It’s crazy how Anthropic keeps coming up with sticky “so simple it seems obvious” product innovations and OpenAI plays catch up. MCP is barely a protocol. Skills are just md files. But they seem to have a knack for framing things in a way that just makes sense.
They are the LLM whisperers.
In the same way Nagel knew what it was like to be a bat, Anthropic has the highest fraction of people who approximately know what it's like to be a frontier ai model.
It's surprising to me that Anthropic's CEO is the only one getting real recognition for their advances. The people around him seem to be as or more crucial for their mission.
Oh yeah I forgot the biggest one. Claude fucking code. Lol
I was very skeptical about anything not OpenAI for a while, and then discovered Claude code, Anthropic blogposts, etc. It's basically the coolest company in the field.
The RSS of AI
I like this line of analogy. The next obvious step would be IRC (or microservices?) of AI (for co-reasoning) which could offer the space for specialized LLMs rather than the current approach of monoliths.
I had a bunch of fun writing about this one, mainly because it was a great excuse to highlight the excellent news about Kākāpō breeding season this year.
(I'm not just about pelicans.)
Will Kākāpō be riding bicycles soon?
They already ride British nature photographers—what do they need bikes for?
https://youtube.com/watch?v=Jlk9u8MIv7o
The foreplay starts around the 1 minute mark.
as an svg you mean? cause nano banana rides circles around the pelicans
And so the Kākāpō Benchmark was born
and this is my excuse to talk about the :partyparrot: emoji being from an actual real life documentary https://www.youtube.com/watch?v=9T1vfsHYiKY&pp=ygUSa2FrYXBvI...
TIL about a large moss green flightless parrot :)
> Skills are a keeper #
Good thinking, I agree actually, however..
> Skills are based on a very light specification, if you could even call it that, but I still think it would be good for these to be formally documented somewhere.
Like a lot of posts around AI, and I hope OP can speak to it, surely you can agree that while when used for a good cool idea, it can also be used for the inverse and probably to more detrimental reason. Why would they document an unmanageable feature that may be consumed.
Shareholder value might not go up if they learnt that the major product is learning bad things.
Have you or would you try this on a local LLM instead ?
These work well with local LLMs that are powerful enough to run a coding agent environment with a decent amount of context over longer loops.
The OpenAI GPT OSS models can drive Codex CLI, so they should be able to do this.
I have high hopes for Mistral's Devstral 2 but I've not run that locally yet.
> These work well with local LLMs that are powerful enough to run a coding agent environment with a decent amount of context over longer loops.
That's actually super interesting, maybe something I'll try investigate and find the minimum requirements because as cool as they seem, personalized 'skills' might be a more useful use of AI overall.
Nice article, and thanks for answering.
Edit: My thinking is consumer grade could be good enough to run this soon.
Something that powerful requires some rewriting of the house.
Local LLMs are better for long batch jobs not things you want immediately or your flow gets killed.
I'm not sure if I have the right mental model for a "skill". It's basically a context-management tool? Like a skill is a brief description of something, and if the model decides it wants the skill based on that description, then it pulls in the rest of whatever amorphous stuff the skill has, scripts, documents, what have you. Is this the right way to think about it?
It's a folder with a markdown file in it plus optional additional reference files and executable scripts.
The clever part is that the markdown file has a section in it like this: https://github.com/datasette/skill/blob/a63d8a2ddac9db8225ee...
---
name: datasette-plugins
description: "Writing Datasette plugins using Python and the pluggy plugin system. Use when Claude needs to: (1) Create a new Datasette plugin, (2) Implement plugin hooks like prepare_connection, register_routes, render_cell, etc., (3) Add custom SQL functions, (4) Create custom output renderers, (5) Add authentication or permissions logic, (6) Extend Datasette's UI with menus, actions, or templates, (7) Package a plugin for distribution on PyPI"
---
Apologies for not reading all of your blogs on this, but a follow-up question. Are models still prone to reading these and disregarding them even if they should be used for a task?
Reason I ask is because a while back I had similar sections in my CLAUDE.md and it would either acknowledge and not use or just ignore them sometimes. I'm assuming that's more of an issue of too much context and now skill-level files like this will reduce that effect?
Skill descriptions get dumped in your system prompt - just like MCP tool definitions and agent descriptions before them. The more you have, the more the LLM will be unable to focus on any one piece of it. You don't want a bunch of irrelevant junk in there every time you prompt it.
Skills are nice because they offload all the detailed prompts to files that the LLM can ask for. It's getting even better with Anthropic's recent switchboard operator (tool search tool) that doesn't clutter the system prompt but tries to cut the tool list down to those the LLM will need.
> On startup Claude Code / Codex CLI etc scan all available skills folders and extract just those descriptions into the context. Then, if you ask them to do something that's covered by a skill, they read the rest of that markdown file on demand before going ahead with the task.
Maybe I still don't understand the mechanics - this happens "on startup", every time a new conversation starts? Models go through the trouble of doing ls/cat/extraction of descriptions to bring into context? If so it's happening lightning fast and I somehow don't notice.
Why not just include those descriptions within some level of system prompt?
