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MediaSquirrel

3,021 カルマ登録 17 年前
Founder/CEO at TalkTastic.com

Previously, I co-founded SpeakerText (speakertext.com), a interactive/social layer on top of online video, and Humanoid (gethumanoid.com), a crowdsourced virtual labor service that uses machine learning to track worker reputation on Mechanical Turk and ensure quality.

In previous lives, I fought forest fires in Montana & Alaska for the US Forest Service, worked as a freelance news reporter for the NY Times, and drove a 911 ambulance in Harlem and the South Bronx.

投稿

Show HN: Local Motion – Use Cursor Agents and Chat with a Local LLM

github.com
1 ポイント·投稿者 MediaSquirrel·17 時間前·0 コメント

Show HN: Gist Discover – TikTok for ArXiv Summaries

gist.is
4 ポイント·投稿者 MediaSquirrel·9 日前·1 コメント

Scalable GANs with Transformers

arxiv.org
3 ポイント·投稿者 MediaSquirrel·11 日前·0 コメント

Cheaper Than Concrete: Robots and the New Stone Age

originals.is
4 ポイント·投稿者 MediaSquirrel·11 日前·0 コメント

Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild

arxiv.org
3 ポイント·投稿者 MediaSquirrel·18 日前·0 コメント

Death Is an Engineering Problem

originals.is
3 ポイント·投稿者 MediaSquirrel·18 日前·1 コメント

Non-frontal face recognition using GANs and memristor-based classifiers

arxiv.org
2 ポイント·投稿者 MediaSquirrel·24 日前·0 コメント

MambAdapter: Lightweight Mamba-Based Adapters for Transfer Learning

arxiv.org
2 ポイント·投稿者 MediaSquirrel·25 日前·0 コメント

Can I Buy Your KV Cache?

arxiv.org
36 ポイント·投稿者 MediaSquirrel·29 日前·28 コメント

Before You Think: System 0, AI-Mediated Cognition and Cognitive Colonization

arxiv.org
1 ポイント·投稿者 MediaSquirrel·29 日前·0 コメント

Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Reasoning

arxiv.org
1 ポイント·投稿者 MediaSquirrel·29 日前·0 コメント

Superficial Beliefs in LLM Decision-Making

arxiv.org
3 ポイント·投稿者 MediaSquirrel·30 日前·0 コメント

Show HN: Magenta Real-Time Music Generation Locally on iPhone, Without the GPU

github.com
9 ポイント·投稿者 MediaSquirrel·先月·0 コメント

Feedback Alignment in Self-Distillation

arxiv.org
2 ポイント·投稿者 MediaSquirrel·先月·0 コメント

WWDC 2026 – On-Device AI Deep Dive

gist.is
1 ポイント·投稿者 MediaSquirrel·先月·0 コメント

Show HN: Gemma 4 Multimodal Fine-Tuner for Apple Silicon

github.com
235 ポイント·投稿者 MediaSquirrel·3 か月前·28 コメント

We Melted iPhones for Science – Generating Real-Time Video with On-Device AI

accelerateordie.com
1 ポイント·投稿者 MediaSquirrel·10 か月前·0 コメント

[untitled]

1 ポイント·投稿者 MediaSquirrel·10 か月前·0 コメント

We Melted iPhones for Science

accelerateordie.com
67 ポイント·投稿者 MediaSquirrel·10 か月前·44 コメント

コメント

MediaSquirrel
·9 日前·議論
Ha, I built a version of the same thing as a Cursor plugin. Check it out:

https://open-vsx.org/extension/Transcendence/gist-discover https://gist.is/discover

We literally posted our Show HNs within minutes of each other: https://news.ycombinator.com/item?id=48768342

Great minds think alike.
MediaSquirrel
·11 日前·議論
[flagged]
MediaSquirrel
·18 日前·議論
[dead]
MediaSquirrel
·18 日前·議論
Dr. Alex Mathiasen, PhD wants to build a pause button for human biology – so that you can live forever. All he needs is enough GPUs to simulate quantum physics.
MediaSquirrel
·24 日前·議論
[flagged]
MediaSquirrel
·25 日前·議論
[dead]
MediaSquirrel
·29 日前·議論
[flagged]
MediaSquirrel
·29 日前·議論
[dead]
MediaSquirrel
·30 日前·議論
Do share.
MediaSquirrel
·30 日前·議論
[flagged]
MediaSquirrel
·先月·議論
[dead]
MediaSquirrel
·3 か月前·議論
yeah, it came out after I stared on my project last year. Only issue is that you can't fine-tune it on Apple Silicon.
MediaSquirrel
·3 か月前·議論
depends on the model!

If you run a smaller whisper-distil variant AND you optimize the decoder to run on Apple Neural Engine, you can get latency down to ~300ms without any backend infra.

The issue is that the smaller models tend to suck, which is why the fine-tuning is valuable.

My hypothesis is that you can distill a giant model like Gemini into a tiny distilled whisper model.

but it depends on the machina you are running, which is why local AI is a PITA.
MediaSquirrel
·3 か月前·議論
Look inside here: https://github.com/mattmireles/gemma-tuner-multimodal/tree/m...

Here’s the trick: use Gemini Pro deep research to create “Advanced Hacker’s Field Guide for X” where X is the problem that you are trying to solve. Ask for all the known issues, common bugs, unintuitive patterns, etc. Get very detailed if you want.

Then feed that to Claude / Codex / Cursor. Basically, create a cheat sheet for your AI agents.

This will unlock a whole new level of capability.

I’m @mattmireles on Twitter — feel free to DM me.
MediaSquirrel
·3 か月前·議論
More data -> better, faster on-device models

The actual plan was to distill Gemini 2.5 Pro into the best on-device voice dictation model.

Pretty sure it would have worked. Alas.
MediaSquirrel
·3 か月前·議論
re: Whisper v3 -- how is this possible? Whisper has a 30s context window. You have to chunk it.
MediaSquirrel
·3 か月前·議論
Great minds think alike!

Also, I had a huge head start, as I spent a month or two working on this in September 2025, shelved it and dusted it back off this weekend.
MediaSquirrel
·3 か月前·議論
Haven’t tried yet. That’s on the do list. But good suggestion.
MediaSquirrel
·3 か月前·議論
you are welcome! It was a fun side quest
MediaSquirrel
·3 か月前·議論
Memory usage increases quadratically with sequence length. Therefore, using shorter sequences during fine-tuning can prevent memory explosions. On my 64GB RAM machine, I'm limited to input sequences of about 2,000 tokens, considering my average output for the fine-tuning task is around 1,000 tokens (~3k tokens total).