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Изходен канал @clockstackwheels · Post #81 · 6.10

Делали в кладовой ремонт, из-за этого немного поменялось местоположение базы робота-пылесоса. Обычной функции "задать точку новой базы на карте" в приложении нет. Робот после включения отображал сам себя в старой точке. Я в режиме ручного управления привёл его в нужную комнату (так и знал, что годы обучения поездкам на радиоуправляемой машинке не зря!). Так вот он мне, пока ехал, стал лидаром рисовать новую квартиру поверх старой со смещением. Тогда я нагуглил алгоритм обновления базы, он довольно забавный: 1. Привести робота в комнату, которую он знает, и дать ему убрать. Тогда он себя верно определит в этой комнате (кстати круто работает, неужели по форме стен и стоящих у стен предметов?) 2. Привести его в точку новой базы вручную или с помощью команды "ехать в заданную точку на карте". Он не знает пока ещё, что там база, но положение своё уже понимает. 3. Отправить его на базу. Он поедет в точку старой базы, где она когда-то была. И начнёт её искать, очень мило выглядит :) 4. Не найдя базу, станет спрашивать по своим датчикам, а где же всё-таки база, и, наконец, увидит новую и поедет к ней. Профит. Малютка-робот нашёл свой дом. #gadgets

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GitHub Trends

@githubtrending · Post #15143 · 14.09.2025 г., 12:00

#python#llms#mlx MLX LM is a Python tool that helps you run and fine-tune large language models (LLMs) efficiently on Apple Silicon Macs. It connects easily to thousands of models on Hugging Face, supports model quantization to save memory, and allows distributed training. You can generate text or chat with models via simple commands or Python code. It also offers features like prompt caching and memory optimization for handling long texts, making it faster and less resource-heavy. This means you can run powerful AI models locally on your Mac without needing expensive cloud services, saving cost and improving speed. https://github.com/ml-explore/mlx-lm

GitHub Trends

@githubtrending · Post #14655 · 01.05.2025 г., 13:30

#typescript#electron#llama#llms#lora#mlx#rlhf#transformers Transformer Lab is a free, open-source tool that lets you easily work with large language models on your own computer, offering one-click downloads for popular models like Llama3 and Mistral, fine-tuning across different hardware (including Apple Silicon and GPUs), and features like chatting, training, and evaluating models through a simple interface—saving you from complex setups like CUDA or Python version issues[1][2][5]. https://github.com/transformerlab/transformerlab-app

GitHub Trends

@githubtrending · Post #15614 · 13.04.2026 г., 11:30

#typescript#ai#cuda#mlx#qwen3_tts#qwen3_tts_ui#voice_ai#voice_clone#whisper Voicebox is a free, open-source voice synthesis studio that lets you clone voices, generate speech in 23 languages, and apply audio effects—all running privately on your computer. You can create realistic voice clones from just seconds of audio, use five different text-to-speech engines for different needs, add effects like reverb and pitch shift, and build multi-voice projects with a timeline editor. The key benefit is complete privacy: your voice data and AI models never leave your machine, unlike cloud-based alternatives. It also includes an API for building voice-powered applications and works across Mac, Windows, and Linux with GPU acceleration support. https://github.com/jamiepine/voicebox

GitHub Trends

@githubtrending · Post #14684 · 08.05.2025 г., 12:00

#python#apple_silicon#audio_processing#mlx#multimodal#speech_recognition#speech_synthesis#speech_to_text#text_to_speech#transformers MLX-Audio is a powerful tool for converting text into speech and speech into new audio. It works well on Apple Silicon devices, like M-series chips, making it fast and efficient. You can choose from different languages and voices, and even adjust how fast the speech is. It also includes a web interface where you can see audio in 3D and play your own files. This tool is helpful for making audiobooks, interactive media, and personal projects because it's easy to use and provides high-quality audio quickly. https://github.com/Blaizzy/mlx-audio

GitHub Trends

@githubtrending · Post #15600 · 04.04.2026 г., 11:30

#python#apple_silicon#florence2#idefics#llava#llm#local_ai#mlx#molmo#paligemma#pixtral#vision_framework#vision_language_model#vision_transformer MLX-VLM lets you run, chat with, and fine-tune Vision Language Models (VLMs) plus audio/video models on your Mac using MLX—install easily with `pip install -U mlx-vlm`. Use CLI for quick text/image/audio generation (e.g., `mlx_vlm.generate --model ... --image photo.jpg`), Gradio UI for chats, Python scripts, or a FastAPI server with OpenAI-compatible endpoints supporting multi-images/videos. Features like TurboQuant cut KV cache memory by 76%, and LoRA/QLoRA fine-tuning works on consumer hardware. You benefit by experimenting with powerful multimodal AI locally—fast, memory-efficient, no cloud costs, perfect for Mac users tweaking models affordably. https://github.com/Blaizzy/mlx-vlm