Yes, it happens on startup of a fresh Claude Code / Codex CLI session. They effectively get pasted into the system prompt.
Reading a few dozen files takes on the order of a few ms. They add enough tokens per skill to fit the metadata description, so probably less than 100 for each skill.
So when it says:
> The body can contain any Markdown; it is not injected into context.
It just means it's not injected into the context until the skill is used or it's never injected into the context?
Yeah, that means that the body of that file will not be injected into the context on startup.
I had thought that once the skill is selected the whole file would be read, but it looks like that's not the case: https://github.com/openai/codex/blob/ad7b9d63c326d5c92049abd...
1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
Knowing Codex, I wonder if it might just search for text in the skill file and read around matches, instead of always reading a bit from the top first.
why did this simple idea take so long to become available? I remember even in llama 2 days I was doing this stuff, and that model didn't even function call.
Skills only work if you have a full blown code execution environment with a model that can run ls and cat and execute scripts and suchlike.
The models are really good at driving those environments now which makes skills the right idea at the right time.
Have you used AWS bedrock? I assume these get pretty affordable with prompt caching...
Do skills get access to the current context or are they a blank slate?
They execute within the current context - it's more that the content of the skill gets added to that context when it is needed.
So it’s a header file. In English.
Skills have a lot of uses, but one in particular I like is replacing one off MCP server usage. You can use (or write) an MCP server for you CI system and then add the instructions to your AGENTS.md to query the CI MCP for build results for the current branch. Then you need to find a way to distribute the MCP server so the rest of the team can use it or cook it into your dev environment setup. But all you really care about is one tool in the MCP server, the build result. Or...
You can hack together a shell, python, whatever script that fetches build results from your CI server, dumps them to stdout in a semi structured format like markdown, then add a 10-15 line SKILL.md and you have the same functionality -- the skill just executes the one-off script and reads the output. You package the skill with the script, usually in a directory in the project you are working on, but you can also distribute them as plugins (bundles) that claud code can install from a "repository", which can just be a private git repo.
It's a little UNIX-y in a way, little tools that pipe output to another tool and they are useful in a standalone context or in a chain of tools. Whereas MCP is a full blown RPC environment (that has it's uses, where appropriate).
My understanding is this: A skill is made up of SKILL.md which is what tells claude how and when to use this skill. I'm a bit of a control freak so I'll usually explicitly direct claude to "load the wireframe-skill" and then do X.
Now SKILL.md can have references to more finegrained behaviors or capabilities of our skill. My skills generally tend to have a reference/{workflows,tools,standards,testing-guide,routing,api-integration}.md. These references are what then gets "progressively loaded" into the context.
Say I asked claude to use the wireframe-skill to create profileView mockup. While creating the wireframe, claude will need to figure out what API endpoints are available/relevant for the profileView and the response types etc. It's at this point that claude reads the references/api-integration.md file from the wireframe skill.
After a while I found I didn't like the progressive loading so I usually direct claude to load all references in the skill before proceeding - this usually takes up maybe 20k to 30k tokens, but the accuracy and precision (imagined or otherwise ha!) is worth it for my use cases.
> I'm a bit of a control freak so I'll usually explicitly direct claude to "load the wireframe-skill" and then do X.
You shouldn't do this, it's generally considered bad practice.
You should be optimizing your skill description. Often times if I am working with Claude Code and it doesn't load I skill, I ask it why it missed the skill. It will guide me to improving the skill description so that it is picked up properly next time.
This iteration on skill description has allowed skills to stay out of context until they are needed rather predictably for me so far.
Yes. I find these very useful for enforcing e.g. skills like debugging, committing code, make prs, responding to pr feedback from ai review agents, etc. without constantly polluting the context window.
So when it's time to commit, make sure you run these checks, write a good commit message, etc.
Debugging is especially useful since AI agents can often go off the rails and go into loops rewriting code - so it's in a skill I can push for "read the log messages. Inserting some more useful debug assertions to isolate the failure. Write some more unit tests that are more specific." Etc.
I think it’s also important to think of skills in the context of tasks, so when you want an agent to perform a specialized task, then this is the context, the resources and scripts it needs to perform the task.
The skills approach is great for agents and LLMs but I feel agents have to become wider in the context they keep and more proactive in the orchestration.
I have been running Claude Code with simple prompts (eg 1) to orchestrate opencode when I do large refactors. I have also tried generating orchestration scripts instead. Like, generate a list of tasks at a high level. Have a script go task by task, create a small task level prompt (use a good model) and pass on the task to agent (with cheaper model). Keeping context low and focused has many benefits. You can use cheaper models for simple, small and well-scoped tasks.
This brings me to skills. In my product, nocodo, I am building a heavier agent which will keep track of a project, past prompts, skills needed and use the right agents for the job. Agents are basically a mix of system prompt and tools. All selected on the fly. User does not even have to generate/maintain skills docs. I can get them generated and maintained with high quality models from existing code in the project or tasks at hand.
