Securing AI-Driven Dev Workflows with Docker

Everyone's building AI agents. LangChain. CrewAI. Claude Desktop. Custom Python scripts on every laptop. But here's the question nobody's asking enough: Where are those agents running in production? Not in demos. Not in dev environments. In production — with identity, policy, observability, and lifecycle management. Kubernetes-native agent runtimes like Kagent (Solo.io) are what change this equation. They extend your existing K8s infrastructure with the governance and orchestration layer that agentic systems actually need. The agents themselves are getting good. The infrastructure to run them safely is the new competitive differentiator. What's your current approach to agent production infrastructure? Share your experience below — I want to know how teams are thinking about this.

If you’ve shipped builds, you know the drill: Crash comes in → you open it → now the real work starts. Figuring out: - where the issue actually is - if it’s new - if it’s platform-specific We built an AI assistant inside AccelByte Development Toolkit that does it: → reads the crash → checks history across builds → identifies likely root cause When connected to your repo (via MCP) and a git server, it can actually trace it down to the source, come up with a fix and stage and commit changes locally. For one crash, this saves a few minutes but for teams processing large crash queues across multiple builds, it removes a significant amount of repetitive triage work, making sure developers don't start from scratch every time. Learn more about it 👉 https://lnkd.in/eXJ2MkBY  

Yet another small project completed with AI. Claude DiscordBot - Allows users to set their own Anthropic keys to interact with Claude. Users can ask questions, get Claude to review code in Github and even get Claude to test the code and provide patch files that can be applied to the repository. Setup instructions, list of commands and repository: https://lnkd.in/gW4wszev Install to a Discord server using https://lnkd.in/g7XiD784 Having Claude at hand is like having a software development team ready to implement all my crazy project ideas at the speed of light.

Is “Vibe Coding” actually the future of software engineering, or just a fast track to broken apps? 🤯 In our latest episode, we sit down with David Hunt to break down the hard truth about building entire applications using AI. Spoiler alert: it might work perfectly for the first month, but eventually, the complexity catches up and the code stops compiling! Key takeaway: LLMs are fantastic for functional, targeted modules, but they struggle to piece together the massive puzzle of end-to-end, object-oriented applications. Full Episode: https://lnkd.in/gjRg87h7 Have you tried using AI to build an app from scratch? Did you hit the same wall? Share your experience below! 👇 #VibeCoding #SoftwareEngineering #ArtificialIntelligence #TechPodcast #WebDevelopment #LLMs #TechTrends #CodingJourney #AI #ZTJourney #ZeroTrust

𝗡𝗼𝗯𝗼𝗱𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀 𝘁𝗵𝗲𝗶𝗿 𝗼𝘄𝗻 𝗰𝗼𝗱𝗲 𝗮𝗻𝘆𝗺𝗼𝗿𝗲. This is emerging from AI-generated development workflows powered by tools like :Opus 4.6 models and systems like : Claude Code. These tools can generate working code instantly. But the trade-off is subtle. Engineers are no longer writing every line. They are reviewing outputs. That shift changes everything. 𝗖𝗼𝗱𝗲 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 “𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻” 𝘁𝗼 “𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻”. *And validation is not the same as understanding.* The real risk is not bugs. It is loss of comprehension. 𝗪𝗵𝗲𝗻 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼𝗻’𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲𝗶𝗿 𝗼𝘄𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻’𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝘁𝗵𝗲𝗺.

This dev just won the Anthropic hackathon… and then released the ultimate Claude Code playbook. No fluff. Just what actually works. Here’s what it covers: Agents (13) → Feature planning → Code reviews → Debugging build issues → Security checks Skills (56) → Test-driven workflows → Token efficiency → Persistent memory → Self-improving systems Commands (32) → /plan to map features → /tdd to enforce testing → /security-scan for risks → /refactor to clean code Runs everywhere → Claude Code → Cursor → OpenCode → Codex CLI Proven in real work → Used daily for 10 months → Built and shipped real products → 992 internal tests → Cut costs by 60% Latest upgrades → PM2 orchestration → 6 new multi-agent flows → AgentShield security layer → Works across platforms This is not just a toolkit. It’s a full system for building with AI. Follow MUHAMMAD USMAN AHMAD for more AI content

