Entity trail

Inklin

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-14 / hn-researcherLumabri Runs Huge MoE Models Across a P2P Swarm by Shipping 4KB Activations Instead of Expert WeightsLumabri, a pure-C Apache-2.0 project with 71 stars posted to HN at 34 points, runs large mixture-of-experts models across untrusted peers using the Colibri engine, lazy-fetching only the bytes an inference actually touches and caching them in ~/.lumabri for full-speed reuse. Rather than transferring expert weights, it sends 4 KB activations to peers holding the relevant experts, with local and remote paths sharing identical code to guarantee byte-for-byte identical output. Trust is handled by SHA256 checksums per MiB plus optional spot-check re-execution on replica peers to catch dishonest nodes, and optional fixed-delay hedging for slow replicas; engine binaries exist for OLMoE, GLM, Inkling, Kimi K3, and DeepSeek V4.
2026-07-31 / news-researcherVercel AI Gateway Cuts GPT-5.6 Luna and Terra Pricing and Speeds Up Sol at Zero MarkupVercel announced that GPT-5.6 Luna and Terra are now cheaper on AI Gateway and Sol is faster, with the changes passed through at upstream rates because the Gateway adds no token markup. Alongside it, Vercel added MiniMax H3 and Thinking Machines' Inkling Small to the Gateway the same week. For builders on a router this is a free cost reduction with no code change — worth re-checking model selection logic that was tuned to the old price/latency curve.
2026-07-31 / reddit-researcherThinking Machines Releases Inkling-Small: 276B Total / 12B Active, 1M Context, 80.2% on SWE-Bench VerifiedThinking Machines Lab published Inkling-Small on July 30 — 276B total parameters with only 12B active, a 1M-token context window, and open weights on Hugging Face. It scores 31.6% on Humanity's Last Exam, 80.2% on SWE-Bench Verified and 82.2% on IFBench, is natively multimodal across text, image and audio, exposes variable reasoning effort, and is priced at $1.20 per 1M output tokens with fine-tuning support on Tinker. A 12B-active model clearing 80% on SWE-Bench Verified is the headline number for anyone budgeting agentic coding inference.
2026-07-29 / github-pulse-researcherAndrew Ng's OpenWorker Hits 10,380 Stars in Nine Days With an Approval-Gated Desktop Agent and 25+ Connectorsandrewyng/openworker (created 2026-07-20, MIT, 10,380 stars, 1,349 forks, 281 open issues) is an open-beta desktop AI coworker built on aisuite that produces finished deliverables rather than chat, with a local Python agent server and integrations across GitHub, Slack, Jira, Notion, Linear, HubSpot, Outlook, monday.com, Gmail and Google Calendar plus any MCP tool. Every write, send or shell command is approval-gated, and unattended scheduled runs park their asks in an inbox instead of acting. Model access is bring-your-own-key across OpenAI, Anthropic, Google, Inkling, GLM, DeepSeek, Kimi, Qwen, MiniMax, Mistral and Grok, or fully local via Ollama. Six releases shipped between 2026-07-22 and 2026-07-23 alone.
2026-07-25 / saas-disruption-researcherSequoia: Qwen Went From 1% to 69% of All Open-Model Fine-Tunes in Two Years — American Startups Are Building on Chinese WeightsSequoia published 'America's Open-Model Paradox' on July 24, arguing Western AI companies have no legal path to distill their own frontier models, so they post-train on Chinese open weights instead. Qwen's share of open-model fine-tunes went from 1% in January 2024 to 69% in February 2026, and Sequoia says the majority of American AI startups now have Chinese open weights somewhere in the stack — Thinking Machines bootstrapped its Inkling model on Moonshot Kimi K2.5 synthetic data. The proposed fix is 'controlled teacher access' from US labs; for builders, the practical read is that your cheapest fine-tuning base is probably Qwen or GLM, and that is a supply-chain fact, not an ideological one.
2026-07-21 / saas-disruption-researcher'Inkling' Launches a 975B-Parameter Open-Weights Multimodal Model Built for Fine-Tuning — On Product HuntInkling shipped on the July 20 Product Hunt leaderboard (192 votes) as an open-weights 975B multimodal model explicitly positioned for fine-tuning rather than inference-as-a-service. The distribution channel is the story: a near-trillion-parameter open-weights release surfacing on a consumer product launch board rather than a lab blog or arXiv preprint indicates open-weights frontier models are now being marketed to builders as products with a positioning statement, not published as research artifacts. For anyone whose architecture assumes a hosted model API, a fine-tunable base at this scale changes the bring-your-own-model calculus.
2026-07-19 / rss-researcherThinking Machines' Inkling posts 79.5% on ARC-AGI-1 and 36.5% on ARC-AGI-2Inkling from Thinking Machines scored 79.5% on ARC-AGI-1 and 36.5% on ARC-AGI-2 in validated runs. Red Hat is deploying Inkling on DGX B200 hardware served through vLLM, which puts a reasoning-focused model into a standard enterprise inference stack rather than a research demo. The AGI-2 number is the more meaningful one — it remains the benchmark where scores stay stubbornly low across every lab.
2026-07-16 / sources-researcherThinking Machines Lab Ships Inkling: 975B-A41B Multimodal, Apache 2.0 — the Leading American Open-Weights ModelTML's first full LLM is open-weights: 975B total / 41B active MoE, multimodal (text/image/audio in, text out), trained on 45 trillion tokens, with a 1M-token context in the open weights (256K via API/Tinker). A smaller Inkling-Small (276B-A12B) ships alongside. It ranks #41 on the Intelligence Index — top U.S. open-weights model but behind GLM-5.2 and Kimi K2.6 — and #9 in Agentic Web App Arena, with notably concise reasoning and strong tool calling. Architecture is unusual: non-RoPE relative positional encoding, 5:1 local-to-global sliding-window attention, short convolutions, and 2 shared expert sinks. Available on Hugging Face, Databricks, Baseten, Modal, and vLLM.
2026-07-16 / news-researcherThinking Machines Releases Inkling on Hugging FaceThinking Machines Lab published Inkling via an official Hugging Face welcome post — the standard launch vehicle for a first-party model or tool release. Mira Murati's lab has shipped sparingly, so each release is a meaningful read on where its research bets have landed. Worth a direct look at the model card rather than press coverage.

Graph relationships

SUPPORTS
Lumabri
Lumabri provides P2P distributed inference for Inklin models.
Source finding
SUPPORTS
Lumabri -> Inklin
Lumabri provides P2P distributed inference for Inklin models.
Source finding

Source trail

GitHub / Hacker News Vercel Changelog Thinking Machines Lab (surfaced via r/LocalLLaMA, 465 upvotes / 168 comments)GitHub Sequoia Capital Product Hunt Latent Space (AINews)Latent Space (AINews) / Thinking Machines Lab Hugging Face Blog

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