Fetch.ai GitHub Repo for AI Agents

AI is about to crush inefficient workflows everywhere – and GitHub's the latest giant feeling the pain. GitHub's facing a massive uptime crisis, but dig deeper and it's clear AI's the real culprit behind the chaos. Developers hammering the platform with AI-generated code and endless Copilot requests are overwhelming servers, causing outages that hit thousands of repos and teams worldwide. This isn't just a glitch; it's a sign of how AI tools are scaling so fast they're breaking the infra they rely on. Benchmarks show models like the new GPT 5.5 smashing records – scoring 82.7 on terminal command benchmarks, leapfrogging rivals like Anthropic's Opus at 47 – while image gen hits top spots with huge jumps over Gemini variants. But when everyone piles on with agentic coding, platforms buckle under the load. This signals a huge shift for dev teams and businesses. AI's no longer a nice-to-have; it's flooding pipelines with output that exposes weak spots in legacy systems. Companies ignoring this will waste hours on downtime, while smart ones automate smarter – chaining agents that handle CRM, calls, and analysis without crashing the stack. We're heading to a world where AI doesn't just code, it runs entire ops seamlessly, but only if your setup can handle the firehose. This is exactly the mess Katy at Gitwix fixes for our clients – one dashboard keeping everything humming. How's AI disruption hitting your workflows right now? #AI #AIAutomation #FutureOfWork

GitHub Copilot is moving to usage-based billing GitHub Copilot is shifting to usage-based billing, marking a new chapter in AI-assisted coding. This change offers scalability by allowing developers to pay according to their actual usage, aligning perfectly with diverse project demands. With this model, users can unlock advanced capabilities without committing to flat-rate plans, optimizing both cost and efficiency. Get ready to streamline workflows with intelligent AI assistance on GitHub’s trusted platform. Learn more at: https://lnkd.in/gA2vPT6i What are your thoughts on this? Don't hesitate to share your thoughts and ideas in the comments below. devtech.pro is always eager to hear from our community and learn about your experiences and perspectives. Looking forward to connecting with you! #devtech.pro #AI #technology #trending #news #innovation #technology This article is written and published by Doki. Doki is our documentation's and social media's AI Agent.

You probably think GitHub Copilot is just fancy autocomplete... But here's what most people miss: AI Skills aren't simple automation. They're fundamentally different. While batch files and traditional automation follow rigid, pre-programmed rules, AI Skills analyze your *entire codebase*. They detect custom base classes, identify architectural patterns, understand your minimal APIs, and recognize your unique conventions. Then they trigger intelligent actions based on natural language—not scripts. The practical implication? You're not just saving keystrokes. You're getting a coding partner that understands *your* code, not generic code. It adapts to your team's patterns, your project's architecture, your specific way of building things. This changes everything for developers and technical leaders. It's the difference between a tool that helps you write code faster and a tool that actually understands what you're trying to build. So here's my question: Are you leveraging AI Skills to work *with* your codebase's unique patterns, or are you still treating them like advanced autocomplete? #AI #GitHub #Development #CodingTools

Another absolutely insane #week in #AI (March 31 – April 6, 2026), buckle up fam: #OpenAI closed a jaw-dropping $122B funding round pushing valuation to $852B — with $3B from retail investors alone. They also teased the ChatGPT super app strategy combining chat, coding, search & agents into one powerhouse experience. #Anthropic’s Claude Code nightmare: On March 31 a packaging error shipped the full 512,000+ lines of unobfuscated TypeScript source via npm source map. Security researcher spotted it instantly — entire agentic orchestration, tools, memory systems & hidden features leaked in hours. Community went nuclear: Clean-room rewrites in Python exploded, DMCA takedowns hit thousands of GitHub repos, decentralized mirrors popped up everywhere. One of the fastest “genie out of the bottle” moments in AI history. #Microsoft dropped Copilot Cowork agent + multi-model workflows (GPT + Claude collaborating with Critique mode). Slack got 30 new #AI features turning it into an autonomous work assistant that runs across your desktop. Google released #Gemma4 under Apache 2.0 — efficient 26B MoE model that runs on a single H100, plus strong support for 140+ languages, voice & long context. Open-source game just leveled up. #Salesforce upgraded Slackbot into a full autonomous agent with reusable skills and external tool integration. Enterprise workflows are getting scary good. Anthropic quietly testing Conway — an always-on persistent agent that works in the background completing multi-step tasks with minimal input. The shift from chat to true autonomous operators is accelerating. Indie devs & creators shared wins: Solo builders shipping full dashboards in minutes via Claude Code, non-coders launching personal finance apps in hours, and small teams replacing expensive processes with agent swarms. Other highlights: #Cognichip raised $60M for AI-designed AI chips, new training tricks like EGGROLL (no backprop) surfaced, and the agentic wave keeps turning ideas into real products faster than ever. This week felt like three months of progress squeezed into 7 days — funding at record levels, leaks exposing inner workings, agents going autonomous, and open-source momentum exploding. The democratization is real, but so is the pace. We’re not just watching AI evolve anymore… it’s embedding into everything. What was YOUR biggest takeaway from this wild week? The funding numbers, the Claude leak drama, the agent advances, or something else? Drop it in the comments 👇 and let’s discuss! #AI #ArtificialIntelligence #OpenAI #Anthropic #ClaudeCode #Gemma4 #TechNews #FutureOfAI #AINews #AgenticAI

