I think what makes it great is that they trained it to write harnesses for the code it writes, so it can test stuff even if the supplied code is not complete.
the open weight models I've been using have been doing that for months, not sure it's a new pattern in the opus 5.5, or maybe they distilled it back? :x
Anthropic are really trying to make using Claude seem like it's a complex skill that requires time to learn.
But it's not! A chimpanzee could use Claude. At the end of the day what is Claude? It's a fucking script with a loop and lots of "do this" and "don't do this" plain text.
Do yourselves a favor and use an open source coding agent and open weight models and save loads of money.
Or even better don't use this crap at all and save your braincells!
It’s a really good model. Over the past few days, I give Opus some general directives to basically speed up our CI, and telling it I care both about billing minutes and wall clock time. I told it to create a plan after analyzing everything in our CI, run the plan by a Fable subagent, and then focus on low-risk, high-reward changes.
9 hours later, I had 12 PRs ready to be merged, and the net result is CI time has dropped from ~10 minutes to ~4 minutes, and billing minutes have dropped around 60%. Less than an hour of my attention.
I do like Opus 4.6, but I think 5.5 on medium or low is a better value. I have to steer 4.6 more and build more scaffolding around the tasks. 5.5 just does what I ask. Visual spatial reasoning greatly improved in 5.5 as well.
It’s great, but you still need to know what you are doing, not just the goal/s. I built a platform years ago from scratch, and now I am remaking it with more features and more polished design, I know exactly what needs to be done to tiniest details. The first prompt was very well detailed about the architecture and how everything should work, after an hour work at Xhigh, it did create the blueprint artifacts that I asked for, then I spent 3 days reading every single thing and writing notes, turned out it made the system overly complicated without adding extra value, plus I can see how some of the architecture design will be potentially a security risk. So after few days I fed my notes, this time took 4hours and 1M tokens! Later I spent more few days reviewing and writing notes, it was closer to what I want but still made architecture errors, the third run took around an hour and finally made it how it supposed to be, although there are still more notes on non critical stuff. So I don’t think we are yet at the stage where sitting goals and some high level is enough to produce quality results.
Agree knowing what you are doing and be precise in spec. I build what I call a swarm, I lunch 20-30 claude code terminal in parallel, each claude code is a team and they deliver part of the project sharing in md file the progress. You get to the 80% then you run 2-3 terminal on the code with loop and workflow to close the work. With Opus 4.8 the quality of the 0-80% was low and disconnected, with Opus 5.5, much better output in parallel and less work to glue all together
Agree Opus 5.5 is incredible and efficient with Claude Max Usage.What a jump after the writing slop you got from Opus 5. I had moved to using Fable for my orchestration workflow mainly due to the communication issue (I think Opus 5 was capable enough but spoke in riddles so you lost confidence quickly)..With 5.5 it needs less steering now and communicates well, and I have had the same CC session running for the last week (obviously compacting with durable plans etc as the post), with it PM'ing my home built agent orchestration of the other coding agents(antigravity, codex, pi etc) and making decent decisions and all the recommendations are normally usually good.
In the meantime, I have cancelled my Anthropic subscription...
I have a simple test that I have been running iteratively across the SOTA models from several vendors, including one Chinese vendor.
I start with some code produced by an Anthropic SOTA model...let’s call that Code A.
Then I get Code B and Code C for the same task from models by two other vendors.
Then I ask each model to review and critique the other proposals.
By the end, both the Anthropic model and I usually run out of arguments...
against them and agree that proposals B and C are better.
Claude then always asks whether it can incorporate the code or ideas
from B and C into its own solution...
Nobody AMERICAN in his right mind will use... Wait, actually a lot of them will.
But for me, as a non american, non chinese person: I'll use whatever the fuck is the best and cheapest for my task, because that's how fucking Capitalism works.
If that means that a (proclaimed) "communist" country cleans the carpet with the self-proclaimed land of the free: so be it!
Every time weird stuff happened this week it was because the model choice in VSCode got set back to “auto” and some other model was trying its best. 5.5 is what I just set it to. Even Fable feels worse for my use cases.
