyone who works with large codebases :) Link to Code Wiki: https://codewiki.google/ #AI #LLMs #AIAgents

GitHub just flipped their AI pricing model. Out: flat monthly subscriptions. In: pay-per-token consumption. We've been tracking our Copilot usage across client projects for months. The math is brutal under subscription pricing. Some weeks we burn through completions. Other weeks, barely touch it. Consumption pricing fixes this. Now we pay for what we actually use. No more subsidizing GitHub's enterprise customers who code 12 hours a day. No more eating fixed costs during discovery phases. This shift matters beyond GitHub. It signals the AI tooling market is maturing. Moving from "land grab" subscription models to actual usage-based economics. For development shops like us, this changes budget planning entirely. AI costs now scale with project intensity, not calendar months. The subscription era is ending. #AI #GitHub #Development