Signal Radar

2026-10-23

Tech news with the retellings removed: first-party announcements, measured numbers, and things you can use today.

234papers read
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21shortlisted
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9must-read

360 s · 201,381 input tokens · ≈ $0.0085 · jev-1.13.0 via typesafe

Must-read ≥ 90%

98%measurementreddit-ai

Meta's 816B Muse Spark Open scores 39 on Artificial Analysis, the 180B Qwen3.8-Flash-Next scores 40. Score vs size for 11 open-weight models

/u/BTA_Labs

Measured result 98%
Abstract

Made this from the Artificial Analysis model pages after Muse Spark Open and Beam got their scores last week. X axis is total parameters as AA lists them, log scale. Orange = released in October 2026. - Muse Spark Open (816B, 42B active) scores 39. Qwen3.8-Flash-Next (180B, 6B active) scores 40. - The Unsloth UD-Q4_K_XL GGUFs are about 506 GB for Muse Spark Open and 111 GB for Qwen3.8-Flash-Next. - Meta's recommended local setup for Muse Spark Open is two M5 Ultra Mac Studios with 512 GB each. - Qwen3.8 27B scores 34, above Beam (501B) at 30 and Nemotron 3.5 Bolt (74B) at 21. Unsloth lists 16 to 19 GB of memory for its 4-bit quants. - Highest on the chart is MiMo-V2.6-Pro (1T) at 46, then DeepSeek V4.1 Flash 1017 (552B) and GLM-5.3 (753B) at 45. The index is one aggregate number from AA's own eval runs. It says nothing about speed, long context, vision, or how a model does on your own tasks. Table with active params, licenses and links in the comments. submitted by /u/BTA_Labs to r/LocalLLaMA [link] [comments]

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94%announcementreddit-ai

Q-Labs Presents "Noise": Pretraining Without Backpropagation | "In The 10 Days Since We Released The Noise Speedrun, The Time To Pretrain A Transformer Went From 1.76 Hours To 1.6 Seconds, A 4,000x Speedup!!!"

/u/Lord_44th__Raikage

Measured result 94%First-party 89%Usable today 68%
Abstract

https://freeimage.host/i/neCfqyx Expounding Information: Backprop is the only credit assignment algorithm that trains modern neural nets, and the field has co-evolved around it: it needs differentiability and gives first-order gradients, and our architectures, optimizers and hardware are built around it. As compute keeps growing, brute-force learning algorithms based on search may overtake it. The bitter lesson is that general methods which scale with compute win; AlphaGo Zero is the obvious example: bootstrapping on human games helped at first, and pure self-play won once there was enough compute. Differentiability and backprop may be the same kind of bias: efficient while compute is scarce, limiting once there is enough of it. They constrain which architectures work, and even for the ones that do, they explore the loss landscape poorly, which may be why today's networks need so much data to generalize. We think replacing backprop with a much better credit assignment algorithm is inevitable. Noise tests that on the hardest task we know, pretraining language models, and scores two things: how low the validation loss goes given a lot of compute, and how fast it gets there. We just published Noise, our attempt to replace backprop with brute-force computation rather than analytic structure. Noise perturbs activations instead of weights, independently at every token, so one forward pass evaluates thousands of population members, and the reward-weighted noise becomes a gradient estimate for an ordinary optimizer. Activations are where interpretability work finds the model's reasoning, so training becomes a search over latent reasoning. Link to the GitHub: https://github.com/qlabs-eng/noise Link to the Announcement Article: https://t.co/uUF3VnQ2LN submitted by /u/Lord_44th__Raikage to r/accelerate [link] [comments]

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94%releasereleases-ollama

ollama v0.40.9

github-actions[bot]