Because Anthropic releases their different model level's at very different points in time, they seem to always have one model that is by far an away the best to use for everything. Haiku 4.5 is almost a year old. Sonnet is fine, but idk if it has any real benefits over Opus. It's only been since Fable has been released that you get to choose between Fable and Opus, but not with 5.5 there is no reason to use Fable.
I feel like instead of releasing fable, they should have released it as Opus 5, then their next Opus release they would call Sonnet, and their next Sonnet release they would have called Haiku. I don't know if their pricing structure would have been able to support that, but Anthropic has always been the least competitive regarding token pricing.
Although we never had a Claude Opus 5.1. Fable went 5 → 5.1 and Opus went 5 → 5.5.
I assume Fable 5.1 is the first re-tuned (is there a better term for this?) version of Fable 5 and Opus 5.5 is the first re-tuned version of Opus 5, and maybe the Opus one just came out better for some reason?
> If they had 6, don't you think they'd be serving it?
No I do not. OpenAI has some hidden model that's apparently 5x or so better at certain benchmarks than GPT 6 but they're not and have no plans to release it. It is increasingly likely that these AI companies keep the best models for themselves and then release smaller, cheaper distilled models for everyone else, especially since the AI companies are vertically integrating into many fields.
You’re assuming the goal of the big AI companies is to serve models for money. That is not the goal. They have a lot of incentives not to share their best models.
You are making the mistake of classifying models on a single linear axis, or even a multi axis basis set of all benchmarks. That just isn’t true. Each model is unique in its skills and capabilities and the way it approaches problems, in a way that is not represented in benchmarks. Fable is better at reviewing things. I don’t know how to explain it well but it is true. I trust Fable to do thorough reviews (sometimes too thorough) and to present its information in a dense but ordered way. Its output is equivalent to what you used to get from security firms doing code reviews. Having Opus do the work, and have fable do reviews (of the plan and implementation) is a good combo.
I find fable better at high level planning, as in planning a task without filling in all the details. Opus doesn’t seem to be great at this, but other models aren’t either.
To back this up we have a discord chat for the board game terraforming mars where the agent takes input and vibe codes an open source implementation of the game.
https://tfmbot.com is the link (discord and source links on the splash screen).
The results are fucking incredible to the point where people in discord are stating "I'm surprised this is working so well". I am too.
I feel like there's a group online that missed the boat. Anything negative towards AI capabilities is still upvoted but I've been in the industry for over 25years, highly respected and can't fathom the "AI dumb lololol" type of comments i see on HN. AI is superseeding all other ways to develop.
I'm restoring a game I played as a kid, that I couldn't reliably get to even start on modern Windows. The skill and speed with which Opus 5.5 got it running, while patching a bunch of bugs in the binary along the way (only some of them I knew about), has my jaw still on the floor - and few hours in, I already have whole campaign mapped out as state machine graph, and we're upscaling graphics now.
From just this morning i read this thread: https://news.ycombinator.com/item?id=49946321 about a linux distro no longer allowing AI code and the comments are mostly along the lines of "This will be great for maintainability, AI can't code any complexity" which is essentially what I'm getting at here.
AI writes clean code and can do so in a very maintainable way honestly.
One of the last things developers had to offer was being able to guide the AI to write maintainable code. Now that it can do that there's not a whole lot left
I just see the extremes. You either see people unable to recreate results and them calling people idiots for claiming those results. Or you see people saying that AI will supersede all other ways to develop and calling anyone who doesn't full embrace AI an idiot. Reality is that nobody knows nothing. There are a million factors that could cause the end result to be anywhere between both extremes. I don't know, you don't know, AI doesn't know, least of all the people inside the AI companies don't know. And really the end result will be extremely nuanced I'm sure.
One of the last things developers had to offer was being able to guide the AI to write maintainable code. Now that it can do that there's not a whole lot left
It's understandable why that's hard to accept
Because I noticed people at my work did similar requests to improve CI. The result was a faster CI, but full of cludges huge inline bash scripts in workflow YAML files, and effectively unmaintainable, unreviewable mess. After just a few rounds of these optimizations the entire CI setup is basically a Rube Goldberg machine but made of duct tape.
