This definitely is the case. I was talking to someone complaining about how llms don't work good.

They said it couldn't fix an issue it made.

I asked if they gave it any way to validate what it did.

They did not, some people really are saying "fix this" instead of saying "x fn is doing y when someone makes a request to it. Please attempt to fix x and validate it by accessing the endpoint after and writing tests"

Its shocking some people don't give it any real instruction or way to check itself.

In addition I get great results doing voice to text with very specific workflows. Asking it to add a new feature where I describe what functions I want changed then review as I go vs wait for the end.

I have 30 years of experience delivering code and 10 years of leading architecture. My argument is the only thing that matters is does the entire implementation - code + architecture (your database, networking, your runtime that determines scaling, etc) meet the functional and none functional requirements. Functional = does it meet the business requirements and UX and non functional = scalability, security, performance, concurrency, etc.

I only carefully review the parts of the implementation that I know “work on my machine but will break once I put in a real world scenario”. Even before AI I wasn’t one of the people who got into geek wars worrying about which GOF pattern you should have used.

All except for concurrency where it’s hard to have automated tests, I care more about the unit or honestly integration tests and testing for scalability than the code. Your login isn’t slow because you chose to use a for loop instead of a while loop. I will have my agents run the appropriate tests after code changes

I didn’t look at a line of code for my vibe coded admin UI authenticated with AWS cognito that at most will be used by less than a dozen people and whoever maintains it will probably also use a coding agent. I did review the functionality and UX.

Code before AI was always the grind between my architectural vision and implementation

It's not skill with talking to an LLM, it's the users skill and experience with the problem they're asking the LLM to solve. They work better for problems the prompter knows well and poorly for problems the prompter doesn't really understand.

Try it yourself. Ask claude for something you don't really understand. Then learn that thing, get a fresh instance of claude and try again, this time it will work much better because your knowledge and experience will be naturally embedded in the prompt you write up.

I review most of the code I get LLMs to write and actually I think the main challenge is finding the right chunk size for each task you ask it to do.

As I use it more I gain more intuition about the kinds of problems it can handle on it's, vs those that I need to work on breaking down into smaller pieces before setting it loose.

Without research and planning agents are mostly very expensive and slow to get things done, if they even can. However with the right initial breakdown and specification of the work they are incredibly fast.

I think that entirely disregarding the fundamental operation of LLMs with dismissiveness is ungrounded. You are literally saying it isn’t a skill issue while pointing out a different skill issue.

It is absolutely, unequivocally, patently false to say that the input doesn’t affect the output, and if the input has impact, then it IS a skill.

you are overestimating the skill of code review. Some people have very specific ways of writing code and solving problems which are not aligned what LLMs wrote, but doesn't mean it's wrong.

I know senior developers that are very radical on some nonsense patterns they think are much better than others. If they see code that don't follow them, they say it's trash.

Even so, you can guide the LLM to write the code as you like.

And you are wrong, it's a lot on how people write the prompt.

> Developers who have experience reviewing code are more likely to find problems immediately and complain they aren't getting great results without a lot of hand holding

this makes me feel better about the amount of disdain I've been feeling about the output from these llms. sometimes it popsout exactly what I need but I can never count on it to not go offrails and require a lot of manual editing.

It's always easier to blame the model and convince yourself that you have some sort of talent in reviewing LLM's work that others don't.

In my experience the differences are mostly in how the code produced by LLM is prompted and what context is given to the agent. Developers who have experience delegating their work are more likely to prevent downstream problems from happening immediately and complain their colleagues cannot prompt as efficiently without a lot of hand holding. And those who rarely or never delegated their work are invariably going to miss crucial context details and rate the output they get lower.

Unfortunately it is impossible to ascertain what is what from what we read online. Everyone is different and use the tools in a different way. People also use different tools and do different things with them. Also each persons judgement can be wildly different like you are saying here.

We can't trust the measurements that companies post either because truth isn't their first goal.

Just use it or don't use it depending on how it works out imo. I personally find it marginally on the positive side for coding

I thought I try to debunk your argument with a food example. I am not sure I succeeded though. Judge for yourself:

It's always easier to blame the ingredients and convince yourself that you have some sort of talent in how you cook that others don't.

