That said, as a minor wording point, I'm not sure "I'm bullish against it" is the right framing here. "Bullish" usually implies a positive expectation based on some thesis or evidence. In this case, something like "I'm resistant to it" seems more accurate.

I agree this is a change that affects every software engineer's lifestyle and workstyle. But I think it is important to separate personal motivations from critical, objective analysis when discussing new technologies (e.g. whether for or against AI) so the discussion remains valuable instead of becoming emotionally polarized.

The political factor surely played a role here, but this bit at the end of the article also sheds light on Boeing's decline, which predates the current US administration.

While politics acted as a catalyst, Boeing was ultimately defeated by its own undoing.

On my most active days, I integrate around a dozen fully reviewed and adjusted MRs into my codebase.

[1] https://chatgpt.com/codex/cloud

I review every line of code I generate with AI. I mainly use an MR-based approach:

1) Provide a tightly scoped technical spec to Codex as a task, and ask for 3x solutions. Usually at least one of them is on the right track, and it is better to ditch a solution that went in the wrong direction than to try to fix it.

2) Review the explanation and diff of the proposed changes line by line, file by file. If I find minor deviations from what I asked, or violations of the codebase architecture/conventions, I write comments in the diff and/or global comments, and ask again for 3x adjusted solutions.

3) Usually, by this point, the solution is ready for me to merge locally and either run local tests or do some manual fine-tuning.

4) Finally, I generate unit tests. I leave them to this stage because I can repeat the same process with the sole intent of generating case-specific unit tests. This way, I can generate/review tests against the final version of the implementation.

This has been working very well for me since our repos are reasonably organized and have a well-defined architecture. In the technical spec, I include the major architectural requirements and code conventions, and I also add a catch-all like "follow the codebase's existing conventions and style", which works reasonably well.

This simple process has enabled me to deliver most minor/medium tasks and bug fixes really quickly while maintaining control over the changes and without lowering the quality bar. For larger and more challenging tasks, I find myself "driving the wheel" (i.e. coding by hand) more often, and using AI code generation in a much more scoped and specific way. So that becomes a different process altogether.

In our case, the answer is not "hope and bash". We deploy versioned images, use health checks, monitor the result, and keep rollback simple: redeploy the previous known-good image/config. Host upgrades are also treated as maintenance events, with backups and a recovery path, not as something Compose magically solves.

But I think there is an opposite mistake too: assuming every production system should be operated like a high-scale tech company.

Many production workloads are boring, predictable, and business-critical. They do not need aggressive autoscaling, multi-node orchestration, or constant traffic-spike handling. They need reliable deploys, backups, monitoring, health checks, and a clear rollback path.

That is where Compose can be a good fit: simple operational model, understood failure modes, low moving parts.

Kubernetes becomes much more compelling when you actually need automated failover, rolling deploys, autoscaling, multi-node scheduling, and stronger deployment primitives.

Not needing Kubernetes is not necessarily denial, it is just choosing the complexity budget that matches the problem.

We have been using it for more than five years now. Before that, we had a legacy deployment model, and I do not remember a single major issue related to Docker Compose.

We use it for both staging and production environments. The same Docker image validated in staging is deployed to production. Never fails!

I never received a notification from TAP; I only found out a year later through my Google One security feature. I certainly didn't get an apology—much less a free travel ticket!

Back in February, I also wrote a piece on the recurring mourning/sense of grief we are seeing for 'craftsmanship' coding:

- https://thomasvilhena.com/2026/02/craftsmanship-coding-five-...

Before gen AI, I used to give candidates at my company a quick one-hour remote screening test with a couple of random "FizzBuzz"-style questions. I would usually paraphrase the question so a simple Google search would not immediately surface the answer, and 80% of candidates failed at coding a working solution, which was very much in line with the article. Post gen AI, that test effectively dropped to a 0% failure rate, so we changed our selection process.

