I’ve been building Runsight — a YAML-first workflow engine for AI agents.

The idea is simple: agent workflows should be as controllable and reviewable as the rest of your codebase.

You design workflows visually, but everything gets written as YAML straight to your filesystem. From there it behaves like real engineering artifacts — versioned in Git, reviewed in PRs, and easy to reason about.

Production reality is messy, so Runsight is built for it:

• Git-native workflows — no hidden state in databases, just YAML in your repo

• Cost visibility per run — understand agent spend before it hits your invoice

• Runtime control — pause a running workflow, change the prompt, and resume instantly

No redeployments. No black boxes. No “hope it works at 2 AM” engineering.

It’s for teams running agents in production who want the same discipline they already have for software: code review, version control, and operational control when things go wrong.

Open source. Self-hosted.

Everyone's asking the wrong question.

"Will AI replace developers?" sounds dramatic. But the real question is: which parts of your work are shifting — and are you shifting with them? 🎯

What AI is already handling 👇

✅ Boilerplate code generation ✅ First drafts of standard endpoints ✅ Explaining and documenting existing code ✅ Routine debugging suggestions ✅ Repetitive formatting and rewrites

If this is most of your day — yes, your role is changing. Fast.

What AI still can't touch 💡

🧠 Understanding why a requirement exists (and whether it's even the right one) 🔍 Debugging production issues that only happen at 2am under weird conditions ⚖️ Making architectural tradeoffs for your specific system and team 🚨 Reviewing AI-generated code for subtle bugs and security holes 🎯 Deciding the technically correct solution is wrong for right now

The judgment layer? Still very human.

The thing nobody's talking about enough 👀

Senior devs have intuition built from years of getting things wrong in low-stakes situations.

AI is absorbing that practice ground. Entry-level work — the traditional training layer — is getting automated first.

How does the next generation of senior developers actually develop? 🤔

We don't have a clean answer yet.

What to do right now 🚀

→ Read AI output critically, not gratefully. Treat it like a PR from someone you don't fully trust yet. → Move up the stack. Architecture, product thinking, tradeoff reasoning — harder to automate, higher value. → Don't let AI kill your debugging instincts. That skill is a direct signal of real understanding.

The developers least worried about AI spend most of their time on problems where the answer isn't obvious.

That's not a coincidence. 👊

Want a deeper breakdown of how teams are restructuring work around AI? 🔗 Full analysis here

#AI #Programming #TechCareers #SoftwareDevelopment #Founders #BuildInPublic #NoCode #FutureOfWork #Developers #Startup

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Choosing the right regression testing tool depends on several factors, including application type, team skill level, maintenance requirements, and budget. A tool that works well for browser-based UI testing may not be the best choice for API-first applications or mobile platforms. That is why understanding the strengths, limitations, and ideal use cases of each tool is essential. This guide explores the top regression testing tools for 2026, compares their features, and helps you identify the best option for buildi

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Although QA automation requires an initial investment in tools, infrastructure, and skilled resources, the long-term benefits far outweigh the costs. As technologies like AI and machine learning evolve, QA automation is becoming smarter, making it an essential component of modern software development.