In my experience the differences are mostly in how the dishes produced in the kitchen are tasted. Chefs who have experience tasting dishes critically are more likely to find problems immediately and complain they aren't getting great results without a lot of careful adjustments. And those who rarely or never tasted food from other cooks are invariably going to miss stuff and rate the dishes they get higher.

> It's always easier to blame the prompt and convince yourself that you have some sort of talent in how you talk to LLMs that other's don't.

Well, it's easily the simplest explanation, right?

I think that code review experience is a big driver of success with the llms, but my take away is somewhat different. If you’ve spent a lot of time reviewing other people’s code you realize the failures you see with llms are common failures full stop. Humans make them too.

I also think reviewable code, that is code specifically delivered in a manner that makes code review more straightforward was always valuable but now that the generation costs have lowered its relative value is much higher. So structuring your approach (including plans and prompts) to drive to easily reviewed code is a more valuable skill than before.

In my experience the differences are mostly between the chair and the keyboard.

I asked Codex to scrape a bunch of restaurant guides I like, and make me an iPhone app which shows those restaurants on a map color coded based on if they're open, closed or closing/opening soon.

I'd never built an iOS app before, but it took me less than 10 minutes of screen time to get this pushed onto my phone.

The app works, does exactly what I want it to do and meaningfully improves my life on a daily basis.

The "AI can't build anything useful" crowd consists entirely of fools and liars.

That seems to make sense. Any suggestions to improve this skill of reviewing code?

I think especially a number of us more junior programmers lack in this regard, and don't see a clear way of improving this skill beyond just using LLMs more and learning with time?

That's what I meant, though. I didn't mean "I say the right words", I meant "I don't give them a sentence and walk away".

The same here. I find big AI-corpos pretty evil and drastically misaligned with broader well-being of the society.

This is anecdotal but just a couple days ago, with some colleagues, we conducted a little experiment to gather that evidence.

We used a hierarchy of agents to analyze a requirement, letting agents with different personas (architect, business analyst, security expert, developer, infra etc) discuss a request and distill a solution. They all had access to the source code of the project to work on.

Then we provided the very same input, including the personas' definition, straight to Claude Code, and we compared the result.

They council of agents got to a very good result, consuming about 12$, mostly using Opus 4.6.

To our surprise, going straight with a single prompt in Claude Code got to a similar good result, faster and consuming 0.3$ and mostly using Haiku.

This surely deserves more investigation, but our assumption / hypothesis so far is that coordination and communication between agents has a remarkable cost.

Should this be the case, I personally would not be surprised:

- the reason why we humans do job separation is because we have an inherent limited capacity. We cannot reach the point to be experts in all the needed fields : we just can't acquire the needed knowledge to be good architects, good business analysts, good security experts. Apparently, that's not a problem for a LLM. So, probably, job separation is not a needed pattern as it is for humans.

- Job separation has an inherent high cost and just does not scale. Notably, most of the problems in human organizations are about coordination, and the larger the organization the higher the cost for processes, to the point processed turn in bureaucracy. In IT companies, many problems are at the interface between groups, because the low-bandwidth communication and inherent ambiguity of language. I'm not surprised that a single LLM can communicate with itself way better and cheaper that a council of agents, which inevitably faces the same communication challenges of a society of people.

There's a lot of cargo culting, but it's inevitable in a situation like this where the truth is model dependent and changing the whole time and people have created companies on the premise they can teach you how to use ai well.

I think this is just anthropomorphism. Sub agents make sense as a context saving mechanism.

Aider did an "architect-editor" split where architect is just a "programmer" who doesn't bother about formatting the changes as diff, then a weak model converts them into diffs and they got better results with it. This is nothing like human teams though.

The different models is a big one. In my workflow, I've got opus doing the deep thinking, and kimi doing the implementation. It helps manage costs.

Sample size of one, but I found it helps guard against the model drifting off. My different agents have different permissions. The worker can not edit the plan. The QA or planner can't modify the code. This is something I sometimes catch codex doing, modifying unrelated stuff while working.

