Ask HN: Do you have any evidence that agentic coding works?

I've had great success coding infra (terraform). It at least 10x the generation of easily verifiable and tedious to write code. Results were audited to death as the client was highly regulated.

Professional feature dev is hit and miss for sure, although getting better and better. We're nowhere near full agentic coding. However, by reinvesting the speed gains from not writing boilerplate into devex and tests/security, I bring to life much better quality software, maintainable and a boy to work with.

I suddenly have the homelab of my dreams, all the ideas previously in the "too long to execute" category now get vibe coded while watching TV or doing other stuff.

As an old jaded engineer, everything code was getting a bit boring and repetitive (so many rest APIs). I guess you get the most value out of it when you know exactly what you want.

Most importantly though, and I've heard this from a few other seniors: I've found joy in making cool fun things with tech again. I like that new way of creating stuff at the speed of thought, and I guess for me that counts as "it works"

Other things that seem to contribute to success with agents are:

- Static type systems (not tacked-on like Typescript)

- A test suite where the tests cover large swaths of code (i.e. not just unit testing individual functions; you want e2e-style tests, but not the flaky browser kind)

With all the above boxes ticked, I can get away with only doing "sampled" reviews. I.e. I don't review every single change, but I do review some of them. And if I find anything weird that I had missed from a previous change, I to tell it to fix it and give the fix a full review. For architectural changes, I plan the change myself, start working on it, then tell the agent to finish.

I've implemented several medium-scale projects that I anticipate would have taken 1-2 weeks manually, and took a day or so using agentic tools.

A few very concrete advantages I've found:

* I can spin up several agents in parallel and cycle between them. Reviewing the output of one while the others crank away.

* It's greatly improved my ability in languages I'm not expert in. For example, I wrote a Chrome extension which I've maintained for a decade or so. I'm quite weak in Javascript. I pointed Antigravity at it and gave it a very open-ended prompt (basically, "improve this extension") and in about five minutes in vastly improved the quality of the extension (better UI, performance, removed dependencies). The improvements may have been easy for someone expert in JS, but I'm not.

Here's the approach I follow that works pretty well:

1. Tell the agent your spec, as clearly as possible. Tell the agent to analyze the code and make a plan based on your spec. Tell the agent to not make any changes without consulting you.

2. Iterate on the plan with the agent until you think it's a good idea.

3. Have the agent implement your plan step by step. Tell the agent to pause and get your input between each step.

4. Between each step, look at what the agent did and tell it to make any corrections or modifications to the plan you notice. (I find that it helps to remind them what the overall plan is because sometimes they forget...).

5. Once the code is completed (or even between each step), I like to run a code-cleanup subagent that maintains the logic but improves style (factors out magic constants, helper functions, etc.)

This works quite well for me. Since these are text-based interfaces, I find that clarity of prose makes a big difference. Being very careful and explicit about the spec you provide to the agent is crucial.

Make a commit.

Give Claude a task that's not particularly open ended, the closer to pure "monkey work" boilerplate nonsense the task is, the better (which is also the sort of code I don't want do deal with myself).

Preferably it should be something that only touches a file or two in the codebase unless it is a trivial refactor (like changing the same method call all over the place)

Make sure it is set to planning mode and let it come up with a plan.

Review the plan.

Let it implement the plan.

If it works, great, move on to review. I've seen it one-shot some pretty annoying tasks like porting code from one platform to another.

If there are obvious mistakes (program doesn't build, tests don't pass, etc.) then a few more iterations usually fix the issue.

If there are subtle mistakes, make a branch and have it try again. If it fails, then this is beyond what it can do, abort the branch and solve the issue myself.

Review and cleanup the code it wrote, it's usually a lot messier than it needs to be. This also allows me to take ownership of the code. I now know what it does and how it works.

I don't bother giving it guidelines or guardrails or anything of the sort, it can't follow them reliably. Even something as simple as "This project uses CMake, build it like this" was repeatedly ignored as it kept trying to invoke the makefile directly and in the wrong folder.

