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Raink – Document ranker using LLMs

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Raink – Document ranker using LLMs

I think a lot of AI-augmented security problems can be decomposed into "show me the best thing in this list of things": - the changed function in a patch diff that most closely relates to a given security advisory - the injection point in a webapp that seems most likely to cause a state change on the backend - the static code analyzer result that would have most severe impact if a sink were actually reachable It's notoriously difficult to get an LLM to seriously consider all items when presented with a big list of input—so I built raink, a CLI tool to harness LLMs for general purpose document ranking. Blog post here: https://bishopfox.com/blog/raink-llms-document-ranking

Visit productView on Hacker NewsJan 15, 2025
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
59%59% 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 HuntUnlikely to reach the leaderboard · Strong signals: using, code · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
48%48% 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
44%44% 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
13%13% 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
1%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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