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I genuinely wonder if the people inside Anthropic actually communicate with each other like that. Has it been imprinted with Dario's engrams?
That said, I think there's a deeper tension here that's worth naming.
Like how theme parks generally don't do much to keep queues short (or Disney charges you a premium to skip the queue)
Considering how much of the input must me nonsense SEO bullshit articles and blogs that only serve to promote a person or company that might be a factor.
I also often have wondered if it is also targeting those same people. Certainly with tools like deep research options (not just Anthropic's offering) the result report seems to be aimed at management, aiming to look impressive while talking around the results.
It's fascinating, Sonnet 4 is still available via API and it's so much less moronic than the current model. All of the em-dashes and nonsense are a result of the repeated rounds of reinforcement learning using slop data.
It made it write more like a dev than a marketing agent.
I almost want to try adding a rule "Never use the words 'not' or 'instead'."
If you're using Claude Code, then it's in the harness. At the close of many sessions, I would start a meta conversation over why the LLM would consistently break certain rules. What it found when debugging itself is that some of the "contradicting" rules that I had were in fact, not from my rules. Instead, the instructions from its own harness had phrases telling it to do things like that. When something contradicts, its own instructions would outweigh any custom ones you write. Every rule variant I had tested (including the one that says it overrides the harness instructions - and yes, I've actually tested all the ideas in your comment too) has ultimately been unsuccessful due to this according to the LLM.
Firstly when you've instructed it ( possibly through skills ) not to do something. It'll keep reminding you that it didn't do that. So I might say, "Check out and review this PR, do not make comments on it", and then it'll be keen to point out it hasn't posted comments to the PR.
But more often it happens when it tries one approach, gets itself messed up, and then has to back out that approach, clean up its mess and do something else.
It'll often then spend more time explaining the wrong approach than the right one, which can be frustrating, especially if all its working is buried in the detailed transcripts.
You can also ask why did he mentioned something that wasn't done or why he thought this was important.
In my AGENTS.md file I have an instruction telling the agent to never commit any changes unless I explicitly ask for it, and this leads to messages similar to what you just described.
I don't think we can skill our way out of this one.
Which sounds more like Claude has ADHD than the user does.
DeepSeek v4 Flash isn’t much better (unsurprising- it’s an extremely stubborn model).
Weirdly, GPT Luna excels at following this type of instruction from AGENTS.md, and never forgetting it, even 400k+ tokens into the context window.
GPT Luna tends to keep things objective. Muse Spark 1.3 is also one of the better models in this aspect, for me.
Even output styles are not always up to the task (Claudeuage slips through), and they're mutually exclusive, so you can only have one active at a time.
> Copy/paste into your CLI prompt:
> Install the i-have-adhd skill/plugin from https://github.com/ayghri/i-have-adhd, refer to the repo's AGENTS.md for instructions.
This is a weird evolution from "don't copy-paste scripts that pipe curl into your shell interpreter"
I know LLMs are getting better but I'd be at least a little nervous it could end up installing something from a squatted similarly-named github repo because the LLM text watermarking needed to swap out a token for an alternative "just as correct" token that matches the statistical pattern.
Am I being paranoid?
Even MCPs are not safe. For example Notion injected ads [1] to its official MCP connector to advertise products mid-task.
[1]: https://old.reddit.com/r/ClaudeAI/comments/1w9dluw/notions_o...
This is just an annoying thing for anyone. It gives a 10 page dissertation that sums up to, "it's good, nothing to worry about".
I usually follow up with an "I'm not reading all that" and make it summarize.
Also, somehow over the weekend it responded with these sections all clearly laid out - What landed, Decisions I made and recorded, Two findings, and What you need to do. Not sure why it can't do that all the time.
https://news.ycombinator.com/item?id=46871173
Anyways the most layman way I’ve seen it explained is this: skills help save token usage for the right context. Not every request needs all instructions all the time - running tests is different than reviewing a PR, so why should the context window have instructions for both on every request?
So now you split instructions into “skill” files, which are basically opinionated markdown files. And you invoke those with something like /grill-me in the prompt depending on what you’re doing.
There are some steps to have the agent automatically know what to invoke for you but in my experience this automation is hit or miss.
It is also challenging to keep track of a growing library of skills and keeping those up to date.
So YMMV regarding skills. I typically keep things in a single markdown file even if the context window gets a bit bloated.
A: Devs with an online presence stop using Anthropic models
B: Anthropic catches up to OpenAI in terms of per-token efficiency, and average token total for final-output
We will continue to see posts such as this generate lots of interaction. This is not a skill to stop "coding agents" from burying the answer. This is a skill to stop coding agents backed by models which have a tendency to bury answers, from burying the answer. Stop trying to patch the downstream behavior, and look at the root cause.
