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Sep 23

Sep 23Wed
  1. Liquid AI BlogOfficialAI score46

    LFM2.5-VL-DSpark speeds up vision-language model decoding on GPUs and edge devices

    AILiquid AI released an experimental DSpark draft model for its LFM2.5-VL-3B vision-language model, delivering decoding throughput gains of up to 2.66× on GPUs and 3.13× on edge devices. The drafter adds about 280M parameters, an 8.9% increase in the deployed model's parameter count, and is available on Hugging Face with support in llama.cpp, SGLang, and MLX-VLM.

  2. vLLM BlogOfficialAI score54

    vLLM adds distortion-free Gumbel-max watermarking for text provenance

    AIvLLM now supports Gumbel-max watermarking, which embeds a keyed signal into generated text without changing the expected token distribution. Detection requires the secret key and tokenizer, and the signal accumulates over longer outputs. Benchmarks on Qwen3.5-27B with MTP-3 show throughput changes between -1.1% and +2.0% across batch sizes, with no consistent slowdown.

  3. Google Developers BlogOfficialAI score62

    Google reproduces Olmo 3 7B pre-training in MaxText on TPUs

    AIGoogle Developers reproduced Ai2's Olmo 3 7B from scratch in MaxText on Google Cloud TPUs, covering both the stage-1 pre-training run and the stage-2 mid-training anneal. The match was checked on held-out C4 loss, an 8-task accuracy suite, multi-domain perplexity, and token-level KL, not just the training loss curve. The post also describes a data-loader bug that made training loss look better than the reference while held-out metrics did not move.

    Why it matters: The post documents how a faithful reproduction was verified on held-out metrics, including a data bug that training loss alone would have hidden.

  4. Google GemmaOfficialAI score60

    Google's Antigravity SDK adds local execution with Gemma 4 and LiteRT

    AIGoogle says the Antigravity SDK now supports running agents entirely on a local machine with Gemma 4 and LiteRT. The post adds support for OpenAI-compatible endpoints, naming Ollama, llama.cpp, and vLLM as options for serving Gemma, and gives the install command pip install google-antigravity litert-lm.

    Why it matters: The post names the specific runtimes and serving endpoints supported, letting developers judge whether their current local setup fits the new SDK path.

    Video from @googlegemma's post
  5. InferactOfficialAI score44

    vLLM maintainers show TPUv7 megakernels beat GB200 NVL72 on Kimi K3

    AIInferact says vLLM maintainers used megakernel optimization to reach 700 tokens per second per user on TPUv7 running Kimi K3. SemiAnalysis, which shared the work, reports this is 56% better performance than Nvidia's GB200 NVL72. Inferact links a full technical breakdown of the TPU megakernel work on its blog.

  6. Google AntigravityOfficialAI score38

    Antigravity SDK runs Gemma 4 fully offline on local GPUs

    AIGoogle Antigravity says developers can now run open models such as Gemma 4 completely offline in its SDK. The setup uses Google AI Edge's LiteRT to run the model directly on a local GPU, with no API costs and no internet connection required.

    Video from @antigravity's post
  7. InferactOfficialAI score49

    Inferact's TPU megakernel runs Kimi K3 at 709 tokens/s

    AIInferact says its first TPU megakernel for Kimi K3 reaches 709 tokens/s on low-concurrency decode with DSpark speculative decoding, versus 450 tokens/s for its GB200 baseline. The company claims it is the first TPU inference megakernel, running the whole model in a single Pallas kernel, and says it is roughly 1.4 to 2x the GB200 baseline at batch sizes 1 through 8 without speculative decoding. Inferact says it is open-sourcing the kernel today.

    Video from @inferact's post
  8. LM StudioOfficialAI score28

    Bionic adds a built-in interactive canvas for shared diagrams

    AIBionic now includes a built-in interactive canvas where users can create Excalidraw diagrams that both they and Bionic can view and edit. The canvas supports collaboration on mockups, system designs, and process maps, and users can ask Bionic to implement what is drawn.

