rdli

I would also say: I think this works great because the goal is well-defined and measurable.

I’ve also used Opus 5.5 on some hill-climbing, and a lot more steering is required here, because … eval is hard.

I’m on the $100/month subscription; this session took about $500 in token-equivalent costs.

(Note that it wasn’t all Opus 5.5; I have a setup that uses Fable 5.1 as an advisor, Sonnet 5.5 for mechanical changes, etc.)

Yes. It spawned multiple subagents to run different experiments to benchmark a lot of different things, reviewed CI logs from past runs, etc. In the end, there were changes to what/how we cached, various code quality checks, speeding up test runners, and many other things.

It’s a really good model. Over the past few days, I give Opus some general directives to basically speed up our CI, and telling it I care both about billing minutes and wall clock time. I told it to create a plan after analyzing everything in our CI, run the plan by a Fable subagent, and then focus on low-risk, high-reward changes.

9 hours later, I had 12 PRs ready to be merged, and the net result is CI time has dropped from ~10 minutes to ~4 minutes, and billing minutes have dropped around 60%. Less than an hour of my attention.

One thing that wasn't obvious in the post: it's usage (token) based pricing, not including in your existing Claude subscriptions.

I get that but just not entirely obvious how you do that for the Notion AI.

Securing LLMs is just structurally different. The attack space is "the entirety of the human written language" which is effectively infinite. Wrapping your head around this is something we're only now starting to appreciate.

In general, treating LLM outputs (no matter where) as untrusted, and ensuring classic cybersecurity guardrails (sandboxing, data permissioning, logging) is the current SOTA on mitigation. It'll be interesting to see how approaches evolve as we figure out more.

Nothing super-fancy. We have a common GitHub repo in our org for skills, and everyone checks out the repo into their preferred setup locally.

(To clarify, I meant that some engineers mostly use CC while others mostly use Codex, as opposed to engineers using both at the same time.)

This is great. At my startup, we have a mix of Codex/CC users so having a common set of skills we can all use for building is exciting.

It’s also interesting to see how instead of a plan mode like CC, Codex is implementing planning as a skill.

Polar Sky | Founding AI Lead | Bay Area/Seattle | Hybrid/Onsite | Full-time

We're a well-funded, pre-seed cybersecurity startup focused on data security. I'm looking for a founding AI lead with experience in fine-tuning LLMs (expertise around RL + reasoning models a big plus). This person would own the full AI stack from data to training to eval to test-time compute.

Who's a good fit:

* If you've always thought about starting a company, but for whatever reason (funding, life, idea), this is a great opportunity to be part of the founding team. We're 2 people right now.

* You enjoy understanding customer problems and their use cases, and then figuring out the best solution (sometimes technical, sometimes not) to their problems.

* You want to help figure out what a company looks like in this AI era.

* You enjoy teaching and sharing knowledge.

Questions, interest, just email [email protected].