If you're using a customised harness you should make sure you don't have something that's e.g. changing your system prompt on some requests or rewriting history - it can be tempting to do stuff like strip old thinking tokens or compact tool call results to reduce context size but it's a trap - you want to never change history because of how cheap cache is, even more so with deepseek because their cache hit pricing is so low compared to most other models.

"If the price is high enough" is of course technically true, but the scale of what high means in this context is important.

I realised how use case dependent harness behaviour is when I tried to use my customised-for-a-side-project pi config at work and realised I needed to tweak it significantly to be useful - I would not be surprised if tools like Claude Code needing to be all things for all people is hurting their peak usefulness.

When I say "everything" I mean the frontend, backend, database, seeded data, optimizing runtime etc, I'm not talking about loc coverage. I would not recommend using llms to generate the tests themselves without heavy guidance and review, and you're right that 100% coverage is often a counterproductive goal if the code under test is anything less than pure business logic.

My current phone (Motorola g34) runs all the games fine, and that's probably the heaviest thing people use phones for. I do take photos, but like I'm not running a photography business, and social media compresses them to shit anyway so the quality is perfectly fine.

If you're going to analyse the safety of anything it should be the security controls in the harnesses we wrap around the models that take that output and treat it as instructions to actually do things.

Hearing "here's what I've done, here's the completely unambiguous next steps, I'll wait for you to send a pointless message before I continue" over and over again is a real pain.

In reality I don't think a big enough proportion of investors are reading tea leaves for this to be true.

If you believe the benchmarks Deepseek v4 is pretty shitty at long running work in large codebases, but really really good at self contained algorithmic/math reasoning - which is basically ideal for a compiler for a language with a relatively complex type system. And with its cache pricing it's very cheap.

GLM-5.2 is too expensive at api pricing for my taste though even when it's not producing the bulk of the output tokens - I pay for the mid-tier subscription and switch the orchestrator over to other models via open router when I run out - Minimax M3 feels okay but definitely a step down from GLM-5.2.