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Principles of programming:

1. Break things down into small units

2. Think about sequence

3. Find patterns

4. Focus on the important things

5. Visualize sequences in your mind

Love the silly music and the way they teach, thanks for sharing this!


Also relevant to this is the newest episode of The Lightcone Podcast with Quan Vuong, co-founder of PI and, one of many co-authors of that paper.


Thanks for sharing this here, it's a beautiful video. The images are linked in the description but if anybody is reading this, check: https://www.flickr.com/photos/nasa2explore/


"... the first early version of Claude Mythos Preview was made available for internal use on February 24. In our testing, Claude Mythos Preview demonstrated a striking leap in cyber capabilities relative to prior models, including the ability to autonomously discover and exploit zero-day vulnerabilities in major operating systems and web browsers."

More infos here: https://red.anthropic.com/2026/mythos-preview/


As a big Codex user, with many smaller requests, this one is the highlight: "In Codex, GPT‑5.4 mini is available across the Codex app, CLI, IDE extension and web. It uses only 30% of the GPT‑5.4 quota, letting developers quickly handle simpler coding tasks in Codex for about one-third the cost." + Subagents support will be huge.


Having to invoke `/model` according to my perceived complexity of the request is a bit of a deal breaker though.


you use profiles for that [0], or in the case of a more capable tool (like opencode) they're more confusing referred to as 'agents'[1] , which may or may not coordinate subagents..

So, in opencode you'd make a "PR Meister" and "King of Git Commits" agent that was forced to use 5.4mini or whatever, and whenever it fell down to using that agent it'd do so through the preferred model.

For example, I use the spark models to orchestrate abunch of sub-agents that may or may not use larger models, thus I get sub-agents and concurrency spun up very fast in places where domain depth matter less.

[0]: https://developers.openai.com/codex/config-advanced#profiles [1]: https://opencode.ai/docs/agents/


Not sure why you think Anthropic has not the same problems? Their version numbers across different model lines jump around too... for Opus we have 4.6, 4.5, 4.1 then we have Sonnet at 4.6, 4.5, and 4.1? No version 4.1 here, and there is Haiku, no 4.6, but 4.5 and no 4.1, no 4 but then we only have old 3.5...

Also their pricing based on 5m/1h cache hits, cash read hits, additional charges for US inference (but only for Opus 4.6 I guess) and optional features such as more context and faster speed for some random multiplier is also complex and actually quiet similar to OpenAI's pricing scheme.

To me it looks like everybody has similar problems and solutions for the same kinds of problems and they just try their best to offer different products and services to their customers.


With Anthropic you always have 3 models to choose from: Opus-latest, Sonnet-latest, and Haiku-latest, from the best/slowest to the worst/fastest.

The version numbers are mostly irrelevant as afaik price per token doesn't change between versions.


Three random names isn't ideal. I'm often need to double check which is which. This is why we use numbers


They aren't random. Opus's are very long poems, haikus are very short ones (3 lines), sonnets are in between (~14 lines)


What's next? Claude Iliad?


How are the names random?

https://en.wikipedia.org/wiki/Masterpiece

https://en.wikipedia.org/wiki/Sonnet

https://en.wikipedia.org/wiki/Haiku

They dropped the magnum from opus but you could still easily deduce the order of the models just from their names if you know the words.


It's much more consistent. Only 3 lines, numbered 4.6, 4.6, and 4.5, and it's clear they're tiers and not alternate product lines. It wasn't until recently that GPT seems to have any kind of naming convention at all and it's not intuitive if every version number is a whole different class of tool.

The pricing is more complex but also easy, Opus > Sonnet > Haiku no matter how you tweak those variables.


Perhaps useful, I discovered: https://github.com/agent-infra/sandbox

> All-in-One Sandbox for AI Agents that combines Browser, Shell, File, MCP and VSCode Server in a single Docker container.


Some more info here: https://developers.openai.com/api/docs/models/gpt-realtime-1...

- $4 input, $0.4 cached input, $16 output

- 32,000 context window

- 4,096 max output tokens

- Sep 30, 2024 knowledge cutoff

Love the models, speed, and capabilities. Just sad that they are not getting the publicity and adoption right now, but hopefully in the future.


Sound on!

Song name is: Windowdipper from ꪖꪶꪶ ꪮꪀ ꪗꪖꪶꪶ by Jib Kidder

https://jibkidder.bandcamp.com/track/windowdipper


what's the symbols for that `ꪖꪶꪶ ꪮꪀ ꪗꪖꪶꪶ ` font? where can I find these



thank you!


also, seems like they have another project https://feel.thatsh.it/ and I'd love to find that song as well if you can help haha


That song is also pretty nice. I wonder if that was an earlier project?



Do opus 4.6 or gemini deep think really use test time adaptation ? How does it work in practice?


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