alt Hacker NewsVibe coding and agentic engineering are getting closer than I'd like
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People in the future are going to wonder what the hell we were thinking, when 30 years down the line everything is a hot mess of billions of lines of code generated by LLMs that no human has read almost any of it and is no longer possible for anyone to maintain neither with nor without LLMs. And the LLM generated garbage will have drowned out all of the good quality code that ever existed and no one will be able to find even human generated code anymore on the internet.
Makes me want to just give up programming forever and never use a computer again.
Vibe Coding (and LLMs) did not create undisciplined engineering organizations or engineers. They exposed and accelerated them.
Plenty of engineers have loose (or no!) standards and practices over how they write coee. Similarly, plenty of engineering teams have weak and loose standards over how code gets pushed to production. This concept isn't new, it's just a lot easier for individuals and teams who have never really adhered to any sort of standards in their SDLC to produce a lot more code and flesh out ideas.
For me the distinction is the quality and rigor of your pipeline.
Vibe coding: one shot or few shot, smoke test the output, use it until it breaks (or doesn't). Ideal for lightweight PoC and low stakes individual, family or small team apps.
Agentic engineering: - You care about a larger subset of concerns such as functional correctness, performance, infrastructure, resilience/availability, scalability and maintainability. - You have a multi-step pipeline for managing the flow of work - Stages might be project intake, project selection, project specification, epic decomposition, d=story decomposition, coding, documentation and deployment. - Each stage will have some combination of deterministic quality gates (tests must pass, performance must hit a benchmark) and adversarial reviews (business value of proposed project, comprehensiveness of spec, elegance of code, rigor and simplicity of ubiquitous language, etc)
And it's a slider. Sometimes I throw a ticket into my system because I don't want to have to do an interview and burn tokens on three rounds of adversarial reviews, estimating potential value and then detailed specification and adversarial reviews just to ship a feature.
Perhaps I've missed a few weeks worth of progress, but I don't think that AIs have become more trustworthy, the errors are just more subtle.
If the code doesn't compile, that's easy to spot. If the code compiles but doesn't work, that's still somewhat easy to spot.
If the code compiles and works, but it does the wrong thing in some edge case, or has a security vulnerability, or introduces tech debt or dubious architectural decisions, that's harder to spot but doesn't reduce the review burden whatsoever.
If anything, "truthy" code is more mentally taxing to review than just obviously bad code.
> If you can go from producing 200 lines of code a day to 2,000 lines of code a day, what else breaks? The entire software development lifecycle was, it turns out, designed around the idea that it takes a day to produce a few hundred lines of code. And now it doesn’t.
It is so embarrassing that LOC is being used as a metric for engineering output.
What an excellent article by a smart, humble, still-learning person!
Favorite quote:" There are a whole bunch of reasons I’m not scared that my career as a software engineer is over now that computers can write their own code, partly because these things are amplifiers of existing experience. If you know what you’re doing, you can run so much faster with them. [...]
I’m constantly reminded as I work with these tools how hard the thing that we do is. Producing software is a ferociously difficult thing to do. And you could give me all of the AI tools in the world and what we’re trying to achieve here is still really difficult. [...]"
> It’s not just the downstream stuff, it’s the upstream stuff as well. I saw a great talk by Jenny Wen, who’s the design leader at Anthropic, where she said we have all of these design processes that are based around the idea that you need to get the design right—because if you hand it off to the engineers and they spend three months building the wrong thing, that’s catastrophic.
This is spot on. I think the tooling is evolving so much particularly on the design side that its not worth the "translation cost" to stay (or even be) on the Figma side anymore.
Have you noticed that the coding agents get really close to the solution on the first one shot and then require tons of work to get that last 10% or 5%?
If we shift the paradigm of how we approach a coding problem, the coding agents can close that gap. Ten years ago every 10 or 15 minutes I would stop coding and start refactoring, testing, and analyzing making sure everything is perfect before proceeding because a bug will corrupt any downstream code. The coding agents don't and can't do this. They keep that bug or malformed architecture as they continue.
The instinct is to get the coding agents to stop at these points. However, that is impossible for several reasons. Instead, because it is very cheap, we should find the first place the agent made a mistake and update the prompt. Instead of fixing it, delete all the code (because it is very cheap), and run from the top. Continue this iteration process until the prompt yields the perfect code.
Ah, but you say, that is a lot of work done by a human! That is the whole point. The humans are still needed. The process using the tool like this yields 10x speed at writing code.
