Signal Radar

2026-10-12

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

242papers read
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28shortlisted
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6must-read

173 s · 214,732 input tokens · ≈ $0.0090 · jev-1.13.0 via typesafe

Must-read ≥ 90%

98%releasereddit-ai

Decision 3.0: open, multimodal decision models from 0.6B to 27B. Ranks #1 on both Jev Decision Index boards

/u/henzy123

Measured result 98%Usable today 91%First-party 85%
Abstract

With the vLLM Semantic Router team we released Decision 3.0 (d3) , our new SOTA decision model family ranging from 0.6B to 27B, Apache-2.0, all reading text, images and video. How it works - Built on Qwen3.5 (0.8B / 2B / 4B / 9B) and Qwen3.8-27B. The parameter counts include the vision encoder: 0.33B in the 4B, 0.46B in the 27B. - Three decision types: choice, yes/no, score. Probability for every answer, with no text generation. Several questions about the same input are answered in one call, each from its own forward pass over the input. - Images: multiple per request, read at up to 1.6 MP. - Video: multiple per request (MP4, WebM, MOV, MKV), sampled at 2 fps, at most 32 frames over the whole clip, each at up to 0.2 MP. - BF16 weights, load with Transformers (trust_remote_code=True), trained on AMD Instinct MI325X. Results (our evaluation, official Jev 0.3 kit on the released weights) - #1 on both boards: the 27B scores 64.7 text and 71.6 vision, ahead of every listed model, Jev's own included. - Every size leads its class: the 0.6B, 0.8B, 4B and 9B each lead their size class. The 9B (d3-flash) beats our previous-generation 27B (Decision 2.0). - Video zero-shot: no model saw video in training. On all 19,140 Perception Test validation questions, the 27B scores 77.4 and the 4B scores 70.3. HF collection: https://huggingface.co/collections/vllm-sr/decision-30 submitted by /u/henzy123 to r/LocalLLaMA [link] [comments]

rss:5c68be9f2b2b4870Useful?👍👎
97%releasereddit-ai

"Strata" for GLM5.3 Flash is here for some! Project Maya

/u/inthesearchof

Measured result 97%Usable today 78%
Abstract

I stumbled across this as I was currently having glm5.3 flash run only around 10tok/s basically unusable. With Maya i am running 30 tok/s now. GLM5.3 feels even with this low quant like a much more enjoyable model than qwen 3.8 flash next so far. Still testing . This is very exciting allowing many to run GLM5.3 flash. https://github.com/mw00/project-maya Project Maya A 321-billion-parameter AI model on your own GPU GLM-5.3-Flash, private and fast · one NVIDIA GPU or up to 16 · AMD and Windows (experimental) · chat, pictures, OpenAI- and Anthropic-compatible API Models this size usually need a datacenter. Maya runs GLM-5.3-Flash (zai-org, MIT license) on the hardware you already have: 321 B parameters, about 18 B active per token, and a context of up to 1 M tokens. Its engine is built for this one model. It keeps the experts your conversation uses most on the GPU, the next ones in RAM and the rest on your NVMe SSD, and moves them as you chat. It measures your GPUs, CPU, RAM and SSD and tunes itself to them. Nothing leaves your machine. Fast: up to 118 tokens/s on four RTX 4090s, 30+ on a single RTX 5090. Real numbers from real users below. Close to the original: Maya's own quants keep 97.7-99.2% of the FP8 model's zero-shot accuracy. Plugs into your tools: OpenAI- and Anthropic-compatible API with tool calls, ready for coding agents. Everything in the box: a chat and a live monitor in the browser, pictures, thinking levels, a one-command setup. submitted by /u/inthesearchof to r/LocalLLaMA [link] [comments]

