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  "description": "Mingbird is a local-first agent harness that gets small 2–9B models to finish real multi-step tasks on an ordinary laptop, running offline against a local Ollama backend. Open source: yes (Apache-2.0); self-hostable (local Ollama; one-click zero-outbound mode; config in ~/.ollama_agent/config.json). Free OSS; ~1 star (brand-new, created 2026-09-17); active (30 commits, v1.8.2, 461-test suite, CI on Linux/macOS).",
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  "one_liner": "Mingbird gets small 2–9B local models to finish real multi-step tasks on a laptop, running offline against a local Ollama backend.",
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  "self_hostable": "yes",
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  "who_its_for": "A builder who wants an offline, self-hosted agent that runs on small local models (2–9B via Ollama) on an ordinary laptop without a big GPU or a cloud API — and who believes reliability comes from the harness, not just the model.",
  "who_its_not_for": "A team that needs frontier-model reasoning, a managed cloud agent, or production maturity today — Mingbird is brand-new (~1 star) and bets on small-model task completion that depends heavily on the chosen local model.",
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    "note": "Apache-2.0 per repo (license read, not guessed); ~1 star; created 2026-09-17; 30 commits; v1.8.2; 461-test suite; CI on Linux/macOS. Local-first: runs against a local Ollama backend with a one-click zero-outbound mode, config in ~/.ollama_agent/config.json, no network required. Maker's thesis is 'harness defects, not model defects,' backed by a 288-cell public benchmark (github.com/Mingbird/Mingbird-agent, verified 2026-09-30)."
  },
  "caveats": "Verified from the primary repo (2026-09-30); no independent testing here. Brand-new (created 2026-09-17, ~1 star) — adoption and durability are unproven. The 'harness defects, not model defects' framing and the 288-cell benchmark are the maker's own (vendor-claim), not independently reproduced. Small-model task completion varies sharply by the local model you point it at; evaluate on your own tasks before relying on it.",
  "lead": "Mingbird is a local-first agent harness that gets small 2–9B models to finish real multi-step tasks on an ordinary laptop, running offline against a local Ollama backend. Open source: yes (Apache-2.0); self-hostable with a one-click zero-outbound mode and all config in `~/.ollama_agent/config.json`; free OSS. It is brand-new (~1 star, created…",
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      "est_tokens": 109,
      "text": "Mingbird is a local-first agent harness that gets small 2–9B models to finish real multi-step tasks on an ordinary laptop, running offline against a local Ollama backend. Open source: yes (Apache-2.0); self-hostable with a one-click zero-outbound mode and all config in `~/.ollama_agent/config.json`; free OSS. It is brand-new (~1 star, created 2026-09-17) but active — 30 commits, v1.8.2, a 461-test suite, and CI on Linux and macOS."
    },
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      "heading_path": [
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      "text": "on an ordinary laptop, running offline against a local Ollama backend. Open source: yes (Apache-2.0); self-hostable with a one-click zero-outbound mode and all config in `~/.ollama_agent/config.json`; free OSS. It is brand-new (~1 star, created 2026-09-17) but active — 30 commits, v1.8.2, a 461-test suite, and CI on Linux and macOS.\n\nMingbird is the scaffolding around a small local model, not another model. Its bet is that most agent failures are harness defects — bad tool wiring, lost state, no retries — rather than the model being too small, so it focuses on the loop that turns a 2–9B model running under Ollama into something that actually completes a multi-step task on a normal laptop. You run it fully offline: it talks to a local Ollama backend, offers a one-click zero-outbound mode so nothing leaves the machine, and keeps its configuration in `~/.ollama_agent/config.json`. The project ships a 461-test suite and a 288-cell public benchmark of harness-vs-model behavior. Open source: yes (Apache-2.0); self-hostable; free OSS."
    },
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      "heading_path": [
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      "text": "laptop. You run it fully offline: it talks to a local Ollama backend, offers a one-click zero-outbound mode so nothing leaves the machine, and keeps its configuration in `~/.ollama_agent/config.json`. The project ships a 461-test suite and a 288-cell public benchmark of harness-vs-model behavior. Open source: yes (Apache-2.0); self-hostable; free OSS.\n\n- Apache-2.0 per repo (license read from GitHub metadata + README, not guessed); ~1 star; created 2026-09-17, pushed 2026-09-23; 30 commits; v1.8.2; 461-test suite; CI on Linux/macOS (github.com/Mingbird/Mingbird-agent, verified 2026-09-30).\n- Local-first/offline-first: local Ollama backend, one-click zero-outbound mode, config in `~/.ollama_agent/config.json`, no network required.\n- Vendor framing: \"harness defects, not model defects,\" backed by the maker's own 288-cell benchmark — a vendor-claim, not independently reproduced here.\n- Surfaced in the GTM Stacker studio daily pull (2026-09-30 pass); license/facts independently verified 2026-09-30."
    },
    {
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        "Why it matters for a GTM stack"
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      "text": "Local-first/offline-first: local Ollama backend, one-click zero-outbound mode, config in `~/.ollama_agent/config.json`, no network required. - Vendor framing: \"harness defects, not model defects,\" backed by the maker's own 288-cell benchmark — a vendor-claim, not independently reproduced here. - Surfaced in the GTM Stacker studio daily pull (2026-09-30 pass); license/facts independently verified 2026-09-30.\n\nMost GTM agent work does not need a frontier model: classify a reply, draft a follow-up, extract fields, decide the next step. Mingbird is a bet that a small model on your own laptop can do that reliably if the harness around it is good — which, if it holds, means an offline, zero-cost, zero-data-egress agent for the routine steps of an outbound or ops workflow. The honest read: this is a brand-new project (~1 star) and the \"it's the harness, not the model\" thesis is the maker's own, benchmarked only by the maker. It belongs on the watch list as part of the small-local-model lineage (alongside jeff and jeeves for decisions, openshell for isolation) — try it on a low-stakes task first and judge completion quality on your own data before it runs anything that touches a real system."
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