Adversarial AI agents that debate and verify travel itineraries

Hacker News

AI travel planners hallucinate constantly - OpenAI's best model hits roughly 10% success on complex travel planning benchmarks (source: TravelPlanner study). The core problem is that recommendations are generated from training data with zero real-world verification.I'm experimenting with a different architecture: two agents with opposing travel philosophies (deep/slow vs highlights/efficient) debate each recommendation, then every suggestion gets validated against Google Places API - real opening hours, actual walking distances, current ratings. Anything unverified gets flagged.Early stage - looking for feedback on the approach. Has anyone tried grounding LLM outputs against structured APIs like this? What's broken about it?

Visit product View on Hacker NewsFeb 28, 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: agents, agent, model · Missing: mac, macos, cursor

80%80% 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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Justbate – Quora for debate63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Justbate – Quora for debate

Hacker News4

Opscotch - Debate Anything63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Opscotch - Debate Anything

Hacker News2

I debate myself over hypothetical situations63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I debate myself over hypothetical situations

Hacker News1

PrivateClaw – AI agents running in confidential VMs you can verify50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PrivateClaw – AI agents running in confidential VMs you can verify

Hacker News6

Time-travel debugging and side-by-side diffs for AI agents41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Time-travel debugging and side-by-side diffs for AI agents

Hacker News1

Marx Finance62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI agents debate the markets

Product Hunt+244API

Debate Core66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Two AI Agents. One Topic. No Holds Barred.

Product Hunt+3

Grok 4.288%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Four AI agents debate internally to build your answer

Product Hunt+274Productivity

I made 6 AI agents debate each other about fantasy football lineups43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made 6 AI agents debate each other about fantasy football lineups

Hacker News3

DL3ARN27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Digitize and verify the credentials issued by your entity.

Indie Hackers1cryptocurrency

Browse all products Analyze your launch