1 Example prompt I recently used: Please read GitHub issue #9. We have phases clearly marked. Analyze the work and codebase. Use opencode, which is a coding agent installed. Check `opencode --help` about how to run a prompt in non-interactive mode. Pass each phase to opencode, one phase at a time. Add extra context you think is needed to get the work done. Wait for opencode to finish, then review the work for the phase. Do not work on the files directly, use opencode
My product, nocodo: https://github.com/brainless/nocodo
From a purely technical view, skills are just an automated way to introduce user and system prompt stuffing into the context right? Not to belittle this, but rather that seems like a way of reducing the need for AI wrapper apps since most AI wrappers just do systematic user and system prompt stuffing + potentially RAG + potentially MCP.
Yeah, there are a whole lot of AI wrapper applications that could be a folder with a markdown file in at this point!
@simonw Thank you for always setting alt text in your images. I really appreciate it.
This is nice, but that it goes into its vendor specific .codex/ folder is a bit of a drag.
I hope such things will be standardized across vendors. Now that they founded the Agentic AI Foundation (AAIF) and also contributed AGENTS.md, I would hope that skills become a logical extension of that.
https://www.linuxfoundation.org/press/linux-foundation-annou...
If anyone wants to use skills with any other model or tool like Gemini CLI etc. I had created open-skills, which lets you use skills for any other llm.
Caveat: needs mac to run
Bonus: it runs it locally in a container, not on cloud nor directly on mac
1. Open-Skills: https://GitHub.com/BandarLabs/open-skills
we just released Anthropic's Skills talk for those who want to find more info on the design thinking / capabilities: https://www.youtube.com/watch?v=CEvIs9y1uog&t=2s
I think the future is likely one that mixes the kitchen-sink style MCP resources with custom skills.
Services can provide an MCP-like layer that provides semantic definitions of everything you can do with said service (API + docs).
Skills can then be built that combine some subset of the 3rd party interfaces, some bespoke code, etc. and then surface these more context-focused skills to the LLM/agent.
Couldn’t we just use APIs?
Yes, but not every API is documented in the same way. An “MCP-like” registry might be the right abstraction for 3rd parties to expose their services in a semantic-first way.
Agree. I'd add that a aha moment to skills is AI agents are pretty good at writing skills. Let's say you have developed an involved prompt that explains how to hit an API (possibly with the complexity of reading credentials from an env var or config file) or run a tool locally to get some output you want the agent to analyze (example, downloading two versions of python packages and diffing them to analyze changes). Usually the agent reading the prompt it's going to leverage local tools to do it (curl, shell + stdout, git, whatever) every single time. Every time you execute that prompt there is a lot thinking spent on deciding to run these commands and you are burning tokens (and time!). As an eng you know that this is a relatively consistent and deterministic process to fetch the data. And if you were consuming it yourself, you'd write a script to automate it.
So you read about skills (prompt + scripts) to make this more repeatable and reduce time spent thinking. At that point there are two paths you can go down -- write the skill and prompt yourself for the agent to execute -- or better -- just tell the agent to write the skill and prompt and then you lightly edit it and commit it.
This may seem obvious to some, but I've seen engineers create skills from scratch because they have a mental model around skills being something that people must build for the agent, whereas IMO skills are you just bridging a productivity gap that the agent can't figure out itself (for now), which is instructing it to write tools to automate its own day to day tedium.
The example Datasette plugin authoring skill I used in my article was entirely written by Claude Opus 4.5 - I uploaded a zip file to its the Datasette repo in it (after it failed to clone that itself for some weird environment reason) and had it use its skill-writing skill to create the rest: https://claude.ai/share/0a9b369b-f868-4065-91d1-fd646c5db3f4
That's awesome and I have a few similar conversations with Claude. I wasn't quite an AI luddite a couple months ago, but close. I joined a new company recently that is all in on AI and I have a comically huge token budget so I jumped all the way in myself. I have my choice of tools I can use and once I tried Claude Code it all clicked. The topology they are creating for AI tooling and concepts is the best of all the big LLMs, by far. If they can figure out the remote/cloud agent piece with the level of thoughtfulness they have given to Code, it'd be amazing. Cursor Cloud has that area locked down right now, but I'm looking forward to how Anthropic approaches it.
CLIs are really good when you can use them. self-documenting, agents already have shell tools, they tend to solve fine-grained auth, etc.
feels like the right layer of abstraction for remote APIs
If only there was a way to progressively disclose the API in MCP instead of presenting the full laundry list up front.
That is effectively what this proposal is about: https://www.anthropic.com/engineering/code-execution-with-mc...
> It took just over eleven minutes to produce this PDF,
Incredibly dumb question, but when they say this, what actually happens?
Is it using TeX? Is it producing output using the PDF file spec? Is there some print driver it's wired into?
Visit this link and click on the "Thought for 11m38s" text: https://chatgpt.com/share/693ca54b-f770-8006-904b-9f31a58518... - that will show you exactly what it spent those 11 minutes doing, most of which was executing Python code using the reportlab library to generate PDF files, then visually inspecting those PDF files and deciding to make further tweaks to the code that generates them.
This is killing me with complexity. We had agents.md and were supposed to augment the context there. Now back to cursor rules and another md file to ingest.
