Astra writes well (which is why I often like to work with it over Claude), but it tends to produce code golf that's borderline unreadable, like completely omitting line breaks. I thought I had something funky with my config, but it seems to be a known issue.
EDIT: You can search for "tenobrus astra" on X, but link removed.
The generated code certainly does what's asked of it, but it's as sloppy as anything else I've seen. I don't think this benchmark can control for that. Maybe if it had a metric for minimum diff to satisfy the new requirement with the existing constraints on dead code etc?
I use the frontier models only in plan mode; coding is held out to cheaper models.
I run Paseo as an orchestrator with agent profiles. "Distinguished Member of Technical Staff" -> Fable, plan mode. "End-User Customer Advocate" -> Astra, plan mode. A few others. Basically, these agents (with profile prompts) can only be "consulted" for input on design elements; it's OK if they produce a bit of verbosity here. This has worked fantastically for me, really awesome to see a self-constructed team own the work end-to-end.
A "Director of Engineering" (6.1 Sol or Opus 5.5) also in plan mode organizes the team for the task and then owns coordination and instruction. You can think of this in essence like a "model router".
Coding is handled by Haiku 5.5/GPT 6 Luna/DeepSeek v4.1 Flash. One "Principal Engineer" Sonnet 5.5 or 6.1 Sol for some tasks.
You are correct, at least based on my experimentation. This subagent workflow really only delivers meaningful results when working across model families (ex: Opus asking Astra for a review) or when working across conceptual hardware boundaries (cloud offloading to local inference, etc). The planner / overseer model ends up reading all the output of the coder model anyway, at least if you want quality output, so there's not much benefit otherwise.
> The planner / overseer model ends up reading all the output of the coder model anyway
I mean, even that's fine because output tokens cost ~5x input tokens. So that's still a win if the agent that does the heavy code reading and writing produces output that may be read again by the lead (not my observation of how agents in Paseo interact and of course, correctable to degrees with shared system prompt).
Here's a snippet of a sample prompt downwards from the lead for a piece of code I'm working on:
From agent: 3ff73f80-9713-458c-8271-d275f03609c6
<role>
You are the Principal Engineer on <snipped ... /> (.NET 11 / Vue MCP server; read CLAUDE.md in your working directory first). ...
</role>
<lead_role>
I am the Director of Engineering. The System Architect and the DMTS reviewed this approach ...
</lead_role>
<sniped ... />
<comms>
Run all gating commands in the FOREGROUND. End your turn ONLY with the final report: pass/fail per success criterion (a)-(i) with evidence, the branch tip SHA and `git log --oneline origin/main..HEAD`, lines added/deleted, the exact 0factor image digest and version you pinned, every deviation from this brief and why, risks you saw,...
</comms>
The agent is given explicit "comms" instructions upwards and I find that both agents in this exchange stick to the contract.
> even that's fine because output tokens cost ~5x input tokens.
That's assuming you come in under a certain threshold of revisions, and excludes the tokens generated for all the exchanges. The later can be be significant, especially when there are significant issues to correct.
Nah; I can use my token budget across both providers this way, use tokens more sparingly with smaller models for heavy text in/out (why pay Opus prices to babysit a PR? run tests?), and you'd be surprised when the agents talk to each other and "role play" how much this improves things like the UX as well as technical design which are more subjective.
Distinguished Member of Technical Staff, for example, is asked to "expand the solution space" which I find has a strong effect on approaches considered (it doesn't know what was already considered on the main thread; only the domain and problem space).
This also helps with context growth since the sub-agents do the heavy lifting (e.g. search, read docs) and just provide the key bits back to the lead.
Like you, I was a single-model, single-TUI user up until three weeks ago when I picked up Paseo and it is the only tool that I've found that has significantly positively improved my throughput without sacrificing quality of product.
Is it "larping"? I ran an eval today on a contract ingest and extract agent I'm working on. Changing the <role> fragment of the prompt lifted the results by +7 points by simply changing the text to the effect of "You are a senior partner at a prestigious law firm that focuses on reading licensing contracts..." and some bits about the content the agent is reviewing + objective. No one would believe it without an eval.