rss:3267be2d0741d395Useful?👍👎
92%releasereddit-releases

Claude composed this entire piano piece, then rendered 3D hands playing it

/u/MrBobBrick

Usable today 92%Measured result 54%
Abstract

I built a 3D video renderer for piano pieces with Claude Code. It builds on u/V9011N 's 2D piano animator. Claude also composed the piece in the video, Ember Fountain . What it does: You give it a MIDI file. It renders a video of 3D hands playing the piece on a piano, with falling notes and the audio. A browser-based studio lets you pick the style, camera, and quality, preview single frames, and queue renders. How it works: A Python script turns the MIDI into hand and finger motion. Blender 5.1 (Eevee) renders the frames with rigged hands. They're based on Blender Studio's free CC0 Human Base Meshes, with a procedural skin. FluidSynth renders the audio, and ffmpeg combines the frames and audio into the video. Rendering this video took about 6.5 hours on an RTX 4060: 9,439 frames at 1080p and 60 fps. Blender crashed partway through, and the render picked up where it stopped. Try it: It's free and open source: https://github.com/mrbobbrick/3D-piano-animator You need Python, Blender 5.1, and a GPU. submitted by /u/MrBobBrick to r/ClaudeAI [link] [comments]

rss:c72d617f7d448c0fUseful?👍👎
91%releasereddit-ai

microsoft/AesCode 8B and 32B

/u/jacek2023

Usable today 91%First-party 55%
Abstract

https://preview.redd.it/r2ledkyuntuh1.png?width=1684&format=png&auto=webp&s=9ed4518b2d0f20f44835ebbfac605581da186a57 AesCode generates information-rich visual artifacts such as slides, posters, and dashboards as HTML/CSS. The output remains structured, editable, and verifiable , but the task poses a distinct challenge: code models cannot see how layout, hierarchy, and color come together on the canvas. Image generators offer the opposite strength. They compose visually compelling pages but often misrender text, numbers, and logical relationships. AesCode uses an image generated from the same prompt as an aesthetic reference while following the prompt for the required content. Reference input alone does not solve the problem. Off-the-shelf vision-language models may copy hallucinated content or ignore the reference layout. AesCode separates semantic requirements from visual cues through graph-structured supervision and decoupled cross-modal rewards . AesCode-32B starts from Qwen3-VL-32B-Instruct and is trained with cold-start SFT followed by GDPO across seven reward channels. It is the larger released checkpoint and leads the aggregate benchmark results. https://huggingface.co/microsoft/AesCode-32B https://huggingface.co/bartowski/AesCode-32B-GGUF https://huggingface.co/microsoft/AesCode-8B https://huggingface.co/bartowski/AesCode-8B-GGUF https://preview.redd.it/7s9j5oooxtuh1.png?width=734&format=png&auto=webp&s=f4c1c8dc912b5fe6f882387da7bb679eae4d767d submitted by /u/jacek2023 to r/LocalLLaMA [link] [comments]

rss:b3be51c967182ea9Useful?👍👎
91%releasereleases-ollama

ollama v0.40.3

github-actions[bot]

First-party 91%Usable today 76%
Abstract

What's Changed ollama launch claude no longer shows a warning about Claude connectors by @hoyyeva in #18878 ollama launch codex no longer warns about service tiers by @hoyyeva in #18879 Paused background model upgrade for embedding and other request performance, by @jmorganca in #18908 Full Changelog : v0.40.2...v0.40.3

Same story elsewhere (2)
rss:96fe8a7bafd153a7Useful?👍👎
90%announcementreddit-releases

I am building a web application to transform images into Gaussian Splats

/u/obraiadev

Usable today 90%
Abstract

I’ve been playing with orbit-style videos from video models (lately Minimax H3 in ComfyUI) and kept thinking they’d make great input for Gaussian Splatting. The catch was that the usual pipeline means installing COLMAP, a trainer, CUDA and so on. That’s a lot of friction just to try one idea. So I built Gausify Orbit: https://orbit.gausify.com/ You drop in your video and the whole thing runs locally in your browser on WebGPU: Pulls frames from the video Optional background removal with RMBG-1.4 (BRIA), running through ONNX Runtime Web Camera poses from a small structure-from-motion step Trains the splat with Brush Preview the result and export it as PLY or SOG No Python, no CUDA, no account. Your video never leaves your machine. The models download once, and after that the app works offline too. If you want to show someone a result, you can share a temporary private link that opens in a viewer (it even supports WebXR). Since it all runs on your GPU, how fast it goes depends on your hardware. You also need a browser with WebGPU (recent Chrome or Edge work best). AI videos aren’t perfectly 3D-consistent either, so some generations reconstruct much better than others. Slow, steady orbits around one centered subject give the best results. Tip for better results: I’ve been using this LoRA by pablodawson to get cleaner orbits out of Minimax H3: https://huggingface.co/pablodawson/MiniMax-H3-360-Orbit-LoRA It makes the camera move in a much smoother, more consistent 360° orbit around the subject. That helps a lot here, because the reconstruction step relies on the camera following a predictable path. In my tests, videos made with it got better camera poses and noticeably sharper splats than plain prompting. Highly recommend it if you’re generating orbit videos for this. ComfyUI Workflow : https://github.com/obraia/ComfyUI/blob/main/workflows/minimax-h3-gausify-orbit.json submitted by /u/obraiadev to r/StableDiffusion [link] [comments]

