building an AI language learning app · Local LLM junkie

Learn Japanese with anime 👉
Joined June 2013
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Built a fully local voice AI assistant on react native - Zero token cost. Speech-to-Text → LLM → Text-to-Speech. All on-device. All private. The Stack: - Whisper.RN for STT (OpenAI's Whisper ported to RN) - Ollama + Qwen model for local LLM inference - Supertonic (via Node.js/ONNX) for TTS Forget token counting. Thing is, Supertonic has Python, Node.js, and Flutter bindings, but not React Native 😅 So I had to run a local server. But hetzner servers are cheap. Follow for regular Ai news from the trenches
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Claude code $200 was the best value
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llama.cpp at 100k stars now that 90% of the code worldwide is being written by AI agents, I predict that within 3-6 months, 90% of all AI agents will be running locally with llama.cpp 😄 Jokes aside, I am going to use this small milestone as an opportunity to reflect a bit on the project and the state of AI from the perspective of local applications. There is a lot to say and discuss and yet it feels less and less important to try to make a point. Opinions about viability of local LLMs are strongly polarized, details are overlooked, the scientific approach is lacking. Arguments are predominantly based on vibes and hype waves. One thing is clear though - local LLMs are used more and more. I expect this trend to continue and likely 2026 will end up being one of the most important years for the local AI movement. I admit that I didn't expect the agentic era to come so quickly to the local LLM space. One year ago, the available models were too computationally expensive for doing long-context tasks. There wasn't an obvious path towards meaningful agentic applications. The memory and compute requirements were huge. Last summer, with the release of gpt-oss, things started to change. It was the first time we saw a glimpse of tool calling that actually works well within the resource constraints of our daily devices. Later in the year, even better models were released and by now, useful local agentic workflows are a reality. Comparing local vs hosted capabilities at a given moment of time is pointless. To try put things into perspective: - We don't need frontier intelligence to automate searches and sending emails - We don't need trillion parameter models to be able to summarize articles or technical documents - We don't need massive GPU data centers to control our home appliances or turn the lights off in the garage I believe that there is a certain level of intelligence we as humans can comprehend and meaningfully utilize to improve our working process. Beyond that level, access to more intelligence becomes unnecessary at best and counterproductive at worst. I also believe that that level of useful artificial intelligence is completely within reach locally and it has always been just a matter of implementing the right software stack to bring it to the end user. With llama.cpp, I am confident that we continue to be on the right track of building that software stack! The llama.cpp project is going stronger than ever. With more than 1500 contributors, the project keeps growing steadily. From technical point of view, I think that llama.cpp + ggml is the only solution that actually makes sense. That is, the software stack must run efficiently on every possible device, hardware and operating system. The technology is too important to be vendor-locked. It has to be developed in the open, by the community, together with the independent hardware vendors. This is the only right way to build something that will truly make a difference in the long run. I won't try to convince you about what is currently and will be possible with local AI. We will just continue to build as usual. I am confident that after the smoke clears and we look objectively at what we have built together, the benefits will be obvious to everyone. Big shoutout to all llama.cpp maintainers. I feel extremely lucky to be able to work together with so many talented contributors. Every day I learn something new and I feel there is so much more cool stuff that we are going to build. Also, I am really thankful that the project continues to have reliable partners to support it! Cheers!
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Wow! This is amazing! Segmented every car locally in real time with Meta's SAM3 converted to MLX. Just on-device (M2 laptop) vision getting absurdly good. Local AI is moving faster than most people realize! What other models should we test? what kind of videos?
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What An episode !!!
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Worth investigating for those with nvidia 5060 gpu. its an entry level card costing around $400-$500
These ultra geniuses beat Sonnet 4.5 performance on LiveCodeBench with Qwen3-14B, a single RTX 5060 and a great harness.
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If you are considering buying the intel 32 GB gpu that is under $1k, then read this before you buy.