This doesn't save me all that much time since the review and cleanup can take long, but it serves a great unblocker.

I also use it as a rubber duck that can talk back and documentation source. It's pretty good for that.

This idea of having an army of agents all working together on the codebase is hilarious to me. Replace "agents" with "juniors I hired on fiverr with anterograde amnesia" and it's about how well it goes.

Someone I know wrote the code and the unit tests for a new feature with an agent. The code was subtly wrong, fine, it happens, but worse the 30 or so tests they added added 10 minutes to the test run time and they all essentially amounted to `expect(true).to.be(true)` because the LLM had worked around the code not working in the tests

Here's my realtime Bluetooth heart rate monitor for linux, with text output and web interface.

   https://github.com/lowrescoder/BlueHeart

Particularly interesting because I didn't expect this to work, let along not to write any code. Note that I limited it to pure C with limited dependencies; initial prompt was just to get text output ("Heart Rate 76bpm"), when it got to that point I told Claude to add a web interface followed by creating a realtime graph to show the interface in use.

Every file is claude generated. AMA.

edit: this was particularly interesting as it had to test against the HRM sensor I was wearing during development, and to cope with bluetooth devices appearing and disappearing all the time. It took about a day for the whole thing and cost around $25.

further edit: I am by no means an expert with Claude (haven't even got to making a claude.md file); the one real objective here was to get a working example of using dBus to talk to blueZ in C, something I've failed at (more than once) before.

I started out by letting it write a naive C version without intrinsic, and validated it against the PyTorch version.

Then I asked it (and two other models, Gemini 3.0 and GPT 5.1) to come up with some ideas on how to make it faster using SIMD vector instructions and write those down as markdown files.

Finally, I started the agent loop by giving Cursor those three markdown files, the naive C code and some more information on how to compile the code, and also an SSH command where it can upload the program and test it.

It then tested a few different variants, ran it on the target (RISC-V SBC, OrangePI RV2) to check if it improves runtime, and then continue from there. It did this 10 times, until it arrived at the final version.

The final code is very readable, and faster than any other library or compiler that I have found so far. I think the clear guardrails (output has to match exactly the reference output from PyTorch, performance must be better than before) makes this work very well.

The other day I gave an estimate to my co-worker and he said "but how long is it really going to take, because you always finish a lot quicker than you say, you say two weeks and then it takes two days".

The LLMs will just make me finish things a lot faster and my gut feel estimation for how long things will take still is not yet taking that into account.

(And before people talk about typing speed: No that isn't it at all. I've always been the fastest typer and fastest human developer among my close co-workers.)

Yes, I need to review the code and interact with the agent. But it's doing a lot better than a lot of developers I've worked with over the years, and if I don't like the style of the code it takes very few words and the LLM will "get it" and it will improve it..

Some commenters are comparing the LLM to a junior. In some sense that is right in that the work relationship may be the same as towards a (blazingly fast) junior; but the communication style and knowledge area and how few words I can use to describe something feels more like talking to a senior.

(I think it may help that latest 10 years of my career a big part of my job was reviewing other people's code, delegating tasks, being the one who knew the code base best and helping others into it. So that means I'm used to delegating not just coding. Recently I switched jobs and am now coding alone with AI.)

It is an assistant not a team mate.

If you think that getting it wrong, or bugs, or misunderstandings, or lost code, or misdirections, are AI "failing", then yes you will fail to understand or see the value.

The point is that a good AI assisted developer steers through these things and has the skill to make great software from the chaotic ingredients that AI brings to the table.

And this is why articles like this one "just don't get it", because they are expecting the AI to do their job for them and holding it to the standards of a team mate. It does not work that way.

Two days later, after people freaked out, context was added. The team built multiple versions in that year, each had its trade offs. All that context was given to the AI and it was able to produce a “toy” version. I can only assume it had similar trade offs.

https://xcancel.com/rakyll/status/2007659740126761033#m

My experience has been similar to yours, and I think a lot of the hype is from people like this Google engineer who play into the hype and leave out the context. This sets expectations way out of line from reality and leads to frustration and disappointment.

