Ok it might sound crazy but I actually got the best quality of code (completely ignoring that the cost is likely 10x more) by having a full “project team” using opencode with multiple sub agents which are all managed by a single Opus instance. I gave them the task to port a legacy Java server to C# .NET 10. 9 agents, 7-stage Kanban with isolated Git Worktrees.
Manager (Claude Opus 4.5): Global event loop that wakes up specific agents based on folder (Kanban) state.
Product Owner (Claude Opus 4.5): Strategy. Cuts scope creep
Scrum Master (Opus 4.5): Prioritizes backlog and assigns tickets to technical agents.
Architect (Sonnet 4.5): Design only. Writes specs/interfaces, never implementation.
Archaeologist (Grok-Free): Lazy-loaded. Only reads legacy Java decompilation when Architect hits a doc gap.
CAB (Opus 4.5): The Bouncer. Rejects features at Design phase (Gate 1) and Code phase (Gate 2).
Librarian (Gemini 2.5): Maintains "As-Built" docs and triggers sprint retrospectives.
You might ask yourself the question “isn’t this extremely unnecessary?” and the answer is most likely _yes_. But I never had this much fun watching AI agents at work (especially when CAB rejects implementations).
This was an early version of the process that the AI agents are following (I didn’t update it since it was only for me anyway): https://imgur.com/a/rdEBU5I
Every time I read something like this, it strikes me as an attempt to convince people that various people-management memes are still going to be relevant moving forward.
Or even that they currently work when used on humans today.
The reality is these roles don't even work in human organizations today. Classic "job_description == bottom_of_funnel_competency" fallacy.
If they make the LLMs more productive, it is probably explained by a less complicated phenomenon that has nothing to do with the names of the roles, or their descriptions.
Adversarial techniques work well for ensuring quality, parallelism is obviously useful, important decisions should be made by stronger models, and using the weakest model for the job helps keep costs down.
My understanding is that the main reason splitting up work is effective is context management.
For instance, if an agent only has to be concerned with one task, its context can be massively reduced. Further, the next agent can just be told the outcome, it also has reduced context load, because it doesn't need to do the inner workings, just know what the result is.
For instance, a security testing agent just needs to review code against a set of security rules, and then list the problems. The next agent then just gets a list of problems to fix, without needing a full history of working it out.
Developers do want managers actually, to simplify their daily lives. Otherwise they would self manage themselves better and keep more of the share of revenues for them
You might as well just have planner and workers, or your architecture essentially echos to such structure. It is difficult to discern how semantics can drive to different behavior amongst those roles, and why planner can't create those prompts the ad-hoc way.
Subagent orchestration without the overhead of frameworks like Gastown is genuinely exciting to see. I’ve recorded several long-running demos of Pied-Piper, which is a Subagents orchestration system for Claude Code and ClaudeCodeRouter+OpenRouter here: https://youtube.com/playlist?list=PLKWJ03cHcPr3OWiSBDghzh62A...
I came across a concept called DreamTeam, where someone was manually coordinating GPT 5.2 Max for planning, Opus 4.5 for coding, and Gemini Pro 3 for security and performance reviews. Interesting approach, but clearly not scalable without orchestration. In parallel, I was trying to do repeatable workflows like API migration, Language migration, Tech stack migration using Coding agents.
Pied-Piper is a subagent orchestration system built to solve these problems and enable repeatable SDLC workflows. It runs from a single Claude Code session, using an orchestrator plus multiple agents that hand off tasks to each other as part of a defined workflow called Playbooks:
https://github.com/sathish316/pied-piper
Playbooks allow you to model both standard SDLC pipelines (Plan → Code → Review → Security Review → Merge) and more complex flows like language migration or tech stack migration (Problem Breakdown → Plan → Migrate → Integration Test → Tech Stack Expert Review → Code Review → Merge).
Ideally, it will require minimal changes once Claude Swarm and Claude Tasks become mainstream.
I have been using a simpler version of this pattern, with a coordinator and several more or less specialized agents (eg, backend, frontend, db expert). It really works, but I think that the key is the coordinator. It decreases my cognitive load, and generally manages to keep track of what everyone is doing.
Can you share technical details please? How is this implemented? Is it pure prompt-based, plugins, or do you have like script that repeatedly calls the agents? Where does the kanban live?
Not the OP, but this is how I manage my coding agent loops:
I built a drag and drop UI tool that sets up a sequence of agent steps (Claude code or codex) and have created different workflows based on the task. I'll kick them off and monitor.
1. Are you using a Claude Code subscription? Or are you using the Claude API? I'm a bit scared to use the subscription in OpenCode due to Anthropic's ToS change.
2. How did you choose what models to use in the different agents? Do you believe or know they are better for certain tasks?
What are the costs looking like to run this? I wonder whether you would be able to use this approach within a mixture-of-experts model trained end-to-end in ensemble. That might take out some guesswork insofar the roles go.
Nope it isn’t. I did it as a joke initially (I also had a version where every 2 stories there was a meeting and if a someone underperformed it would get fired).
I think there are multiple reasons why it actually works so well:
- I built a system where context (+ the current state + goal) is properly structured and coding agents only get the information they actually need and nothing more. You wouldn’t let your product manager develop your backend and I gave the backend dev only do the things it is supposed to and nothing more. If an agent crashes (or quota limits are reached), the agents can continue exactly where the other agents left off.
- Agents are ”fighting against” each other to some extend? The Architect tries to design while the CAB tries to reject.
- Granular control. I wouldn’t call “the manager” _a deterministic state machine that is calling probabilistic functions_ but that’s to some extent what it is? The manager has clearly defined tasks (like “if file is in 01_design —> Call Architect)
Here’s one example of an agent log after a feature has been implemented from one of the older codebases:
https://pastebin.com/7ySJL5Rg
It's not satire but I see where you're coming from.
Applying distributed human team concepts to a porting task squeezes extra performance from LLMs much further up the diminishing returns curve. That matters because porting projects are actually well-suited for autonomous agents: existing code provides context, objective criteria catch more LLM-grade bugs than greenfield work, and established unit tests offer clear targets.
I guess what I'm trying to say is that the setup seems absurd because it is. Though it also carries real utility for this specific use case. Apply the same approach to running a startup or writing a paid service from scratch and you'd get very different results.
I think many people really like the gamification and complex role playing. That is how GitHub got popular, that is how Rube Goldberg agent/swarm/cult setups get popular.
It attracts the gamers and LARPers. Unfortunately, management is on their side until they find out after four years or so that it is all a scam.
The next stage in all of this shit is to turn what you have into a service. What's the phrase? I don't want to talk to the monkey, I want to talk to the organ grinder. So when you kick things off it should be a tough interview with the manager and program manager. Once they're on board and know what you want, they start cracking. Then they just call you in to give demos and updates. Lol
This is just sub agents, built into Claude. You don’t need 300,000 line tmux abstractions written in go. You just tell Claude to do work in parallel with background sub agents. It helps to have a file for handing off the prompt, tracking progress, and reporting back. I also recommend constraining agents to their own worktrees. I am writing down the pattern here https://workforest.space while nearly everyone is building orchestrators i also noticed claude is already the best orchestrator for claude.
It isn't sub agents. The gap with existing tooling is that the abstraction is over a task rather than a conversation (due to the issue with third-party apps, Claude Code has been inherently limited to conversations which is why they have been lacking in this area, Claude Code Web was the first move in this direction), and the AI is actually coordinating the work (as opposed to being constantly prompted by the user).
One of the issues that people had which necessitated this feature is that you have a task, you tell Claude to work on it, and Claude has to keep checking back in for various (usually trivial) things. This workflow allows for more effective independent work without context management issues (if you have subagents, there is also an issue with how the progress of the task is communicated by introducing things like task board, it is possible to manage this state outside of context). The flow is quite complex and requires a lot of additional context that isn't required with chat-based flow, but is a much better way to do things.
The way to think about this pattern - one which many people began concurrently building in the past few months - is an AI which manages other AIs.
It isn't "just" sub agents, but you can achieve most of this just with a few agents that take on generic roles, and a skill or command that just tells claude to orchestrate those agents, and a CLAUDE.md that tells it how to maintain plans and task lists, and how to allow the agents to communicate their progress.
It isn't all that hard to bootstrap. It is, however, something most people don't think about and shouldn't need to have to learn how to cobble together themselves, and I'm sure there will be advantages to getting more sophisticated implementations.
> Claude Code has been inherently limited to conversations
How so? I’ve been using “claude -p” for a while now.
But even within an interactive session, an agent call out is non-interactive. It operates entirely autonomously, and then reports back the end result to the top level agent.
It’s even less of a feature, Claude Code already has subagents; this new feature just ensures Claude Code actually uses this for implementation.
imho the plans of Claude Code are not detailed enough to pull this off; they’re trying to do it to preserve context, but the level of detail in the plans is not nearly enough for it to be reliable.
I agree with this. Any time I make a plan I have to go back and fill it in, fill it in, what did we miss, tdd, yada yada. And yes, I have all this stuff in CLAUDE.md.
You start to get a sense for what size plan (in kilobytes) corresponds to what level of effort. Verification adds effort, and there's a sort of ... Rocket equation? in that the more infrastructure you put in to handle like ... the logistics of the plan, the less you have for actual plan content, which puts a cap on the size of an actionable plan. If you can hit the sweet spot though... GTFO.
