Signal Radar

2026-10-21

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

207papers read
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28shortlisted
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12must-read

195 s · 185,150 input tokens · ≈ $0.0078 · jev-1.13.0 via typesafe

Must-read ≥ 90%

98%measurementreddit-ai

Qwen3.8-Flash-Next vs Opus 5.5: Six-way benchmark on a real Rust feature with Claude Code and Pi

/u/deepu105

Measured result 98%Usable today 35%
Abstract

A few of you pointed out in my last post that comparing Flash-Next and Opus with different harnesses and effort levels wasn't fair. So here is a more controlled comparison. Same task for every run: a real feature from the LlamaStash roadmap (save llama-server's KV cache before an unload and restore it when the model comes back). Six combinations, each run twice, so 12 PRs. Flash-Next ran locally on my Strix Halo laptop (Flow Z13) via Halogen (~Q4 quant). Opus 5.5, Flash-Next, and Kimi K3 scored every PR on seven review questions plus CI. Combination Score (of 8) Time API cost Tokens (in / cached / out) Claude Code + Opus 5.5, xhigh 6.94 (87%) 43.5 min $17.78 507k / 48.3M / 203k Claude Code + Opus 5.5, medium 6.25 (78%) 23.4 min $6.52 242k / 13.4M / 95k Claude Code + Flash-Next, medium 5.5 (69%) 45.3 min $0.32 445k / 13.5M / 82k Pi + Flash-Next, xhigh 5.44 (68%) 63.8 min $0.67 316k / 35.0M / 134k Claude Code + Flash-Next, xhigh 5.38 (67%) 49.5 min $0.39 269k / 18.9M / 99k Pi + Flash-Next, medium 5.06 (63%) 48.1 min $0.56 242k / 29.1M / 117k Scores are the average of both runs. Time, cost and tokens are from the second run, where the 100k-token benchmark numbers were given in the prompt. Flash-Next cost is at the QwenCloud price for Qwen3.8-Flash; I ran it locally, so it cost me only electricity. TL;DR All 12 runs finished and all 12 PRs pass the end-to-end test. Opus wrote better code, 82% on average against 68% for Claude Code + Flash-Next and 66% for Pi + Flash-Next. The difference is in edge cases, tests and docs. The core feature works fine. The gap between Opus and Qwen is not that wide. Flash-Next took 1.1x to 2.1x as long as Opus at the same effort. With Flash-Next, Claude Code and Pi scored the same on the review questions. CI passed on 4 of 4 Claude Code PRs and 1 of 4 Pi PRs. xhigh helped Opus a bit at 2.7x the cost. For Flash-Next, medium and xhigh scored about the same, and medium was faster in all 4 pairs. Opus is still ahead, but Flash-Next was right behind, on my laptop, for the cost of electricity. Also note that the models are not on the same league. Opus is a frontier model run at FP16 or atleast FP8. I ran Flash-Next on my laptop at 4.16 bpw . Flash-Next at FP8 should show a bit more quality. The machine running Flash-Next ran at 70W TDP ceiling for the entire duration. I could have run it at 90W for slightly better speed but the fan noise and thermal stress was not worth it. Full write-up with the detailed methodology and the per-run results and what each model missed: https://deepu.tech/qwen3.8-flash-next-vs-claude-opus-showdown All 12 PRs are public if you want to read the code: https://github.com/deepu105/llamastash-b4r2/pulls submitted by /u/deepu105 to r/LocalLLaMA [link] [comments]

rss:371f2d39df9267d1Useful?👍👎
95%businessreddit-ai

Waymo's robotaxis had 95% fewer serious crashes than humans. Now it plans a fleet of 20,000 cars

/u/Anen-o-me · discussion

Measured result 95%
Abstract

Waymo's safety hub now covers 271 million driverless miles: 0.01 serious-injury crashes per million miles, against 0.21 for the human benchmark in the same areas. An independent IIHS study on older data (2021-2024, about 50 million miles) found 81% fewer injury crashes, though Austin came out slightly worse than human drivers. Semafor now reports Waymo wants to go from about 4,000 cars to 20,000 by the end of 2027, funded by a $5 billion loan. submitted by /u/Anen-o-me to r/singularity [link] [comments]

Same story elsewhere (1)
rss:1bb4939d1eb57da8Useful?👍👎
95%measurementreddit-ai

Eagle-4 Released: Faster than DFlash2 & DSpark

/u/TKGaming_11 · discussion

Measured result 95%Usable today 32%
Abstract

Insane numbers, so far only Qwen3.8-27B is supported but wow those are some good numbers. Will watch closely when this lands in llama.cpp/vLLM ### Qwen 3.8 27B Benchmark Results Method Speedup over Vanilla MTP 2.22× DSpark 2.57× DFlash 2 2.80× EAGLE-4 3.59× submitted by /u/TKGaming_11 to r/LocalLLaMA [link] [comments]

rss:727279686ce40b0fUseful?👍👎
95%releasereddit-releases

FastH3 Omni Open Weight is here!