Claude docs explicitly say to LARP[0]:
> Give Claude a role
>
> Setting a role in the system prompt focuses Claude's behavior and tone for your use case. Even a single sentence makes a difference
My conclusion: the agents like to LARP so I'm happy to LARP with them.
X links are fine. People who don't want to visit the site can choose not to visit it.
This current situation where people get shamed for sharing the information until they edit it out and leave obscure "search for___ on X" is infinitely worse.
I think it's the best possible approach. It lets you refer to content that's unfortunately stuck on the site, while still signaling clearly that it's something to avoid if possible and to apologize for if not. Omitting the link just removes information without influencing consensus on whether to use X.
edit: I wish it would let me view the darn screenshot tho :c
I removed it, but it's frustrating when the options are someone's (reasonable) post on X, or slop-spam aggregators. I don't really want to drive visits to either, but at least the X poster can get the views. I don't really like xcancel either as it still encourages indirect engagement with X.
I've noticed the line breaks issue. It's easily fixed with a formatter like prettier and, frankly, that kind of quality-of-life thing is best handled deterministically anyway.
I haven't used prettier much but this is what the prettier docs say about empty lines.
> It turns out that empty lines are very hard to automatically generate. The approach that Prettier takes is to preserve empty lines the way they were in the original source code. There are two additional rules:
>
> - Prettier collapses multiple blank lines into a single blank line.
> - Empty lines at the start and end of blocks (and whole files) are removed. (Files always end with a single newline, though.)
The last time I used astra it wrote a lot of rust code where it would insert almost no empty lines at all. Not before functions, structs, impl blocks, etc., and not inside functions to group statements. Similar to prettier, rustfmt doesn't really insert empty lines.
Edit: hmm maybe I misunderstood your and the parent's comment. Are you talking about missing regular newlines after statements, i.e. multiple statements on one line?
Doesn’t this mess up the context though? I use ruff and my agents get confused if I edit and run ruff out of band. How do people deal with that? I’m always having to remind agents to re-read files because they’ve changed
Very interesting. If I’m honest with myself it could be very much a reality that I would be better off in only using for medium effor size tasked instead of trying to "offload" the necessary architecture thinking to "high" or even more.
They are good for inspiration though, but maybe I rather let me build a demo and then tell a medium model what I want from it.
Is there a reasonable way to run a representative subset myself?
Edit:
> For folks who want to try to repro these findings: I used the pinned v1.0 catalog, the stock just-solve prompt, seed 42, the any-case runner pass policy, and Docker with Python 3.12 and uv.
Yeah and I started with subsets although I did not find them representative, eg I picked 6 challenges for a subset and fable scored 33%, while on the full challenge it landed closer to 14-15% range
The part II post linked in my top comment has a list of the challenges I used for that.
To run them, just point your model at the paper, it can find the repo, etc and with a key or sub, get it running
For most things I find medium to be the highest quality output. However if I want it to do spatial reasoning or if I want it be funny / write something (not code) of almost-passable quality, then xhigh is where it's at.
I have a simplify skill built from a rich hickey talk that basically solves the erosion and verbosity metrics SCB flagged (i.e. erosion and verbosity are way better and improve over time). However, after running the SCB benchmark I cannot find any evidence that it actually improves test pass rate trajectories. Food for thought on maintainability metrics.
what are the odds of as purely side effect of benchmaxing that the models get actually good because the benchmarkers the nittier they get the better the model has to become?
corrolary: just as cars evolved for worst because they were measured for "race numbers", what are the odds that the models just become "that uber nerd that degrades anyone who doesn't follow all his uber-rules for uber-code" and in the process start creating code that cannot be read or maintained?
This is interesting. Is nobody training LLMs to repeat resolving issues by patches from real projects where refactoring is required, judging the refactor similarity and not just tests passing?
Secondly, I’m mostly using Astra Ultra or Sol 6.1 when tight on quota. I pay $200/month and rarely if ever run out. Am I doing something wrong? Or perhaps I’m not that productive as you guys?