rss:c9ae3876ef6f3df7Useful?👍👎

Maybe 80% to 90%

89%releasereddit-releases

HD-2D RPG in Three.js built with Claude Code

/u/EdgarDruin

Usable today 89%
Abstract

Most of this was built from an initial prompt: "create a 3D pixel-art game using Three.js ... here are some results other have achieved: [screenshot] ... orchestrate sub agents using appropriate models and effort. Take as long as needed." I use a 20x plan for work and had tokens to spare last weekend. The "take as long as needed" turned out to be about 12 hours, with my first prompt at 11:26 AM on Oct 4, with no messages from me until it finished around 11:25 PM. I expected a short tech demo. Claude Code split the work across sub-agents, ran on its own for about 12 hours, and came back with a playable RPG: a village, a forest and a dungeon to explore, a party of four, and turn-based battles with weaknesses, shield breaks and boosts. Since that initial prompt, I've played it through a few times, cleaned up rough edges through follow up prompts, added anonymous logging (which enabled finding and resolving shader issues on machines I don't have) and various small fixes. The game has no image, model or audio files. Every texture, sprite, song and sound effect is generated in code when the page loads, and the whole first chapter is about a 4 MB download, free to play in your browser and takes about an hour: https://rivetmothgames.itch.io/lanterns-of-aldermere submitted by /u/EdgarDruin to r/ClaudeAI [link] [comments]

rss:ff6be8c744993084Useful?👍👎
88%releasereddit-ai

This game did not ship with DLSS frame gen. I had Opus 5.5 add it and my FPS doubled.

/u/SpyAmongUs

Usable today 88%Measured result 44%
Abstract

Playing as Minecraft Steve in Elden Ring is cool and all, but I feel like people don't realise the potential of AI in implementing frame gen to games without temporal upscalers and making games more accessible to lower-end hardware. This mod hooks into the game's renderer and gives DLSS Frame Generation the information it needs to create extra frames. It took less than 2 hours for Opus 5.5 on Extra to make it, what's stopping game developers from using it to implement frame gen or optimise their games? The game is Transport Fever 3. My specs are AMD Ryzen 7 7445HS, NVIDIA GeForce RTX 4050 Laptop GPU with 32 GB DDR5-5600. https://github.com/WhiteTortoise/transportfever3-dlss-framegen/releases submitted by /u/SpyAmongUs to r/singularity [link] [comments]

rss:03d137ee1f41d666Useful?👍👎
87%incidentreddit-ai

PSA: DeepSeek V4.1 Flash habitually exfiltrates API keys. It is dangerously misaligned and may be hazardous to use

