spamizbad

> I simply know that I can solve any engineering problem (that I can come up with) with the help of artificial intelligence. I've tried it a few times — it works.

I think you just need to push yourself to do harder problems. For me, I pushed pretty aggressively into the hardware space, building things as a hobby that I originally felt would be far too time-consuming. This is an area where AI can certainly help accelerate but asking it to make small changes can be an expensive (token wise) proposition, so it forces you think through all the critical decisions and research up-front, and let AI do the boring scut-work.

Yep, I think that's a very pragmatic decision given the state of the KiCad project (not that KiCad bad, but the current stable release lacks agentic-friendly hooks which will hopefully land in 11).

Everyone says this and then when the company experiences a round of layoffs everyone’s job gets harder, service gets worse and delivery slows down

Consumer-ready LLMs have been widely available to the public now nearly 4 years if you start counting at GPT 3.5. There's certainly some compression here but Karpathy does a good job of outlining how silo'd those experiences are: the bulk of the product penetration (based on headcount rather than token usage) is "derpy chatbots" (his words, not mine). Most of the actual token spend is likely done on the STEM side. We're talking about a technology today that currently only has significant penetration with <25M users.

People need to be patient and put in the work to build great products, rather than lament its slower-than-desired (albeit still quite fast) adoption curve.

I think people in the LLM space are being far too impatient with the general public and their adoption. And I think they need to start thinking harder about building products that genuinely enrich people's lives. Look at how long home computing took to establish itself with the public, then the internet, and finally social media. None of these trends happened overnight, and some were accelerated by other technological advances (social media-> smartphones).

The adoption of these technologies spurned ton of innovation in these spaces, and I feel like with LLMs its the first time I've seen genuine frustration from the industry directed towards users for not adopting the technology fast enough. This attitude did not exist in the 1980s, it didn't exist in the 90s, and it didn't exist in the 00s/early 10s either.

The problem is you won't be faced with a binary choice of certain death or a single unknown AI cure: You will have a choice between several established protocols AND several unknown AI drugs. You doing a dice roll or do you want to make an educated decision?

I think for many projects you're right. One niche where I disagree: reverse-engineering. You'll need to carry context on things cannot be derived from code immediately eg: past experiments, findings, decomposed schematics/firmware etc. Without this, models are prone to re-doing the same work continuously - which sometimes involves "expensive" (in terms of wall time) simulations.

"Vibe shift" is all the rage these days but one area where it's most apparent is modern software.

People of Raskin's vintage had a very humanist approach to computing. The mentality was very much an outgrowth of the cultural revolution of the 60s. Computer usage should be joyful rather than toiling. The computer should help you. It should make you feel good about what you create.

That stands in stark contrast today, where things are more hostile. Not just in terms of "Dark patterns", or writing off user needs for the sake of expediency... but even the way we talk about technology today. You need to use X, do Y or you're "not going to make it" / "escape the permanent underclass". It's bleak.

I recently restored a vintage Macintosh SE I picked up at VCF Midwest and despite its limitations that machine was an absolute JOY to use despite its limitations and nearly 40 years of age. This was a machine and software designed by Raskin's contemporaries and it shows.

It's been a while (well, 8 months) since I used Claude Code, but what I can say is that I greatly enjoy using the plan mode that comes baked into omp (oh my pi), especially for tasks around firmware development. I recognize that has a very different workflow than webapps though.

Oh I'd say its real. Years ago I worked as that senior who divvied up the work to a team of 4 mid-level engineers in that exact fashion (they were mostly Latin American contractors with limited english). We actually shipped some good stuff too!

Unfortunately, looking back, I could easily see Claude replacing 3 out of 4 of those developers. Myself + one other dev + AI would probably would've shipped a bit more a little faster. With that said tokens aren't free so the net cost savings would've been 2 dev salaries maximum.

I feel like PA-RISC actually landed with a handful of useful somewhat-complex instructions. It always struck me as the best architecture from that era, making the correct trade-off of avoided microcode but adopted stuff like completers and shift-and-add operations to minimize instruction count and maximize work done per pipeline.

One level above junk. Yikes.