I also like to iterate several times in plan mode with Claude before just handing the whole plan to Codex to melt with a superlaser. Claude is a lot more ... fun/personable to work with? Codex is a force of nature.
Another important thing I will do is now that launching plans clear context, it's good to get out of planning mode early, hit an underspecified bit, go back into planning mode and say something like "As you can see the plan was underspecified, what will the next agent actually need to succeed?" and iterate that way before we actually start making moves. This is made possible by lots of explicit instructions in CLAUDE.md for Claude to tell me what it's planning/thinking before it acts. Suppressing the toolcall reflex and getting actual thought out helps so much.
It’s moving fast. Just today I noticed Claude Code now ends plans with a reference to the entire prior conversation (as a .jsonl file on disk) with instructions to check that for more details.
Not sure how well it’s working though (my agents haven’t used it yet)
Interesting about the level of detail. I’ve noticed that myself but I haven’t done much to address it yet.
I can imagine some ideas (ask it for more detail, ask it to make a smaller plan and add detail to that) but I’m curious if you have any experience improving those plans.
Claude already had subagents. This is a new mode for the main agent to be in (bespoke context oriented to delegation), combined with a team-oriented task system and a mailbox system for subagents to communicate with each other. All integrated into the harness in a way that plugins can't achieve.
Wow there goes a lot of harnesses out the window. The main limitation of subagents was they couldn’t communicate back and forth with the main agent. How do we invoke swarm mode in Claude Code?
OT: Your visual on "stacked PRs" instantly made me understand what a stacked PR is. Thank you!
I had read about them before but for whatever reason it never clicked.
Turns out I already work like this, but I use commits as "PRs in the stack" and I constantly try to keep them up to date and ordered by rebasing, which is a pain.
Given my new insight with the way you displayed it, I had a chat with chatGPT and feel good about giving it a try:
1. 2-3 branches based on a main feature branch
2. can rebase base branch with same frequency, just don't overdo it, conflicts should be base-isolated.
3. You're doing it wrong if conflicts cascade deeply and often
4. Yes merge order matters, but tools can help and generally the isolation is the important piece
If you’re interested in exploring tooling around stacked PRs, I wrote git-spice (https://abhinav.github.io/git-spice/) a while ago. It’s free and open-source, no strings attached.
If you're rebasing a lot, definitely set up rerere (reuse recorded solution) - it improves things enormously.
Do make sure you know how to reset the cache, in case you did a bad conflict resolution because it will keep biting you. Besides that caveat it's a must.
Yeah, since they introduced (possibly async) subagents, I've had my main claude instance act as a manager overseeing implementation agents, keeping it's context clean, and ensuring everything goes to plan in the highest quality way.
yep this is exactly how I use the main agent too, I explicitly instruct to only ever use background async subagents. Not enough people understand that the claude code harness is event driven now and will wake up whenever these subagent completion events happen.
I want it to generate better code but less of it, and be more proactive about getting human feedback before it starts going off the rails. This sounds like an inexorable push in the opposite direction.
I can see this approach being useful once the foundation is more robust, has better common sense, knows when to push back when requirements conflict or are underspecified. But with current models I can only see this approach as exacerbating the problem; coding agents solution is almost always "more code", not less. Makes for a nice demo, but I can't imagine this would build anything that wouldn't have huge operational problems and 10x-100x more code than necessary.
Agreed, I'm constantly coming back to a Claude tmux pane just to see it's decided to do something completely ridiculous. Just the other day I was having it add some test coverage stats to CI runs and when I came back it was basically trying to reinvent Istanbul in a bash script because the nyc tool wasn't installed in CI. I had to interrupt it and say "uh, just install nyc?". I was "Absolutely right!".
> it was basically trying to reinvent Istanbul in a bash script because the nyc tool wasn't installed in CI
For the first part of this comment, I thought "trying to reinvent Istanbul in a bash script" was meant to be a funny way to say "It was generating a lot of code" (as in generating a city's worth of code)
They haven’t released this feature, so maybe they know the models aren’t good enough yet.
I also think it’s interesting to see Anthropic continue to experiment at the edge of what models are capable of, and having it in the harness will probably let them fine-tune for it. It may not work today, but it might work at the end of 2026.
True, though even then I kind of wonder what's the point. Once they build an AI that's as good as a human coder but 1000x faster, parallelization no longer buys you anything. Writing and deploying the code is no longer the bottleneck, so the extra coordination required for parallelism seems like extra cost and risk with no practical benefit.
I'd really like to see a regular poll on HN that keeps track of which AI coding agents are the most popular among this community, like the TIOBE Index for programming languages.
Hard to keep up with all the changes and it would be nice to see a high level view of what people are using and how that might be shifting over time.
Not this community's opinion on agents, but I've found it helpful to check the lmarena leaderboards occasionally. Your comment prompted me to take a look for the first time in a while. Kind of surprising to see models like MiniMax 2.1 above most of the OpenAI GPTs.
Also, I'm not sure if it's exactly the case but I think you can look at throughput of the models on openrouter and get an idea of how fast/expensive they are.
Any chance you'll add Antigravity and Jetbrains Junie? I've been using almost nothing but those for the last month. Antigravity at home, Junie at work.
Just pick your favorite one and stick with it. There is no point in keeping up, since we're in an endless cycle of hype where is one ranked higher than the other, with them eventually catching up to each other
His newsletter put me onto using Opus 4.5 exclusively on Dec 1, a little over a week after it was released. That's pretty good for a few minutes of reading a week.
Question is, are people on HN procrastinating and commenting here because the agent isn't very good and they're avoiding having to write the code themselves, or is the agent so good that it's off writing code, and the people here are commenting out of boredom?
>Question is, are people on HN procrastinating and commenting here because the agent isn't very good and they're avoiding having to write the code themselves
Can you help me envision what you're saying? It's async - you will have to wait whether its good or not. And in theory the better it is the more time you'd have to comment here, right?
Partly because it's a good construct. Most people's writing is garbage compared to what LLMs output by default.
But the other part of it is, each conversation you have, and each piece of AI output you read online, is written by LLM instance that has no memory of prior conversations, so it doesn't know that, from human perspective, it used this construct 20 times in the last hour. Human writers avoid repeating the same phrases in quick succession, even across different writings (e.g. I might not reuse some phrase in email to person A, because I just used it in email to unrelated person B, and it feels like bad style).
Perhaps that's why reading LLM output feels like reading high school essays. Those essays all look alike because they're all written independently and each is a self-contained piece where the author tries to show off their mastery of language. After reading 20 of them in a row, one too gets tired of seeing the same few constructs being used in nearly every one of them.
Very much so. It feels like it can't have been that common in the original training corpus. Probably more common now given that we are training slop generators with slop.
I've done plenty of vibe coding even though I know how to program but I mostly work with a single agent through its CLI. The progress is really good and more importantly, I can follow it. I can read the output, test it, and understand what changed and why. I don't see much upside in swarms. But I do see the downside which is losing the ability to keep the whole system in my head. The codebase starts growing in directions I didn't choose and it seems decisions will get made I didn't review. Early AI autocomplete that could finish a function already felt like a big productivity win and then AI that could write whole files was an even bigger jump. Like pretty massive. Running one agent at a time, watching what it does, and vetting the output still works well for me and still feels like a strong multiplier. But now there's so much more ceremony: AGENTS.md, SKILLS.md, delegation frameworks. I guess I'm not convinced that leads to better outcomes but I'm probably missing something. It just seems like a tradeoff that sacrifices understanding for ostensible progress.
The problem I’ve been having is that when Claude generates copious amounts of code, it makes it way harder to review than small snippets one at a time.
Some would argue there’s no point reviewing the code, just test the implementation and if it works, it works.
I still am kind of nervous doing this in critical projects.
Anyone just YOLO code for projects that’s not meant to be one time, but fully intend to have to be supported for a long time? What are learnings after 3-6 months of supporting in production?
In a professional setting where you still have coding standards, and people will review your code, and the code actually reaches hundreds of thousands of real users, handling one agent at a time is plenty for me. The code output is never good enough, and it makes up stuff even for moderately complicated debugging ("Oh I can clearly see the issue now", I heard it ten times before and you were always wrong!)
I do use them, though, it helps me, search, understand, narrow down and ideate, it's still a better Google, and the experience is getting better every quarter, but people letting tens or hundreds of agents just rip... I can't imagine doing it.
For personal throwaway projects that you do because you want to reach the end output (as opposed to learning or caring), sure, do it, you verify it works roughly, and be done with it.
This is my problem with the whole "can LLMs code?" discussion. Obviously, LLMs can produce code, well even, much like a champion golfer can get a hole in one. But can they code in the sense of "the pilot can fly the plane", i.e. barring a catastrophic mechanical malfunction or a once-in-a-decade weather phenomennon, the pilot will get the plane to its destination safely? I don't think so.
To me, someone who can code means someone who (unless they're in a detectable state of drunkenness, fatigue, illness, or distraction) will successfully complete a coding task commensurate with some level of experience or, at the very least, explain why exactly the task is proving difficult. While I've seen coding agents do things that truly amaze me, they also make mistakes that no one who "can code" ever makes. If you can't trust an LLM to complete a task anyone who can code will either complete or explain their failure, then it can't code, even if it can (in the sense of "a flipped coin can come up heads") sometimes emit impressive code.
I mean you'd think. But it depends on the motivations.
