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  "title": "Jeeves",
  "description": "Jeeves is a reasoning-augmented Qwen3.5-9B decision classifier that produces calibrated yes/no/rating calls and exposes the reasoning behind each one. Open source: yes (MIT); self-hostable (download weights, local CUDA inference or Python SDK). Free; ~211 stars; maker PostHog.",
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  "one_liner": "Jeeves is a Qwen3.5-9B decision classifier that returns calibrated yes/no/rating calls together with the reasoning trace behind each verdict.",
  "open_source": "yes",
  "self_hostable": "yes",
  "pricing_model": "free",
  "who_its_for": "A team that wants a self-hosted decision classifier whose calibrated yes/no or rating output comes with a visible reasoning trace — so a human can audit why a call was made — running locally via CUDA or the Python SDK.",
  "who_its_not_for": "A team on lightweight hardware that cannot host a 9B model, or one that needs sub-30ms inline decisions rather than reasoned, auditable judgments.",
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    "note": "MIT per repo; ~211 stars. A reasoning-augmented Qwen3.5-9B decision classifier producing calibrated yes/no/rating calls with transparency into the reasoning. Self-hostable: download weights, run local CUDA inference or use the Python SDK. Maker is PostHog, a well-known analytics company (PostHog/jeeves, verified 2026-09-29)."
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  "caveats": "Verified from the primary repo (2026-09-29); no independent benchmarking here. 'Calibrated' and the reasoning-transparency claim are the vendor's own (vendor-claim) — validate calibration on your own labeled data. A 9B base model needs real GPU memory to self-host, so this is heavier to run than the sub-2B decision models in this space.",
  "lead": "Jeeves is a reasoning-augmented decision classifier built on Qwen3.5-9B that produces calibrated yes/no and rating calls while exposing the reasoning behind each one. Open source: yes (MIT); self-hostable by downloading the weights and running local CUDA inference or using the Python SDK. It is free, has ~211 stars, and is…",
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      "text": "Jeeves is a reasoning-augmented decision classifier built on Qwen3.5-9B that produces calibrated yes/no and rating calls while exposing the reasoning behind each one. Open source: yes (MIT); self-hostable by downloading the weights and running local CUDA inference or using the Python SDK. It is free, has ~211 stars, and is maintained by PostHog — a well-known analytics company, which is a useful credibility signal for the project's provenance."
    },
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      "heading_path": [
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      "text": "the reasoning behind each one. Open source: yes (MIT); self-hostable by downloading the weights and running local CUDA inference or using the Python SDK. It is free, has ~211 stars, and is maintained by PostHog — a well-known analytics company, which is a useful credibility signal for the project's provenance.\n\nJeeves takes a decision prompt and returns a structured verdict — yes/no or a rating — that the project describes as calibrated, and crucially it surfaces the reasoning trace that led to the verdict rather than emitting a bare label. That transparency is the differentiator: where a small classifier gives you an answer, Jeeves gives you an answer you can audit. It is built on Qwen3.5-9B, so it is a larger model than the sub-2B single-purpose decision models; you self-host it by downloading the weights and running CUDA inference or calling it through the Python SDK. Open source: yes (MIT); self-hostable; free."
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      "text": "answer, Jeeves gives you an answer you can audit. It is built on Qwen3.5-9B, so it is a larger model than the sub-2B single-purpose decision models; you self-host it by downloading the weights and running CUDA inference or calling it through the Python SDK. Open source: yes (MIT); self-hostable; free.\n\n- MIT per repo; ~211 stars.\n- Reasoning-augmented Qwen3.5-9B decision classifier; calibrated yes/no/rating output with visible reasoning.\n- Self-hostable: download weights, run local CUDA inference or use the Python SDK (PostHog/jeeves, verified 2026-09-29).\n- Maker is PostHog, an established analytics company — a provenance/credibility signal.\n- Surfaced via the GTM Stacker studio daily pull (2026-09-29 pass); license/facts verified against the primary repo 2026-09-29."
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      "text": "output with visible reasoning. - Self-hostable: download weights, run local CUDA inference or use the Python SDK (PostHog/jeeves, verified 2026-09-29). - Maker is PostHog, an established analytics company — a provenance/credibility signal. - Surfaced via the GTM Stacker studio daily pull (2026-09-29 pass); license/facts verified against the primary repo 2026-09-29.\n\nMany GTM decisions are judgment calls that later need defending — is this lead a fit, does this reply signal intent, should this account be escalated. A bare classifier label is hard to trust or debug; Jeeves pairs the calibrated verdict with the reasoning behind it, so an operator can see why the model decided as it did and catch systematic errors before they compound across a pipeline. The honest read: \"calibrated\" is the vendor's own claim and should be checked against your labeled outcomes, and a 9B model is materially heavier to self-host than the tiny decision models — so choose Jeeves when the auditability of each call is worth the extra GPU footprint, and a smaller model when you only need a fast inline label."
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