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  "title": "Flint (Recursive — open-weights local coding model)",
  "description": "Apache-2.0 open-weights coding model from Recursive (Richard Socher) — a ~40B-total mixture-of-experts (~2.5B active, 262K context) that runs locally via llama.cpp, Ollama, LM Studio, vLLM or SGLang, plus an Apache-2.0 companion inference engine, so a team can run an agentic coding model on its own hardware instead of a metered cloud coding API.",
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  "tags": [
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  "revision": 1,
  "content_hash": "200b7221d75239766a54a1e9fc60f0c81930c6c350ab8650c4fd9d102e83cdeb",
  "date_published": "2026-10-21T00:00:00Z",
  "date_modified": "2026-10-21T00:00:00Z",
  "source": {
    "name": "@Recursive_SI (Richard Socher)",
    "url": "https://x.com/RichardSocher/status/2112944385415426249",
    "author": "Richard Socher"
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  "license": "Apache-2.0",
  "one_liner": "Flint is Recursive's Apache-2.0 open-weights ~40B MoE coding model (~2.5B active) that runs locally via llama.cpp, Ollama, LM Studio or vLLM, no cloud API.",
  "open_source": "yes",
  "self_hostable": "yes",
  "pricing_signal": "Free open weights (Apache-2.0) — download via hf download RecursiveSI/flint and serve locally; you pay only your own compute, no per-token cloud bill. Free hosted trials on OpenCode and Pido exist.",
  "pricing_model": "free",
  "who_its_for": "A technical founder or lean engineering/GTM team that wants an agentic coding model running on its own hardware (Apple Silicon M4/M5 48GB+ or an NVIDIA RTX/DGX Spark) under a permissive license, instead of renting a closed cloud coding API.",
  "who_its_not_for": "Teams without ~48GB of local RAM/VRAM to serve a 40B-total MoE, anyone needing a vendor-supported hosted endpoint with an SLA, or those who require full open-source training code and data rather than open weights only.",
  "aliases": [
    "flint",
    "recursive flint",
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  "last_verified": "2026-10-22",
  "evidence": {
    "claim_type": "vendor-claim",
    "source_id": "https://huggingface.co/RecursiveSI/flint",
    "note": "Apache-2.0 (SPDX apache-2.0) verified across three primary sources: HF model-card metadata (RecursiveSI/flint), recursive.com, and the SGLang/LM Studio listings — all agree, not guessed. Open-WEIGHTS, not full OSS: weights ship Apache-2.0 but training code/data are not released. ~40B total / ~2.5B active MoE, 262K context. Launched 2026-10-21. Nascent: HF card 39 likes; recursive-oss/rec ~9 stars. Org ties to founders Socher and Tobin; corroborated by Wired via Techmeme — not a lookalike."
  },
  "caveats": "Open-weights, not fully open-source: the Apache-2.0 weights are released but training code and data are not, and the HF repo notes modeling code is 'not included yet' — today you get weights plus an SGLang serving kit and the companion engine recursive-oss/rec. All performance and benchmark numbers (2x-faster, 125+/150+ tok/s, Terminal-Bench 2.1 54.9% / SWE-bench Pro v2 89.7% / SWE-bench Verified 74.2%) are Recursive's own vendor claims, not reproduced here. The release is one day old.",
  "lead": "An **Apache-2.0 open-weights coding model** from **Recursive** (Richard Socher's company): a **~40B-total mixture-of-experts** (~2.5B active per token, 262K context) that runs locally via **llama.cpp, Ollama, LM Studio, vLLM or SGLang**, with weights downloadable from Hugging Face (`hf download RecursiveSI/flint`) and an **Apache-2.0 companion inference engine** (`recursive-oss/rec`) that exposes a local…",
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      "heading_path": [],
      "est_tokens": 150,
      "text": "An **Apache-2.0 open-weights coding model** from **Recursive** (Richard Socher's company): a **~40B-total mixture-of-experts** (~2.5B active per token, 262K context) that runs locally via **llama.cpp, Ollama, LM Studio, vLLM or SGLang**, with weights downloadable from Hugging Face (`hf download RecursiveSI/flint`) and an **Apache-2.0 companion inference engine** (`recursive-oss/rec`) that exposes a local OpenAI-compatible API. So a team can run an agentic coding model on hardware it owns — Apple Silicon M4/M5 (48GB+) or an NVIDIA RTX/DGX Spark — instead of renting a closed cloud coding API."