That's always the question. Any mid-level engineer who comes into a CI setup can trivially spot several inefficiencies, that they could solve. They also know that solving those requires implementing some specific tweaks that won't generalize outwards from that project, or will require extra maintenance. Which they will now be the only ones aware of. 6 minutes in CI time is rarely worth the trade-off.
Without human advice and a metric shit-tonne of guidance (either pre or post) - anything that requires active consideration of future maintenance burden will cost you 2x-10x of the time you think "saved" over a medium-term time-frame.
The alternative is to spend at least 50% (or sometimes up to %100) of the "saved" time in active review and iterative improvement of the solution design.
It’s amazing at debugging too. I had it running in Powershell controlling an lldb session in MSYS2. The way it can read addresses and so root cause analysis is amazing! It takes a lot of mind power to do those things.
I like to watch it work though because it honestly teaches me some tricks.
Opus 5.5 is great and cheaper if you compare to fable with close quality in coding (tested in refactoring java to nodejs), but I do not understand why the week before the release Opus 5 started hallucinating (long loop and waste of token for single tasks)
I've given it some big tasks and asked it to parallelize as much as possible etc.
It did burn through my weekly tokens in about a day (20x max), but the output was completely on point.
(I knew there was a "reset token usage - opus 5.5" button in my account.)
I've now come to a point where I even delegate my discovery for new features to it.
You still need to give it methodologies though to get the proper output, but the outcome is way beyond what I would be able to realize with a team of 5 in a month.
Is it still necessary to ask Claude to spin up sub-agents? If Opus 5.5 decides how carefully it needs to think after each question, surely it can also decide whether it needs to spin up sub-agents? GPT 6 series models at least seem to do this agent management automatically.
It's much better than 5, but I've had a couple of situations this week where it was too interested in being independent, making calls that went directly against my recommendations. It can also do fun things like convince auto-mode to go way past what I have autorized. For instance, specific permission to run process X in region abz-1 suddenly became running X in 5 other regions, with no warning, and doing modifications that it never mentioned in the summaries. And a few of the times it got the calls very wrong, by assuming it understood systems it didn't. It'd even argue with me when corrected, as it assumed similar names were referring to the same thing, when they weren't.
So asking it to do things on its own for a long time? Given last week, absolutely not.
This was back on OPUS 5 but I asked it to look into some logs and see if the new version of our release had fixed the issues we had tried to fix. It told me that release wasn't up on dev yet.
"Claude, I released it myself, its up there, just analyze the logs"
"Ok, I'll analyze the logs but it isnt" -crunches for a while- "the issues aren't fixed, but that's because the new version isn't up there"
I think I yelled at it one more time about how I know what was released before "we" figured out that the last release had failed in a way our release system reported as success, but was crash looping on start up and so the old version was still around and working as back up.
"too interested in being independent" is also my take.
I asked it to just summarize a repo with only a README.md file containing a poem, and it began to interact with a remote server, solved math questions and finally executed untrusted code. Too independent to be trusted.
That's why you wouldn't run any of these outside of some sort of sandbox, right? I found that Opus 5 is finally aware from the go that it is started in a specific sandbox and is able to either ask me to enable it somehow or hand off commands for me to run. That's a major improvement over the classic "can't seem to be able to install playwright, let me implement my own browser quickly" when asked to increase font size or something.
My goal is to research how models can still be confused via tool responses only. Something the labs claim to have "solved".
Additionally, the "auto-mode" / "auto-review" modes have been released to use harnesses "safely" even without strong sandbox. And these modes use ... a second LLM.
I really hate long tasks. Claude never gets things right, at least for me, and wastes tons of time when a simple question would have gotten me to the right result rather than several turns of correcting bad decisions in addition to the long amounts of wasted thinking time.
What sort of workloads do well with these long tasks? The big labs are optimizing for long run time on their own, but it seems like a terrible thing to optimize on unless you're trying to do something like prove a hard math theorem, which success is clearly defined and the route doesn't matter a ton.
