IMO a much better test would be designs that aren't AI to begin with. Much more useful to see how well a model can html an image design without slopping it up
It's not about figuring out if it's LLM written though. The style is hard to read and annoying. With the kind of sentences GP was talking about it's actually harder to get the substance.
Curious whether you were just bare asking it questions, or whether you provided it with lessons one by one with instruction that the lesson is the baseline truth etc
This has been the case since the early days. Aider had a bunch of code to be very forgiving with formatting of tool calls (file editing in particular at first). It's just the nature of the beast. It surprises me that Pi doesn't have a lot of this kind of stuff built in too
Maybe I'm too optimistic, but given appropriate skills and references (not just for writing but also reviewing) and intelligent use of subagents for isolated reviews and checks, you can lengthen the leash a bit.
But you still need to properly review plans and PRs to keep a good mental model of the codebase. This effectively limits the number of tasks being done in parallel to maybe 2-3. Though you'll be mentally exhausted and probably start to make mistakes or take shortcuts in reviews yourself.
We selected PRs (real ones we merged over the 6 months prior) and have an "LLM as judge" score how close the AI generated code is to the PR. Same as how other benchmarks do it, but it's with tasks we actually do and code we have decided is actually up to scratch for us
Similar result on our kotlin coding benchmark at work. It measures how close agents can get to a small mergable PR (according to my team). 20 tasks of varying difficulty, with 5 attempts each, LLM as judge to evaluate accuracy (same outcome and quality but allowing for acceptable variances).
Fable 5 sits ahead of Opus 4.7, but behind Opus 4.6, Sonnet 4.6, Opus 4.8, GPT-5.4, GPT-5.5.
Fable isn't a good coding workhorse. That doesn't mean it's not good for actually complex problems and long horizon tasks (big POCs, complex research and such). But I only have vibes and Anthropics own benchmarks and marketing to guide me there.
> The dynamic of agent codes human reviews does seem like the only sane one for the foreseeable future. Even Anthropic themselves still fall back to this.
Do they? I saw some crazy stat from the guy who built claude code that he was pushing hundreds of PRs a day. There's no way you can human review that much code. It's probably closer to heavily AI assisted review and planning.
This is a branching point. One dev would find someone else and convince them to approve it. Another would redo the task (code is cheap now, right?) in a PR stack that can actually be reviewed, cleaned up etc.
That's an example of why it would be useful for someone to actually do it. A random commenter on HN is one thing. A direct comparison on a brand new app that isn't part of any training is another
It's very addictive when you're working on something cool and the agents are iterating nicely. Instead of browsing reddit / HN / instagram etc during downtime, I find it much more fun to build something.
> When we first started experimenting with AI code review, we took the path that most other people probably take: we tried out a few different AI code review tools and found that a lot of these tools worked pretty well, and a lot of them even offered a good amount of customisation and configurability! Unfortunately, though, the one recurring theme that kept coming up was that they just didn’t offer enough flexibility and customisation for an organisation the size of Cloudflare.
Most people I know had the experience that signal to noise was way off, regardless of scale. So it was a burden rather than a help. Code review by AI ended up being a skill before creating the PR so the dev owning the PR addressed everything before the team got bogged down with it in review
It's the later. You can view it and see fine grained progress, but you can't interact with it. I hope that's coming next, because it would be useful to steer later phases or even agents
I tried this out yesterday - lucky enough to have access through EAP at work. The workflows that are generated are quite good - smart parallelisation and phasing. End results for larger chunks of work are also much better, which I attribute to more of the work having clean context windows (Opus 4.7 is unusable past 200k conversation length, and each subagent ends up using less than that IME). They also seem to have a validation phase hint in the workflow generator which also helps a lot. Speed is a bonus.
You can achieve a similar result manually prompting to use subagents, yes. But the TUI for in flight dynamic workflows is really nice - great visibility into exactly what's happening.
Honesty, for anything larger than a 1 shot PR, it's worth firing off a workflow for better automatic context management alone (more work done in the first 20% sweet spot)