Hacker Neue

Preferences
abrichr
Joined 1,452 karma
Machine learning scientist, engineer, and entrepreneur.

- https://github.com/OpenAdaptAI/OpenAdapt - https://openadapt.ai - https://linkedin.com/in/richard-abrich - https://github.com/abrichr - https://richardabrich.com/resume - https://MLDSAI.com - richard at openadapt dot ai

abrichr.at.hn

meet.hn/city/ca-Old-Toronto

Socials: - github.com/abrichr - x.com/abrichr - linkedin.com/in/richard-abrich

---

  1. abrichr
    You can reach much higher spend through the API (which you can configure `$claude` to use)
  2. abrichr
    > Like, Copilot could watch my daily habits and offer automation for recurring things.

    We're working on it at https://github.com/openadaptai/openadapt.

  7. abrichr
    > But that one year of knowledge building, distribution, early customers, then dominates who has control over the cap table for a decade.

    Can you recommend any resources for learning how to do this work yourself?

  8. abrichr
    > While the LLMs get to blast through all the fun, easy work at lightning speed, we are then left with all the thankless tasks: testing to ensure existing functionality isn’t broken, clearing out duplicated code, writing documentation, handling deployment and infrastructure, etc.

    I’ve found LLMs just as useful for the "thankless" layers (e.g. tests, docs, deployment).

    The real failure mode is letting AI flood the repo with half-baked abstractions without a playbook. It's helpful to have the model review the existing code and plan out the approach before writing any new code.

    The leverage may be in using LLMs more systematically across the lifecycle, including the grunt work the author says remains human-only.

  11. abrichr
    Corporate income taxes are treated differently than personal income taxes. You absolutely can deduct corporate expenses in Canada.
  15. abrichr
    The "things" you mention may correspond to internal concept representations encoded in the model's weights. See e.g. https://arxiv.org/abs/2206.13289
  16. abrichr
    Interesting, thanks!

    Would something like https://github.com/OpenAdaptAI/OmniMCP make sense to include here?

  17. abrichr
    > Twitter was making around $500 billion a quarter before the Apple ad targeting changes

    From https://archive.is/rfBcg:

    > Advertising revenue was $1.14 billion during the quarter ended Sept. 30 [2021], in line with consensus estimates.

  18. abrichr
    > I just need to verify whichever part I need to use for something else. I can run the code it produces to see if it works. I can look up the reference to see if it exists. I can Google the particular fact to see if it's real. It's really very little effort. And the verification is orders of magnitude easier and faster than coming up with the information in the first place. Which is what makes LLM's so incredibly helpful.

    Well put.

    Especially this:

    > I can run the code it produces to see if it works.

    You can get it to generate tests (and easy ways for you to verify correctness).

  19. abrichr
    Thank you both!

    Follow-up question: do US employers ever provide assistance with O-1 or E-2? What is considered "a relatively high level of achievement"?

This user hasn’t submitted anything.