Same here. I’m feeling productive enough. If I was more “productive”, I wouldn’t be able to keep up with what I was producing. My attention and cognitive broadband is the bottleneck. I find it hard to believe that people are actually productive using 4 active chat windows simultaneously for 8+ hours a day.
IMO slop usually comes from overly defensive programming from LLMs. They’re loath to remove code and simplify, because unless we humans tell them some code / use case is unimportant, they have no reason to not care about it.
So agents can make these overly complex solutions trying to make bulletproof implementations and prevent regressions.
It requires a human to look at code, ask about complexity, and guide simplification by subtracting requirements implicit in the code.
They also seem to really bias towards tacking on new code to "work around" what's there, rather than breaking down what's there into smaller more composable bits that can serve the pre-existing use case and the new requirement elegantly.
LLMs write. It sounds glib but it's what they do, and so every problem they encounter they will try to write their way out of it without lots of hand holding.
I believe effort can mean both "solve harder problems" and "produce more stuff". I mostly don't want more stuff than I asked for, so a capable model at lower effort can indeed be the wiser choice. Faster response too.
GPT 5.5 on Medium is all I use lately, I don't understand why anyone would pay drastically more for slightly more, when Opus has been good enough for a year now to do most things.
You unknowingly substituted the name of another model when you’re otherwise talking about the competitor fwiw. Some if those in the reply chain didn’t even seem to notice.
That’s a little telling of the state of ai atm though isn’t it? It’s all pretty similar. The latest Opus/GPT/Gemini/Deepseek/Kimi on medium does indeed do the job
It would be a little strange but not that strange for someone to have an idiosyncratic preference for GPT-5.5. It's still available as a Codex model (I think? It's selectable in Pi, not Codex itself), and it might perform differently enough from later GPT models that someone prefers it.
To your point, though, I don't know if I've noticed that big of an impact going from 5.5 to newer GPT models. If I had to switch back, I'm not sure how much I'd miss the newer ones. People talk about e.g. Opus 4.6 similarly, I've noticed.
The bigger change in OpenAI-land IMO is the models got cheaper, generally.
I find QFN on xhigh usually overruns the context window for no good result. If it can do the task, it'll do it quicker on medium. If it can't do the task, xhigh won't change that.
https://lucumr.pocoo.org/2026/9/7/astra-why/
EDIT: You can search for "tenobrus astra" on X, but link removed.
The generated code certainly does what's asked of it, but it's as sloppy as anything else I've seen. I don't think this benchmark can control for that. Maybe if it had a metric for minimum diff to satisfy the new requirement with the existing constraints on dead code etc?
I run Paseo as an orchestrator with agent profiles. "Distinguished Member of Technical Staff" -> Fable, plan mode. "End-User Customer Advocate" -> Astra, plan mode. A few others. Basically, these agents (with profile prompts) can only be "consulted" for input on design elements; it's OK if they produce a bit of verbosity here. This has worked fantastically for me, really awesome to see a self-constructed team own the work end-to-end.
A "Director of Engineering" (6.1 Sol or Opus 5.5) also in plan mode organizes the team for the task and then owns coordination and instruction. You can think of this in essence like a "model router".
Coding is handled by Haiku 5.5/GPT 6 Luna/DeepSeek v4.1 Flash. One "Principal Engineer" Sonnet 5.5 or 6.1 Sol for some tasks.
You may be larping for no additional benefit vs just using Opus 5.5 and just asking it to implement a feature.
Here's a snippet of a sample prompt downwards from the lead for a piece of code I'm working on:
The agent is given explicit "comms" instructions upwards and I find that both agents in this exchange stick to the contract.That's assuming you come in under a certain threshold of revisions, and excludes the tokens generated for all the exchanges. The later can be be significant, especially when there are significant issues to correct.
Distinguished Member of Technical Staff, for example, is asked to "expand the solution space" which I find has a strong effect on approaches considered (it doesn't know what was already considered on the main thread; only the domain and problem space).
This also helps with context growth since the sub-agents do the heavy lifting (e.g. search, read docs) and just provide the key bits back to the lead.
Like you, I was a single-model, single-TUI user up until three weeks ago when I picked up Paseo and it is the only tool that I've found that has significantly positively improved my throughput without sacrificing quality of product.