People keep saying “VRAM is all that matters” for local LLMs > It’s not just wrong, it’s misleading When running LLMs locally, the bottleneck is NOT just “VRAM size” It’s: - memory bandwidth - interconnect (PCIe vs NVLink vs RDMA) - inference engine (vLLM, TensorRT-LLM, SGLang) (Also, Unified Memory ≠ VRAM - and it’s much slower) > People still evaluate hardware in isolation A piece of hardware is USELESS if the software stack isn’t there You are not buying silicon you are buying: - kernels - runtime - framework support - model and quantization compatibility > Buying this Intel GPU? Good luck waiting years for that ecosystem to mature Meanwhile even Blackwell isn’t fully mature and it’s already widely adopted > There are already too many problems across the stack (e.g. GPU → framework → model → quantization) Example of what people keep falling for: “32GB VRAM card for $1K???” But: > unified memory class bandwidth > weak software stack > no CUDA You didn’t buy a GPU you bought a debugging project > Yes, you can make it work You might see: > ~54 tok/s aggregate on 3x Arc A770 But it comes with: - buggy drivers - missing tooling - kernel level hacks And real performance looks like: > ~13 to 14 tok/s single request > unstable configs and constraints This is the trap... On paper it looks competitive in practice it’s fragile > My take This class of products gets killed in less than 2 generations The stack won’t mature fast enough and it wastes your time > If you care about local AI Optimize for: - bandwidth - interconnect - software maturity Not just VRAM TLDR > The stack is the moat > CUDA still wins > Unless you wanna waste your time, this is a NO BUY —Buy a GPU, The Movement
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I played this game around 1996 ! 30 years ago !
Visco’s Goal! Goal! Goal! is a spectacular Neo Geo soccer classic that captures the pure spirit of arcade sports. With its fast-paced gameplay, powerful special shots, and vibrant graphics, it delivers a thrilling international tournament experience.
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kaiyes retweeted
GLM-5.1 is available to ALL GLM Coding Plan users! z.ai/subscribe
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meet Intel's Arc pro B70 - 32 GB Vram below $999. there are pros & cons to it though - availability, software support etc. Supports vLLm from the get go. Hopefully rocM or vulkan picks up the pace with it. Don't have much hope for intel to provide the software support
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VLMs too slow for production? Not anymore: 46ms end-to-end inference, 60+ fps on a single H100. Introducing Photon, Moondream's inference engine. Runs on everything from edge to server. moondream.ai/blog/photon-rea…
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if ADHD had a face
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Super happy with hermes agent. up & running in like 5 minutes using local QWEN 9B Q4 model. With openclaw, the setup is so tedious. Thanks to @sudoingX , I gave it a try
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How in the hell are these accounts claiming to run entire companies w/ OpenClaw. I just spent 1.5 HRS trying to get my claw to use X API for reading tweets. FAIL We have a whole SOP documenting exactly how to do it from previous failures. I can’t imagine running 10 of these…
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agreed 💯. thats what I have been doing too
I'm convinced if you want to maximize productivity, you shouldn't be using openclaw or hermes - they take so much time bug fixing that you're better off just using claude code/codex directly there's maybe 1% of people who are the exception to this
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new twitter algorithm is really good ! it picked up my slight interests & put tweets in my timeline from amazing accounts. my feed is now all AI + anime. Normally it puts me in the react native Jail. Never knew these many great manga/anime accounts were there in twitter
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Transformers are Turing complete and can be trained to run arbitrary programs Turns out you can embed a relatively efficient assembly interpreter in the forward pass. This allows the LLM to execute deterministic code at inference time in its own weights, no sandbox
1/4 LLMs solve research grade math problems but struggle with basic calculations. We bridge this gap by turning them to computers. We built a computer INSIDE a transformer that can run programs for millions of steps in seconds solving even the hardest Sudokus with 100% accuracy
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take a look guys !
QWEN 3.5 4b MLX running locally with a milk avatar Model by @toxsam
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had to post this
Replying to @souljagoyteller
who the hell is claude?
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