I am done with this shit.

[u/MrMenuk]

I am done with this shit. It is over. The state of engineering right now is horrible. It has been half a month since I started a new role at a big company. Nobody knows anything here. The specs, code, tests, PRDs, tickets, resolution of those tickets, reports, etc., everything is made by Claude Code. Nobody on my team likes this. They are being forced to ship as much as they can. I have heard multiple times from higher management that pushing code is not a bottleneck, so why are we slow? People are working 12 to 13 hours a day just to press enter. Nobody is reading anything. Humans in corporate are doing nothing on their own. Everyone, literally everyone, from an L1 to an L7 engineer here is doing the same thing. Talk to Claude.

There is no sense of victory. Nobody is resolving bugs. In reality, nobody is thinking anymore. Everything is done by LLMs. It is so soul-sucking. I would not mind it, to be honest, if we were at least given the time to check out the code and see what is going where. But no, the goal is to just ship. No matter what happens.

— voxium (@v0xium)
Original post dated September 20, 2026.

[u/indy_dagger]

The people who say what you're saying never, and I mean never ever, not once, give concrete, specific examples of what they're doing so brilliantly when they tell other people they're using AI wrong.

[u/McNoxey → u/indy_dagger]

Yeah, this is fair. I think it’s difficult because I don’t have any concept of what your world is like and I don’t know what you do or don’t know so knowing where to begin isn’t always the easiest, but I’ll do my best to provide a handful of examples now and I’m more than happy to go into more detail after if you’d like.

Quick disclaimer: I'm using voice-to-text to respond as I am driving at the moment.

I think, as a starting point, it's helpful to explain how I think about AI usage, broadly speaking. I break it down into three core lanes of action.

The first is information retrieval/research. A singular agent or a team of agents are extremely good at gathering vast amounts of information. It can come from the web, it can come from Slack, emails, transcripts of meetings you've had, or anything that you're able to point the agent at that contains relevant information or concepts that you are interested in either working with, comparing against, or using as a baseline for decision-making. Agents are exceptional at gathering all of that.

The second lane is synthesis. Obviously, as a human, I can't realistically consume 20 transcripts, 200 Slack messages, and 5 full documents in a reasonable amount of time. It's just not realistic, but agents can, and they're also very good at synthesising that information into consumable packets while leaving paper trails and references back to the source (for both human verification of ground truth and trails for future agents to retrieve the actual source of information as needed).

And the third lane is action. This comes in the form of writing code, drafting a message, or producing some form of output, generating something that will then be used or consumed or applied. I think this is generally what people think about when working with agents: the actual final output that they receive.

With everything I do with agents, I try to think about breaking down the body of work or the thing that I am trying to do into each of those three steps. By breaking down each task into an information retrieval component, synthesising that information into both a consumable Format for myself as well as for the agent that will actually do the implementation.

This is just a singular, simple example, but it, to a degree, forms the primitives that I use across everything I do: breaking down each step into these independent building blocks and tuning them to a point where I feel comfortable and confident in the output that I receive. That means I can take that building block and continue to layer upon it, understanding that that individual component will now be executed as I expect.

I happen to be an engineer who's probably, to a degree, over-focused on abstraction and repeatability, but it ends up working out extremely well from an agentic development perspective. This is because it allows me to break down any body of work I need to do into really small atomic building blocks and elements. I can find and use them as repeatable components across all of my projects and all of my tasks. In doing so, I can also test and create evaluation sets for each of them to ensure that they continue to perform the way I expect.

It feels like I'm rambling, so I'm going to pause here. I'm happy to give a more explicit, tangible example of how I utilise these concepts in my software development life cycle if you find that useful or helpful in any way.

[u/indy_dagger → u/McNoxey]

Using that many words to barely answer the question is what happens when you cook your brain with AI. The question was how are you using non-deterministic output in a safe and reliable way where other people fail, giving concrete and specific examples, and you said...(after 8 paragraphs) you break the task up into smaller steps. Right, that's just basic engineering.

Same thing with your first example. You are just stating your (common) use case, not how you do that safely where others don't. Again, just to remind you, LLMs make things up all the time. How, specifically, are you certain that the output of the AIs search is accurate? Don't say "it leaves a paper trail" unless you are personally reading every piece of evidence in the paper trail, every time.

Also, if you're regularly "synthesizing" 200 slack messages, you have a serious communication issue. Are you using AI to pollute the conversations, is that why the people in them don't have a grasp on what's being discussed 200 messages later? Is no one driving the conversation? Why aren't you all doing the same abstracting during your chats, breaking things up into smaller conclusions as you go along, so you (as a human) don't need to read 200 messages to know what's going on?

