I think you mean "sandbox" like what is in the Codex TUI. Parent poster meant a VM or container (or bubblewrap/firejail) that doesn't let the agent to edit files outside of specific paths or run dangerous commands, so you can turn the whole confirmation off.
CloudFlare AI Gateway was quite convenient for me - I wanted to give my zeroclaw instance a limited budget to services like Image generation/Replicate/Fal.ai, which would mean for each service I'd have to run my own proxy that stores the keys and cuts off the real calls if we go over the limits. Easy to do but still extra thing to build and maintain.
Instead I put my API keys to cloudflare, set limits, and gave the agent the CloudFlare token, and in minutes it could contact tens of services.
edit: not to mention instead of loading balance to each service I could just keep balance on cloudflare that covers them all
I know that's the way omp is often introduced but I don't think it's fair for either omp or pi - after lately switching my usage more towards omp I'd say by this point they're almost more different than Pi and Codex. And I like them both.
Interesting idea, but at the same time it is just so cheap that you can just run it with API pricing. I sometimes do even if I have available usage that I'm going to cap so I'll save it for bigger models
Cache reads priced the same as Opus 5.5? So there won't be that much price difference in agentic coding. Or is that a mistake in the table, that seems quite weird
I think gpt 5.6 family also dropped pricing but didn't give any more usage for the subscriptions. Maybe it's a way to silently lower the value given to subscriptions while keeping API pricing competitive
1M cached tokens on deepseek is $0.006, the big labs can't sell anywhere close to this, they have funders expecting returns and huge overhead.
btw I've had a ton of fun with the new deepseek today, I was waiting for my OpenAI 5h limit reset and decided to give it some problems for fun, got pretty great results. Tried some harder problems and still got great results. I don't expect it to be Sol class or anything but I really didn't expect it to be anywhere near this good so we'll see where it ends up. And it's really fun throwing crazy amount of tokens at the wall for ~free instead of watching the subscription limits tick closer while your agents churn away.
> 1M cached tokens on deepseek is $0.006, the big labs can't sell anywhere close to this, they have funders expecting returns and huge overhead.
Losing most of your customers tends to sharpen the mind a bit. They could eg stop pushing out the absolute frontier for a while and focus on making what they have run cheaper. Or they go and do more lobbying against China. Or a million other little things that take more than 30 seconds to come up with when writing a HN comment, but less than a week for someone who's smart and paid to do this for a living.
I did a lot of that kind of work with the older version of DeepSeek before they upped the prices.
For example, it was quite good to get a decent Sashiko review. Sashiko is a Linux kernel review agent with interchangeable LLM driver. It's very good, but it eats tokens like crazy.
This clearly is Codex-specific, not as much a feature of the model (though obviously they probably have trained it to be great at working with their own tools). Sounds somewhat similar to pi-observational-memory I'm using with Pi.
