Some of us do actually have intimate knowledge in certain areas where guidance of an AI takes longer than doing it yourself. It's not about typing speed, it's that when you know something really really well the solution/code is already known to you or the very act of thinking about the problem makes the solution known to you in full. When that happens it's less text to write that solution than it is to write a sufficient description of the solution to AI (not even counting the back and forth required of reviewing the AI output and correcting it).
Giving a precise description of what the computer is supposed to do is exactly what programming is.
The more specific your requirements the closer you get to natural language not being useful anymore.
I code mostly in APL and J. It’s much faster to type the code than explain everything to AI.
The exceptions that prove the rule. When your programming language is built up of singular Unicode characters with specific meanings, of course that's faster than typing out in English what you want.
What do you use them for? For most AI users it's usually CRUD and I've never seen a web server or frontend in APL like languages.
The exception is the rule.
The reason why programming is hard is because most languages force you to use a hammer when you need a screw driver. LLMs are very good at misusing hammers and most people find them useful for that reason.
If you use a sane dsl instead the natural language description of a problem is always more complex and much longer than the equivalent description in a dsl. It's also usually wrong to boot.
This is what algebra used to look like before variables: https://en.wikipedia.org/wiki/Archimedes%27s_cattle_problem#...
I don't think you will find anyone who can do better than an LLM at one shotting the prose version of the problem. Both will of course be wrong.
But I also don't think you will find an LLM that can solve the problem faster than a human with Prolog when you have to use the prose description of the problem.
Using esoteric programming languages doesn’t suddenly make it true for the majority of development, which is web apps, CRUD stuff, some data science, etc.
This is actually my biggest gripe with vibecoding. The single best feature of any programming language is that it is precise. And that is what we throw out?! I favor of natural language, of all things?! We're insane!
It turns out an awful lot of precision (plenty for many things) lives in library and web APIs, documentation, header files and dependency manifests. Language can literally just point at it without repeating it all. Avoidance of mistake through elimination of manual copying in things like actuarial and ballistics tables was what the original computers were built for.
Custom written code can also point at those APIs and libraries without repeating it all? Or am I missing your point?
API Glue is the easy and boring part in programming. Nobody really enjoys wiring API A to API B, combining the results and using API C to push it forwards.
Any semi-competent AI Agent can do that with a plan you've written in 5 minutes.
I would love to see an AI try to make sense of GTK API.
I may be wrong, but it seems when people are talking about easy glue code, they’re talking about web services API, not OS API, not graphics or sound API, not file formats libraries,…
That's because very often the precision is just common sense that can be derived, either from general knowledge, or from your existing code.
[dead]
Yes, there are still many areas where skilled humans are faster than AI (meaning faster coding yourself, than providing so much context and guidance that the AI can do it on its "own").
But in general the statement is really not true anymore, generic projects/problems have a pretty good chance that the AI can one shot a working solution from a lazily typed vague prompt.
Maybe a failure to automate?
The volume of people successfully adopting agentic engineering practices suggests this stuff isn't rocket science, but it is a learned skill and takes setup.
A year later into heavy AI coding, my experience is what you're describing should aid in being able to run 5+ agents simultaneously on a project because you know what you're doing, you set it up right, and you know how to tell agents to leverage that properly.
Maybe you're the exception and are actually doing it right and actually getting good results, but every time I have heard this, it has been an ignorance-is-bliss scenario where the person saying it is generating massive amounts of code that they don't understand, not because they're incapable but because they don't care to, and immediately wiping their hands of it afterward.
To give an example of where I hear this, it is indistinguishable from the things I hear from my coworkers: "You just need the right setup!" (IMO the actual difference is I need to turn off the part of my brain that cares about what the code actually does or considers edge cases at all) What I actually see, in practice, are constant bugs where nobody ever actually addresses the root cause, and instead just paves over it with a new Claude mass-edit that inevitably introduces another bug where we'll have to repeat the same process when we run into another production issue.
We end up making no actual progress, but boy do we close tickets, push PRs, and move fast and oh man do we break things. We're just doing it all in-place. But at least we're sucking ourselves off for how fast we're moving and how cutting edge we are, I guess.
I dunno, maybe I'm doing it wrong, maybe my team is all doing it wrong. But like I said the things they say are indistinguishable from the common HN comment that insists how this stuff is jet fuel for them, and I see the actual results, not just the volume of output, and there's no way we're occupying the same reality.
1. If what you're replying to was a thing, wouldn't there be a open source project where I could see this in action? or Some sort of example I could watch on youtube somewhere. 2. The people that talk like this in my company, spin up new projects all the time and then just get to hand them off for other teams to clean up the mess and decode what the heck is going on.
You seem to have missed OP's point: some things are only encoded in our brains when you are sufficiently experienced.
Translating that into code can happen directly by you, or into prompt iterations that need to result in the same/similar coded representation.
