Measured result 97%Usable today 85%First-party 32%
Abstract
Introducing Mistral Large 4: Le chonk Mistral are back in the game. Today they're releasing a preview of Mistral Large 4, a 1 trillion parameter, 49 billion active parameter model trained on their own cluster of 3,800 NVIDIA Grace Blackwell GPUs. The preview is available via their API. They promise to release the open weights model at the "end of this month". The model only supports two reasoning levels - "none" and "high" - via the Mistral API. Here are both pelicans - the "high" one looks better, though surprisingly it only used 2,717 output tokens compared to "none" which used 3,275: On Artificial Analysis it scores 38 , just behind DeepSeek 4.1 Flash, which is a 552B model. It's a huge improvement on last December's Mistral Large 3, which drew this terrible pelican and scored 9 on AA . It's certainly not a Fable-class model, but it's great to see Mistral put out a model that's back to being maybe about 6 months behind the frontier. Via Hacker News Tags: ai , generative-ai , llms , mistral , pelican-riding-a-bicycle , llm-release
I've been working on native ComfyUI support for Tencent's HunyuanImage 3.0 , the 80B mixture-of-experts image model (13B active per step). It isn't a wrapper around Tencent's pipeline: it uses the normal KSampler, the normal VAE Decode and ComfyUI's own memory management, which streams the experts from system RAM so the model fits on one consumer GPU. What's in the gallery (all Instruct-Distil, 8 steps): 4-bit vs int8 , same prompt and seed. Times are the whole generation on an RTX 3090. image editing with the 4-bit weights. The instruction is at the top of each image. style transfer with the 4-bit weights: two input images, the photo and a style reference. These are picked from a bigger run: 60 prompts × 2 formats, 60 edits and 14 styles, one seed each, no rerolls. Most of the edits worked; a few didn't (snow that barely shows, a logo it wouldn't remove, a "make it night" that stayed day). The text-to-image prompts come from popular prompt posts on X. What you get All three models: Instruct-Distil (8 steps, the one to start with), Instruct (50 steps) and Base Text-to-image, image editing , and multi-image fusion with up to 3 input images (that's how the style transfer works) Optional prompt rewriting : the model expands your prompt first (slow, about 1 s per token) Optional Spectrum speed-up: about 3.4× faster for the 50-step models Ready-made weights: 4-bit W4A8 (44 GB) , int8 (76 GB) and bf16 (150 GB, mostly for comparisons) Example workflows for each model and task Nothing to pip install Speed (Instruct-Distil, about 1 megapixel): GPU 4-bit W4A8 int8 RTX 4090 ~22–26 s ~47–49 s RTX 3090 ~29 s ~54 s It also runs with only 16 GB or 12 GB of VRAM (~30 s and ~32 s per image on a 4090 limited to that). What you need An NVIDIA GPU with 12 GB+ Lots of system RAM : ComfyUI held about 50 GB with the 4-bit file loaded. This is the real requirement, since the experts live in RAM and stream over PCIe every step. A recent ComfyUI (late September 2026 or newer) Links Nodes: https://github.com/PedroMarinhoDev/ComfyUI-HunyuanImage3 Weights: Instruct-Distil · Instruct · Base Every model, format and Spectrum setting side by side: https://pedromarinhodev.github.io/ComfyUI-HunyuanImage3/ Happy to answer questions. If something breaks, open an issue on GitHub with the traceback. submitted by /u/LatentSpacer to r/StableDiffusion [link] [comments]
Measured result 96%Usable today 93%First-party 62%
Abstract
