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<feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><title>Shipped</title><id>https://shipped.anth.us/</id><updated>2026-10-21T00:00:00Z</updated><link rel="alternate" type="text/html" href="https://shipped.anth.us/"/><link rel="self" type="application/atom+xml" href="https://shipped.anth.us/atom.xml"/><author><name>Shipped</name></author><subtitle>Announcements and real-world deployments of AI, checked against their sources and scored: open-weights releases, decision models in the wild, and what is actually in use.</subtitle><entry><title>REAX 1.1: a 27B decision model fine-tuned from pplx-decider</title><id>https://shipped.anth.us/decisions/decisionmodels-reax-1-1/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/decisionmodels-reax-1-1/"/><updated>2026-10-21T00:00:00Z</updated><published>2026-10-21T00:00:00Z</published><summary>Apache-2.0 weights built on REAX 1, itself a fine-tune of pplx-decider-v1.2. The card reports 71.27 on its own SN92 scoring.</summary><category term="decision-models"/><category term="classifier"/><category term="reax"/></entry><entry><title>Decision4Rec releases Kev models that rerank recommender candidates</title><id>https://shipped.anth.us/decisions/polignano-decision4rec-kev/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/polignano-decision4rec-kev/"/><updated>2026-10-19T00:00:00Z</updated><published>2026-10-19T00:00:00Z</published><summary>Apache-2.0 adapters from 0.8B to 27B that read a pool of 30 candidates and output a probability for each. Development results only.</summary><category term="decision-models"/><category term="ranking"/><category term="classifier"/><category term="marco-polignano"/></entry><entry><title>AccuKnox Inc. releases Guard 1.1, an Apache-2.0 guardrail for agent inputs and prompt injection</title><id>https://shipped.anth.us/decisions/accuknox-guard-1-1/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/accuknox-guard-1-1/"/><updated>2026-10-16T00:00:00Z</updated><published>2026-10-16T00:00:00Z</published><summary>A 4.5B guard fine-tuned from a Qwen3.5-4B base that returns a block probability. The card reports 0.802 on a 27,416-item safety set.</summary><category term="decision-models"/><category term="guard-model"/><category term="classifier"/><category term="accuknox-inc"/></entry><entry><title>Maincode releases Matilda Decision v1, a 27B multimodal decision model</title><id>https://shipped.anth.us/decisions/maincode-matilda-decision-v1/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/maincode-matilda-decision-v1/"/><updated>2026-10-15T00:00:00Z</updated><published>2026-10-15T00:00:00Z</published><summary>Apache-2.0 weights on Qwen3.8-27B. The maker's own run scores 65.38 on the public Jev Decision Index 0.3 suite.</summary><category term="decision-models"/><category term="classifier"/><category term="maincode"/></entry><entry><title>decisionmodels releases s1-llm-auto-router, a 183 MB CPU router under MIT</title><id>https://shipped.anth.us/decisions/decisionmodels-s1-llm-auto-router/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/decisionmodels-s1-llm-auto-router/"/><updated>2026-10-15T00:00:00Z</updated><published>2026-10-15T00:00:00Z</published><summary>A pruned mmBERT-small that answers seven routing questions in one CPU pass. The card reports 0.898 category accuracy on its held-out set.</summary><category term="decision-models"/><category term="router"/><category term="classifier"/><category term="decisionmodels"/></entry><entry><title>Liquid AI releases LFM2.5-24B-A2B, a 24B on-device reasoning MoE with 2B active</title><id>https://shipped.anth.us/open-weights/liquidai-lfm2-5-24b-a2b/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/open-weights/liquidai-lfm2-5-24b-a2b/"/><updated>2026-10-12T00:00:00Z</updated><published>2026-10-12T00:00:00Z</published><summary>LFM2.5-24B-A2B is a 24B-parameter reasoning model with about 2.45B active, built for agents on-device, under the LFM1.0 licence.</summary><category term="open-llms"/><category term="mixture-of-experts"/><category term="small-model"/><category term="multilingual"/><category term="liquid-ai"/></entry><entry><title>StartLux releases Decision v1.1, from 0.8B to 27B, with non-commercial weights</title><id>https://shipped.anth.us/decisions/startlux-decision-v1-1/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/startlux-decision-v1-1/"/><updated>2026-10-11T00:00:00Z</updated><published>2026-10-11T00:00:00Z</published><summary>StartLux-Decision-v1.1 ships Instant and AutoThink variants. Weights are CC BY-NC 4.0; commercial use needs a separate licence.