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Oct 5

Oct 5Mon
  1. Cursor ChangelogOfficialAI score58

    Cursor iOS app adds remote control for local agents on your computer

    AICursor's iOS app now lets users see and reply to local agents running on their computer. Remote control is on by default except for Enterprise organizations, and agents keep running on the computer rather than moving to the cloud. The computer must stay on and online, and users can enable Keep this computer awake in desktop settings.

  2. meng shaoXAI score47

    Reflection previews Beam, a 501B-parameter open agentic model

    AIReflection AI previewed Beam, an MoE open model with 501B total and 23B active parameters, claiming 3–4x better inference efficiency than GLM 5.2. The model was pretrained from scratch on 23.8T tokens in four weeks, and its RL run used 10,500 GB300 GPUs over four weeks, which the post describes as possibly the largest publicly recorded. Reflection positions Beam as a workhorse open model for enterprises, governments, and developers, with full weights due this month.

    Image from @shao__meng's post
  3. Aravind SrinivasXAI score35

    Perplexity Mac app adds tabs and multi-window spatial canvas

    AIPerplexity's Mac app now supports tabs, and sessions can open in separate windows for parallel multitasking. Users can run tasks side by side or spread sessions across different screens. The feature is live in version 26.37.1 for all Computer users on Mac.

  4. Ethan MollickXAI score46

    Cowork moves inference and VM to the cloud, with local file access

    AIEthan Mollick reports that he moved much of his complex Cowork work to the new Claude Projects, which persistently chat with a dedicated cloud VM, finding them much better in most ways but poorly documented. Felix Rieseberg, who works on Cowork, explains that the new version runs model inference and the VM in the cloud, with each session in its own sandbox that is destroyed when the session ends. Files are accessed only from folders the user explicitly adds, with the desktop app handling those requests.

  5. Sophia YangXAI score62

    Reflection AI's Beam open model has 501B total parameters and 23B active

    AISophia Yang congratulated Reflection AI on Beam, a 501B-parameter open model with 23B active per token. She attributes its efficiency to an RL length penalty that discourages unnecessary tokens and a sparse MoE architecture. Reflection says full weights will be released this month, and the quoted post reports training over 100 million rollouts on 10.5K NVIDIA GB300 GPUs over four weeks.

  6. PyTorch BlogOfficialAI score40

    PyTorch Consolidates Media Decoding and Encoding Into TorchCodec, Narrows TorchVision and TorchAudio

    AIPyTorch has consolidated all media decoding and encoding for images, video, and audio into TorchCodec, which now runs on CPU and CUDA. TorchVision and TorchAudio are narrowed to focus on their transforms, with models, datasets, and pipelines no longer under active development. All three libraries are now ABI stable and no longer need rebuilding for each PyTorch release.

  7. clem 🤗XAI score72

    Reflection AI announces Beam, a 501B-parameter agentic open model

    AIReflection AI introduced Beam, an agentic open model with 501B total parameters and 23B active parameters, trained end-to-end from scratch. The quoted announcement says it targets frontier reasoning efficiency and coding and agentic tasks, with full weights due this month. Clément Delangue, Hugging Face's CEO, reposted it with a welcome to the Reflection organization on Hugging Face.

    Why it matters: The quoted announcement names Beam's parameter scale, active-parameter count, and coding and agentic focus, which helps readers gauge where it fits among open models.

    Image from @ClementDelangue's post
  8. PikaOfficialAI score22

    Pika lets users try Ideogram 4.5 on its platform

    AIPika announces that users can try Ideogram 4.5 through its create platform, linking to an Ideogram app page in its image tools section. The post provides no details on features, pricing, or capabilities.

  9. ReflectionOfficialAI score23

    Reflection AI's Beam model pretrained in four weeks on 24T tokens

    AIReflection AI says its Beam model was pretrained in 4 weeks on 24T high-quality tokens, giving it innate coding capabilities. The company credits MoE stability improvements and large-scale data curation and deduplication for a base model it claims outperforms open-source base models of the same class. It presents this strong reasoning foundation as what makes sustained reinforcement learning gains possible.

    Image from @reflection_ai's post
  10. ReflectionOfficialAI score42

    Reflection AI previews Beam, a 500B open model under Apache 2.0

    AIReflection AI says its Beam model, with a 500B form factor, combines strong agentic performance and efficient reasoning for enterprises, governments, and developers. Beam is in final red-teaming and will be released this month under an Apache 2.0 license, with quantized FP8 and NVFP4 versions for efficient deployment. Early access sign-ups are open on the company's platform.

  11. Vaibhav (VB) SrivastavXAI score42

    OpenAI speeds up GPT-6 Astra and GPT-6.1 Sol inference by 50%

    AIOpenAI has optimized inference for GPT-6 Astra and GPT-6.1 Sol, making them about 50% faster by default across subscription plans and Sign in with ChatGPT partners. The change requires no action from users and should be noticeable within two hours of rollout.

  12. Liquid AIOfficialAI score37

    Liquid AI's d1 decision model adds vision, rivaling GPT-6.1 Sol at lower cost

    AILiquid AI released d1 with vision support, accepting images, text, or both as inputs. In tests on six real applications, d1 matched or beat GPT-6.1 Sol on four while costing 19x to 200x less than both GPT-6.1 Sol and Claude Opus 5.5. It returns probabilities for yes/no, choice, or score questions in one forward pass, with text decisions in 200 to 300 ms.

    Image from @liquidai's post
  13. Liquid AIOfficialAI score36

    Liquid AI's d1 model inspects parts from camera images with 85-97% accuracy

    AILiquid AI's vision-enabled decision model d1 inspects parts directly from camera images and is described as the best such model currently on the market. It reaches 85% to 97% accuracy across four VisA inspection tasks covering circuit boards, candles, cashews, and chewing gum. It understands each task from a short description without task-specific training.

    Video from @liquidai's post
  14. MuseOfficialAI score22

    Muse runs natively on a 15-year-old PSP with voice replies

    AIDeveloper Wob Soriano ported Muse's gadget client to C so it runs natively on a PSP, a handheld kept in his house for over 15 years. Holding R and speaking into the built-in microphone gets an answer from Muse, with OpenAI providing the voice.

  15. Liquid AI · new models on Hugging FaceOfficialAI score44

    LiquidAI releases d1-omni-600M, a 600M decision model for text, image and audio

    AILiquidAI has released d1-omni-600M on Hugging Face, a 587M-parameter model that answers named yes/no, choice and score questions over text, images or up to 30 seconds of speech in a single forward pass. It returns typed answers with zero output tokens by reading the model's distribution over options, and is built on LFM2.5-Encoder-350M with a 16,384-token context length. The model is not a chat model and does not generate text.

  16. LiveKitOfficialAI score30

    LiveKit demos Microsoft speech models in a voice support agent

    AILiveKit Agents pairs MAI-Transcribe-2-Streaming for speech-to-text, Gemma 4 on LiveKit Inference for reasoning and tool calls, and MAI-Voice-2.1-Flash for speech output in a demo support call. The post links separate speech-to-text and text-to-speech resources for developers.

    Video from @livekit's post
  17. Google AIOfficialAI score46

    Gemma 4 and BOTANIC-1 pinpoint crop-yield DNA mutations in minutes

    AILiving Models paired Google's Gemma 4 with BOTANIC-1, a plant-DNA model trained on 320 species, to identify causal genetic variants. In a melon yield test, the pipeline ranked the target mutation first out of 2,494 possibilities in under four minutes. The approach aims to speed up breeding of climate-resilient crops that would otherwise take years of field trials.