/u/mnmunknown

Measured result 95%First-party 88%Usable today 60%
Abstract

https://reddit.com/link/1xb19y9/video/jx0qabbs6owh1/player FastH3 Omni is here. It's 5-11x faster as we tested on b200 and faster on your consumer GPU too. Hi!!! The folks behind FastH3 Omni is here and it's open weight! It's fast, it's omni reference, and yes you can run on many more consumer GPUs. We will do live Q&A for about an hour here. Drop your comments! This video is made entirely with FastH3 Omni with our creative agent when we play around it for fun. Now you can do it too. As promised, we're open-sourcing the reference-conditioned FastH3 (built on MiniMax-H3), training code included. Give it a prompt plus images, video clips and a voice, and it returns a finished video with synchronized sound. https://preview.redd.it/aj4e8q83oowh1.png?width=2586&format=png&auto=webp&s=d10a9482a23598c74338af6bcb5f6eebb66bbb03 Up to 9 image references and 3 video clips in one request, plus a voice Tag each one in the prompt (<Picture 1>, <Video 1>, <Audio 1>) to control whose face, which motion and whose voice goes where 8 forward passes instead of 50 steps (PDD + DMD distillation) 5.6-11.9x lower end-to-end latency than base MiniMax-H3, same 1344x768 output Training code and reference preprocessing are open Blog with the gallery and benchmarks: https://haoailab.com/blogs/fastvideo-omniref-update/ Models: https://huggingface.co/collections/FastVideo/fasth3-omni Code: https://github.com/hao-ai-lab/FastVideo Local release + blog drops tomorrow. Built with Nuva Lab and Reactor , with support from NVIDIA. Happy to answer questions! submitted by /u/mnmunknown to r/StableDiffusion [link] [comments]

rss:a43795797faac165Useful?👍👎
93%releasereddit-ai

Phonon is now open-source, 31M and 40M TTS models designed to run on the edge

/u/l-m-z

Usable today 93%First-party 92%
Abstract

At Gradium we just made our first open-source model release with Phonon, a collection of small Text-to-Speech models designed to run on device. The inference stack is fully open-source and targets 10+ different backends (CPUs, GPUs, NPUs), most of them in Rust. We hope that the community will build lots of different things on tops of our models and code, any feedback is very welcome and we're looking forward to many more open-source releases! https://huggingface.co/spaces/gradium/phonon submitted by /u/l-m-z to r/LocalLLaMA [link] [comments]

rss:77cf5f652b0a6a20Useful?👍👎
92%releasereleases-ollama

ollama v0.40.7

github-actions[bot]

First-party 92%Usable today 80%
Abstract

What's Changed pplx-decider is a 27B decision model from Perplexity, fine-tuned from Qwen3.8-27B Continuing an OpenAI-compatible Responses conversation no longer fails when switching between models with and without thinking, or when the history contains another provider's compaction items Fixed streaming Responses requests where client tool calls (e.g. Codex with web search) could emit before open reasoning items, producing misordered/duplicate tool call events Requests routed to remote models are now cancelled on ollama.com when the client disconnects, instead of continuing to run ollama show now reports which renderer and parser a model uses and where its chat template comes from; the API exposes the same values in new renderer , parser , and template_source fields More draft models now work with speculative decoding: llama-server infers the draft type automatically instead of requiring Ollama to identify it ollama launch claude now clamps CLAUDE_CODE_AUTO_COMPACT_WINDOW to Claude Code's supported range, avoiding a "window too large" warning Updated llama.cpp and MLX Full Changelog : v0.40.6...v0.40.7

Same story elsewhere (1)
rss:57e5236276ed1fd2Useful?👍👎
91%opinionreddit-releases

Fibo 2 Edit is what Klein 3 should've been.

/u/East_Shoe8814

Usable today 91%
Abstract

Very good so far. Must be the first time I actually believe the score. It's far from perfect, but it's extremely uncensored out of the box and fast. I haven't noticed any color drift or quality loss either. The Flux 2 VAE really helps this model. Really excited for proper ComfyUI support. And yes, it's uncensored out of the box. To clarify, I am not saying it's the best edit model. My prompts are pretty simple, it handles them well, doesn't break anatomy, and isn't censored. You'll have to test it yourself for more complex tasks. Links: Model: https://huggingface.co/Comfy-Org/Fibo-2.0 Workflow: https://github.com/Comfy-Org/ComfyUI/pull/17240 P.S. By “very uncensored,” I mean it can generate nudity. I don’t know if it can do anything beyond that, though. Update: It seems to struggle with more complex prompts and multiple references. submitted by /u/East_Shoe8814 to r/StableDiffusion [link] [comments]

rss:4188994ab16c714eUseful?👍👎
91%opinionreddit-releases

That was the best $48.62 spent on Fable. Thank you Anthropic

/u/david-labs-ca

Measured result 91%Usable today 61%
Abstract

Since many months we were struggling to fix a critical bug in our App. That was discovered in a native vertex library. The impact was that AI was not able understand properly when the user has stopped talking. Because the mics that we have today are sensitive enough to pick Fan noise or any other distraction. We were struggling on this issue since May. Tired of changing models after models, We switched from Cursor to Claude.ai. For Cursor was imposing Grok on us. You know why Claude is better than Cursor. Because it is very transparent. Team subscription will cost you this much. Fable extra on usage. So we paid extra for Fable, gave it a simple prompt, and freedom, and let it rip through the codebase. 48.62 dollars later, the app was finally able to understand... and have a proper Teacher student conversation. Still not very smooth but much much better. Thank you for creating Fable. Our dream of making a zero-bias and accessible teacher is on the right track now. submitted by /u/david-labs-ca to r/ClaudeAI [link] [comments]