</summary><category term="decision-models"/><category term="classifier"/><category term="startlux"/></entry><entry><title>AI Singapore releases Gemma-SEA-LION-Guard v2, MIT-licensed safety models for Southeast Asian languages</title><id>https://shipped.anth.us/decisions/aisingapore-sea-lion-guard-v2/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/aisingapore-sea-lion-guard-v2/"/><updated>2026-10-11T00:00:00Z</updated><published>2026-10-11T00:00:00Z</published><summary>A 31B decision model (plus E4B and E2B) for content moderation and action governance across eight languages. Scores AUPRC 93.5 on general safety.</summary><category term="decision-models"/><category term="guard-model"/><category term="public-sector"/><category term="ai-singapore"/></entry><entry><title>Microsoft releases Decision-1, a 9B model that scores fixed answer options</title><id>https://shipped.anth.us/decisions/microsoft-decision-1/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/microsoft-decision-1/"/><updated>2026-10-09T00:00:00Z</updated><published>2026-10-09T00:00:00Z</published><summary>Microsoft-Decision-1 returns a probability per answer option instead of prose. Hosted on Foundry and OpenRouter; no licence stated.</summary><category term="decision-models"/><category term="classifier"/><category term="router"/><category term="microsoft"/></entry><entry><title>Amazon publishes Strands Decider 2B, a small decision model for agent workflows</title><id>https://shipped.anth.us/decisions/amazon-strands-decider-2b/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/amazon-strands-decider-2b/"/><updated>2026-10-09T00:00:00Z</updated><published>2026-10-09T00:00:00Z</published><summary>A 2B LoRA on Qwen3.5-2B-Base, Apache-2.0, with a /v1/systemone server. The card reports 0.779 on 231 public JevBench tasks.</summary><category term="decision-models"/><category term="classifier"/><category term="router"/><category term="amazon"/></entry><entry><title>Reflection releases Beam, a 501B open-weights MoE under Apache 2.0</title><id>https://shipped.anth.us/open-weights/reflection-beam-1-0-501b-a23b/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/open-weights/reflection-beam-1-0-501b-a23b/"/><updated>2026-10-07T00:00:00Z</updated><published>2026-10-07T00:00:00Z</published><summary>Beam is Reflection's first open model: 501B total and 23B active parameters, 1M-token context, under Apache 2.0.</summary><category term="open-llms"/><category term="mixture-of-experts"/><category term="reasoning-model"/><category term="code-model"/><category term="reflection"/></entry><entry><title>OpenAI opens its Decisions API in public beta, built on GPT-6 Luna</title><id>https://shipped.anth.us/decisions/openai-decisions-api/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/openai-decisions-api/"/><updated>2026-10-06T00:00:00Z</updated><published>2026-10-06T00:00:00Z</published><summary>A /v1/decisions endpoint returns typed predicate, choice and score answers. Public beta, $0.10 per million input tokens, GA promised in weeks.</summary><category term="decision-models"/><category term="router"/><category term="classifier"/><category term="openai"/></entry><entry><title>Torchcast AI releases Torchcast Decision 27B, built on StartLux weights</title><id>https://shipped.anth.us/decisions/torchcast-decision-27b/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/torchcast-decision-27b/"/><updated>2026-10-03T00:00:00Z</updated><published>2026-10-03T00:00:00Z</published><summary>A LoRA fine-tune of StartLux-Decision-27B that speaks the /v1/systemone API. Weights are CC BY-NC 4.0; card reports 65.00 on Index 0.2.1.</summary><category term="decision-models"/><category term="classifier"/><category term="torchcast-ai"/></entry><entry><title>Aleph Alpha releases Kolibri 1, a 78B German and English reasoning MoE under Apache 2.0</title><id>https://shipped.anth.us/open-weights/aleph-alpha-kolibri-1/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/open-weights/aleph-alpha-kolibri-1/"/><updated>2026-10-03T00:00:00Z</updated><published>2026-10-03T00:00:00Z</published><summary>Kolibri 1 is a 78B mixture-of-experts reasoning model with 3.46B active parameters, tuned for German and English, under Apache 2.0.</summary><category term="open-llms"/><category term="mixture-of-experts"/><category term="reasoning-model"/><category term="multilingual"/><category term="aleph-alpha"/></entry><entry><title>SberAI releases FRIDA-Decisions, a Russian and English encoder for structured decisions</title><id>https://shipped.anth.us/decisions/sberai-frida-decisions/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/sberai-frida-decisions/"/><updated>2026-10-02T00:00:00Z</updated><published>2026-10-02T00:00:00Z</published><summary>A 0.8B MIT-licensed encoder that picks, rates, answers yes/no or ranks in one pass. The card reports 0.893 on 735 razvilka items.