Strong agree. Most orgs will stay tangled in the mess they hand-coded over the years, a few greenfield teams will pull ahead, but until some LLM-fuelled startup displaces a strong incumbent I'm skeptical that we're on the cusp of anything other than a K-shaped transition. I see already low quality software and orgs getting flushed to make room for some new ideas now that the barrier to entry is slightly lower (but far from free). I just wish the transition was done with more humanity.
I think all coding will become vibe coding, but it will be no less an engineering discipline.
Note: I still review pretty much every line of code that I own, regardless of who generates it, and I see the problems with agents very clearly... but I can also see the trends.
My take: Instead of crafting code, engineering will shift to crafting bespoke, comprehensive validation mechanisms for the results of the agents' work such that it is technically (maybe even mathematically) provable as far as possible, and any non-provable validations can be reviewed quickly by a human. I would also bet the review mechanisms would be primarily visually, because that is the highest bandwidth input available to us.
By comprehensive validations I don't mean just tests, but multiple overlapping, interlocking levels of tests and metrics. Like, I don't just have an E2E test for the UI, I have an overlapping test for expected changes in the backend DB. And in some cases I generate so many test cases that I don't check for individual rows, I look at the distribution of data before and after the test. I have very few unit tests, but I do have performance tests! I color-code some validation results so that if something breaks I instantly know what it may be.
All of this is overkill to do manually but is a breeze with agents, and over time really enables moving fast without breaking things. I also notice I have to add very few new validations for new code changes these days, so once the upfront cost is paid, the dividends roll in for a long time.
Now, I had to think deeply about the most effective set of technical constraints that give me the most confidence while accounting for the foibles of the LLMs. And all of this is specific to my projects, not much can be generalized other than high-level principles like "multiple interlocking tests." Each project will need its own custom validation (note: not just "test") suites which are very specific to its architecture and technical details.
So this is still engineering, but it will be vibe coding in the sense that we almost never look at the code, we just look at the results.
This is a timely observation and feels right to me. I needed to get a relatively simple batch download -> transform -> api endpoint stood up. I wrote a fairly detailed prompt but left a lot of implementation details out, including data sources.
Opus 4.7 built it about 90% the same way I would, but had way more convenience methods and step-validations included.
It's great, and really frees me up to think about harder problems.
The real paradigm shift is not here yet, but not very far away. I'm talking about the single unified codebase. Agents building a unique codebase for all your software needs.
Because most of the complexity in software comes from interfacing with external components, when you don't need to adapt to this you can write simpler and better code.
Rather than relying on an external library, you just write your own and have full control and can do quality control.
Linux kernel is 30 000 000 LOC. At 100 tokens /s, let's say 1 LOC per second produced for a single 4090 GPU, in one year of continuous running 3600 * 24 * 365 = 31 536 000 everyone can have its own OS.
It's the "Apps" story all over again : there are millions of apps, but the average user only have 100 max and use 10 daily at most.
Standardize data and services and you don't need that much software.
What will most likely happen is one company with a few millions GPUs will rewrite a complete software ecosystem, and people will just use this and stop doing any software because anything can be produced on the fly. Then all compute can be spent on consistent quality.
I agree somewhat, but I do still think there is a decently sized separation between true vibe coding (the typical "make me an app...fix this bug") and actual AI assisted development. I personally think that if you are a dev and you simply trust the AI's output, that is still vibe coding.
I am not a developer and have very basic code knowledge. I recently built a small and lightweight Docker container using Codex 5.5/5.4 that ingests logs with rsyslog and has a nice web UI and an organized log storage structure. I did not write any code manually.
Even without writing code, I still had to use common sense in order to get it in a place I was happy with. If i truly knew nothing, the AI would have made some very poor decisions. Examples: it would have kept everything in main.go, it would have hardcoded the timezone, the settings were all hardcoded in the Go code, the crash handling was non existent, and a missing config would have prevented start. And that is on a ~3000 line app. I cannot imagine unleashing an AI on a large, complex. codebase without some decent knowledge and reviewing.
The scary part is that codebases are getting layers of AI complexity, that it's going to cost $$$ to have the latest model decipher and make changes as no human can understand the code anymore.
Pretty soon there is no code reuse and we're burning money reinventing the wheel over and over.
Claude often does things in more detail, and even better, than I would, in the first pass. But I don't understand how anybody stands comments generated by an LLM?
It's seriously the thing that worries (and bothers) me the most. I almost never let unedited LLM comments pass. At a minimum.
Most of the time, I use my own vibe-coded tool to run multiple GitHub-PR-review-style reviews, and send them off to the agent to make the code look and work fine.