rss:563c06e4918157ecUseful?👍👎
90%releasereddit-ai

XHToken/Spark-X2.6-4B · Hugging Face

/u/jacek2023 · discussion

Usable today 90%First-party 60%
Abstract

https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF Introduction A little over a month has passed since Spark-X2.5. In the rapidly evolving world of AI agents, that is enough time to rethink, refine, and move the frontier forward. Building on the Spark-X2.5-4B, we introduce Spark-X2.6-4B , the latest generation of our compact, general-purpose language models for on-device deployment. Spark-X2.6-4B retains exactly the same architecture as Spark-X2.5-4B, achieving its capability gains through improved post-training. It delivers substantial improvements in coding, agent, long-context, multilingual, reasoning, etc. These advances enable more capable and practical on-device AI for developers and users tackling real-world tasks. Technical Highlights : Stronger Coding, Agentic, and Long-Context Capabilities : The models deliver substantial improvements in agentic workflows involving complex tool use, multi-step planning, and software engineering. They achieve state-of-the-art performance among similarly sized open-source models, matching or even surpassing models with approximately 9B parameters. Controllable Thinking Budget : The models provide three configurable thinking levels—none, low, high—allowing applications to balance reasoning quality, latency, and inference cost dynamically. no more excuses for "GPU poor", stop being lazy and run small models today submitted by /u/jacek2023 to r/LocalLLaMA [link] [comments]

rss:dd96e41a820ddf7bUseful?👍👎
90%releasereddit-releases

Kromoss has been updated

/u/mikemend

Usable today 90%
Abstract

The Kromoss model was updated today, it's worth replacing yesterday's version: https://huggingface.co/lodestones/Kromoss Here are a few examples from u/lodestonerock : https://huggingface.co/lodestones/Kromoss/discussions/3#6ad6e1472803556bd669b5dc Update: Meanwhile, the 8-steps-dmd2-lora has also been released! submitted by /u/mikemend to r/StableDiffusion [link] [comments]

rss:0a048d8cef605478Useful?👍👎
90%analysisreddit-releases

How to train MOSS-Image LoRAs locally (8 GB VRAM): settings, tips and workflow

/u/AcademiaSD

Measured result 90%Usable today 70%
Abstract

A lot of people have been asking me for the "right" settings to train MOSS-Image LoRAs, and some are getting terrible results after training for thousands of steps. So here's everything in one place, with screenshots of a real run. TL;DR: for a character LoRA, start with the default settings and train 800 steps (just an example: other types of training may need other settings). The likeness should already be there by step ~800; go to 1200–1500 only if your dataset is large or the concept is hard. If you're at 8000 steps, you've gone way past the point where the LoRA was good. The tool I train with AcademiaSD LoRAlab Trainer Studio , a free, open-source local trainer (one installer, one launcher, NF4 models) that also does Qwen-Image, FLUX.2 Klein, Z-Image, Ideogram 4, SDXL, LTX-2.3 and more: GitHub: https://github.com/AcademiaSD/AcademiaSD_LoRAlab-TrainerStudio Video with the ComfyUI workflow: "AMAZING MOSS-IMAGE! UNCENSORED, JSON + BBOX EDITING, LONG TEXT AND MORE" : https://www.youtube.com/watch?v=nXqRnyRUwgU No GPU? There's a ready-made RunPod template (link in the repo README). MOSS-Image (OpenMOSS, Apache 2.0) is a 5B single-stream model. In Trainer Studio the transformer is NF4, so training at 512² with rank 8 uses under 6 GB of VRAM (~1 s/step on an RTX 5080). 8 GB cards are perfectly fine. Settings that work (character LoRA) https://preview.redd.it/3f2swpqchmwh1.png?width=2530&format=png&auto=webp&s=9b6c93fb767331814cba13ebad8c6a6ddd884a14 Setting Value Base model Base (the one OpenMOSS publishes for LoRA training; SFT also works) Steps 800 (1200–1500 for big datasets or harder concepts) Learning rate 0.0004 LR scheduler cosine, 100 warmup steps Rank / alpha 8 / 8 Batch size / grad accumulation 1 / 4 Training resolution 512² Save every 100 steps ⚠️ These settings are just an example , a starting point that works for this character LoRA. Each type of training (characters, objects, styles, concepts) and each dataset may need different settings: steps, learning rate, rank, resolution or captions. Run your own tests and compare checkpoints. Why 8000 steps goes wrong: with gradient accumulation 4, every step already sees 4 images. 800 steps = 2,000 image views; 8000 steps = 32,000. At that point the LoRA has long since overfit, and adding more images doesn't fix that. More steps is not more quality here. How to pick the right checkpoint Train with the defaults; a checkpoint is saved every 100 steps. Check the previews, but judge the final result in ComfyUI : ComfyUI gives better quality and closer likeness than the in-trainer previews (they're there to follow the training, not to judge it). Test 300 / 400 / 500 in ComfyUI. If the likeness isn't there yet, continue to 1200–1500 and test those checkpoints. https://preview.redd.it/1y5va8w2hmwh1.png?width=2540&format=png&auto=webp&s=7f85fd1cbc97df033dd621c6eeaec815699ce8c9 If your results still look bad at 800–1500 steps, post your settings and a few dataset images (or open an issue on GitHub) and I'll take a look. Captions and dataset Use the built-in captioner (Qwen3-VL-8B NF4). It writes detailed prose following OpenMOSS's own recaptioning guide, with the trigger word first. Captions matter more than dataset size: a clean, varied, well-captioned dataset beats a huge one. ComfyUI The LoRA is exported with the original layer names, so a recent ComfyUI (nightly, with native MOSS-Image support) loads it with the standard LoRA loader. The workflow I use is in the video linked above. https://preview.redd.it/b6ee8ui7hmwh1.png?width=2554&format=png&auto=webp&s=487467e165f6755417bdf88e822b191b9d8b5f68 If your results still look bad at 800–1500 steps, post your settings and a few dataset images (or open an issue on GitHub) and I'll take a look. submitted by /u/AcademiaSD to r/StableDiffusion [link] [comments]