I made AnimeGen , app that allows you to run Anima on your mobile phone. Since last post I added hd image generation, and different aspect ratios. The app is now available on the App Store: https://apps.apple.com/pl/app/animegen-anime-art-generator/id6786438562 I am an iOS developer and unfortunately can't make an Android app. Sorry for that. What it can do now Prompt-to-image generation powered by Anima HD and 540r image generation in different ratios Runs locally on your device Performance iPhone 14 : approximately 15–20 seconds per image iPhone 17 : approximately 10–15 seconds per image M1 iPad : approximately 15–20 seconds per image It uses native Apple Neural Engine, so it is very efficient. Probably display takes more energy then AI. Before you install On the first launch, the app needs to compile its models directly on your device, similar to how games compile shaders: It takes around 1-2 minutes and happens only once per installation/update. It also loads models on device in parallel with preparation. Once finished, everything runs fully offline on your iPhone. Technical requirements Devices: iPhone/iPad only Designed for iPhone 12, m1 iPad and newer devices OS: iOS 18 or newer Free space: at least 10 GB available for smooth operation Planned features not yet available: Support for custom LoRAs and checkpoints(Probably from hugging face and CivitAI) Image editing and ControlNet 4k and 2k Upscaler Feedback & community For questions, bug reports, feature requests, or sharing your generations, join the subreddit: r/animegen_tech . It is the best place to follow development updates and discuss AnimeGen. I also have a website, where I plan to update with current status of project: animegen.tech If you find AnimeGen useful, please consider leaving a review on the App Store. It is the best way to support the project submitted by /u/Agitated-Pea3251 to r/StableDiffusion [link] [comments]
Sorry, reposted from earlier because the video wasn't working. Play it: gravwars.com (free, no account, phone or computer) Gravity Wars is a two-player artillery game in curved spacetime. You take turns firing missiles past planets, stars and black holes, and gravity bends every shot. It started as a remake of a public-domain 1989 DOS game (by Sohrab Ismail-Beigi) and grew into a small relativity sim: orbits precess near black holes, black holes are ray-traced with lensing, and missiles feel time dilation. Almost all of the code was written by Claude Code, with me playing, testing and deciding what to build next. How it's built Claude Code in the desktop app, over many sessions. A roadmap folder and a CLAUDE.md in the repo hold the plan and the rules, so each new session picks up where the last one stopped. Plain JavaScript. No frameworks, no npm packages, no build step. A small Python script bundles the game into one HTML file. WebGL shaders draw the universe and ray-trace the black holes, Canvas 2D draws the ships, trails and readouts, and the Web Audio API generates the music from the physics. Testing: Node scripts check the physics before every commit (energy conservation, orbital precession, CPU accuracy). Playwright screenshots every change in headless Chrome and Firefox. An Android emulator covers phone layouts. Hosting: static files on Cloudflare Pages. The trailer was rendered frame by frame from the game itself, with Playwright and ffmpeg. Bug reports: the in-game ! button sends a report to a Cloudflare Worker, which files it as an issue in a private GitHub repo (with Cloudflare Turnstile and rate limits against spam). Claude reads and triages them. Design choices No dependencies. The game opens straight from a file and has nothing to update or audit. Two views, one physics. Classic is flat 2D; "3D lite" tilts space into the rubber-sheet picture of gravity. Missiles fly by the same equations in both. Same universe everywhere. The world is a fixed 16:9 field, and every universe comes from a seed, so a link rebuilds the exact same layout on any device. Phones first-class. Played sideways, installable, works offline, resumes after switching apps, and drops to lighter graphics on weaker GPUs. CPU opponents simulate their shots instead of cheating with a formula. Private by default. No accounts, no tracking, no ads. What I'd love from you Play a few rounds against the CPU or a friend. Best in Chrome, Firefox or Edge. Safari and iPhones are untested, so reports from those are especially welcome. If something breaks or confuses you, press ! , say what happened, and press Send . It adds your device and the match details automatically. Happy to answer questions about the workflow. submitted by /u/gmccolgan3 to r/ClaudeAI [link] [comments]