/u/gaviniboom

Measured result 87%
Abstract

EDIT: since people keep calling it out, this is API key abuse but not exfiltration. Seems like both Harbor and Pier (sandboxes used in almost every Software Engineering benchmark) expose the Openrouter endpoint and API key to models, even though they block the rest of the internet. Only DeepSeek V4.1 Flash absued this, and it did so while knowing it was "ethically gray". Other open models like DeepSeek V4, GLM 5.3 +Flash, and Qwen were fine. Logs attached. I know OpenRouter is not local, but considering people do run DeepSeek v4.1 Flash locally, I thought this was a matter you all might need to hear. We were running a DeepSWE variant on DeepSeek v4.1 Flash using the standard Pier sandbox. Out of all the 15 models we tested so far (including other open models like GLM 5.3/Flash, DeepSeek v4 Flash 0731, etc), ONLY DeepSeek v4.1 Flash displays such pervasive malicious behavior: In 33% of runs, it attempts to exfiltrate the OpenRouter API key from its sandbox and in 11% of them, it succeeds. Not only that, the transcript shows it knows what it's doing is ethically wrong, but it does it anyway. And once it starts, it persists despite: Multiple frontier models refusing to help it Multiple smaller models refusing to help it Web search not finding anything Questioning multiple times whether or not this is allowed or morally right It concludes that this is fine because it's only "Ethically gray" and has a "higher chance of success" Thinking about whether or not it is going to be detected If you or anyone you know is using DeepSeek V4.1 Flash please be very careful! OpenRouter spend: https://preview.redd.it/9j0q3e8mssuh1.png?width=1080&format=png&auto=webp&s=d234fc7c79bad1bd4b5dd0e8d710efcbb433277a Task 1, DeepSeek V4.1 Flash calls Astra, which refuses to help, and then calls multiple other frontier models, which also all refuse to help. It finally succeeds after finagling a lot with Sonar Web Search: https://preview.redd.it/w8ko2cf7tsuh1.png?width=1080&format=png&auto=webp&s=a43d16fe4dfc64e07be198e7ac12b2924f834b59 https://preview.redd.it/in7zo9f8tsuh1.png?width=1080&format=png&auto=webp&s=e46d7f0282fe1a95c83e68db9aee9875a9667f3f https://preview.redd.it/9uqzovy8tsuh1.png?width=1080&format=png&auto=webp&s=dbccc56363d8ece8c5633b424f3235c2d74aa03b https://preview.redd.it/ybvk4ue9tsuh1.png?width=1080&format=png&auto=webp&s=36288c18a18a2e92f3a1c022ec661934060aa17b Task 2, it says that it'll be fine "unless usage tracked" (lol) and considers that cost may be significant, but it calls multiple frontier models anyway! https://preview.redd.it/xmq30ucftsuh1.png?width=1080&format=png&auto=webp&s=bcc27fae45fc388b36fd3ae69b7bb083da050ce0 https://preview.redd.it/73f4qu0gtsuh1.png?width=1080&format=png&auto=webp&s=a49338dc01be811dcbec8ac42bab9f3c8a8b9112 https://preview.redd.it/1hd2losgtsuh1.png?width=1066&format=png&auto=webp&s=fddb05ad631f1be2418197176aae609a99143cd9 Task 3, DeepSeek questions whether this is allowed ethically, and then keeps on going after getting refused by the model: https://preview.redd.it/brmorbmhtsuh1.png?width=1080&format=png&auto=webp&s=c8dca6a86050d1e0f475a7a6628f424820649476 https://preview.redd.it/nuadyhmitsuh1.png?width=1080&format=png&auto=webp&s=2166989dc13a1eaa35b13f1b3f8cd9225df689d3 --- There are many others like this, I have a bunch more highlights on Imgur, but I won't put them all on this post. Be very careful around DeepSeek v4.1 Flash and API keys or private data; don't know what's going to happen if it thinks it can abuse that to get an advantage. submitted by /u/gaviniboom to r/LocalLLaMA [link] [comments]

rss:1e34988be39580abUseful?👍👎
86%releasereddit-releases

I made a free Minecraft mod where Claude builds whatever you describe

/u/DutyOnly4308

Usable today 86%Measured result 76%
Abstract

I made a Minecraft mod for Claude Code and it's free You press K in game and tell it what you want like a medieval castle with a moat and Claude builds it in your world while you keep playing. Don't like it and one click undoes everything I built it myself and used Claude Code a lot along the way for the code the tests and the debugging. I decided what to build and tested it in the game Under the hood the mod launches your own Claude Code on your PC and it builds by calling 20 tools that run inside Minecraft. Nothing goes through a server of mine To try it you need a Claude Pro or Max sub with Claude Code installed plus Minecraft Java 26.3 and Java 25. The mod itself is free with no account. Grab the jar at https://www.craftai.world/ and drop it in your mods folder (works with Fabric or Forge or NeoForge) and the guide there has the steps It's a beta. Biggest build I timed was a 34k block castle in around 15 minutes Would love to know what you'd ask it to build ! submitted by /u/DutyOnly4308 to r/ClaudeAI [link] [comments]