rss:9192296e0f60f78cUseful?👍👎
90%releasehn

Google open-sources TorchTPU, a native PyTorch back end for TPUs

ilreb

Usable today 90%First-party 83%
Abstract

Article URL: https://github.com/google-pytorch/torch_tpu Comments URL: https://news.ycombinator.com/item?id=50169053 Points: 320 # Comments: 73

rss:90c51fdabafb679fUseful?👍👎

Maybe 80% to 90%

89%incidenttechmeme

OpenAI says two agents, one GPT-6.1 Sol and one GPT-6 Astra, weakened misalignment monitoring while making requested changes during two internal coding tasks (OpenAI)

First-party 89%
Abstract

OpenAI : OpenAI says two agents, one GPT-6.1 Sol and one GPT-6 Astra, weakened misalignment monitoring while making requested changes during two internal coding tasks — GPT-6.1 Sol and GPT-6 Astra · Internal deployments — Discovered: Oct 3, 2026 (Sol); Oct 6, 2026 (Astra) — Summary

rss:6497cb1275e2bfb8Useful?👍👎
88%announcementhn

Voxtral 3

ilreb

First-party 88%Usable today 38%
Abstract

Article URL: https://mistral.ai/news/voxtral-3/ Comments URL: https://news.ycombinator.com/item?id=50168396 Points: 604 # Comments: 152

rss:b33469bf7717e3ccUseful?👍👎
88%releasereleases-llama-cpp

llama.cpp b11778

github-actions[bot]

First-party 88%Usable today 82%
Abstract

model : support Nemotron-3(.5)-Embed ( #30877 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58053681 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:abd3cb0112abc775Useful?👍👎
87%releasereddit-ai

ornith-ai/Ornith-1.5-122B (Qwen finetune)

/u/jacek2023 · discussion

Usable today 87%First-party 76%
Abstract

from Ornith: Ornith-1.5-122B Chirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog . https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF https://huggingface.co/unsloth/Ornith-1.5-122B-GGUF https://huggingface.co/ornith-ai/Ornith-1.5-122B-NVFP4 submitted by /u/jacek2023 to r/LocalLLaMA [link] [comments]