What's Changed Models run on MLX on Apple Silicon by default In this release, on Apple Silicon devices, model architectures supported by the MLX runtime will automatically run on MLX. ollama pull qwen3.8 ollama run qwen3.8 Additional models include gemma4 , qwen3.6 and qwen3.5 Decision models are now available on MLX as well: Nimble tev1 clef clef-flash MLX now has support for an embedding model: embeddinggemma-2 We will continue testing and enabling additional models. Full Changelog : v0.35.1...v0.40.0
EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders. Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering. EmbeddingGemma 2 builds upon the architectural and capability advancements of Gemma 4, offering several core features: Native multimodality: Native multimodality: Unifies 4 modalities (text, images, video, and audio) in a single shared 768-dimensional embedding space. Multilinguality and code: EmbeddingGemma 2 understands 100+ languages, and achieves a ~14% improvement on code tasks relative to its predecessor. Flexible footprint: Combines a 270M parameter text backbone (130M transformer + 140M embedder) with selectively loadable vision (170M) and audio (300M) encoders, allowing developers to load only the modalities required for their use case. Matryoshka Representation Learning (MRL): Native support for truncated embeddings across 128d, 256d, 512d, and 768d, enabling up to a 6x reduction in vector storage costs with minimal impact on quality. Context length: 8K token context window, capable of processing minutes of audio or video. Task-steered representations: Uses lightweight text instruction prefixes to optimize embeddings for different tasks (search, classification, clustering, semantic similarity, etc.). llama.cpp support https://github.com/ggml-org/llama.cpp/pull/30054 GGUF from GG: https://huggingface.co/ggml-org/embeddinggemma-2-GGUF GGUF from Unsloth: https://huggingface.co/unsloth/embeddinggemma-2-GGUF submitted by /u/jacek2023 to r/LocalLLaMA [link] [comments]
Octop is an open-source, self-hosted AI assistant. Through its multi-agent architecture, it builds an intelligent environment that is both independent and collaborative for teams, families, and individuals. Best of all, it runs entirely on your machine, the fully self-hosted design means privacy is never a compromise, while single-process startup makes the powerful web console, CLI, and IM integrations readily accessible. Surfaces: - Web dashboard — chat, experts / teams, connectors, channels, cron, knowledge, plugins, settings - Desktop client — native apps for Windows / macOS / Linux; FnOS packages for NAS - CLI — octop run, octop chats, octop acp, admin commands - HTTP/SSE/WebSocket API — full programmatic access - Remote desktop — dashboard control of the host desktop session Deploy using either desktop app (Windows, MacOS, Linux) or using Docker GitHub: https://github.com/TencentCloud/Octop submitted by /u/ResearchCrafty1804 to r/LocalLLaMA [link] [comments]
https://github.com/hypersniper05/MCP-Image-Generator-Uncensored Just wanted to share with you guys my workflow converted into an MCP. It's a Qwen Image 2.1 Uncensored MCP with a few extra models I found helpful: a watermark-removal LoRA, a texture-fix VAE and an ESRGAN upscaler. Any LLM client that supports MCP can drive it. Uses 11GB VRAM at peak usage. The entire stack has: - Normal Qwen Image 2.1 image generation in all supported sizes (up to 2K), panoramas, and image editing (up to 10 input images) - Seamless tile generation, and edits of a tile stay seamless too, so you can make height and normal maps for game textures - 2x/4x upscaling, up to 8K - Built-in 360 viewer (the LLM gets a URL that opens the panorama in the viewer) - Watermark removal - Transparent backgrounds (real RGBA PNGs) and background removal - Text in images (quoted text comes out as written) It runs in Docker on an NVIDIA GPU (the whole stack fits on a 12 GB card) or on the CPU (very slow), downloads the models on first start, and works with any MCP client (llama.cpp web UI, VS Code, Cursor, etc.). For the seamless tiles I use stable-diffusion.cpp's circular mode with a tiny patch so it can be turned on per request. The tiles come out seamless in one pass instead of patching the seams afterwards. Note: Project created with the help of Claude. A lot of testing, and back and forth to get things to work smoothly. Hope someone finds it useful. submitted by /u/Delicious-Farmer-234 to r/StableDiffusion [link] [comments]
