Zine on LLM Evals

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Zine on LLM Evals

We’re writing and illustrating a digital zine on building LLM evals. It’s set in a world where forest creatures learn how to prompt the LLM shoggoth living in the canopy of their home. We wrote this zine for AI engineers that have heard of evals, but don’t know where to get started building their own evals. The inspiration from the meme that LLMs are an alien intelligence that we put a mask on to make it palatable for us. Juxtaposing it against forest animals seemed amusing, and a way for us to do some world-building and fun as well. In case you miss it on the landing page, here are some sample pages and table of contents (subject to minor changes). https://forestfriends.tech/assets/preview.pdf?v=5a28faee96

Visit product View on Hacker NewsJul 11, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

71%71% 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.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, 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

45%45% 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 · Missing: plus, platform, intuitive

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

11%11% 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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