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Bibfixer – AI-powered BibTeX cleaner

Hacker News

Bibfixer – AI-powered BibTeX cleaner

Bibfixer is a Python tool to automatically clean and standardize BibTeX files. It often takes hours to make the bib file very clean and consistent. For example, we often have to check if each arXiv paper has been accepted to a conference or journal. The conference names are messed up and not consistent: NeurIPS vs NIPS vs Neural Information Processing Systems vs Neural Information Processing Systems (NeurIPS). The title of the paper is not capitalized properly, e.g., "llm"/"ai" instead of "LLM"/"AI". Some authors may be missing. The list goes on. With LLMs and recent web search tools, this tedious work no longer needs to be manual. Bibfixer will do this for us: it completes entries with accurate metadata via LLM + web search capabilities, but also enforces a consistent style based on your preferences. Of course, we should check the final output since LLMs can also make mistakes, but this is much easier than doing this fully manually.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: mistakes · Missing: supports, reddit linkedin, podcasting
84%84% 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.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
45%45% 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 · Missing: mobile apps, ios, personal
41%41% 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
17%17% 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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