Release v5.19.0 New Model additions EmbeddingGemma2 EmbeddingGemma 2 is a multimodal embedding model from Google built on the Gemma 4 architecture. It encodes text, images, audio, and video, individually or combined in one input, into a shared 768-dimensional vector space for cross-modal retrieval, semantic similarity, clustering, and classification. It uses Matryoshka Representation Learning, so embeddings can be truncated to 512, 256, or 128 dimensions. It also offers configurable visual and video token budgets, and unused vision or audio towers can be disabled at load time to save memory. Links: Documentation Smthn smthn ( #49364 ) by @vasqu in #49364 Breaking changes All MoE models whose routers compute logits now return them when output_router_logits=True , following the Qwen3-MoE pattern (a router_logits recorder on the base model, MoeModelOutputWithPast from the backbone, and a MoE causal LM output from the head), so code that relied on the previous outputs or their absence should read the router logits from these output classes. 🚨 Return router logits from every MoE model that computes them ( #48920 ) by @qgallouedec Owlv2ForObjectDetection.embed_image_query now selects the query box with the highest objectness score, as in the original OWLv2 notebook, instead of the OWL-ViT heuristic, so image-guided query embeddings and detections may differ from earlier releases. 🚨 Select the OWLv2 image query by objectness ( #49200 ) by @qgallouedec The "paged|" prefix for SDPA and flash attention implementations is deprecated, so users should set the regular attention implementation (e.g. sdpa or flash_attention_2 ) for continuous batching instead of paged|sdpa or paged|flash_attention_2 . 🚨 Attention 🚨 Deprecate "paged|" prefix for SDPA and flash ( #49112 ) by @remi-or The regular flash and SDPA attention functions ( flash_attention.py , sdpa_attention.py ) now support continuous batching directly, and "paged|..." implementations for these are redirected to them, while eager still requires the "paged|eager" prefix. 🚨 Attention 🚨 Make regular attention support CB ( #49101 ) by @remi-or In continuous batching, the cache update for the index-based and block-table paths is now fused into a single call, which slightly changes the cache update function's behavior and affects any custom code that calls the separate update paths. 🚨 [CB] 🚨 Fuse update for index and block table path ( #49088 ) by @remi-or Continuous batching internals changed in preparation for removing "paged" : `max 🚨 [CB] 🚨 Little fixes before removing "paged" ( #49069 ) by @remi-or Parallelization Expert parallelism gains a token-dispatch implementation, selected via the new ep_dispatch_experts plan rule and now the default for Qwen3 MoE and Mellum, which removes the requirement that EP size equal TP size. The Trainer was also adapted to work with expert parallelism, and the docs now note that PEFT adapters support tensor parallelism. A CI-related fix for pipeline-parallel chart2table inference was also included. [ distributed ]: adapt Trainer to work Expert Parallel ( #48873 ) by @3outeille in [ #48873 ] [ distributed ] Add expert-parallel token dispatch, default for Qwen3 MoE ( #48865 ) by @3outeille in [ #48865 ] Fix pp chart2table inference ( #49225 ) by @zucchini-nlp in [ #49225 ] [docs] TP for PEFT adapters ( #49060 ) by @stevhliu in [ #49060 ] Cache This release fixes quantized cache handling: generate no longer mutates the user's cache_config , and QuantizedLayer.reorder_cache is repaired. It also adds per-layer cache configuration, so DynamicCache and StaticCache initialize each layer from its own config (sliding window, attention chunk size, conv states, and attention head counts) to better support heterogeneous models. Separately, the deprecation cycle on mask and cache