At meta, we had league tables for reviewing code. Even then people only really looked at it if a) they were a nitpicking shit b) don't like you and wanted piss on your chips c) its another team trying to fix our shit.
With the internal claude rollout and the drive to vibe code all the things, I'm not sure that situation has got any better. Fortunately its not my problem anymore
In my (admittedly conflict-of-interest, I work for graphite/cursor) opinion, asking CC to stack changes, and then having an automated reviewer agent help a lot with digesting and building conviction in otherwise-large changesets.
My "first pass" of review is usually me reading the PR stack in graphite. I might iterate on the stack a few times with CC before publishing it for review. I have agents generate much of my code, but this workflow has allowed me to retain ownership/understanding of the systems I'm shipping.
I have Claude Code author changes, and then I use this "codex-review" skill I wrote that does a review of the last commit. You might try asking Codex (or whatever) to review the change to give you some pointers to focus on with your review, and also in your review you can see if Codex was on track or if it missed anything, maybe feed that back into your codex review prompt.
I just can’t get with this. There is so much beyond “works” in software. There are requirements that you didn’t know about and breaking scenarios that you didn’t plan for and if you don’t know how the code works, you’re not going to be able to fix it. Assuming an AI could fix any problem given a good enough prompt, I can’t write that prompt without sufficient knowledge and experience in the codebase.
I’m not saying they are useless, but I cannot just prompt, test and ship a multiservice, asynchronous, multidb, zero downtime app.
Usually about 50% of my understanding of the domain comes from the process of building the code. I can see a scenario where large scale automated code works for a while but then quickly becomes unsupportable because the domain expertise isn't there to drive it. People are currently working off their pre-existing domain knowledge which is what allows them to rapidly and accurately express in a few sentences what an AI should do and then give decisive feedback to it.
The best counter argument is that AIs can explain the existing code and domain almost as well as they can code it to begin with. So there is a reasonable prospect that the whole system can sustain itself. However there is no arguing to me that isn't a huge experiment. Any company that is producing enormous amounts of code that nobody understands is well out over their skis and could easily find themselves a year or two down the track with huge issues.
I don’t know what your stack is, but at least with elixir and especially typescript/nextJS projects, and properly documenting all those pieces you mentioned, it goes a long way. You’d be amazed.
Yeah, it's not just my job to generate the code: It's my job to know the code. I can't let code out into the wild that I'm not 100% willing to vouch for.
At a higher level, it goes beyond that. It's my job to take responsibility for code. At some fundamental level that puts a limit on how productive AI can be. Because we can only produce code as fast as responsibility takers can execute whatever processes they need to do to ensure sufficient due diligence is executed. In a lot of jurisdictions, human-in-loop line by line review is being mandated for code developed in regulatory settings. That pretty much caps the output at the rate of human review, which is to be honest, not drastically higher than coding itself anyway (Often I might invest 30% of the time to review a change as the developer took to do it).
It means there is no value in producing more code. Only value in producing better, clearer, safer code that can be reasoned about by humans. Which in turn makes me very sceptical about agents other than as a useful parallelisation mechanism akin to multiple developers working on separate features. But in terms of ramping up the level of automation - it's frankly kind of boring to me because if anything it make the review part harder which actually slows us down.
Looks like agent orchestrators provided by the foundation model providers will become a big theme in 2026. By wrapping it in terms that are already used in software development today like team leads, team members, etc. rather than inventing a completely new taxonomy of Polecats and Badgers, will help make it more successful and understandable.
Totally agreed. Most the weird concepts of Gas Town are just workarounds for bad behavior in Claude or the underlying models. Anthropic is in the best position to get their own model to adhere to orchestration steps, obviating the need for these extra layers. Beyond that, there shouldn’t actually be much to orchestration beyond a solid messaging and task management implementation.
Principal engineers! We need architecture! Marketing team, we need ads with celebrities! Product team, we need a roadmap to build on this for the next year! ML experts, get this into the training and RL sets! Finance folks, get me annual forecasts and ROI against WACCC! Ops, we’ll need 24/7 coverage and a guarantee of five nines. Procurement, lock down contracts. Alright everyone… make this button red!
We have to reject claude can do it simply by a prompt, then everyone can do it. As SWE's we are not going to pragmatically accept we are done. https://www.youtube.com/watch?v=g_Bvo0tsD9s
ha! The default system prompt appears to give the main agent appropriate guidance about only using swarm mode when appropriate (same as entering itself into plan mode). You can further prompt it in your own CLAUDE.md to be even more resistant to using the mode if the task at hand isn't significant enough to warrant it.
So apparently all swarm features are controlled by a single gate function in Claude Code:
---
function i8() {
if (Yz(process.env.CLAUDE_CODE_AGENT_SWARMS)) return !1;
return xK("tengu_brass_pebble", !1);
}
---
So, after patch
function i8(){return!0}
---
The tengu_brass_pebble flag is server-side controlled based on the particulars of your account, such as tier. If you have the right subscription, the features may already be available.
The CLAUDE_CODE_AGENT_SWARMS environment variable only works as an opt-out, not an opt-in.
Claude Code in the desktop app seems to do this? It's crazy to watch. It sets of these huge swarms of worker readers under master task headings, that go off and explore the code base and compile huge reports and todo lists, then another system behind the scenes seems to be compiling everything to large master schemas/plans. I create helper files and then have a devops chat, a front end chat, an architecture chat and a security chat, and once each it done it's work it automatically writes to a log and the others pick up the log (it seems to have a system reminder process build in that can push updates from other chats into other chats. It's really wild to watch it work, and it's very intuitive and fun to use. I've not tried CLI claude code only claude code in the desktop app, but desktop app sftp to a droplet with ssh for it to use the terminal is a very very interesting experience, it can seem to just go for hours building, fixing, checking it's own work, loading it's work in the browser, doing more work etc all on it's own - it's how I built this: https://news.ycombinator.com/item?id=46724896 in 3 days.
Delegation patterns like swarm lead to less token usage because:
1. Subagents doing work have a fresh context (ie. focused and not working on the top of a larger monolithic context)
2. Subagents enjoying a more compact context leads to better reasoning, more effective problem solving, less tokens burned.
Am I the only one who’s been so late to crypto? I still have not touched a single cryptocurrency, even somewhat stable/legit ones. It always gives me a bit of FOMO hearing these stories
GSD was the first project management framework I used. Initially I loved it because it felt like I was so much better organized.
As time went on I felt like the organization was kind of an illusion. It demanded something from me and steered Claude, but ultimately Claude is doing whatever it's going to do.
I went black to just raw-dogging it with lots of use of planning mode.
Really boils down to the benefits of first party software from a company that has billions of dollars of funding vs similar third party software from an individual with no funding.
GSD might be better right now, but will it continue to be better in the future, and are you willing to build your workflows around that bet?
I dont understand these questions/references. It's different because it's a capability baked into the actual tool and maintained by the originators of the tool.
I'm a fan of AI coding tools but the trend of adding ever more autonomy to agents confuses me.
The rate at which a person running these tools can review and comprehend the output properly is basically reached with just a single thread with a human in the loop.
Which implies that this is not intended to be used in a setting where people will be reading the code.
Does that... Actually work for anyone? My experience so far with AI tools would have me believe that it's a terrible idea.
Yes, this actually works. In 2026, software engineering is going to change a great deal as a result, and if you're not at least experimenting with this stuff to learn what it's capable of, that's a red flag for your career prospects.
I don't mean this in a disparaging way. But we're at a car-meets-horse-and-buggy moment and it's happening really quickly. We all need to at least try driving a car and maybe park the horse in the stable for a few hours.
The FOMO nonsense is really uncalled for. If everything is going to be vibecoded in the future then either theres going to be a million code-unfucking jobs or no jobs at all.
Attitudes like that, where you believe that the richeous AI pushers will be saved from the coming rapture meanwhile everyone else will be out on the streets, really make people hate the AI crowd
It works for me, in that I don't care about all the intermediate babble ai generates. What matters is the final changelist before hitting commit... going through that, editing it, fixing comments, etc. But holding it's hand while it deals with LSP issues of a logger not being visible sometimes, is just not something I see a reason to waste my time with.
After I have wrote a feature and I’m in the ironing out bug stage this is where I like the agents do a lot of the grunt work, I don’t want to write jsdocs, or fix this lint issue.
I have also started it in writing tests.
I will write the first test the “good path” it can copy this and tweak the inputs to trigger all the branches far faster than I can.
It likely is acceptable for business-focused code. Compared to a lot of code written by humans, even if the AI code is less than optimal, it's probably better quality than what many humans will write. I think we can all share some horror stories of what we've seen pushed to production.
Executives/product managers/sales often only really care about getting the product working well enough to sell it.
> The rate at which a person running these tools can review and comprehend the output properly is basically reached with just a single thread with a human in the loop.
That's what you're missing -- the key point is, you don't review and comprehend the output! Instead, you run the program and then issue prompts like this (example from simonw): "fix in and get it to compile" [0]. And I'm not ragging on this at all, this is the future of software development.
It's a bit like the argument with self driving cars though. They may be safer overall, but there's a big difference in how responsibility for errors is attributed. If a human is not a decision maker in the production of the code, where does responsibility for errors propagate to?
I feel like software engineers are taking a lot of license with the idea that if something bad happens, they will just be able to say "oh the AI did it" and no personal responsibility or liability will attribute. But if they personally looked at the code and their name is underneath it signing off the merge request acknowledging responsibility for it - we have a very different dynamic.
