A locally running (macOS) iOS build size analysis tool. Been tinkering with Apple Intelligence, trying to implement an on-device size optimization engineer for my users. The small context window for Apple Intelligence has been tricky to get right.
I maintain an app size inspection tool that runs locally on your macOS and added the file inspector (Sunburst chart) for Gmail if anyone is interested in exploring its contents.
As others have pointed out, the main executable is huge (~300MB) and there are a lot of localization files, but not too much traditional asset duplication.
Not just about the products imho. I do some consulting for law firms who typically use the MSFT stack, and I was excited about the private ChatGPT services in Azure, because from my (admittedly limited) sample of law firms, nobody likes using Copilot and LLMs need to be private/secure. The amount of outdated and poor quality documentation for Azure services is amazing given how nascent these services are.
> Since the data will always be flawed and the test set won't be blind, the machine learning engineer's priority should be spent working with policy teams to improve the data.
It's interesting to watch this dynamic change from data set size measuring contests to quality and representativeness. In "A small number of samples can poison LLMs of any size" from Claude they hit on the same shift, but their position is more about security considerations than quality.
Hello, I'm a former Staff Engineer branching out to help law firms and attorneys with AI product development and assessments. Looking for partners in the legal space who want to build the next big thing in legaltech or need help understanding how to best adopt AI in the legal space.
Location: Golden, CO
Remote: Onsite, remote, or hybrid are all okay.
Willing to relocate: Negotiable
Technologies: AI / PyTorch / Tensorflow / Typescript / Next.js / AWS / Node.js / Vercel / Swift / iOS / macOS
Résumé/CV: https://fractional-engineer.com
Email: kenny@ndukt.com
Alright let's say im tasked with building a fancy AI-powered research assistant and I need onyx or Vercel's ai-chatbot sdk. Why would I reach for onyx?
I have used vercel for several projects and I'm not tied to it, but would like to understand how onyx is comparable.
Benefits for my use cases for using vercel have been ease of installation, streaming support, model agnosticity, chat persistence and blob support. I definitely don't like the vendor lock in, though.
Not wanting to use Vercel is honestly a good enough reason. If you’re a heavy Vercel user you probably aren’t their target market since they’re aiming at enterprise types from what it looks like.
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