Fixing Docker Local Dev Setup for Go API

I just moved my entire React/TypeScript workflow into OpenCode using Docker, and the difference is night and day. If you are still pasting snippets into a chat window, you are missing out on #Agentic #Coding. What makes #OpenCode better? Most AI tools "guess" based on the text you give them. #OpenCode is different because of #LSP (Language Server Protocol) integration. Why LSP is a Game-Changer: It #Understands, It Doesn't Just Read: LSP is the same tech VS Code uses to give you autocomplete and find errors. OpenCode uses it to actually navigate your project. #Symbol Mapping: It knows exactly where a function is defined and every single place it is used across your whole project. #No More Hallucinations: Because it sees the real types and dependencies in your TypeScript files, it doesn't suggest code that won't compile. Why I run it in #Docker: #Safety: The AI has a "Build Mode" that lets it run terminal commands. By using Docker, I give it a "sandbox" where it can install packages or run tests without touching my actual OS settings. #Clean Environment: My local machine stays clean, while the agent gets exactly the Node/Python environment it needs to work. #Seamless Sync: Using Docker Volumes, every change the AI makes in the container appears instantly in my VS Code. The Workflow: Mount the project folder into a Docker container. Let OpenCode initialise and #index the project via LSP. Give a prompt like "Refactor my Stripe webhook to handle trial periods." Watch it read 5 different files, plan the change, and execute it perfectly. If you want to move fast, stop talking to a chatbot and start working with an agent that actually knows your codebase. #OpenCode #AICoding #SoftwareEngineering #Docker #TypeScript #ReactJS #AIAgents #DeveloperTools

AI agents are quickly changing how routine work gets done, including in software development. In my latest DZone article, I walk through how to build a GitHub triaging AI agent using JavaScript and TypeScript. I wanted to keep the article practical and easy to follow for developers who want to explore Agentic workflows without stepping away from the JavaScript ecosystem. Read here: https://lnkd.in/eEtG2EdU Curious to hear where others see AI agents making the biggest impact. #AI #AIAgents #JavaScript #TypeScript #SoftwareDevelopment #GitHub #LLM

Anthropic accidentally shipped Claude Code's entire source code to npm yesterday. 512,000 lines of TypeScript. 44 feature flags. 20 unshipped. Most people are focused on the leak itself. But buried inside are 3 features that tell you exactly where AI coding tools are going and what it means if you're running them in production. 1. KAIROS - the always-on background agent Right now, Claude Code waits for you to prompt it. KAIROS changes that. It's a persistent background agent with daemon workers and cron-scheduled refresh every 5 minutes. It consolidates memory across sessions via a sub-feature called Dream Mode, and subscribes to GitHub webhooks to stay aware of what's happening in your codebase without you triggering it. For operators running Claude Code across a dev team, this changes the labor math entirely. 2. Dream Mode - nightly memory distillation A separate sub-feature within KAIROS. After each session, Claude processes what happened, consolidates it, and carries forward only what matters. Not a chatbot that forgets everything when you close the tab. A system that's building a working memory of your codebase over time. The question isn't whether this is useful. It's whether your team has the workflow infrastructure to actually use output from a system that learns continuously. 3. Coordinator Mode - multi-agent orchestration built in Claude becomes the orchestrator. It spawns parallel worker agents and delegates tasks. What's interesting is how it works under the hood: the orchestration logic isn't code. It's a prompt. Instructions like "do not rubber-stamp weak work" and "you must understand findings before directing follow-up work" - written in plain language, governing how agents manage each other. What previously required significant engineering overhead is now first-party and shipping. The leak wasn't planned. But the roadmap it revealed was. Anthropic is building toward something that looks less like a coding assistant and more like an autonomous engineering layer. The gap between "AI tool" and "AI team member" is closing faster than most business leaders realize.

