#engineering-factory

23 posts

  1. 01 Agent Skills: packaging what your org knows An Apache licensed spec for portable agent capabilities. The enterprise angle is that know-how stops being locked to one vendor's agent.
  2. 02 Open weights walk in the front door of GitHub Copilot Kimi K2.7 Code is the first open weight model in Copilot's picker. The story is distribution, not benchmarks.
  3. 03 Kimi K3 changes your leverage, not your stack The first open 3T-class model is here. Almost no enterprise will ever host it, and that is not where the value is.
  4. 04 MiniMax M3: judge it by the 24 hour run MiniMax's new flagship pairs a 1M token context with the metric that matters for agentic delivery: how long it stays coherent without a human.
  5. 05 Agents Need Eyes: The Verification Layer Is Arriving Chrome ships DevTools as an MCP server for coding agents. It is one of the clearest signals that verification, not code generation, is where agentic delivery gets decided.
  6. 06 Astryx: Meta Makes the Design System the Agent Contract Meta's new open source design system calls itself agent ready. That phrase is the strategy: components as the guardrails agents build UI through.
  7. 07 OpenAI Shipped a Codex Plugin for Claude Code. Read the Signal. The official codex-plugin-cc repo lets Claude Code users run Codex reviews and delegate tasks. The interop matters more than the feature.
  8. 08 Graphify Turns Your Repo Into a Knowledge Graph Agents Can Query An open source skill that maps code, docs, and media into a queryable graph for coding agents. Context retrieval is the real constraint on agent quality.
  9. 09 TestMu's Browser Cloud Sells Real Browsers to AI Agents The company formerly known as LambdaTest now offers managed Chrome sessions as agent infrastructure. Verification capacity is becoming a line item.
  10. 10 A joke repo just made the best point about token economics Caveman, a Claude Code skill that cuts about 65 percent of tokens by talking like a caveman, went viral. The punchline is a real budget line.
  11. 11 AI as the engineering factory: copilots are stage one, not the strategy The third layer of enterprise AI redesigns how software gets built. A maturity model for going from individual assistants to an agentic delivery system.
  12. 12 GLM-5.2 and the open-weights step change for enterprise agents Z.ai's MIT-licensed flagship claims near-frontier agentic coding with a usable 1M-token context. The enterprise story is what this does to cost and data residency.
  13. 13 Pentesting is becoming a pipeline stage Strix, an open source AI pentest agent, is trending. The interesting question for enterprises is not the tooling, it is who approves what the agent attacks.
  14. 14 Superpowers: agentic skills as an operating methodology A 245,000-star framework for giving coding agents reusable skills. The signal is that agent capability is becoming a managed artifact, like code.
  15. 15 Build Agentic Preview Environments on Your Own VPS Build isolated pull request previews on your VPS with Dokploy, PostgreSQL, dedicated workers, safe credentials, tests, and automatic cleanup.
  16. 16 How to Build a Safe Agentic Software Pipeline Build an agentic delivery pipeline where every pull request gets isolated code, data, workflows, tests, and a live Preview URL.
  17. 17 Stop Overengineering Your App From Day One A cheap VPS can handle more traffic than most teams expect. Avoid premature complexity, tune the basics, and scale only when evidence demands it.
  18. 18 AI Is Quietly Changing How You Should Pick a Tech Stack Tech stack choices now include an AI factor: training data, ecosystem maturity, and how reliably AI can help you ship and maintain code.
  19. 19 How I built this site... or should I say guided my agents to build it? Discover how to build a fast, database-driven site with a static shell and cache-driven fragments using Next.js, Cache Components, and AI-assisted workflows.
  20. 20 PM, Dev, QA Are Merging: Meet the Rise of the Product Engineer Uncover how AI merges PM, dev, and QA into a single product engineer. Learn to specify user needs, criteria, and metrics to accelerate delivery.
  21. 21 Stop Blaming Your Engineers: The Real Reason They're Slow Stop blaming engineers: the perfection loop slows delivery. Learn how rapid deployments, feature flags, and real-time observability speed shipping.
  22. 22 How to Ship Faster by Ditching 'Future-Proof' Over-Engineering Ship simple to learn fast: avoid premature, complex architecture for a future that may not exist. Discover how data should drive scale and momentum.
  23. 23 Tickets are comforting. Outcomes are accountable. Move from closing tickets to delivering real user value. Learn how redefining 'done' and ownership drives end-to-end impact and meaningful shipping.