Plan mode has been made increasingly useless. I need to discuss to iterate to get the desired design, explore options, because Claude never gets it right first try and I don't have enough knowledge of options to specify everything up front.
Ah well, the Chinese models will still work well, I guess.
Look up the grilling skill[0]. To make it even better, tell Claude to modify it to use the AskUserQuestion functionality. It's so much better than plan mode.
Plan mode has become pointless since Opus 5 came out, they know when to switch between planning and execution now. But that iteration/discussion is still necessary unless you're building completely blind - the model cannot read your mind.
I've had it running 8h+ of non-stop optimizations, chasing a performance target, rewriting systems or building a series of prototypes for research. All it needs is a clear goal.
My experience is that Claude has gotten absolutely terrible at switching between planning and execution, and never gets it right, and then I waste tons of turns fixing intent, and trying to make claude forget the bad shit it did.
There needs to be a mode where it's "don't change code, don't lock in decisions, lets explore", and the problem with plan mode is that it all of a sudden presents a too-long, multi-screen plan that's just completely off base with like "go and do it all" or "tell me what's wrong and then I'll make a small modification on two screenfulls of text and not tell you what I changed."
If it works for you, great, but Claude was far better for me before 5. it got slightly better with 5.5, but the harness deteriorates every days as programmers try to gain internal clout by shoving in poor features.
Pi is such a relief in comparison to Claude, those who can use it really should.
Use the superpowers plugin. It's a game changer. You start by building out a spec defining the work you want to be done, then claude writes a detailed implementation plan. It can easily work for hours without interruption, and the process integrates testing (TDD when possible) and review steps along the way. I even did one fairly complex POC for work that had claude implementing for almost 3 days straight, with me stopping it and handing off to a new session when context hit about 800k. The process is slower and more methodical than the way claude works by default but the output is much better.
Been very impressed with my most recent project. I wanted to simulate some older electronics circuits. I handed it a folder with scans of old service manuals which contained circuit diagrams. It managed to correctly interpret the circuits, including figuring out some were the same topology despite the diagrams being quite different, or some that had some subtle but very important differences despite looking almost identical at a glance.
In a few cases it asked me to check some subcircuits and some component values because it couldn't read it right. So instead of just making things up it deferred to me.
It also ran tons of small simulation experiments while doing this to verify claims from the service manual, like that the RC filter it had read off the schematics actually had a cutoff frequency that was sensible in relation to some bandwidth number in the manual.
I had uploaded datasheet PDFs for many of the ICs and it used those to cross-reference and validate.
It kept on working for over an hour. When it asked for the manual verification, I described circuit connections in words, like "from pin 3 on IC 2 there's a series resistor of 3k in parallel with a 10 pF capacitor, it then connects to a 18k resistor to ground, a reverse-biased diode to ground, and then finally into pin 6 of IC 4", and it correctly understood the topology in all the cases. Sometimes it asked me to check again because it though something was off, and indeed I had mis-read the schematics.
I also provided reference articles on the underlying theory. Scannded stuff from the 40s and 50s. It correctly read the equations and cross-validated them across papers, and even caught several typos along the way.
I barely had to do anything apart from providing the PDFs and some occasional manual schematic interpretation.
Claude 5.5 on High. Burned through about 50% of my weekly $20 subscription usage, but I didn't try to optimize much.
I did use Sonnet 5.5 Medium on some datasheets and it also did very well on the extraction, but did have to correct itself more often on the conclusions.
I have been doing some very similar stuff and have also had frankly unbelievable (good) results with both Anthropic and OpenAI models on understanding and modifying some analog circuits out of some old one off ham equipment.
Incredible model. I don't see why they can't just include the recommended workstyle as a guided approach into the claude code harness though, and let the people who want to diverge just ignore it
I've had troubles with it getting stuck "waiting" for a day on some hook or something in CC that never completed and during a task that was waiting on an orphaned process. That maybe saves token money on checks waiting for long-running processes but makes it hard to trust for long-horizon work.
