ahussain

What was in your system prompt / harness? The demo is impressive!

But isn't it only a matter of time before the AI generated autocomplete will contain the correct solution? What then?

I don't think pg is giving advice on what will lead most directly to a job, but rather what is the best learning for a 17yo.

A 17yo who trains their own LLM will have a much richer understanding of what AI is, how it works, what its potential capabilities and pitfalls are, versus someone who spends the same time doing something else.

Quality and speed are not diametrically opposed. A great engineer does well on both axes by building the minimal thing needed now in a way that is easy to extend in the future.

I have also seen projects go badly because the eng was trying to be perfect upfront. Whereas quickly getting to an MVP and then iterating tends to go better.

I’m curious about this scale vs speed distinction.

Every codebase includes parts that are more experimental, and parts that are more core. My sense is that AI can help on both of these fronts (I.e building rapid prototypes on the fringes and hardening the core with better test coverage).

Engineers seem to think business people don’t know what they are doing, but if your post were true, then companies would add slack to outperform their competitors.

The broken system likely doesn’t have enough business impact to justify the investment to maintain it.

> When his supervisor sent him a paper to read, Bob asked the agent to summarize it. When he needed to understand a new statistical method, he asked the agent to explain it. When his Python code broke, the agent debugged it. When the agent's fix introduced a new bug, it debugged that too. When it came time to write the paper, the agent wrote it. Bob's weekly updates to his supervisor were indistinguishable from Alice's.

In my experience, doing these things with the right intentions can actually improve understanding faster than not using them. When studying physics I would sometimes get stuck on small details - e.g. what algebraic rule was used to get from Eq 2.1 to 2.2? what happens if this was d^2 instead of d^3 etc. Textbooks don't have space to answer all these small questions, but LLMs can, and help the student continue making progress.

Also, it seems hard to imagine that Alice and Bob's weekly updates would be indistinguishable if Bob didn't actually understand what he was working on.

My prediction is that we'll start to see a whole new layer of abstraction to help us write high quality code with LLMs - meaning new programming languages, new toolchains, stricter typechecking, in-built feedback loops etc.

The slop we're seeing today comes primarily from the fact that LLMs are writing code with tools meant for human users.

The EV tech seems to be good enough already in China

People were saying the same last year, and then Anthropic launched Claude Code which is already at a $2.5B revenue run rate.

LLMs are useful and these companies will continue to find ways to capture some of the value they are creating.

I was wondering this too. I assume it’s because child care costs are lower when one parent isn’t working(?)

They wrote:

> If you optimize below 1487 cycles, beating Claude Opus 4.5's best performance at launch, email us at [email protected] with your code (and ideally a resume) so we can be appropriately impressed and perhaps discuss interviewing.

That doesn’t seem snarky to me. They said if you beat Opus, not their best solution. Removing “perhaps” (i.e. MAYBE) would be worse since that assumes everyone wants to interview at Anthropic. I guess they could have been friendlier: “if you beat X, we’d love to chat!”