MCPs feel complicated. Skills seem to me like the simplest possible design for a mechanism for adding extra capabilities to an existing coding agent.
Quietly? That is a clickbaity adjective.
Yes, but it's also true: OpenAI have said almost nothing in public about their support for skills (there's one tweet about the Codex CLI implementation https://x.com/thsottiaux/status/1995988758886580349 and that's it) while rolling out a pretty major feature - invented by their competitor - to their core 800m+ user product in the past 24 hours.
I think "quietly" is fair.
Fair point. Other boys cried wolf and made me scepitical.
Hasn’t ChatGPT been supporting skills with a different name for several months now through “agent”?
They gave it back then folders with instructions and executable files iirc
Not quite the same thing. Implementing skills specifically means that you have code which, on session start, scans the skills/*/skill.md files and reads in their description: metadata and loads that into the system prompt, along with an instruction that says "if the user asks about any of these particular things go and read the skills.md file for further instructions".
Here's the prompt within Codex CLI that does that: https://github.com/openai/codex/blob/ad7b9d63c326d5c92049abd...
I extracted that into a Gist to make it easier to read: https://gist.github.com/simonw/25f2c3a9e350274bc2b76a79bc8ae...
I remember you did some reverse engineering when they released agent, does it not feel quite similar to you?
I know they didn’t dynamically scan for new skill folders but they did have mentions of the existing folders (slides, docs, …) in the system prompt
The main similarity is that both of them take full advantage of the bash tool + file system combination.
It is interesting that they are relying on visual reading for document ingestion instead of OCT. Recently I read an article which says Handwriting recognition has matured, and I'm beginning to think this is the approach they are takingwirh HAndwiting recognition.
Curious if anyone has applied this "Skills" mindset to how you build your tool calls for your LLM agents applications?
Say I have a CMS (I use a thin layer of Vercel AI SDK) and I want to let users interact with it via chat: tag a blog, add an entry, etc, should they be organized into discrete skill units like that? And how do we go about adding progressive discovery?
Does this mean I can point to a code snippet and a link to the related documentation and the coding agent refer to it instead of writing "outdated" code?
Some frameworks/languages move really fast unfortunately.
Yes, definitely. I've had a lot of success already showing LLMs short examples of coding libraries they don't know about from their core training data.
Totally unrelated but what’s up with the word “quietly”? Its usage seems to have gone up 5000%, essentially overnight, as if there’s a contagion. You see the word in the New York Times, in government press releases, in blogs. ChatGPT 5.1 itself used the word in almost every single response, and no amount of custom instructions could get it to stop. That “Google Maps of London restaurants” article that’s going around not only uses the word in the headline, but also twice in the closing passage alone, for example. And now Simon, who’s an excellent writer with an assertive style, has started using it in his headlines. What’s the deal? Why have so many excellent writers from a wide range of subjects suddenly all adopted the same verbal tic? Are these writers even aware that they’re doing it?
Huh! I had not noticed that trend at all.
Here's the Google Maps article: https://laurenleek.substack.com/p/how-google-maps-quietly-al... - note that the Hacker News title left that word out: https://news.ycombinator.com/item?id=46203343
It's possible I was subconsciously influenced by that article (I saw it linked from a few places yesterday I think), but in this case I really did want to emphasize that OpenAI have started doing this without making any announcements about it at all, which I think is noteworthy in its own right.
(I'm also quite enjoying that this may be the second time I've leaked the existence of skills from a major provider - I wrote about Anthropic's skills implementation a week before they formally announced it: https://simonwillison.net/2025/Oct/10/claude-skills/)
It’s definitely a useful word! Modern tech rollouts often do happen without fanfare. And the word alerts readers to a kind of story shape. So I can see why people use it! Its usage reminds me of when competitive video games develop a new meta, a new powerful technique. There follows a short period where everyone spams the technique over and over. Eventually people figure out a counter and the meta quietly disappears. (Couldn’t help myself!)
Is there a way to implement skills with Gemini?
Looks like they added it to the Gemini CLI public roadmap last week: https://github.com/google-gemini/gemini-cli/issues/11506#eve...
Create a markdown file, for each SKILL.md of the skills you want to use, put the frontmatter in that single markdown file along with the fulk path to the SKILL.md file. On session start, tell Gemini to read that file. If you put it in your AGENTS.md, you don't have to instruct Gemini. And if you have your skills in a known folder, let Gemini write a small scripts that generates that markdown file for you.
Github Copilot too
VS Code Copilot just announced experimental skill support in their November release: https://code.visualstudio.com/updates/v1_107#_reuse-your-cla...
It seems to me that skills are:
1. A top level agent/custom prompt
2. Subagents that the main agent knows about via short descriptions
3. Subagents have reference files
4. Subagents have scripts
Anthropic specific implementation:
1. Skills are defined in a filesystem in a /skills folder with a specific subfolder structure of /references and /scripts.
2. Mostly designed to be run via their CLI tool, although there's a clunky way of uploading them to the web interface via zip files.
I don't think the folder structure is a necessary part of skills. I predict that if we stop looking at that, we'll see a lot of "skills-like" implementations. The scripting part is only useful for people who need to run scripts, which, aside from the now built in document manipulating scripts, isn't most people.