First-party 94%Usable today 87%
Abstract

Speech Ollama can now generate speech with Phonon on Apple Silicon. ollama run plays text or a chat model's reply aloud as the audio is generated, and apps can stream audio through the OpenAI-compatible /v1/audio/speech endpoint. ollama run phonon " Hello from Ollama. " d3 decision models d3 brings vLLM Semantic Router's decision models to Ollama. Give a model text, structured state, or an image, and ask it to classify, answer yes or no, or score options with probabilities. The family includes 2B, 4B, 9B, and 27B sizes. ollama run d3 " A customer says their card was charged twice. " --ask " Is this a billing issue? " Flint on Linux and Windows Flint now runs on Linux and Windows as well as macOS. Other improvements Streaming OpenAI Responses and Anthropic Messages requests now report generation failures instead of ending silently after partial output. ollama launch now passes cloud models' context lengths and capabilities to coding tools, fixing incorrect context and compaction settings. Anthropic-compatible web search now uses the model's supported thinking setting in follow-up requests, preventing avoidable failures. Full Changelog : v0.40.8...v0.40.9

Same story elsewhere (1)
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93%announcementtechmeme

Anthropic donates the Agent Skills open standard to the Agentic AI Foundation and says 70+ platforms have integrated it, after donating MCP to the AAIF in 2025 (Anthropic)

First-party 93%Usable today 37%
Abstract

Anthropic : Anthropic donates the Agent Skills open standard to the Agentic AI Foundation and says 70+ platforms have integrated it, after donating MCP to the AAIF in 2025 — Today, we're donating Agent Skills, the open standard that gives AI agents specialized knowledge and workflows, to the Agentic AI Foundation (AAIF).

rss:9ff6ee13cacc7004Useful?👍👎
92%releasereddit-releases

MOSS-Image-Edit LoRA

/u/LatentSpacer

Usable today 92%
Abstract

An edit LoRA for MOSS Image has been released. Works with both Base and SFT. Weights: https://huggingface.co/chinoll/Moss-Image-Edit Custom nodes / Workflows: https://github.com/Chenkin-x/ComfyUI-Moss-Image-Edit/blob/main/README.en.md Edit: I implemented it natively with caching for faster inference. The LoRA is a bit rough. Let me know how it's working for you. Custom nodes / Workflows: https://github.com/PedroMarinhoDev/ComfyUI-Moss-Image-Edit-Native ComfyUI-compatible weights: https://huggingface.co/PedroMarinhoDev/Moss-Image-Edit-ComfyUI There's an open PR by Kijai on ComfyUI so it might be merged and supported natively later. https://github.com/Comfy-Org/ComfyUI/pull/17296 https://preview.redd.it/hgdiyvw0d1xh1.png?width=2400&format=png&auto=webp&s=89ac4b4f86a821e2dff5c7c64e4918daa0d1f9c5 I have no relation to the weights or custom nodes, just sharing them. submitted by /u/LatentSpacer to r/StableDiffusion [link] [comments]

rss:9a2036c655b75424Useful?👍👎
92%announcementhn

eMudhra to exit global SSL/TLS certificate business

bo0tzz

First-party 92%
Abstract

Article URL: https://emudhra.com/en/press-release/emudhra-to-exit-global-ssl-tls-certificate-business Comments URL: https://news.ycombinator.com/item?id=50195110 Points: 1006 # Comments: 666

rss:fde4b6eb413619ccUseful?👍👎
91%announcementreddit-ai

Ant Ling just announced a second model today: Ling-3.0-flash-Sante (124B, 5.1B active)

/u/BerryBlush_Rogers691

Usable today 91%First-party 42%
Abstract

InclusionAI just announced Sante too. Damn, two model announcements in one day. They're on a roll. I posted about the 3.1-flash-1019 update earlier today and they've already announced Sante . Two model announcements in one day, damn. This one's built on Ling-3.0-flash , with 124B total parameters, 5.1B active per token and a 256K context window. It's a healthcare-focused version aimed at medical reasoning, literature research and tool-assisted workflows. The weights are already up on Hugging Face . I'm glad they're still building on 3.0. So much of the earlier thread turned into a discussion about finding enough memory for 3.1 that seeing something happening with the older base caught my attention. I'd like more of these focused releases. Pick a field, put some work into it, give people the weights to explore it themselves. This is a more interesting direction to me than another few points on a general leaderboard. submitted by /u/BerryBlush_Rogers691 to r/LocalLLaMA [link] [comments]

