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.
They also did an MRI scan on Honnold and found that he doesn't have the usual fear response. It's not clear if this was trained away, or if it's something innate.
> 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!”
It seems like the biggest downside of this world is iteration speed.
If the AT instagram wants to add a new feature (i.e posts now support video!) then can they easily update their "file format"? How do they update it in a way that is compatible with every other company who depends on the same format, without the underlying record becoming a mess?
I enjoyed hearing Claude Code creator Boris Cherny talk about "latent demand"[0], which is when users start using your product for something it was not intended for. When that happens, it's a great signal that you should go build that into a full product.
Cowork seems like a great application of that principle.
It’s more about cognitive load, and abstraction level. If you’re trying to make an object spin, it’s much easier to use the tailwind class than it is to remember css keyframes.
Sure, when debugging a complex issue, it’s worth knowing the low-level, but CSS is not a great abstraction for day-to-day work.
People like tailwind because it feels like the correct abstraction. It helps you colocate layout and styling, thereby reducing cognitive load.
With CSS you have to add meaningless class names to your html (+remember them), learn complicated (+fragile) selectors, and memorise low level CSS styles.
With tailwind you just specify the styling you want. And if using React, the “cascading” piece is already taken care of.
Agentic AI companies are doing millions in revenue. Just because agents haven’t spread to the entire economy yet doesn’t mean they are not useful for relatively complex tasks.
A philosophy of software design by John Ousterhout is the best software book I have read.
But, from your post it’s not clear specifically what you are looking for. If you think you will level up by learning how to apply numerical modelling techniques, then it’s probably best to focus on that.
I'd bet a lot of people are trying to optimize their codebases for LLMs. I'd be interested to see some examples of your ASI-unlocking codebase in action!
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.