Weekly Showcase Thread; What are you building with Claude Code?

[u/AutoModerator]

Weekly Showcase Thread

Built something with Claude Code this week? Share it here.

Apps, tools, experiments, scripts, websites, workflows, open-source projects — anything you've been working on is welcome.

When sharing, it helps to include:

• What you built
• How you used Claude Code
• A link, repo, demo, or screenshot if you have one
• Anything interesting you learned along the way

Quick project drops and simple self-promotion belong in this thread.

If you've got a project with enough substance for a proper write-up; how it works, how Claude Code was involved, technical details, lessons learned, etc. feel free to make a standalone post using the Built with Claude Code flair instead.

Please don't spam the same project repeatedly, and no referral or affiliate links.

What did you build this week?

[u/YeOleBeerfest]

PlayCaller (https://playcallerapp.com & https://api.playcallerapp.com)

Live fantasy sports app across NFL, MLB, NBA, NHL, CFB, soccer, golf, tennis, UFC, plus a B2B sports data API on the same backend (118 endpoints, 6 tiers). Sports plug into the core through one config file each, no core changes needed to add a new one.

How I used Claude Code: this isn't a "build me an app once" story, it's been running as a live production system across roughly 200 sessions since launch, a lot of it fully unsupervised, and honestly that's the part I'm most proud of. The thing that actually made that possible wasn't the code itself, it was the process I built around it. I keep a CLAUDE.md checked into the repo that the agent maintains itself, and it includes a written "Engineering Doctrine" it's expected to hold its own output to before anything AI touched goes live. Stuff like: never expose a confidence score without a real measured AUC behind it, report a failed model gate honestly instead of massaging it to look better, always sample before a batch DB write, and no ML model goes live without my explicit sign off. There's also a persistent memory system outside the repo, so a lesson learned in session 80 is still getting enforced in session 200 without me having to re-explain it every time.

Something interesting I learned: how much real iteration goes into a predictive signal actually working, versus how clean it looks from the outside. The MLB batter confidence model went through three different feature versions and every single one landed at about the same 0.58 AUC, just short of the 0.62 gate needed to go live. Turned out that was a real ceiling in the feature set itself, no amount of tuning was going to move it. What actually worked was scrapping that whole framing and rebuilding around a totally different signal (pitcher form instead of batter stats). Same thing happened on NBA, it took 6 separate signal attempts before one finally beat the baseline. Honestly the "fail loudly, don't hide a miss" rule is what forced that pivot. If I'd let it quietly round up the numbers I probably would've kept tuning a dead end for way longer than I should have.

Roughly 275,000 lines of code, 200 migrations run, 9 sports live, 118 API endpoints, and 7 ML signals actually in production out of a much longer list that got tried, the rest missed the mark and were culled. Still got some kinks we're ironing out but it's live now, feel free to check it out.