LL

LLMFlows – LangChain alternative for explicit and transparent apps

Hacker News

LLMFlows – LangChain alternative for explicit and transparent apps

Hi HN! Over the last several weekends, I've been building LLMFlows as an alternative to langchain. There's been a lot of discussion on the shortcomings of langchain in the past few weeks, but when I first tried it in March, I thought there are 3 main problems: 1. Too many abstractions 2. Hidden prompts and opinionated logic in chains which makes it hard to customize 3. Hard to debug This inspired me to try and build a framework that solves these 3 issues, and therefore I started building LLFlows with the "philosophy" of being "simple, explicit, and transparent." A few weekends later, I think I finally managed to reach a state where I feel it's ready to be shared. I would love to hear your feedback! Thank you!

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Actual performance

49points
22comments
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps · Missing: mac, agents, macos
78%78% 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: started · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
64%64% 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
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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.

Correct prediction on native model

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