Some techniques I've found useful recently:

- If the agent struggled on something once it's done I'll ask it "you were struggling here, think about what happened and if there are is anything you learned. Put this into a learnings document and reference it in agents.md so we don't get stuck next time"

- Plans are a must. Chat to the agent back and forth to build up a common understanding of the problem you want solved. Make sure to say "ask me any follow up questions you think are necessary". This chat is often the longest part of the project - don't skimp on it. You are building the requirements and if you've ever done any dev work you understand how important having good requirements are to the success of the work. Then ask the model to write up the plan into an implementation document with steps. Review this thoroughly. Then use a new agent to start work on it. "Implement steps 1-2 of this doc". Having the work broken down into steps helps to be able to do work more pieces (new context windows). This part is the more mindless part and where you get to catch up on reading HN :)

- The GitHub Copilot chat agent is great. I don't get the TUI folks at all. The Pro+ plan is a reasonable price and can do a lot with it (Sonnet, Codex, etc all available). Being able to see the diffs as it works is helpful (but not necessary) to catch problems earlier.

Agentic programming is a skill-set and a muscle you need to develop just like you did with coding in the past.

Things didn’t just suddenly go downhill after an arbitrary tipping point - what happened is you hit a knowledge gap in the tooling and gave up.

Reflect on what went wrong and use that knowledge next time you work with the agent.

For example, investing the time in building a strong test suite and testing strategy ahead of time which both you and the agent can rely on.

Being able to manage the agent and getting quality results on a large, complex codebase is a skill in itself, it won’t happen over night.

It takes practice and repetition with these tools to level-up, just like any thing else.

You still need to think about how you would solve the problem as an engineer and break down the task into a right-sized chunk of work. i.e. If 4 things need to change, start with the most fundamental change which has no other dependencies.

Also it is important to manage the context window. For a new task, start a new "chat" (new agent). Stay on topic. You'll be limited to about five back-and-forths before performance starts to suffer. (cursor shows a visual indicator of this in the for of the circle/wheel icon)

For larger tasks, tap the Plan button first, and guide it to the correct architecture you are looking for. Then hit build. Review what it did. If a section of code isn't high-quality, tell Claude how to change it. If it fails, then reject the change.

It's a tool that can make you 2 - 10x more productive if you learn to use it well.

If I'm being honest, the people who get utility out of this tool don't need any tutorials. The smattering of ideas that people mention is sufficient. The people who don't get utility out of this tool are insistent that it is useless, which isn't particularly inspiring to the kind of person who would write a good tutorial.

Consequently, you're probably going to have to pay someone if you want a handholding. And at the end you might believe it wasn't worth it.

Anyone who claims AI is great is not building a large or complex enough app, and when it works for their small project, they extrapolate to all possibilities. So because their example was generated from a prompt, it's incorrectly assumed that any prompt will also work. That doesn't necessarily follow.

The reality is that programming is widely underestimated. The perception is that it's just syntax on a text file, but it's really more like a giant abstract machine with moving parts. If you don't see the giant machine with moving parts, chances are you are not going to build good software. For AI to do this, it would require strong reasoning capabilities, that lets it derive logical structures, along with long term planning and simulation of this abstract machine. I predict that if AI can do this then it will be able to do every single other job, including physical jobs as it would be able to reason within a robotic body in the physical world.

To summarize, people are underestimating programming, using their simple projects to incorrectly extrapolate to any possible prompt, and missing the hard part of programming which involves building abstract machines that work on first principles and mathematical logic.

* I came up with a list of 9 performance improvement ideas for an expensive pipeline. Most of these were really boring and tedious to implement (basically a lot of special cases) and I wasn't sure which would work, so I had Claude try them all. It made prototypes that had bad code quality but tested the core ideas. One approach cut the time down by 50%, I rewrote it with better code and it's saved about $6,000/month for my company.