</summary><category term="decision-models"/><category term="classifier"/><category term="sberai"/></entry><entry><title>H2O.ai releases H2O Lightning 4B, an Apache-2.0 decision model with GGUF builds</title><id>https://shipped.anth.us/decisions/h2oai-h2o-lightning-4b/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/h2oai-h2o-lightning-4b/"/><updated>2026-10-02T00:00:00Z</updated><published>2026-10-02T00:00:00Z</published><summary>Built on Qwen3.5-4B, with GGUF files from 1.43 GB for llama.cpp. The card reports 88.74% on 231 public JevBench text questions.</summary><category term="decision-models"/><category term="classifier"/><category term="h2o-ai"/></entry><entry><title>Perplexity releases pplx-decider, an Apache-2.0 27B decision model</title><id>https://shipped.anth.us/decisions/perplexity-pplx-decider/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/perplexity-pplx-decider/"/><updated>2026-10-01T00:00:00Z</updated><published>2026-10-01T00:00:00Z</published><summary>pplx-decider-v1 and v1.1 are open-weight 27B models that return probabilities for fixed answers. Built on Qwen3.8-27B.</summary><category term="decision-models"/><category term="classifier"/><category term="router"/><category term="perplexity"/></entry><entry><title>Fish Audio releases Drama 3, a 35B mixture-of-experts TTS for multi-character scenes</title><id>https://shipped.anth.us/open-weights/fish-audio-drama-3/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/open-weights/fish-audio-drama-3/"/><updated>2026-10-01T00:00:00Z</updated><published>2026-10-01T00:00:00Z</published><summary>Drama 3 is a 35B MoE speech model (3B active) with up to 16 speaker tags and voice cloning; research licence.</summary><category term="open-tts"/><category term="multi-speaker"/><category term="voice-cloning"/><category term="multilingual-tts"/><category term="fish-audio"/></entry><entry><title>vLLM Semantic Router team releases Decision 2.0 open decision models</title><id>https://shipped.anth.us/decisions/vllm-decision-2-0/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/vllm-decision-2-0/"/><updated>2026-09-29T00:00:00Z</updated><published>2026-09-29T00:00:00Z</published><summary>Decision 2.0 is an Apache-2.0 family from vllm-sr. The 27B Vega card reports 74.0 on JevArena. Lux is a 9B sibling.</summary><category term="decision-models"/><category term="router"/><category term="classifier"/><category term="vllm-semantic-router"/></entry><entry><title>An independent developer releases Jev-Style 2B Decision v3, a 2B model with a free hosted API</title><id>https://shipped.anth.us/decisions/chaoliangunsw-jev-style-2b-decision-v3/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/chaoliangunsw-jev-style-2b-decision-v3/"/><updated>2026-09-27T00:00:00Z</updated><published>2026-09-27T00:00:00Z</published><summary>Apache-2.0 fine-tune of Qwen3.5-2B. The card reports 73.6% on public JevBench items against Jev 1.13 at 86.6%.</summary><category term="decision-models"/><category term="classifier"/><category term="chaoliang-yan"/></entry><entry><title>Shanghai AI Laboratory releases Intern-Decision, multimodal decision models from 0.8B to 4B</title><id>https://shipped.anth.us/decisions/internlm-intern-decision/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/internlm-intern-decision/"/><updated>2026-09-26T00:00:00Z</updated><published>2026-09-26T00:00:00Z</published><summary>Intern-Decision-4B is fine-tuned from Qwen3.5-4B and answers up to 16 questions per pass, with image input. Apache-2.0.</summary><category term="decision-models"/><category term="classifier"/><category term="shanghai-ai-laboratory"/></entry><entry><title>Xiaomi MiMo releases MiMo-V2.6-Pro-RL, a 1.02T omnimodal MoE under MIT</title><id>https://shipped.anth.us/open-weights/xiaomimimo-mimo-v2-6-pro-rl/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/open-weights/xiaomimimo-mimo-v2-6-pro-rl/"/><updated>2026-09-21T00:00:00Z</updated><published>2026-09-21T00:00:00Z</published><summary>MiMo-V2.6-Pro-RL is the flagship of the MiMo-V2.6 series: 1.02T total, 42B active, 1M context, text, image, video and audio in.</summary><category term="open-llms"/><category term="mixture-of-experts"/><category term="multimodal"/><category term="reasoning-model"/><category term="xiaomi-mimo"/></entry><entry><title>distil labs releases an invoice-decision model that approves or holds vendor invoices</title><id>https://shipped.anth.us/decisions/distil-labs-qwen3-5-4b-invoice-decision/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/distil-labs-qwen3-5-4b-invoice-decision/"/><updated>2026-09-21T00:00:00Z</updated><published>2026-09-21T00:00:00Z</published><summary>A 4B Qwen3.5 fine-tune returning one of five decisions. The card reports 98 of 100 correct on synthetic test invoices.