"Congratulations, you have been elevated to manager to agents."
That's not exactly really where I hoped my career would lead. It's like managing junior developers, but without having nice people to work with.
That's not exactly really where I hoped my career would lead. It's like managing junior developers, but without having nice people to work with.
- can write code
- tireless
- have no aspirations
- have no stylistic or architectural preferences
- have massive, but at the same time not well defined, body of knowledge
- have no intrinsic memories of past interactions.
- change in unexpected ways when underlying models change
- ...
Edit: Drones? Drains?
They can usually write code, but not that well. They have lots of energy and little to say about architecture and style. Don't have a well defined body of knowledge and have no experience. Individual juniors don't change, but the cast members of your junior cohort regularly do.
But they don't have a grasp for the project's architecture and will reinvent the wheel for feature X even when feature Y has it or there is an internal common library that does it. This is why you need to be the "manager of agents" and stay on top of their work.
Sometimes it's just about hitting ESC and going "waitaminute, why'd you do that?" and sometimes it's about updating the project documentation (AGENTS.md, docs/) with extra information.
Example: I have a project with a system that builds "rules" using a specific interpreter. Every LLM wants to "optimise" it by using a pattern that looks correct, but will in fact break immediately when there's more than one simultaneous user - and I have a unit test that catches it.
I got bored by LLMs trying to optimise the bit wrong, so I added a specific instruction, with reasoning why it shouldn't be attempted and has been tried and failed multiple times. And now they stopped doing it =)
- don't have career growth that you can feel good about having contributed to
- don't have a genuine interest in accomplishment or team goals
- have no past and no future. When you change companies, they won't recognize you in the hall.
- no ownership over results. If they make a mistake, they won't suffer.
We'll fix that, eventually.
- don't have career growth that you can feel good about having contributed to
Humans are on the verge of building machines that are smarter than we are. I feel pretty goddamned awesome about that. It's what we're supposed to be doing.
- don't have a genuine interest in accomplishment or team goals
Easy to train for, if it turns out to be necessary. I'd always assumed that a competitive drive would be necessary in order to achieve or at least simulate human-level intelligence, but things don't seem to be playing out that way.
- have no past and no future. When you change companies, they won't recognize you in the hall.
Or on the picket line.
- no ownership over results. If they make a mistake, they won't suffer.
Good deal. Less human suffering is usually worth striving for.
Have you ever spent any time around children? How about people who think they're accomplishing a great mission by releasing truly noxious ones on the world?
You just dismissed the entire notion of accountability as an unnecessary form of suffering, which is right up there with the most nihilistic ideas ever said by, idk, Dostoevsky's underground man or Raskolnikov.
Don't waste your life on being the Joker.
It's also the premise of The Matrix. I feel pretty goddamned uneasy about that.
In any case, the matrix wasn't my inspiration here, but it is a pithy way to describe the concept. It's hard to imagine how humans maintain relevancy if we really do manage to invent something smarter than us. It could be that my imagination is limited though. I've been accused of that before.
> Humans are on the verge of building machines that are smarter than we are.
You're not describing a system that exists. You're describing a system that might exist in some sci-fi fantasy future. You might as well be saying "there's no point learning to code because soon the rapture will come".
Most AI experts not heavily invested in the stocks of inflated tech companies seem to agree that current architectures cannot reach AGI. It's a sci-fi dream, but hyping it is real profitable. We can destroy ourselves plenty with the tech we already have, but it won't be a robot revolution that does it.
What I really need to ask an LLM for is a pointer to a forum that doesn't cultivate proud exhibition of ignorance, Luddism, and general stupidity at the level exhibited by commenters in this entire HN story, and in this subthread in particular.
We already had one Reddit, we didn't need two.
Why?
It's a tool, not an intelligent being
Next year there will be AI screwdriver your employer force you to use.
Then I realised that this will actually happen, and was sadly reminded we’re now in the post-sarcasm era.
Whenever I have a model fix something new I ask it to update the markdown implementation guides I have in the docs folder in my projects. I add these files to context as needed. I have one for implementing routes and one for implementing backend tests and so on.
They then know how to do stuff in the future in my projects.
Key words are these.
> They then know how to do stuff in the future in my projects.
No. No, they don't. Every new session is a blank slate, and you have to feed those markdown files manually to their context.
The AI hype folks write massive fan fiction style novellas that don't have any impact.
But there's middle ground where you tell the agent the specific things about your repo that it doesn't know based on its training. Like if your application has a specific way to run tests headless or it's compiled a certain way that's not the default average.
Unless, of course, the phase of the moon is wrong and Claude itself is stupid beyond all reason
AGENTS.md exists, Codex and Crush support it directly. Copilot, Gemini and Claude have their own variants and their /init commands look at AGENTS.md automatically to initialise the project.