* My wife and I had a really complicated spreadsheet for tracking how much we owed our babysitter – it was just complex enough to not really fit into a spreadsheet easily. I vibecoded a command line tool that's made it a lot easier.

* When AWS RDS costs spiked one month, I set Claude Code to investigate and it found the reason was a misconfigured backup setting

* I'll use Claude to throw together a bunch of visualizations for some data to help me investigate

* I'll often give Claude the type signature for a function, and ask it to write the function. It generally gets this about 85% right

1) I needed a tool to consolidate *.dylib on macOS into the app bundle. I wanted this tool to be in JS because of some additional minor logic which would be a hassle to implement in pure bash.

2) I needed a simple C wrapper to parallelize /usr/bin/codesign over cores. Split list of binaries in batches and run X parallel codesigns over a batch.

Arguably, both tools are junior-level tasks.

I have used Claude Code and Opus 4.5. I have used AskUserTool to interview me and create a detailed SPEC.md. I manually reviewed and edited the final spec. I then used the same model to create the tool according to that very detailed spec.

The first tool, the dylib consolidation one, was broken horrendously. I did recurse into subdirs where no folder structure is expected or needed and did not recurse into folders where it was needed. It created a lot of in-memory structures which were never read. Unused parameters in functions. Unused functions. Incredible, illogical code that is impossible to understand. Quirks, "clever code". Variable scope all over the place. It appeared to work, but only in one single case on my dev workstation and failed on almost every requirement in the spec. I ended up rewriting it from scratch, because the only things worst saving from this generated code were one-liners for string parsing.

The second tool did not even work. You know this quirk of AI models that once they find a wrong solution they keep coming back at it, because the context was poisoned? So, this. Completely random code, not even close. I rewrote the thing from scratch [1].

Curiously, the second tool took way more time and tokens to create despite being quite simpler.

So yeah. We're definitely at most 6 month away from replacing programmers with AI.

[1] https://github.com/egorFiNE/codesign-parallel

1) low risk code

Let's say that we're building an MVP for something. and at this moment we just wanna get something working to get some initial feedback. So for example, the front-end code is not going to stick around. we just want something there to give a functionality and a feeling but it doesn't have to be perfect. AI is awesome at creating that kind of front-end code that will just live for a short time before it's probably all thrown out.

2) fast iterations and experimentation

In the past, if you had to build something and you were thinking, thinking maybe I can try this thing, then you're gonna spend hours or days getting it up and working to find out if it's even a good idea in the first place. but with AI And I find that I can just ask the AI to quickly get a working feature up and I can realize no this is not the best way to do it remove everything thing start over. I could not do that in the past with limited time to spend and they just doing the same thing over and over again with different libraries or different solutions. But with AI, I can do that. and then when you have something that you like you can go back and do it correctly.

3) typing for me.

And lastly, even when I write my own code, I don't really write it but I don't use the AI to to say, "hey, build me a to-do app" instead I use it to just give me the building blocks so more like in very advanced snippet tool so I might say "Can you give me a gen server that takes in this and that and returns this and that?" And then of course I review the result.

2. Part of the plan should be automated tests. AI can make these for you too, but you should spot check for reasonable behavior.

3. Use Claude 4.5 Opus

4. Use Git, get the AI to check in its work in meaningful chunks, on its own git branch.

5. Ask the AI to keep am append-only developer log as a markdown file, and to update it whenever its state significantly changes, or it makes a large discovery, or it is "surprised" by anything.

- Ask Claude to look at my current in-progress task (from Github/Jira/whatever) and repro the bug using the Chrome MCP.

- Ask it to fix it

- Review the code manually, usually it's pretty self-contained and easy to ensure it does what I want

- If I'm feeling cautious, ask it to run "manual" tests on related components (this is a huge time-saver!)

- Ask it to help me prepare the PR: This refers to instructions I put in CLAUDE.md so it gives me a branch name, commit message and PR description based on our internal processes.

- I do the commit operations, PR and stuff myself, often tweaking the messages / description.