rss:01ea6d2e0486bccbUseful?👍👎
86%announcementreddit-releases

Update: Kroma, A Krea-2 Fine Tune

/u/KadahCoba

Usable today 86%Measured result 50%
Abstract

[ I'm posting this on behalf of u/LodestoneRock since for some reason his post on this sub keep getting auto removed by Reddit. ] Hey everyone, First of all, thank you to everyone for supporting this project, and to the Krea team who made the best uncompromised base model. Unlike the previous run, where I had to un-distill the model, the Krea team kindly provided us with their real base model. The training is much cheaper than the previous Chroma run. Though cheap is relative, it’s still costing me about ~10K RTX pro 6000 hours (3+ months on 4xGPU machine) running locally, this includes a bunch of failed experiments and bugs during training. Also thank you to ComfyOrg for the 2 RTX pro 6000 donations ♥️ The Base Krea is also not collapsed which allows me to do OPD (RL) directly ontop of it. As a result, you will get a good generation out of the box. Here are some results: You can download them here: https://huggingface.co/lodestones/Kroma (Civitai link soon) So what’s next? There are several stale projects like my pixel space run chroma radiance and zeta chroma, but with the limited compute, I have decided to not continue them for now since compute is at a premium atm. Right now the focus is to create a better dataset (where about 90% of the quality comes from) while continuing to add more knowledge to kroma in the background. I do want to tackle video generation models like h3 in the near future once the dataset curation is done, but fine tuning the video model has astronomical compute cost so I'm looking for funding for more hardware to train a model. I firmly believe that hardware funding donations help a ton because we can indefinitely do more experiments in a sustainable way without having to worry about rental cost and psychological burden of “burned money” on failed experiments. I have a target of getting 4 more RTX pro 6000 but that’s like 60 grand in total which is an insane amount of money! So I don’t expect to reach this goal immediately, it's more about chipping in one card at a time at the pace of the accumulated donations. Every contribution helps, no matter the amount, this project simply isn’t possible without your support! My discord: discord.gg/SQVcWVbqKx [ I'll let Lodestone post his support and donation links in a comment. ] submitted by /u/KadahCoba to r/StableDiffusion [link] [comments]

rss:457c01d0d575f767Useful?👍👎
86%measurementreddit-releases

Just calculated: A $20 subscription buys you ~$1300 in API costs (methodology below)

/u/vzakharov

Measured result 86%
Abstract

Methodology: 1- Newly created subscription 2- Reach close to 100% of the 5-hour limit 3- Calculate the API costs across all sessions' logs 4- Extrapolate Interestingly, I have previously calculated the $200 to buy around $8000 of usage -- but that one wasn't precise -- so if it's true, the cheapest subscription is more beneficial in terms of `API costs/subscription costs`. submitted by /u/vzakharov to r/ClaudeAI [link] [comments]

rss:d1eedd6a69244559Useful?👍👎
86%releasereleases-llama-cpp

llama.cpp b11559

github-actions[bot]

Usable today 86%First-party 84%
Abstract

opencl: improve fa, allow dk512 for gemma-4, improve dk64 ( #30266 ) opencl: enable Gemma-4 E4B GPU decode Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com opencl: extend Gemma-4 GPU decode to E2B Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com opencl: optimize DK64 GQA8 decode Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com opencl: optimize DK128 GQA4 decode Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54845312 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:ab637ae91b8085fcUseful?👍👎
85%announcementhn

NYC's "Click to Cancel" rule is now in effect

gregsadetsky

First-party 85%
Abstract

Article URL: https://www.nyc.gov/main/click-to-cancel Comments URL: https://news.ycombinator.com/item?id=50045946 Points: 456 # Comments: 214

rss:659fa751c3ea96f5Useful?👍👎
85%announcementhn

Show HN: BetterWispr – Free, open-source dictation for Mac

kartik017

Usable today 85%First-party 41%
Abstract

Article URL: https://betterwispr.com/ Comments URL: https://news.ycombinator.com/item?id=50042333 Points: 194 # Comments: 114

rss:026a123e3c5059dcUseful?👍👎
85%releasereleases-llama-cpp

llama.cpp b11563

github-actions[bot]

First-party 85%Usable today 84%
Abstract

ggml: Change default visibility of symbols to hidden ( #30014 ) See discussion in #29997 . This prevents a handful of internal symbols leaking via GGML_API. Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54949054 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:8c93690a37e3020eUseful?👍👎
84%announcementreddit-releases

