dwrodri

As someone who used LaTeX heavily in grad school, using Typst in VSCode to create bespoke invites to an event was quite nice! Would highly recommend giving it a spin.

I could see this being really useful for people who need VM-Sandboxed software, but also need quick and dirty bridges between host/guest. Great contribution!

question to clarify:

If the LLM designs the circuit, it is clear "contamination" if the tool calls which wrote the RTL / Verilog are also in the same context window as the specification design.

If a windows DLL, distributed without a license that says anything regarding, is it a "clean room implementation" if there is some nonzero chance the source code was leaked into the weights at pre-train time? I guess there should be some sort of method for subpoenaing frontier labs to ask "can you grep for this code in the training set for this model", but that might not be practical or feasible.

was about to hop into the thread to make a jeer about the highly compensated engineering team at Anthropic apparently not testing their website on iPhone screens.

Accessibility is important, and I find that "Reader Mode" in most browsers is quite good. Everyone should have access to the tools to consume content. Did your browser not provide that functionality?

When I was a vision engineer at a previous role back in 2022-ish, laptop-class Intel chips used in their SFF NUCs were powerful enough to run small finetuned convolutional nets by dancing across the different on the package: first decode using QSV, and then downsize using the CPU, run the images through an OpenVINO model running on the iGPU, and aggregate results and append metadata using the CPU core. You could have 50% of the hardware in today's high-end security cameras for 10% of the cost.

I have to assume that hardware advancements in the past four years have only pushed the efficiency envelope further.

My deepest conflict with Python as someone who works with it professionally is balancing "keep it simple and clean" with an acknowledgement that reality is often is complex and messy.

Synchronous, mostly functional python scripts written with the standard library and perhaps a few dependencies generally feel like the best the language has to offer, and they frequently make great prototypes (and I have put a few in production).

But I have also dealt with a lot of important stuff that felt like the wrong tool for the job:

- Airflow tasks, and durable workflows as a whole on top of a database

- async stuff, and generally the fact that you need `aiofile` to interact with the filesystem asynchronously

- "Productionized" stuff around NumPy and DataFrame APIs. I think there are genuinely fascinating uses for this stuff and its still great! JAX stood out as the the API that truly "clicked" for achieving "simplicity" despite the difficult challenges that it solves.

Typing woes appear the most when I am handed a prototype to productionize. Good static analysis of types carries you so far, it makes me frequently wonder if something even more aggressive than what TypeScript does would have been ideal. There is a great scripting language trapped underneath a mountain of adjustments that have been made to Python so it can "be used for writing large scalable software systems" and it makes me question whether that ever should have been the point of Python in the first place.

P.S. INB4 Stroustrup quote about languages people complain about vs ones they don't use and all that. You can tell Go was designed for very specific use cases and hasn't catered too much to use cases outside of that.