ChainFactory – Run Structured LLM Inference with Easy Parallelism

Hacker News

Hi HN! Disclaimer: I submitted another post about ChainFactory a few days ago. Here's what has changed since: - Added hash based caching of auto-generated prompts and masks. - Did some internal restructuring and cleanup. - Updated the order in which README doc introduces concepts and terminology. Posting this again because honestly, I am kinda puzzled about what to add/fix/change due to having 0 users and no genuine feedback. By genuine feedback, I mean feedback from strangers who do not have a social pressure to be polite and pull punches. Please take a look if you find this interesting and leave a comment. If you think it's an deranged or stupid idea not worth your time, please at least leave a 'no' - I'd still be delighted as it's an honest opinion. Thanks a lot! PS: Is it okay to post updates and changes at regular intervals?

Visit product View on Hacker NewsJul 30, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos

68%68% 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.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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 · Strong signals: introduce · 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

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