Nobody is feeding aything "manually" to Agents. Only people who think "AI" is a web page do that.
All of them often can't even find/read relevant docs in a new session without prompting
And of course it's up to the developer to keep the documentation up to date. Just like when working with humans. Stuff don't magically document itself.
Yes "good code is self-documenting", but it still takes ages to find anything without docs to tell you the approximate direction.
It's literally a text file the agent can create and update itself. Not hard. Try it.
Humans actually learn from codebases they work with. They don't start with a clean slate every time they wake up in the morning. They know where to find information and how to search for them. They don't need someone to constantly update docs to point to changes.
> but it still takes ages to find anything without docs to tell you the approximate direction.
Which humans, unsurprisingly, can do without wiping their memory every time.
That sounds a lot like '50 First Dates' but for programming.
Yes, this is something people using LLMs for coding probably pick up on the first day. They're not "learning" as humans do obviously. Instead, the process is that you figure out what was missing from the first message you sent where they got something wrong, change it, and then restart from beginning. The "learning" is you keeping track of what you need to include in the context, how that process exactly works, is up to you. For some it's very automatic, and you don't add/remove things yourself, for others is keeping a text file around they copy-paste into a chat UI.
This is what people mean when they say "you can kind of do "learning" (not literally) for LLMs"
It's functionally working the same as learning.
If you look at it like a black box, then you can't tell the difference from the input and output.
For example, let's say LLMs did not have examples of chess gameplay examples in their training data. Would one be able to have an LLM play chess by listing the rules and examples in the context? Perhaps, to some extent, but I believe it would be much worse than if it was part of the training (which of course isn't great either).
Coincidentally, the hippocampus looks like a seahorse (emoji). It's all connected.
Not to mention; hippocampus literally means "seahorse" in Greek. I knew neither of those things before today, thanks!
- constantly give wrong answers, with surprising confidence
- constantly apologize, then make the same mistake again immediately
- constantly forget what you just told them
- ...
NO ONE TALKS TO EACH OTHER unless absolutely necessary for work.
We get on Zooms to talk. Even with the person 1 cubicle over.
Who normalized this?!!
But why? Required? Culture? Maybe it's the company?
> You can verify code quality as a glance, and ship absolute with confidence.
> You can confidently trust and merge the code without hours of manual review.
I couldn't possibly imagine that going wrong.
It's clear now that "agents" in the context of "AI" is really about answering the question "How can we make users make 10x more calls to our models in a way that makes it feel like we're not just squeezing money out of them?" I've seen so many people that think setting some "agents" of on a minutes to hours long task of basically just driving up internal KPIs at LLM providers is cutting edge work.
The problem is, I haven't seen any evidence at all that spending 10x the number of API calls on an agent results in anything closer to useful than last year when people where purely vibe coding all the time. At least then people would interactively learn about the slop they were building.
It's astounding to watch a coworker walk though through a PR with hundreds of added new files and repeatedly mention "I'm not sure if these actually work, but it does look like there's something here".
Now I'm sure I'll get some fantastic "no true Scotsman" replies about how my coworkers must not be skilled enough or how they need to follow xyz pattern, but the entire point of AI was to remove the need for specialize skills and make everyone 10x more productive.
Not to mention that the shift in focus on "agents" is also useful in detracting from clearly diminishing returns on foundation models. I just hope there are enough people that still remember how to code (and think in some cases) to rebuild when this house of cards falls apart.
At least for programming tools, for everything (well, the vast majority, at least) that is sold that way—since long before generative AI—it actually succeeds or fails based not on whether it eliminates need for specialized skills and makes everyone more productive, but whether it further rewards specialized skills, and makes the people who devote time to learning it more productive than if they devoted the same time to learning something else.
Nice? I thought all sycophant LLMs were exceedingly nice.
Someone gave me a great tip though - at least for ChatGPT there's a setting where you can change its personality to "robot". I guess that affects the system prompt in some way but it basically fixes the issue.
Sadly, this is not sustainable and I am not sure what I'm going to do.
With a human, you give them feedback or advice and generally by the 2nd or 3rd time the same kind of thing happens they can figure it out and improve. With an LLM, you have to specifically setup a convoluted (and potentially financially and electrical power expensive) system in order to provide MANY MORE examples of how to improve via fine tuning or other training actions.
The only way that an AI model can "learn" is during model creation, which is then fixed. Any "instructions" or other data or "correcting" you give the model is just part of the context window.
The burden of human interaction is removed from building.
I just need some time by myself to recharge after all the social interactions.
I actually checked that before commenting and went off the Google AI overview -.- eugh