It's not about splitting for quality, it's about cost optimisation (Sonnet implements, which is cheaper). The quality comes with the reviewers.

Notice that I didn't split out any roles that use the same model, as I don't think it makes sense to use new roles just to use roles.

Nitpick: I don’t think architect is a good name for this role. It’s more of a technical project kickoff function: these are the things we anticipate we need to do, these are the risks etc.

I do find it different from the thinking that one does when writing code so I’m not surprised to find it useful to separate the step into different context, with different tools.

Is it useful to tell something “you are an architect?” I doubt it but I don’t have proof apart from getting reasonable results without it.

With human teams I expect every developer to learn how to do this, for their own good and to prevent bottlenecks on one person. I usually find this to be a signal of good outcomes and so I question the wisdom of biasing the LLM towards training data that originates in spaces where “architect” is a job title.

I think the splitting make sense to give more specific prompts and isolated context to different agents. The "architect" does not need to have the code style guide in its context, that actually could be misleading and contains information that drives it away from the architecture

> what's the evidence

What’s the evidence for anything software engineers use? Tests, type checkers, syntax highlighting, IDEs, code review, pair programming, and so on.

In my experience, evidence for the efficacy of software engineering practices falls into two categories:

- the intuitions of developers, based in their experiences.

- scientific studies, which are unconvincing. Some are unconvincing because they attempt to measure the productivity of working software engineers, which is difficult; you have to rely on qualitative measures like manager evaluations or quantitative but meaningless measures like LOC or tickets closed. Others are unconvincing because they instead measure the practice against some well defined task (like a coding puzzle) that is totally unlike actual software engineering.

Evidence for this LLM pattern is the same. Some developers have an intuition it works better.

> the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session?

There's a 63 pages paper with mathematical proof if you really into this.

https://arxiv.org/html/2601.03220v1

My takeaway: AI learns from real-world texts, and real-world corpus are used to have a role split of architect/developer/reviewer

After "fully vibecoding" (i.e. I don't read the code) a few projects, the important aspect of this isn't so much the different agents, but the development process.

Ironically, it resembles waterfall much more so than agile, in that you spec everything (tech stack, packages, open questions, etc.) up front and then pass that spec to an implementation stage. From here you either iterate, or create a PR.

Even with agile, it's similar, in that you have some high-level customer need, pass that to the dev team, and then pass their output to QA.

What's the evidence? Admittedly anecdotal, as I'm not sure of any benchmarks that test this thoroughly, but in my experience this flow helps avoid the pitfall of slop that occurs when you let the agent run wild until it's "done."

"Done" is often subjective, and you can absolutely reach a done state just with vanilla codex/claude code.

Note: I don't use a hierarchy of agents, but my process follows a similar design/plan -> implement -> debug iteration flow.

Yeah always seemed pretty sus to me to.

At the same time I can see a more linear approach doing similar. Like when I ask for an implementation plan that is functional not all that different from an architect agent even if not wrapped in such a persona

“…if you give it good context…” that’s what the architect session is for basically. You throw around ideas and store the direction you want to go.

Then you execute it with a clean context.

Clean context is needed for maximum performance while not remembering implementation dead ends you already discarded

If you know what you need, my experience is that a well-formed single-prompt that fits the context gives the best results (and fastest).

If you’re exploring an idea or iterating, the roles can help break it down and understand your own requirements. Personally I do that “away” from the code though.

In machine learning, ensembles of weaker models can outperform a single strong model because they have different distributions of errors. Machine learning models tend to have more pronounced bias in their design than LLMs though.

So to me it makes sense to have models with different architecture/data/post training refine each other's answers. I have no idea whether adding the personas would be expected to make a difference though.

One added benefit is it allows you to throw more tokens to the problem. It’s the most impactful benefit even.

Context & how LLMs work requires this.

From my experience no frontier model produces bug free & error free code with the first pass, no matter how much planning you do beforehand.

With 3 tiers, you spend your token & context budget in full in 3 phases. Plan, implement, review.

