@Banaxi-Tech stop hiding my comments. AND STOP STEALING PAPERS AND SPREADING MISINFORMATION. your BGA blog is a copy of NSA (deepseek, 2025) branded under your name. literally the same top16 selected blocks, 512 local window, router over block summaries, all you did was change block size from 64 to 128. you didnt cite NSA once but you put a “please cite BGA” bibtex at the bottom. i commented under your post and said that there is no way that you can support claims like: “The Accuracy Should BE WAy better than DSA but untested yet.” you didnt run a single experiment. and the 256x isnt from BGA, its just n/2k with k=2048 so the exact same k DSA uses. if opus wrote this for you, at least read it before posting. i commented again after you hid my comment despite it having constructive and correct feedback and you hid that too. and again. you can hide the truth and just try to get hf post likes..... but is it really the thing that needs to be done? do you really want to take papers and make them yours while barely even changing the params?
admitting your mistakes and doing something about them needs humbleness, intelligence, humanness. i encourage you to admit your mistakes and try to do better next time (at least read what blog your ai wrote or do proper experiments to back your stuff up).
Most NSFW classifiers break the second an image touches the internet.
They look great on pristine benchmarks, but in the wild, every social platform aggressively recompresses, downsamples, and degrades images. The moment JPEG or WebP compression artifacts show up, confidence collapses and false positives spike.
SafeScan was built to survive actual platform pipelines. Trained on 34,000 images under almost every major social media compression profile using a Vision Transformer backbone (google/vit-base-patch16-224). Instead of blunt binary filtering, it breaks decisions down across 5 clear categories:
• safe • drawing • sexy • hentai • porn
The result is a moderation model that actually generalizes to real-world internet feeds instead of fragile, uncompressed datasets. Open-weight and available on Hugging Face:
Most deepfake audio detectors are quietly cheating.
They don’t really listen to the speech — they just look at how long the embedding vector is. Once they figure that out, accuracy looks great on paper and falls apart in the wild.
AIRealNet-Audio was built to stop that shortcut. It forces every feature onto the unit hypersphere (twice) so the model can only use direction, not magnitude. Trained on speech from 100+ different TTS and voice-cloning systems, plus real human recordings under heavy compression and noise.
The result is a detector that actually has to learn the artifacts instead of gaming the feature space.
🕷️ I made a Spider-Man: Miles Morales web-swinging game that runs right in your browser!
Swing, wall-crawl and web-zip through a Spider-Verse style city at sunset: ink outlines, halftone shading, comic caption boxes, "THWIP!"s, wall crashes and all.
🖼️ Nano Banana Editor is now Portrait Editor, now with Qwen-Image-2.1
Some updates to the node-based photo editor 👇
✨ What's new - New name: Nano Banana Editor → Portrait Editor - Qwen-Image-2.1 is the HuggingFace model. It runs on its own ZeroGPU Gradio Space and handles both image editing and text-to-image. FLUX.1-Kontext and Qwen-Image-Edit have been removed. - HuggingFace is the default mode. Sign in with HF and start editing. No API key is needed, and it uses your own ZeroGPU quota. - Bug fix: after signing in with HuggingFace, the app used to switch back to Gemini. You now stay on HuggingFace.
🧩 Gemini and GPT modes are still available for multi-image MERGE nodes.
Pretrained on 4x more tokens than the previous releases (20b vs 5b). Instruct tuned versions are coming soon. Very interesting models are coming soon too (hint: super long context).
Just hit #14 and #15 with out FIRST models on Open SLM Leaderboard. The models were trained on 5B tokens, while competing with similarly sized models trained on more than 6-20x the data.
A new base model Speck1.5-140M being trained right now on a higher quality corpus and will be released soon. SpeckChat3 is coming very soon with 1 million samples, specifically designed to post train small base models.
Also, just to clarify stuff, we will NOT release anything that is NOT MIT licensed EVER. Openness is needed in small language research.
Thanks to everyone supporting the project, and stay tuned for new releases!
SPECK UPDATES: 1 New instruct model tuned on top of Speck1-140M: specklabs/Speck1-140M-Instruct 2 Instruction tuning datasets 2 GGUFs
Much more coming soon: Speck1.1-140M-Instruct that is post trained on SpeckChat2 will be coming very soon New base model Speck1.5-140M is coming with a much higher quality corpus
Thanks to everyone who is already supporting the project, and stay tuned for new releases!
new models coming very soon (both instruct and much better models), with much much higher training scale as i am getting marenostrum5 access soon! we will be looking at 100b-2t token budgets :)
So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
I built an open-source, local-first screen recorder and timeline editor for macOS and Windows. Record your screen, camera and audio; trim clips, add subtitles and export without an account.
Shadows of Tomorrow is finally live on Hugging Face Spaces with Gradio.
It’s a browser-playable RPG built with Godot, set in a post-nuclear future where players explore Magnus Province, collect medicinal plants, craft medicine, and help cure NPCs.
The app introduces the @Coherelabs Tiny Aya series of multilingual AI models to mobile devices. This release is significant as it enhances access to multilingual AI from anywhere, particularly for users who prefer offline capabilities.
We trained an open-source Mythos like cybersecurity LLM for the Build Small Hackathon meet OpenMythos
Trained in two stages: SFT on ~1.84K filtered ArXiv cs.CR papers + real CVE data, then RLVR using paired with past vulnerabilities GitHub repos with a verifier model checking outputs against ground truth.
Trained on: H100s from Modal
The RLVR stage made the biggest difference responses got more precise and less prone to confusing similar vulnerability classes.