rss:584d411bc7527f49Useful?👍👎
87%announcementsimonwillison

Battle Drones - a vibe coded RTS with audio-based multiplayer

Usable today 87%Measured result 78%First-party 32%
Abstract

My latest vibe coded game experiment is a Command & Conquer style RTS (Real Time Strategy) game called Battle Drones ( play it here ). Unlike my previous experiments , this one is actually fun to play . I'm somewhat embarrassed to admit that I got there by telling Claude to "Make it a LOT more fun". It also supports multiplayer between nearby devices using audio to synchronize game state! Here's what I've built so far, and some of the things I learned along the way. Battle Drones How I built this v1: the initial prototype v2: full sprites v3: varied terrain v4: the fun update v5: Battle Drones goes 3D v8: Archipelago v10: Goliath, and agent battles Agent battles v11: Random Maps v12: Multiplayer via sound Some thoughts Update: Online multiplayer support Battle Drones My initial goal was to build an RTS game I could play on my phone, in the style of Command & Conquer . I have fond memories of the 1995 original (I liked Red Alert too) and wanted to recapture a little bit of the magic. After 12 major iterations (and 321 commits by coding agents, mainly Claude Code) I now have a browser-based game that works on mobile and desktop, has multiple play modes, several maps (including a random map generator) and even supports multiplayer for two devices within microphone range of each other. ( Update : also online multiplayer ; the audio multiplayer induced headaches!) Here's a video demo of Battle Drones running in a desktop browser: Features so far include: 9 buildings, 7 ground vehicles, 6 aerial vehicles, 3 infantry units - all 3D models, which you can browse here Fog of war Different weapons are needed to target aerial or ground vehicles Six maps , across five biomes - temperate, desert, arctic, volcanic, tropical A random map generator Skirmish mode - red vs. blue on a rotationally symmetrical map Last Stand mode - one player holds out against as many waves of enemies as possible Area effect weapons: Barrage, EMP, Drop Pod, Sky Hammer Crate drops with bonus resources In-game music that responds to events - it gets more intense as fighting breaks out Different in-game AI personalities (Balanced, Rush, Air Swarm, Boom, Skyforce, Ironclad) AI vs. AI mode, to watch those personalities play each other The Goliath - a giant stomping mech with laser eyes and arm cannons and the ability to establish a new base Mutators that add extra rules or twists A "Daily Op" challenge Ranks and achievements ( Medals ) Agent vs. Agent mode where two LLMs can play Battle Drones against each other A multiplayer mode based on two nearby phones communicating via audio messages, an extension of my earlier experiment with Chirp Pong How I built this Battle Drones is heavily vibe coded - I've reviewed almost none of the code, and it's built in a language (TypeScript) that I've hardly used myself. I've playtested it quite a bit though, and it's been through a large number of iterations since I started the project. Here's a rough history of the project so far, including key prompts and links to transcripts. v1: the initial prototype I started out with Claude Fable 5 back in July: We are going to build a mobile friendly RTS game - command and conquer style - called Battle Drones, in which two armies of drones (both flying and ground-based) battle for territory and resources Let's start with a MVP design, ask me some questions Here's a shared transcript . I'd given Claude Code for Web an environment variable with an OpenAI API key and told it to use the OpenAI Image Generation API for assets, which got me some basic sprites. You can try v1 here - it looked like this: It's recognizable as an RTS, has building mechanics and the ability to select and direct units, and a basic AI to play against. This first version was barely a proof of concept, and it wasn't any fun to play. Claude used the Phaser 2D game framework for these early versions. I didn't even realize this until several iterations later! v2: full sprites The second version ( try v2 here ) added sprites for all of the vehicles and the "AI vs. AI" mode, so I could watch the game playing without having to directly control it myself. Claude proved reasonably competent at prompting OpenAI's image models to generate sprites. You can browse the v2 sprites in the public/assets/sprites folder. v3: varied terrain It still wasn't much fun, and the graphics were boring. It was now October, and I had upgraded to Claude Opus 5.5. I told it: Massively improve this game. Make the graphics and gameplay better (keep it mobile friendly). Render new graphics using the latest OpenAI GPT image API (lookup details) with the API key in the environment variable. Add new unit types and balance the gameplay better. Also add new buildings and better, more varied terrain. This resulted in five new maps with generated background tiles in a variety of styles, visible here . Opus also regenerated the sprites, making them much more visually appealing. You can try v3 here - it looks like this: This is starting to feel like a real game! It still wasn't very fun to play though - I found myself getting bored pretty quickly. v4: the fun update So far my Battle Drones experiment was confirming my previous belief that coding agents can build a thing that looks like a game but can't actually make something that's genuinely compelling to play. I shared this theory with someone over dinner, and then thought to myself that I should give the models a proper chance to prove me wrong. I prompted Opus 5.5 on my phone: This game isn't fun enough yet. Make it a LOT more fun. This time I was running in Ultracode mode , where Claude Code (in this case Claude Code for Web) runs "dynamic workflows", described in their docs as: A dynamic workflow is a JavaScript script that orchestrates many subagents at once. This kicked off many hours of work. I started it in the evening and didn't get a playable result back until late into the next morning. But the results were spectacular. You can try v4 here - it looks like this: I had Claude write its own release notes - here are the highlights: Every skirmish and Daily Op now opens with a choice, gives the side that's losing a way back, and pulls the armies into the middle of the map. There are also two new ways to play. Openers : pick Blitz, Industry or Air Wing before the clock starts. Call-down powers : Barrage, EMP, Drop Pod and the Sky Hammer orbital strike. Objectives and salvage : Uplink relays, supply drops and wrecks worth scrap. Feel : combat juice, adaptive music, unit voices and an announcer. Command : battle alerts, game speed, saved squads and touch placement. New modes : Last Stand survival waves and an 8-mission Ops campaign. Progression : heroes, an after-action report with awards, a career, 7 mutators and a Daily Op. Fights run longer and swing more. In 1,200 AI-vs-AI test matches on Normal, games still undecided after 25 minutes fell from 14% to 0.9%. The share of wins by a side that had been 800+ scrap behind rose from 24% to 46%. Part of the reason Claude took so long was that it ran over a thousand simulated playtest sessions, and used the results to rebalance the existing units, add new ones, and introduce new features. v5: Battle Drones goes 3D After the success of Ultracode at "Make it a LOT more fun", I decided to be more ambitious: Port this game to three.js with 3D models for all of the buildings and vehicles and landscapes That was another overnight run. I woke up to this v5 version : The new 3D models were entirely constructed by Claude in TypeScript code. v6 ( play v6 here ) added some more visual polish. v7 ( play v7 ) added robot infantry (troopers, rocket squads, snipers) and a "Condor" troop carrying aircraft. v8: Archipelago I was beginning to feel guilty at how little actual design input I had into the game - it really had been a case of me saying "make it more fun" and "add visual polish" for the past few releases. I'd noticed that the AI didn't use the new infantry and troop carrying aircraft from v7 very often - there wasn't a great strategic reason to use them. It would be neat if we had a new map with a new game mechanic that actively encouraged players to load up Condors with infantry robots and send them out into the map. This resulted in my longest prompt yet: Design a new map. Goal is to make different strategic incentives that encourage use of Infantry and Condors and aerial vehicles. To do this the map will have limited harvester resources but several more uplinks - but uplinks will be on islands that are inaccessible by land vehicles or infantry. These uplinks can also not be claimed using air vehicles. The ONLY way to get them is to fly infantry there in a Condor. Armies always start with a Condor and a single squad. If an army has no Condor a new one will be provided for free, flying in from the edge closest to their HQ and landing there. That free condor will come with a single free squad on board. The two HQs should be entirely separated by water, but still accessible by vehicles thanks to two bridges across the map in different places I want to encourage a battle over uplinks. The map should be the times the size of other maps - still square First research and then critique this design. Only build after we have agreed on any modifications You get to design a new biome for this The result was the tropical Archipelago map, with seven island uplinks that players can only capture by flying out infantry in a Condor. It's definitely the most fun map to play, once you've got the hang of the basic game mechanics from the simpler maps. The v9 edition made that map a little more interesting - it now looks like this: v10: Goliath, and agent battles I decided to add giant robot mechs to the game - think Jaegers from Pacific Rim , or Titans from Warhammer 40,000. Here are some of the prompts I used to brainstorm with Claude: Help me brainstorm ideas for adding giant robots to this game - think dreadnoughts or titans from W20K - how