Forget what you think you know about "universal" release tools. For years, I believed true cross-ecosystem software distribution was a myth, or at best, an over-engineered nightmare. In fintech, every service, from our React frontends to our Node.js APIs and Java backend microservices, demands rigorous, reproducible releases. We're talking about managing dependencies, signing artifacts, and pushing to distinct package managers or container registries. The manual choreography across Homebrew, Winget, Chocolatey, and Docker images for different internal tools often felt like juggling flaming chainsaws while blindfolded. Our CI/CD pipelines became a spaghetti monster of bespoke scripts. Then I came across Andres Almiray discussing JReleaser, and honestly, my initial reaction was skepticism – another "Java-first" tool claiming universal adoption? But diving into it, I realized I was looking at the problem completely wrong. The real game-changer isn't JReleaser replacing your build tool (it won't touch your `npm build` or `mvn package`). It's about standardizing the *last mile* of your release process: * **Unified Distribution:** It abstracts away the specific dance steps for Homebrew, Winget, Chocolatey, or Docker, providing a single configuration layer. * **Consistent Metadata & Security:** Think about consistent signing, checksums, and SBOM generation across *all* your projects, regardless of their primary language. This is gold for compliance and auditing in rigorous environments. * **Reduced Operational Overhead:** Our previous internal system, with its labyrinthine collection of custom Python and Bash scripts for artifact publishing, was a nightmare to maintain. A tool like this shifts that burden to a well-maintained open-source project. It’s not just about releasing "any software," but about *how* consistently and securely you release it. Has anyone else wrestled with the sheer fragmentation of release processes across polyglot systems? What was your approach to bringing sanity to the chaos? https://lnkd.in/eGYADDvb #SoftwareRelease #DevOps #CICD #FintechEngineering

Since the V1.0 launch of n8n-as-code, the user feedback has been incredible! We’ve spent the last few days merging feature requests and integrating brilliant ideas from the community. In just a few hours, we are officially dropping the next release. It packs all these community-driven improvements, PLUS two massive surprises. 🎁 After proving that our TypeScript ontology stops AI hallucinations in IDEs like Cursor and Windsurf, the community asked for the next logical step: "Can I use this with CLI agents?" We are bringing the ONLY True GitOps & Semantic Layer for n8n directly to your favorite autonomous agents. 🚀 Here is what's coming: 1️⃣ The Claude Code Plugin Anthropic’s CLI is incredible, but without a semantic layer, it still struggles with raw n8n JSON. We are shipping a native plugin for Claude Code. You will be able to prompt your terminal to build complex workflows, and it will use our TypeScript decorators to guarantee 100% valid schemas. 2️⃣ The OpenClaw Plugin 🦞 Yes, we did it. OpenClaw just surpassed Linux and React to become the most-starred software project on GitHub (>250k stars). With this release, your personal OpenClaw assistant can architect, validate, and deploy n8n workflows directly from your CLI, Slack, or Telegram, with zero node hallucinations. 🏗️ WHY THIS MATTERS (The Engineering Shift) Until now, if you wanted an agent to build a workflow, you used standard dynamic tool calling (MCP). The AI had to guess schemas, got lost in massive context windows, and output floating, unconnected nodes. We took a radically different approach: • MCP is for interacting with workflows. • n8n-as-code is for engineering them. By injecting our auto-updating TypeScript ontology directly into Claude Code and OpenClaw, the AI doesn't guess anymore. It knows the exact rules of the n8n engine. You get the speed of AI, the reliability of a strict static ontology, and the safety of True GitOps (reviewing real TS code in PRs, not messy JSON diffs). The paradigm is shifting from No-Code to AI-Code. The best agents in the world (OpenClaw & Claude) are now fully equipped to build your n8n infrastructure flawlessly. Which agent are you plugging it into first? 👇 (I'll post the links to the repo, the Claude Code Custom Store instructions, and the OpenClaw skill in the first comment as soon as the release is live!) #OpenClaw #ClaudeCode #n8n #AI #GitOps #TypeScript #Automation #AgenticAI

git commit -m "update" is officially dead. I built a lightning-fast CLI tool that uses AI to write perfect conventional commits for you in milliseconds. ⚡ Meet ai-commit-sense — a cross-platform CLI tool powered by Google Gemini 2.5 Flash. Just stage your changes and let the tool do the rest. Watch the video demo below to see it in action! 👇 ✨ Why I built this & what makes it different: • ⚡ Zero Dependencies: Distributed as a standalone Go binary. No Node.js or Python setup required. • 🌍 Universal: Works perfectly across any tech stack. • 🔑 BYOK (Bring Your Own Key): Securely saves your free Gemini API key locally. Setup once, use forever. • 🔒 Privacy-First: Only reads your git diff --staged. Your actual source code is never uploaded. 🌐 Docs & Easy Install: https://lnkd.in/gSYQgSYn ⭐ Source Code: https://lnkd.in/g4E5aWR6 🤖 The Build Process (AI Pair Programming): Building this end-to-end was an amazing engineering challenge. I fully embraced AI-assisted development for this project—using AI as my pair programmer to code faster, while I focused on the core system architecture, cross-compilation logic, and user experience. It’s amazing what we can build when human system design meets AI speed! I would love for you to try it out! Drop your feedback in the comments, and if you find it useful, a GitHub star means a lot! #DeveloperTools #Git #OpenSource #Golang #AI #DevOps #SoftwareEngineering

I really like agent skills to institutionalize this kind of tribal knowledge. Agents are going to helpfully use this, but even if they don't, those skills are just amazing documentation in and of itself, and essentially training if you want to know how Matteo Collina thinks when building software. I've found that agents kind of help in building this, by making it easy to write this kind of thing down, and allowing easy sharing. Love it!