    Video from @lmstudio's post
  9. Black Forest LabsOfficialAI score67

    Black Forest Labs releases FLUX 3 Action, an open 7B world action model for robots

    AIBlack Forest Labs says FLUX 3 Action is an open-weights 7B world action model that ranks first on the RoboLab benchmark. The company says it outperforms the previous best open model by 6.1 percentage points while using 56% fewer parameters and running up to 3.95x faster. The model predicts video and actions together, and the company is releasing the weights, code, fine-tuning recipe, benchmarks, and examples. It also integrated the model into Hugging Face's LeRobot with NVIDIA, with edge deployment on NVIDIA Jetson.

    Why it matters: The release pairs benchmark results with the trade-off it claims to remove between world action model performance and VLA speed, which is useful context for robotics teams weighing open models.

    Video from @bfl_ai's post
  10. Thomas DohmkeXAI score22

    Open-source 3D-printed Marvin robot connects to ChatGPT and Entire

    AIDeveloper Stefano (@spedemo) built a 3D-printed, remote-controlled robot named Marvin that uses voice detection and speech, with all of it open source. The post says Marvin connects to ChatGPT and Entire, and it will be shown at the WeAreDevs booth 753 in San Jose.

    Video from @ashtom's post
  11. Microsoft ResearchOfficialAI score60

    Microsoft Research shows offloading robot AI inference improves performance and battery life

    AIMicrosoft Research reports that running physical AI inference on onboard GPUs can limit robot performance and battery life, while offloading inference to edge or cloud GPUs improved results in mobile manipulation tests. In its evaluation, smaller onboard GPUs slowed mapping and planning by up to 383% compared with an A100, and large onboard GPUs such as Jetson Thor drained robot batteries by up to 160%.

    Why it matters: The study measures how offloading robot inference to edge or cloud GPUs changes task success, battery life, and model size, offering evidence for infrastructure design.

  12. Mike KnoopXAI score57

    Tufa Labs reaches 83.06% on ARC-AGI-2, 2% short of the grand prize

    AIMike Knoop says the top ARC Prize 2026 ARC-AGI-2 score of 83.06% by Tufa Labs is only 2% short of the 85% grand prize threshold. The challenge runs under strict Kaggle compute limits with no internet access, and the winning solution is set to be open sourced. The image shows the leaderboard with RabbitHole at 76.94%, nvbanana at 74.17%, Yi-Chia Chen at 55.14%, and Kha Vo at 37.50%.

  13. Lewis Tunstall @ COLM 🌉XAI score40

    Lewis Tunstall makes voice acting debut with Reachy Mini robot

    AILewis Tunstall of Hugging Face shares his first cameo as a voice actor using the Reachy Mini robot, linked to a YouTube video about Australian football. The post itself provides no technical details, and the Reachy Mini context comes from a separate post by Andi Marafioti about NVIDIA's open-source Nemotron 3 Diarization model.

  14. Julien ChaumondXAI score36

    Hugging Face releases JS package to browse LeRobot datasets directly

    AIHugging Face has released a new JavaScript package, huggingface/lerobot, that lets developers read LeRobot datasets on the Hub directly in the browser without downloading them. The post says coding agents can use it to build custom dataset viewers quickly, with an example UI implemented in about 600 lines of JS.

    Image from @julien_c's post
  15. ModelScopeOfficialAI score44

    NVIDIA releases Nemotron 3 Diarization for live speaker attribution

    AINVIDIA's Nemotron 3 Diarization is now available on ModelScope, labeling speakers and timestamps in streaming audio for up to eight speaker slots per conversation. The 99.2M-parameter model uses an end-to-end streaming architecture built on NVIDIA's Streaming Sortformer, running on Ampere, Hopper, and Blackwell GPUs via NeMo Speech C++. It is designed to pair with existing ASR systems such as Nemotron ASR, Parakeet, Canary, or Whisper to produce speaker-attributed transcripts.