If the feature is complex, multiple round of reviews, from scratch.

It works.

Even for reducing the context size it's probably worth it. If you have to go back back and forth on both problem and implementation even with these new "large" contexts if find quality degrading pretty fast.

> Genuine question: what's the evidence that the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session?

Using multiple agents in different roles seems like it'd guard against one model/agent going off the rails with a hallucination or something.

The agent "personalities" and LLM workflow really looks like cargo-cult behavior. It looks like it should be better but we don't really have data backing this.

> produces better results than just... talking to one strong model in one session?

I think the author admits that it doesn't, doesn't realise it and just goes on:

--- start quote ---

On projects where I have no understanding of the underlying technology (e.g. mobile apps), the code still quickly becomes a mess of bad choices. However, on projects where I know the technologies used well (e.g. backend apps, though not necessarily in Python), this hasn’t happened yet

--- end quote ---

I have been using different models for the same role - asking (say) Gemini, then, if I don't like the answer asking Claude, then telling each LLM what the other one said to see where it all ends up

Well I was until the session limit for a week kicked in.

Evidence? My friend, most of the practices in this field are promoted and adopted based on hand-waving, feelings, and anecdata from influencers.

Maybe you should write and share your own article to counter this one.

From my experience, you kinda get what you ask for. If you don't ask for anything specific, it'll write as it sees fit. The more you involve yourself in the loop, the more you can get it to write according to your expectation. Also helps to give it a style guide of sorts that follows your preferred style.

It's the kind of code you should expect if you don't run a harness that includes review and refactoring stages.

It's by no means the best LLMs can do.

I would suggest not delegating the LLD (class / interface level design) to the LLM. The clankeren are super bad at it. They treat everything as a disposable script.

Also document some best practices in AGENT.md or whatever it's called in your app.

Eg

    * All imports must be added on top of the file, NEVER inside the function.
    * Do not swallow exceptions unless the scenario calls for fault tolerance.
    * All functions need to have type annotations for parameters and return types.

I almost always define the class-level design myself. In some sense I use the LLM to fill in the blanks. The design is still mine.

Pine Town [1], the "whimsical infinite multiplayer canvas of a meadow", also looks like pure slop.

[1] https://pine.town/

You can make it better by investing a lot of time playing around with the tooling so that it produces something more akin to what you're looking for.

Good luck convincing your boss that this ungodly amount of time spent messing around with your tooling for an immeasurable improvement in your delivery is the time well spent as opposed to using that same amount of time delivering results by hand.

> is this the kind of code I should expect?

Sadly yes. But it "works", for some definition of working. We all know it's going to be a maintenance nightmare seen the gigantic amount of code and projects now being generated ad infinitum. As someone commented in this thread: it can one-shot an app showing restaurant locations on a map and put a green icon if they're open. But don't except good code, secure code, performant code and certainly not "maintainable code".

By definition, unless the AIs can maintain that code, nothing is maintainable anymore: the reason being the sheer volume. Humans who could properly review and maintain code (and that's not many) are already outnumbered.

And as more and more become "prompt engineers" and are convinced that there's no need to learn anything anymore besides becoming a prompt engineer, the amount of generated code is only going to grow exponentially.

So to me it is the kind of code you should expect. It's not perfect. But it more or less works. And thankfully it shouldn't get worse with future models.

What we now need is tools, tools and more tools: to help keep these things on tracks. If we ever to get some peace of mind about the correctness of this unreviewable generate code, we'll need to automate things like theorem provers and code coverage (which are still nowhere to be seen).

And just like all these models are running on Linux and QEMU and Docker (dev container) and heavily using projects like ripgrep (Claude Code insist on having ripgrep installed), I'm pretty sure all these tools these models rely on and shall rely on to produce acceptable results are going to be, very mostly, written by humans.

I don't know how to put it nicely: an app showing green icon next to open restaurants on a map ain't exactly software to help lift off a rocket or to pilot a MRI machine.

BTW: yup, I do have and use Claude Code. Color me both impressed and horrified by the "working" amount un unmaintainable mess it can spout. Everybody who understands something about software maintenance should be horrified.

