>> just aren't good for end to end agentic workflows.

This is (apparently) the conceit of SteveYegge / GasTown - no model can cope unassisted so chunk it up, run it and if it falls over remember the exact place and restart, merging it all in

But that’s not my point.

I believe that software is a new form of literacy and just as all Companies and societies are literate now, in the future (tm) companies will run exclusively on software - AI developed software and those who go all out will have the sort of advantages the Catholic Church had over .. guilds?

Anyhow, that’s me being AI optimist. But writing the code is going to be a small part of that transition - almost everything to do with LLMs that is claimed amazing (Computer vision is something else) - almost everything people say we need an LLM is stuff you could have done three years ago but your internal politics just would not let you. Oh look we can see if our policies are being met (you could have written the policies in code and solved the whole problem)

Im struggling to get it out but - almost everything AI is proposed for is stuff a well run engineering firm coukd have taken on. A software literate firm could have done without AI is where firms are hoping AI will Get them

Imagine how far ahead real software literate firms will be - as long as they don’t burn their runway in tokens

Which is why, the right play imo is still buy in-house as much as possible, engineer around the problems and explore the phase space at marginal Cost.

Then and only then think frontier models.

Have you been using those models? I've been using a hand-rolled orchestrator with Mimo v2.5 (I seem to be paying $0.017 per million/tokens after their heavy caching) and it's been very impressive. I started with it in Opencode as a harness, then had it build its own micro-harness with stdlib-only Python, then used that to build a local stdlib-only Orchestrator with CLI and web harness, and now I'm using that for improving itself and now multi-project wider-ranging software. I talk to a steward who investigates and plans, then the plans are handed off to parallel worker agents who go through a work, test, interrogate, review, eval state machine for quality (all autonomously) with me at the end just reviewing the work or getting notified if the work items aren't progressing due to the workers getting stuck. So far the only "getting stuck" has been bugs/configs on my part, all at a pretty great quality bar, and at a price that makes me laugh at things like Opus.

I'm still using Claude at work (they're the only approved provider), but wow are the smaller models starting to SMOKE the big ones. At this point, all I'd consider paying out of my own pocket for is the lowest-limit Anthropic/GPT plan to get a big model as the Steward, but I wouldn't pay for ANY of the Anthropic models as the workers who do all the work. And as time passes, I don't know if I'd even do that; the open models are serving SO well.

>end to end agentic workflows.

There are probably 100 competing versions what this phrase might encapsulate. Could you elaborate more on which version you are using exactly?

My experience is that frontier models are only marginally better and not close to the cost/value of the open models which are anywhere from 10-100x cheaper. Perhaps I'm not doing "end to end agentic workflows?"

> People don't like to hear this but the open models just aren't good.

Stuff like the latest DeepSeek, Kimchi and GLM are used and loved by many people. It's not using an open model that is difficult: it's having the hardware allowing to do so. It's pricey and require technical skills.

That's why most people who are using (excellent btw) open-weight models are just renting compute online.