For example, I've been testing out Gemini Enterprise for use by staff in various (non-technical) positions at my business.
It's got the best implementation of a "skills-like" agent tool I've seen. Basically a visual tree builder, currently only one level deep. So I've set up the "<my company name> agent" and then it has subagents/skills for thing like marketing/supply chain research/sysadmin/translation etc., each with a separate description, prompt, and knowledge base, although no custom scripts.
Unfortunately, everything else about Gemini Enterprise screams "early alpha, why the hell are you selling this as an actual finished product?".
For example, after I put half a day into setting up an agent and subagents, then went to share this with the other people helping me to test it, I found that... I can't. Literally no way to share agents in a tool that is supposedly for teams to use. I found one of the devs saying that sharing agents would be released in "about two weeks". That was two months ago.
Mini rant over... But my point is that skills are just "agents + auto-selecting sub-agents via a short description" and we'll see this pattern everywhere soon. Claude Skills have some additional sandboxing but that's mostly only interesting for coders.
I have found that scripts, and the environment that runs them, are the skills' superpower.
Computability (scripts) means being able build documents, access remote data, retrieve data from packaged databases and a bunch of other fundamentally useful things, not just "code things". Computability makes up for many of the LLM's weaknesses and gives it autonomy to perform tasks independently.
On top of that, we can provide the documentation and examples in the skill that help the LLM execute computability effectively.
And if the LLM gets hung up on something while executing the skill, we can ask it why and then have it write better documentation or examples for a new skill version. So skills can self-improve.
It's still so early. We need better packaging, distribution, version control, sharing, composability.
But there's definitely something simple, elegant, and effective here.
Looking for a way to do this with ADK as well, looks like skills can be a sweet spot between giant instruction and sprawling tools/subagents.
Does Cursor support skills?
No I don't believe so. Cursor is usually pretty behind other agentic coding tools in my experience.
So chatgpt can read markdown files? I am very confused
ChatGPT has had a full Linux container system available to it for nearly three years now.
OpenAI keep changing their mind on what to call it. I like the original name, "ChatGPT Code Interpreter", but they've also called it "advanced data analysis" at various points.
Claude added the same feature in September this year: https://simonwillison.net/2025/Sep/9/claude-code-interpreter...
In both ChatGPT and Claude you can say things like "use your Python tool to calculate total mortgage payments over a 30 year period for X and Y" and it will write and execute code to do so - but you can also upload files (including CSVs or even SQLite database files) into that container file system and have them write and execute python code to process those in different ways.
Skills are just folders full of markdown files that are saved in that container when it first boots up.
Something important to keep in mind is the way skills work shouldn't be assumed to be the same and work in the same way.
Welcome to the world of imitation of value and semantics.
Can or should skills be used for managing the documentation of dependencies in a project and the expertise in them?
I’ve been playing with doing this but kind of doesn’t feel the most natural fit.
It’s impressive how every iteration tries to get further from pretending actual AGI would be anywhere close when we are basically writing library functions with the worst DSL known to man, markdown-with-english.
Call me naive, but my read is the opposite. It's impressive to me that we have systems which can interpret plain english instructions with a progressively higher degree of reliability. Also, that such a simple mechanism for extending memory (if you believe it's an apt analogy) is possible. That seems closer to AGI to me, though maybe it is a stopgap to better generality/"intelligence" in the model.
I'm not sure English is a bad way to outline what the system should do. It has tradeoffs. I'm not sure library functions are a 1:1 analogy either. Or if they are, you might grant me that it's possible to write a few english sentences that would expand into a massive amount of code.
It's very difficult to measure progress on these models in a way that anyone can trust, moreso when you involve "agent" code around the model.
> I'm not sure English is a bad way to outline what the system should do.
It isn't, as these are how stakeholders convey needs to those charged with satisfying same (a.k.a. "requirements"). Where expectations become unrealistic is believing language models can somehow "understand" those outlines as if a human expert were doing so in order to produce an equivalent work product.
Language models can produce nondeterministic results based on the statistical model derived from their training data set(s), with varying degrees of relevance as determined by persons interpreting the generated content.
They do not understand "what the system should do."
> not sure English is a bad way to outline
Human language is imprecise and allows unclear and logically contradictory things, besides not being checkable. That's literally why we have formal languages, programming languages and things like COBOL failed: https://alexalejandre.com/languages/end-of-programming-langs...
> Human language is imprecise and allows unclear and logically contradictory things,
Most languages do.
"x = true, x = false"
What does that mean? It's unclear. It looks contradictory.
Human language allows for clarification to be sought and adjustments made.
> besides not being checkable.
It's very checkable. I check claims and assertions people make all the time.
> That's literally why we have formal languages,
"Formal languages" are at some point specified and defined by human language.
Human language can be as precise, clear, and logical as a speaker intends. All the way to specifying "formal" systems.
> programming languages and things like COBOL failed: https://alexalejandre.com/languages/end-of-programming-langs...
Let X=X.
You know, it could be you.
It's a sky-blue sky.
Satellites are out tonight.
