YHK - yet another hacker news clone

Enough, already.

I am gonna appeal to Occam’s razor here and say, the integral variable here is AI, and the only variable you need to know is AI. The problem is AI.

I don't think you need to review every line of code, but you absolutely do need to be able to describe how the system works and its high level structure.

As is so often the case with coding agents, having experience as a tech lead or engineering manager really helps here. You are responsible for a large system that has been worked on by multiple different collaborator (both human and agentic). You need to be able to make smart, informed decisions about that system, and talk with credibility to other stakeholders about what it can and cannot do and sensible next steps for the project.

I know this is being hyperbolic but I thought this was an odd post to include. I've met plenty of data engineers that don't have great knowledge of the business/product and SWEs that do have that. ¯\_(ツ)_/¯

The code change itself doesn't specifically matter. But suffice to say, it was about an AI feature in one of our products.

The code was stamped by Claude driven by a prompt. The prompt was for a ticket generated with the Atlassian AI integration. Atlassian had digested docs made with AI. The docs came from strategy memos I'm 90% sure were written entirely by Claude.

The strategy was chosen by management at the urging of exec leadership. The execs now communicate mostly via AI written memos. I do not know how they make decisions, but they reference tech influencers, market conditions, customer expectations.

This gave me pause. Who had actually made the decision then? Arguably there has been several layers of human review, but the actual source of the decision was hard to pin down.

We were not building the feature because we wanted it. We were building it because we thought other people expected it.

Perhaps reflecting on the state of the market, I thought, could indicate who was actually in control.

Where do investor and customer expectations come from in 2026? It is very murky, at least in tech. There appears to be hype. Some hype comes from true believers, some comes from cynics. But both respond to market incentives that reward bigger and bigger claims.

Where does the market's "action" come from? What is the driver?

Investors do not really seem to understand what the tech is or its limitations. Some are passive operators. Others are just responding to the overall froth and speculation in the market - which becomes a runaway feedback cycle.

This left me lost.

Nobody in this ecosystem, I thought, is actually in control here.

Nobody is actually orienting work and action to real, concrete goals. It's all based on speculation and anxiety about the future.

So it is not only that nobody understands what the code does. It is that we cannot, or at least I cannot, explain the motivation. There doesn't seem to "be" any form of "intention" in this environment.

It has all been hollowed out, replaced either be inscrutable machines, or inscrutable incentives.

Ironically it rather resembles the kind of "misaligned" superintelligence we are supposed to be avoiding.

You can still know things and get force multiplication out of LLMs, if you are disciplined and caring enough. In practice, most people won't be. And you can't force other people to be. But you can force yourself to be.

The labs saw this early, and thus many roles at the labs are "Member of Technical Staff". That's the future for every software team. You're not a software engineer anymore, but you're also not a PM, nor a designer. Think horizontal slices, not vertical: Every human's responsibility is to leverage AI to be an expert on everything necessary to deliver some vertical slice of the business.

It boggles me we completely forgot that the world operated like this just 4 years ago

Always has been, always will be.

I am actually hopeful that AI will finally break the industry and force a reckoning around this. Some of it goes to our economic system. New builds are usually capitalizable, flashy, and a great way to get promoted.

Doing ten to fifteen years of thankless maintenance, keeping a critical system alive with high quality? Usually nobody cares, and it's OPEX, not sexy.

I was in a Hackathon for students which quite a few staff, like myself, infiltrated. The results were completely unfair, staff and teams with staff (like mine) cleaned up the awards.

In my case I was working with a student who was much better at writing platformers in Unity than I was and an another student who could draw the art we needed even if she'd been trained to think every problem we had interacting with each other had something to do with "the patriarchy".

Myself I'd been in many startups where the game was make a half-baked demo that you could demo on stage and get people excited about it. So everything from presenting broken software on stage and making it look not just perfect but enticing and developing software that has the qualities it takes to present it that way was routine for me, the bit that isn't routine is onboarding unexperienced people to this life in two days.

The more things are unprecedented, the more you need a longer view with more experience.

So, if you're pooping out code, and committing it because tests still pass, and that's all you know, you're in for a treat. When an executive wants to know why a b0rked feature lost their department millions of dollars, guess who will have to answer for it, and its not the LLM.

