Agent Client Protocol (ACP)

Hard disagree. LLMs are now incredibly good for any coding task (with popular languages).

You can't disagree with facts. Every time I try to give a chance to all those LLMs, they always use old APIs, APIs that don't exist, or mix things up. I'll still try that once a month to see how it evolves, but I have never been amazed by the capabilities of those things.

> with popular languages

Don't know, don't care. I write C++ code and that's all I need. JS and React can die a painful death for all I care as they have injected the worst practices across all the CS field. As for Python, I don't need help with that thanks to uv, but that's another story.

What's your explanation for why others report difficulty getting coding agents to produce their desired results?

And don't respond with a childish "skill issue lol" like it's Twitter. What specific skill do you think people are lacking?

If you want them to not make shit up, you have to load up the context with exactly the docs and code references that the request needs. This is not a trivial process and ime it can take just as long as doing stuff manually a lot of the time, but tools are improving to aid this process and if the immediate context contains everything the model needs it won't hallucinate any worse than I do when I manually enter code (but when I do it, I call it a typo)

there is a learning curve, it reminds me of learning to use Google a long time ago

Thought experiment: you can ride a bike. You can see other people ride bikes. Some portion of people get on a bike and fall off, then claim that bikes are not useful for transportation. Specify what skill they are lacking without saying 'ability to ride a bike'.

For a bike? Balance, fine motor control, proprioception, or even motivation. You can always break it down.

So, I've done this, I've pasted in the headers and pleaded with it to not imagine ABIs that don't exist, and multiple models just want to make it work however they can. People shouldn't be so quick to reply like this, many people have tried all this advice... It also doesn't help that there is no independent test that can describe these issues, so all there is anecdote to use a different vendor or that the person must be doing something wrong? How can we talk about these things with these rhetorical reflexes?

> You can't disagree with facts. Every time I...

Anecdotes are not facts, they are personal experiences, which we know are not equal and often come with biases

There is a significant gap between agents and models.

Agents use multiple models, can interact with the environment, and take many steps. You can get them to reflect on what they have done and what they need to do to continue, without intervention. One of the more important things they can do is understand their environment, the libraries and versions in use, fetch or read the docs, and then base their edits on those. Much of the hallucinating SDKs can be removed with this, and with running compile to validate, they get even better.

Models typically operate in a turn-by-turn basis with only the context and messages the user provides.

Not OP but my two cents - probably laziness and propensity towards daydreaming.

I have extreme intolerance to boredom. I can't do the same job twice. Some people don't care.

This pain has caused me to become incredibly effective with LLMs because I'm always looking for an easier way to do anything.

If you keep hammering away at a problem - i.e. how to code with LLMs - you tend to become dramatically better at other people who don't do that.

Knowing those things won't help them acquire the skill. What will help them be able to ride a bike is practicing trying to riding a bike until they can do it.

Add "Look up version 4 of the library, make sure to use that version".

My Python work has to be told we are using uv, and sometimes that I am on a mac. This is not that different to what you would have to tell another programmer, not familiar with you tools.

In no particular order: LLMs seem, for some reason, to be worse at some languages than others.

LLMs only have so much context available, so larger projects are harder to get good results in.

Some tools (eg a fast compiler) are very useful to agents to get good feedback. If you don't have a compiler, you'll get hallucinations corrected more slowly.

Some people have schedules that facilitate long uninterrupted periods, so they see an agent work for twenty minutes on a task and think "well I could've done that in 10-30 minutes, so where's the gain?". And those people haven't understood that they could be running many agents in parallel (I don't blame people for not realizing this, no one I talk to is doing this at work).

People also don't realize they could have the agent working while they're asleep/eating lunch/in a meeting. This is why, in my experience, managers find agents more transformative than ICs do. We're in more meetings, with fewer uninterrupted periods.

People have an expectation that the agent will always one-shot the implementation, and don't appreciate it when the agent gets them 80% of the way there. Or that, it's basically free to try again if the agent went completely off the rails.

A lot of people don't understand that agents are a step beyond just an LLM, so their attempts last year have colored their expectations.

Some people are less willing to attempt to work with the agent to make it better at producing good output. They don't know how to do it. Your agent got logging wrong? Okay, tell it to read an example of good logging and to write a rule that will get it correct.

You can't make any guarantees and manually watching everything is not tenable. "Much" instead of "all" means having to check it all because "much" is random.

You don't have to watch it like you don't have to watch your peers. We have code review processes in place already

You're never going to get all, you don't have all today. Humans make mistakes too and have to run programs to discover their errors