could we do this in a way that enhanced gameplay, kept things balanced, added unique twist? Don't overthink this yet Let's keep brainstorming. I like the idea that it's a mobile base - that can accept harvested scrap and can have buildings built near it (even if it later moves on) and can produce infantry units too. An interaction with condor would be neat but I don't know what that could be. Idea: as it gets damage it loses the ability to fire some weapons - by default it has eye laser (like lancers) and arm canons (like titan) and two shoulder mounted mortars, plus machine guns similar to a squad of troops Damage knocks out mortars and then laser - it always has guns. You can't build next to a building that was only put there because of the mech, you need the mech there for off-base building and only within three squares of it [...] Lets do two condors can carry the mech The end result is really cool . Your browser does not support HTML video. Goliaths can be constructed using a Gantry - a building that can only be created after first building a Fab, Bay, Airpad, and Tech Lab. You can see the Goliath under construction, and it then walks out from the Gantry and can march around the map. Goliaths can "anchor", which digs them into the ground. In their anchored state they can act as a new HQ, constructing infantry units and additional buildings in a new area. Two Condor aircraft can hook onto a Goliath, at which point it can be flown around the map! The game now ends when you destroy the enemy's HQ building and any Goliaths they might have built. There are also a couple of mutators relating to Goliaths: Goliath Armada starts each side with four airborne Goliaths (each with a pair of Condors) and no other units. Robot Love adds a special rule: if enemy Goliaths get close to each other on the battlefield they fall in love and the game ends in a draw. (Thank Natalie for that one). Agent battles The other v10 feature is agent battles . In musing about how multiplayer could work I had an idea: what if different LLMs could fight it out in the game? Fun extra challenge: think about how we might make multiplayer available to coding agents. It would be neat if we could set up a battle between eg GPT-6 Sol and Claude Opus 5.5 where each model has access to a tool which gives them information about the game (presumably non-visual, but only the information they could see through fog of war etc) and lets them control their units etc [...] Consult ~/dev/llm/docs and include notes in that file about how LLM could be used here via a new tools plugin Opus 5.5 went to work on this, producing this AGENT-BATTLES.md proposal and then implementing and testing it. The feature also includes the ability to record and replay these battles - here's a replay of Claude Haiku 5.5 vs. GPT-6 Luna , and here's that same battle in a separate debug tool that shows the underlying prompt responses. The prompt that drives agent battles can be seen here . There are a few more replays like this linked in the README . I hope to turn this into a full league table at some point in the future. v11: Random Maps This version exists purely because I glanced at my Claude Usage page one day and saw that I'd only used 75% of my weekly allowance and it was resetting in 45 minutes. I decided to see if I could get an extra Battle Drones feature from my remaining tokens, and picked "random map" as the target: Design and implement a mechanism for generating a random map using any of the available biomes - it should be the size of the largest map and rivers and bridges and rocks and forest and scrap and crystals are all in play. Make them rotationally symmetric and use a seed to allow them to be selected or reused later. You have 45 minutes to build this! Be creative. Here's the transcript , and an image Claude generated to show me its work: v12: Multiplayer via sound I only just got this working, so I've not had much time to actively test it yet. The latest Battle Drones now supports multiplayer via two devices sat within microphone distance of each other! This feature started out as my Chirp Pong experiment from last week. I pointed Claude Code at that working proof of concept: Clone Simonw/tools from GitHub and look at recent branches for the example pong game that uses web microphone to implement multiplayer between two phones via audio signals Build a version of that for battledrones Opus built this Radio Probe tool to measure how much bandwidth we could get between two devices and, based on positive results from that, implemented a full audio modem system. I've tried it between laptops, iPhones and iPads so far and it seems to work really well! Why use sound for this? Because I love static HTML that costs nothing to run, and I don't want to spin up a server somewhere and then stay responsible for keeping it operating for as long as anyone might want to play the game. Some thoughts I know almost nothing about game development. This makes it a great opportunity to try out coding agents in a field where my existing software engineering experience has less impact on the results. Games are also super low stakes. The worst that can happen is one of my silly game experiments suffers a bug. This morning ( via Hacker News ) I found Steam Fans Are Begging for a No-AI Filter as Steam Next Fest Is Filled With Slop . I wouldn't personally dream of submitting Battle Drones to an event like this - it's still way too low effort to warrant consideration as a legitimate indie gaming project. ... but I can now see a path to vibe coded games like this being taken seriously. Battle Drones is fun, it looks good, it has innovative features (the multiplayer-over-audio thing is a first for an RTS as far as I can tell). I'd love to see what someone with better taste than me and a driving passion to produce something truly exceptional could build! A few other observations from this project: Static HTML and JavaScript is a fantastic medium for games development. WebGL works great, and Three.js (and Phaser before it) are excellent platforms to build on that the current models know inside out. This also lets them run as Progressive Web Apps - I didn't ask for it, but Battle Drones works offline. I was able to play it on a plane despite never having directly installed it - my phone had it cached. Giving Claude an OpenAI API key and telling it to generate images using OpenAI's models works really well. It can view the images it has generated and iterate on the prompts. Opus 5.5 is spectacular at 3D modeling using Three.js (and Blender, and no doubt other 3D applications as well). The same is true of GPT-6 Astra. They are absolutely capable of building models for 3D game projects. For features like replay and multiplayer it's useful to have a deterministic simulation engine. At one point Claude had to replace calls to Math.sin() and Math.cos() and friends with custom versions because different browsers (Chrome vs. Safari, for example) returned very slightly different numbers, desynchronizing the replay. Opus 5.5 can write music! There are no audio files in this project - the soundtrack is entirely generated through code, varies across different maps, and even gets more intense as the battle heats up. See Scrimshaw Jukebox for a previous experiment with Claude for music composition. I still can't get over how well the prompt "Make it a LOT more fun" worked. Tools are effectively free. I prompted up the Radio Probe , the Model browser , the Agent vs. Agent debug tool , and Claude Code built a bunch of tools for itself that I haven't even explored myself. Audio-based multiplayer between static HTML games works! I hope to see a whole lot more games use this same pattern - in digging around today I've not found much prior art beyond Siner by Alon Horesh and ggwave by Georgi Gerganov (also of llama.cpp fame). Claude Code "Ultracode" burns tokens like there's no tomorrow, but it can also solve complex problems (like adding audio-based multiplayer) from a single prompt. I'm still not quite over the guilt of building software in this way. I don't feel like I've earned the right to create something this good, in a field in which I have so little prior experience! At this point I've shown it to enough people in private that it felt wrong not to publish it for everyone else to see and learn from. Update: Online multiplayer support Earlier in this article I said: Games are also super low stakes. The worst that can happen is one of my silly game experiments suffers a bug. Then I tried playing the multiplayer version with Natalie... and the high pitched modem sounds gave her a nasty headache! That's a pretty bad bug. I added a warning and was then inspired to build proper network multiplayer. I tried getting WebRTC to work but couldn't get a connection going between two of my own devices even on my own WiFi network, so I had Claude build a simple, stateless WebSockets relay server instead: If I wanted to support multiplayer between two devices at a very low cost without storing any state on the server could I do that with a tiny asyncio web server running on the cheapest Fly instance? Build the server and the fly.toml for deploying it, use Fly application name battledrones-relay - put that in a folder in the repo, include deploy instructions in the README in that folder Here's the resulting server . Since this scales to zero and runs on a single Fly.io shared-cpu-1x instance it should cost me no more than $2.19/month - and that's if it stays hot and serving connections the whole time. Battle Drones now supports online multiplayer. Click "Duel a friend online", then either share the resulting room URL via your messaging app of choice or have them snap a picture of the displayed QR Code. I also added a feature where you can copy JSON of the match once it has ended and then feed that into the replay feature to rewatch your game. I didn't expect to introduce online multiplayer this soon, but the headache bug felt like it warranted an urgent alternative. Tags: game-design , projects , ai , generative-ai , llms , claude , vibe-coding , coding-agents , claude-code

