Ll
Llmcurator.io- A front end and annotation tool for LLM
Hacker NewsLlmcurator.io- A front end and annotation tool for LLM
Hi HN, I built this data annotation tool to curate training data for your LLM. You can select the best response from the model or edit the response when needed. You can also use this as a frontend for any LLM by setting up a simple API. This way, you can process input before sending it to the LLM. All the data is stored locally, which you can export as JSON. Looking forward for some feedback! Regards, Pushpankar
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Product HuntOn track for Day 1 leaderboard · Strong signals: model · Missing: mac, agents, macos79%79% 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, podcasting55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released48%48% 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: way · Missing: mobile apps, ios, personal43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · 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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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