I'm in that camp -- I have the max-tier subscription to pretty much all the services, and for now Codex seems to win. Primarily because 1) long horizon development tasks are much more reliable with codex, and 2) OpenAI is far more generous with the token limits.
Gemini seems to be the worst of the three, and some open-weight models are not too bad (like Kimi k2.5). Cursor is still pretty good, and copilot just really really sucks.
I've done this kind of thing many times with codex and sqlite, and it works very well. It's one prompt that looks something like this:
- inspect and understand the downloaded data in directory /path/..., then come up with an sqlite data model for doing detailed analytics and ingest everything into an sqlite db in data.sqlite, and document the model in model.md.
Then you can query the database adhoc pretty easily with codex prompts (and also generate PDF graphs as needed.)
I typically use the highest reasoning level for the initial prompt, and as I get deeper into the data, continuously improve on the model, indexes, etc., and just have codex handle any data migration.
Expiries are a defence-in-depth that exist primarily for crypt hygiene, for example to protect from compromised keys. If the private key material is well protected, the risk is very low.
However, an org (particularaly a .mil) not renewing its TLS certs screams of extreme incompetence (which is exactly what expiries are meant to protect you from.)
> Also MCP is very obviously dead, as any of us doing heavy agentic coding know.
As someone that does heavy agentic coding (using basically all the tools), this is so far from the truth. People claiming this have probably never worked in large enterprise environments where things like authentication, RBAC, rate limiting, abuse detection, centralized management/updates/ops, etc. are a huge part of the development and deployment workflow.
In these situations you can't just use skills and cli tools without a gigantic amount of retooling and increased operational and security complexity. MCP is really useful here, and allows centralized eng and ops teams to manage their services in a way that aligns with the organizations overall posture, policies, and infrastructure.
> Google is so far behind agentic cli coding. Gemini CLI is awful.
This part I totally agree. It's really hard to express how bad it is (and it's really disappointing.)
> At some point you need to treat people as adults, which includes letting them make very bad decisions if they insist on doing so.
The world does not consist of all rational actors, and this opens the door to all kinds of exploitation. The attacks today are very sophisticated, and I don't trust my 80-yr old dad to be able to detect them, nor many of my non-tech-savvy friends.
> any more than it would be acceptable for a bank to tell an alcoholic "we aren't going to let you withdraw your money because we know you're just spending it at the liquor store".
What's bullshit about it? This is how TRNGs in security enclaves work. They collect entropy from the environment, and use that to continuously reseed a PRNG, which generates bits.
If you're talking "true" in the philosophical sense, that doesn't exist -- the whole concept of randomness relies on an oracle.
It's not a waste of time, it's a responsibility. All things need steering, even humans -- there's only so much precision that can be extrapolated from prompts, and as the tasks get bigger, small deviations can turn into very large mistakes.
There's a balance to strike between micro-management and no steering at all.
I've used both gVisor and microvms for this (at very large scales), and there are various tradeoffs between the two.
The huge gVisor drawback is that it __drastically_ slows down applications (despite startup time being faster.)
For agents, the startup time latency is less of an issue than the runtime cost, so microvms perform a lot better. If you're doing this in kube, then there's a bunch of other challenges to deal with if you want standard k8s features, but if you're just looking for isolated sandboxes for agents, microvms work really well.
It's waaay better than GLM 4.7 (which was the open model I was using earlier)! Kimi was able to quickly and smoothly finish some very complex tasks that GLM completely choked at.
It seems to work with OpenCode, but I can't tell exactly what's going on -- I was super impressed when OpenCode presented me with a UI to switch the view between different sub-agents. I don't know if OpenCode is aware of the capability, or the model is really good at telling the harness how to spawn sub-agents or execute parallel tool calls.
That tends to work quite poorly because Claude Code does not use standard completions APIs. I tried it with Kimi, using litellm[proxy], and it failed in too many places.
Running it via https://platform.moonshot.ai -- using OpenCode. They have super cheap monthly plans at kimi.com too, but I'm not using it because I already have codex and claude monthly plans.
I've been using this model (as a coding agent) for the past few days, and it's the first time I've felt that an open source model really competes with the big labs. So far it's been able to handle most things I've thrown at it. I'm almost hesitant to say that this is as good as Opus.
"Economy" doesn't necessarily mean "monetization" -- there are lots of parallel and competing economies that exist, and that we actively engage in (reputation, energy, time, goodwill, etc.)
Money turns out to be the most fungible of these, since it can be (more or less) traded for the others.
Right now, there are a bunch of economies being bootstrapped, and the bots will eventually figure out that they need some kind of fungibility. And it's quite possible that they'll find cryptocurrencies as the path of least resistance.
Gemini seems to be the worst of the three, and some open-weight models are not too bad (like Kimi k2.5). Cursor is still pretty good, and copilot just really really sucks.