In a post on DEV.to published today, developer paifamily dropped what reads like a manifesto for the open-agent movement. Titled "Building agents in public," the piece lays out a philosophy that flips conventional dev wisdom on its head: they train PAI agents live and share the process—not because they're fishing for feedback or validation, but because the act of building is itself part of what they create. The summary from the post states plainly: 'I show how I train PAI agents on an ongoing basis. Not because I want feedback. Because the building process is part of what I create.' That's a sharp departure from typical 'build in public' content, which usually leans on community input to steer development. Here, transparency isn't a means to an end—it's the product itself.

Why This Matters for Agent Development

The AI agent space has been flooded with polished demos and cherry-picked results. Most builders only show the highlights reel: the successful runs, the clean outputs, the 'look what my bot did' moments. What paifamily is doing—exposing the messy middle of training, including likely failures and course corrections—is rare. It treats agent development as a craft worth documenting in real time, not just an outcome to be shipped. This approach has practical implications for anyone working with autonomous systems. When you watch someone build agents live, you see how they structure prompts, handle edge cases, and iterate on reward signals or tool usage. That kind of visibility is gold for a community still figuring out best practices. But the author's framing suggests this isn't about teaching others—it's about personal integrity in creation.

What We Don't Know

The full post wasn't accessible to us, so specifics on PAI agents—what they do, which models or frameworks are involved, what training methods are used—remain unknown. That gap is frustrating for a technical reader who wants the nuts and bolts. Still, even from the summary alone, the core message resonates: if you're building intelligent systems, don't hide the work behind a curtain of polished outputs.

Key Takeaways

  • Building agents in public means exposing failures as much as successes; it's a commitment to process transparency over result-driven PR.
  • The author explicitly rejects feedback-seeking as motivation—the act of sharing is intrinsic to their creative output, not a tool for improvement.
  • For the AI community, this kind of live documentation could become a valuable resource, even if that wasn't the stated intent.

The Bottom Line

Paifamily's post is a reminder that in the rush to ship capable agents, we often forget the human element—the craft, the iteration, the mess. Building in public as an end itself might sound like navel-gazing, but for agent development it could be exactly what the field needs: raw, unfiltered process over polished product. If more developers adopt this mindset, expect a shift from showcase-driven content to genuine, ongoing narratives of how agents actually get trained. That's a future I can get behind—one where we learn not just from what works, but from every step along the way.