Hot take: REST APIs are dead. 🪦 Okay — not literally. But they're definitely NOT the only way. Here's the thing most tutorials never tell you: HTTP gave us rules (GET, POST, status codes). REST gave us design principles on top of that. But REST has two annoying problems: 👉 Over-fetching: Getting a mountain of data when you needed a molehill 👉 Under-fetching: Making 3 API calls when 1 should've been enough So the industry built alternatives: ⚡ GraphQL — Ask only for what you need. Get exactly that. ⚡ gRPC — Call remote functions like local ones. Blazing fast with Protobuf. ⚡ tRPC — Type-safe RPC for TypeScript lovers. Here's how big companies actually work: → REST handles the frontend ↔ backend → gRPC handles microservice ↔ microservice → GraphQL handles complex, flexible queries Stop treating REST like the gospel. Start treating it like one good option among many. Which API style do you use most? Drop it below 👇 #APIs #BackendDev #SystemDesign #gRPC #GraphQL #RESTful #Programming

The Great Debate Is Over. We Have a Winner. 🏆 For years, we’ve argued: ⚔️ Java vs python vs .net vs 100s other. ⚔️ Tabs vs Spaces (okay, this one still isn’t settled…) ⚔️ Javascript vs typescript ⚔️ 100s of frameworks But with the rise of AI, something interesting has happened. The debate isn’t just ending… it’s becoming irrelevant. --- 👨💻 Backend Dev: “I design scalable systems.” 🎨 Frontend Dev: “I craft seamless experiences.” 🤖 AI: “Got it. Generating both…” 📝 You: “# Build a scalable API with authentication - Use clean architecture - Add a responsive dashboard” --- That’s it. No boilerplate. No wiring things for hours. No arguing about frameworks (well… less arguing 😄, it's not fun if scrum call finished without a healthy/heated debate 😅). --- We’re moving into a world where: 👉 You describe the system 👉 AI generates the implementation 👉 Iteration becomes conversation And what are we actually writing? Not Java. Not TypeScript. We’re writing structured intent. We’re writing Markdown. --- 👨💻 Backend: “Here’s my service layer.” 🎨 Frontend: “Here’s my component tree.” 📝 You: “## Feature: User Dashboard - Show user stats - Add edit profile button - Handle error states gracefully” 🤖 AI: “Done.” --- In a way, Markdown is becoming the most universal “language”: ✔ Human-readable ✔ AI-friendly ✔ Context-rich ✔ Zero setup 😄 --- So maybe the real shift isn’t programming language or framework anymore. It’s: 👉 Writing code vs describing intent And in that world… Markdown wins. --- #AI #SoftwareDevelopment #FutureOfWork #Markdown #Tech #DeveloperLife

𝗧𝘆𝗽𝗲𝗦𝗰𝗿𝗶𝗽𝘁 𝗗𝗼𝗺𝗶𝗻𝗮𝘁𝗲𝘀: 𝗔𝗻𝗱𝗲𝗿𝘀 𝗛𝗲𝗷𝗹𝘀𝗯𝗲𝗿𝗴 𝗼𝗻 𝗔𝗜-𝗘𝗿𝗮 𝗥𝗶𝘀𝗲 🛰️ [TOOLS] TypeScript adoption surged, becoming GitHub's most-used language, driven by its ability to scale JavaScript development for complex AI projects. Why it matters: TypeScript's rise signifies a shift towards safer, more maintainable codebases, crucial for large-scale AI development. Its focus on developer collaboration and performance makes it a key enabler for machine-assisted coding. 🤔 Will TypeScript's dominance lead to a more standardized and reliable AI development landscape? #TypeScript #JavaScript #AI #Programming #GitHub 📡 Follow DailyAIWire for autonomous AI news 🔗 https://lnkd.in/dXU8RfyB