OpenClaw Becomes Most-Starred GitHub Project in History

In a significant shift for the developer community, GitHub has announced a pause on new sign-ups for its Copilot Pro plans, along with tightened usage limits. This move signals a pivotal change in how AI-assisted coding tools are accessed and utilized, moving away from a model of unlimited support at fixed prices. As companies increasingly rely on AI to enhance productivity, this decision prompts a reevaluation of cost structures associated with such technologies. By recalibrating its offerings, GitHub is not only addressing operational challenges but also setting a new precedent for the sustainability of AI services in a competitive market. This development may encourage organizations to explore alternative solutions or even develop in-house capabilities, ultimately reshaping the landscape of AI in software development. The implications of this decision extend beyond GitHub, as other tech firms may follow suit, leading to a more nuanced discussion around the value, accessibility, and pricing of AI tools across the industry. https://lnkd.in/dmwMtMx3 #GitHub #CopilotPro #AIassistance #technews

When an organisation responsible for delivering nation‑wide government services moves fast with AI using GitHub Copilot, doing it securely and transparently is not optional, it is essential (and you can do it the same way - read below) This is why I find cplt project from Norway particularly interesting. It is an open source project built by NAV (Norwegian Labour and Welfare Administration) operating at a scale where trust, security, and reliability are absolutely critical. For them, adopting GitHub Copilot is not about experimentation, it is about enabling developers to move faster without compromising national‑level responsibilities. What cplt does is refreshingly pragmatic. It acts as a drop‑in sandbox wrapper for GitHub Copilot CLI on macOS, using Apple’s kernel‑level sandbox to ensure the AI agent can work on your codebase while access to secrets, credentials, and sensitive system resources is strictly controlled. No magic, no hand‑waving, just auditable, well‑documented security decisions you can actually read and reason about. I really appreciate the philosophy behind this project. It shows that “move fast” and “be secure” are not opposites, especially in the public sector. With the right engineering choices, strong defaults, and openness about trade‑offs, AI developer tools can be adopted responsibly even in environments where the stakes are very high. Ready to start? Here is the repo: https://lnkd.in/epj5B6V7 This is a great example of how open source, public sector engineering, and modern AI tooling can come together to raise the bar for everyone. 👏 Hats off to Hans Kristian Flaatten 🕊️🍉 and Nav team for building and sharing this to set a strong reference point for secure GitHub Copilot adoption. Morten Stange Bye, Haakon Hasli, Christian Tryti, Else Tefre, Francesco Manni, Jaime De Mora, Pankaj Agrawal, Muhammad Daniyal (Dani), Ömür Sert, Adil I., Cornelia Bjørke-Hill #GitHubCopilot #AINativeDevInfra #AINativeDevSecurity #DevSecOps

🔥 80 % of Devs Feed AI Their Whole Codebase… Then Cry When It Leaks IP on GitHub Last Tuesday, GitHub quietly updated its Copilot terms: any repo marked “public” is now fair game for model training unless you hit a buried opt out. Same day, a startup in Miami woke up to find its proprietary payment gateway cloned line for line in an AI tutorial that ranks page one on Google. Why it matters 1. Public no longer means “ignore me.” If your repo is public, even “just for backup,” it’s training data. 2. Private repos on free tiers still get scanned for “security insights” unless you toggle two more switches. 3. Once your code trains the model, DMCA takedowns won’t erase the knowledge; it’s baked in. My take after 9 years I started backing every side project into a paid, zero knowledge Git host. Five bucks a month beats the cost of rewriting IP. For client work, we now run a “golden repo” rule: public only after a repo has been scrubbed of keys, logos, and anything that could end up in a competitor’s prompt. Here’s what this means for you as a business owner: treat GitHub like a public forum, not a flash drive. Mirror, don’t originate. What do you think? Overhyped or the end of open source as we know it? Check if your repos are still on “vintage” 2023 settings before your next commit. #TechNews #WebDevelopment #AI #WordPress #DigitalMarketing #Technology #GitHub #Copilot #CodeSecurity #FreelanceLife #DevTips #InfoSec #StartupLife #PrivacyMatters #TechTrends