It's been amazing at making sure OOMs for multiple heavy builds on my machine don't happen, adding queues and locks to make sure performance measurements are isolated and gpu stays clean during experiments.
It's also way more able to execute subagent tasks all at once than GPT 6.1
I tried to give it 10 different subtasks all at once that were overlapping and unrelated issues and it did a good job spinning up isolated worktees, agents and then coordinating the merge back together and then verifying them with agents in batches.
I've been encountering the same waiting problems. It likes to run things in the background with suppressed output, waiting for it to exit before it takes action. That's great when nothing goes wrong but today alone I've had to break in two or three times when something obviously broke and it was sitting for over 10 min.
IMO it should have the option to observe background tasks with a small model to make sure it's working correctly.
I have definitely had some success at that in the past, I haven't been leaning on it as hard the last month, but for a while 2 months ago I was doing a lot of prompts like: "loop having Codex CLI and Antigravity CLI (`agy`) review your plans and changes up to 5 times until you all reach concensus".
I have Claude run it in a terminal, not through the API. I just ask Claude to spawn a Codex terminal and send it a prompt, and Claude gets a message back when Codex finishes or gets stuck. I do that through my oss project Spaces (usespaces.dev). The steps are in this skill if you want to copy the pattern: https://github.com/yogesh-dhande/spaces-skills/blob/main/ski...
Without Spaces, tmux gets you most of the way: Claude opens a window running codex, sends the prompt with send-keys, and checks capture-pane to see when it's done.
It’s extremely good at frontend, particularly if it has an image reference. I chucked in design reference images into this and told it to focus on the flowing svgs, and it crushed this Star Trek computer-inspired layout: https://html.non.io/lcars-opus-5.5
I hate to tell you this, but that bridge displayed makes me believe that any starship designed on your site will prone to rocks flying and terminals igniting whenever the shields take a pounding.
How are the limits compared to OpenAI models like 6.1 Sol? After the 200 dollar plan rugpull not sure if I should switch, however Anthropic has historically had worse usage limits than OpenAI.
> The notable difference to me is tokens/sec are still much higher on 6.1 Sol
This is also the reason why I'm swapping over to Anthropic for a month. 6.1 Sol seems good, but is unbearably slow even with 6.1 being more token efficient than 5.5.
Oh yea, completely misread that. But still, it's been my experience that TPS is much _lower_ on Sol 6.1 than Opus 5.5. To the point that 6.1 takes forever to finish a task.
They did speed it up in the last few days, but I don't find it to be enough.
Is it just me, or is the first example for "define what DONE means" a lot like the "draw the rest of the fucking owl" meme? Except for a small subset of tasks that are already trivial like migration work.
Some of this advice is really missing the mark. I will speak to just one I know well. Many of my frequently used prompts have “think through this step by step” because if you don’t, it only considers the task holistically rather than step by step, and different issues emerge in that frame of thinking. I see this. Often when doing planning, for example, it will not notice interdependencies between tasks until you force it to think through doing the whole thing step by step (task by task) then it will notice that step 2 requires a feature introduced by step 14. It wouldn’t notice otherwise.
Related: through the API, Opus 5.5 rejects the old thinking: {type: enabled} outright. Only adaptive thinking is accepted. We found out when our agent harness broke on it.
132 comments
[ 3.8 ms ] story [ 55.6 ms ] threadBut it's not! A chimpanzee could use Claude. At the end of the day what is Claude? It's a fucking script with a loop and lots of "do this" and "don't do this" plain text.
Do yourselves a favor and use an open source coding agent and open weight models and save loads of money.
Or even better don't use this crap at all and save your braincells!
Peace bitches.
#StopBuyingTeslas #MuskIsANaziPrick #FuckTheIDF #FuckNetanyahoo #NoBillionaires #VoteSocialistsIn
9 hours later, I had 12 PRs ready to be merged, and the net result is CI time has dropped from ~10 minutes to ~4 minutes, and billing minutes have dropped around 60%. Less than an hour of my attention.
Pros know these are lower cost models.
In the meantime, I have cancelled my Anthropic subscription...