I also managed to find a 1000 line .cpp file in one of the projects. The article's content doesn't match his apps quality. They don't bring any value. His clock looks completely AI generated.

I think it mattered a lot more a few years ago, when the user's prompts were almost all context the LLM had to go by. A prompt written in a sloppy style would cause the LLM to respond in a sloppy style (since it's a snazzy autocomplete at its core). LLMs reason in tokens, so a sloppy style leads it to mimic the reasoning that it finds in the sloppy writing of its training data, which is worse reasoning.

These days, the user prompt is just a tiny part of the context it has, so it probably matters less or not at all.

I still do it though, much like I try to include relevant technical terminology to try to nudge its search into the right areas of vector space. (Which is the part of the vector space built from more advanced discourse in the training material.)

I can't speak for everyone, but to me the most accurate answer is that I'm role-playing, because it just flows better.

In the back of my head I know the chatbot is trained on conversations and I want it to reflect a professional and clear tone.

But I usually keep it more simple in most cases. Your example:

> I'd like to add email support to this bot. Let's think through how we would do this.

I would likely write as:

> if i wanted to add email support, how would you go about it

or

> concise steps/plan to add email support, kiss

But when I'm in a brainstorm/search/rubber-duck mode, then I write more as if it was a real conversation.

The reasoning is by being polite the LLM is more likely to stay on a professional path: at its core a LLM try to make your prompt coherent with its training set, and a polite prompt + its answer will score higher (gives better result) than a prompt that is out of place with the answer. I understand to some people it could feel like anthropomorphising and could turn them off but to me it's purely about engineering.

Edit: wording

Because some people like to be polite? Is it this hard to understand? Your hand-written prompts are unlikely to take significant chunk of context window anyway.

My view is that when some "for bots only" type of writing becomes a habit, communication with humans will atrophy. Tokens be damned, but this kind of context switch comes at much too high a cost.

For models that reveal reasoning traces I've seen their inner nature as a word calculator show up as they spend way too many tokens complaining about the typo (and AI code review bots also seem obsessed with typos to the point where in a mid harness a few too many irrelevant typos means the model fixates on them and doesn't catch other errors). I don't know if they've gotten better at that recently but why bother. Plus there's probably something to the model trying to match the user's style (it is auto complete with many extra steps) resulting in sloppier output if you give it a sloppier prompt.

Related to this, has anyone investigated how much typos matter in your chats? I would imagine that typing 'typescfipt' would not be a token in the input training set, so how would the model recognize this as actually meaning 'typescript'? Or does the tokenizer deal with this in an earlier stage?

I write "properly" (and I do say "please" and "thank you"), just because I like exercising that muscle. The LLM doesn't care, but I do.

I prompt politely for two reasons: I suspect it makes the model less likely to spiral (but have no hard evidence either way), and I think it's just good to keep up the habit for when I talk to real people.

There is evidence of that, but more importantly, it wouldn't occur to me to write "wanna add email support". That's not my natural voice.

I just don't want to build the habit of being a sloppy writer, because it will eventually leak into the conversations I have with real humans.

With current models this isn't as big of a deal, but why risk being an asshole in any context? I don't think treating something like shit simply because it's a machine is a good excuse.

Also consider the insanity of intentionally feeding bullshit into an information engine and expecting good things to come out the other end. The fact that they often perform well despite the ugliness is a miracle, but I wouldn't depend on it.

Just stream of consciousness into the context window works wonders for me. More important to provide the model good context for your question

Some people are just polite by nature & habits are hard to break

I suspect they just find it easier and more natural to write with proper grammar.

one reason to do that could be it’s trained on conversations happened between humans.

I choose to talk in a respectful way, because that's how I want to communicate: it's not because I'm afraid of retaliation or burning bridges. It's because I am caring and conscious. If I think that something doesn't have feelings or long-term memory, whether it's AI or a piece of rock on the side of a trail, it in no way leads me to be abusive to it.

