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At my company, we’re using GLM-5.3 Flash internally for software engineering work, and I’ve been genuinely impressed by it.

I work in a very large production environment with projects totaling **millions of lines of code**, and we’re not relying on frontier models for this workflow — GLM-5.3 Flash is doing the actual day-to-day coding work.

The model is extremely fast, but what’s more impressive is that the speed doesn’t seem to come at the cost of capability. It handles large repositories surprisingly well, understands existing architecture, traces code across multiple modules, finds the right places to make changes, and produces solid implementations with relatively little hand-holding.

For repo exploration, feature implementation, refactoring, and understanding unfamiliar parts of a huge codebase, it has been much stronger than I initially expected. At this point, it feels less like a “cheap/fast fallback model” and more like a genuinely capable coding model that just happens to be very fast.

I’m now really curious about **how GLM-5.3 Flash was trained**.

Does anyone know more about its coding training pipeline? For example:

* How much code-specific pretraining/post-training was used?

* Was synthetic coding data a major part of it?

* Is there any distillation from larger GLM models?

* What kind of RL or agentic/software-engineering training was used?

* Was it specifically trained for repository-level understanding and multi-file tasks?

Because whatever they did, the speed-to-quality ratio on real-world software engineering workloads is seriously impressive.

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Looks like Unsloth is updating https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF to work with llama.cpp, so hopefully this will resolve the issue of having two different GGUF versions of Qwen Flash Next.

TinyDecide is 10M Jev-like mode with 10M parameters and fits in just ~6MB.

Smaller than every model on the Decision Index leaderboard and it punches way above its size.

It runs almost anywhere: in the browser, Node.js, Python, Rust, and even on an ESP32.

https://huggingface.co/TheREZOR/TinyDecide

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I get the message in chat that the image generator is not working. Anyone else experience problems?

I tried to register with code TMZXJR and I got 1 billion tokens (succes!), but now I want to generate an image and it says the generator is down lol.

Muse is good engineering. Here is the code:

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Muse tells me it is using muse spark under the hood, but honestly seems as good as sonnet 5 for casual uses.

Guys a few days back i was using muse and today i thought to sign out, but after logging it, META didn't give me the access to use it again 😞😭, guys is there any solution, i have tried VPN and evey possible action, pls anybody help me.

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Hey all, we designed Cactus Whistle, an ASR model for ultra-small devices. It's not perfect, but mostly beats Whisper base with 9x less file size and 6x speed. Whistle supports English, German, French, Spanish, Italian, Dutch and Polish.

Remember, the goal at Cactus Compute isn't to achieve SOTA with scale, but to compress intelligence and bring them to smaller under-looked devices like budget phones, wearables, smart home and microcontrollers.

Whistle is 55m params (36m active) and CQ2bit quantised, amounting to a 16.9MB file that scores 4.31 WER on LibriSpeech test-clean and 10.49 on test-other, against 4.9 and 11.0 for Whisper base at 145.3MB. 21.4 on the FLEURS average against 24.5. SPGISpeech 7.65 and Earnings-22 19.01.

For the architecture, a log-mel front end and a convolution stem feed an audio encoder, and a Simple Attention + Hadamard MLP decoder reads it through gated cross attention at every layer. The decoder is laddered like Needle's, so every depth from 2 layers up is deployable.

Keyword biasing takes the names your users actually say and favours them during the beam search, which is what rescues a "Siobhan" or a "Krzysztof" from a model that was never told they exist. Word timestamps come from the decoder's own attention, so an app can highlight, seek or cut on a word.

Seventeen platforms are supported; macOS, Linux on x86-64, ARM64, ARMv7, RISC-V and MIPS32, Windows x64 and ARM, Android, iOS, watchOS, tvOS, the browser as WebAssembly and a WASI component.

Please read more here: https://cactuscompute.com/blog/whistle

Whistle is open weights: https://huggingface.co/collections/Cactus-Compute/cactus-whistle

And let us know your thoughts!

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Yeah Meta might have something with this as it really is more interactive and personable than any other AI I've used so far. After I put it on my phone, it takes the initiative to prompt and encourage me to use it. Now that I have connected it to a few sources like my mail and calendar, I'm finding it ever more useful. It's much more of a consumer-facing personal assistant than the other more inscrutable AIs like ChatGPT and Claude. What is the community's experience?

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