Tyler Williams PRO

unmodeled-tyler

https://quantaintellect.com

AI & ML interests

AI research engineer & solo operator of VANTA Research/Quanta Intellect

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reacted to cahlen's post with 🔥 about 2 hours ago

It’s wild to me how you can just make shit now. You can take a weekend with a raspberry pi 5, a pi camera, a 3d printer, and a smidgen of custom fine tuning (wakeword, whisper, tinybert, and pipertts) and you have physical device as a talking personal assistant. What a time to be alive. Edge ai, physical ai, ai augmented animatronics… tiny models. Tiny agents. Going to be a wild year.

reacted to aufklarer's post with 🔥 about 2 hours ago

We benchmarked https://github.com/soniqo/speech-swift, our open-source Swift library for on-device speech AI, against Whisper Large v3 (FP16) on LibriSpeech test-clean. Three models beat it. Two architectural approaches: Qwen3-ASR (LALM — Qwen3 LLM as ASR decoder, AuT encoder pretrained on ~40M hours) hits 2.35% WER at 1.7B 8-bit, running at 43x real-time on MLX. Greedy decoding matches beam search — the LLM decoder is strong enough that the greedy path is nearly always optimal. Parakeet TDT (non-autoregressive transducer — FastConformer + TDT joint network) hits 2.74% WER in 634 MB as a CoreML INT8 model on the Neural Engine. No generative hallucination by design. Leaves GPU completely free. Two findings worth flagging: - 4-bit quantization is catastrophic for non-English: Korean 6.89% → 19.95% WER on FLEURS. Use 8-bit for multilingual. - On CoreML, INT8 is 3.3x *faster* than INT4 — opposite of GPU behavior. Native ANE INT8 MACs vs INT4 lookup table indirection. All numbers reproducible in 15 minutes. Full article: https://blog.ivan.digital/we-beat-whisper-large-v3-with-a-600m-model-running-entirely-on-your-mac-20e6ce191174 Library: https://github.com/soniqo/speech-swift Models: Qwen/Qwen3-ASR-0.6B, Qwen/Qwen3-ASR-1.7B, nvidia/parakeet-tdt-0.6b-v2

updated a dataset about 2 hours ago

unmodeled-tyler/vessel-browser-tool-loop

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LINK: https://github.com/unmodeled-tyler/vessel-browser

Hey Hugging Face!

It's been quiet from me over here for the last few weeks, but I've been busy building! I just submitted my project to the Hermes Agent Hackathon, and wanted to share it with all of you.

This is Vessel Browser - an AI-native web browser that runs locally on Linux, and is operated by your personal AI agent via MCP server. Vessel is built from the ground up around the agent as first-class and visible UI for human-in-the-loop with 3 different levels of permissions.

Your agent finds, reads, and organizes the web for you, based on what you actually care about - not what a platform's algorithm thinks you care about.

Once your agent finds what it's looking for, it can organize bookmarked pages into custom folders with summaries for later browsing, take screenshots with highlighted text, and integrate with Obsidian for long-term browsing related-memory.

Check it out!