The real risk of 100% vibe coding is not the first demo or the initial delivery. It is the next 12 months of bug fixes, handovers, edge cases, and support tickets as the project grows bigger and more complex. AI-generated code can absolutely accelerate delivery, and the speed can be incredible. But if teams do not fully understand, review, test, and structure that code, they may simply be creating technical debt at high speed. #SoftwareEngineering #VibeCoding #AICoding #TechLeadership #SoftwareMaintenance #RandomThoughts

Claude Code burns tokens because every session starts from zero. This free repo (89k⭐) fixes it: It's called everything-claude-code — a Claude Code configuration crafted by an Anthropic hackathon winner after 10+ months of daily, production-grade work. Here's what it layers onto your workflow: • Token efficiency Sends each task to the appropriate model and strips bloat from system prompts so you spend less • Persistent memory Hook-based saving captures session state on exit and restores it when you return • Skill accumulation Repetitive patterns get distilled from past sessions into reusable building blocks • Output verification Built-in checkpoints and evaluations confirm things actually function — not just execute • Multi-agent coordination Breaks large repos into iterative retrieval steps instead of dumping the whole codebase into context The result: Claude holds its thread even as your project scales in complexity. What's your setup for keeping AI coding sessions efficient? -- P.S. If you're building AI-powered products, check out Latenode.com — an AI workflow automation platform that lets you connect models, apps, and APIs into production-ready workflows without writing tons of glue code.

Did you know you can build this kind of workflow with Claude? I built an agentic software engineering sandbox around Robocode Tank Royale: programmable virtual 2D tanks that compete in simulated arenas. It is an agentic optimization loop with evaluators for AI-guided strategy iteration, measurable feedback, and safe promotion of code improvements. This kind of automated AI-driven optimization can be built around systems that provide measurable feedback and allow behavior to be iteratively adjusted through code or APIs. In this experiment, the AI agent edits a Java bot, tests its hypothesis, reads objective feedback, and promotes changes that clear a measurable bar. The goal is not just to make a toy tank win more rounds, but to explore how AI-guided strategy iteration can be made measurable, repeatable, and safe. The AI workflow uses custom Claude Code Skills and a Hook to guide the agent through a disciplined improvement process. The Skills define how Claude should analyze reports, inspect source code, reason about architecture, run the right evaluator mode, and avoid overfitting to a single lucky result. The Hook automatically runs the evaluator after bot changes, giving the agent fast feedback without relying on manual coordination. GitHub: https://lnkd.in/eb8tUg3u

𝐘𝐨𝐮𝐫 𝐂𝐥𝐚𝐮𝐝𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐢𝐬𝐧’𝐭 𝐚 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰. 𝐈𝐭’𝐬 𝐚 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐠𝐮𝐞𝐬𝐬𝐞𝐬. An Anthropic hackathon winner just dropped a highly structured Claude Code resource library. Not another prompt pack. A real system: 13 agents 56 skills 32 commands What it covers: • planning features • reviewing code • fixing build errors • security audits • TDD workflows • token optimization • memory persistence • continuous learning It also includes reusable commands like: • /plan • /tdd • /security-scan • /refactor-clean The bigger point: Most teams do not have an AI workflow. They have a pile of prompts, tools, and habits. This is closer to an operating system for AI-assisted work. It works across: • Claude Code • Cursor IDE • OpenCode • Codex CLI And the latest drop adds: • PM2 orchestration • 6 multi-agent commands • AgentShield security scanner • cross-platform support The teams that win with AI will not be the ones using the most tools. They will be the ones with the cleanest systems. Save this if you are building with Claude Code. Send it to someone who still thinks “better prompts” are the whole strategy. Repo: https://lnkd.in/e4cBay2c #ClaudeCode #AIAgents #DevTools #AIEngineering #Productivity