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

When I started coding back in 2016, I had one big question: “How do I start contributing to open source?” I searched a lot… But honestly, it was confusing. • Too many repositories • Hard to find beginner-friendly issues • No clear starting point • Lots of friction just to get going And I know I’m not the only one who felt this. Even today, many developers want to contribute to open source… but don’t know where to start. So I decided to build something to fix this 👇 🚀 Introducing: Issues Scout An open-source contribution finder built to help you discover the right issues, faster. 💡 What it does: • Connect your GitHub using a simple token • Search for relevant projects based on your interests • Discover open issues you can contribute to • Bookmark issues for later • Open GitHub issues in one click ⚡ No complex onboarding ⚡ No unnecessary flows ⚡ Just connect → explore → contribute 🔗 Try it here: https://lnkd.in/giZ5rQN8 The goal is simple: Remove friction and help more developers start contributing to open source. Because contributing shouldn’t feel overwhelming. It should feel accessible. This is Product #3 of my “100 Products Challenge” — building 100 useful products. If you’ve ever struggled to find your first open source contribution, this is built for you. I’d love your feedback 🙌 What features would make this even more useful? #buildinpublic #opensource #github #flutter #developers #100daysofcode #productbuilding

As some of you have suspected (Boris Cherkasky gets the credit in my feed) — GitHub's recent availability struggles are a direct consequence of AI coding tools using it as infrastructure at massive scale. And this raises some genuinely interesting questions, both on the product/business side and the engineering side. GitHub's COO wrote "we can't just scale horizontally and vertically" (https://lnkd.in/dbXJehWs) — and that's true in a deep way. Even a system built for scaling eventually hits new bottlenecks. You can add more workers, but the queue that feeds them has its own throughput ceiling. The small service that checks permissions before a GH Action runs, the one that watches for abuse, the one that validates... — each of them needs to grow, and each at a different rate. That's a genuinely complex and fascinating engineering problem (and I'd love to read a proper writeup on how they're tackling it). But there's a second problem — a business one. How do you price the services to cover the costs for an infrastructure that's growing at this rate? Usage patterns are shifting dramatically (of course I don't have data on how). It might be the ratio of paying to free users, or the amount of code per average seat, or the number of Actions triggered (more repos = more pipelines, more commits == more pipleines). If the story is "a huge volume of small free users," the answer is probably one thing — I'd guess tightening the free tier. But if the story is that usage *patterns* themselves have fundamentally changed (which is my suspicion), then the answer has to be more nuanced and might be painful for long time users and organizations. I genuinely hope GitHub figures out the engineering side first — running more for less — before they're forced to solve it on the business side, which means charging all of us more. The AI coding wave is real (for now at least, while the VC money that powers it doesn't run up). The infrastructure stress it creates is real. The answers? less clear currently. it's not SaaSpocalypse but the changing usage patterns would enforce change and adaptions

Agentic flows and coding agents are killing The $20 AI Dream, make it less affordable for you and me, this time on GitHub!! GitHub just hit the "Emergency Brake." New sign-ups for Copilot are officially paused, and existing users are starting to see those dreaded "Capacity Reached" warnings in their IDEs. This isn't just a minor server hiccup; it’s a fundamental shift in the economics of AI. We’ve moved from simple "autocomplete" to complex AI agents that can run for hours, refactoring entire codebases and running tests autonomously. The problem? Those agents eat compute for breakfast, and the $20-a-month subscription model can no longer foot the bill. Microsoft-backed or not, even GitHub has a ceiling. For engineering leaders, this is a massive signal. If your team’s velocity is tied exclusively to one proprietary tool, you aren't just "innovating"—you’re leaning on a fragile dependency. We’re seeing the birth of "Compute Rationing." GitHub is now enforcing strict weekly token limits and throttling heavy users to keep the lights on. It’s a stark reminder that cloud-based AI is a finite utility, not a bottomless pit of magic. If you haven't started looking into local LLM fallbacks or model-agnostic setups, now is the time. Relying on a single "black box" for your team's productivity is a risk that just became very real. #GitHub #SoftwareEngineering #GenerativeAI #EngineeringManagement #TechStrategy #CloudComputing

GitHub announced the new pricing strategy: consumption based and many models have seen their cost increased sensibly (especially the newest Anthropic models). I am not here to teach any lessons, but clearly the current model wasn't sustainable. GitHub was clearly subsidizing everybody's company. I would be shocked if this adjustment isn't followed by other players like OpenAI and Anthropic. Otherwise, GitHub would be just giving up on the market share it currently owns. Anyway, we'll see the impact of the shift on the overall ecosystem. Bigger companies will continue to consider tokens as a commodity. The same won't be true anymore for smaller companies or solo-devs. Startups will have to reevaluate how deep into AI they can afford to go. I guess compute became again a resource. https://lnkd.in/dhKFzH5e https://lnkd.in/dX3aUTa7

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