AI Agent System to Analyze ArXiv AI Papers

Hacker News

Building a multi-agent system to analyze new AI research papers from 3 distinct perspectives: - Deep learning researcher agent: extract interesting deep learning methods that are related to paper - Theoretical mathematician agent: figure out theoretical mathematical concepts that are important in this paper and additional theoretical references that will be useful in understanding it - Skeptic agent: find unjustified assumptions that lack supporting evidence For this mvp, I used low-code agent platform StackAI (YC W23) and wrote about my process: https://www.sabrina.dev/p/building-ai-agent-system-to-analyz...

Visit product View on Hacker NewsJun 12, 2024

Share card

Actual performance

21points

10comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: agent, new, code · 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.

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

55%55% 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 · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

17%17% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

PagePeek59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Your AI Professor for drafting and evaluating papers in secs

Product Hunt+6

The “tl;dr” of Recent Transformer Papers58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The “tl;dr” of Recent Transformer Papers

Hacker News5

The Federalist Papers, typeset as the 1787 newspapers they ran in67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The Federalist Papers, typeset as the 1787 newspapers they ran in

Hacker News58

Back Me Up – Find papers that back your argument63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Back Me Up – Find papers that back your argument

Hacker News2

AI Agent in Jupyter – Runcell41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI Agent in Jupyter – Runcell

Hacker News8

MVAR – Deterministic sink enforcement for AI agent20%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

MVAR – Deterministic sink enforcement for AI agent

Hacker News1

Kollab14%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

All-in-one AI Agent

Indie Hackers

Kollab37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

All-in-one AI Agent

Indie Hackerscommitment-full-time

LinkedNav14%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI Agent

Indie Hackers

Bilbo84%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Metabase AI Agent

Product Hunt+171Chrome Extensions

Browse all products Analyze your launch