In other words, when it matters how something works and it is full of intricate details, you do not need to specify it, you just do it (eg. as an example which is probably not the best is you knowing how to avoid N+1 query performance issue — you do not need a ticket or spec to be explicit, you can just do it at no extra effort — models are probably OK at this as it is such a pervasive gotcha, but there are so many more).
That's the failure to automate. The AI isn't telepathic, so agentic engineers not automating this stuff is skipping out on the engineering part.
You setup the environment and then you do the work. Unless you are switching employers every week, you invest in writing that stuff down so the generation is right-ish and generate validation tooling so it auto-detects the mistakes and self-repairs.
sometimes you write the feature and write it well so it's reusable.
imagine you have to implement a specific algorithm for a quantum computer.
There's no value setting up AI to do the writing for you. That might be orders of magnitude harder then writing the algorithm directly.
For highly specialized one-off features, it doesn't always pay off.
On the other hand, if all you do are some generic items that AI can do well... then I'm not sure you're going to have a job long term, your prompts and automation will be useful for the new junior hires that will be specialized in using these and cost effective.
I think there's a level above that where the words to describe such structure are familiar and readily available and hey guess what? The model understands those too. Just about every pattern has a name. Or a shape. Or an analog or metaphor in other languages or codebases. All work as descriptors.
This presumes that most of this stays encoded as words in our brains: the effort to translate some of these into words might be similar to translating it into code (still words, just very precise).
It's like talking legalese vs plain English; or formal logic vs English. Some people have the formal stuff come more naturally, and then spitting code out is not a burden.
> successfully adopting agentic engineering practices
What's your definition of "successfully"?
More LOC committed per day is probably the only one that's guaranteed when you let spicy autocomplete take the wheel.
I don't think it's at all possible to reason about the other more meaningful metrics in software development, because we simply don't have the context of what each human is working on, and as with the WYSIWYG fad of 3 decades ago, "success" is generally self-reported, by people who don't know what they don't know, and thus they don't know what spicy autocomplete is getting woefully wrong.
"But it {compiles,runs,etc}" isn't a meaningful metric when a large portion of the code in question is dynamic/loosely typed in a non-compiled language (JavaScript, Python, Ruby, PHP, etc).
Also, if your boss tells you "we're AI company now, you will use AI or be fired" then of course you will use AI and claim it is productive.
If you are on the right team with the right professionals you can measure. when we first started using LLMs we decided to run the same process as if they did not exist, same sprint planning meetings, same estimation. we did this for 6 months and saw roughly 55% increase in output compared to pre-LLM usage. there are biases in what were tried to achieve, it is not easy to estimate something will take XX hours when you know some portion (for example writing documentation or portions of the test coverage) you won’t have to write but we did our best. after we convinced ourselves of productivity gains we stopped doing this. saying you can’t measure something is typical SWE BS like “we can’t estimate” and the other lies we were able to convince everyone off successfully
Yeah it’s when you go off the happy path that it gets difficult. Like there’s a weird behaviour in your vibe-coded app that you don’t quite know how to describe succinctly and you end up in some back-and-forth.
But man AI is phenomenal for getting stuff out of your head and working quick.
That doesn't matter. The statement wasn't "faster than AI right now", it was "will always be faster than AI". And that's just nonsense.
Current AI systems are extremely serial, in that very little of the inherent parallelism of the problem is utilized. Current-gen AI systems run at most a few hundreds of thousands of operations in parallel, while for frontier models, billions of operations could be run in parallel. Or in other words, what currently takes AI 8 hours will take it barely long enough for you to perceive the delay after you release the enter key.
For a demo, play around with https://chatjimmy.ai/ , the AI chatbot of Taalas, where they etched the model into silicon in a distributed way, instead of saving it in RAM and sucking it to execution units by a straw. It's a 8B parameter model, so it's unsuitable for complex problems, but the techniques used for it will work for larger models too, and they are working to get there.
And even Taalas is very far from the limits. Modern better quality LLM chatbots operate at ~40 tokens per second. The Taalas chatbot operates at 17000 tokens/s. If you took full advantage of parallelism, you should be able to have a latency of low hundreds of clock cycles per token, or single request throughput of tens of millions of tokens per second. (With a fully pipelined model able to serve one token per clock cycle, from low hundreds of requests.) Why doesn't everyone do it like that right now? Because to do this, you need to etch your model into silicon, which on modern leading edge manufacturing is a very involved process that costs hundreds of millions+ in development and mask costs (we are not talking about single chips here, you can barely fit that 8B model into one), and will take around a year. So long as the models keep improving so much that a year-old model is considered too old to pay back the capital costs, the investment is not justified. But when it will be done, it will not just make AI faster, it will also make it much more energy-efficient per token. Most of the energy costs are caused by moving data around and loading/storing it in memory.
