my stack in a sentence: refine the docs/prompts/skills often, that's your biggest job, use both frontier labs models reviewing each other, don't solve individual problems only the systemic ones (set standards strategically, don't define tactics)
If I had that many tokens/dollars I would be running canaries and adversarial verification in prod based on e.g. traffic replay, live fuzzing, all kinds of things to build confidence without direct human line-by-line review. If I had $100k to spend next month I could probably get through it, I'm running $2500+-api-equivalent a week at this point and I feel very token limited. Will be time for a 2nd or 3rd subscription soon for both labs I think.
Fable was a revolution, still learning how best to use it, 5.1 felt like a notable upgrade. At this point I launch a workflow with 10-20 minutes of interactive setup (and even that I feel might be too much), it runs for hours, and the PR is trivially mergeable (I still review every line, but 95% are just merge, maybe 4% are feedback needed, 1% are thrown away and regenerated, which implies I'm being insufficiently ambitious)
Thanks!
> If I had that many tokens/dollars I would be running canaries and adversarial verification in prod based on e.g. traffic replay, live fuzzing, all kinds of things to build confidence without direct human line-by-line review.
This part jumped out at me. There's something to watch out for here.
I recently had a funny experience. I delegated a major feature to an agent.
It turned out that it had implemented it precisely backwards, in a way which was pointless and which made things worse.
But it had written countless tests for the feature and all the tests were green.
I realised in that moment that even formal verification would not have helped, because it would simply have written a mathematical proof of the correctness of the incorrect feature...
Yes, you need some kind of other source of truth. I think the best way to get that is to do clean room development with a different agent, but ultimately if you give them the wrong idea they'll do the wrong thing.
The other thing I do, not as much as I should, but it's very powerful, is to generate spikes and deliberately throw them away to understand how to prompt better. Like I generated a swift version of the react native app I'm working on, and Alloy provers for the state transitions. None of it is production quality but getting great results that way is useful to scope future work.
If you've managed people, these are all familiar problems. I found you need much more than a functional specification, you also need motivation, background, related work, ideas tried, etc., because those help disambiguate the right path in the inevitable situation where your original task description is unclear or conflicts with itself.
Yes I'm using the entire consultancy stack - define values, etc, and work your way down the "where do these not match reality on the ground and need change", but for little robot people instead of (arguably less messy) humans.
> I think the best way to get that is to do clean room development with a different agent
What is this about? Could you give an example?