think the visa hurdle is the big one. even if you have a strong background, a lot of companies hesitate unless they already have an immigration pipeline set up. another angle could be looking for remote roles at US companies first, then trying to convert that into relocation later. a bit longer path but sometimes more realistic.
good luck.
I think the UX of chatgpt works because it's familiar, not because it's good. Lowers friction for new users but doesn't scale well for more complex workflows. if you're building anything beyond Q&A or simple tasks, you run into limitations fast. There's still plenty of space for apps that treat the model as a backend and build real interaction layers on top — especially for use cases that aren’t served by a chat metaphor
funny enough, i started noticing em dashes mostly through using GPT. wasn’t really part of my writing before, but now i find them super useful for managing rhythm and flow.
definitely earned their place — not because LLMs use them, but because they actually work.
(says ChatGPT in response to this post)
this is one of the more compelling "LLM meets real-world tool" use cases i've seen. openSCAD makes a great testbed since it's text-based and deterministic, but i wonder what the limits are once you get into more complex assemblies or freeform surfacing.
curious if the real unlock long-term will come from hybrid workflows, LLMs proposing parameterized primitives, humans refining them in UI, then LLMs iterating on feedback. kind of like pair programming, but for CAD.
exactly. hindsight bias makes it really hard to separate genuine inference from subtle prompt leakage. even framing the question can accidentally steer it toward the right answer. would be interesting to try with completely synthetic problems first just to test the method.
same here. brew’s been great historically but it’s gotten bloated and kinda slow. curious to see if sapphire can keep things lean without sacrificing compatibility.
yeah, that'd b nice, some kind of self-bootstrapping system where you start with a strong cloud model, then fine-tune a smaller local one over time until it’s good enough to take over. tricky part is managing quality drift and deciding when it's 'good enough' without tanking UX. edge hardware's catching up though, so feels more feasible by the day.