Fix quantized cache ( #48700 ) by @jiqing-feng in [ #48700 ] [CI] check_bad_commit: use EFS cache to avoid Xet FUSE OOM (exit 137) ( #49273 ) by @ydshieh in [ #49273 ] Support per-layer cache configuration ( #48178 ) by @eladsegal in [ #48178 ] Remove deprecation cycle on mask and cache ( #49231 ) by @Cyrilvallez in [ #49231 ] Bugfixes and improvements Revert "[CI] Temporarily disable AMD scheduled CI trigger" ( #49271 ) ( #49362 ) by @ydshieh in [ #49362 ] ALM testx fIxing... ( #49355 ) by @zucchini-nlp in [ #49355 ] Fix DataCollatorForLanguageModeling ignoring seed=0 ( #49349 ) by @akanyaani in [ #49349 ] Fix explicit kernelization modes for evaluation models ( #49339 ) by @DimensionSTP in [ #49339 ] fix(cohere_compass): add video to input_modalities ( #49320 ) ( #49323 ) by @destopianpirate in [ #49323 ] GPT-OSS: read the clamped SwiGLU's alpha and limit from the config ( #49346 ) by @IlyasMoutawwakil in [ #49346 ] [ distributed ] Default MoE ep_plans to token dispatch ( #49160 ) by @3outeille in [ #49160 ] Move CI to Python 3.11, with the version set in .python-version ( #49311 ) by @tarekziade in [ #49311 ] Bump doc-builder main docs workflow pin ( #49350 ) by @paulinebm in [ #49350 ] [ distributed ] decoupled tp_plan and ep_plan ( #48859 ) by @3outeille in [ #48859 ] [ distributed ] Add dense and expert device mesh ( #48857 ) by @3outeille in [ #48857 ] Bump doc-builder workflow pin ( #49342 ) by @paulinebm in [ #49342 ] Allow registering new processing backends ( #48984 ) by @zucchini-nlp in [ #48984 ] Fix WatermarkDetector repeated-ngram counting ( #49315 ) by @LE0-Lin in [ #49315 ] Fix minicpm 4.6V video batching and CI ( #49259 ) by @zucchini-nlp in [ #49259 ] remove colwise_gather_output for lm_head for DeepseekV4 ( #49314 ) by @3outeille in [ #49314 ] Fix the labels docstring of CLIPSegForImageSegmentation ( #49001 ) by @qgallouedec in [ #49001 ] [CI] Temporarily disable AMD scheduled CI trigger ( #49271 ) by @ydshieh in [ #49271 ] Remove the unused pandas entry from setup.py deps ( #49329 ) by @tarekziade in [ #49329 ] Fix run_glue.py label ids becoming strings with pandas>=3 ( #49326 ) by @tarekziade in [ #49326 ] [Fix] Remove old test file with two ancient tests ( #49280 ) by @remi-or in [ #49280 ] Fix chunked prefill with multi-axis position ids (Qwen3-VL) ( #49319 ) by @dacorvo in [ #49319 ] Why override when you can fix in Mixin ( #49105 ) by @zucchini-nlp in [ #49105 ] Do RoPE with mul instead of matmul ( #49265 ) by @Rocketknight1 in [ #49265 ] Deprecated pipelines should redirect; removed pipelines should raise ( #49308 ) by @LysandreJik in [ #49308 ] Enable model test on upcoming torch_tpu backend ( #49207 ) by @tengomucho in [ #49207 ] Fix encoder repetition penalty source rows after batch expansion ( #49262 ) by @gitedmond in [ #49262 ] Preserve masked EOS scores in exponential decay length penalty ( #49260 ) by @gitedmond in [ #49260 ] [CI] Avoid fetching all artifacts when only specific ones are needed ( #49282 ) by @ydshieh in [ #49282 ] Revert "[CI] ssh-runner: add optional cache_type input to switch between bucket and EFS runners ( #49159 )" ( #49270 ) by @ydshieh in [ #49270 ] Add Trainer.loss_is_scaled_for_ga to declare whether compute_loss already scales for gradient accumulation ( #49240 ) by @qgallouedec in [ #49240 ] fix precomputed flash kwargs clashing ( #47928 ) by @IlyasMoutawwakil in [ #47928 ] Reapply modular examples ( #49230 ) by @Cyrilvallez in [ #49230 ] Remove some stuff that does not exist ( #49232 ) by @Cyrilvallez in [ #49232 ] Remove rotaries deprecation cycle ( #49228 ) by @Cyrilvallez in [ #49228 ] [ GTE ] Add memory mixin ( #49234 ) by @vasqu in [ #49234 ] [CB] Make CB more device agnostic and add support for XPU ( #49156 ) by @remi-or in [ #49156 ] correct final metrics due to resuming from checkpoint ( #49208 ) by @SunMarc in [ #49208 ] [ESM/Persimmon/NllbMoe/ESMFold] use safetensors repos to fix Xet FUSE SIGBUS/IOError on bucket CI runners ( #49194 ) by @ydshieh in [ #49194 ] Dev update ( #49212 ) by @vasqu in [ #49212 ] Significant community contributions The following contributors have made significant changes to the library over the last release: @vasqu Smthn smthn ( #49364 ) [ GTE ] Add memory mixin ( #49234 ) Dev update ( #49212 ) @remi-or [Fix] Remove old test file with two ancient tests ( #49280 ) [CB] Make CB more device agnostic and add support for XPU ( #49156 ) 🚨 Attention 🚨 Deprecate "paged|" prefix for SDPA and flash ( #49112 ) 🚨 Attention 🚨 Make regular attention support CB ( #49101 ) 🚨 [CB] 🚨 Fuse update for index and block table path ( #49088 ) [Refactor] Make some flash-attention utils more readable ( #49071 ) 🚨 [CB] 🚨 Little fixes before removing "paged" ( #49069 )
