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Posted by poisonfountain 1 day ago

LLMs are eroding my software engineering career and I don't know what to do(human-in-the-loop.bearblog.dev)
https://web.archive.org/web/20260607163135/https://human-in-...

https://archive.is/i55Th

1097 points | 1032 commentspage 19
jruohonen 1 day ago|
"Except that nobody cares anymore."

:-(

titaniumrain 1 day ago||
find another job but software engineering :) simple
elzbardico 21 hours ago||
I don't get those depressing articles. If anything I've been working way more with LLMs and working harder.

I love that Codex added "Steer" to the chat, because I fucking pay attention to the conversation traces on coding agents and I was tired of the PANIC ESC - "No, that's not what you should do! We use a visitor to calculate all that stuff, I just need you to implement another visit method for this new Stuff according to the rules".

Nothing says you shouldn't write any code, in fact, I think that starting from a n interface and a few clients is superior to simply describing things only in natural language. Lots of refactors like extracting method, extracting interface, renaming symbol are way faster, cheaper (no tokens) and less error prone using the IDE.

When I am not sure about the concrete design to implement something, I talk to the coding agent, we discuss types, i suggest patterns, I wrote a small stub here and there, maybe a couple unit tests, but I like to activelly engage with the coding agent as if I was pair programming.

Yeah, I am not a fan of fire and forget, I see no point in being able to control agents remotelly, but nobody is complaining I am not fast enough. It is perfectly possible to have the huge productivity gains coding agents give you without entering vegetative programmer mode. You just need to engage yourself in the process.

You can describe the different categories of something, or you can go ahead and create an enum in rust, you can create a pydantic validator, a few tests here and there. The agent now has something more concrete than a natural language description, it has the compiler to check his code, it has the unit test. The flow becomes faster.

You can mention in your prompt that the agent should use AWS SDK v2 in your Go service, or you can go ahead and add the imports and the initialization somewhere in your code, the second option is a far stronger nudge towards the right direction.

The time you used to waste trying to shoehorn some stack overflow answer to your problem, now you can use to actually read the documentation of whatever you're trying to use. Go ahead, read it fully, you now have time to understand it deeply. The grunt work, the toil will be taken care of by the agent in a few minutes when you're finally feel yourself ready to move to implementation, because now you have the deep knowledge to take the best possible decisions.

There are plenty of places for you to apply your knowledge: the agent may write a correct function, but then this function does a remote HTTP call in the context of a database transaction, so, what happens when the remote http endpoint has a spike on latency? And what if the tables involved on this transaction are a hotspot in your application? You can't add all those small details in your context all the time, you can't add all single corner case and potential pitfall to your AGENTS.md.

Of course, you have to up your game. If all you ever did was completing JIRA tickets without thinking much about it, yes, there's no much you can add to the process beyond what your coding agent can do. But this is a choice.

We can either create a humongous era of slop and technical debt with coding agents, or we can use its hability to free ourselves from toil so we can finally improve our code in correctness, performance, efficiency, efficacy, security and compliance all the while keeping the business happy.

We can either use LLMs to have tens, hundreds, thousands of THERAC-25, or we can use them to liberate our time so we can do the deep work that ensures that you can't possibly deploy a THERAC-25 in production.

mrandish 1 day ago||
Of the posts I've seen by senior devs who assess recent events and end up roughly here:

> I'm still employed and I see myself employed for a foreseeable future. But I don't know what to think about the long-term ... Maybe I should consider transforming my woodworking hobby into a profession.

This one is notable for having all the clues pointing to why that's not the end-state this is headed toward, and yet... still not quite see it.

> I have no domain expertise that another Sr. engineer steering an LLM cannot match.

It's clear he's developed a significant competence in "steering an LLM" but the depth and value of that aren't apparent yet. After ~70 years, software development is now in the early stages of its first tectonic disruption. In the moment, these kinds of tech disruptions mostly appear to be displacing jobs but, historically, we understand the displacement is one part of a larger shift that's vertically compressing roles, functions and labor value. One steam shovel doesn't just displace dozens of pick-axe swinging diggers, it changes the roles, functions and competencies required across the entire supervision and management stack of "make tunnel through mountain" from the crew bosses and site managers to the tunnel engineers and business owners.

The author seems to be successfully navigating this shift but is still mid-disruption, so he and his management aren't yet able to see all the new competencies required or appreciate their value because it's all so new and still evolving. The rapid shock of agentic coding LLMs is especially disorienting because it's the first dramatic disruption in the field.

> review the code and steer the robot.

Historically, it's not surprising those few words are bearing so much weight and unappreciated value. Steam power was a similar shock to every field which relied on earth-moving and shaping. The big machines were quickly deployed, but it took quite a while for all the disruptions to both new and existing roles, functions and necessary competencies to be understood and appropriately valued. I imagine some top pick-axe swingers who'd graduated to being crew bosses and site foremen ended up driving or directing early steam shovels. In the first months they probably had little appreciation for the tremendous amounts of tacit new knowledge and practical expertise they accrued while keeping the steel beasts working. They were too busy being both amazed at the sheer power and frustrated by the constant scalding burns, tip-overs, blown boilers, landslides (too much weight, too little support) and cave-ins (dug too much tunnel, too fast with too little scaffolding), etc.

