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Monday13 items

  1. English text is being prepared.Model releases
    AI score52

    China's AI 3D model Hi3D V1.0 goes open source, releasing weights and inference code

    China's AI 3D creation platform Hi3D has announced the open-sourcing of the V1.0 model weights and inference code, with the resources now available on GitHub and Hugging Face. The model is based on the Sparc3D architecture, and its training data has been expanded from about 500,000 to about 2 million curated 3D assets. It supports an effective sparse grid resolution of up to 1536³ and takes a reference image and a coarse mesh as input to progressively refine the geometric structure, but it outputs only the geometric model and does not generate textures, materials, or vertex colors. V1.0 uses a custom license agreement: upon exceeding 200,000 monthly active users or reaching US$1 million in monthly revenue, the licensee must notify the licensor within 60 days and apply for a separate written authorization.

    #Model releases#Open-source ecosystem#Hugging Face

  2. English text is being prepared.Model releases
    AI score71

    Moonshot AI Releases Kimi K3.1 Pro, Strengthening 3D Modeling and Spatial Reasoning

    Moonshot AI has released Kimi K3.1 Pro, strengthening 3D creation and spatial reasoning capabilities, and launched the 3D Make feature, which supports online preview and editing of 3D models, downloading files for 3D printing, or exporting as videos and Live Photos. The model is the first product in the K3.1 series, supports a 1 million Token context, and can write and run scripts for software such as Blender, SolidWorks, and Rhino to complete modeling. Official BenchCAD results show that, in a test subset consisting of 1,000 files, the voxel IoU after tool invocation was 92.7%, versus 72.8% without tools; the model is currently integrated into the Kimi web client, Kimi Work, Kimi Code, and the API, the Highspeed version outputs at roughly 2 to 3 times the speed of the standard version, and the full model weights are planned for release before November 4.

    Same story · 34 reports

    #Model releases#Multimodal#Reasoning

  3. CaixinModel releases
    AI score66

    Moonshot AI launches Kimi K3.1 Pro, a post-trained version of K3

    In the early hours of October 24, Moonshot AI (Kimi) officially launched Kimi K3.1 Pro. This is the post-trained version of Kimi K3 and its most capable 3-trillion-parameter model to date, supporting a 1 million context window. Kimi says the model shows particularly notable improvements in coding, 3D creation, financial research and other long-horizon Agent capabilities. This is the first time Kimi has introduced a Pro version in its model versions, and K3.1 Pro is also the first model in the K3.1 series, leaving room for subsequent releases of cost-effective models.

    Same story · 34 reports

    #Industry news#Model releases#Agents

  4. MarkTechPostModel releases
    AI score60

    Nanbeige Releases Nanbeige4.3-33B Open-Source Agentic Coding Model

    Nanbeige LLM Lab released Nanbeige4.3-33B on Hugging Face under the Apache 2.0 license. It is a 33B dense model aimed at agentic coding and office collaboration tasks, supports a 524,288 token context window, and is released alongside Nanbeige4.3-33B-Base, which supports customizable post-training. In the lab's self-built comparison table, it leads in 17 of 20 benchmarks against Qwen3.8-27B, Muse-Glimmer-30B, and Gemma4-31B, scoring 80.9 on Terminal-Bench 2.1, but trails Qwen3.8-27B on SWE-Bench Pro with 60.3 versus 61.7. The article points out that some competitor figures in its table are inconsistent with the official model cards from Meta and Google, and that two SWE-Bench Pro entries appear to be swapped; these results need to await independent reproduction. The model is positioned as the teacher model for the Nanbeige4.3 series, with plans to subsequently distill agentic capabilities into Nanbeige4.3-3B.

    Same story · 3 reports

    #Model releases#Open-source ecosystem#Agents

  5. MarkTechPostModel releases
    AI score66

    Hugging Face Releases 14.8B Open-Source DNA Language Model Carbon-X

    Hugging Face Bio has released Carbon-X, a 14.8B-parameter decoder-only DNA language model that can directly read raw DNA and answer in English, with weights released under Apache 2.0. The model is based on Qwen3-14B and uses a hybrid tokenizer that adds 5,515 DNA tokens, supports a 40,960-token context, and can run in bfloat16 on a single 80GB GPU; the official GGUF Q8_0 is 15.8GB. Hugging Face self-reports an average of 66.7% across 5 DNA benchmarks, with BioReason Coding reaching 74.9%, but only 29.8% on GenomeQA splice-site questions, and it has not undergone clinical validation.

