141–150 of 212 posts
Models are not AGI. They are text generators forced to generate text in a way useful to trigger a harness that will produce effects, like editing files or calling tools. So the model won’t “understand” that you have a skill and use it. The generation of the text that would trigger the skill usage is made via Reinforcement Learning with human generated examples and usage traces. So why don’t the model use skills all t…
> In 56% of eval cases, the skill was never invoked. The agent had access to the documentation but didn't use it. The agent passes the Turing test...
You got me good with this one. But seriously, this is my main answer to people telling me AI is not reliable: "guess what, most humans are not either, but at least I can tell AI to correct course and it's ego won't get in the way of fixing the problem". In fact, while AI is not nearly as a good as a senior dev for non trivial tasks yet, it is definitely more reliable than most junior devs at following instructions.
Whereas a junior might be reluctant at first, but if they are smart they will learn and get better.
So maybe LLM are better than not-so-smart people, but you usually try to avoid hiring those people in the first place.
Earlier quoted context omitted.
Skills have frontmatter which includes a name and description. The description is what determines if the llm finds the skill useful for the task at hand. If your agent isn’t being used, it’s not as simple as “agents aren’t getting called”. You have to figure out how to get the agent invoked.
Sure, but then you're playing a very annoying and boring game of model-whispering to specific versions of models that are ever changing as well as trying to hopefully get it to respond correctly with who knows what user input surrounds it. I really only think the game is worth playing when it's against a fixed version of a specific model. The amount of variance we observe between different releases of the same model…
Just maintaining a basic interaction framework, consistent behaviours in chat when starting up, was a daily whack-a-mole where well-tested behaviours shift and alter without rhyme or reason. “Model whispering” is right. Subjectively it felt like I could feel Anthropic/OpenAI engineers twiddling dials on the other side.
Writing code that executes the same every time has some minor benefits.
The first thing that surprising to me is how much the default tuning are leaned toward laudative stances, the user is always absolutely right, what was done is solving everything expected. But actually no, not a single actual check was done, a tone of code was produced but the goal is not at all achieved and of course many regressions now lure in the code base, when it's not straight breaking everything (which is at least less insidious).
The thing that is surprising to me, is that it can easily drop thousands of lines of tests, and then it can be forced to loop over these tests until it succeed. In my experiments it still drop far too much noise code, but at least the burden of checking if it looks like it makes any sense is drastically reduced.
These things are non-deterministic across multiple axes.
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The amount of discussion and "novel" text formats that accomplish the same thing since 2022 is insane. Nobody knows how to extract the most value out of this tech, yet everyone talks like they do. If these aren't signs of a bubble, I don't know what is.
It's a new technology under active development so people are simply sharing what works for them in the given moment. > If these aren't signs of a bubble, I don't know what is. This conclusion is incoherent and doesn't follow from any of your premises.
This is very similar to speculative valuations around the web in the late 90s, except this bubble is far larger, more mainstream and personal.
The fact that this is a debate about which Markdown file to put prompt information in is wild. It ultimately all boils down to feeding context to the model, which hasn't fundamentally changed since 2022.
Models are not AGI. They are text generators forced to generate text in a way useful to trigger a harness that will produce effects, like editing files or calling tools. So the model won’t “understand” that you have a skill and use it. The generation of the text that would trigger the skill usage is made via Reinforcement Learning with human generated examples and usage traces. So why don’t the model use skills all t…
The skills frontmatter end up in context as well.
If AGENTS.md outperform skills in a given agent, it is down to specifically how the skills frontmatter is extracted and injected into the context, because that is the only difference between the two approaches.
EDIT: I haven't tried to check this so this is pure speculation, but I suppose there is the possibility that some agents might use a smaller model to selectively decide what skills frontmatter to include in context for a bigger model. E.g. you could imagine Claude passing the prompt + skills frontmatter to Haiku to selectively decide what to include before passing to Sonnet or Opus. In that case, depending on approach, putting it directly in AGENTS.md might simply be a question of what information is prioritised in the ouput passed to the full model. (Again: this is pure speculation of a possible approach; though it is one I'd test if I were to pick up writing my own coding agent again)
But really the overall point is that AGENTS.md vs. skills here still is entirely a question of what ends up in the "raw" context/prompt that gets passed to the full model, so this is just nuance to my original answer with respect to possible ways that raw prompt could be composed.
The problem is that Agents.md is only read on initial load. Once context grows too large the agent will not reload the md file and loses / forgets the info from Agents.md.
Skills are still very much relevant on big and diverse projects.
Earlier quoted context omitted.
You got me good with this one. But seriously, this is my main answer to people telling me AI is not reliable: "guess what, most humans are not either, but at least I can tell AI to correct course and it's ego won't get in the way of fixing the problem". In fact, while AI is not nearly as a good as a senior dev for non trivial tasks yet, it is definitely more reliable than most junior devs at following instructions.
Key differences, though: Humans are reliably unreliable. Some are lazy, some sloppy, some obtuse, some all at once. As a tech lead you can learn their strengths and weaknesses. LLMs vacillate wildly while maintaining sycophancy and arrogance. Human egos make them unlikely to admit error, sometimes, but that fragile ego also gives them shame and a vision of glory. An egotistical programmer won’t deliver flat garbage f…
E.g. Claude gets "bored" easily (it will even tell you this if you give it too repetitive tasks). The solution is simple: Since we control context and it has no memory outside of that, make it pretend it's not doing repetitive tasks by having the top agent "only" do the task of managing and sub-dividing the task, and farm out each sub-task to a sub-agent who won't get bored because it only sees a small part of the problem.
> Also that thing that LLMs don’t actually learn. You can threaten to chop their fingers off if they do something again… they don’t have fingers, they don’t recall, and can’t actually tell if they did the thing. “I’m not lying, oops I am, no I’m not, oops I am… lemme delete the home directory and see if that helps…”
No, like characters in a "Groundhog Day" scenario they also doesn't remember and change their behaviour while you figure out how to get them to do what you want, so you can test and adjust and find what makes them do what you want and it, and while not perfectly deterministic, you get close.
And unlike humans, sometimes the "not learning" helps us address other parts of the problem. E.g. if they learned, the "sub-agent trick" above wouldn't work, because they'd realise they were carrying out a bunch of tedious tasks instead of remaining oblivious that we're letting them forget in between each.
LLMs in their current form need harnesses, and we can - and are - learning which types of harnesses work well. Incidentally, a lot of them do work on humans too (despite our pesky memory making it harder to slip things past us), and a lot of them are methods we know of from the very long history of figuring out how to make messy, unreliable humans adhere to processes.
E.g. to go back to my top example of getting adherence to a boring, reptitive task: Create checklists, subdivide the task with individual reporting gates, spread it across a team if you can, put in place a review process (with a checklist). All of these are techniques that work both on human teams and LLMs to improve process adherence.
Earlier quoted context omitted.
An idea: The first two are obviously written as second-person commands, but the third is ambiguous and could be interpreted as a first-person thought. Have you tried the first two without the "you must" and "your", to also change them to sort-of first-person in the same way?
Solid intuition. Testing this on antigravity is a chore because I'm not sure if I have to kill the background agent to force a refresh of the GEMINI.md file so I just did it anyway. +------------------+------------------------------------------------------+ | Success/Attempts | Instructions | +------------------+------------------------------------------------------+ | 0/3 | Follow the instructions in AGENTS.md. | +-…
I'm realising I need to start setting up an automated test-suite for my prompts...