</summary><category term="decision-models"/><category term="classifier"/><category term="distil-labs"/></entry><entry><title>JetBrains releases Mellum2.1 Thinking, a 12B coding MoE with 2.5B active, under Apache 2.0</title><id>https://shipped.anth.us/open-weights/jetbrains-mellum2-1-12b-a2-5b-thinking/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/open-weights/jetbrains-mellum2-1-12b-a2-5b-thinking/"/><updated>2026-09-20T00:00:00Z</updated><published>2026-09-20T00:00:00Z</published><summary>Mellum2.1 Thinking is a 12B mixture-of-experts reasoning model with 2.5B active, retrained mostly with reinforcement learning on real repositories.</summary><category term="open-llms"/><category term="mixture-of-experts"/><category term="code-model"/><category term="reasoning-model"/><category term="jetbrains"/></entry><entry><title>Jared Palmer releases Kev-9B, an Apache-2.0 LoRA decision model on Qwen3.5-9B</title><id>https://shipped.anth.us/decisions/jaredpalmer-kev-9b/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/jaredpalmer-kev-9b/"/><updated>2026-09-20T00:00:00Z</updated><published>2026-09-20T00:00:00Z</published><summary>An independent adapter that implements TypeSafe's System One API. The card reports 0.852 on a locked out-of-domain test.</summary><category term="decision-models"/><category term="classifier"/><category term="jared-palmer"/></entry><entry><title>Convai Innovations releases laya-typed-decisions, a 0.4B ModernBERT decision model</title><id>https://shipped.anth.us/decisions/convai-laya-typed-decisions/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/convai-laya-typed-decisions/"/><updated>2026-09-18T00:00:00Z</updated><published>2026-09-18T00:00:00Z</published><summary>An Apache-2.0 encoder fine-tuned on four synthetic workflows. The card reports 0.766 accuracy and warns its probabilities are over-confident.</summary><category term="decision-models"/><category term="classifier"/><category term="convai-innovations"/></entry><entry><title>TypeSafe AI launches Jev, a hosted model for typed software decisions</title><id>https://shipped.anth.us/decisions/typesafe-jev/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/decisions/typesafe-jev/"/><updated>2026-09-15T00:00:00Z</updated><published>2026-09-15T00:00:00Z</published><summary>Jev takes state and typed questions and returns probabilities. Early access via API, $0.042 per million input tokens; weights not stated.</summary><category term="decision-models"/><category term="classifier"/><category term="router"/><category term="typesafe-ai"/></entry><entry><title>Yandex releases AliceAI-Foundation-80B-A3B-Base, an Apache 2.0 base MoE for Russian and English</title><id>https://shipped.anth.us/open-weights/yandex-aliceai-foundation-80b-a3b-base/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/open-weights/yandex-aliceai-foundation-80b-a3b-base/"/><updated>2026-09-12T00:00:00Z</updated><published>2026-09-12T00:00:00Z</published><summary>AliceAI-Foundation-80B-A3B-Base is an 80B base model with 3B active parameters and 262,144-token context, trained from scratch.</summary><category term="open-llms"/><category term="mixture-of-experts"/><category term="base-model"/><category term="multilingual"/><category term="yandex"/></entry><entry><title>DeepSeek releases V4.1-Flash, a multimodal MoE with a much smaller KV cache, under MIT</title><id>https://shipped.anth.us/open-weights/deepseek-ai-deepseek-v4-1-flash/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/open-weights/deepseek-ai-deepseek-v4-1-flash/"/><updated>2026-09-10T00:00:00Z</updated><published>2026-09-10T00:00:00Z</published><summary>DeepSeek-V4.1-Flash is a 552B-backbone multimodal MoE with 1M-token context and a 890-byte-per-token KV cache, under MIT.</summary><category term="open-llms"/><category term="mixture-of-experts"/><category term="multimodal"/><category term="reasoning-model"/><category term="deepseek-ai"/></entry><entry><title>OpenBMB releases MiniCPM5-2B, a dense 2B model with open training data, under Apache 2.0</title><id>https://shipped.anth.us/open-weights/openbmb-minicpm5-2b/</id><link rel="alternate" type="text/html" href="https://shipped.anth.us/open-weights/openbmb-minicpm5-2b/"/><updated>2026-09-06T00:00:00Z</updated><published>2026-09-06T00:00:00Z</published><summary>MiniCPM5-2B is a dense 2B on-device model for coding, tool use and long context, released with its UltraData training sets.</summary><category term="open-llms"/><category term="small-model"/><category term="dense-model"/><category term="openbmb"/></entry></feed>
