We review YC applications for free – with feedback from YC founders

Hacker News

Hi HN, We just launched YC Roaster, a free YC application review service run by Lobster Capital. If you’re applying to Y Combinator, you can submit your application and get written feedback from founders who’ve actually gone through YC and built real companies. How it works * You submit your YC application (PDF) * It’s reviewed by successful YC founders in our network * Everyone gets written feedback * If we think you have a strong shot, we offer a short 1-on-1 Zoom call Why are we doing this for free? Lobster Capital is a VC fund that exclusively invests in YC companies. We’ve reviewed hundreds of YC applications over the years and backed 100+ YC startups. This is our way of giving back, and hopefully helping more strong teams get in. There’s no guarantee of acceptance, no pitch requirement, and no obligation to talk to us afterward. What we’re curious about * What parts of the YC application do founders struggle with the most? * What feedback have you found most useful when applying? Website: https://www.ycroaster.com Happy to answer questions or take feedback (including criticism).

Visit product View on Hacker NewsJan 29, 2026

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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