And I want to stress that none of the above is dependent on any kind of new developments or inventions. We know how to do it, it's held back only by the pace of model improvement and economics. When models reach a state of truly "good enough", it will happen. It feels perverse to me that people are treating this situation as "there was a per-AI period that worked like X, now we are in a post-AI period and we have figured out that it will work like Y". No. We are at the very bottom of a very steep curve, and everything will be very different when it's over.
I don't believe this. Either you're lying, or you just haven't caught on with how to use Agentic AI.
Everything I do to interact with my computer is through an agent now.
I don't believe this. Either you're lying, or you just haven't caught on how to use a computer.
Everything I do to interact with my computer is still the same.
See how boring you are?
Ok sorry about that. I seriously don't believe him. The Agent is so fast there's literally no way you can be faster.
Telling the agent your high level plan that you are extremely familiar with and then having the agent execute on 2000 lines of code is FASTER then having you execute on that 2000 lines of code. There is no reality where that can be physically beaten by even someone who's typing really quickly with zero pause. Physically impossible.
Less boring or not? Another way to put it... although my answer is boring, I think I'm right. He is either a liar or like many other people lacks skill in using AI... because the transition to AI is happening so fast... not many people are fully utilizing AI to it's maximum potential. Many still use IDEs, many still interact with terminal. Many people still don't use it to configure infrastructure, do database administration, deploy code... etc.
Why are you starting the clock at the time when you have a "high level plan that you are extremely familiar with"? I think it's fairer to start from "I received a bug report/feature request" or similar.
Also, haven't you ever had a situation where the prompt you started with ends up being longer than the final code diff? Perhaps a subtle bug that's hard to describe/trigger, but ended up having a simple root cause like an off-by-one error?
Also also, coding agents are infamous for generating way more code than is strictly necessary. The 2000 lines of code that the agent generated may well have been only 200 lines had you written it yourself.
AI can write 2000 lines faster than you, but you can write the 2000 lines correctly first shot faster than having AI do 10 iterations on these 2000 lines with your guidance to finally get it right
I know that a better plan could mean fewer iterations, but again that extends the time you need to spend on that plan => the total time of the AI solution
Care to explain which particular intimate knowledge allowed you in the last 6-9 months to be faster than AI in certain area?
Honestly, I'm still faster than AI cooking scrambled eggs, but definitely not faster than neither AI (or compiler) in translating stuff into code.
I interpret "faster than AI" to include writing the prompt. For me (scientific computing) it is more often than not faster to write out a simulation or design in a language I know inside out like fortran or mathematica than explicate the requirements to an LLM to request the code. Obviously if someone wrote out a prompt to me and the LLM it would be way faster, but I don't think that's what the commenter had in mind.
If you're good at SQL, or SQL-like languages like Linq, it might be more efficient precisely writing a reasonably complex query than trying to explain it in detail to an AI.
I am very good at SQL, I worked half my life with SQL and teached it and know all kinds of SQL flavour. But good luck getting ahead of AI on a complex query with recursive CTEs, left outers, 625-column tables that change semantics conditional to certain prop, and then some obscure Oracle package APIs.
No way U beat an LLM on this, even on trivial ones. LLMs are better at that since at least 2024, if you haven't noticed, then you're not doing enough SQL perhaps.
But, of course it took years for people to realize they cannot outpace Visual Studio in the 90s by being very good at x86 assembly.
Not the parent but I've had this happen when debugging for sure. Sometimes I ask Claude Code to help me debug something and it makes a wrong assumption and just churns in circles burning tokens. While it's doing that I realize the problem and fix it.
Sometimes debuggind is faster indeed, and making small very focused changes too.
But during feature development? Not possible. And I consider myself a very fast developer
Don't you find that debugging takes place as part of feature development though?
What I meant is that only sometimes I am faster than Claude with debugging. When it's a standalone problem, a report in Sentry, and I just know immediately where I need to go to fix it. Then it's faster to do myself, than telling Claude what's the problem and where to look and wait.
Bugs happen during feature development, as you say, but then Claude is in the context, and I don't need to tell it where to go, it sees the bug with failing tests, or smth similar.
BTW. One thing that helps my Claude with debugging harder problems is that I tell it to apply scientific method to debugging. Generate hypotheses, gather pros/cons evidence, write to a journal file debug-<problem>.md, design minimal experiments to debunk hypotheses.
You can add that as a skill, and sometimes it will pick it up automatically, but it works wonders just as a single sentence in the input.
..but then you ignore all other times CC got it right, and statistically I would put my bets CC does it right (or Codex (or PI)) than you would, and more often is right than tis not.
besides it is a system that you query, it responds. I'm sure your dbs are not always 'right' and particularly when you as the wrong questions.
[dead]
> Some of us do actually have intimate knowledge in certain areas where guidance of an AI takes longer than doing it yourself.
You speak as if AI development is frozen, and you ignore the poster's point:
> that gap will only increase as LLMs get more intelligent