It also struggles with doing things the idiomatic way for huge codebases, or sometimes it's just plain wrong about why something works, even if it gets it right.
And I say this despite the fact that I don't really write much code by hand anymore, only the important ones (if even!) or the interesting ones.
Also, don't even get me started on AI-generated READMEs... I use Claude to refine my Markdown or automatically handle dark/light-mode, but I try to write everything myself, because I can't stand what it generates.
It's already the case that you get much better results out of LLMs by forcing agents using them to go through additional layers of planning, design & review.
The future is going to dynamically budget and route different parts of the SLDC through different models and subagents running on the cloud. Over time, more and more of that process will be owned by robots and a level of economic thinking will be incorporated into what is thought of today as "software engineering." At some point vibe coding _is_ coding and we're maybe closer to that point than popularly believed.
There are techniques for improving our confidence in our software: unit testing, integration testing, fuzz testing, property-based testing, static analysis, model checking, theorem proving, formal methods, etc. The LLM is not only a tool for generating lines of code. It can also generate lines of testing. The goal is that the tests are easier to audit by the humans than the code.
One-shot "vibe coding" is generally a mistake.
But using an agentic LLM to complete boilerplate is attractive simply because we've created a mountain of accidental and intentional complexity in building software. It's more of a regression to the mean of going back to the cognitive load we had when we simply built desktop applications.
Given rapidly decelerating quality of, at least, claude code output, the agentic coding use may decrease. It is insane how bad the results of background agents are now: constant hallucinations, nonsensical outputs.
When I was in grad school I graded homework for first year math classes, and the thing about math homework is that the perfect homework takes almost no time to grade.
It's the bad, semi-coherent submissions that eat up your time, because you do want to award some points and tell students where they went wrong. It's the Anna Karenina principle applied to math.
Code review is the same thing. If you're sure Claude wrote your endpoint right, why not review it anyway? It's going to take you two minutes, and you're not going to wonder whether this time it missed a nuance.
> The thing that really helps me is thinking back to when I’ve worked at larger organizations where I’ve been an engineering manager. Other teams are building software that my team depends on.
> If another team hands over something and says, “hey, this is the image resize service, here’s how to use it to resize your images”... I’m not going to go and read every line of code that they wrote.
The distance of accountability of the output from its producer is an important metric. Who will be held accountable for which output: that's important to maintain and not feel the "guilt".
So, organizations would need to focus on better and more granular building incentives and punishment mechanisms for large-scale software projects.
I think I'm just too opinionated to go there. If I see something that works fine, but isn't the way I'd do it, it doesn't matter if a human or an LLM wrote it I'm still in there making it match my vision.
The current state of the technology is that you must read at least some of the code, but everyone keeps shipping tools that are focussed on churning out more and more stuff without giving you any affordances to really understand the output.
Claude Code in particular seems really uninterested in this aspect of the problem and I've stopped using entirely because of this.
the discourse around "code quality" has always attracted the least nuanced minds, ones who see the world and the phenomenon of life as nothing but territory to be divided up by the latest buzzwords. the worst ones insist that we narrow the discussion even further, to focus on the conflicts between these buzzwords. whenever i have to sit through such discussions, i try to meditate on the irony of mother nature weaving the most functionally brutal, ruthlessly redundant poetry that is the genetic code, only for the resulting creatures to deny themselves the power of the principles inherent in their own construction.
I think this is what people mean when they say LLMs are a higher level abstraction. We still need to consider edge cases and have tests. We still to sweat the architecture and understand how the pieces fit together and have a mental map of the codebase. But within each bottom node of that architecture we don't sweat the details. Anything obvious gets caught right away. Most subtle/interaction-based issues occur at the architecture level. Anything that bypasses those filters is a weird bug that is no worse or different from a normal bug fixes - an edge case that was hit in a real world scenario that gets flagged by a user or a logged as an error.
There are certain codebases and pieces of code we definitely want every line to be reasoned and understood. But like his API endpoint example, no reason to fuss with the boilerplate.
This has definitely been my shift over the past few months, and the advantage is I can spend much more time and energy on getting the code architecture just right, which automatically prevents most of the subtle bugs that has people wringing their hands. The new bar is architecting code to be defined as well as an API endpoint->service structure so you can rely on LLMs to paint by numbers for new features/logic.
As agents get better at code we trust them to produce more of it. There are still bugs to find, but the haystack gets bigger.
So the number of bugs to find remains constant but the amount of code to review scales with the capability of the agent.
