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Discussion (49 Comments)Read Original on HackerNews
> On the flip side, this may imply that as the models get better, they’ll become harder to control.
Love this. "The models are getting better, which means they're going to perform worse on the task".
They will keep poking at the problem, drive it to directions you did not intend to and ultimately they will be worse at the task.
It's genius like that that sets human apart from machine!
Sometimes these dumb processes are there for a reason and you just have to follow them, no questions asked.
Example: military. They literally get rid of anyone who will question the processes. They might be right to question them, but it does not matter.
Sounds like a very reasonable thing to do unless the author explicitly asked it to not search the web.
- lets you configure different models for planner, coder, and reviewer roles. (e.g., using Claude as an adversarial reviewer against Codex)
- breaks your plan up into reasonable-sized chunks of work with clearly defined success criteria
- runs each chunk of work through a coder / read-only reviewer loop. Once both agents are satisfied, neal moves on to the next chunk. Once everything is complete there is a final pass through the coder / reviewer loop to ensure the implementation satisfies the entire plan.
- resets the coder's context with each chunk of work to prevent context drift, leaving the reviewer's context long-running.
It also told me that in a spec it generated that I wasn't allowed to allow it to ignore a requirement and proceed to the next task. When I finally got it to obey it passive aggressively decided that stories needed more than just a "open|blocked|closed" status but also an "exempted by product owner" status to indicate that it doesn't believe that the task is done but I've told it that it was.
I have to repeatedly tell it that I am the product owner and that I don't care what one of it's subagents told it, I make the decisions. This behavior seems to get worse the higher the reasoning level
A .github/CODEOWNERS file seems to help when it’s going down that path, but I don’t like to indulge it..
Also, a skill like grill-me from Matt P. https://github.com/mattpocock/skills.
One thing I discovered was that the worker agent, having access to all the skills, would sometimes expand scope unnecessarily.
This led to the agent making the solution "better" than the initial request, which is what I want most of the time in my actual development (e.g. /tmp/frame-N.bmp instead of a single /tmp/frame.bmp).
I ended up testing a flow where the supervisor chooses the skill(s), and only injects the subset into the worker. Not sure I love it, but it made the worker execution cleaner.
For the verifier (not documented in the blog post), I used a fresh-worker context that would attempt to adversarially poke holes in the solution. This worked pretty well, but required increasing the timeout by 2-3x (thus invalidating the benchmark).
[0]https://github.com/jumploops/chum
Once the specs are being completed and splitted into beads, I span multiple agents (ultreworkers) and as part of a contributing guidelines I specify to use gitflow + git worktrees, then pr.
Could this be fixed with better harness restrictions/tool sandboxing?
In my early testing with 5.5, I didn't see this behavior, so I didn't lock down the sandbox.
For the vanilla Codex runs, I just used the benchmark's built-in Codex package, so it's not clear to me if the published benchmarks have access to the internet or not.
If I were to continue benchmarking, I would allowlist certain package repository URLs, instruct the agent not to cheat, etc.
As noted at the bottom of the post, Terminal Bench 3.0 explicitly asks the agent not to cheat[0].
[0]https://github.com/harbor-framework/terminal-bench/blob/v3.0...
First one of these I've seen using DOM manipulation and CSS transitions instead of canvas, so that's neat.
Hard disagree. Sol (and the entire new 5.6 series) is one of the most steerable models I've seen in years. Sol literally follows every instruction in my CLAUDE.md and AGENTS.md, something that Opus 5 and Fable just casually skip.
E.g. ask it to make contract for a spec in code and then ask it to violate that contract. Overall an excellent model, just need to stop and put it back into we are harnessing or specing not building for a few turns not just try to pivot it off with one prompt.
> Before asking an LLM to do something, I first ask it to draft a doc for what it needs to do
Just no. That's not spec-driven development if AI is writing the spec for you. The spec needs to be in your own words. You must use AI to refine it, but not to write it. If you leave it to the AI, it will bloat the spec with 10x the details, many of which should be left out of the spec.
The spec needs to be something that you can take to any AI for development. If it's too rigid, it constrains the AI into suboptimal or obsolete paths. If it's too bloated, AI risks losing track of what really matters.
I'm already sick of this current look of the hard squares and solid colours.
Cheaters are the people behind it...