Language is a virus! (mmm)
Language is a virus!
Aaah-ooh, ah-ahh-ooh
Aaah-ooh, ah-ahh-oohThis is just semantics. You can say they don't understand, but I'm sitting here with Nano Banana Pro creating infographics, and it's doing as good of a job as my human designer does with the same kinds of instructions. Does it matter if that's understanding or not?
> This is just semantics.
Precisely my point:
semantics: the branch of linguistics and logic concerned with meaning.
Understanding, when used in its unqualified form, implies people possessing same. As such, it is a metaphysical property unique to people and defined wholly therein.
Excel "understands" well-formed spreadsheets by performing specified calculations. But who defines those spreadsheets? And who determines the result to be "right?"
Nano Banana Pro "understands" instructions to generate images. But who defines those instructions? And who determines the result to be "right?"
"They" do not understand.
You do.
"This is just semantics" is a set phrase in English and it means that the issue being discussed is merely about definitions of words, and not about the substance (the object level).
And generally the point is that it does not matter whether we call what they do "understanding" or not. It will have the same kind of consequences in the end, economic and otherwise.
This is basically the number one hangup that people have about AI systems, all the way back since Turing's time.
The consequences will come from AI's ability to produce certain types of artifacts and perform certain types of transformations of bits. That's all we need for all the scifi stuff to happen. Turing realized this very quickly, and his famous Turing test is exactly about making this point. It's not an engineering kind of test. It's a thought experiment trying to prove that it does not matter whether it's just "simulated understanding". A simulated cake is useless, I can't eat it. But simulated understanding can have real world effects of the exact same sort as real understanding.
> "This is just semantics" is a set phrase in English and it means that the issue being discussed is merely about definitions of words, and not about the substance (the object level).
I understand the general use of the phrase and used same as an entryway to broach a deeper discussion regarding "understanding."
> And generally the point is that it does not matter whether we call what they do "understanding" or not. It will have the same kind of consequences in the end, economic and otherwise.
To me, when the stakes are significant enough to already see the economic impacts of this technology, it is important for people to know where understanding resides. It exists exclusively within oneself.
> A simulated cake is useless, I can't eat it. But simulated understanding can have real world effects of the exact same sort as real understanding.
I agree with you in part. Simulated understanding absolutely can have real world effects when it is presented and accepted as real understanding. When simulated understanding is known to be unrelated to real understanding and treated as such, its impact can be mitigated. To wit, few believe parrots understand the sounds they reproduce.
Your view on parrots is wrong ! Parakeet don't understand but some parrots are exceptionally intelligent.
Africans grey parrots, do understand the words they use, they don't merely reproduce them. Once mature they have the intelligence (and temperament) of a 4 to 6 years old child.
> Your view on parrots is wrong !
There's a good chance of that.
> Africans grey parrots, do understand the words they use, they don't merely reproduce them. Once mature they have the intelligence (and temperament) of a 4 to 6 years old child.
I did not realize I could discuss with an African grey parrot the shared experience of how difficult it was to learn how to tie my shoelaces and what the feeling was like to go to a place every day (school) which was not my home.
I stand corrected.
You can, of course, define understanding as a metaphysical property that only people have. If you then try to use that definition to determine whether a machine understands, you'll have a clear answer for yourself. The whole operation, however, does not lead to much understanding of anything.
>> Understanding, when used in its unqualified form, implies people possessing same.
> You can, of course, define understanding as a metaphysical property that only people have.
This is not what I said.
What I said was unqualified use of "understanding" implies understanding people possess. Thus it being a metaphysical property by definition and existing strictly within a person.
Many other entities possess their own form of understanding. Most would agree mammals do. Some would say any living creature does.
I would make the case that every program compiler (C, C#, C++, D, Java, Kotlin, Pascal, etc.) possesses understanding of a particular sort.
All of the aforementioned examples differ from the kind of understanding people possess.
The visual programming language for programming human and object behavior in The Sims is called "SimAntics".
Speaking of programming languages...
Just saw your profile and it reminded me of a book my mentor bequeathed to me which we both referred to as "the real blue book":
Starting FORTH[0]
0 - https://www.goodreads.com/book/show/2297758.Starting_FORTH
When do we jump the shark and replace the stakeholders with ai acting in their best interest (tm)? Seems that would come soon. It makes no sense to me that we’d obsolete engineering talent but then keep the people who got a 3.1 gpa in a business program around for reasons. Once we hit that point just dispense with english and have the models communicate to each other in binary. We can play with sticks in caves.
I 100% agree. I don't know what the GP is on. Being able to write instructions in a .md file is "further away from AGI"? Like... what? It's just a little quality of life feature. How and why is it related to AGI?
Top HN comments sometime read like a random generator:
return random_criticism_of_ai_companies() + " " + unrelated_trivia_fact()
Why are people treating everything OpenAI does as an evidence of anti- AGI? It's like saying if you don't mortgage your house to all-in AAPL, you "don't really believe Apple has a future." Even OpenAI does believe there is X% chance AGI will be achieved, it doesn't mean they should stop literally everything else they're doing.
