LLFn – A Light-Weight Framework for Building AI (LangChain Alternative)

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Hi HN, After building a few AI apps with LLMs and LangChain (including https://github.com/orgexyz/BlockAGI ), we finally felt that it's time for a change. LLFn was born out of our necessity to iterate faster on building AI agents. We found that the current frameworks are convoluted with complex abstractions which over time has present more of a road block than saving our time. LLFn is an open-source framework that turns any prompt into a function and let you decoratively define their output. This abstraction allows us to build AI apps, especially those with multiple agents interacting with each other, much faster. Because each "agent" is just a function, you can compose it any way you want with minimum boilerplate. This turned out to be 10x productivity for our team. We're super excited to share it with you and hear your feedback! https://github.com/orgexyz/LLFn If you want to try LLFn on Colab, here's the link: https://colab.research.google.com/drive/11cYRchBaKftxGJvyacd...

Visit product View on Hacker NewsJul 27, 2023

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

88%88% 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.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% 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 · Strong signals: apps, google, 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

25%25% 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

19%19% 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.

Incorrect prediction on native model

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