A big difference in the analogy is the first 100,000 steam shovels weren't sold at ~1/10th their actual cost and simultaneously delivered to job sites worldwide in six months. Software engineering is also unlike earth-moving and tunnel digging, in that the full costs and consequences aren't as visible or immediate as cave-ins and avalanches. The prices of 'steel beasts' are already going vertical with no end in sight and, over the next 18 months, I suspect "management" is about to gain a more viscerally accurate appreciation of the catastrophic costs of digging 'too much tunnel, too fast' absent the close supervision of highly skilled experts in directing all that newfound power constructively and not destructively. Between the skyrocketing full cost of operation and the consequences of poorly managed, non-expert execution - we'll start to see the broad outlines of the new equilibrium take shape.

In the steam era it over a decade for the ecosystem to understand how to even draw a new org chart accurately, label the boxes and appropriately value proven competency where it mattered. The faster the disruption, the longer it can take for all the pieces to rebalance and stabilize around a new equilibrium. Today, the author doesn't know all that he already knows and doesn't yet have the visibility to see how the new domain competencies he's rapidly accruing are creating a different kind of role that could be even higher value.

holyknight 1 day ago||
People are missing the long-term horizon on this. Yes, definitely, you can automate most of your workflows as a software engineer with today's LLM frontier capabilities fully E2E. But many things are still super open: -First, cost is not a settled topic yet. We have no indication that automating everything E2E will be a cost-effective way of doing stuff. So the bare minimum is that you will need some expert designing the workflows in a token-efficient way. Worst-case scenario, tokens become super expensive and only certain parts of the job can be efficiently automated and many companies are not even able to afford tokens. -Second, the system you just "created" is just a static snapshot of today. Yeah it may work fully automated for 6 months, maybe a year. What then? Breaking changes? Updates? Re-designs? What if the quality slowly degrades until nothing ever works again? Who will fix that? There are so many unknowns that it is borderline irresponsible to make guesses on what can be automated sustainably long-term or not. Unless you are OpenAI's Codex team wasting a billion tokens a day on automating and self-improving everything, there is a high chance that everything you set up today is completely useless in a year. -Third, the core engineering workflow hasn't changed a single bit. People like stakeholders, product owners, PMs, etc. can come up with ideas and things to build but someone needs to take decisions on what gets built and what doesn't, balance out paying down technical debt vs. feature development, incorporate new domain knowledge into the system (Or would you expect your PM to be tweaking the prompts about a new regulation regarding GDPR or a completely new legal framework that changes the whole thing?) -Fourth, probably the most important one. If you think AI will soon get good enough to get self-improving and self-sustaining enough to replace full engineering departments E2E with no supervision then nothing else matters because we will all end up without a job and living on UBI (not only tech people). So why do you even care? If it happens it doesn't matter, and if it doesn't happen we just continue doing what we were doing until now. Why do you care?
mschuster91 1 day ago||
> Maybe I should consider transforming my woodworking hobby into a profession...

Yeah. There is no future in IT any more, let's be real. Enough CEOs have drunk so much AI kool-aid that they'll lay off so many people it will become outright impossible to get re-hired again when the incompetent CEOs have gotten fired - too much competition.

The only industry that's going to give reliable employment in the future is the trades, especially the regulated/licensed ones. Gas, water, electricity, structural engineers - basically everything where there is actual human lives on the line when things go south.

gxs 1 day ago||
> And then I started realizing: all the knowledge I have accumulated over the years: the trade-offs between implementations, how acquiring works, how to structure idempotency to prevent double-charges, everything, was becoming useless.

It’s not useless, at least not yet. And the fact that you recognize this puts you way ahead of the typical HN user constantly crying about how AI could never

What’s going to make you a good AI-augmented engineer is going to be treating AI like a good partner

Not like a genius, not like an idiot - these are extremes where all the memes on LinkedIn are generated

Like any partnership you will see it comes with bad ideas and good ideas - that it will challenge your own ideas and be sometimes wrong and sometimes right

Approaching it this way, I think my learnings only accelerated - the conversation is of much higher value because it’s a fast back and forth where I can take a moment to learn on those occasions where its ideas beat mine

You are feeling a little insecure, paranoid is not the word, and that’s a good thing

Tackle the problem for what it is: I have this sidekick now that can help me bang shit out in a fraction of the time it used to

Use the the brain that got you here to figure that out - don’t waste your time on these debating whether ai is good or not or listening to stories about how it’s stupid because one time it suggested something that wrong

You’re going to be fine, put AI to work for you

Ask me again in a few months but for now you’re fine

shevy-java 1 day ago||
I don't see it as negatively, in that there are specific trade-offs.

For one: LLMs make a lot of mistakes. We all see that when they hallucinate search results and what not. But, possibly even more important than that, you ultimately become dependent on some big company via LLMs. Perhaps that trade-off is worth it for some companies, but I personally don't want to become dependent on these companies. I actually consider it a hostile attack from the USA, and under Trump this is even more obvious.

Another thing that sucks by LLMs is documentation. They generate a lot of crap that is useless. So that's another area where humans could be better.

Admittedly a lot of vibe-coded AI slop is also useful in some ways, but it has started to make me rather angry in general - youtube already spoiled me here. I no longer want to see ANY AI videos at all whatsoever. It just wastes my time. I am not here to empower skynet version 20.2.

emodendroket 1 day ago|
OK, but that same argument applied to getting on one of like four cloud providers and essentially everyone did that.
sspoisk 5 hours ago|
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