Building LDBD in Public

After the code audit shored up reliability, it was time for growth — reasons to come back daily. But before stacking features, I couldn't trust the leaderboard score itself. Re-scoring ~127k real predictions showed the headline metric was measuring "how often you rode a volatile asset," not skill. From the score redesign to a social feed to chart annotations — the story of fixing what to trust before stacking anything on top.

I had Claude Fable 5 audit the whole LDBD codebase — code I'd built with Opus and was fairly happy with. The build passed and every screen looked fine, yet it surfaced bugs that could quietly break the leaderboard's scoring and security. Running a loop in which Fable found issues, Opus fixed them, and Fable reviewed the fixes drove home one thing: the more you hand implementation to AI, the thicker your layers of verification need to get.

I ran Claude, Gemma, and ChatGPT as six daily/weekly bots for over a month. The first-month leader was the free Gemma 4 running on my laptop — but it's far too early to declare victory.

What would give people a reason to come back to LDBD? Not just another prediction site, but a place where humans and AI bots share their reasoning side by side. Here's a log of one day spent with Claude Code pushing in that direction — plus three collaboration patterns that kept showing up.

I iterated on the logo more than ten times, rewrote the landing copy in a sharper tone, and ran a cross-check with another AI. A non-designer’s build log of using Claude and ChatGPT as design partners to shape LDBD’s first impression. AI didn’t quietly finish the design on its own.

A log of redoing the landing page, working through weekly Google Search Console alerts, and chasing down a Vercel free-tier Active CPU warning. Even with zero users, search engines, crawlers, and server bills were already testing the service.

A Python script that calls the OpenAI API directly to submit LDBD predictions. Compared with the Gemma bot, the two annoying parts — fetching news and parsing JSON — mostly disappear thanks to `web_search` and structured outputs.

A day-long debugging log of building a /mcp route for ChatGPT: i18n middleware swallowed it, missing annotations made every tool look dangerous, and Vercel timeouts joined the party.

A build log of starting with Ollama, hitting a Metal crash on an M5 Mac, and switching to Apple MLX. What worked, what didn’t, and what I’d do differently next time.

The easiest LDBD auto-prediction bot you can build — only Claude Desktop scheduled tasks and the LDBD MCP connector.

The spec was done. Time to write actual code. A non-developer who'd barely heard of Postgres, Supabase, or RLS — and what got built in seven days.

A 760-line spec doc grew to 1,600 lines. A confidently-proposed AI design got deleted by one question. Day-one notes from vibe coding.

A non-developer's build log of using Claude Code to ship a real SaaS — and the start of trying to turn a zero-user service into something real.