My advice is to find ways to keep on top of how it all works, and if you're the only one who cares, well, then, that makes you even more valuable, not less.

We have both the tools and the skills.

Later, we will loose the skills because of AI and the depletion of natural resources will lead to the scarcity of the tools.

https://www.youtube.com/watch?v=Jt0OoXluC8g at 4:08:

  Writers disagree on what effect word processing will have on the quality of our written language.

  Some writers are concerned that computer assistance may promote dry bland writing.

Here's an anecdote about a way to do this wrong.

I have found with AI coding methods that there's a line where it becomes a hail-mary (in the American Football sense).

A hail-mary is when you throw the ball to the end zone and just pray someone catches it. This is almost always at the end of the game.

This moment with AI code is indicative that you can't put together a coherent plan so you just tell the agent to "make it good". It used to be that the results here would suck, but now the agents are really competent, so the results might be good.

But at that moment, that's your cue to back up. Because as soon as you take a solution that's so far detached from your understanding, you're underwater. The hail mary is not part of a larger game plan. It's the last play of the game. There's nothing after.

So as soon as you reach that moment in your coding, you're signaling that you're done understanding not just the code, but even the way it works at a high level. If you're still going to work with this code after, then back up and work with the AI to get more understanding of the problem.

Really? Seems like they're not making proper use of the tool. I've been reading MORE not less, and also learning more along the way. Just hitting "enter" is a choice, these tools are so powerful if you invest your curiosity, time and experience.

Sounds like they don't care about what they're doing in the first place, writing code by hand won't fix that.

> You may not write the code by hand but you understand it enough to investigate and fix it when it fails. It is how I think we should leverage AI instead of becoming a meat proxy.

[0]: https://raahelbaig.com/entry/responsible-human-in-the-loop/

Aside: this reminds me of running a “negative split” in a long distance race, where you aim to run the second half faster than the first. It’s very hard to do this because you have to be willing to let everyone else in your pace group pull waaay ahead, and running above race pace in the beginning feels “free” with all of the adrenaline. But if you do manage to stay disciplined, it’s a fantastic feeling to reach half-way with gas in the tank, and then start to reel in all those runners who sped by in the beginning.

https://youtu.be/JrBdYmStZJ4

And having AI code to review is no different than any other code that ever was to review, so the review tooling is - as it necessitates - also benefited by lugubrious application of .. more AI. But: all AI is human reviewed.

So it's not a big impact. We just don't ship code that isn't 100% human reviewed, If that's insurmountable: you're doing it wrong. Use AI to make code readable again.

And then, also, put AI back in its box. Don't give devs 100% full-time API access to subscriptions: give them actual hardware to use, to go 100% local.

Local AI is, thus, the best AI, folks. Don't use more than you can run locally, is a great way to keep AI code properly maintainable.

The industry will prove this, itself, sooner or later: If you can't put your AI in its box for safe-keeping, you're doing it wrong, anyway... and should've already learned this practice as a habit, decades ago, vis a vis future-proof tooling... (See also: not logging everything you do with an AI? Big fail.)

Sure, the absolutely intoxicating addiction of Big Metal AI™ is going to put a lot of consumers in a deep, deep pit of Neo-Illiteracy - however: 'good' AI code is actually just good code.

Dealing with legacy messes I used to get frustrated and bored of making the improvements, now it's easy to clean up code bases and write loads of tests. I asked Astra to come up with a plan for Playwright testing the whole App, I have not built it yet but the flows suggested were fantastic as was the ephemeral database we plan to create for CI.

I built my friends portfolio website almost entirely vibe coded in 4 hours and it looks unbelievable, we added so much slickness (he's a designer) just prompting together. I used a CMS I had never used once before and it was so so easy to do without any of the usual need to read docs about everything.

I've done so much devops now I'm actually fairly confident that me and an AI can do anything you want in terms of deployment/infra and scaling from AWS to Terraform to whatever.

Anyway my main concern about this technology is not that it is crap at coding it's that the improvements in how it codes and thinks are absolutely dramatic which is extremely scary - it has come so far in a year I wonder what the next year will bring.

If you can really get a good set of requirements, go and write all of your test cases out, and then throw it at an AI that will one shot it. Its perfect.