LLMs degrade with long input regardless of caching.

  > AI is best suited for augmentation not automation.

the most frustrating part of these interactive ai assistants is when it sends me down a rabbit hole of an api that doesn't exist (but looks almost right)

Reliability means 99.9% of the time when I hand something off to someone else it's what they want.

Availability means I'm at my desk and not at the coffee machine.

Humans very much are 99.9% accurate, and my deliverable even comes with a list of things I'm not confident about

I used to work on a backup application, it ran locally on our clients' machines. We had over 10000 clients. A 99.9% reliability would mean that there are 10 of our customers, at any one point, having a problem. It's not a question of uptime. It's a question of data integrity in this case. So 99.9% reliability could even leave us open to, potentially, 10 lawsuits. Also, about 10 support calls per day.

Now we only had about 10k customers at the time. Imagine if it were millions.

That said, I have been using LLMs for a while now with great benefit. I did not notice anything missing, and I am not sure what agents bring to the table. Do you know?

I think it is a mix of fomo and the 'upside' potential of being able to minimize ( ideally remove ) the expensive "human component". Note, I am merely trying to portray a specific world model.

<< In the time spent toying around with agents there are a lot of interesting applications that could have built, some of which may be technically an agent but without so much focus and effort on trying to solve for all use cases.

Preaching to the choir man. We just got custom AI tool ( which manages to have all my industry specific restrictions rendering it kinda pointless, low context making it annoying, and slower than normal, because it now has to go through several layers of approval including 'bias' ).

At the same time, committee bickers over minute change to a process that has effectively no impact on anything of value.

Bonkers.

For me the only problem I have is I find typing slow and laborious. I've always said if I could find a way to type less I would take it. That's why I've been using tab completion and refactoring tools etc for years now. So I'm kind of excited about being able to get my thoughts into the computer more quickly.

But having it think for me? That's not a problem I have. Reading and assimilating information? Again, not a problem I have. Too much of this is about trying to apply a solution where there is no problem.

So until these techniques are baked into the model by OpenAI, you have to come up with these ideas yourself.

Something seems off about that...

But it generates mistakes like say 1 in 10 times and I do not see it getting fixed unless we drastically change the LLM architecture. In future I am sure we will have much more robust systems if the current hype cycle doesn't ruin its trust with devs.

But the hit is real, I mean I would hire a lot less If i were to hire now as I can clearly see the dev productivity boost.. Learning curve for most of the topics are also drastically reduced as the loss in Google search result quality is now supplemented by LLMs.

But thing I can vouch for is automation and more streamlined workflows. I mean having normal human tasks being augmented by an LLM in a workflow orchestration framework. The LLM can return its confidence % along with the task results and for anything less than ideal confidence % the workflow framework can fall back on a human. But if done correctly with proper testing, guardrails and all, I can see LLM is going to replace human agents in several non-critical tasks within such workflows.

The point is not replacing humans but automating most of the work so the team size would reduce. For e.g. large e-commerce firms have 100s of employees manually verifying product description, images etc, scanning for anything from typos to image mismatch to name a few. I can see LLMs going to do their job in future.

I wrote a little about one such task, getting agents to supplement my markdown dev-log here: https://github.com/sutt/agro/blob/master/docs/case-studies/a...

ChatGPT's Deep Research mode is also an agent: it will keep crawling the web and refining things until it feels it has enough material to write a good response.

_Knowing how way leads to way_, the larger the task, the more chance there is for an early deviation to doom the viability of the solution in total. Thus for even the SOTA right now, agents that can work in parallel to generate several different solutions can reduce your time of manually refactoring the generation. I wrote a little about that process here: https://github.com/sutt/agro/blob/master/docs/case-studies/a...

And I have an AI workflow that generates much better posts than this.

"We can't allow this post to create FUD about the current hype on AI agents and we need the scam to continue as long as possible".

This phrase is usually followed by some, you know...Math?

> AI tools aren't perfect yet. They sometimes make mistakes, and they can't always understand what you are trying to do. But they're getting better all the time, In the future, they will be more powerful and helpful. They'll be able to understand your code even better, and they'll be able to generate even more creative ideas.

From another post on the same site. [0]

Yup, slop.

[0]: https://utkarshkanwat.com/writing/review-of-coding-tools/

Humans can try things, learn, and iterate. LLMs still can't really do the second thing, you can feed back an error message into the prompt but the learning isn't being added to its weights so its knowledge doesn't compound with experience like it does for us.

I think there are still a few theoretical breakthroughs needed for LLMs to achieve AGI and one of them is "active learning" like this.

Even for software applications like the Linux kernel, there would have been a theory in Linus' head - for example of what an operating system is, and how it should work.

A theory gives 100% correct predictions. Although the theory itself may not model the world accurately. Such feedback between the theory, and its application in the world causes iterations to the theory. From newtonian mechanics to relativity etc. From euclidean geometry to geometry of curved spaces etc.

