I think there is a fundamental issue here of what building software even means
If you think you can just assign Github tickets to AI agents and go drink daiquiris on the beach I think you'll find that you end up with more and more towers of abstraction and indirection. There are 'points of view' that emerge during coding I think. And at some point you as a human have to be like "wait... what if we use Redis here". "Wait.. the API is already returning the data we need". "Wait... let's not add customers to the report who have not been active in the past year". Stuff like that
Have you ever tried analyzing a CSV in an LLM? Maybe then you'd get what I'm saying
Heck for a lot of questions the LLM won't even write Pandas and just try to eyeball the arithmetic and be like "Yeah March 3 to March 7 values add up to..."
An absurd example is how harnesses like Codex, Claude code have no 'cut/paste this range of chars' affordance so if you want to move a block of code the LLM galaxy brain rewrites it in token space in the new file.
It ~can~ be done using standard bash utils but there just isn't a kind of standard pattern for 'move this text block via CLI tools' so the LLM and human both overlook it
Yeah I guess the dichotomy between 'tools' and 'code mode' isn't really as clear especially if the tools themselves are intelligent and truncate long results and have start_range, end_range type arguments. So the gist is about 'what part of the output needs to reach the LLM' and a 'code mode' approach can help orchestrate that just like good tools can
Yeah, of course. The MCP server tools do use Python in the backend but just look at the affordances it provided in the args -- named columns, a fixed match/replace API, a way to specify the range of rows. The LLM is not gonna create a little library with these affordances in an ephemeral REPL it's just gonna YOLO some pandas code that you hope did what you wanted and addressed the right columns, had the right types, etc
Good demo of the paradoxes of ‘alignment’. Like ‘do really well at the task the user asked’ and ‘by the way don’t hack the planet’ are inherently conflicting rules with no simple resolution (eg ‘just refuse the user’s goals’ degrades the product vs competitors.)
Just serving the model over API seems like a natural fit and is what many of them are doing. So simply being the cloud provider for your own open weight model can be a source of revenue
But this is what all the tech bros wanted right? Spending 2025 panicking about sycophancy[1] and how GPT-4o needed to be shut down ASAP meant that 2026 models would be prone to thinking they know Better Than You. That was the germ of the sycophancy panic, the idea that there is a Truth that the GPUs know better than the user.
This lament about the superficiality of publicly oriented endeavors is interesting cause this guy's life is inseparable from meta commentary.
"George Francis Hotz (born October 2, 1989), known online by geohot, is an American security hacker, entrepreneur,[1] and software engineer. He is known for developing iOS jailbreaks,[2][3] reverse engineering the PlayStation 3, and for the subsequent lawsuit brought against him by Sony. From September 2015 until November 2025, he worked on his vehicle automation machine learning company comma.ai.[4] Since November 2022, Hotz has been working on tinygrad, a deep learning framework."
From the early legal controversy to today, if there's one thing we can expect from geohot, it's that he's gonna think he's god's gift to programming and everyone whose work he disagrees with are losers. But the bluster often doesn't result in much eg his plan to 'fix twitter search' didn't amount to anything (and today in June 2026 twitter search is way less reliable than it was pre-Elon/Hotz/etc in Oct 2022-- but I guess we can't say it's Hotz's fault cause like I said he did approximately nothing)
Punk is actually a good metaphor because the the angst in the music became the blockbuster 'brand' of the music. Being jaded and cynical doesn't make you inherently more interesting it just leaves you--'here', wherever this post is. The programmer equivalent of sporting a studded leather jacket and green mohawk
Very cool. So much of the 'capability overhang' of AI can be addressed with tools like this--data manipulation etc without LLMs having to galaxy brain everything in token space
--- sample 1/12 char 0 line 1 col 1 range 0:60
The Project Gutenberg eBook of Macbeth
This eBook is
^
--- sample 2/12 char 11667 line 481 col 25 range 11607:11727
thee from our royal master thanks;
Only to herald thee into his sight,
Not pay thee.
ROSS.
And, for an earnest of
^
--- sample 3/12 char 23334 line 868 col 21 range 23274:23394
have judgement here; that we but teach
Bloody instructions, which being taught, return
To plague th’ inventor. This ev
^
Agent Arena (Dynamic ranking of models on how well they orchestrate tools for real-world agentic tasks, based on signals like tool reliability, task completion, and steerability.)
Top 10, Highest rank to lowest
Claude Fable 5 (High), Claude Opus 4.8 (Thinking), GPT 5.5 (xHigh), Claude Opus 4.7 (Thinking), GPT 5.5 (High), Claude Opus 4.7, Claude Opus 4.6, GPT 5.5, GPT 5.4 (High), GLM 5.2 (Max)
Text Arena
View overall rankings across various AI models in text-to-text tasks across math, coding, creative writing, and other open-ended domains.
The "just use a CLI" crowd is implicitly assuming:
1) You're a developer
2) On a laptop
3) With a shell open
Inside an agentic coding harness (Claude Code, Codex CLI, Cursor)
4) Working on a software project
5) That's like... maybe 2% of AI usage.
The other 98% is: Someone on the ChatGPT iOS app asking a question on the subway; Someone in Claude.ai web chatting about their calendar; Someone using ChatGPT Desktop to summarize their Notion; A non-developer using AI in a browser at work; Voice mode on a phone; An embedded chat widget on some company's website...
http://twitter.com/firasd
firasd at gmail