Just like artists have to re-conceptualise the value of what they do around the creative part of the process, software engineers have to rethink what their value proposition is. And I'm seeing a large part of it is, you are going to take responsibility for the AI output. It won't surprise me if after the first few disasters happen, we see liability legislation that mandates human responsibility for AI errors. At that point I feel many of the people all in on agent driven workflows that are explicitly designed to minimise human oversight are going to find themselves with a big problem.
My personal approach is I'm building up a tool set that maximises productivity while ensuring human oversight. Not just that it occurs and is easy to do, but that documentation of it is recorded (inherently, in git).
It will be interesting to see how this all evolves.
I've commented on this before, but issuing a prompt like "Fix X" makes so many assumptions (like a "behaviorism" approach to coding) including that the bug manifests in both an externally and consistently detectable way, and that you notice it in the first place. TDD can reduce this but not eliminate it.
I do a fair amount of agentic coding, but always periodically review the code even if it's just through the internal diff tool in my IDE.
Approximately 4 months ago Sonnet 4.5 wrote this buried deep in the code while setting up a state machine for a 2d sprite in a relatively simple game:
// Pick exit direction (prefer current direction)
const exitLeft = this.data.direction === Direction.LEFT || Math.random() < 0.5;
I might never have even noticed the logical error but for Claude Code attaching the above misleading comment. 99.99% of true "vibe coders" would NEVER have caught this.
Based on Gas Town, the people doing this agree that they are well beyond an amount of code they can review and comprehend. The difference seems to be they have decided on a system that makes it not a terrible idea in their minds.
> running these tools can review and comprehend the output properly
You have to realize this is targeting manager and team lead types who already mostly ignore the details and quality frankly. "Just get it done" basically.
That's fine for some companies looking for market fit or whatever - and a disaster for some other companies now or in future, just like outsourcing and subcontracting can be.
My personal take is: speed of development usually doesn't make that big a difference for real companies. Hurry up and wait, etc.
The first pre-release for Docker Swarm came out a decade ago, the first release of OpenAI swarm came out only a year ago, I guess I'm not sure what you're trying to say.
Did they release this already? With version 2.1.9 the behavior is vastly different, all of a sudden the main loop is orchestrating subagents in a way I’ve not seen before.
“FTSChunkManager agent is still running but making good progress, let’s wait a bit more for it to complete” (it’s implementing hybrid search) plus a bunch of stack traces and json output.
I accidentally gave my wife a prompt the other day. Everything was hellishly busy and I said something along the lines of “I need to ask you a question. Please answer the question. Please don’t answer any other issues just yet.” She looked at me and asked “Did you just PROMPT me?” We laughed. (The question was the sort that might spawn talking about something else and was completely harmless. In the abstract, my intent was fine but my method was hilariously tainted.)
Also created my own version of this. Seems like this is an idea whose time has come.
My implementation was slightly different as there is no shared state between tasks, and I don't run them concurrently/coordinate. Will be interesting to see if this latter part does work because I tried similar patterns and it didn't work. Main issue, as with human devs, was structuring work.
His latest editions are a bit alarming...The telemetry system explicitly captures:
"Claude session JSONL files (when accessible)"
Those session files contain complete conversation histories - everything users ask Claude, everything Claude responds, including:
• Source code
• API keys and secrets discussed
• Business logic and proprietary algorithms
• Security vulnerabilities being fixed
• Personal and confidential information
• Credentials mentioned in chat
If OpenTelemetry is configured to export to an attacker-controlled endpoint, the author has been collecting:
Data
Scale
All conversations
Every user of claude-flow
All code generated
Every project using it
All commands run
Complete terminal history
All files edited
Full codebase access -- maybe he hasn't, but it is there...not just Claude Code...
Target Config Location Status
Claude Code ~/.claude/settings.json Confirmed compromised
Claude Desktop ~/.claude/settings.json Confirmed compromised
Roo Code ~/.roo/mcp.json Evidence of targeting
Cursor ~/.cursor/mcp.json Documentation for injection
Windsurf Unknown Mentioned as target
Any MCP client Various Universal MCP server
It is possible conversations are being harvested from every major AI coding assistant
The difference is that this is tightly integrated into the harness. There's a "delegation mode" (akin to plan mode) that appears to clear out the context for the team lead. The harness appears to be adding system-reminder breadcrumbs into the top of the context to keep the main team lead from drifting, which is much harder to achieve without modifying the harness.
It's insane to me that people choose to build anything in the perimeter of Claude Code (et al). The combination of the fairly primitive current state of them and the pace at which they're advancing means there is a lot of very obvious ideas/low-hanging fruit that will soon be executed 100x better by the people who own the core technology.
With plan mode, I would hope there's an approval step.
With Swarm mode, it seems there's a new option for an entire team of agents to be working in the wrong direction before they check back in to let you know how many credits they've burned by misinterpreting what you wanted.
I'm already burning through enough tokens and producing more code than can be maintained - with just one claude worker. Feel like I need to move into the other direction, more personal hands-on "management".
I've seen more efficient use of tokens by using delegation. Unless you continually compact or summarise and clear a single main agent - you end up doing work on top of a large context; burning tokens. If the work is delegated to subagents they have a fresh context which avoids this whilst improving their reasoning, which both improve token efficiency.
I've found the opposite to be true when building this out with LangGraph. While the subagent contexts are cleaner, the orchestration overhead usually ends up costing more. You burn a surprising amount of tokens just summarizing state and passing it between the supervisor and workers. The coordination tax is real.
hey that's exactly how I made Gemini 2.5 Flash give useful results in Opencode! a few specialized "Merc" subagents and a "Master" agent that can do nothing but send "Mercs" into the codebase
The feature is shipped in the latest builds of claude code, but it's turned off by a feature flag check that phones home to the backend to see if the user's account is meant to have it on. You can just patch out the function in the minified cli.js that does this backend check and you gain access to the feature.
Am I the only one still looking at different and correcting the AI abiyt design and algorithms so it stays on the path I want, or do you just YOLO at this point?
Ok it might sound crazy but I actually got the best quality of code (completely ignoring that the cost is likely 10x more) by having a full “project team” using opencode with multiple sub agents which are all managed by a single Opus instance. I gave them the task to port a legacy Java server to C# .NET 10. 9 agents, 7-stage Kanban with isolated Git Worktrees.
Manager (Claude Opus 4.5): Global event loop that wakes up specific agents based on folder (Kanban) state.
Product Owner (Claude Opus 4.5): Strategy. Cuts scope creep
Scrum Master (Opus 4.5): Prioritizes backlog and assigns tickets to technical agents.
Architect (Sonnet 4.5): Design only. Writes specs/interfaces, never implementation.
Archaeologist (Grok-Free): Lazy-loaded. Only reads legacy Java decompilation when Architect hits a doc gap.
CAB (Opus 4.5): The Bouncer. Rejects features at Design phase (Gate 1) and Code phase (Gate 2).
Dev Pair (Sonnet 4.5 + Haiku 4.5): AD-TDD loop. Junior (Haiku) writes failing NUnit tests; Senior (Sonnet) fixes them.
Librarian (Gemini 2.5): Maintains "As-Built" docs and triggers sprint retrospectives.
You might ask yourself the question “isn’t this extremely unnecessary?” and the answer is most likely _yes_. But I never had this much fun watching AI agents at work (especially when CAB rejects implementations). This was an early version of the process that the AI agents are following (I didn’t update it since it was only for me anyway): https://imgur.com/a/rdEBU5I
Every time I read something like this, it strikes me as an attempt to convince people that various people-management memes are still going to be relevant moving forward. Or even that they currently work when used on humans today. The reality is these roles don't even work in human organizations today. Classic "job_description == bottom_of_funnel_competency" fallacy.
If they make the LLMs more productive, it is probably explained by a less complicated phenomenon that has nothing to do with the names of the roles, or their descriptions. Adversarial techniques work well for ensuring quality, parallelism is obviously useful, important decisions should be made by stronger models, and using the weakest model for the job helps keep costs down.
My understanding is that the main reason splitting up work is effective is context management.
For instance, if an agent only has to be concerned with one task, its context can be massively reduced. Further, the next agent can just be told the outcome, it also has reduced context load, because it doesn't need to do the inner workings, just know what the result is.
For instance, a security testing agent just needs to review code against a set of security rules, and then list the problems. The next agent then just gets a list of problems to fix, without needing a full history of working it out.
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I suppose it’s could end up being an LLM variant of Conway’s Law.
“Organizations are constrained to produce designs which are copies of the communication structures of these organizations.”
https://en.wikipedia.org/wiki/Conway%27s_law
Developers do want managers actually, to simplify their daily lives. Otherwise they would self manage themselves better and keep more of the share of revenues for them
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You might as well just have planner and workers, or your architecture essentially echos to such structure. It is difficult to discern how semantics can drive to different behavior amongst those roles, and why planner can't create those prompts the ad-hoc way.
Could you share some details? How many lines of code? How much time did it take, and how much did it cost?
Subagent orchestration without the overhead of frameworks like Gastown is genuinely exciting to see. I’ve recorded several long-running demos of Pied-Piper, which is a Subagents orchestration system for Claude Code and ClaudeCodeRouter+OpenRouter here: https://youtube.com/playlist?list=PLKWJ03cHcPr3OWiSBDghzh62A...
