It'd be nice to have some info on how it's substantially different from something like https://github.com/earthtojake/text-to-cad (besides from being hosted). Is there a 'document' format you're working with besides from being able to export to stl? Can we download that / continue our work with another editor if we need to make updates? I guess how far into production parts is this project aiming to go?

Useful to understand in the context of the current DRAM supply constraints since it also goes into what capacity the other 3 players are bringing online in the next 2 years.

Comments URL: https://news.ycombinator.com/item?id=48648255

Points: 4

# Comments: 1

UE is definitely used to obtain simulation data in other domains (this is coming from first hand experience in big tech), but usually through scripting UE handmade levels in python which also needed convoluted server systems at the time (hopefully this has gotten better now).

Gemini 3.1 and GLM 5 came out around the same time as Sonnet 4.6 (~Feb 2026) so it's strange that they are missing, but Gemini 2.5 Flash, Gemini 3 Flash, and GLM 4.7 are there.

There’s other options like photonic computing which might be able to reduce power significantly but are still in research as far as I can tell. Because so much money is invested in AI & traditional gpu inference is so power hungry, I would expect significant improvements in this space quickly.

Now that it's at least here, hopefully Google can continue updating it instead of giving up on it if their metrics don't show as fast growth as iOS or Chrome usage.

Then langchain and structured schemas for the output along w/ a specific system prompt for the LLM. Do you know which open source models work best or do you just use gemini in production?

Also, looking at the docs, Gemini 2.5 flash is getting deprecated by June 17th https://ai.google.dev/gemini-api/docs/deprecations#gemini-2.... (I keep getting emails from Google about it), so might want to update that to Gemini 3 Flash in the examples.

Github seems to want to do nothing about this & I'm tired of wasting my time reading issues that no one actually validated or are just used as marketing for yet another AI slop tool.

In the past week I've had a number of "security audits" generated completely by AI that do not help my project at all. If this continues, I might have to just block github issues entirely in my public repos.

Comments URL: https://news.ycombinator.com/item?id=47339814

Points: 2

# Comments: 1

AWS has information about their UAE data centers, but haven't seen any confirmation from Amazon itself that amazon.com is having issues.

Just noting that I'm not against differentiation in products, but it gets very confusing for users when there's too many options (in the case of the consumer ChatGPT at least this is still more limited than in pre-GPT 5 days). The issue is that there's differentiation at what I pay monthly (free vs plus vs pro) and also at the model layer - which essentially becomes this matrix of different options / limits per model (and we're not even getting into capabilities).

For someone who uses codex as well, there are 5 models there when I use /model (on Plus plan, spark is only available for Pro plan users), limits also tied to my same consumer ChatGPT plan.

I imagine the model differentiation is only going to get worse as well since with more fine tuned use cases, there will be many different models (ie health care answers, etc.) - is it really on the user to figure out what to use? The only saving grace is that it's not as bad as Intel or AMD cpu naming schemes / cloud provider instance naming, but that's a very low bar.

It doesn't even look like they added cellular as an option with their own C1X chip (getting around the licensing / cost issues since it's their own chip now).

I feel like openai is going to get right back to where they were pre GPT-5 with a ton of different options and no one knows which model to use for what.

It’s interesting that they only have 70 people for this - I can understand the outside the US ones for nighttime assistance and they need to be able to scale for other countries too in the future.

What I’m still wondering is what is limiting the scaling for Waymo - just cars or also the sensor systems? They’ve had their new test vehicles in SF for a while but I still think that most customers only get their Jaguars right now (and still limited on highway driving to specific customers in the Bay Area).

I think that the github repo's README may be more useful: https://github.com/webmachinelearning/webmcp?tab=readme-ov-f...

Also, the prior implementations may be useful to look at: https://github.com/MiguelsPizza/WebMCP and https://github.com/jasonjmcghee/WebMCP

Question is how you stay motivated to keep at it - looks like it took about 4 years before you made similar to your Google salary, did family pressure or external pressure ever impact you? Or is it mainly just keep your eyes on the longer term goal?

I'm also quite lucky that I was aiming for lean-FIRE before I left Facebook, so I have the luxury of being able to keep at it, but sometimes it is demotivating seeing peers / others.