And without expertise you end up asking stupid things to a token producer machine, however godlike it can be.

It's hard not to extrapolate...

My favorite paragraph:

> The fact that AI wrote 1M LOC and then refined it over the course of the next couple of months to produce a reliable piece of software that is currently running on millions of developer machines is absolutely mind blowing. And you can say, “well it’s not that impressive because they had an oracle to compare against, so it was simple to go from one language to another”, but I think that’s selling this entire thing short. If you can build a verification system and give proper direction, AI can produce a highly complex, highly sophisticated piece of software and it can continue to refine it until it just works.

For me, this captures what's special about the Claude Fable 5 and GPT-5.6 Sol class of models. If you can reduce a problem to a clearly verifiable end state, provide the necessary context, and equip a model with the necessary tools it can usually get to a good solution.

Reducing problems to that state and designing that environment remains a skill, and one that I expect we will be paid handsomely for.

“Since FORTRAN should virtually eliminate coding and debugging…” -- FORTRAN report, 1954 [1]

And the FORTRAN report was both right and wrong. What was meant by "coding" back then, carefully crafting machine instructions from higher level specifications, was almost entirely eliminated. It was replaced by something else, which we now call coding.

[1] http://www.softwarepreservation.org/projects/FORTRAN/BackusE...

Loud with lots of smoke, but didn't go anywhere!

Neither 1 nor 2 can be done by someone who doesn't even know what Bun, Zig, or Rust is, let alone deeply understand how those work. In fact, even I as a programmer with decades of experience in PHP/Python/JS but without specific experience in Zig and Rust probably couldn't do a proper rewrite for a project the size of Bun.

If you discard coding purity questions like style, architecture, cleanliness - the stuff they come out with is buggy & error prone.

The problems seems architectural - in that context windows are limited and you need more compute to increase them, married with the fact the models are really over confident. But if you do increase them it causes mode collapse. Yann LeCun has a really good graphic in his slides of a circle (all possible answers) and a red line coming from the centre depicting the one correct path. How do you actually stop the model going into the subsequence of wrong paths? I don't think it's possible.

I've had so many times in my day job someone has told me (Claude told them) there is a bug in my code, I look at it and nope - it just didn't look up the right file. Then you push back on it and it completely crumbles and says sorry.

I wouldn't keep an employee hired who did that over and over again and never learned

Our SaaS company is making plans to move towards bespoke development, which until now has been far too costly for most customers to accept. It also violates the multi-tenant cost/business model, so we're scrambling to figure out what hosting and ops and support agreements look like in a bespoke future.

The implications here go well beyond development. I'm seeing pretty massive changes happening in finance, consulting, HR, accounting, law, design, architecture, health, and everything else. When the value of intelligence is effectively zero, how are humans supposed to market and sell themselves in the job market? Some white collar people might try to move into physical jobs for job security, but it will only take a fraction of white collar workers to migrate to crater wages there too.

We're not ready for this. Socially, economically, and politically. Look at how we treated middle Americans who lost their manufacturing jobs when they were offshored to China. Hillary Clinton famously laughed at them and told them to "learn to code." We'll watch jobs and industries disappear while clinging to our own and praying that it's not us today.

UBI is going to be inevitable soon, but it's also woefully insufficient. Giving a developer who used to earn $100k $20k per year UBI isn't going to placate their white hot rage at the social contract being broken. We're going to need universal high income, and paying for that is going to be such a radical social change that I worry voters won't accept it until things are dire.

New versions ship daily and I seem to hit brand new issues every day. They disappear after a few more updates, but it's damn annoying to keep up instead of doing work, and the direction of changes is lacking at best. The underlying technology otherwise seems nothing short of a miracle, but the contrast is eye-opening.

In industries where this is acceptable, that's the future. In ones that can't tolerate it, it isn't. I hadn't expected software engineering itself to be one of the former but it's kinda obvious in retrospective.

On the other hand, if you're working in an industry where you and your coworkers are developing novel solutions that aren't widely known, chances are you'll still be doing that by hand with some light LLM automation. There are industries with a lot of gatekept secrets where LLMs will be less useful. What I will say on that matter is this: if you weren't surprised frontier labs stole copyrighted work for training data, you'd do well to mind what you push through their systems.

Or maybe 'code' just gets pushed down a level like assembly did when C was invented, for example. As a percentage very few people look at assembly in comparison to the 1960s-80s.

on the bun rewrite, this matches my experience

it's always been worth completely ignoring people that had a negative experience with AI-assisted coding, because others of us have been entirely functional. the slow trickle of examples has always been because it was counterproductive to tell anyone that your project or service was AI-assisted in coding or entirely vibe coded.

but now, with bun being an Anthropic project and their whole thing is showing examples that would speed up everyone else, the truth is out there. good move by them.

bun is a fast runtime and will save millions of projects time and compute costs in CI/CD and redeployments

on the title, I disagree, but with a twist. manually coding is just not necessary, but the ability to get results, and use available compute resources efficiently and at low cost are still disciplines. I don't just mean "architecture", I mean a more specific part of architecture like knowing that your postgres instance can already be an event based architecture and you don't need the cloud provider's pub/sub arrangement at all.

right now, AI isn't going to do that for you. but regarding the discipline, I don't have pride over that, I think that's a major point for people, a level of gatekeeping and pride. this isn't what we need, this isn't what companies need, everyone always needed results.

people in this career are concerned about the billable rate when people with less familiarity with the discipline can deliver results, and thats where I also see a different outcome: delivering results in software still has some unique attributes compared to almost every role in an organization.

A, it just has to be done. B, it's still one of the only roles where your workers can compete directly against you whenever they feel content enough to do so, or discontent with how you are paying them.

Software engineers have little to no overhead costs of running their own thing, they have no licenses and hence no geographic limitation compared to your accountants and lawyers, and they also have enough money to cover the little overhead costs they do incur, something even your sales team doesn't have before getting caught in a lead trading ponzi scheme. The major markets for software engineers (California) nullifies any non-compete you were relying on. It doesn't matter who is doing the software, whether it was someone that was previously doing HR, or someone that built an identity around coding, it just has to be done.

But this should translate into hiring, right now, the importance and cost reinforces teams to want to interview for something that they think "should" be gatekept. multi week, multi month, hiring processes to prove someone has a cognitive ability. but this isn't necessary, people should be able to come into the effort, the same way someone can pick up a broom. the same way a sailor on an old expedition picked up an oar and splits the bounty during the trip.

It's not. If you give it a reference implementation, and unless the LLM/agent cheats, you'd get the migration minus undocumented/untested behavior. I've done that with different domain, migrating existing numerical code from Fortran to Rust. When working on the same codebase, agents routinely shit the bed unless given increasingly specific and concrete specs.

It's simply a spectrum between "make an airbnb for ants, make no mistakes" and providing an actual specification (and I consider an implementation to be such). What you don't provide and account for from the beginning will be paid in tokens later. That puts a price on technical debt, which is interesting in itself.