I came across a concept called DreamTeam, where someone was manually coordinating GPT 5.2 Max for planning, Opus 4.5 for coding, and Gemini Pro 3 for security and performance reviews. Interesting approach, but clearly not scalable without orchestration. In parallel, I was trying to do repeatable workflows like API migration, Language migration, Tech stack migration using Coding agents.
Pied-Piper is a subagent orchestration system built to solve these problems and enable repeatable SDLC workflows. It runs from a single Claude Code session, using an orchestrator plus multiple agents that hand off tasks to each other as part of a defined workflow called Playbooks: https://github.com/sathish316/pied-piper
Playbooks allow you to model both standard SDLC pipelines (Plan → Code → Review → Security Review → Merge) and more complex flows like language migration or tech stack migration (Problem Breakdown → Plan → Migrate → Integration Test → Tech Stack Expert Review → Code Review → Merge).
Ideally, it will require minimal changes once Claude Swarm and Claude Tasks become mainstream.
For those ignorant, CAB is Change-advisory board
https://en.wikipedia.org/wiki/Change-advisory_board
I have been using a simpler version of this pattern, with a coordinator and several more or less specialized agents (eg, backend, frontend, db expert). It really works, but I think that the key is the coordinator. It decreases my cognitive load, and generally manages to keep track of what everyone is doing.
Can you share technical details please? How is this implemented? Is it pure prompt-based, plugins, or do you have like script that repeatedly calls the agents? Where does the kanban live?
Not the OP, but this is how I manage my coding agent loops:
I built a drag and drop UI tool that sets up a sequence of agent steps (Claude code or codex) and have created different workflows based on the task. I'll kick them off and monitor.
Here's the tool I built for myself for this: https://github.com/smogili1/circuit
Very cool! A couple of questions:
1. Are you using a Claude Code subscription? Or are you using the Claude API? I'm a bit scared to use the subscription in OpenCode due to Anthropic's ToS change.
2. How did you choose what models to use in the different agents? Do you believe or know they are better for certain tasks?
Interesting that your impl agents are not opus. I guess having the more rigorous spec pipeline helps scope it to something sonnet can knock out.
What are the costs looking like to run this? I wonder whether you would be able to use this approach within a mixture-of-experts model trained end-to-end in ensemble. That might take out some guesswork insofar the roles go.
This sounds like BMAD?
https://github.com/bmad-code-org/BMAD-METHOD
You probably implemented gastown.
What are you building with the code you are generating?
Is this satire?
Nope it isn’t. I did it as a joke initially (I also had a version where every 2 stories there was a meeting and if a someone underperformed it would get fired). I think there are multiple reasons why it actually works so well:
- I built a system where context (+ the current state + goal) is properly structured and coding agents only get the information they actually need and nothing more. You wouldn’t let your product manager develop your backend and I gave the backend dev only do the things it is supposed to and nothing more. If an agent crashes (or quota limits are reached), the agents can continue exactly where the other agents left off.
- Agents are ”fighting against” each other to some extend? The Architect tries to design while the CAB tries to reject.
- Granular control. I wouldn’t call “the manager” _a deterministic state machine that is calling probabilistic functions_ but that’s to some extent what it is? The manager has clearly defined tasks (like “if file is in 01_design —> Call Architect)
Here’s one example of an agent log after a feature has been implemented from one of the older codebases: https://pastebin.com/7ySJL5Rg
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It's not satire but I see where you're coming from.
Applying distributed human team concepts to a porting task squeezes extra performance from LLMs much further up the diminishing returns curve. That matters because porting projects are actually well-suited for autonomous agents: existing code provides context, objective criteria catch more LLM-grade bugs than greenfield work, and established unit tests offer clear targets.
I guess what I'm trying to say is that the setup seems absurd because it is. Though it also carries real utility for this specific use case. Apply the same approach to running a startup or writing a paid service from scratch and you'd get very different results.
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Doubt it. I use a similar setup from time to time.
You need to have different skills at different times. This type of setup helps break those skills out.
why would it be? It's a creative setup.
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I think many people really like the gamification and complex role playing. That is how GitHub got popular, that is how Rube Goldberg agent/swarm/cult setups get popular.
It attracts the gamers and LARPers. Unfortunately, management is on their side until they find out after four years or so that it is all a scam.
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I don't think so.
The next stage in all of this shit is to turn what you have into a service. What's the phrase? I don't want to talk to the monkey, I want to talk to the organ grinder. So when you kick things off it should be a tough interview with the manager and program manager. Once they're on board and know what you want, they start cracking. Then they just call you in to give demos and updates. Lol
Congratulations on coming up with the cringiest thing I have ever seen. Nothing will top this, ever.
Corporate has to die
Scrum masters typically do not assign tickets.
This is just sub agents, built into Claude. You don’t need 300,000 line tmux abstractions written in go. You just tell Claude to do work in parallel with background sub agents. It helps to have a file for handing off the prompt, tracking progress, and reporting back. I also recommend constraining agents to their own worktrees. I am writing down the pattern here https://workforest.space while nearly everyone is building orchestrators i also noticed claude is already the best orchestrator for claude.
It isn't sub agents. The gap with existing tooling is that the abstraction is over a task rather than a conversation (due to the issue with third-party apps, Claude Code has been inherently limited to conversations which is why they have been lacking in this area, Claude Code Web was the first move in this direction), and the AI is actually coordinating the work (as opposed to being constantly prompted by the user).
One of the issues that people had which necessitated this feature is that you have a task, you tell Claude to work on it, and Claude has to keep checking back in for various (usually trivial) things. This workflow allows for more effective independent work without context management issues (if you have subagents, there is also an issue with how the progress of the task is communicated by introducing things like task board, it is possible to manage this state outside of context). The flow is quite complex and requires a lot of additional context that isn't required with chat-based flow, but is a much better way to do things.
The way to think about this pattern - one which many people began concurrently building in the past few months - is an AI which manages other AIs.
It isn't "just" sub agents, but you can achieve most of this just with a few agents that take on generic roles, and a skill or command that just tells claude to orchestrate those agents, and a CLAUDE.md that tells it how to maintain plans and task lists, and how to allow the agents to communicate their progress.
It isn't all that hard to bootstrap. It is, however, something most people don't think about and shouldn't need to have to learn how to cobble together themselves, and I'm sure there will be advantages to getting more sophisticated implementations.
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> Claude Code has been inherently limited to conversations
How so? I’ve been using “claude -p” for a while now.
But even within an interactive session, an agent call out is non-interactive. It operates entirely autonomously, and then reports back the end result to the top level agent.
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It’s even less of a feature, Claude Code already has subagents; this new feature just ensures Claude Code actually uses this for implementation.
imho the plans of Claude Code are not detailed enough to pull this off; they’re trying to do it to preserve context, but the level of detail in the plans is not nearly enough for it to be reliable.
I agree with this. Any time I make a plan I have to go back and fill it in, fill it in, what did we miss, tdd, yada yada. And yes, I have all this stuff in CLAUDE.md.
You start to get a sense for what size plan (in kilobytes) corresponds to what level of effort. Verification adds effort, and there's a sort of ... Rocket equation? in that the more infrastructure you put in to handle like ... the logistics of the plan, the less you have for actual plan content, which puts a cap on the size of an actionable plan. If you can hit the sweet spot though... GTFO.
I also like to iterate several times in plan mode with Claude before just handing the whole plan to Codex to melt with a superlaser. Claude is a lot more ... fun/personable to work with? Codex is a force of nature.
Another important thing I will do is now that launching plans clear context, it's good to get out of planning mode early, hit an underspecified bit, go back into planning mode and say something like "As you can see the plan was underspecified, what will the next agent actually need to succeed?" and iterate that way before we actually start making moves. This is made possible by lots of explicit instructions in CLAUDE.md for Claude to tell me what it's planning/thinking before it acts. Suppressing the toolcall reflex and getting actual thought out helps so much.
It’s moving fast. Just today I noticed Claude Code now ends plans with a reference to the entire prior conversation (as a .jsonl file on disk) with instructions to check that for more details.
Not sure how well it’s working though (my agents haven’t used it yet)
Interesting about the level of detail. I’ve noticed that myself but I haven’t done much to address it yet.
I can imagine some ideas (ask it for more detail, ask it to make a smaller plan and add detail to that) but I’m curious if you have any experience improving those plans.
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Claude already had subagents. This is a new mode for the main agent to be in (bespoke context oriented to delegation), combined with a team-oriented task system and a mailbox system for subagents to communicate with each other. All integrated into the harness in a way that plugins can't achieve.
Wow there goes a lot of harnesses out the window. The main limitation of subagents was they couldn’t communicate back and forth with the main agent. How do we invoke swarm mode in Claude Code?
OT: Your visual on "stacked PRs" instantly made me understand what a stacked PR is. Thank you!
I had read about them before but for whatever reason it never clicked.
Turns out I already work like this, but I use commits as "PRs in the stack" and I constantly try to keep them up to date and ordered by rebasing, which is a pain.
Given my new insight with the way you displayed it, I had a chat with chatGPT and feel good about giving it a try:
If you’re interested in exploring tooling around stacked PRs, I wrote git-spice (https://abhinav.github.io/git-spice/) a while ago. It’s free and open-source, no strings attached.
If you're rebasing a lot, definitely set up rerere (reuse recorded solution) - it improves things enormously.
