Rahul Gupta’s Post

3 free AI coding tools died this month. And nobody's talking about what comes next. In April alone: → Tabnine went enterprise-only ($39-59/seat) → Qwen killed its free OAuth tier (Apr 15) → Claude Code's $5 starter credits evaporate in hours, not days — Pro is $20/mo, Max is $200/mo → GitHub Copilot paused new sign-ups and tightened limits The pattern is unmistakable: the free AI coding era is ending. Here's why — and it's not greed. Agentic workflows consume 10-50x more compute than autocomplete. A single "fix this entire test suite" command can burn 100K+ tokens. No company can subsidize that at scale. The economics simply don't work. But here's what most developers are missing — the alternatives are actually BETTER: • Gemini CLI — 1,000 free requests/day with Gemini 2.5 Pro. That's the most generous free tier in the industry. • Aider + DeepSeek — frontier-quality coding for ~$10/month (DeepSeek V3.2 at $0.28/M tokens) • OpenCode — 95K+ GitHub stars, works with 75+ model providers • Cline — 59K stars, VS Code native, bring any model you want The real shift isn't from free to paid. It's from "locked ecosystem" to "bring your own key." BYOK sounds worse. It's actually better. You pick the model. You control costs. You route cheap tasks to cheap models and hard tasks to Claude Opus. You're never locked out by someone else's rate limits. My prediction: flat-rate AI coding subscriptions will be dead within 12 months. Usage-based billing wins because it's the only model where both sides can actually scale. What's your AI coding stack right now — still riding free tiers, or have you already started budgeting for this? #AICoding #DeveloperTools #GitHubCopilot #AgenticAI #DevProductivity

Ghostty Is Leaving GitHub When Ghostty, once a promising open-source project, leaves GitHub, it's more than just another software going dormant. It highlights a fundamental truth about our AI tools: their longevity and community support are crucial for sustainability. AI and backend systems thrive on continuous collaboration and iteration. Tools like Ghostty might offer a unique feature set or solve a specific problem initially, but without an active community to maintain them, they become outdated faster than expected. This underscores the importance of not just building robust tools, but also fostering vibrant communities around them. What lessons can we draw from projects like Ghostty for our own AI and backend initiatives? How do we ensure that our tools remain relevant and supported in the long term? Let’s discuss how to build sustainable ecosystems around our technologies. #AI #DevOps #SustainableTech #AI #NoCode #AITools #Productivity

People are worried that GitHub might use developers' code to train AI 🤖 But honestly… what’s wrong with that? If AI learns from more real-world code: • Tools will get smarter • Development will get faster • And bigger companies competing means more benefits for us And we all know one thing 👇 👉 More competition = better products + lower costs Instead of fearing it, maybe it’s time to adapt and take advantage of it. What do you think is a threat or opportunity? Learn More Here: https://lnkd.in/dKfzq3ZS #AI #GitHub #Developers #Tech #Innovation #Engineers #coding

GitHub's upcoming policy shift on Copilot data—using interaction data to train models by default starting April 2026—raises an important question for our industry: who owns the intelligence generated during development? This isn't just a privacy issue. It's about the feedback loop that makes AI coding tools better. Every autocomplete, every rejection, every edit is training signal. GitHub is essentially saying: "Your coding patterns belong to us, unless you opt out." For teams building with AI agents, this matters deeply. If you're using Copilot while developing agentic systems, your architectural decisions, error patterns, and problem-solving approaches are being absorbed into the next generation of models. That's powerful for the ecosystem—but it also means you're contributing to the competitive landscape without explicit choice. The opt-out mechanism is important, but opt-out policies historically have low adoption rates. Most developers won't know this changed, let alone how to disable it. We think developers deserve clarity here: understand what data you're contributing, what it trains, and whether that aligns with your company's IP strategy. For enterprises building proprietary agents, this is a conversation worth having with your legal and security teams now—before April 2026. The broader lesson? As AI tools become infrastructure, the terms of engagement matter. The models that power our work are shaped by collective data. That's a feature, not a bug. But it should be intentional. What's your take—does this change how you think about using AI coding assistants? #AI #Developers #AgenticEngineering #GitHub

Documentation often breaks due to the curse of knowledge and silent drift. Drasi's team built an AI agent that acts as a synthetic user to test tutorials for accuracy. Using GitHub Copilot CLI, Dev Containers, and Playwright, they identified critical bugs and improved docs. #AI #Tech 🤖

GitHub has paused new Copilot individual sign-ups and tightened usage limits amid a surge in agentic workflows, signaling rising compute costs for AI coding tools. By Paul S.