Further, an LLM being inherently sycophantic leads to it mimmicking me, so if I talk to it in a stupid or abusive (which is just another form of stupidity, in my eyes) manner, it will behave stupid. Or, that's what I'd expect. I've not researched this in a focused way, but I've seen examples where people get LLMs to be very unintelligent by prompting riddles or intelligence tests in highly-stylized speech. I wanted to say "highly-stupid speech", but "stylized" is probably more accurate, e.g.: `YOOOO CHATGEEEPEEETEEE!!!!!!1111 wasup I gots to asks you DIS.......`. Maybe someone can prove me wrong.

agree, prompting a token predictor like you’re talking to a person is counterproductive and I too wish it would stop

the models consistently spew slop when one does it, I have no idea where positive reinforcement for that behavior is coming from

I don't think that splitting into subagents that use the same model will really help. I need to clarify this in the post, but the split is 1) so I can use Sonnet to code and save on some tokens and 2) so I can get other models to review, to get a different perspective.

It seems to me that splitting into subagents that use the same model is kind of like asking a person to wear three different hats and do three different parts of the job instead of just asking them to do it all with one hat. You're likely to get similar results.

that reference you give is pretty dated now, based on a talk from August which is the Beforetimes of the newer models that have given such a step change in productivity.

The key change I've found is really around orchestration - as TFA says, you don't run the prompt yourself. The orchestrator runs the whole thing. It gets you to talk to the architect/planner, then the output of that plan is sent to another agent, automatically. In his case he's using an architect, a developer, and some reviewers. I've been using a Superpowers-based [0] orchestration system, which runs a brainstorm, then a design plan, then an implementation plan, then some devs, then some reviewers, and loops back to the implementation plan to check progress and correctness.

It's actually fun. I've been coding for 40+ years now, and I'm enjoying this :)

[0] https://github.com/obra/superpowers

re: breaking into specialized subagents -- yes, it matters significantly but the splitting criteria isn't obvious at first.

what we found: split on domain of side effects, not on task complexity. a "researcher" agent that only reads and a "writer" agent that only publishes can share context freely because only one of them has irreversible actions. mixing read + write in one agent makes restart-safety much harder to reason about.

the other practical thing: separate agents with separate context windows helps a lot when you have parts of the graph that are genuinely parallel. a single large agent serializes work it could parallelize, and the latency compounds across the whole pipeline.

No criticism or anything, but it really does feel / sound like you (and others who embraced LLMs and agentic coding) aspire to be more of a product manager than a coder. Thing is, a "real" PM comes with a lot more requirements and there's less demand for them - more requirements in that you need to be a people person and willing to spend at least half your time in meetings, and less demand because one PM will organize the work for half a dozen developers (minimum).

Some people say LLM assisted coding will cost a lot of developers' jobs, but posts like this imply it'll cost (solve?) a lot of management / overhead too.

Mind you I've always thought project managers are kinda wasteful, as a software developer I'd love for Someone Else to just curate a list of tasks and their requirements / acceptance criteria. But unfortunately that's not the reality and it's often up to the developers themselves to create the tasks and fill them in, then execute them. Which of course begs the question, why do we still have a PM?

(the above is anecdotal and not a universal experience I'm sure. I hope.)

Others have already partially answered this, but here’s my 20 cents. Software development really is similar to architecture. The end result is an infrastructure of unique modules with different type of connectors (roads, grid, or APIs). Until now in SW dev the grunt work was done mostly by the same people who did the planning, decided on the type of connectors, etc. Real estate architects also use a bunch of software tools to aid them, but there must be a human being in the end of the chain who understands human needs, understands - after years of studying and practicing - how the whole building and the infrastructure will behave at large and who is ultimately responsible for the end result (and hopefully rewarded depending on the complexity and quality of the end result). So yes we will not need as many SW engineers, but those who remain will work on complex rewarding problems and will push the frontier further.

> Then what is our use?

You will have to find new economic utility. That's the reality of technological progress - it's just that the tech and white collar industries didn't think it can come for them!

A skill that becomes obsoleted is useless, obviously. There's still room for artisanal/handcrafted wares today, amidst the industrial scale productions, so i would assume similar levels for coding.