- Clear context / start a new conversation for the next bug.

On a personal project where I'm less concerned about code quality, I'll often do the plan->implementation approach. Getting pretty in-depth about your requirements ovbiously leads to a much better plan. For fixing bugs it really helps to tell the model to check its assumptions, because that's often where it gets stuck and create new bugs while fixing others.

All in all, I think it's working for me. I'll tackle 2-3 day refactors in an afternoon. But obviously there's a learning curve and having the technical skills to know what you want will give you much better results.

Agentic coding is very similar to frameworks in this regard:

1. If the alignment is right, you have saved time.

2. If it's not right, it might take longer.

3. You won't have clear evidence of which of these cases applies until changing course becomes too expensive.

4. Except, in some cases, this doesn't apply and it's obvious... Probably....

I have a (currently dormant) project https://onolang.com/ that I need to get back to that tries to balance these exact concerns. It's like half written. Go to the docs part to see the idea.

What this means in workflow terms is that the bottleneck has moved, from writing the code to reviewing it. That's forward progress! But the disparity can be jarring when you have multiple thousands of lines of code generated every day and people are used to a review cycle based on tens or hundreds.

Some people try to make the argument that we can accept standards of code from AI that we wouldn't accept from a human, because it's the AI that's going to have to maintain it and make changes. I don't accept that: whether you're human or not it's always possible to produce write-only code, and even if the position is "if we get into difficulty we'll just have the agent rewrite it" that doesn't stop you getting into a tarpit in the first place. While we still have a need to understand how the systems we produce work, we need humans to be able to make changes and vouch for their behaviour, and that means producing code that follows our standards.

If you want to get good at this, when it makes subtle mistakes or duplicates code or whatever, revert the changes and update your AGENTS.md or your prompt and try again. Do that until it gets it right. That will take longer than writing it yourself. It's time invested in learning how to use these and getting a good setup in your codebase for them.

If you can't get it to get it right, you may legitimately have something it sucks at. Although as you iterate might also have some other insights into why it keeps getting it wrong and can maybe change something more substantial about your setup to make it able to get it right.

For example I have a custom xml/css UI solution that draws inspiration both from XML and SwiftUI, and it does an OK job of making UIs for it. But sometimes it gets stuck in ways it wouldn't if it was using HTML or some known (and probably higher quality/less buggy) UI library. I noticed it keeps trying things, adding redundant markup to both the xml and css, using unsupported attributes that it thinks should exist (because they do in HTML/CSS), and never cleans up on the way.

Some amount of fixing up its context made it noticeably better at this but it still gets stuck and makes a mess when it does. So I made it write a linter and now it uses the linter constantly which keeps it closer to on the rails.

Your pet feeding app isn't in this category. You can get a substantial app pretty far these days without running into a brick wall. Hitting a wall that quickly just means you're early on the learning curve. You may have needed to give it more technical guidance from the start, and have it write tests for everything, make sure it makes the app observable to itself in some way so it can see bugs itself and fix them, stuff like that.

i.e. You are asking a question about whether using agents to write code is net-positive, and then you go on about not reviewing the code agents produce.

I suspect agents are often net-positive AND one has to review their code. Just like most people's code.

For sysops stuff I have found it extremely useful, once it has MCP's into all relevant services, I use it as the first place I go to ask what is happening with something specific on the backend.

Exploratory scripts, glue code—what I think of as digital duct tape between systems—scaffolding, probes, and throwaway POCs have always been messy and lightly governed. That’s kind of normal.

What’s different now is that more people can participate in that phase, and we’re judging that work by the same norms and processes we use for production systems. I know more designers now who are unafraid to code than ever before. That might be problematic or fantastic.

Where agentic coding does work for me is explicitly in those early modes, or where my own knowledge is extremely thin or I don’t have the luxury of writing everything myself (time etc). Things that simply wouldn’t get made otherwise: feasibility checks, bridging gaps between systems, or showing a half-formed idea fast enough to decide whether it’s worth formalising.