Long story short, the LLM is a long way away from any of this. And to be fair to LLMs, the average human is not creating theories, it takes some genius to create them (newton, turing, etc). The average human is trading memes on social media.

Someone was saying that with an increasing number of attempts, or increasing context length, LLMs are less and less likely to solve a problem

(I searched for it but can't find it)

That matches my experience -- the corrections in long context can just as easily be anti-corrections, e.g. turning something that works into something that doesn't work

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Actually it might have been this one, but there are probably multiple sources saying the same thing, because it's true:

Context Rot: How Increasing Input Tokens Impacts LLM Performance - https://news.ycombinator.com/item?id=44564248

In this report, we evaluate 18 LLMs, including the state-of-the-art GPT-4.1, Claude 4, Gemini 2.5, and Qwen3 models. Our results reveal that models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows.

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As far this question: how do we manage to create things like the Linux kernel or the Mars landers without AI

It's because human intelligence is a totally different thing than LLMs (contrary to what interested people will tell you)

Carmack said there are at least 5 or 6 big breakthroughs left before "AGI", and I think even that is a misleading framing. It's certainly possible that "AGI" will not be reached - there could be hardware bottlenecks, software/algorithmic questions, or other obstacles we haven't thought of

That is, I would not expect AI to create anything like the Linux kernel. The burden of proof is on the people who claim that, not the other way around !!!

Hey, maybe humans aren't just like LLMs after all.

In fact, the point of the whole article isn't that AI doesn't work; to the contrary, it's that long chains of (20+) actions with no human intervention (which many agentic companies promise) don't work.

And you mention testing, which certainly can be done. But when you have a large product and the code generator is unreliable (which LLMs always are), then you have to spend most of your time testing.

For better or worse, everything we see and do ends up modifying our "weights", which is something current LLMs just architecturally can't do since the weights are read-only.

Do they? I certainly don't. I don't know if it's my memory deficiency, but I frequently hit my "context window" when solving problems of sufficient complexity.

Can you provide some examples of problems where humans have such large context windows?

For context, relevant information from steps can be cherrypicked to next stage.

The math works differently because AI (mostly) ignores irrelevant results. So steps actually increase reliability overall.

In the software world (like the article is talking about) this is the logic that has ruthlessly cut software QA teams over the years. I think quality has declined as a result.

Verifiers are hard because the possible states of the internal system + of the external world multiply rapidly as you start going up the component chain towards external-facing interfaces.

That coordination is the sort of thing that really looks appealing for LLMs - do all the tedious stuff to mock a dependency, or pre-fill a database, etc - but they have an unfortunate tendency to need to be 100% correct in order for the verification test that depends on them to be worth anything. So you can go further down the rabbit hole, and build verifiers for each of those pre-conditions. This might recurse a few times. Now you end up with the math working against you - if you need 20 things to all be 100%, then even high chances of each individual one starts to degrade cumulatively.

A human generally wouldn't bother with perfect verification of every case, it's too expensive. A human would make some judgement calls of which specific things to test in which ways based on their intimate knowledge of the code. White box testing is far more common than black box testing. Test a bunch of specific internals instead of 100% permutations of every external interface + every possible state of the world.

But if you let enough of the code to solve the task be LLM-generated, you stop being in a position to do white-box testing unless you take the time to internalize all the code the machine wrote for you. Now your time savings have shrunk dramatically. And in the current state of the world, I find myself having to correct it more often then not, further reducing my confidence and taking up more time. In some places you can try to work around this by adjusting your interfaces to match what the LLM predicts, but this isn't universal.

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In the non-software world the situation is even more dire. Often verification is impossible without doing the task. Consider "generate a report on the five most promising gaming startups" - there's no canonical source to reference. Yet these are things people are starting to blindly hand off to machines. If you're an investor doing that to pick companies, you won't even find out if you're wrong until it's too late.

Something I knew all along was that you build the system that lets you do it with the human in the loop, collect evaluation and training data [1] and then build a system which can do some of the work and possibly improve the quality of the rest of it.

[1] in that order because for any 'subjective' task you will need to evaluate the symbolic system even if you don't need to train it -- if you need to train the system, on the other hand, you'll still need to eval

If the LLM can’t answer a query it usually forwards the chat to a human support agent.

That isn't reliable either, but it supports the person who gets the mail on his desk in the end.

We sometimes get handwritten service protocols and the model we are using is very proficient in reading handwritten notes which you would have difficulties to parse yourself.

It works most of the time, but not often enough that AI could give autogenerated answers. For service quality reasons we don't want to impose any chatbot or AI on a customer.

Also data protection issues arise if you use most AI services today, so parsing customer contact info is a problem as well. We also rely on service partners to tell the truth about not using any data...