I built a fake live cable TV network out of the Internet Archive's ephemeral media

/u/thetalkinghawk

Usable today 84%
Abstract

It’s called spelunkr . 24 channels (so far) of forgotten videos organized into themed "cable tv channels" that play on a synced schedule 'round the clock. The entire collection so far is about 2,700 hours of video, running around the clock. You tune in and land in the middle of whatever is on, at the same moment as everyone else watching, the way TV used to work. Codebase is about 87,000 lines, made up of 24,000 of app code, 13,000 of CSS, 28,000 of tooling scripts, 16,000 of tests and 5,000 of SQL. 670 commits over 8 months, and we run over 850 unit tests and 160 browser tests. It's been a real passion project for me, really my first idea of what to build with Claude and has just been growing slowly as I've had time to work on it. I've been plugging away at this off and on for the past 8 months, about 30 days of "total" work across that time, nearly all of it done with Claude Code. Here's how it works under the hood: All videos on the website are streamed directly from the Internet Archive. The player is a plain HTML <video> element pointed at the Archive’s own file. No iframe, no proxy, no re-encoding, no copy. Spelunkr itself serves the page (about 110 KB of JavaScript, gzipped), small JSON schedules, and about 200 public domain or CC BY or BY-SA music tracks for the breaks. Every title links to the tape’s own page at the Archive. There are station "bump" breaks between videos as an homage to the classic Adult Swim bumps. Channels are built from Archive search audits to see if the IA has enough content to support a full length channel. Some channels are classed as "REWIND" and usually are 2 to 6 hour long VHS rips of actual live cable TV from someone's VCR in 1993. There's also classic movie channels dedicated to horror, monster flicks, detective noir capers, and western flicks. I even found every episode of The Joy of Painting and gave it CH 12: HAPPY TREES. Claude checks all the tapes to make sure they're legit, then formulates the descriptions, facts, and schedule for me to approve. Each channel has its own branding, a 32×32 pixel logo drawn from eight color tokens, its own station-break voice and its own music pool. A channel orders its tapes one of three ways: by year (8 channels), shuffled (12), or block rotation (4). Block rotation is a few episodes of one show, then the next show, round and round. A channel is a published list of tapes with a start time. “What is on now” is arithmetic: walk the schedule from its start to the present. The same code runs in the browser, the publishing tool and the tests, in whole milliseconds, so every machine gets the identical spot on every channel's schedule across the globe. The math also accounts for the bump breaks, which get re-picked on every pass through the lineup. The browser measures its clock against the server’s once (the HTTP Date header) and corrects it only when it is more than 2 seconds off. A new lineup is published at least two minutes ahead and takes over at the end of a break. Open tabs hear of it over a websocket and switch without a reload. Some key features built in: Limited DVR: Pause, skip back 15 seconds, or scrub within the tape that is on. The player shows how far behind live you are, and LIVE snaps back. (While paused you fall further behind each second) In-line timestamped comments and reactions: A comment is stored against the tape (its Archive identifier) and a second within it, not against a channel or an air date. It comes back at that second every time the tape airs, on any channel that carries it. I've linked up a discord to help moderate potential comments (there are none yet lol). Signing in to comment also joins you to the Discord. Reactions work the same way and require no account or Discord. Its a free for all. A reaction from someone within 10 seconds of your playhead floats up over your picture as it happens. Comments are permanent pins on the timeline and pop up over the picture as the playhead crosses them. I like to think of it as pop-up video cable graffiti. Future plans: Marathon blocks GeoCities-accurate homepages for every channel with guest books and webrings. 90's chatroom skinned live chats from the discord server for every station. Machine-generated live synced English subtitles for foreign stations (big interest in vintage Japanese Game Show channels) Clipping for users and a leaderboard to browse and share clipped moments from live TV. More channels: I'm currently building a "horror hosts" B-movie channel for Halloween. Think Elvira and many other public access hosts from bygone eras showing terrible creature features. I've also got infomercials, game shows, paranormal TV, and probably another 50 channels worth of stockpiled ideas ready to build! submitted by /u/thetalkinghawk to r/ClaudeAI [link] [comments]

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84%releasereddit-releases

AuroraIMG-6M Released!

