SOMEONE VIBE CODED A VIDEO STREAM THAT IS SECRETLY 100% TEXT SO IT CANT BE BLOCKED
it plays 360p video at 30fps, but theres no actual video on the page. every frame is just colored text characters being repainted on a canvas
to the browser its not media at all, its javascript updating some text
its called asciline, and here's the trick:
> the server decodes the real video and streams it as binary packed text over websockets
> the browser paints thousands of colored block characters fast enough to look like 360p
> ad blockers and autoplay blockers cant catch it because theres no video element to catch
> it streams in kilobytes since its just strings, so it runs on trash internet
since the video is literally text, you can apply css glows to it, let people copy paste a moving frame, or feed it straight to a local llm
however, an unblockable stream is also an unblockable ad as well
Gemma 4 MTP just got officially merged into llama.cpp
This means you can use Gemma 4 QAT + MTP for a lightweight + super fast setup. Excited to see what the community builds with it
https://t.co/1te7tgdi2H
MOSS-TTS-v1.5 just reached #1 on Hugging Face Trending for Text-to-Speech, with 20.6K downloads.
A multilingual, controllable TTS model with stable voice cloning, long-form generation, and precise pause control.
MOSS-TTS-v1.5 is now officially supported by vLLM-Omni and SGLang-Omni.
Built by OpenMOSS-Team.
Try it:
GitHub: https://t.co/mSlALD6Fzy
Hugging Face: https://t.co/qTv7xu1MZ5
ModelScope: https://t.co/NzAXgAzagL
Qwen3.6 35B A3B can't fill out a paper form on its own. But give it NVIDIA's LocateAnything-3B — the #1 trending model on HuggingFace — as its eyes, and the two small models get it done together.
(The test: place each element at the right pixel position on a blank form image, not type into a field.)
Setup:
> Qwen is the brain (main model), LocateAnything is the eyes (helper model acting as a tool).
> I gave Qwen a new tool: ask "where's the email field?" and LocateAnything returns the exact x, y, width, height.
> The blue boxes on the screen are its detections. Look how tight they are — it nails every field.
Result:
> Qwen3.6 35B A3B + LocateAnything-3B: form completed, all info correct.
> Name, DOB, ID, gender, marital status, nationality, email, phone, address, postal code: all landed in the right field areas.
> Character-box alignment still a touch loose, but every value is where it belongs.
> 9m10s, 224.5k input, 24.3k output, 21 turns.
Why it matters:
> Qwen alone can't finish this test. Bolt on a 3B model that does exactly one thing > locate > and suddenly it can.
> A combination of small models can do the work of a single large one.
@GetRammed_69@Breaking911 So he was a copilot to an Estonian Tech Entrepreneur who was also piloting the plane for passengers with undisclosed cargo... And the tech entrepreneur left Paraguay before they arrested Jabari.. Kinda misleading title tbh.
@47_ways@Breaking911 So he was a copilot to an Estonian Tech Entrepreneur who was also piloting the plane for passengers with undisclosed cargo... And the tech entrepreneur left Paraguay before they arrested Jabari.. Kinda misleading title tbh.
llama.cpp now has an official website: https://t.co/vztdUpdBWL
Our goal is to make local AI accessible to everyone, and improving the user experience is a big part of that. On the new landing page you’ll find a single-line cross-platform installer. The installation provides a single unified `llama` entrypoint which you can use to run/serve models and interface with 3rd-party agentic applications.
While oriented towards simplified user experience, the new `llama` application also provides all the advanced functionality of the existing llama.cpp tooling with which experienced users are already familiar. Also note that all GGUF models that you might have already downloaded with llama.cpp in the past will be automatically available to use without downloading again (they are stored in the common HF cache on your machine).
We have many improvements in the pipeline both at the UX and at the engine level and we plan to iteratively ship new things over the coming months. One of the main focuses will be seamless integration with local-friendly 3rd-party agents (such as Pi). In the meantime, we’ll continue to listen for feedback from the community and adjust accordingly, so keep letting us know what you think and need.
Almost all animals sleep. Why don’t LMs?
Introducing our new work on language model sleep.
tl;dr : A periodic, recurrent “sleep” phase allows LMs to digest their context and transfer it into their weights, improving recall and reasoning on challenging tasks.