I notice in myself a preemptive feeling of ‘if the project doesn’t launch, whatever’ before the start.
Like during development I look like Schwarzenegger at the gym, but during the presentation I’m a shy girl going ‘you probably won’t like it, but…’
This is bad.
@openai please add a checkbox to NOT include general user memory / RAG retrieval of past conversations into the current chat
this would make the chat experience 100% more convenient, because ChatGPT’s judgment is sometimes polluted by context I never asked for
@marclou Hey Marc. Can organic growth of $66 ARR over six months be considered validation for a lightweight project (a Chrome extension)? Is there any point in continuing it with such a small ARR?
i met a very attractive korean girl for dinner tonight
last time i saw her was 6 months ago
when we sat down she immediately said "your got more muscles" and "your skin looks much better" etc
only been looksmaxxing for a month
you can do it too anon
@airkatakana Beauty is in the eye of the beholder. Left: astigmatism; right: healthy eyes. So it’s not that I’m fat—it’s that your eyes are bad
(le chaton fat)
anthropic ignoring "haiku" models this hard is beyond ridiculous
haiku is a subagent powerhouse and the most important model for cost efficiency in subagents
it's criminal that anthropic hasn't updated haiku since 4.5, while openai has gpt-5.4 nano, which is cheaper and better
If AGI is achievable & labs can be banned from using a model internally ONLY if they release the model publicly, the Big Three labs may decide it is better to capture all the value from AGI themselves by expansion & acquisition. Sharing AI access with other firms triggers risk.
was this a surprise to anyone?
anthropic has said 100 times that tools matter just as much as raw intelligence.
did you really expect a tech company full of TOP engineers NOT to build software dev tooling for their own AI?
it’s just so dumb that anyone finds this “wow” worthy
Researchers show that Claude Code is 98% not AI.
Anthropic never gave us the architecture for Claude Code. There were no docs. Just a tool that every developer is currently obsessing over.
Until it leaked recently.
A research team pulled the source code, analyzed all 500,000 lines, and found something ridiculous.
Only 1.6% of the codebase actually interacts with the AI model.
The core of Claude Code is literally just a simple while-loop. It asks the model what to do, runs a tool, and repeats.
So what is the other 98.4%?
It is hardcore, traditional software engineering.
The researchers found a massive, complex infrastructure designed entirely to babysit the AI and keep it from hallucinating or destroying your computer:
- A 7-mode permission system acting as a security bouncer.
- A 5-layer context compaction pipeline so the AI doesn't forget its goal.
- A subagent delegation mechanism with strict worktree isolation.
- Four different extensibility hooks to manage external tools safely.
Every startup right now is trying to build a better AI model to get better results.
Anthropic did the exact opposite.
They took an existing model and built a fortress of deterministic software around it.
They realized that the AI doesn't need to be smarter. It needs to be managed.
good posture is the cheapest way to boost your brain performance by 5-10% through improved blood flow. as you know from LLMs, a 5% difference in intelligence can feel like 500%
lol, you're not using Haiku right. You just need to give it a set of smoke and e2e tests, the ability to call gpt xhigh, write a detailed prompt, and list the errors it made. Basically, you can do all of this yourself and just give him a script that will insert.
The real trick with Opus 4.8 is not to ask it to “do the task.”
That is exactly why some people mistakenly think Codex 5.5 is smarter.
For simple execution — “do this,” “build that,” “fix this” — Codex is often faster, cleaner, and more direct.
But Opus has another gear entirely.
Don’t just give it a task. Give it a quality system.
Example:
“Complete task X, but do not deliver the final result until you verify it through three different review methods, and until an Opus-based reviewer confirms it meets these project standards: A, B, and C.”
This is where Opus separates itself.
The same methodology does not work nearly as well with Codex 5.5.
Codex is strong as an executor, but its agent coordination is weak. When you try to make it work like a multi-agent review system, the final output still feels like it came from one mind, one lane, one narrow execution path.
Opus is different.
Inside Claude, when prompted this way, it can actually feel like a council of experts: builder, critic, strategist, reviewer, and quality gatekeeper all pushing the work from different angles.
That is why the result often has better judgment, less hallucination, more depth, and a level of polish Codex 5.5 rarely reaches through raw execution alone.
One warning: don’t use this casually unless you’re on the $200 Max plan.
This method is powerful, but it burns usage fast.