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New AI Grant Fellows

Hacker News

New AI Grant Fellows

Hi HN, A few months ago, we announced our AI Grant project. We give $2,500 in cash and $20,000 in GPU training credits to anyone who wants to work on AI research. Today, we're excited to announce our second batch of Fellows! We were flooded with nearly 1,000 applications. Topics were diverse: robotics, NLP, biology, tooling, physics, dataset acquisition, fundamental research and more. The applicants were also diverse, spanning from world-class Google researchers to high school students. Check out https://blog.aigrant.org/new-ai-grant-fellows-43f1c26c13d9 for an overview of the 20 new Fellows. Einstein was a patent clerk. In Bern. The weirdest and best research ideas often come from outsiders. Our goal is to find and fund their work. What should we add to our program to improve it? Thanks! - Daniel Gross & Nat Friedman. P.S. I'm a partner at YC. This is Nat's and my side project, not a YC effort.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
92%92% 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 · Strong signals: google, new · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month, google · Missing: mobile apps, ios, personal
53%53% 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
41%41% 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
26%26% 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.

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