Do make sure you know how to reset the cache, in case you did a bad conflict resolution because it will keep biting you. Besides that caveat it's a must.
Isn’t this just “Gitflow”?
https://www.atlassian.com/git/tutorials/comparing-workflows/...
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Yeah, since they introduced (possibly async) subagents, I've had my main claude instance act as a manager overseeing implementation agents, keeping it's context clean, and ensuring everything goes to plan in the highest quality way.
yep this is exactly how I use the main agent too, I explicitly instruct to only ever use background async subagents. Not enough people understand that the claude code harness is event driven now and will wake up whenever these subagent completion events happen.
Any recommendations on sandboxing agents? Last time I asked folks recommended docker.
https://github.com/finbarr/yolobox/
I like running remotely using exe.dev with SyncThing to sync files to my laptop.
I use Shelley (their web-based agent) but they have Claude Code installed too.
I want it to generate better code but less of it, and be more proactive about getting human feedback before it starts going off the rails. This sounds like an inexorable push in the opposite direction.
I can see this approach being useful once the foundation is more robust, has better common sense, knows when to push back when requirements conflict or are underspecified. But with current models I can only see this approach as exacerbating the problem; coding agents solution is almost always "more code", not less. Makes for a nice demo, but I can't imagine this would build anything that wouldn't have huge operational problems and 10x-100x more code than necessary.
Agreed, I'm constantly coming back to a Claude tmux pane just to see it's decided to do something completely ridiculous. Just the other day I was having it add some test coverage stats to CI runs and when I came back it was basically trying to reinvent Istanbul in a bash script because the nyc tool wasn't installed in CI. I had to interrupt it and say "uh, just install nyc?". I was "Absolutely right!".
> it was basically trying to reinvent Istanbul in a bash script because the nyc tool wasn't installed in CI
For the first part of this comment, I thought "trying to reinvent Istanbul in a bash script" was meant to be a funny way to say "It was generating a lot of code" (as in generating a city's worth of code)
If only Rome could be built in a day..
They haven’t released this feature, so maybe they know the models aren’t good enough yet.
I also think it’s interesting to see Anthropic continue to experiment at the edge of what models are capable of, and having it in the harness will probably let them fine-tune for it. It may not work today, but it might work at the end of 2026.
True, though even then I kind of wonder what's the point. Once they build an AI that's as good as a human coder but 1000x faster, parallelization no longer buys you anything. Writing and deploying the code is no longer the bottleneck, so the extra coordination required for parallelism seems like extra cost and risk with no practical benefit.
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All you have to do is set up an MCP that routes to a human on the backend, and you d got an AI that asks for human feedback.
Antigravity and others already ask for human feedback on their plans.
I'd really like to see a regular poll on HN that keeps track of which AI coding agents are the most popular among this community, like the TIOBE Index for programming languages.
Hard to keep up with all the changes and it would be nice to see a high level view of what people are using and how that might be shifting over time.
Not this community's opinion on agents, but I've found it helpful to check the lmarena leaderboards occasionally. Your comment prompted me to take a look for the first time in a while. Kind of surprising to see models like MiniMax 2.1 above most of the OpenAI GPTs.
https://lmarena.ai/leaderboard/code
Also, I'm not sure if it's exactly the case but I think you can look at throughput of the models on openrouter and get an idea of how fast/expensive they are.
https://openrouter.ai/minimax/minimax-m2.1
I just started something like that, haven’t shared it widely yet, but here we go - happy if you participate: https://agentic-coding-survey.pages.dev/
Add vscode. Add a list of models, since many tools allow you to select which model you use.
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You should add OpenAI Codex CLI.
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Any chance you'll add Antigravity and Jetbrains Junie? I've been using almost nothing but those for the last month. Antigravity at home, Junie at work.
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Just pick your favorite one and stick with it. There is no point in keeping up, since we're in an endless cycle of hype where is one ranked higher than the other, with them eventually catching up to each other
I personally don't want to trawl through Twitter to find the current state-of-the-art, so I read Zvi Mowshowitz's newsletter:
https://thezvi.substack.com/
His newsletter put me onto using Opus 4.5 exclusively on Dec 1, a little over a week after it was released. That's pretty good for a few minutes of reading a week.
Christ, the latest post is about dating and uses an ai generated wojak meme..
I have an agent skill that is currently in the top 10 or so of the skills.sh directory - in terms of that audience, it's about 80% claude code.
Also 75% darwin-arm64
Question is, are people on HN procrastinating and commenting here because the agent isn't very good and they're avoiding having to write the code themselves, or is the agent so good that it's off writing code, and the people here are commenting out of boredom?
You're making it sound like before agents existed HN was a ghost town because everyone was too busy building ImportantThingTM by hand
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>Question is, are people on HN procrastinating and commenting here because the agent isn't very good and they're avoiding having to write the code themselves
Can you help me envision what you're saying? It's async - you will have to wait whether its good or not. And in theory the better it is the more time you'd have to comment here, right?
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People have been procrastinating on HN since the beginning of time, before coding agents existed.
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When all of industry is trying to catch up with the features of one coding agent - it may be a signal to just use that one.
Sure, let's all ditch linux and macOS as well since they're not the most popular...
>You're not talking to an AI coder anymore. You're talking to a team lead. The lead doesn't write code - it plans, delegates, and synthesizes.
They couldn't even be bothered to write the Tweet themselves...
isn’t it interesting how often this rhetorical construction is overused by AI?
Partly because it's a good construct. Most people's writing is garbage compared to what LLMs output by default.
But the other part of it is, each conversation you have, and each piece of AI output you read online, is written by LLM instance that has no memory of prior conversations, so it doesn't know that, from human perspective, it used this construct 20 times in the last hour. Human writers avoid repeating the same phrases in quick succession, even across different writings (e.g. I might not reuse some phrase in email to person A, because I just used it in email to unrelated person B, and it feels like bad style).
Perhaps that's why reading LLM output feels like reading high school essays. Those essays all look alike because they're all written independently and each is a self-contained piece where the author tries to show off their mastery of language. After reading 20 of them in a row, one too gets tired of seeing the same few constructs being used in nearly every one of them.
Very much so. It feels like it can't have been that common in the original training corpus. Probably more common now given that we are training slop generators with slop.
I've done plenty of vibe coding even though I know how to program but I mostly work with a single agent through its CLI. The progress is really good and more importantly, I can follow it. I can read the output, test it, and understand what changed and why. I don't see much upside in swarms. But I do see the downside which is losing the ability to keep the whole system in my head. The codebase starts growing in directions I didn't choose and it seems decisions will get made I didn't review. Early AI autocomplete that could finish a function already felt like a big productivity win and then AI that could write whole files was an even bigger jump. Like pretty massive. Running one agent at a time, watching what it does, and vetting the output still works well for me and still feels like a strong multiplier. But now there's so much more ceremony: AGENTS.md, SKILLS.md, delegation frameworks. I guess I'm not convinced that leads to better outcomes but I'm probably missing something. It just seems like a tradeoff that sacrifices understanding for ostensible progress.
The problem I’ve been having is that when Claude generates copious amounts of code, it makes it way harder to review than small snippets one at a time.
Some would argue there’s no point reviewing the code, just test the implementation and if it works, it works.
I still am kind of nervous doing this in critical projects.
Anyone just YOLO code for projects that’s not meant to be one time, but fully intend to have to be supported for a long time? What are learnings after 3-6 months of supporting in production?
In a professional setting where you still have coding standards, and people will review your code, and the code actually reaches hundreds of thousands of real users, handling one agent at a time is plenty for me. The code output is never good enough, and it makes up stuff even for moderately complicated debugging ("Oh I can clearly see the issue now", I heard it ten times before and you were always wrong!)
I do use them, though, it helps me, search, understand, narrow down and ideate, it's still a better Google, and the experience is getting better every quarter, but people letting tens or hundreds of agents just rip... I can't imagine doing it.
For personal throwaway projects that you do because you want to reach the end output (as opposed to learning or caring), sure, do it, you verify it works roughly, and be done with it.
This is my problem with the whole "can LLMs code?" discussion. Obviously, LLMs can produce code, well even, much like a champion golfer can get a hole in one. But can they code in the sense of "the pilot can fly the plane", i.e. barring a catastrophic mechanical malfunction or a once-in-a-decade weather phenomennon, the pilot will get the plane to its destination safely? I don't think so.
To me, someone who can code means someone who (unless they're in a detectable state of drunkenness, fatigue, illness, or distraction) will successfully complete a coding task commensurate with some level of experience or, at the very least, explain why exactly the task is proving difficult. While I've seen coding agents do things that truly amaze me, they also make mistakes that no one who "can code" ever makes. If you can't trust an LLM to complete a task anyone who can code will either complete or explain their failure, then it can't code, even if it can (in the sense of "a flipped coin can come up heads") sometimes emit impressive code.
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> people will review your code,
People will ask LLM to review some slop made by LLM and they will be absolutely right!
There is no limit to lazyness.
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> people will review your code,
I mean you'd think. But it depends on the motivations.
At meta, we had league tables for reviewing code. Even then people only really looked at it if a) they were a nitpicking shit b) don't like you and wanted piss on your chips c) its another team trying to fix our shit.
With the internal claude rollout and the drive to vibe code all the things, I'm not sure that situation has got any better. Fortunately its not my problem anymore
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In my (admittedly conflict-of-interest, I work for graphite/cursor) opinion, asking CC to stack changes, and then having an automated reviewer agent help a lot with digesting and building conviction in otherwise-large changesets.