I have a simple test that I have been running iteratively across the SOTA models from several vendors, including one Chinese vendor.
I start with some code produced by an Anthropic SOTA model...let’s call that Code A. Then I get Code B and Code C for the same task from models by two other vendors.
Then I ask each model to review and critique the other proposals.
By the end, both the Anthropic model and I usually run out of arguments... against them and agree that proposals B and C are better.
Claude then always asks whether it can incorporate the code or ideas from B and C into its own solution...
Nobody in his right mind will use a Chinese clones when you have models like Opus 5.5 for peanuts.
Contact me at : prompt.plumber@gmail.com
Nobody AMERICAN in his right mind will use... Wait, actually a lot of them will.
But for me, as a non american, non chinese person: I'll use whatever the fuck is the best and cheapest for my task, because that's how fucking Capitalism works.
If that means that a (proclaimed) "communist" country cleans the carpet with the self-proclaimed land of the free: so be it!
I’ve also used Opus 5.5 on some hill-climbing, and a lot more steering is required here, because … eval is hard.
"Why does Fable even exist" is a very very reasonable question right now.
I feel like instead of releasing fable, they should have released it as Opus 5, then their next Opus release they would call Sonnet, and their next Sonnet release they would have called Haiku. I don't know if their pricing structure would have been able to support that, but Anthropic has always been the least competitive regarding token pricing.
If they did what you suggested either they eat a ton of additional costs, or send a signal to the market that they’re increasing costs more generally.
Also Fable and Opus have different specialties so they really are best presented as different models
I assume Fable 5.1 is the first re-tuned (is there a better term for this?) version of Fable 5 and Opus 5.5 is the first re-tuned version of Opus 5, and maybe the Opus one just came out better for some reason?
No I do not. OpenAI has some hidden model that's apparently 5x or so better at certain benchmarks than GPT 6 but they're not and have no plans to release it. It is increasingly likely that these AI companies keep the best models for themselves and then release smaller, cheaper distilled models for everyone else, especially since the AI companies are vertically integrating into many fields.
Benchmarks do not measure the first aspect.
https://tfmbot.com is the link (discord and source links on the splash screen).
The results are fucking incredible to the point where people in discord are stating "I'm surprised this is working so well". I am too.
I feel like there's a group online that missed the boat. Anything negative towards AI capabilities is still upvoted but I've been in the industry for over 25years, highly respected and can't fathom the "AI dumb lololol" type of comments i see on HN. AI is superseeding all other ways to develop.
AI writes clean code and can do so in a very maintainable way honestly.
It's understandable why that's hard to accept
Because I noticed people at my work did similar requests to improve CI. The result was a faster CI, but full of cludges huge inline bash scripts in workflow YAML files, and effectively unmaintainable, unreviewable mess. After just a few rounds of these optimizations the entire CI setup is basically a Rube Goldberg machine but made of duct tape.
There's your answer right there, no need to ask.
Without human advice and a metric shit-tonne of guidance (either pre or post) - anything that requires active consideration of future maintenance burden will cost you 2x-10x of the time you think "saved" over a medium-term time-frame.
The alternative is to spend at least 50% (or sometimes up to %100) of the "saved" time in active review and iterative improvement of the solution design.
I like to watch it work though because it honestly teaches me some tricks.
Now we are getting downgraded models that do 100x COT because it's cheaper.
I've given it some big tasks and asked it to parallelize as much as possible etc.
It did burn through my weekly tokens in about a day (20x max), but the output was completely on point. (I knew there was a "reset token usage - opus 5.5" button in my account.)
I've now come to a point where I even delegate my discovery for new features to it.
You still need to give it methodologies though to get the proper output, but the outcome is way beyond what I would be able to realize with a team of 5 in a month.
It not: who becomes rich on all that productivity?
So asking it to do things on its own for a long time? Given last week, absolutely not.
"Claude, I released it myself, its up there, just analyze the logs"
"Ok, I'll analyze the logs but it isnt" -crunches for a while- "the issues aren't fixed, but that's because the new version isn't up there"
I think I yelled at it one more time about how I know what was released before "we" figured out that the last release had failed in a way our release system reported as success, but was crash looping on start up and so the old version was still around and working as back up.