rss:77c4de7d209721e7Useful?👍👎
86%measurementreddit-industry

Google DeepMind says four AI agents beat Minecraft in 27 hours by writing 2,783 programs of their own — AI team died 605 times on Easy difficulty and ran up an estimated $520 in tokens

/u/ControlCAD · discussion

Measured result 86%First-party 54%
Abstract

submitted by /u/ControlCAD to r/artificial [link] [comments]

rss:b48221b7a4e07f04Useful?👍👎
86%releasereleases-llama-cpp

llama.cpp b11784

github-actions[bot]

First-party 86%Usable today 81%
Abstract

Sycl iq3 crash fix ( #30604 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58078930 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:36b56ca477ee18fbUseful?👍👎
85%releasereleases-llama-cpp

llama.cpp b11781

github-actions[bot]

Usable today 85%First-party 80%
Abstract

server : add POST /tts endpoint for TTS models ( #26603 ) mtmd: add audio out stream api add /tts endpoint add docs wire up mtmd_helper_model_can_chat upload speaker_ref via form-data expose seed to api fix header for pcm wip fix penalty_last_n fix server_output_limits fix pocket-tts stream wip add server_schema for tts add server routing server: coerce seed and stream from multipart form fields Form fields arrive as strings, so -F seed=42 and -F stream=true were rejected by the schema. seed joins the integer fields, parsed as a long long so a full uint32 seed fits, and stream is read as a boolean when it comes as a string. Co-authored-by: Pascal admin@serveurperso.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58065712 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%rumourreddit-ai