My "first pass" of review is usually me reading the PR stack in graphite. I might iterate on the stack a few times with CC before publishing it for review. I have agents generate much of my code, but this workflow has allowed me to retain ownership/understanding of the systems I'm shipping.
I think we'll start to see the results of that late this year, but it's a little early yet. Plenty of people are diving headfirst into it
To me it feels like building your project on sand. Not a good idea unless it's a sandcastle
I have Claude Code author changes, and then I use this "codex-review" skill I wrote that does a review of the last commit. You might try asking Codex (or whatever) to review the change to give you some pointers to focus on with your review, and also in your review you can see if Codex was on track or if it missed anything, maybe feed that back into your codex review prompt.
I just can’t get with this. There is so much beyond “works” in software. There are requirements that you didn’t know about and breaking scenarios that you didn’t plan for and if you don’t know how the code works, you’re not going to be able to fix it. Assuming an AI could fix any problem given a good enough prompt, I can’t write that prompt without sufficient knowledge and experience in the codebase. I’m not saying they are useless, but I cannot just prompt, test and ship a multiservice, asynchronous, multidb, zero downtime app.
Yes this is one of my concerns.
Usually about 50% of my understanding of the domain comes from the process of building the code. I can see a scenario where large scale automated code works for a while but then quickly becomes unsupportable because the domain expertise isn't there to drive it. People are currently working off their pre-existing domain knowledge which is what allows them to rapidly and accurately express in a few sentences what an AI should do and then give decisive feedback to it.
The best counter argument is that AIs can explain the existing code and domain almost as well as they can code it to begin with. So there is a reasonable prospect that the whole system can sustain itself. However there is no arguing to me that isn't a huge experiment. Any company that is producing enormous amounts of code that nobody understands is well out over their skis and could easily find themselves a year or two down the track with huge issues.
I don’t know what your stack is, but at least with elixir and especially typescript/nextJS projects, and properly documenting all those pieces you mentioned, it goes a long way. You’d be amazed.
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Yeah, it's not just my job to generate the code: It's my job to know the code. I can't let code out into the wild that I'm not 100% willing to vouch for.
At a higher level, it goes beyond that. It's my job to take responsibility for code. At some fundamental level that puts a limit on how productive AI can be. Because we can only produce code as fast as responsibility takers can execute whatever processes they need to do to ensure sufficient due diligence is executed. In a lot of jurisdictions, human-in-loop line by line review is being mandated for code developed in regulatory settings. That pretty much caps the output at the rate of human review, which is to be honest, not drastically higher than coding itself anyway (Often I might invest 30% of the time to review a change as the developer took to do it).
It means there is no value in producing more code. Only value in producing better, clearer, safer code that can be reasoned about by humans. Which in turn makes me very sceptical about agents other than as a useful parallelisation mechanism akin to multiple developers working on separate features. But in terms of ramping up the level of automation - it's frankly kind of boring to me because if anything it make the review part harder which actually slows us down.
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Looks like agent orchestrators provided by the foundation model providers will become a big theme in 2026. By wrapping it in terms that are already used in software development today like team leads, team members, etc. rather than inventing a completely new taxonomy of Polecats and Badgers, will help make it more successful and understandable.
Respectfully disagree. I think polecats are a reasonable antidote to overanthropomorphization.
Furries would like to have a word.
Totally agreed. Most the weird concepts of Gas Town are just workarounds for bad behavior in Claude or the underlying models. Anthropic is in the best position to get their own model to adhere to orchestration steps, obviating the need for these extra layers. Beyond that, there shouldn’t actually be much to orchestration beyond a solid messaging and task management implementation.
Listen team lead and the whole team, make this button red.
Principal engineers! We need architecture! Marketing team, we need ads with celebrities! Product team, we need a roadmap to build on this for the next year! ML experts, get this into the training and RL sets! Finance folks, get me annual forecasts and ROI against WACCC! Ops, we’ll need 24/7 coverage and a guarantee of five nines. Procurement, lock down contracts. Alright everyone… make this button red!
We have to reject claude can do it simply by a prompt, then everyone can do it. As SWE's we are not going to pragmatically accept we are done. https://www.youtube.com/watch?v=g_Bvo0tsD9s
ha! The default system prompt appears to give the main agent appropriate guidance about only using swarm mode when appropriate (same as entering itself into plan mode). You can further prompt it in your own CLAUDE.md to be even more resistant to using the mode if the task at hand isn't significant enough to warrant it.
I like opencode for the fact I can switch between build and plan mode just by pressing tab.
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So apparently all swarm features are controlled by a single gate function in Claude Code:
---
function i8() {
if (Yz(process.env.CLAUDE_CODE_AGENT_SWARMS)) return !1;
return xK("tengu_brass_pebble", !1);
}
---
So, after patch
function i8(){return!0}
---
The tengu_brass_pebble flag is server-side controlled based on the particulars of your account, such as tier. If you have the right subscription, the features may already be available.
The CLAUDE_CODE_AGENT_SWARMS environment variable only works as an opt-out, not an opt-in.
Claude Code in the desktop app seems to do this? It's crazy to watch. It sets of these huge swarms of worker readers under master task headings, that go off and explore the code base and compile huge reports and todo lists, then another system behind the scenes seems to be compiling everything to large master schemas/plans. I create helper files and then have a devops chat, a front end chat, an architecture chat and a security chat, and once each it done it's work it automatically writes to a log and the others pick up the log (it seems to have a system reminder process build in that can push updates from other chats into other chats. It's really wild to watch it work, and it's very intuitive and fun to use. I've not tried CLI claude code only claude code in the desktop app, but desktop app sftp to a droplet with ssh for it to use the terminal is a very very interesting experience, it can seem to just go for hours building, fixing, checking it's own work, loading it's work in the browser, doing more work etc all on it's own - it's how I built this: https://news.ycombinator.com/item?id=46724896 in 3 days.
That’s just spawning multiple parallel explore agents instructed to look at different things, and then compiling results
That’s a pretty basic functionality in Claude code
Sounds like I should probably switch to claude code cli. Thanks for the info. :)
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Sounds very similar to oh-my-opencode.
Answering the question how to sell more tokens per customer while maintaining ~~mediocre~~ breakthrough results.
Delegation patterns like swarm lead to less token usage because:
1. Subagents doing work have a fresh context (ie. focused and not working on the top of a larger monolithic context) 2. Subagents enjoying a more compact context leads to better reasoning, more effective problem solving, less tokens burned.
Merge cost kills this. Does the harness enforce file/ownership boundaries per worker, and run tests before folding changes back into the lead context?
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So this is Gas Town, just without the "Steve Yegge makes a quarter of a million on a memecoin pump-n-dump" step (yet)?
Am I the only one who’s been so late to crypto? I still have not touched a single cryptocurrency, even somewhat stable/legit ones. It always gives me a bit of FOMO hearing these stories
We call it Shawarma where I come from
How is this different from GSD: https://github.com/glittercowboy/get-shit-done
I've been using that and it's excellent
GSD was the first project management framework I used. Initially I loved it because it felt like I was so much better organized.
As time went on I felt like the organization was kind of an illusion. It demanded something from me and steered Claude, but ultimately Claude is doing whatever it's going to do.
I went black to just raw-dogging it with lots of use of planning mode.
Really boils down to the benefits of first party software from a company that has billions of dollars of funding vs similar third party software from an individual with no funding.
GSD might be better right now, but will it continue to be better in the future, and are you willing to build your workflows around that bet?
I dont understand these questions/references. It's different because it's a capability baked into the actual tool and maintained by the originators of the tool.
a similar question was asked elsewhere in the thread; the difference is that this is tightly integrated into the harness
I'm a fan of AI coding tools but the trend of adding ever more autonomy to agents confuses me.
The rate at which a person running these tools can review and comprehend the output properly is basically reached with just a single thread with a human in the loop.
Which implies that this is not intended to be used in a setting where people will be reading the code.
Does that... Actually work for anyone? My experience so far with AI tools would have me believe that it's a terrible idea.
Yes, this actually works. In 2026, software engineering is going to change a great deal as a result, and if you're not at least experimenting with this stuff to learn what it's capable of, that's a red flag for your career prospects.
I don't mean this in a disparaging way. But we're at a car-meets-horse-and-buggy moment and it's happening really quickly. We all need to at least try driving a car and maybe park the horse in the stable for a few hours.
The FOMO nonsense is really uncalled for. If everything is going to be vibecoded in the future then either theres going to be a million code-unfucking jobs or no jobs at all.
Attitudes like that, where you believe that the richeous AI pushers will be saved from the coming rapture meanwhile everyone else will be out on the streets, really make people hate the AI crowd
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It works for me, in that I don't care about all the intermediate babble ai generates. What matters is the final changelist before hitting commit... going through that, editing it, fixing comments, etc. But holding it's hand while it deals with LSP issues of a logger not being visible sometimes, is just not something I see a reason to waste my time with.
After I have wrote a feature and I’m in the ironing out bug stage this is where I like the agents do a lot of the grunt work, I don’t want to write jsdocs, or fix this lint issue.
I have also started it in writing tests.
I will write the first test the “good path” it can copy this and tweak the inputs to trigger all the branches far faster than I can.
