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Llamaphone- Single-file Front end for Llamafile
Hacker NewsLlamaphone- Single-file Front end for Llamafile
Llamafile might just be the easiest way to run a LLM locally. I tried it out and thought it was awesome, but perhaps a bit lacking in the server UI. In the spirit of simplicity, I made a single-file web page for interacting with Llamafile that I'm calling Llamaphone. The goal of this project is to make the barrier of entry as low as possible for an average user, and it doesn't get much simpler than a single html file.
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Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, single · Missing: mac, agents, macos87%87% 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 · Missing: supports, reddit linkedin, podcasting64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: llama · Missing: https docs, excited, just released62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit15%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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