    Same story · 9 reports

    #Model releases#Open-source ecosystem#English text is being prepared.

  6. Testing CatalogModel releases
    AI score62

    Nanbeige Releases the Open-Source Nanbeige4.3-33B Model

    Nanbeige has released Nanbeige4.3-33B, claiming that it achieves SOTA agent performance among open-source models with roughly 30B parameters, with outstanding results on agentic coding and cowork tasks. The model surpasses Qwen3.8-27B, Muse-Glimmer-30B, and Gemma4-31B on most benchmarks, and once integrated into mainstream agent frameworks, it can handle real-world tasks at relatively low inference cost. The company also plans to release a smaller Nanbeige4.3-3B, with the 33B version serving as its teacher model.

    Quoted postNanbeige · @nanbeige

    🚀Excited to announce Nanbeige4.3-33B! To push the performance ceiling of small agents, our strategy in Nanbeige4.3 starts with building a powerful teacher first. Nanbeige4.3-33B delivers SOTA agentic performance among open models at ~30B parameters: * Strong results on agentic coding and cowork benchmarks. * When integrated into mainstream agent harnesses, it tackles real-world tasks with cost-efficient inference. * It will serve as a teacher for upcoming smaller Nanbeige models, starting with Nanbeige4.3-3B. https://t.co/tSToQgZf2O https://t.co/D228euvybz #LLM #Agent

    Same story · 3 reports

    #Model releases#Open-source ecosystem#Agents

Sunday6 items

  1. The DecoderModel releases
    AI score60

    Block releases speech model Dessa, trained entirely on human speech

    Block has released the speech model Dessa, which it says is the first AI model trained from scratch entirely on human speech, without any text data. Dessa processes audio directly rather than first converting it to text, allowing it to capture emotion, sarcasm, laughter, and pauses in real time. It supports full-duplex communication and can run locally on a MacBook Pro M4 Max, with training performed on a single NVIDIA GB200 NVL72. The model has not yet been made public, and the team is recruiting AI safety researchers to test potential risks.

    Same story · 10 reports

    #Model releases#Speech#English text is being prepared.

  2. IT Home · Artificial IntelligenceModel releases
    AI score62

    Block launches full-duplex speech model Dessa, supporting local operation on MacBook

    Block launches the full-duplex speech model Dessa, which can listen to the user's speech while speaking; emotional expression is natively built into the model, and it can simulate states such as laughter, pauses, and choking up. The company says the model can run locally on consumer-grade computers, with the device threshold being MacBooks with M4 Max or higher configurations, and that training used only a single rack of NVIDIA GPUs. At this stage Dessa is not open for public trial; Block is sending testing invitations to safety researchers, focusing on risk scenarios in which such speech agents deceive humans.

    Same story · 10 reports

    #Model releases#Speech#English text is being prepared.

  3. Shao MengModel releases
    AI score76

    Block releases Dessa, a full-duplex model trained purely on speech, which can run locally in real time on a MacBook Pro M4 Max

    Block, founded by Twitter co-founder Jack, has released Dessa, a speech-to-speech model trained from scratch purely on human speech, with no text and no pretrained base, supporting full duplex so it can listen and speak at the same time. The model claims inference faster than real time and can run locally on a MacBook Pro M4 Max and above, and the entire model is said to have been trained on a single NVIDIA GB200 NVL72. The team says its goal is to let the model understand tone and emotion, avoiding the safety risk of continuing to respond without perceiving the other person's distress.

    Quoted postjack · @jack

    We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence. https://t.co/r0qFLdvd6c

    Same story · 10 reports

    #Model releases#Speech#English text is being prepared.

  4. Phoenix TechnologyModel releases
    AI score66

    Block launches full-duplex speech model Dessa, supports local operation

    Block has launched the full-duplex speech model Dessa, which can listen to the user's speech while speaking, and can simulate emotional expressions such as laughter, pauses, and choking up. The company says it is trained entirely on human speech data, using only a single rack of NVIDIA GPUs for training, and can run locally on MacBooks with M4 Max and higher configurations. At this stage Dessa is not open for public trial; Block is sending testing invitations to security researchers, focusing on risk scenarios in which speech agents deceive humans.

