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CyteType – AI agents that annotate cell types in scRNA-seq data

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CyteType – AI agents that annotate cell types in scRNA-seq data

Been annotating single-cell data for 7 years now. Got tired of second-guessing CellTypist/SingleR outputs in disease datasets. Reference methods pattern-match against atlases. Works until it doesn't: disease, ectopic expression, unexpected tissue context. You get an annotation label and no way to know it's wrong. CyteType runs multiple LLM agents that propose and critique annotations and surface all the ambiguity instead of hiding it. The output is an interactive report you can chat with to interrogate the reasoning. Linked cell ontology terms and relevant literature for each cluster. Confidence score and match score against author labels help with triaging. Model-agnostic. Benchmarked across 16 LLMs and against reference-based methods. Integrates with Seurat/Scanpy/Anndata.

Visit productView on Hacker NewsNov 24, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
93%93% 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, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% 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
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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