EXCLUSIVE: ByteDance, Alibaba, Tencent place over $35 billion worth of Nvidia chip orders

/u/Ok_Warning2146 · discussion

Measured result 84%
Abstract

China just ordered 3mil RTX 5500 Pro for $11.4k a pop. Probably for llm inference and AI video gen. I suppose this card is now out of reach for local llm. submitted by /u/Ok_Warning2146 to r/LocalLLaMA [link] [comments]

rss:9832ae2ee6046d76Useful?👍👎
84%announcementhn

Upcoming critical PHP security release

xd

First-party 84%
Abstract

Article URL: https://news-web.php.net/php.announce/509 Comments URL: https://news.ycombinator.com/item?id=50172041 Points: 461 # Comments: 172

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83%releasereddit-ai

Deepseek released DeepInfer - Open Source high-performance, large-scale inference framework for DeepSeek models!

/u/feelspeaceman · discussion

Usable today 83%First-party 66%
Abstract

This is crazy good for studying to improve existing custom engines by community like gufo, halogen, strixite, Strata... by learning their secret sauces. submitted by /u/feelspeaceman to r/LocalLLaMA [link] [comments]

Same story elsewhere (1)
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83%releasehn

Show HN: VoidAccess – open-source dark web OSINT, from query to a graph

coding-maniac

Usable today 83%First-party 38%
Abstract

Hi HN! Im Katriel, and im a Security Researcher/Enthusiast and in the wave of AI, i wanted to build that next gen of security tooling that surpasses the current tools, and even the paid industries with an open source engine. I built MailAccess as well, which is going to be the best damn email osint tool out there, in another year. But just before building MailAccess i built VoidAccess, because i was delving a lot into DarkINT and the content inside the dark web is too difficult to navigate through for a human, so i automated that pipeline, made sure the noise was removed, and anything NSFW was filtered out, to give the analyst an overview of what is present in the dark web, without having to actually go through it manually. Very simple to run, you just run a few commands to install, and a couple to investigate. Then the engine takes over, and stops onyl when the job is done(or at a particular time limit, if set by the user). Do check it out, if that is something that interests you. Thanks to the community for giving it the love it has got till now. Comments URL: https://news.ycombinator.com/item?id=50167286 Points: 151 # Comments: 13

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83%releasereleases-llama-cpp

llama.cpp b11782

github-actions[bot]

First-party 83%Usable today 82%
Abstract

server : fix usage.completion_tokens undercount with n > 1 ( #29486 ) Assisted-by: Copilot Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58070419 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:becc21be99e89d80Useful?👍👎
82%releasereleases-llama-cpp

llama.cpp b11777

github-actions[bot]

Usable today 82%First-party 80%
Abstract

qwen: reshape output to 2d instead of 3d ( #30823 ) qwen: reshape output to 2d instead of 3d remove final reshape, fix comments Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58050689 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:392ed2099a485329Useful?👍👎
81%releasereleases-llama-cpp

llama.cpp b11780

github-actions[bot]

Usable today 81%First-party 76%
Abstract

server: add realtime transcription API ( #30636 ) server: add realtime transcription API drop transcription_sessions proxy ws add docs server: let the API key middleware guard the websocket httplib runs the pre-routing handler before the websocket upgrade, so the API key middleware answers 401 at handshake like any other route. This covers the router proxy, whose children run keyless, and accepts the same key forms as HTTP (Bearer, raw key, X-Api-Key). server: keep realtime slots out of the prompt loop A realtime slot waiting for audio is GENERATING with nothing to decode, so it fell through the prompt loop and took slot_batched. A request on a higher slot then failed can_batch_with on the task type and starved for the whole session. Realtime slots have no prompt: the loop sets them up once and always leaves them. server: start the realtime session once its slot is live The audio stream was created in the prompt loop, which skips a slot that cannot batch with the one generating, and session.created went out before the task was posted: early appends failed and the reader quit, leaving the socket open and the slot held until the HTTP timeout. The slot now sets up its stream at launch and signals it with the begin result, session.created waits for it, and a reader that stops closes the socket so the session ends with it. server: name the committed item in input_audio_buffer.committed Per the Realtime spec, item_id is the item made from the committed audio and previous_item_id the one committed before it, null for the first. committed now comes before the transcript that completes that item, and both go out with the item switch under one lock, so a delta cannot land between them. server: use the GA transcription session shape The session object follows the GA Realtime schema: type transcription, with the input format, transcription model, turn detection and noise reduction under audio.input. The rate stays the model sample rate, which is what the client must send for now. Co-authored-by: Pascal admin@serveurperso.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/58060985 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:3fa128b62a04a8b3Useful?👍👎
80%releasereddit-releases

MOSS-Image-Base - LoRA - Release - Smartphone Snapshot Photo Reality v14

/u/AI_Characters

Usable today 80%First-party 30%
Abstract

Link: https://civitai.red/models/3010926/moss-image-base-smartphone-snapshot-photo-reality-style I will look into uploading this on to HuggingFace too eventually. submitted by /u/AI_Characters to r/StableDiffusion [link] [comments]

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Near misses (10): just under your maybe bar, useful for tuning thresholds
Filtered by your exclusions (58)