rss:e44cb2cc9cf89769Useful?👍👎
91%releasehn

Crystal 1.22

ksec

First-party 91%Usable today 71%
Abstract

Article URL: https://crystal-lang.org/2026/10/12/1.22.0-released/ Comments URL: https://news.ycombinator.com/item?id=50194938 Points: 173 # Comments: 90

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90%announcementhn

Quad9 to exit France

farrokhi

First-party 90%
Abstract

Article URL: https://quad9.net/news/blog/au-revoir-france/ Comments URL: https://news.ycombinator.com/item?id=50195959 Points: 1124 # Comments: 389

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Maybe 80% to 90%

87%releasereddit-ai

inclusionAI/Ling-3.0-flash-Sante · Hugging Face

/u/jacek2023 · discussion

Usable today 87%First-party 71%
Abstract

from inclusionAI: Introduction Ling-3.0-flash-Sante is our healthcare-specialized model built on Ling-3.0-flash . Its name comes from the French word santé , meaning “health.” With 124B total parameters and 5.1B active parameters per token , Sante is designed for medical reasoning, healthcare research, deep research, and tool-assisted workflows while retaining the general capabilities of Ling-3.0-flash. Model weights are available on Hugging Face and ModelScope . Important: Ling-3.0-flash-Sante is a research model, not a medical device. Its outputs may be incomplete or incorrect and must not be used as a substitute for professional medical advice, diagnosis, or treatment. High-stakes use requires qualified human review, independent verification, and compliance with applicable privacy, security, and healthcare regulations. Highlights Strong healthcare capabilities: Sante performs well across professional healthcare tasks, complex medical reasoning, medical knowledge, and healthcare safety benchmarks. Efficient at scale: Its hybrid-linear MoE architecture combines Kimi Delta Attention (KDA), gated MLA, and sparse MoE layers, activating 5.1B of 124B parameters per token. Built for long contexts: Training extends to 256K tokens, supporting long-document analysis, evidence synthesis, and multi-step research. Beyond medical QA: Sante is designed for deep research, tool-assisted workflows, and multi-agent collaboration as well as question answering. Native reasoning and tool use: Thinking mode is enabled by default, with tool calling supported through compatible serving frameworks.Introduction submitted by /u/jacek2023 to r/LocalLLaMA [link] [comments]

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85%releasereddit-ai

Introducing NPCTalk: Make the NPCs of any game talk in realtime, in character, in their own cloned voice, in under 1s, and they act on what you say.

/u/Prime-Paradox

Measured result 85%Usable today 69%
Abstract

From Universal Modder on X: "Introducing NPCTalk: make the NPCs of any game talk in realtime. I made every NPC in GTA V answer to me in character, in their own cloned voice, in under 1s, and they act on what you say.” “Packaged all the skills Claude learned into a single repo, built on open models: NVIDIA Nemotron ASR for transcription, Qwen 3.8 27B for realtime dialogue, and Fish Audio Drama 3 for the cloned voices, self-hosted on a serverless GPU. " submitted by /u/Prime-Paradox to r/singularity [link] [comments]

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85%releasereddit-releases

testing minimax h3 ref2va with my ps2 style lora

/u/aziib

Usable today 85%
Abstract

you can get my lora in here: https://civitai.com/models/2983280/modern-ps2-style works with text to image and image to video too. submitted by /u/aziib to r/StableDiffusion [link] [comments]

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84%releasereleases-llama-cpp

llama.cpp b11827

github-actions[bot]