Bought an old mining farm of a guy on avito (Russian eBay), guy had bought a garage a couple of years ago and it was sitting there for a while, found out it was a mining farm and put it up on there for sale for 5000 rub (~60 USD) since he wasn't sure if it works. I negotiated down to 3000 rub (~35 USD), it turned out to have 9x p106 6gb (gtx 1060 6gb) gpus, with 54gb vram total, all working, the only thing missing was an SSD, I booted from USB and it works fine. submitted by /u/markpronkin to r/LocalLLaMA [link] [comments]
Patch release v5.15.1 This patch most notably solves a few issues with DFlash and MTP candidate generators, as well as an issue where images could sometimes not be processed on accelerator if using Lanczos filter. It contains the following commits: Fix DFlash candidate token device mismatch with device_map="auto" ( #47877 ) by @sywangyi and @Cyrilvallez Align logit distributions for CandidateGenerators using sampling ( #48007 ) by @Cyrilvallez Fix MTP config when mlp_layer_types is absent ( #48015 ) by @Cyrilvallez Fallback from 'lanczos' to 'bicubic' when on cuda ( #48026 ) by @zucchini-nlp Fix gemma4 video to device ( #47896 ) by @guarin
https://huggingface.co/google/embeddinggemma-2 EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders. Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering. submitted by /u/Recoil42 to r/LocalLLaMA [link] [comments]
Entire analysis here, including methodology to measure: https://newsletter.semianalysis.com/p/anthropic-subscriptions-offer-5x Tl;dr: > At this mid-tier of models—which both companies market as the intended daily driver for most users—Anthropic is an overwhelmingly better deal, offering ~5x the API-equivalent value across the board. You could argue that this is unfair for OAI because 6.1 Sol is much cheaper per token than Opus 5.5, but the gap is still massive even if you switch to comparing the number of tokens. a/b testing confirmed on subscriptions: > While refining our methodology, we ran into a very confusing situation where just 1 of 3 of the same subscription we tested for a particular provider had ~20% lower limits than the other 2. This unlucky account happened to be significantly older as well, and we were worried that the provider had some cursed setup where they changed subscription limits depending on the age of the account. > Fortunately, after reaching out to the provider, we were able to confirm this was not the case and that our unlucky account was part of an “extremely tiny” A/B test they were running on limits. Additionally, they wanted to emphasize that they “didn’t just decrease limits wholesale” and were instead testing “how to better balance when people hit limits”. > This conclusion is interesting for two reasons: > It proves providers can silently change subscription limits at any time > Our method is sensitive enough to detect these subtle changes! submitted by /u/fsharpman to r/ClaudeAI [link] [comments]
First-party 82%Measured result 48%Usable today 46%
Abstract
OpenAl released 722 mathematical manuscripts across 372 families of related results, produced by an unreleased frontier model. Many have Lean proofs; others remain unverified. Average compute per result: roughly three hours of ChatGPT Pro thinking. They're working toward releasing the model. Github: https://github.com/openai/math submitted by /u/socialistshroom to r/singularity [link] [comments]