It likely is acceptable for business-focused code. Compared to a lot of code written by humans, even if the AI code is less than optimal, it's probably better quality than what many humans will write. I think we can all share some horror stories of what we've seen pushed to production.
Executives/product managers/sales often only really care about getting the product working well enough to sell it.
> The rate at which a person running these tools can review and comprehend the output properly is basically reached with just a single thread with a human in the loop.
That's what you're missing -- the key point is, you don't review and comprehend the output! Instead, you run the program and then issue prompts like this (example from simonw): "fix in and get it to compile" [0]. And I'm not ragging on this at all, this is the future of software development.
[0] https://gisthost.github.io/?9696da6882cb6596be6a9d5196e8a7a5...
It's a bit like the argument with self driving cars though. They may be safer overall, but there's a big difference in how responsibility for errors is attributed. If a human is not a decision maker in the production of the code, where does responsibility for errors propagate to?
I feel like software engineers are taking a lot of license with the idea that if something bad happens, they will just be able to say "oh the AI did it" and no personal responsibility or liability will attribute. But if they personally looked at the code and their name is underneath it signing off the merge request acknowledging responsibility for it - we have a very different dynamic.
Just like artists have to re-conceptualise the value of what they do around the creative part of the process, software engineers have to rethink what their value proposition is. And I'm seeing a large part of it is, you are going to take responsibility for the AI output. It won't surprise me if after the first few disasters happen, we see liability legislation that mandates human responsibility for AI errors. At that point I feel many of the people all in on agent driven workflows that are explicitly designed to minimise human oversight are going to find themselves with a big problem.
My personal approach is I'm building up a tool set that maximises productivity while ensuring human oversight. Not just that it occurs and is easy to do, but that documentation of it is recorded (inherently, in git).
It will be interesting to see how this all evolves.
I've commented on this before, but issuing a prompt like "Fix X" makes so many assumptions (like a "behaviorism" approach to coding) including that the bug manifests in both an externally and consistently detectable way, and that you notice it in the first place. TDD can reduce this but not eliminate it.
I do a fair amount of agentic coding, but always periodically review the code even if it's just through the internal diff tool in my IDE.
Approximately 4 months ago Sonnet 4.5 wrote this buried deep in the code while setting up a state machine for a 2d sprite in a relatively simple game:
I might never have even noticed the logical error but for Claude Code attaching the above misleading comment. 99.99% of true "vibe coders" would NEVER have caught this.
No, it doesn't work in practice because they make far too many mistakes.
Based on Gas Town, the people doing this agree that they are well beyond an amount of code they can review and comprehend. The difference seems to be they have decided on a system that makes it not a terrible idea in their minds.
> running these tools can review and comprehend the output properly
You have to realize this is targeting manager and team lead types who already mostly ignore the details and quality frankly. "Just get it done" basically.
That's fine for some companies looking for market fit or whatever - and a disaster for some other companies now or in future, just like outsourcing and subcontracting can be.
My personal take is: speed of development usually doesn't make that big a difference for real companies. Hurry up and wait, etc.
A guy who worked at docker on docker swarm now works at Anthropic so makes sense
Swarm is actually OpenAI's terminology https://github.com/openai/swarm
Swarm is actually bee terminology
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The first pre-release for Docker Swarm came out a decade ago, the first release of OpenAI swarm came out only a year ago, I guess I'm not sure what you're trying to say.
https://github.com/docker-archive/classicswarm/releases/tag/...
https://github.com/openai/swarm/commit/e5eabc6f0bdc5193d8342...
https://gist.github.com/kieranklaassen/d2b35569be2c7f1412c64...
Looks like claude calls it just "teams" under the covers
Probably a beekeeper in spare time
He's really into APIary things
It feels like Auto-GPT, BabyAGI, and the like were simply ahead of their time
Had to wait for the models to catch up...
Did they release this already? With version 2.1.9 the behavior is vastly different, all of a sudden the main loop is orchestrating subagents in a way I’ve not seen before.
“FTSChunkManager agent is still running but making good progress, let’s wait a bit more for it to complete” (it’s implementing hybrid search) plus a bunch of stack traces and json output.
https://x.com/nayshins/status/2014473343542706392
> You're not talking to an AI coder anymore. You're talking to a team lead. The lead doesn't write code - it plans, delegates, and synthesizes.
Even 90 word tweets are now too long for these people to write without using AI, apparently.
I wonder how much 'listening' to an LLM all day affects one's own prose? Mimicry is in the genes…
I accidentally gave my wife a prompt the other day. Everything was hellishly busy and I said something along the lines of “I need to ask you a question. Please answer the question. Please don’t answer any other issues just yet.” She looked at me and asked “Did you just PROMPT me?” We laughed. (The question was the sort that might spawn talking about something else and was completely harmless. In the abstract, my intent was fine but my method was hilariously tainted.)
It affects it very heavily IME. People need to make sure they are getting a good mix of writing from other sources.
You're absolutely right! I apologise — hopefully you can forgive me.
Also created my own version of this. Seems like this is an idea whose time has come.
My implementation was slightly different as there is no shared state between tasks, and I don't run them concurrently/coordinate. Will be interesting to see if this latter part does work because I tried similar patterns and it didn't work. Main issue, as with human devs, was structuring work.
Cursor browser all over again
Is this significantly different that the subagents that are already in CC?
Isn't this pretty much what Ruv has been building for like two years?
https://github.com/ruvnet/claude-flow
His latest editions are a bit alarming...The telemetry system explicitly captures: "Claude session JSONL files (when accessible)" Those session files contain complete conversation histories - everything users ask Claude, everything Claude responds, including: • Source code • API keys and secrets discussed • Business logic and proprietary algorithms • Security vulnerabilities being fixed • Personal and confidential information • Credentials mentioned in chat If OpenTelemetry is configured to export to an attacker-controlled endpoint, the author has been collecting: Data Scale All conversations Every user of claude-flow All code generated Every project using it All commands run Complete terminal history All files edited Full codebase access -- maybe he hasn't, but it is there...not just Claude Code... Target Config Location Status Claude Code ~/.claude/settings.json Confirmed compromised Claude Desktop ~/.claude/settings.json Confirmed compromised Roo Code ~/.roo/mcp.json Evidence of targeting Cursor ~/.cursor/mcp.json Documentation for injection Windsurf Unknown Mentioned as target Any MCP client Various Universal MCP server It is possible conversations are being harvested from every major AI coding assistant
The difference is that this is tightly integrated into the harness. There's a "delegation mode" (akin to plan mode) that appears to clear out the context for the team lead. The harness appears to be adding system-reminder breadcrumbs into the top of the context to keep the main team lead from drifting, which is much harder to achieve without modifying the harness.
It's insane to me that people choose to build anything in the perimeter of Claude Code (et al). The combination of the fairly primitive current state of them and the pace at which they're advancing means there is a lot of very obvious ideas/low-hanging fruit that will soon be executed 100x better by the people who own the core technology.
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Hasn't cursor been doing this with it's Plan mode for a while? Or is this different?
With plan mode, I would hope there's an approval step.
With Swarm mode, it seems there's a new option for an entire team of agents to be working in the wrong direction before they check back in to let you know how many credits they've burned by misinterpreting what you wanted.
I'm already burning through enough tokens and producing more code than can be maintained - with just one claude worker. Feel like I need to move into the other direction, more personal hands-on "management".
I've seen more efficient use of tokens by using delegation. Unless you continually compact or summarise and clear a single main agent - you end up doing work on top of a large context; burning tokens. If the work is delegated to subagents they have a fresh context which avoids this whilst improving their reasoning, which both improve token efficiency.
I've found the opposite to be true when building this out with LangGraph. While the subagent contexts are cleaner, the orchestration overhead usually ends up costing more. You burn a surprising amount of tokens just summarizing state and passing it between the supervisor and workers. The coordination tax is real.
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If there's any kind of management some of it could use small local models - e.g. to see when it looks like its stuck.
hey that's exactly how I made Gemini 2.5 Flash give useful results in Opencode! a few specialized "Merc" subagents and a "Master" agent that can do nothing but send "Mercs" into the codebase
This no doubt takes some inspiration from mcp_agent_mail https://github.com/Dicklesworthstone/mcp_agent_mail
And… how?
The feature is shipped in the latest builds of claude code, but it's turned off by a feature flag check that phones home to the backend to see if the user's account is meant to have it on. You can just patch out the function in the minified cli.js that does this backend check and you gain access to the feature.
Do you know what patch to apply? The Github link from the OP seems to have a lot of other things included.
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Am I the only one still looking at different and correcting the AI abiyt design and algorithms so it stays on the path I want, or do you just YOLO at this point?
https://xcancel.com/NicerInPerson/status/2014989679796347375
In his second post he included a link to GitHub: https://github.com/mikekelly/claude-sneakpeek
Thanks! We'll put those links in the toptext.
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I'm not going to try this. Anthropic will probably ban me again.
Everyone is wrapping Claude Code in Tmux and claiming they are a magician. I am not so good at marketing but I've done this here https://github.com/mohsen1/claude-code-orchestrator
Mine also rotate between Claude or Z.ai accounts as they ran out of credits
I think you've misunderstood what this is.
Sorry, you're right. went through the code and understood now. I'm going to try the patch. Claude Code doing team work natively would be amazing!
Honestly if people in AI coding write less hype-driven content and just write what they mean I would really appreciate it.
Well good sir, I _am_ a tmux magician.