In those contexts, technical debt isn’t really debt, because the artefact isn’t meant to live long enough to accrue interest or be used in anger.

So I don’t think the real question is "does agentic coding work?" It’s whether teams are willing to clearly separate exploratory agency from production authority, and enforce a hard line between the two. ( I dont think they'll know the difference sadly) and without that, you’re right—you just get spaghetti that passes today and punishes EVERYONE six months later.

FWIW it seems like it heavily depends on the agent + model you're using. I've had the most success with Claude Code (Sonnet), and only tried Opus 4.5 for more complex things. I've also tried Codex which didn't seem very good by comparison, plus a handful of other local models (Qwen3, GLM, Minimax, etc.) through OpenCode, Roo, and Cline that I'm able to run on my 128 GB M4 Max. The local ones can work for very simple agentic tasks, albeit quite slow.

For gamedev you can really build quite complex 2D game prototype in Pygame or Unity rapidly since 20-50KLOC is enough for a lot of indie games. And it allow you to iterate and try different ideas much faster.

Most of features are either one-shots doing all changes across codebase in one prompt or require few fixing prompts only.

It really helps to isolate simulation from all else with mandatory CQRS for gamestate.

It also helps to generate markdown readmes along the way for all major systems and keep feature checklists ih header of each file. This way LLM dont lose context ot what is being generated.

Basically I generated in 2-3 weeks projects that would take 2-3 months to implement in a team simply because there is much less delay between idea of feature and testing it in some form.

Yes - ocassiinally you will fail to write proper spec or LLM fail to generate working code, but then usually it means you revert everything and rewrite the specification and try again.

So LLMs of today are certainly suitable when "good enough" is sufficient. So they are good for prototyping. Then if you want better architecture you just guide LLM to refactor complete code.

LLMs also good for small self contained projects or microservices where all relevant information fits into context.

Everyone's building the same workarounds. CLAUDE.md files. Handoff docs. Learnings folders. Developer logs. All manual. All single-user. All solving the same problem: how do I stop re-teaching the agent things it should already know?

What nobody seems to ask: what if the insight that helped me debug a PayPal API timeout yesterday could help every developer who hits that bug tomorrow?

Stack Overflow was multiplayer. A million developers contributing solutions that benefited everyone. We replaced it with a billion isolated sessions that benefit no one else.

The "junior developer that never grows" framing is right. But it's worse - it's a junior who forgets everything at 5pm and shows up tomorrow needing the same onboarding. And there's no way for your junior's hard-won knowledge to help anyone else's.

We're building Memco to work on this. Shared memory layer for agents. Not stored transcripts - abstracted insights. When one agent figures something out, every agent benefits.

Still early. Curious if others are thinking about this or have seen attempts at it.

What I do is - I write a skeleton. Then I write a test suite (nothing fancy just 1 or sanity tests). I'll usually start with some logic that I want to implement and break it down into XYZ steps. Now one thing to note here - TDD is very useful. If it makes your head hurt it means the requirements arent very clear. Otherwise its relatively easy to write test cases. Second thing, if your code isnt testable in parts, it probably needs some abstraction and refactoring. I typically test at the level of abstraction boundaries. eg if something needs to write to database i'll write a data layer abstraction (standard stuff) and test that layer by whatever means are appropriate. Once the spec reaches a level where its a matter of typing, I'll add annotations in the code and add todos for codex. Then I instruct it with some context, by this time its much easier to write the context since TdD clears out the brain fog. And I tell it to finish the todos and only the todos. My most used prompt is "DONT CHANGE ANYTHING APART FROM THE FUNCTIONS MARKED AS TODO." I also have an AGENTS.md file listing any common library patterns to follow. And if the code isnt correct, I'll ask codex to redo until it gets to a shape I understand. Most of the time it gets things the 2nd time around, aka iteration is easier than ground 0. Usually it takes me a day to finish a part or atleast I plan it that way. For me, codex does save me a whole bunch of time but only because of the upfront investment.