Vibe coding is just coding now. Writing assembly used to be a thing too until higher and higher languages were created. LLM is like that except it compiles English to code. This scares lot of professionals understandably.
I am experimenting with writing en entire TypeScript compiler[1] with AI assistant. I've spent 4 months on it already. It might not be successful at the end of the day but my thinking is that if LLMs are going to write a lot of the code I better learn how this can and can not work. I've learned a lot from this project already. I think we're still in charge of design and big ideas even if all of the code is written by AI
The problem with vibe coding closer is that the agentic makes a very plasticy samey feel unless you work with something that makes it unique or can pass a template through it.
Correct me if I’m wrong Simon, but weren’t you highly optimistic about llm’s and agentic-use of them?
I believe this is a common fault of not being able to zoom out and look at what trade offs are being made. There’s always trade-offs, the question is whether you can define them and then do the analysis to determine whether the result leaves you in a net benefit state.
Agentic engineering? That reads to me a little like amateur oncologist. How are you defining engineering?
Can agentic engineers adhere to a similar code of ethics that a professional engineer is sworn to uphold?
https://www.nspe.org/career-growth/nspe-code-ethics-engineer...
What the F is "agentic" really?
> It used to be if you found a GitHub repository with a hundred commits and a good readme and automated tests and stuff, you could be pretty sure that the person writing that had put a lot of care and attention into that project.
I think this highlights a problem that has always existed under the surface, but it's being brought into the light by proliferation of vibeslop and openclaw and their ilk. Even in the beforetimes you could craft a 100.0% pure, correct looking github repo that had never stood the test of production. Even if you had a test suite that covers every branch and every instruction, without putting the code in production you aren't going to uncover all the things your test suite didn't--performance issues, security issues, unexpected user behavior, etc.
As an observer looking at this repo, I have no way to tell. It's got hundreds of tests, hundreds of commits, dozens of stars... how am I to know nobody has ever actually used it for anything?
I don't know how to solve this problem, but it seems like there's a pretty obvious tooling gap here. A very similar problem is something like "contributor reputation", i.e. the plague of drive-by AI generated PRs from people (or openclaws) you've never seen before. Stars and number of commits aren't good enough, we need more.
> I know full well that if you ask Claude Code to build a JSON API endpoint that runs a SQL query and outputs the results as JSON, it’s just going to do it right. It’s not going to mess that up. You have it add automated tests, you have it add documentation, you know it’s going to be good.
> But I’m not reviewing that code. And now I’ve got that feeling of guilt: if I haven’t reviewed the code, is it really responsible for me to use this in production?
Answer: it wholly depends upon what management has dictated be the goal for GenAI use at the time.
There seems to be a trend of people outside of engineering organizations thinking that the "iron triangle" of software (and really, all) engineering no longer holds. Fast, cheap, good: now we can pick all three, and there's no limit to the first one in particular. They don't see why you can't crank out 10x productivity. They've been financially incentivized to think that way, and really, they can't lose if they look at it from an "engineer headcount" standpoint. The outcomes are:
1) The GenAI-augmented engineer cranks out 10x productivity without any quality consequences down the line, and keeps them from having to pay other people
or
2) The GenAI-augmented engineer cranks out 10x productivity with quality consequences down the line, at which point the engineer has given another exhibit in the case as to why they should no longer be employed at that organization. Let the lawyers and market inertia deal with the big issues that exist beyond the 90-day fiscal reporting period.
Either way, they have a route to the destination of not paying engineers, and that's the end goal.
If you don't like that way of running a software engineering organization, well, you're not alone, but if nothing else, you could use GenAI to make working for yourself less risky.
I feel like an outlier in all of this. But isn't this just more AI slop? How is this different from text generation or image generation?
Like many people I have used AI to generate crap I really don't care about. I need an image. Generate something like, whatever. Great hey a good looking image! No that's done I can do something I find more interesting to do.
But it's slop. The image does not fit the context. Its just off. And you can tell that no one really cared.
This isn't good.
Every time I do deep work, and think of solutions to a complex problem. I always have the opportunity to ask claude to implement a sub-par AI slop solution.
Do this enough times, and I will have forgotten how to think.
huh. i honestly never thought they were all that different. didn't the same guy coin them both to refer to the same thing?
For work I do agentic engineering. As the code that I submit for a code review is hand reviewed by me. I know every line and file that I submit.
My side project is 80% vibe code. Every now and then I look and see all the bad stuff, then I scold Codex a bit and it refactors it for me. So I do see the author's point.
Instead of "vibe coding" by asking the AI to design and write code, I'm having it refine my own designs, and write code under strict supervision and guidance, that I carefully review and iterate on.