    Image from @ModelScope2022's post
  16. QwenOfficialAI score60

    Qwen Intelligence launches three mobile agents and opens its benchmark suite

    AIAlibaba's Qwen launched Qwen Intelligence with three mobile agents: a Mobile Planner Agent, a Mobile-Use Agent, and a Mobile Creative Agent. The post reports benchmark results including MobileWorld 82.1, MobileWorld-Real 92.2, and AndroidDaily 97.2, plus a 90% end-to-end success rate, and says the MobilePA-Bench, MobileWorld, MobileWorld-Real, and MobileWorld-Safety benchmarks are open.

    Why it matters: The post names three mobile agents and their benchmark results, while also releasing the benchmark suite, so readers can check the claims against the reported figures.

    Image from @Alibaba_Qwen's post
  17. ModelScopeOfficialAI score40

    TeleOCR: 1.2B vision-language model parses documents, tops OmniDocBench v1.6

    AITeleOCR, a lightweight 1.2B vision-language model released under Apache 2.0, parses digital PDFs and warped phone photos without a separate dewarping model. It scores 96.87 overall on OmniDocBench v1.6, the highest among listed specialized VLMs, and ranks #1 in the ICDAR 2026 Sci-ImageMiner Challenge. It supports structured parsing of text, tables, formulas, layouts, and reading order, with synchronous or asynchronous vLLM inference.

    Image from @ModelScope2022's post
  18. ModelScopeOfficialAI score62

    Xiaomi MiMo-V2.6 open-sourced as a multimodal agent model family under MIT License

    AIXiaomi has released MiMo-V2.6 as an open model family under the MIT License, designed for large-scale reinforcement learning. MiMo-V2.6-Pro scores 46 on the Artificial Analysis Intelligence Index, with 71.9 on DeepSWE v1.1, 89.9 on Terminal-Bench 2.1, and 82.0 on OSWorld-Verified. The 1.02T-parameter MoE activates 42B parameters and supports text, image, video, and audio input with a 1M-token context.

    Why it matters: The post links benchmark results, parameter scale, and a multi-agent RL training run, giving readers concrete figures to compare against other open models.

    Image from @ModelScope2022's post
  19. ModelScopeOfficialAI score62

    Shanghai AI Lab and SJTU release open-weight 8.9B NCP-ArchPreview model under Apache 2.0

    AIShanghai AI Lab and SJTU's LUMIA Lab released NCP-ArchPreview, an 8.9B open-weight language model under Apache 2.0. The model reportedly reaches OLMo-3-7B's final Stage 1 loss using 51.3% of the tokens from the 5.73T Dolma 3 corpus, a 1.95× convergence gain. Its concept module jointly predicts tokens and concepts, and domain adaptation updates only its 17M parameters while the token backbone stays frozen.

    Why it matters: The post pairs an Apache 2.0 open-weight release with training-efficiency figures, showing how the concept module adapts to new domains with few trainable parameters.

    Image from @ModelScope2022's post

Sep 22

Sep 22Tue
  1. ModelScopeOfficialAI score62

    inclusionAI open-sources Ming-Image-0.1-Design models for visual design

    AIinclusionAI open-sources the Ming-Image-0.1-Design family, two complementary 6B models for visual-design workflows, under an MIT License. Design generates complete UIs, dashboards, infographics, and posters up to 2048×2048 with native transparent RGBA output, and Layer decomposes flattened graphics into independently editable RGBA layers.

    Why it matters: The post separates a design-generation model from a layer-decomposition model, letting users compare two distinct visual-design workflows under one MIT license.

    Image from @ModelScope2022's post
  2. Daniel HanXAI score22

    Unsloth Desktop hotfix adds Qwen-Image-2.1 image editing and fixes

    AIUnsloth Desktop received a hotfix update adding image editing for Qwen-Image-2.1. The update also fixes diffusers update issues, GGUF loading failures for Qwen-Image, and black artifacts during diffusion on A100 and consumer GPUs. Users should receive a banner prompting them to update.