    Same story · 10 reports

    #Model releases#Speech#English text is being prepared.

  5. Hacker News TrendingModel releases
    AI score80

    Dream Labs Releases Robot Model Dream-1 and Opens Public API

    Dream Labs has released the world-action model Dream-1, which can control unseen dual-arm parallel-jaw gripper robots to perform unseen tasks via a public API, without fine-tuning or demonstration data from that robot. The company also announced it is emerging from stealth with a $90 million seed round. Dream-1 costs $3 per hour to run, and Dream-1-flash costs $2 per hour; each call returns 1.6 seconds of actions. If zero-shot performance is insufficient, it can be fine-tuned via FaaS with 5 minutes of robot data.

    Same story · 2 reports

    #Model releases#Embodied AI#Open-source ecosystem

  6. TechCrunch AIModel releases
    AI score67

    Block releases full-duplex voice model Dessa, which can run locally on consumer-grade GPUs

    Block's research lab has released the voice model Dessa, which uses a full-duplex architecture, can listen and speak at the same time, and can run locally on a MacBook M4 Max or a more powerful consumer-grade computer. The model was trained entirely on human speech and a single NVIDIA GPU, and the company says expressions such as emotion, pauses, and laughter are built into the model rather than driven by a text pipeline. Dessa is not currently publicly available, and Block is recruiting security researchers to test the risk that it could be used to deceive conversation partners.

    Same story · 10 reports

    #Model releases#Speech#English text is being prepared.

Saturday41 items

  1. NumeramaModel releases
    AI score63

    Block releases speech model Dessa, trained only on human conversation and able to run locally

    Block releases speech model Dessa, saying it is trained only on human conversation, without using text or pretrained models. Dessa uses a full-duplex approach, allowing it to keep listening while speaking and to interject, laugh, or respond; Block says it can run locally on MacBook Pro models with M4 Max and above chips. Dessa is currently a research preview, with no release date or pricing, and early access is open only to a small number of AI safety researchers.

    Same story · 10 reports

    #Model releases#Speech#English text is being prepared.

  2. English text is being prepared.Model releases
    AI score90

    Moonshot AI Releases Kimi K3.1 Pro: 3 Trillion Parameters, 1 Million Context, Coding Agent Benchmarks Surpass GPT-6 Astra

    Moonshot AI officially launched K3.1 Pro, the first model in the Kimi K3.1 series. It is a 3-trillion-parameter model supporting a 1-million-token context, and a Highspeed version with roughly 2–3x the output speed is being released alongside it. On Terminal-Bench 4.0, K3.1 Pro scored 65.7%, surpassing GPT-6 Astra's 58.2% and coming close to Claude Opus 5.5's 66.4%; on OfficeQA Pro v2 it scored 64.4%, achieving the current best. Starting today, it is available via kimi.com, the Kimi App, Kimi Work, Kimi Code, and the Kimi API. The full model weights will be released by November 4. API input pricing is 1.3 yuan per million tokens (cache hit) and 13 yuan (cache miss), with output at 68 yuan.

    Same story · 34 reports

    #Model releases#Agents#English text is being prepared.

  3. WallstreetcnModel releases
    AI score80

    Kimi K3.1 Pro Released: Intelligence Index Ranks Fourth Globally, Agent Benchmark Nears Opus 5.5

    Moonshot AI has released Kimi K3.1 Pro, which ranks fourth globally on the Artificial Analysis Intelligence Index with a score of 55, an 11-point improvement over its predecessor. On Terminal-Bench 4.0, it rose from 13% to 65.7%, narrowing the gap with Opus 5.5 to less than 1%. The model has 3 trillion-level parameters and supports a 1 million context window; it was post-trained on K3 with increased reinforcement learning compute, and a Highspeed version with 3x faster output has also been launched. The 3D Make feature is now available on kimi.com and the Kimi app.

    #Model releases#Agents#English text is being prepared.