Usable today 84%First-party 80%
Abstract

opencl: add depthwise conv2d ( #30900 ) opencl: add Adreno depthwise conv2d Assisted-by: OpenCode opencl: use local program for depthwise conv2d Assisted-by: OpenCode opencl: use singed type for args Co-authored-by: Li He lih@qti.qualcomm.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58789398 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:927bd9de2f971e53Useful?👍👎
83%opinionreddit-releases

The Fifth Element But Making More Sense

/u/Dry-Statistician-684

Usable today 83%
Abstract

Minimax H3 Ref2v, RTX 3060, 64 GBs RAM. I use a workflow with latent upscaling. The first pass is a low-res video which is upscaled later. The overall quality looks better and it also helps to fix smearing. https://drive.google.com/file/d/1hPqPPRdvox_5vVL3mGPgnTlJ1gTmhmcd/view The upscaler https://huggingface.co/LBH-123-AI/Minimax_h3_latent_Upscaler/tree/main Turbo Lora https://huggingface.co/lightx2v/Minimax-h3-Turbo/tree/main You also need to install these nodes https://github.com/LBH-123-AI/Comfyui_Minimax_h3_latent_Upscaler submitted by /u/Dry-Statistician-684 to r/StableDiffusion [link] [comments]

rss:3454fdfa3b6b072cUseful?👍👎
83%releasereleases-llama-cpp

llama.cpp b11829

github-actions[bot]

Usable today 83%First-party 82%
Abstract

server: Substitute rerank placeholder from the original template only. ( #30864 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58867720 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:c1d2480ebecb4c26Useful?👍👎
83%releasereleases-llama-cpp

llama.cpp b11836

github-actions[bot]

Usable today 83%First-party 80%
Abstract

CANN: missing const qualifiers added for consistency ( #30933 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58989960 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:de817523b27a18a4Useful?👍👎
82%releasereleases-llama-cpp

llama.cpp b11826

github-actions[bot]

First-party 82%Usable today 81%
Abstract

mtmd : add MiniCPM-OCR 1.0 support ( #30979 ) Signed-off-by: tc-mb tianchi_cai@icloud.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58775194 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:52c49415a862c0a4Useful?👍👎
82%releasereleases-ollama

ollama v0.40.9-rc1

dhiltgen

First-party 82%Usable today 54%
Abstract

llama.cpp update ( #19194 )

rss:3def2c71946c50beUseful?👍👎
80%releasereddit-ai

MiniMax-M3.1-Flash

/u/mrtime777

Usable today 80%
Abstract

https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash submitted by /u/mrtime777 to r/LocalLLaMA [link] [comments]

rss:87880c7392e5dd54Useful?👍👎
80%announcementtechmeme

Canva launches Craft, its first image generation model, trained on human-designed templates, available to all users, with plans to support third-party chatbots (Ivan Mehta/TechCrunch)

Usable today 80%
Abstract

Ivan Mehta / TechCrunch : Canva launches Craft, its first image generation model, trained on human-designed templates, available to all users, with plans to support third-party chatbots — Design platform Canva released its first image generation model, called Canva Craft, developed by its research team to compete …

rss:a2c60356b9b11591Useful?👍👎
80%releasereleases-llama-cpp

llama.cpp b11828

github-actions[bot]

Usable today 80%First-party 74%
Abstract

server: realtime API: send complete after committed audio drains ( #30994 ) server: realtime API: send complete after committed audio drains server: realtime API: complete a commit at its end row Completion is triggered when the stream runs dry, which is a timing proxy: audio appended while the tail drains leaks into the committed item, a final commit before the drain merges both items, and back to back commits collapse into one. mtmd_audio_stream_flush() records the row count at the end of the flushed audio (mtmd_audio_stream_get_n_rows_flushed), next to the rows produced so far (mtmd_audio_stream_get_n_rows). The slot keeps a FIFO of these end rows and completes each commit once every row up to it is decoded. Co-authored-by: Pascal admin@serveurperso.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58863355 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

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Near misses (10): just under your maybe bar, useful for tuning thresholds
Filtered by your exclusions (71)