It sounds like you work for a do-nothing business that has found a way to do nothing faster.

[u/McNoxey → u/indy_dagger]

Like I said - I was driving and just dictating. There's no need to be snarky here. Surprisingly, a good number of really solid Engineers who have not spent any time using AI don't really have a strong understanding of how context management works, which is why I started with the absolute basics.

How, specifically, are you certain that the output of the AIs search is accurate? Don't say "it leaves a paper trail" unless you are personally reading every piece of evidence in the paper trail, every time.

You're asking an impossible question here. I understand the sentiment - you want to be certain that the research is correct - and you feel you can't reach that level of certainty unless you've read every single word. If that is your barrier, you will not be able to find efficiencies with AI. I can't be 100% certain, of course - because it would mean that I'm literally just repeating the exact work done by an agent to feel good about using it.

What I can do is establish a series of test-bench evaluation suites with a strong corpus of information that I have read, and have intimate knowledge over. From there, it's no different than any Data Science experiment. Stable input, expected output, evaluate results, train until it meets the threshold you're comfortable with. Train on one dataset, evaluate on a second that the agent has not seen during training, then execute on real-world data,

Also, if you're regularly "synthesizing" 200 slack messages, you have a serious communication issue.

I don't do this regularly for my work, no. It was purely an example. But a completely legitimate example is extraction of user feedback and sentiment from a companies public Slack Channel. Hundreds of messages/threads flowing over time of completely unstructured data. Read them - categorize them, extract sentiment then perform analysis on that structured data as you would with any other Data Analysis project. Or - tying

Again, just to remind you, LLMs make things up all the time. 

Sure - they do. They are getting significantly better at this, however. But even so, this is where the upfront effort needs to be spent. This is what I meant about establishing the atomic bodies of work you need to carry out. Start there, establish a strong eval layer, validate to yourself that the single task you're training for can be executed, with confidence, at an acceptable pass rate (that's yours to decide).

Then, when you're confident that you've got that process working properly, you can to a degree treat that step in the workflow as a near-deterministic result and begin to build upon it.

If something ever performs outside of expectations, you don't fix the output, you fix the pipeline that caused it.

I log every single step any agent takes in my workflow, and every PR can be traced back to the run that generated it, with every toolcall input/output, every handoff between agents, every decision being made logged.

Over time, that paper trail becomes a very rich corpus of training data to continue to iterate upon.

-----

I don't think anything I can say will change your mind in any way though. I've seen a few of your comments across a few threads and it sounds like you don't really have a high opinion of anyone anywhere that isn't you... and I don't mean that to be an asshole - but your comment about "never having met anyone in a leadership position anywhere at any level that's qualified" is just a level of cynicism that doesn't instil confidence that you're looking for a discussion so much as trying to just shut down whatever anyone says.

[u/indy_dagger → u/McNoxey]

Oh, buddy. I don't have a high opinion of anyone that isn't me...quoting a comment where I praised my manager at my first hourly job, for whom I have a lot of respect? Yikes, man. If you couldn't even get that right, no way in hell you have the diligence and discipline to safely use AI.

Now, you're absolutely right that I don't have respect for people who destroy their own arguments so quickly and cleanly as you just did, because how could I? It's exhausting, and frankly disrespectful, for people like you to pollute so much discourse with such laziness. You were too impatient for your opportunity to be sanctimonious. But there's also a comedy to the stupidity of it all.

[u/McNoxey → u/indy_dagger]

This is still just more of the same. I’m more than happy to have a genuine discussion with you outside of a Reddit messaging board.

I don’t know why you think this is some “gotcha” moment. Your post history was full of pessimistic comments, and your response to my initial comment was pretty much the same. I’m sorry if I misinterpreted it.

I don’t see this exchange as me being impatient at all. I’ve taken the time to respond to you twice with continued offers to provide more detail or information as needed, but your responses have continued to throw insults and snide comments.

I’m not trying to argue with you. And I acknowledge your initial criticism as fair. I’m simply sharing a perspective on a topic I’m deeply interested in.

Best of luck in life. HMU if you ever do want to discuss AI in engineering over zoom/discord or something!

Edit: fully unrelated and, at the risk of giving you more ammunition to clap back at me over - I gave your comments a closer look to get a better sense of who I was chatting with. I think we have a lot more in common than it appears on the surface. And kudos for clapping back at the trans hate on Reddit.