This looks great, will try it out. I had a crazy idea of doing a full agentic demo video narration and cutting, so might try your tool with some tweaks.
TTS has come incredible long way, there are so many options. There is Kokoro of course, then there is Pocket TTS which is also a tiny 100M model that allows voice cloning. There is also Chatterbox Turbo, which is bit bigger but also allows for more emotional control of the voice. And then finally there is the Fish Audio S2, which is even bigger but allows even larger and essentially unbounded finegrained control of tone and emotion. And all of these can easily run on your Macbook.
I find it astounding that ppl still comment “it’s still behind” or “it’s not the best model”. Everything is about the harness. Even the big AI labs are focusing on managing agents - sandboxes, memory, context, skills, loops. With the right harness GLM 5.2 can do no wrong.
I have the opposite experience with this, and I personally also default to simplest solutions at least as a baseline. However it’s important to distinguish between simple solutions that approximate the problem very well, to simple solutions that work in limited context or with heavy sacrifice in assumptions because those will hurt you in the long run.
I mean it's always nice to play around with sLLM finetuning, but for practical purposes I would always start with a lazy learner using embeddings (something like a small Stella model), pre-embed the topics/categories, embed the question, perform a kNN using cosine distance. You can use an LLM to "expand" the topics before embedding to make them more contextual. This is usually super fast and super simple and gives you a nice baseline. Then I would add a classification head after embedding layer (with maybe some dropout + 2-3 MLP layers) and train my own classifier, and compare that to lazy learner. Only after that would I start finetuning an LLM.
Neko Health has been doing this now for a few years. What I heard is that ultimately it doesn’t solve much (other than them privately collecting all your data) because there are lot of false positives and these false positives are deferred to the general healthcare system, which is a major bottleneck.
Can this be used for ML/AI projects as well? I'm thinking for version controlling LoRa finetunes, finetuning data (which can consist of text, images and audio), safetensors, etc?
As an amateur watercolour artist (shameless plug: https://www.instagram.com/p/DBlKG5cMPxa) I have to say the feeling your made with this wash is gorgeous. Back in the analogue world - paper grain and type/brand has a lot to do with it. Watercolour is really about unpredictability - it's about taking advantage of this unpredictability in terms of how the water travels down the grain and the impact that it makes, combined with light/shadow and "confidence" the artist brings with the brush. So of course it's never going to be truly transferrable digitally, but I still love the work you put into this.
I keep seeing these "sovereign" LMs time and time again. In Sweden we had GPT-SW3 (https://www.ai.se/en/project/gpt-sw3) and same story there. Instead of burning money on "sovereign" claims, national research labs should instead focus on building on top of solid baselines (like Qwen/Kimi) and finetuning frontier models with real agentic utility that can be applied across actual use cases and can be widely used by its people, basically for free. Nations should mirror what Cursor has done with Composer 2.5 for example.
This looks beautiful and I'm sorry the current state of affairs has made you not want to publish the code, I would love to play around with it. Regarding your decision to build - I feel you, I've had the same happen to me for everything from charting libs to various web components.
As an aside, I really like your web page - simple and clean with images and demos, no bloat.
What an awesome story. Not too many stories about Aussies out there, but what Han brothers are doing with Unsloth in AI, and stories like this one, makes this fellow Aussie super proud!
Any particular reason for BM25? Why not just a table of contents or index structure (json, md, whatever) that is updated automatically and fed in context at query time? I know bag of words is great for speed but even at 1000s of documents, the index can be quite cheap and will maximise precision
As someone who's been working in legaltech space where MS Word add-in chatbot was a killer feature, this is brutal. And in their demo they are hammering on the legal case (redline chat).
https://x.com/acatovicx