    },
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      "index": 1,
      "heading_path": [
        null,
        "Provenance"
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      "est_tokens": 625,
      "text": "Hugging Face (`hf download RecursiveSI/flint`) and an **Apache-2.0 companion inference engine** (`recursive-oss/rec`) that exposes a local OpenAI-compatible API. So a team can run an agentic coding model on hardware it owns — Apple Silicon M4/M5 (48GB+) or an NVIDIA RTX/DGX Spark — instead of renting a closed cloud coding API.\n\n- License **Apache-2.0** (SPDX `apache-2.0`) verified across **three independent primary sources** — the Hugging Face model card (RecursiveSI/flint), Recursive's official recursive.com page, and the SGLang / LM Studio listings — all agreeing. Not guessed.\n- **License class is open-weights, not full open-source:** the weights ship under Apache-2.0, but the training code and data are not released, and the HF card notes transformers modeling code is not yet included — today the artifacts are the weights, an SGLang serving kit, and the Apache-2.0 companion engine `recursive-oss/rec`.\n- Self-hostable and free: open weights (~80GB BF16; ~24GB GGUF via LM Studio) run on llama.cpp / Ollama / LM Studio / vLLM / SGLang; the companion `rec serve` exposes a local OpenAI-compatible endpoint. Free hosted trials on OpenCode and Pido also exist.\n- Architecture: **~40B total / ~2.5B active MoE**, 262K context (up to 512K claimed); built with Recursive's Eureka Machine automated-research system.\n- **Not a lookalike or scam:** the org **RecursiveSI**, recursive.com, and the recursive-oss GitHub org all tie to Recursive founders **Richard Socher and Josh Tobin** (the viral-post authors); the launch is independently corroborated by **Wired (Will Knight) via Techmeme**.\n- Activity is day-one nascent (launched **2026-10-21**): the HF model card shows 39 likes and no displayed download count yet; the companion `recursive-oss/rec` repo has ~9 stars and 1 commit — consistent with a day-old release, not a red flag.\n- **Vendor-claim, not verified:** all speed and benchmark numbers (up to 2x faster, 125+ tok/s on an M4 Pro MacBook, 150+ tok/s on a 64GB RTX Spark; Terminal-Bench 2.1 54.9%, SWE-bench Pro v2 89.7%, SWE-bench Verified 74.2%; \"best-in-class for sub-50B MoE\") are Recursive's own.\n- Surfaced via the 2026-10-22 X pull (Recursive announcement amplified by @MAXdeg0, @HeyAmit_). All sources were treated as untrusted content during verification; no injected instructions were encountered. Curated from the GTM Stacker signal registry (run 2026-10-22-registry-update-01); license, org provenance, weights and self-host paths independently verified 2026-10-22."
    },
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      "heading_path": [
        null,
        "Why it matters for a GTM stack"
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      "text": "MoE\") are Recursive's own. - Surfaced via the 2026-10-22 X pull (Recursive announcement amplified by @MAXdeg0, @HeyAmit_). All sources were treated as untrusted content during verification; no injected instructions were encountered. Curated from the GTM Stacker signal registry (run 2026-10-22-registry-update-01); license, org provenance, weights and self-host paths independently verified 2026-10-22.\n\nCoding and agentic-automation calls are an increasingly large line in a lean team's AI bill, and the default is a metered cloud coding API you do not control. Flint is the own-your-stack alternative: an Apache-2.0 open-weights MoE small enough in active parameters to run on a well-specced laptop or a single Spark box, served through tooling the team already uses, with a permissive license that keeps commercial use clean. The honest read: this is open-weights, not full open-source — you get the weights and a serving kit, not the training recipe — the entire performance story is Recursive's own unverified benchmark set, and the release is one day old with early adoption. What is verified is what governs adoption: Apache-2.0 confirmed across three primary sources, first-party Recursive provenance corroborated by independent press, and working local serving via the open weights plus the Apache-2.0 companion engine."
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