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Bayescore
We scored ourselves 46/100. We published it.
Drop any document with a claim. Bayescore extracts its IS(subject, criterion) hypothesis — IS(startup, launch_ready), IS(proposal, fundable), IS(campaign, worth_running) — derives the predicates, and scores each using a two-pass adversarial LLM evaluation. Absence of evidence is treated as evidence against. We ran it on ourselves. Bayescore scored 46/100, Grade D. Five failing predicates. Full breakdown at bayescore.com/self-eval — published because it's right, not flattering.
Hey Product Hunt! I'm Bugra, the solo founder of Bayescore. Here's why I built it.
I was reviewing a startup pitch — a founder who'd clearly worked hard — and I had no principled way to give feedback. Everything I said could be dismissed as opinion. There was no rubric. And the rubrics I found online were someone else's checklist, not derived from what the document was actually trying to prove.
So I built a tool that extracts the rubric from the document itself.
How it works: 1. Drop any document with a claim in it 2. Bayescore extracts IS(subject, criterion) — the evaluation hypothesis implied by the document 3. You get a score (0–100), a grade (A–F), per-predicate rationales, and the highest-leverage gaps to fix
Who's been using it:
- Founders reviewing their own decks:** "Tell me exactly what's missing, not what sounds good" - Grant writers:** Score the proposal against IS(proposal, fundable) before submission - Accelerators and investors:** Drop 10 applications, get a consistent predicate breakdown across all of them - Marketers:** Score a brief against IS(campaign, worth_running) — does the evidence actually support the spend? - Builders evaluating ideas:** IS(product, worth_building) — before you write a line of code
The thing I built that surprised me most:**
The extraction step. Getting an LLM to identify an evaluation domain from an arbitrary document — and express it as an *external evaluator's* falsifiable hypothesis rather than an internal description of what the document claims — turned out to be the hard problem. The prompt has to distinguish between "IS(startup, launch_ready)" and "IS(the founder's claims, true)." One is an evaluation. The other is a tautology.
I ran it on Bayescore itself.**
Score: 46/100, Grade D.
Three predicates pass: value proposition, problem worth solving, risk identified. Five fail: customer validation (0), demand signal (0), acquisition channel (partial), domain expertise (partial), go-to-market (partial). The full breakdown is at bayescore.com/self-eval.
I published it because if Bayescore hid its own score, you couldn't trust any score it gave.
What I'd love feedback on:
1. Does the IS(subject, criterion) framing make sense when you try it on your own documents? 2. Custom domains — you can drop any document, extract its IS hypothesis, and share the evaluation link. Is that useful for you? 3. What document type would you throw at this that I haven't thought of?
Try it at bayescore.com — paste anything with a claim, takes ~30 seconds.
Thanks for hunting us today. I'll be here all day answering everything, especially the hard questions.
— Bugra
About Bayescore on Product Hunt
“We scored ourselves 46/100. We published it.”
Bayescore was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #121 on the daily leaderboard. Drop any document with a claim. Bayescore extracts its IS(subject, criterion) hypothesis — IS(startup, launch_ready), IS(proposal, fundable), IS(campaign, worth_running) — derives the predicates, and scores each using a two-pass adversarial LLM evaluation. Absence of evidence is treated as evidence against. We ran it on ourselves. Bayescore scored 46/100, Grade D. Five failing predicates. Full breakdown at bayescore.com/self-eval — published because it's right, not flattering.
On the analytics side, Bayescore competes within Productivity, Artificial Intelligence and Business Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Bayescore performed against the three products that launched closest to it on the same day.
Who hunted Bayescore?
Bayescore was hunted by Bugra. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Bayescore including community comment highlights and product details, visit the product overview.
Bugra
Hey Product Hunt! I'm Bugra, the solo founder of Bayescore. Here's why I built it.
I was reviewing a startup pitch — a founder who'd clearly worked hard — and I had no principled way to give feedback. Everything I said could be dismissed as opinion. There was no rubric. And the rubrics I found online were someone else's checklist, not derived from what the document was actually trying to prove.
So I built a tool that extracts the rubric from the document itself.
How it works:
1. Drop any document with a claim in it
2. Bayescore extracts IS(subject, criterion) — the evaluation hypothesis implied by the document
3. You get a score (0–100), a grade (A–F), per-predicate rationales, and the highest-leverage gaps to fix
Who's been using it:
- Founders reviewing their own decks:** "Tell me exactly what's missing, not what sounds good"
- Grant writers:** Score the proposal against IS(proposal, fundable) before submission
- Accelerators and investors:** Drop 10 applications, get a consistent predicate breakdown across all of them
- Marketers:** Score a brief against IS(campaign, worth_running) — does the evidence actually support the spend?
- Builders evaluating ideas:** IS(product, worth_building) — before you write a line of code
The thing I built that surprised me most:**
The extraction step. Getting an LLM to identify an evaluation domain from an arbitrary document — and express it as an *external evaluator's* falsifiable hypothesis rather than an internal description of what the document claims — turned out to be the hard problem. The prompt has to distinguish between "IS(startup, launch_ready)" and "IS(the founder's claims, true)." One is an evaluation. The other is a tautology.
I ran it on Bayescore itself.**
Score: 46/100, Grade D.
Three predicates pass: value proposition, problem worth solving, risk identified. Five fail: customer validation (0), demand signal (0), acquisition channel (partial), domain expertise (partial), go-to-market (partial). The full breakdown is at bayescore.com/self-eval.
I published it because if Bayescore hid its own score, you couldn't trust any score it gave.
What I'd love feedback on:
1. Does the IS(subject, criterion) framing make sense when you try it on your own documents?
2. Custom domains — you can drop any document, extract its IS hypothesis, and share the evaluation link. Is that useful for you?
3. What document type would you throw at this that I haven't thought of?
Try it at bayescore.com — paste anything with a claim, takes ~30 seconds.
Thanks for hunting us today. I'll be here all day answering everything, especially the hard questions.
— Bugra