  3. Unsloth AIOfficialAI score26

    Qwen-Image-2.1 FP8 and GGUF quants now run in Unsloth Desktop

    AIUnsloth announced that Qwen-Image-2.1 FP8 and GGUF quantized versions should now run properly in Unsloth Desktop. The app supports both image generation and image editing with these quants. Further details are available on the Unsloth GitHub repository.

    Image from @UnslothAI's post
  4. Fireworks AI BlogOfficialAI score46

    Fireworks ARCv3 cuts RL weight-update payloads nearly 50% for cross-region training

    AIFireworks released ARCv3, a lossless compressor for BF16 weight-update deltas sent from trainers to RL rollout machines. Across 1,000 production RL deltas, ARCv3 produced payloads nearly 50% smaller than ARCv2, averaging about 0.19% of the BF16 weight size versus 0.36%. ARCv3 is available through the Fireworks Training API as fireworks-delta-compression.

  5. Boris ChernyXAI score42

    Boris Cherny Uses Opus 5.5 to Formally Verify Claude Agent SDK

    AIBoris Cherny used Opus 5.5 to formally verify the Claude Agent SDK with Lean, and short prompts produced 16 PRs fixing bugs and race conditions. He also combines Lean and TLA+ to find issues in data flow, concurrency, and state management, and says Claude is strong in both languages even though he does not know them well.

    Video from @bcherny's post
  6. whXAI score34

    MiMo-V2.6 paper details data and RL results for open model

    AIThe MiMo-V2.6 paper thread reports on the newest open model, which also streams its RL run, focusing on data and RL experimental results rather than architecture. The Pro model reportedly rose from 58.41 to 72.57 on DeepSWE after RL, with the top published DeepSWE score cited at 74.

    Image from @nrehiew_'s post
  7. Google GemmaOfficialAI score31

    Deploy DiffusionGemma-Jev on Google Cloud Run with one command

    AIGoogle Gemma says DiffusionGemma-Jev (djev) can now be deployed as a Jev API-compatible endpoint on Google Cloud Run with a single command. The post reports about 35-60 ms single-step latency and roughly 100-123 requests/sec at batch size 32, at about $3/hr that drops to $0 when idle.

    Video from @googlegemma's post
  8. Google GemmaOfficialAI score22

    Google Gemma credits DiffusionGemma-Jev deployment on Cloud Run

    AIGoogle Gemma credits @mmastrac and @dylayed for work on DiffusionGemma-Jev (djev), a Jev API-compatible endpoint. Per @dylayed, djev can be deployed to Google Cloud Run with a single gcloud command, at roughly $3/hr while active and $0 when idle.

  9. Comfy BlogOfficialAI score42

    ComfyUI Speeds Up MiniMax H3 Video VAE Encoding and Decoding

    AIComfyUI's update makes the MiniMax H3 video VAE encode up to about 2.2x faster and decode 1.4-2.7x faster, cutting a 1344x768, 129-frame round trip on an RTX 5090 from 24.3 to 12.7 seconds. The gains come from a fused encoder kernel enabled by default, fp16 accumulation support in a custom convolution, and an int8 decoder, and the source says the changes are visually lossless to the eye. Users need ComfyUI v0.36.0 or above, and the int8 VAE file is a drop-in replacement for the standard one.

  10. StepFunOfficialAI score43

    StepFun open-sources onPanda for token-level LLM annotation and inspection

    AIStepFun has open-sourced onPanda, a tool used internally for LLM data annotation and model inspection, letting users correct tokens and let models continue. The company reports a 52% lower median annotation time versus manual post-editing, with SFT and preference data combined in one workflow. It also supports token probability and top-k inspection, token-by-token decoding control, and browser-based testing across SVG generation, web development, and agent tasks.