LLMs can build anything. The real question is what is worth building, and how it’s delivered. That is what is still human. LLMs, by nature of not being human, cannot understand humans as well as other humans can. (See every attempt at using an LLM as a therapist)

In short: LLMs will eventually be able to architect software. But it’s still just a tool

Agreed, I code for fun. But I am not sure if I still find it fun if the LLM just makes what I want.

This used to be one of my recurring nightmares when I was a child. The three I remember were (1) clocks suddenly starting to go backwards, either partially or completely; (2) radio turning on without being able to turn it off, and (3) house fire. There really is something about clocks.

I don't know if I explained this clearly enough in the article, but I have the LLM write the plan to a file as well. The architect's end result is a plan file in the repo, and the developer reads that.

You can see one here: https://github.com/skorokithakis/sleight-of-hand/blob/master...

> The main difference between my workflow and the authors, is that I have the LLM "write" the design/plan/open questions/debug/etc. into markdown files, for almost every step. > > This is mostly helpful because it "anchors" decisions into timestamped files, rather than just loose back-and-forth specs in the context window.

Would you please expand on this? Do you make the LLM append their responses to a Markdown file, prefixed by their timestamps, basically preserving the whole context in a file? Or do you make the LLM update some reference files in order to keep a "condensed" context? Thank you.

Yeah same. The markdown thing also helps with the multi model thing. Can wipe context and have another model look at the code and markdown plan with fresh eyes easily

Hot take: you can't have your cake and eat it too. If you aren't writing code, designing the system, creating architecture, or even writing the prompt, then you're not understanding shit. You're playing slots with stochastic parrots

    The code grows beyond my usual comprehension, I'd have to really read through it for a while. Sometimes the LLMs can't fix a bug so I just work around it or ask for random changes until it goes away. It's not too bad for throwaway weekend projects, but still quite amusing. I'm building a project or webapp, but it's not really coding - I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.

Writing pieces of code that beat average human level is solved. Organizing that code is on its way to being solved (posts like this hint at it). Finding problems that people will pay money to have solved by software is a different entirely more complicated matter (tbh I doubt anyone could prove right now that this absolutely is or isn’t solvable - but given the change we’ve seen already I place no bets against AI).

Also even if agents could do everything the societal obstacles to change are extensive (sometimes for very good, sometimes for bad reasons) so I’m expecting it to take another year or two serious change to occur.

They’re busy writing applications for their dogs and building “jerk me off” functionality into their OpenClaw fork. Once they’re done you’ll be sorry you ever asked.

Last time I read about a Codex update, I think it mentioned that a million developers tried the tool.

Don't most companies use AI in software development today?

And yes, I know that some companies are not doing that because of privacy and reliability concerns or whatever. With many of them it's a bit of a funny argument considering even large banks managed to adopt agentic AI tools. Short of government and military kind of stuff, everybody can use it today.

I guess you’re probably looking for someone who uses cursor etc to answer but here’s a data point from someone a bit off the beaten path.

My editor supports both modes (emacs). I have the editor integration features (diff support etc) turned off and just use emacs to manage 5+ shells that each have a CLI agent (one of Claude, opencode, amp free) running in them.

If I want to go deep into a prompt then I’ll write a markdown file and iterate on it with a CLI.

For me, I use an IDE if I plan to look at the code.

I don't think there is a meaningful difference.

Whether I use Antigravity, VS Code with Claude Code CLI, GitHub Copilot IDE plugins, or the Codex app, they all do similar things.

Although I'd say Codex and Claude Code often feel significantly better to me, currently. In terms of what they can achieve and how I work with them.

These skills live in my home directory, that's why they aren't in the repos. I can upload them if you want.

I played with this over the weekend:

https://github.com/marcosloic/notion-agent-hive

Ultimately, it's just a bunch of markdown files that live in an `/agents` folder, with some meta-information that will depend on the harness you use.

Solidifying the ideas in writing helps the author improve them, and helps them and the rest of us understand what to look for in the next generation of models.

On the front page at the moment. Position 12

Anti-AI conspiracy, obviously.