I took a rock carving course in school that really enlightened me about software engineering, and it still applies today, especially to AI. You can't just decide what you want to carve, hold the chisel in just the right spot, and whack it with a hammer just perfectly so all the rock you want falls away leaving a perfect statue behind.
"I saw the angel in the marble and carved until I set him free." -Michelangelo
It's a long drawn out iterative process of making millions of tiny little chips, and letting the statue inside find its way out, in its natural form, instead of trying to impose a pre-determined form onto it.
Vibe coding is hoping your first whack of the hammer is going to make a good statue, then not even looking at the statue before shipping it!
But AI assisted conscientious coding (or agentic engineering as Simon calls it) is the opposite of that, where you chip away quickly and relentlessly, but you still have to carefully control where you chisel and what you carve away, and have an idea in your mind what you want before you start.
Simon,
Just piggy backing on this post since I'm early:
Would love to see your take on how the AI and Django worlds will collide.
I agree, I'm actually generating just over of 20,000 lines of code each day at my company. Part of that was the mandate and leaderboards around token usage, but also they started using pull requests as an explicit metric. What I do is usually pull around 5 or so tickets at once, spin up 5 different agents on their own branch, have them work until completion, and then spin up two more agents to handle the merge request.
I'm not checking the code since the code doesn't really matter anymore anyways - I just have the agent write passing tests for the changes or additions I make, and so even if something breaks I can just point to the tests.
Some days, the tickets are completed much faster than I expect and I don't hit my daily token expenditure goal, so I have my own custom harness that actually hooks up an agent to TikTok, basically it splits up the reel into 1 second increments and then feeds those frames to the LLM for it's own consumption. I can easily burn 10m tokens a day on this, and Claude seems to enjoy it.
Personally I want to thank you Simon for putting me onto this "vibe engineering" concept, I really didn't expect an archaeology major like myself to become a real engineer but thanks to AI now I can be! Truly gatekeeping in tech is now dead.
I'd be lying if I said I was not worried about the future. I am not necessarily worried in the sense that there will be some grave, impeding doom that awaits the future of humanity.
Rather, I just feel like I have to constantly remind myself of the impermanence of all things. Like snow, from water come to water gone.
Perhaps I put too much of my identity in being a programmer. Sure, LLMs cannot replace most us in their current state, but what about 5 years, 10 years, ..., 50 years from now? I just cannot help be feel a sense of nihilism and existential dread.
Some might argue that we will always be needed, but I am not certain I want to be needed in such a way. Of course, no one is taking hand-coding away from me. I can hand-code all I want on my own time, but occupationally that may be difficult in the future. I have rambled enough, but all and all, I do not think I want to participate in this society anymore, but I do not know how to escape it either.
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> And that feels about right to me. I can plumb my house if I watch enough YouTube videos on plumbing. I would rather hire a plumber.
I don't buy this argument at all. I think if we could pay $20/month to a service that would send over a junior plumber/carpenter/electrician with an encyclopedic knowledge of the craft, did the right thing the majority of the time, and we could observe and direct them, we'd all sign up for that in a heartbeat. Worst case, you have to hire an experienced, expensive person to fix the mess. Yes, I can hear everyone now, "worst case is they burn your house down." Sure, but as we're reminded _constantly_ when we read stories about AI agent catastrophes -- a human could wipe your prod database too. wHy ArE yOu HoLdInG iT tO a DiFfErEnT sTaNdArD???
The business side of the house is getting to live that scenario out right now as far as software goes. Sure you've got years of expertise that an LLM doesn't have _yet_. What makes you think it can't replace that part of your job as well?
It is pure arrogance to expect that machines will never be able to code as good as a skilled human.
And AI generated code should be different than human code. AI has infinite memory for details. AI doesn’t need organizational patterns like classes. Potentially AI can write code that is more performant than any human.
Will it look like garbage? Sure. Will the code be more suited to the task? Yes.
Repeat after me: most software spends the majority of its time in the maintenance phase.
Repeat after me: it follows that most of the money the software makes occurs during the maintenance phase.
Repeat after me: our industry still does not understand this after almost 100 years of being in existence.
Alan Kay was 100% right when he said that the computer revolution hasn't occurred yet. For all of our current advancements all tools are more or less in the Stone Age.
My great hope is that AI will actually accelerate us to a point where the existing paradigm fully breaks beyond healing and we can finally do something new, different, and better.
So for now - squeee! - put a jetpack on your SDLC with AI and go to town!!! Move fast and break things (like, for real).