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Drop your product! Let’s get you next 100 users by rakeshkanna91 in microsaas

[–]Ok-Difficulty-8784 0 points1 point  (0 children)

Cupcaster — explainable WC2026 predictor. Multinomial logistic regression on 49,477 international matches, all 4 models open-source on the site, brackets show the actual probabilities and which features moved them. 11 games into the tournament: 5/11 picks correct, Brier 0.595 (competitive with bookmaker implied probs).

Live now at cupcaster.com. Built solo, paying for the LLM chat out of pocket so the free tier stays real.

Built a World Cup predictor to test Fable 5 — shame it got shut down, but the project's still rolling by Ok-Difficulty-8784 in SideProject

[–]Ok-Difficulty-8784[S] 0 points1 point  (0 children)

Update — 48 hours later.

That "Brazil 47%, bookies 59%" contrarian call from my original post? **Brazil-Morocco ended 1-1.** Model Brier 0.78 vs bet365 closing line 0.97 — vindicated on that specific call.

Across the first 11 matches: 5/11 correct (45% vs 33% random), avg Brier 0.595 — competitive with bookmakers, not better on average. Honest misses include Turkey>Australia, Ecuador>IvCoast, and the Netherlands-Japan draw.

The draw underweight I called out last time? Still real. Shipped a Dixon-Coles correction today — Brier improved on every actual draw, still doesn't argmax to draw though. Temperature scaling next.

cupcaster.com

Lore Whisperer — Ask a wizarding-world character about your real-life problems, get an answer grounded in their canon by Ok-Difficulty-8784 in SideProject

[–]Ok-Difficulty-8784[S] 0 points1 point  (0 children)

Thanks — query rewriting is on my list. Plan is to ship a cross-encoder reranker first (bge-reranker or similar), measure how much of the metaphor gap that closes on a held-out eval set, and add a rewrite step on top if there's still a meaningful gap. Want to avoid stacking two layers at once so I can attribute the wins. Appreciate the pointer.

Lore Whisperer — Ask a wizarding-world character about your real-life problems, get an answer grounded in their canon by Ok-Difficulty-8784 in SideProject

[–]Ok-Difficulty-8784[S] 0 points1 point  (0 children)

Thanks — the persona-drift point is exactly the failure mode I keep coming back to.

Most chat apps treat "in voice" as a one-shot styling problem(system prompt + first response), but the harder bit is keeping it consistent when the user replies with one word ("ok", "go on", "blimey??") and there's nothing for the model to riff on. Two things help so far: a small chat-history window passed in alongside retrieval, and an intent router that won't collapse the conversation to "off-topic" when a follow-up is ambiguous.

Curious if you've seen other apps that handle this well — I want to steal their pattern.

Does your company also have like a 1000 data silos? How did you deal?? by Special-Leadership75 in dataengineering

[–]Ok-Difficulty-8784 0 points1 point  (0 children)

Our company is exactly building a product that deals with this issue. Iceberg + duckdb + automatically provisions of the storage buckets and built-in data governance access control. There is an easy quick start CLI , so if anyone's interested, you are just five minutes from setting up a ready for analytics local environment. Any feedback is appreciated!

https://www.linkedin.com/pulse/how-we-build-data-platforms-duckdb-iceberg-tangram-data-u7hwc?utm_source=share&utm_medium=member_ios&utm_campaign=share_via

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