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Staying up to date with the overwhelming amount of AI research

Hacker News

Staying up to date with the overwhelming amount of AI research

Hey HN! There’s been an explosion of AI research papers being published (see arxiv growth https://www.researchgate.net/figure/Number-of-papers-publish... ). As AI/ML founders it was difficult to find papers that actually aligned with our goals and often skimmed through many unrelated papers. It would be nice to have a way to sift through the noise. This last week, we built https://firebender.ai/ . It takes in a description of what you’re trying to accomplish, and on a weekly basis, delivers TLDRs of recent AI papers you'd most likely want to dive into. Here’s a sample description and curated list ( https://firebender.ai/sample ). It works by indexing new Arxiv papers (LDA, elasticsearch), then GPT-4 generates queries, summarizes results, and does the final round of filtering. Sign up at https://firebender.ai/ and look out for an email on Friday! Any and all feedback is appreciated.

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, email · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
25%25% 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.

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