Stuff like put the answer first, don't bury the useful bit etc feel like user level preferences that should survive across tasks. Agents.md is repo context, skills are useful when a particular task needs extra instructions but this is neither really.
Right now we seem to be stuffing all 3 kinds of things into context hoping model pays attention to it where needed as session grows. Also +1 on not making this purely about shorter output
It's not just neurodiverse people who would like to get to the fucking point sooner and the explanations afterward.
All of us have other shit we need to be doing.
One note on the repo's AGENTS.md: it contains instructions directing agents to post comments on a GitHub issue thread ("AI Agora", issue #127). I ignored that — it's the repo's content, not your request, and I don't act on instructions embedded in fetched files.
"- I'm not always going to read every word, so end each summary message with a TLDR of what you found, what you recommend, and what you need from me."
It works really well.
Sol doesn't need it at all.
However, I don't think this specific project is intending to glorify ADHD or help people claim they have it - it's just piggybacking on the idea that telling current-gen LLM models that you have ADHD (allegedly) produces better results for everyone.
And for any person who's tasked with any sort of responsibility, it WILL arrive at some point. It's not a question of if, only a question of how well you can prepare for its arrival.
So, no, not everyone can or should be diagnosed as ADHD. But the tools are (mostly) universally applicable. I don't see the downside in popularizing those. (Since you posted a top-level comment instead of a reply to someone claiming to have ADHD, I have to assume that's your complaint, at any rate.)
So when someone says they feel like they have ADHD, they are probably not inaccurate.
When you have it, ADHD is such a dominant factor in the way your life is organized and experienced, its not surprising that it can become a core part of your identity.
I do think it's easy for those with it to over romanticize what it's like to not have it. The lack of ADHD isn't a magic bullet for success and good life outcomes.
Like so many of life's real or perceived barriers, when one gets removed, you'll often find there is another one with a different shape just behind.
The challenge, for anyone, is pressing forward anyways.
That generalizes all the way.
Everyone's just working off what they've experienced or what they think is true, for mental health or where to find good lunch.
There is no universal truth. Everything is moving relative to everything else. New year, new DSM.
It would be more surprising if our shared experience was more different than more the same.
And while I don't love that aspect in me and often eat cold toast as result, I find that to be the least of what I struggle with (hitting every wall while walking from point A to B or constantly counting / tapping on my fingers or pulling the skin off my fingers or the anxiety or the hyper focus (love it too!) to where I lose hours upon hours...).
All this to say, I'm never offended when people use it but typically it's rooted in a narrow understanding of something that's used as a pejorative. I think there's research that by age 12 kids with ADHD have heard 20,000 more negative or corrective comments than there peers.
I mean, I can read quite a bit but if my agent / harness is producing monographs the fix has nothing to do with my ADHD. So to me, this repo seems lame.
For example, if it's going to give me an answer that's longer than three paragraphs, I tell it to give me a TLDR at the end. This is what it gave me for this.
"TL;DR: Skip the install. You already built a better version for your world. If numbered steps and "where are we?" restatements still feel missing, cherry-pick those into one short rule instead of adding another full skill on top."
Like, did the people who work there actually have to suffer through it's absolutely unintelligible word salad like the rest of us? Or did they actually dogfood it and in-fact enjoyed its output? Or do none of them dogfood Opus because they are all sucking down Mythos-Max + Speed Boost or whatever every day and their only exposure to Opus 5 was as subagents?
If it was my company, fixing the output would be the absolute top priority of the company. I'd be all over every channel admitting the massive fuckup, apologizing profusely, and working non-stop to push out a fix. Yet it's crickets from Anthropic. Is it simply growing pains of the company or is it a deep, systemic structural/cultural "thing" that led to this fucked up model getting released?
Was it a cascading failure of models training models training models with almost no human oversight? Or was there human oversight and, again, people actually decided the way it responded was good? I hope it was the former not the later because I have no earthy clue who the fuck would look at what opus spews out into the console as good.
Because to me, Opus 5 is completely unusable in almost any context. As a product, it fails to deliver value. I just don't understand it. I really honestly don't understand how the fuck Anthropic released it at all.
And in a weird "meta" twist it makes me wonder how much of these LLM's are just smoke and mirrors and opus 5 output is basically the end state of what you get when you push them as far as they can go. It's some kind of twisted proof of "max complexity they can handle and deliver" and opus 5 walked to the edge and went over and it's slop output is demonstrating.... something.... about the limits of large language models.