Launch Intel
Sn

Sneakily steer candidates toward naive brute-force solutions

Hacker News

Sneakily steer candidates toward naive brute-force solutions

I've noticed that several startups have been switching from leetcode-style assessments to some version of "clone starter code, build feature, submit code". A key issue with this seems to be that smarter AI models (like Opus 4.6) end up spoiling key insights of the problem by helping them too much with system design and ideation. I set up an assessment platform which basically serves as a middle-man proxy to record all requests between Claude Code and the Anthropic endpoint. I've recently been experimenting with a feature which prevents Opus-class models from providing too much insight by instead making sure that the LLM's suggestions are geared towards only naive and brute-force problem insights unless explicitly challenged. This should prevent increasingly intelligent models from collapsing the resolution of signal that would normally be obtained from such an assessment. Live demo: https://app.gonfire.io (showhn@gonfire.io / Aa123123123123)

Visit productView on Hacker NewsMay 27, 2026
Share card

Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, models · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
20%20% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Ta
Target demanded candidates for your apprenticeship31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Target demanded candidates for your apprenticeship

Hacker News1
Fi
Find data correlations with presidential candidates37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find data correlations with presidential candidates

Hacker News3
Fr
FreshCandidate, Search Through Speeches from Presidential Candidates32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

FreshCandidate, Search Through Speeches from Presidential Candidates

Hacker News5
Ne
New Cancer Drug Candidates30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New Cancer Drug Candidates

Hacker News1
Ne
New Cancer Drug Candidates30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New Cancer Drug Candidates

Hacker News2
Ne
New Cancer Drug Candidates30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New Cancer Drug Candidates

Hacker News1
Ne
New Cancer Drug Candidates30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New Cancer Drug Candidates

Hacker News1
Ne
New Cancer Drug Candidates30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New Cancer Drug Candidates

Hacker News1
Sq
SquareComposer – Scala solutions for CubeComposer33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

SquareComposer – Scala solutions for CubeComposer

Hacker News3
UV
UVA Solutions35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

UVA Solutions

Hacker News1
Browse all productsAnalyze your launch