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Make your own end2end platform for LLMs in under 4 minutes
Hacker NewsMake your own end2end platform for LLMs in under 4 minutes
OK, it may take you a couple more minutes than that, but it really is quick. And open source. I'm struggling to get people to use the tool, and I'd love feedback to get the features right! The accompanying written guide for the video is here: https://kalavai-net.github.io/kalavai-client/self_hosted_llm... This is the repo: https://github.com/kalavai-net/kalavai-client Thank you HN folks!
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting64%64% 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.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal38%38% 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, revenue15%15% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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