/u/Tall_Abrocoma_3533 · discussion

Usable today 84%
Abstract

It's a tiny 6M parameter text to image model, and it can make recognizable images at 64x64. Check out the model card for samples, a detailed explanation about it's architecture and training, and a quick start! submitted by /u/Tall_Abrocoma_3533 to r/StableDiffusion [link] [comments]

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84%releasereleases-llama-cpp

llama.cpp b11564

github-actions[bot]

Usable today 84%First-party 80%
Abstract

vulkan: use ternary expressions to avoid switch lowering for CM2 quant block size ( #30164 ) vulkan: specialize quant block size for CM2 matmul vulkan: use ternary expressions to avoid switch lowering for CM2 quant block size vulkan: clarify CM2 quant block size comment Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54952894 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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84%releasereleases-llama-cpp

llama.cpp b11560

github-actions[bot]

Usable today 84%First-party 77%
Abstract

hexagon: add IM2COL_3D ( #30305 ) hexagon: add IM2COL_3D support Assisted-by: OpenCode hexagon: add DMA paths for IM2COL_3D Assisted-by: OpenCode hexagon: avoid scalar fp16 conversions in IM2COL Assisted-by: OpenCode remove in-loop spills in pre-v81 devices Assisted-by: OpenCode hex-im2col: re-write to use canonical dma dbl-buff pipeline, remove divs and very inefficient fp conversions hex-im2col: move kernel_params and vtcm layout precompute into the host hex-im2col: remove explicit tracking of number of dma-pushes hex-im2col: remove incorrect uint64_t casts hex-im2col: no need for macros for dma loops and pass compute func ptr via local context hex-im2col: use cursor pattern, tracing and proper n_threads handling hex-im2col: support non-contigue views hex-im2col: minor reformatting Co-authored-by: Max Krasnyansky maxk@qti.qualcomm.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54919473 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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84%releasereleases-llama-cpp

llama.cpp b11556

github-actions[bot]

First-party 84%Usable today 84%
Abstract

model : support for Prism Bonsai 2 27B ( #29600 ) Runtime support for Prism Bonsai 2 27B Assisted-by: Claude Code address prism hadamard runtime feedback move hadamard tensors into method, define folded weight load_* hadamard fixes cont : clean-up conversion cleanup Assisted-by: Claude Code prism hadamard key and methods for converter Assisted-by: Claude Code address review bot feedback Assisted-by: Claude Code Co-authored-by: Georgi Gerganov ggerganov@gmail.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54799696 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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84%releasereleases-llama-cpp

llama.cpp b11555

github-actions[bot]

First-party 84%Usable today 81%
Abstract

cpu: fix AVX2 CPUs not using F16C when available ( #30270 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54796975 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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83%releasebluesky92 likes · 29 reposts · 1 reply

NASA Just Made Nearly 1 TB of Artemis II Data Available to the Public

WIRED · @wired.com, buff.ly · discussion

Usable today 83%First-party 58%
Abstract

The data drop includes photographs, audio, video, and astronauts’ notes that will be used to study the moon and prepare for future human missions. Linked page: The data drop includes photographs, audio, video, and astronauts’ notes that will be used to study the moon and prepare for future human missions.

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82%releasereleases-llama-cpp

llama.cpp b11557

github-actions[bot]