  4. CaixinModel releases
    AI score77

    Kimi Releases 3-Trillion-Parameter Model K3.1 Pro, Launching a Pro Version for the First Time

    In the early hours of October 24, Moonshot AI (Kimi) officially launched Kimi K3.1 Pro, calling it its most capable 3-trillion-level parameter model to date, supporting a 1-million context window, with notable improvements in coding, 3D creation, financial research, and long-horizon Agent capabilities. Kimi K3.1 Pro is a post-trained version of Kimi K3; through enhanced post-training compute and technical optimization, it achieves comprehensive improvements across all domains from large-scale RL. This is the first time Kimi has introduced a Pro model in its model versions, and it is also the first model in the K3.1 series, leaving room for subsequent cost-effective models.

    Same story · 34 reports

    #Model releases#Agents#Open-source ecosystem

  5. Latent SpaceModel releases
    AI score84

    Moonshot AI releases Kimi K3.1 Pro: 2.8T total parameters, 104B activated parameters, weights scheduled to be opened on November 4

    Moonshot AI releases Kimi K3.1 Pro, a multimodal MoE model with 2.8T total parameters, 104B activated parameters, and a 1 million token context window. The Standard and Highspeed versions are now available on the Kimi website, App, Kimi Work, Kimi Code and API. The company says Highspeed offers 2–3x faster output at twice the price of the Standard version. Artificial Analysis ranks it fourth on the Intelligence Index with a score of 55, behind Opus 5.5's 58; on Arena's Code Arena WebDev it ranks 8th with 1692 points, up nine places from K3. Standard pricing is $2 per million tokens for input, $10 for output, and $0.2 for cached input, one-third lower than K3; the weights have not yet been released, and the company promises to open them on November 4.

    Same story · 34 reports

    #Model releases#Open-source ecosystem#Agents

  6. ScienceNetModel releases
    AI score78

    Moonshot AI releases Kimi K3.1 Pro with 3-trillion-level parameters and a 1-million-token context

    Moonshot AI has released Kimi K3.1 Pro, a new-generation open-source large model with 3-trillion-level parameters that supports a 1-million-token context window, retaining its title as the open-source model with the largest parameter scale in the world. The model is a post-trained version of Kimi K3, with a substantial increase in post-training compute, and it shows notable improvements in coding, 3D creation, financial research and long-horizon Agent capabilities; across multiple international benchmarks, its overall intelligence level is now approaching that of the world's leading closed-source models. The report also noted that OpenRouter statistics show global total AI large-model API calls from October 12 to 18 reached 164 trillion tokens, and that the weekly call volume of Chinese large models has exceeded that of the United States for 25 consecutive weeks.

    Same story · 34 reports

    #Model releases#Open-source ecosystem#Agents

  7. MarkTechPostModel releases
    AI score70

    NVIDIA releases Nemotron 3.5 Bolt E4B 4GB open-source small model

    NVIDIA releases Nemotron 3.5 Bolt E4B 4GB open-source small language model, aimed at local voice assistants, game NPCs, and robot interfaces, post-trained with reinforcement learning only based on Google Gemma 4 E4B. The model has 8B total parameters, 4B effective parameters, and 64K context; the Q4_K_M version uses 3.1 to 4.3 GB of VRAM on RTX and can run on GeForce RTX, Jetson, and DGX Spark. In NVIDIA's own testing, it leads Gemma 4 E4B, Qwen3.5-4B, and Nemotron 3 Nano 4B on 15 of 21 benchmarks, but scores 55.1 on Tau2-Bench, behind Qwen3.5-4B's 75.1.

    Same story · 2 reports

    #Model releases#Open-source ecosystem#English text is being prepared.

  8. The Beijing NewsModel releases
    AI score81

    Moonshot AI releases open-source model Kimi K3.1 Pro, 3 trillion parameters, 1 million token context

    Moonshot AI released a new-generation open-source model Kimi K3.1 Pro in the early hours of October 24, at a 3 trillion-level parameter scale, supporting a 1 million token context window, with full model weights to be released before November 4. The model increases compute investment in the reinforcement learning stage and improves capabilities in code writing, 3D creation, financial research, and long-horizon Agents. On safety, the team says its cyber offense and defense capabilities, though improved over the previous generation, still lag behind frontier closed-source models that have been publicly released for at least three months, and it explicitly will not release open weights that advance cyber offense and defense capabilities to the absolute frontier.

    Same story · 34 reports

    #Model releases#Open-source ecosystem#Agents