Hey guys, I have been working on the consistent character portable format for a while & I was able to achieve consistent face, cloths & body, but I felt voice is also something should be consistent across video generation. So in the recent tests, I was able to achieve a consistent voice with lip sync across multiple video generation, You just need a 10-30sec voice sample in mp3 or wav & character will say things in a cloned voice from your sample. Reddit post: Details on face, body & cloth consistency You can read more about .char, comfyui nodes & prompting details here. Voice prompts - chris giving an interview & says "Time can bend. Dreams can fold. But a character's voice should never change. With OmniChar, it doesn't. Consistent voice is here." - chris giving an interview with little hand movements & says "I am surprised. It is not just the voice. It is also the face, the clothes and the body. Dot char is a full portable pack." ComfyUI node is updated with the optional sample voice input. Get the latest comfy node: https://github.com/omnichar/ComfyUI-Omnichar Workflows: Build a .char with a voice MiniMax H3 with voice Full face. body, cloth workflows Limitations: - Good with English but might blabber with non-english languages. - Lip sync comes from H3 itself; nothing is added on top. - Avoid multiple voices in sample. Sample inputs are added in node repo. Note: ComfyUI node is still in nightly release, so update your settings accordingly or install via direct git repo url. Related resources: Omnichar repo: https://github.com/omnichar/OmniChar (GPLv3), supports .char for krea2 & more features e.g. character finetuning Community characters: https://www.omnichar.org/characters Hope it's helpful. submitted by /u/ashishsanu to r/StableDiffusion [link] [comments]
Measured result 82%Usable today 70%First-party 32%
Abstract
Matthias Bastian / The Decoder : Google releases Nano Banana 2.1, based on Gemini 3.6 Flash, saying it improves on previous versions “across the board”; pricing is ~50% lower than Nano Banana 2 — Google has released Nano Banana 2.1, a new model for image generation and editing.
First-party 81%Measured result 66%Usable today 63%
Abstract
Still a bit stunned typing this. Five months ago I was refreshing Stripe hoping for one sale. Now there's a small but growing group of people paying every month to keep their apps from leaking. My SaaS is a security scanner for vibecoded apps shipped fast with AI tools. You paste a URL or hook up a GitHub repo and it surfaces what's leaking: secrets in the frontend, open database rules, missing headers. Three months in and we've done about 11k in gross volume, 350+ all time paying customers, almost 8k signups. Public Stripe link in case anyone's seen too many fake numbers: https://profile.stripe.com/@checkvibedev/m/cD8ANzKd A few things that actually worked: TikTok slideshows have carried us. Aesthetic Pinterest-style backgrounds with tool names overlaid, five slides, no branding on the account. One slideshow alone hit almost a million views and is still quietly sending signups weeks later. Takes me 1 - 2 minutes to make and post each one (I use slideys.app for this, since a few people have asked). As a 2-person team that can't spend hours on content every day, this format is unreasonably good. Cold outreach worked, but only the version where I scanned the prospect's app first and DMed them what I found. Generic pitches got ignored. Useful findings got replies almost every time. Paywall design was a 3x lever. The first version blurred all results, which felt clever and barely converted. Switched to one that just shows the count of critical issues with the actual findings locked. Conversion tripled. Curiosity beats obfuscation. What nearly killed me was mobile activation tanking compared to desktop and not catching it for weeks. Onboarding had too many steps on small screens. Cut two and the gap basically closed overnight. submitted by /u/funfunfunzig to r/SaaS [link] [comments]
The Verge · @theverge.com, theverge.com · discussion
Usable today 81%
Abstract
Getting some extra storage space on your Mac could be as easy as deleting Apple’s AI features with a new open-source command line tool called RemoveMacAI. Linked page: Delete Apple’s AI with a single command.