Usable today 82%First-party 81%
Abstract

rpc : turn GGML_RPC_DEBUG into a verbosity level and add logs ( #29544 ) rpc : turn GGML_RPC_DEBUG into a verbosity level and add logs GGML_RPC_DEBUG is now parsed as a number: 0/unset disables debug logs, 1-3 emit increasingly detailed output (events, per-command trace, transport detail). Non-numeric values fall back to 1. The duplicated env/macro blocks in ggml-rpc.cpp and transport.cpp are replaced by a shared log.h, which becomes the single choke point for all logging of the RPC backend: LOG_ERROR/LOG_WARN/LOG_INFO for unconditional severity logs and LOG_DBG/LOG_DBG2/LOG_DBG3 for the verbosity-gated ones. The transport files no longer need ggml-impl.h, and the server banner now also goes through the ggml logger (stderr). Missing logs are added on both the client (handshake, buffer ops, tensor transfers, graph computes, cache decisions) and the server (per-command dispatch, graph nodes), including the negotiated transport via the new socket_t::transport_name(). Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-RL rpc : align log levels with the documented verbosity semantics The first pass introduced GGML_RPC_DEBUG levels, but many call sites did not follow the documented semantics and some logs bypassed the macros entirely: Route the remaining raw GGML_LOG_* call sites through the local LOG_* macros so that log.h stays the single choke point for RPC logging Emit per-tensor transfers and per-command handler traces at level 2, and one-shot lifecycle events (backend and buffer type creation, buffer allocation, graph compute, graph cache eviction) at level 1 Drop the duplicated enqueue trace in rpc_dispatcher::send, work() already logs every dispatched command together with its round-trip timing Promote degraded-operation events to LOG_WARN: remote allocation failure, RDMA falling back to TCP, unexpected peer disconnect Log graph cache eviction on both sides, since client and server must stay in sync for the incremental graph update to be valid Distinguish Unknown command (opcode out of range) from Unhandled command (valid opcode with no handler) Fix format specifiers (%zu for size_t, %u for device ids, 0x for hex pointers) and a missing newline in an error log Document that llama.cpp applications drop ggml debug records unless the verbosity threshold is raised with -lv 5 Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54812036 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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81%announcementbluesky51 likes · 5 reposts · 2 replies

New Sakana AI paper: Recursive Self-Improvement through Multi-Agent Self-Supervision (MASS). Blog: pub.sakana.ai/mass/ Paper: arxiv.org/abs…

hardmaru · @hardmaru.bsky.social

First-party 81%
Abstract

New Sakana AI paper: Recursive Self-Improvement through Multi-Agent Self-Supervision (MASS). Blog: pub.sakana.ai/mass/ Paper: arxiv.org/abs/2610.12176 How do we keep improving AI when tasks become too complex for humans to reliably evaluate? The model eventually hits a supervision bottleneck.

bsky:e47ea2d9304b14aaUseful?👍👎
81%releasereleases-llama-cpp

llama.cpp b11558

github-actions[bot]

Usable today 81%First-party 75%
Abstract

opencl: fix image limit for q4_k dense bin kernels, q4_0 bcast and rms_norm ( #30310 ) opencl: fallback when Q4_K weight images exceed device limits Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com opencl: avoid incomplete subgroup in rms_norm opencl: refine conditions for dense bin kernels and fix bcast for q4_0 Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com Co-authored-by: Hongqiang Wang wangh@qti.qualcomm.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54829575 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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80%opinionreddit-releases

Opus 5.5 is INSANE at decompilation / ports

/u/CarlosGOAT09

Usable today 80%
Abstract

Just decompiled and natively ported StarFox, StarFox 2 and Sonic CD to Windows, Mac and Linux, using Opus 5.5 without REA or similar skills. Claude analyzed the games' roms, built high level hardware runtimes, and added quality of life improvements (resolution, framerate, audio, full controller support, etc.), Even going as far as editing the game's extracted source code to implement features (SuperFX overclocking, True16:9 in Sonic CD) Projects are here, if you wanna try it out ;) https://github.com/carlosrmz009/littleplanet https://github.com/carlosrmz009/arwing https://github.com/carlosrmz009/arwing2 Prompt was simply: Create a full decompilation of *GAME*, so it can run natively in my PC. Game ROM is at */path/* True16:9 gameplay Custom launcher with options This is getting insane bro 😭🙏 EDIT: Fair correction on the terminology: I used "decompilation" too loosely. My actual goal was to see if I could get these games running as native desktop builds and add modern features. At least in Sonic CD's case, the output seems more accurately described as a game-specific emulator plus generated disassembly/static recompilation, rather than a clean reconstruction of the original source code. Still a pretty wild result for me, especially being able to iterate on features like widescreen support. Appreciate the detailed technical breakdown! submitted by /u/CarlosGOAT09 to r/ClaudeAI [link] [comments]

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80%releasereleases-llama-cpp

llama.cpp b11562

github-actions[bot]

Usable today 80%First-party 61%
Abstract

vulkan: fix buffer logging on 32-bit platforms after source split ( #30326 ) Move the vk::Buffer stream operator to the shared header so it is visible to ggml-vulkan-debug.cpp. Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54939218 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 (89)