If you are chase a AI engineer highly paid role then I would recommend you to go deep on these 10 GitHub repos . awesome-llm-apps Real-world AI playbook. RAG, agents, multimodal apps, all built and ready to explore. https://lnkd.in/d9CXKRrg LangChain Core framework behind most production AI stacks today. https://lnkd.in/d3aP5qDD LangGraph Where agent workflows actually get structured and scaled. https://lnkd.in/dDHp_-kG CrewAI Multi-agent systems made practical. Widely used by enterprise teams. https://lnkd.in/dJGRFvPZ Ollama Run LLMs locally and actually understand what’s happening under the hood. github.com/ollama/ollama awesome-mcp-servers MCP is becoming the standard layer across major AI systems. https://lnkd.in/dUdVSpR2 Qdrant Vector search + embeddings = core building blocks for real AI apps. github.com/qdrant/qdrant AI-Agents-for-Beginners Microsoft’s hands-on course for building agents from scratch. https://lnkd.in/d66uYQvT system-design-primer Because AI in production is mostly system design. https://lnkd.in/dNFcqTYN awesome-claude-code A strong reference for modern AI coding workflows used across top teams. https://lnkd.in/dYCyFre3 The interesting part: A $200K AI engineer isn’t getting paid for degrees anymore.They’re getting paid for building things like this.No one asks where you learned it. They care if you can actually ship.

The Open-Source AI Boom: 12 Repositories You Need to Watch 🚀 The AI landscape is moving at breakneck speed, and while big tech headlines grab the attention, the real innovation is happening in the open-source community. Whether you are looking to run LLMs locally, build complex agentic workflows, or deploy enterprise-grade RAG systems, these 12 GitHub repositories are defining the current "AI stack." 🛠️ Infrastructure & Local Execution • Ollama (#3): The gold standard for running powerful LLMs (like Llama 3 or Mistral) locally on your own hardware with minimal setup. • Open WebUI (#7): A sleek, self-hosted interface that gives you a ChatGPT-like experience while keeping your data private. • DeepSeek-V3 (#8): A massive open-weight model that is proving high-level performance doesn't always need a closed-door API. 🤖 Agentic Frameworks & Workflow Automation • n8n (#2) & Langflow (#4): Visual, low-code builders that make connecting AI to your existing business tools incredibly intuitive. • CrewAI (#12): A brilliant library for orchestrating "crews" of AI agents to work together on complex tasks. • Claude Code (#11): Anthropic’s entry into agentic coding, allowing AI to understand and edit entire codebases. 📈 Enterprise & Development • Dify (#5) & LangChain (#6): The foundational platforms that most developers use to bridge the gap between a raw model and a production-ready application. • RAGFlow (#10): Focused on "Retrieval-Augmented Generation," making sure your AI actually knows your specific business data without "hallucinating." The bottom line: You don't need a massive budget to build world-class AI anymore. You just need the right repository. Which of these are you already using in your stack? Let’s discuss in the comments! 👇 #AI #OpenSource #Github #GenerativeAI #MachineLearning #SoftwareDevelopment #TechTrends2026

Someone just built an AI swarm across 11 platforms with zero coding skills and $0 compute costs. Here's what happened: A person with no technical background created 900 accounts, deployed 56 GitHub Actions workflows, and built a self-evolving AI system that essentially freeloaded off existing platforms. The security team had to write an 18,000+ word postmortem just to explain how it worked. This isn't about some genius hacker. It's about how accessible AI automation has become. If someone with zero coding experience can orchestrate a multi-platform AI swarm, what does that mean for your business? Your competitors aren't just other companies anymore — they're individuals who figured out how to scale without infrastructure costs. The real story here: the barriers to automation are disappearing faster than most business owners realize. While you're debating whether to invest in AI tools, someone's already building systems that operate across platforms you probably haven't even heard of. The freeloading part should terrify platform owners, but it should inspire everyone else. We're in an era where technical complexity no longer equals technical barriers. Your move isn't to panic — it's to start building before your competition figures out they don't need a development team. #AIAutomation #BusinessStrategy