Former GitHub Innovators Revamping Developer Tools for the AI Revolution https://lnkd.in/gqFNBrki 🚀 The Next Evolution in Developer Tools: GitButler & Entire In early 2026, two former GitHub leaders, Scott Chacon and Thomas Dohmke, made headlines with groundbreaking funding for their new developer tools aimed at transforming AI-assisted coding. Key Highlights: Scott Chacon's GitButler Funding: $17M from Andreessen Horowitz. Innovation: A next-gen Git client enabling simultaneous work on multiple branches with virtual branches and AI integration. Goal: Redefine version control for AI workflows. Thomas Dohmke's Entire Funding: $60M in the largest-ever seed round for a developer tool. Focus: Governance and observability of AI-generated code, capturing the intent behind AI commits. Benefit: Enhances auditing and understanding of AI's role in coding. Both tools serve different needs—GitButler innovates the daily developer experience, while Entire adds a critical oversight layer to AI contributions. 💡 Ready to explore the future of coding? Share your thoughts below! #AI #DeveloperTools #Innovation Source link https://lnkd.in/gqFNBrki

We have all seen the rise of AI coding agents lately. Tools like GitHub Copilot, Cursor, Antigravity, Claude-based agents, and autonomous coding workflows like Openclaw, OpenCode etc are getting insanely powerful. You give them a task, and they plan it, build it, and execute it. Sounds perfect, right? Not really. Here is the problem I kept running into: They do the job, but they do not really involve you in the process. You often do not know why something was done. You do not get asked for decisions unless you explicitly force it. And slowly, your understanding starts falling behind while the tool keeps moving forward. That knowledge gap is real. On top of that, setting these systems up is still painful. Too many steps, too many configs, too much friction. So we asked a simple question: What if an AI agent actually worked with you, not just for you? That is where CodeTwin started. CodeTwin is a terminal-first AI coding agent that gives you control at every step. You can run it locally from your CLI. Control it remotely from your phone directly through our app. Approve actions, monitor execution, and stay in the loop in real time. Want full control? It asks for your decisions. Want speed? Switch to no-questions mode and let it run. Your workflow, your rules. Built on top of existing agent frameworks, we added a decision layer and interaction system that keeps you involved instead of replacing you. And yeah, we built the first version in just 2 days. Shipped fast because I somehow ended up with teammates who think "let's build it over the weekend" is a normal sentence XD Sahnik Biswas Atanu Saha Aninda Debta Shreyas Saha Still rough around the edges, but we are shipping, learning, and making it better every day. The demo video gives you the basic idea. If you want to try it: You can get started in under 2 minutes by following the README / docs. Website: https://lnkd.in/gqTA_4Vf GitHub: https://lnkd.in/gPvVGwBP #CodeTwin #AI #Technology #AIAgents #DevTools #CodingAgent #DeveloperProductivity #OpenSource #ClaudeCode #OpenCode

Take your AI-assisted development to the next level. 🚀 Integrating specialized skills into your AI agent is a game-changer for any IDE workflow. By leveraging a modular "skills" approach, you can transform your coding environment into a high-performance workspace. Ready to set it up? 1️⃣ Install the skills directory: test -d ~/.gemini/antigravity/skills && echo "Skills installed in ~/.gemini/antigravity/skills" 2️⃣ Activate a skill: When starting a conversation with Google Antigravity, simply use the @ trigger: @"insert_skill_name" [your prompt] With over 1,300+ specialized skills available, you can tailor your AI agent to handle almost any technical challenge. 📂 Explore the full library here: https://lnkd.in/dw3gzx8J #AI #SoftwareEngineering #Productivity #GitHub #AIAgents #CodingLife

What if you could have an AI assistant right in your terminal, helping you with code suggestions and explanations? That's exactly what GitHub Copilot CLI offers, and it's now available for everyone to use. This tool integrates with the GitHub CLI and provides natural language command suggestions and code explanations, making it a game-changer for developers. The recent updates to GitHub Copilot CLI introduce 'agentic' workflows, which allow for more complex and automated tasks. This means developers can focus on higher-level tasks and let the AI handle the more mundane aspects of coding. It's an exciting development that could significantly boost productivity and efficiency in the development process. As someone interested in data analytics and AI, I'm intrigued by the potential applications of GitHub Copilot CLI. It could revolutionize the way we approach coding and development, making it more accessible and efficient for everyone. The fact that it's now available for general use means we can expect to see more innovative solutions and projects emerge. In Dublin's thriving tech sector, tools like GitHub Copilot CLI could have a significant impact. With many top tech companies having a presence here, the demand for skilled developers and data analysts is high. The availability of AI-powered tools like GitHub Copilot CLI could give companies a competitive edge and help them stay ahead of the curve. So, what does the future hold for AI-powered development tools like GitHub Copilot CLI? Will they become an essential part of every developer's toolkit, or will they remain a niche product? I'd love to hear your thoughts on this. #AI #Dublin #Ireland #BusinessAnalyst #DataAnalytics #GitHub #Copilot #ArtificialIntelligence #NaturalLanguageProcessing #DevTools #Coding #Productivity, — Nikhil Upadhyay 📧 nikhil25000@gmail.com | 📞 +353 89 456 4932 🔍 Open to full-time opportunities in Analytics, Research & AI roles O Captain, my Captain — a thinker navigating the age of AI 🧭