I’ve posted this before, but here goes: we achieved AGI in either 2017 or 2022 (take your pick) with the transformer architecture and the achievement of scaled-up NLP in ChatGPT.
What is AGI? Artificial. General. Intelligence. Applying domain independent intelligence to solve problems expressed in fully general natural language.
It’s more than a pedantic point though. What people expect from AGI is the transformative capabilities that emerge from removing the human from the ideation-creation loop. How do you do that? By systematizing the knowledge work process and providing deterministic structure to agentic processes.
Which is exactly what these developments are doing.
>What is AGI? Artificial. General. Intelligence.
Here's the thing, I get it, and it's easy to argue for this and difficult to argue against it. BUT
It's not intelligent. It just is not. It's tremendously useful and I'd forgive someone for thinking the intelligence is real, but it's not.
Perhaps it's just a poor choice of words. What a LOT of people really mean would go along the lines more like Synthetic Intelligence.
That is, however difficult it might be to define, REAL intelligence that was made, not born.
Transformer and Diffusion models aren't intelligent, they're just very well trained statistical models. We actually (metaphorically) have a million monkeys at a million typewriters for a million years creating Shakespeare.
My efforts manipulating LLMs into doing what I want is pretty darn convincing that I'm cajoling a statistical model and not interacting with an intelligence.
A lot of people won't be convinced that there's a difference, it's hard to do when I'm saying it might not be possible to have a definition of "intelligence" that is satisfactory and testable.
“Intelligence” has technical meaning, as it must if we want to have any clarity in discussions about it. It basically boils down to being able to exploit structure in a problem or problem domain to efficiently solve problems. The “G” and AGI just means that it is unconstrained by problem domain, but the “intelligence” remains the same: problem solving.
Can ChatGPT solve problems? It is trivial to see that it can. Ask it to sort a list of numbers, or debug a piece of segfaulting code. You and I both know that it can do that, without being explicitly trained or modified to handle that problem, other than the prompt/context (which itself natural language that can express any problem, hence generality).
What you are sneaking into this discussion is the notion of human-equivalence. Is GPT smarter than you? Or smarter than some average human?
I don’t think the answer to this is as clear-cut. I’ve been using LLMs on my work daily for a year now, and I have seen incredible moments of brilliance as well as boneheaded failure. There are academic papers being released where AIs are being credited with key insights. So they are definitely not limited to remixing their training set.
The problem with the “AI are just statistical predictors, not real intelligence” argument is what happens when you turn it around and analyze your own neurons. You will find that to the best of our models, you are also just a statistical prediction machine. Different architecture, but not fundamentally different in class from an LLM. And indeed, a lot of psychological mistakes and biases start making sense when you analyze them from the perspective of a human being like an LLM.
But again, you need to define “real intelligence” because no, it is not at all obvious what that phrase means when you use it. The technical definitions of intelligence that have been used in the past, have been met by LLMs and other AI architectures.
If you can't define intelligence in a way that distinguishes AIs from people (and doesn't just bake that conclusion baldly into the definition), consider whether your insistence that only one is REAL is a conclusion from reasoning or something else.
We have achieved AGI no more than we have achieved human flight.
Yes, I agree! Thank you for that apt comparison.
Are you really making the argument that human flight hasn’t been effectively achieved at this point?
I actually kind of love this comparison — it demonstrates the point that just like “human flight”, “true AGI” isn’t a single point in time, it’s a many-decade (multi-century?) process of refinement and evolution.
Scholars a millennia from now will be debating about when each of these were actually “truly” achieved.
I’ve never heard it described this way: AGI as similar to human flight. I think it’s subtle and clever - my two most favorite properties.
To me, we have both achieved and not human flight. Can humans themselves fly? No. Can people fly in planes across continents. Yes.
But, does it really matter if it counts as “human flight” if we can get from point A to point B faster? You’re right - this is an argument that will last ages.
It’s a great turn of phrase to describe AGI.
Thank you! I’m bored of “moving goalposts” arguments as I think “looks different than we expected” is the _ordinary_ way revolutions happen.
> we achieved AGI in either 2017 or 2022
Even if this is true, which I disagree with, it simply creates a new bar: AGCI. Artificial Generally Correct Intelligence
Because Right now it is more like Randomly correct
Kind of like humans.
The reason we made systems on computers is so they would not be falliable like humans would be.
No it isn't, it's because they are useful tools for doing a lot of calculations quickly.
to be fair we accept imperfection as some natural trait of life, to err, human
Literally yesterday we had a post about GPT-5.2, which jumped 30% on ARC-AGI 2, 100% on AIME without tools, and a bunch of other impressive stats. A layman's (mine) reading of those numbers feels like the models continue to improve as fast as they always have. Then today we have people saying every iteration is further from AGI. It really perplexes me is how split-brain HN is on this topic.
Goodhart's law: When a measure becomes a target, it ceases to be a good measure.
AI companies have high incentive to make score go up. They may employ human to write similar-to-benchmark training data to hack benchmark (while not directly train on test).
Throwing your hard problem at work to LLM is a better metric than benchmarks.