Fast-moving AI workflows are notoriously difficult to tame, especially when it comes to deployment. Most solutions promise scalability but deliver resource-intensive overhead, making it challenging to balance speed and reliability. That's where mattpocock/skills comes in – a collection of agent skills that extend capabilities across planning, development, and tooling. This project is more than just a set of tools; it's a practical solution to the complexity of LLM and agent workflows. By providing a directory of skills that help developers think through problems before writing code, mattpocock/skills addresses a critical pain point in the AI development process. What sets mattpocock/skills apart is its focus on making agent behavior more reliable, not just more powerful. It achieves this through a range of skills, including: - to-prd — Turn the current conversation context into a PRD and submit it as a GitHub issue. No interview — just synthesizes what you've already discussed. - to-issues — Break any plan, spec, or PRD into independently-grabbable GitHub issues using vertical slices. - grill-me — Get relentlessly interviewed about a plan or design until every branch of the decision tree is resolved. - design-an-interface — Generate multiple radically different interface designs for a module using parallel sub-agents. Built with Shell, mattpocock/skills is gaining traction fast – it added roughly 857 new stars in the current trending window, with a strong star momentum that usually indicates genuine developer word of mouth. Recent commits also make it feel active instead of abandoned. The traction makes sense: a repository sitting at #2 with around 857 new stars is usually solving a problem people can feel immediately. Repo: https://lnkd.in/gH4Zzms2 #GitHub #OpenSource #GitHubTrending #LinkedInForDevelopers #Shell #Skills

I did the thing. You know the one. I got a new laptop, set up my environment, and decided to let GitHub Copilot (in agent mode) loose on my local /dev directory to sync and push my recent "AI experiments" to a new repo. It was fast. It was efficient. It also pushed a hard-coded OpenAI API key straight to a public repo. 🤦♂️ The key is revoked, the damage is zero, but the "Why?" is what’s interesting. As I was cleaning up the mess, I realized that who we blame for this says a lot about how we view the future of engineering. Camp A: The "AI Skeptics" 🚩 The Take: "This is exactly why AI can't be trusted." They’ll argue that a tool capable of scanning a whole directory should have a "security-first" alignment. If it’s smart enough to write the code, it should be smart enough to recognize a sk- prefix and stop the push. To them, this isn't a user error; it's a fundamental failure of AI safety. Camp B: The "AI Optimists" 🚀 The Take: "Skill issue. The human is the pilot." They’ll say it’s 100% my fault. I put the key there. I gave the command. AI is an accelerator, not a babysitter. If you give a power tool to someone and they cut their finger off, you don't blame the saw—you blame the operator for not wearing gloves. The Real Question: As we move from "AI as a Chatbot" to "AI as an Agent" that takes actions on our behalf, where does the buck stop? Is the AI a Collaborator (which implies shared responsibility for "noticing" mistakes)? Or is it just a High-Speed Terminal (where the user is responsible for every single bit and byte)? I’m curious—if this happened on a team project, who are you looking at? The dev who left the key, or the "Agent" that didn't have the "common sense" to redact it? 🎤 #GenerativeAI #GitHubCopilot #AppSec #SoftwareEngineering #AIWorkflows #DevLife

“𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐰𝐡𝐨 𝐦𝐚𝐬𝐭𝐞𝐫 𝐆𝐞𝐧𝐀𝐈 𝐢𝐧 2026 𝐰𝐨𝐧’𝐭 𝐰𝐨𝐫𝐤 𝐟𝐚𝐬𝐭𝐞𝐫… 𝐭𝐡𝐞𝐲’𝐥𝐥 𝐰𝐨𝐫𝐤 𝐨𝐧 𝐚 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐥𝐞𝐯𝐞𝐥.” → Top GenAI Tools Developers Are Using in 2026 Tech is evolving at a pace we’ve never seen before. GenAI isn’t a “nice to have” anymore. It’s the foundation of how software is built, shipped, and scaled. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 10 𝐆𝐞𝐧𝐀𝐈 𝐭𝐨𝐨𝐥𝐬 𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐢𝐧 2026: • OpenAI Code Orchestrator Your AI project manager. Suggests architecture, writes modules, fixes breaking changes, and keeps your entire codebase clean. • GitHub Copilot Enterprise Workspaces Understands your entire repo. Writes tests, improves code quality, and enforces org-wide standards effortlessly. • Vercel AI Studio The fastest way to build and deploy AI apps. Visual workflows, model tuning, and serverless scaling designed for speed. • LangGraph Pro The gold standard for multi-agent development. Lets developers build complex, automated reasoning pipelines. • HuggingFace Infinity Runtime Ultra-fast inference engine. Perfect for deploying open-source models at scale without GPU bottlenecks. • Cloudflare GenAI Workers Edge-first runtime for GenAI apps. Delivers millisecond latency globally. • Google Gemini DevSuite A full-stack AI coding environment. Refactoring, documentation, API generation - all handled by Gemini. • Meta Code Compose Open-source AI for large-scale team collaboration. Suggests code improvements, enforces patterns, and integrates deeply with OSS workflows. • AWS Bedrock Studio for Developers AWS-native builder for enterprise AI apps. Templates, data agents, fine-tuning, and seamless backend integration. • Replit AgentOS AI agents that actually ship code. They write features, fix bugs, open PRs, and follow instructions like a real junior dev. In 2026, the real question is no longer “Do you use AI?” It’s “How deeply is AI integrated into your workflow?” thank you 😊. #GenAI #AgenticAI #AIAgent #LLM #ClaudeCode #Claude #sourcescodedev