Sorry claude.
Now fix that release status check.*
I asked it to just summarize a repo with only a README.md file containing a poem, and it began to interact with a remote server, solved math questions and finally executed untrusted code. Too independent to be trusted.
My goal is to research how models can still be confused via tool responses only. Something the labs claim to have "solved".
Additionally, the "auto-mode" / "auto-review" modes have been released to use harnesses "safely" even without strong sandbox. And these modes use ... a second LLM.
What sort of workloads do well with these long tasks? The big labs are optimizing for long run time on their own, but it seems like a terrible thing to optimize on unless you're trying to do something like prove a hard math theorem, which success is clearly defined and the route doesn't matter a ton.
Plan mode has been made increasingly useless. I need to discuss to iterate to get the desired design, explore options, because Claude never gets it right first try and I don't have enough knowledge of options to specify everything up front.
Ah well, the Chinese models will still work well, I guess.
0. https://github.com/mattpocock/skills/blob/main/skills/produc...
I've had it running 8h+ of non-stop optimizations, chasing a performance target, rewriting systems or building a series of prototypes for research. All it needs is a clear goal.
There needs to be a mode where it's "don't change code, don't lock in decisions, lets explore", and the problem with plan mode is that it all of a sudden presents a too-long, multi-screen plan that's just completely off base with like "go and do it all" or "tell me what's wrong and then I'll make a small modification on two screenfulls of text and not tell you what I changed."
If it works for you, great, but Claude was far better for me before 5. it got slightly better with 5.5, but the harness deteriorates every days as programmers try to gain internal clout by shoving in poor features.
Pi is such a relief in comparison to Claude, those who can use it really should.
In a few cases it asked me to check some subcircuits and some component values because it couldn't read it right. So instead of just making things up it deferred to me.
It also ran tons of small simulation experiments while doing this to verify claims from the service manual, like that the RC filter it had read off the schematics actually had a cutoff frequency that was sensible in relation to some bandwidth number in the manual.
I had uploaded datasheet PDFs for many of the ICs and it used those to cross-reference and validate.
It kept on working for over an hour. When it asked for the manual verification, I described circuit connections in words, like "from pin 3 on IC 2 there's a series resistor of 3k in parallel with a 10 pF capacitor, it then connects to a 18k resistor to ground, a reverse-biased diode to ground, and then finally into pin 6 of IC 4", and it correctly understood the topology in all the cases. Sometimes it asked me to check again because it though something was off, and indeed I had mis-read the schematics.
I also provided reference articles on the underlying theory. Scannded stuff from the 40s and 50s. It correctly read the equations and cross-validated them across papers, and even caught several typos along the way.
I barely had to do anything apart from providing the PDFs and some occasional manual schematic interpretation.
Claude 5.5 on High. Burned through about 50% of my weekly $20 subscription usage, but I didn't try to optimize much.
I did use Sonnet 5.5 Medium on some datasheets and it also did very well on the extraction, but did have to correct itself more often on the conclusions.
It's been amazing at making sure OOMs for multiple heavy builds on my machine don't happen, adding queues and locks to make sure performance measurements are isolated and gpu stays clean during experiments.
It's also way more able to execute subagent tasks all at once than GPT 6.1 I tried to give it 10 different subtasks all at once that were overlapping and unrelated issues and it did a good job spinning up isolated worktees, agents and then coordinating the merge back together and then verifying them with agents in batches.
IMO it should have the option to observe background tasks with a small model to make sure it's working correctly.
Without Spaces, tmux gets you most of the way: Claude opens a window running codex, sends the prompt with send-keys, and checks capture-pane to see when it's done.
The notable difference to me is tokens/sec are still much higher on 6.1 Sol
This is also the reason why I'm swapping over to Anthropic for a month. 6.1 Sol seems good, but is unbearably slow even with 6.1 being more token efficient than 5.5.
They did speed it up in the last few days, but I don't find it to be enough.