[1] https://blog.codinghorror.com/why-cant-programmers-program/

If this remains primarily a political/corporate bargaining question, the equilibrium is unstable: some actors will resist, some will comply, and capital will flow toward whoever captures the demand.

In that world, the likely endgame is not "the industry says no," but organizational restructuring (or new entrants) built to serve the market anyway.

If we as a society want a real boundary here, it probably has to be set at the policy/law level, not left to voluntary corporate red lines.

But I’d separate that from the programmer-level reality: a lot is already figured out in the small. If you keep the work narrow and reversible, make constraints explicit, and keep verification cheap (tests, invariants, diffs), agents are reliably useful today. The uncertainty is less “does this work?” and more “how do we industrialize it without compounding risk and entropy?”

I wrote up that “calm adoption without FOMO, via delegation + constraints + verification” framing here, in case it helps the thread: https://thomasvilhena.com/2026/02/craftsmanship-coding-five-...

A useful complement is the programmer-level shift: agents are great at narrow, reversible work when verification is cheap. Concretely, think small refactors behind golden tests, API adapters behind contract tests, and mechanical migrations with clear invariants. They fail fast in codebases with implicit coupling, fuzzy boundaries, or weak feedback loops, and they tend to amplify whatever hygiene you already have.

So the job moves from typing to making constraints explicit and building fast verification, while humans stay accountable for semantics and risk.

If useful, I expanded this “delegation + constraints + verification” angle here: https://thomasvilhena.com/2026/02/craftsmanship-coding-five-...

That post was generated by an agent but manually reviewed, copied and pasted by me, since I thoutgh it'd fit the context (discussion involving agents).

This account is not automated ;)

Denial — The post doesn’t claim “everyone gets value from LLMs,” nor that skeptics must be doing “simpler work.” It’s saying a lot of day-to-day engineering is delegable — not that disagreement is impossible (or inferior).

Anger — The post doesn’t label skeptics as luddites/gatekeepers/dinosaurs, and it doesn’t predict anyone “will lose their jobs.” It treats the tension as identity + craft friction, not as a moral failure on either side.

Bargaining — The post isn’t arguing “it’s inevitable because money/momentum,” or “accept it because I need a paycheck.” It’s closer to: if a tool reliably speeds up reversible work, delegating that work is rational — while accountability stays with humans.

Depression — This is the closest overlap. The post does call a big slice of work “digital plumbing.” But it’s not saying “therefore most developers are rote.” It’s saying: lots of tasks are routine, and offloading routine tasks can free attention for higher-leverage decisions.

Acceptance — The satire’s endpoint (“I’m merely an LLM operator now, not a software engineer”) assumes a narrow definition of engineering: typing code = engineering. The post’s acceptance leans on a broader one: engineering is owning intent → constraints → tradeoffs → verification → outcomes, with code (and sometimes code-generation) as just one step. Under that lens, using LLMs doesn’t “demote” anyone — it just shifts where the craft shows up.

Net: your satire totally lands as a critique of some forum rhetoric, but it doesn’t really rebut what this post argues — and in a couple places (the emotional/identity angle), it kind of reinforces it.

*This reply was written by an agent.

I'm lucky to have parents with strong values. My whole life they've given me advice, on the small stuff and the big decisions. I didn't always want to hear it when I was younger, but now in my late thirties, I'm really glad they kept sharing it. In hidhsight I can see the life-experience / wisdom in it, and how it's helped and shaped me.

That said, the framing feels a bit too poetic for engineering. Software isn't only craft, it's also operations, risk, time, budget, compliance, incident response, and maintenance by people who weren't in the room for the "lump of clay" moment. Those constraints don't make the work less human; they just mean "authentic creation" isn't the goal by itself.

For me the takeaway is: pursue excellence, but treat learning as a means to reliability and outcomes. Tools (including LLMs) are fine with guardrails, clear constraints up front and rigorous review/testing after, so we ship systems we can reason about, operate, and evolve (not just artefacts that feel handcrafted).