Is it "larping"? I ran an eval today on a contract ingest and extract agent I'm working on. Changing the <role> fragment of the prompt lifted the results by +7 points by simply changing the text to the effect of "You are a senior partner at a prestigious law firm that focuses on reading licensing contracts..." and some bits about the content the agent is reviewing + objective. No one would believe it without an eval.
Claude docs explicitly say to LARP[0]:
My conclusion: the agents like to LARP so I'm happy to LARP with them.[0] https://platform.claude.com/docs/en/build-with-claude/prompt...
I have other products and I work for private equity (no affiliation with Paseo at all).
You've entirely missed the lede.
This current situation where people get shamed for sharing the information until they edit it out and leave obscure "search for___ on X" is infinitely worse.
edit: I wish it would let me view the darn screenshot tho :c
> It turns out that empty lines are very hard to automatically generate. The approach that Prettier takes is to preserve empty lines the way they were in the original source code. There are two additional rules: > > - Prettier collapses multiple blank lines into a single blank line. > - Empty lines at the start and end of blocks (and whole files) are removed. (Files always end with a single newline, though.)
The last time I used astra it wrote a lot of rust code where it would insert almost no empty lines at all. Not before functions, structs, impl blocks, etc., and not inside functions to group statements. Similar to prettier, rustfmt doesn't really insert empty lines.
Edit: hmm maybe I misunderstood your and the parent's comment. Are you talking about missing regular newlines after statements, i.e. multiple statements on one line?
https://xxcancel.com/tenobrus/status/2096636223414808719
Edit: disable JS to prevent click-jacking
They are good for inspiration though, but maybe I rather let me build a demo and then tell a medium model what I want from it.
Edit:
> For folks who want to try to repro these findings: I used the pinned v1.0 catalog, the stock just-solve prompt, seed 42, the any-case runner pass policy, and Docker with Python 3.12 and uv.
And https://github.com/gabeorlanski/scb-problems/
The part II post linked in my top comment has a list of the challenges I used for that.
To run them, just point your model at the paper, it can find the repo, etc and with a key or sub, get it running
corrolary: just as cars evolved for worst because they were measured for "race numbers", what are the odds that the models just become "that uber nerd that degrades anyone who doesn't follow all his uber-rules for uber-code" and in the process start creating code that cannot be read or maintained?
Secondly, I’m mostly using Astra Ultra or Sol 6.1 when tight on quota. I pay $200/month and rarely if ever run out. Am I doing something wrong? Or perhaps I’m not that productive as you guys?
So agents can make these overly complex solutions trying to make bulletproof implementations and prevent regressions.
It requires a human to look at code, ask about complexity, and guide simplification by subtracting requirements implicit in the code.
I believe effort can mean both "solve harder problems" and "produce more stuff". I mostly don't want more stuff than I asked for, so a capable model at lower effort can indeed be the wiser choice. Faster response too.
That’s a little telling of the state of ai atm though isn’t it? It’s all pretty similar. The latest Opus/GPT/Gemini/Deepseek/Kimi on medium does indeed do the job
To your point, though, I don't know if I've noticed that big of an impact going from 5.5 to newer GPT models. If I had to switch back, I'm not sure how much I'd miss the newer ones. People talk about e.g. Opus 4.6 similarly, I've noticed.
The bigger change in OpenAI-land IMO is the models got cheaper, generally.
Though, it has been a while since I've used 5.5.
But for webdev, deep research, code review, and a few other tasks I find the current 6.1 sol better.
But I use both. And I use codex more.
Benchmarking Opus 5 on SlopCodeBench - https://news.ycombinator.com/item?id=49076391 - July 2026 (119 comments)
I am currently investigating SCB results and a larger, more representative dataset to work on would be really helpful!
Recent evaluation result.
I find QFN on xhigh usually overruns the context window for no good result. If it can do the task, it'll do it quicker on medium. If it can't do the task, xhigh won't change that.
Oh, it's there at bottom under "my takeaway". But I still didn't get it initially.
Slop Code Bench? Need a Slop Writing Bench too
That is not how data physics work. It should not be how data science works.