More importantly, they recently started trading in yuans.
Source? How recently?
Readit Newsabrichr commented on Trump says Venezuela’s Maduro captured after strikes reuters.com/world/america... · Posted by u/jumpocelot
abrichr commented on Opus 4.5 is the first model that makes me fear for my job old.reddit.com/r/ClaudeAI... · Posted by u/nomilk
I think MSFT really needs some validated user stories. How many users want to, "Improve my writing," "Create an image," "Understand what is changed" (e.g. recent edits), or "Visualize my data."?
Those are the four use cases featured by the Microsoft 365 Copilot App (https://m365.cloud.microsoft/).
Conversely, I bet there are a lot of people who want AI to improve things they are already doing repeatedly. For example, I click the same button in Epic every day because Epic can't remove a tab. Maybe Copilot could learn that I do this and just...do it for me? Like, Copilot could watch my daily habits and offer automation for recurring things.
> Like, Copilot could watch my daily habits and offer automation for recurring things.
We're working on it at https://github.com/openadaptai/openadapt.
See also: https://audionotch.com/app/tune/
abrichr commented on Five years as a startup CTO: How, why, and was it worth it? (2024) distinctplace.com/2024/09... · Posted by u/mooreds
Right, sure, there is an initial pain of building domain knowledge. But that one year of knowledge building, distribution, early customers, then dominates who has control over the cap table for a decade. Why not just do this work yourself? Its some weird myth that someone capable of being a technical leader cannot also flesh out the distribution
Especially with the existence of AI, it really makes no sense and is some weird hierarchical system built by business people
> 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?
> 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.
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