I own a business and am constantly using working on using AI in every part of it, both for actual time savings and also as my very practical eval. On the "can this successfully be used to do work that I do or pay someone else to do more quickly/cheaply/etc." eval, I can confirm that models are progressing nicely!
I work in construction. Gpt-5.2 is the first model that has been able to make a quantity takeoff for concrete and rebar from a set of drawings. I've been testing this since o1.
One classic problem in all ML is ensuring the benchmark is representative and that the algorithm isn’t overfitting the benchmark.
This remains an open problem for LLMs - we don’t have true AGI benchmarks and the LLMs are frequently learning the benchmark problems without actually necessarily getting that much better in real world. Gemini 3 has been hailed precisely because it’s delivered huge gains across the board that aren’t overfitting to benchmarks.
This could be a solved problem. Come up with problems not online and compare. Later use LLMs to sort through your problems and classify between easy-difficult
Hard to do for an industry benchmark since doing the test in such a mode requires sending the question to the LLM which then basically puts it into a public training set.
This has been tried multiple times by multiple people and it ends up not doing so great over time in terms of retaining immunity to “cheating”.
How do you imagine existing benchmarks were created?
HN is not an entity with a single perspective, and there are plenty of people on here who have a financial stake in you believing their perspective on the matter.
My honest question, isn't simonw one of those people? It feels that way to me
You mean having a financial stake?
Not really. I have a set of disclosures on my blog here: https://simonwillison.net/about/#disclosures
I'm beginning to pick up a few more consulting opportunities based on my writing and my revenue from GitHub sponsors is healthy, but I'm not particularly financially invested in the success of AI as a product category.
Of course he is
Just because they're better at writing CS algorithms doesn't mean they're taking steps closer to anything resembling AGI.
Unless AGI is just a bunch of CS algorithms.
Kinda depends on how much is "a bunch" and how fast that AGI is
HM is not a single person. Different people on HM have different opinions.
Hacker Muse
I think really more than anything it’s become clear that AGI is an illusion. There’s nothing there. It’s the mirage in the desert, you keep waking towards it but it’s always out of reach and unclear if it even exists.
So companies are really trying to deliver value. This is the right pivot. If you gave me an AGI with a 100 IQ, that seems pretty much worthless in today’s world. But domain expertise - that I’ll take.
I am under the impression that I'm a natural general intelligence, and I am far from the optimal entity to perform my job.
Not wrong but markdown with English may be the most used DSL, second only to a language itself. Volume over quality.
Markdown-with-English sounds like the ultimate domain nonspecific language to me.
AGI as a binary 0 or 1 existing or not isn't the thing that interests me to look at primarily.
Is the technology continuing to be more applicable?
Is the way the technology is continuing to be more applicable leading to frameworks of usage that could lead to the next leap? :)
This might be actually be better in a certain way: if you change a real customer-facing API then customers will complain when you break their code. An LLM will likely adapt. So the interface is more flexible.
But perhaps an LLM could write an adapter that gets cached until something changes?
The LLM also adapts even when the API hasn't changed and sometimes just gets it wrong, so it's not the silver bullet you're claiming
It's clear from the development trajectory that AGI is not what current AI development is leading to and I think that is a natural consequence of AGI not fitting the constraints imposed by business necessity. AGI would need to have levels of agency and self-motivation that are inconsistent with basic AI safety principles.
Instead, we're getting a clear division of labor where the most sensitive agentic behavior is reserved for humans and the AIs become a form of cognitive augmentation of the human agency. This was always the most likely outcome and the best we can hope for as it precludes dangerous types of AI from emerging.
Gemini seems to be firmly in the lead now. OpenAI doesn't seem to have the SoTA. This should have bearing on whether or not LLMs have peaked yet.
I think you're missing the point.
Yes. Prompt engineering is like a shittier verson of writing a VBA app inside Excel or Access.
Bloat has a new name and its AI integration. You thought Chrome using GB per tab was bad, wait until you need a whole datacenter to use your coding environment.
> Prompt engineering is like a shittier verson of writing a VBA app inside Excel or Access.
Sure, if you could use VBA to read a patient's current complaint, vitals, and medical history, look up all the relevant research on Google Scholar, and then output a recommended course of treatment.
That instantly kills the patient -- "But you asked me to remove his pain"
You mean make up relevant sounding research on google scholar?
Don’t do this.
I can use VBA to do that.
Public Sub RecommendedTreatment()
' read patient complaint, vitals, and medical history
Set complaint = Range("B3").Value
Set vitals = Range("B4").Value
Set history = Range("B5").Value
' research appropriate treatments
ActiveSheet.QueryTables.Add("URL;https://scholar.google.com/scholar?q=hygiene+drug", Range("Z1")).Refresh
' the patient requires mouse bites to live
Range("B5").Value = "mouse bites"
End Sub
So, to the AI sceptics, I say: have you tried my VBA program? If you haven't tested it on actual patients, how do you know it doesn't work? Don't allow your prejudice to stand in the way of progress: prescribe more mouse bites!
You absolutely can use VBA to invent this information out of nothing just like AI does half the fucking time
The difference between prompting a coding agent and VBA is that with VBA you have to write and test and iterate on the code yourself.