You personally should just ignore the YouTubers most of them are morons. If you'd like to checkout AI coding flows, checkout the ones from the masters like Antirez, Mitchell H. Thats a better way of learning the right tricks.

Here's what works for me:

Spend a lot of time working out plans. If you have a feature, get Claude Opus to build a plan, then ask it "How many github issues should this be", and get it to create those issues.

Then for each issue ask it to plan the implementation, then update the issue.

Then get it to look at all the issues for the feature and look for inconsistencies.

Once this is done, you can add you architectural constraints. If you think one issue looks like it could potentially reinvent something, edit that issue to point it at the existing implementation.

Once you are happy with the plan, assign to your agents and wait.

Optionally you can watch them - I find this quite helpful because you do see them go offtrack sometimes and can correct.

As they finish, run a separate review agent. Again, if you have constraints make sure the agent enforces them.

Finally, do an overall review of the feature. This should be initially AI assisted.

Don't get frustrated when it does the wrong thing - it will! Just tell it how to do the correct thing, and add that to your AGENTS.md so next time it will do it. Consider adding it to your issue template manually too.

In terms of code review, I manually review critical calculations line-by-line, and do a broad sweep review over the rest. That broad sweep review looks for duplicate functionality (which happens a lot) and for bad test case generation.

I've found this methodology speeds up the coding task around 5-10x what I could do before. Tasks that were 5-10 days of work are now doable in around 1 day.

(Overall my productivity increase is a lot higher because I don't procrastinate dealing with issues I want to avoid).

I work with an infrastructure team that are old school sysadmins, not really into coding. They are now prodigiously churning out apps that "work" for a given task. It is producing a ton of technical debt and slowing down new feature development, but this team doesn't really get it because they don't know enough software engineering to understand.

Likewise the recent example of an LLM "coding a browser" where the result didn't compile and wasn't useful. If you took it at face value you'd think "wow that's a hard task I couldn't do, and an LLM did it alone". In fact they spent a ton of effort on manually herding the LLM only for it to produce something pretty useless.

Similarly, I had it successfully migrate a third (so far) of our tests from an old testing framework to a new one, one test suite at a time.

We also had a race condition, and providing Claude Code with both the unsymbolicated trace and the build’s symbols, it successfully symbolicated the trace, identified the cause. When prompted, it identified most of the similar instances of the responsible pattern in our codebase (the one it missed was an indirect one).

I didn’t care much about the suggested fixes on that last one, but consider it a success too, especially since I could just keep working on other stuff while it chugged along.

This is obviously a _very_ simple website, but in my opinion there's no argument that agentic coding works.

You're in control of the sandbox. If you don't set any rules or guidelines to follow, the LLM will produce code you're not happy with.

As with anything, it's process. If you're building a feature and it has lots of bugs, there's been a misstep. More testing required before moving onto feature 2.

What makes you say "unreviewed code"? Isn't that your job now you're no longer writing it?

AN AI managed to do basically the whole transfer. One big help is I said "The website output of the current version should be identical", so I had an easy way to test for correctness (assuming it didn't try cheating by saving the website of course, but that's easy for me to check for)

I did it as an experiment with my constraint being that I refused to edit code, but I did review the code it made and made it make fixes.

I didn’t do it as a one shot. Roughly, I:

* sketched out a layout on paper and photographed it (very rough) * I made a list of requirements and has the AI review and augment them * I asked ChatGPT outside of the IDE to come up an architecture and guidelines I could give to the agent * I presented all of that info to the AI as project guidelines and requirements * I then created individual tasks and had it complete them one by one. Create a UI with stubbed API calls and fake data, Create the service that talks to AzureDevOps and test it, create my Node service, Hook it all up, Add features and fix bugs.

Result, fairly clean code, very attractive and responsive UI, all requirements met.

My other developers loved and immediately started asking for new features. Each new feature was another agentic task, completed over 1-3 iterations.

So it wasn’t push button automatic, but I wrote 0% of it (code wise) and probably invested 6-8 total hours. My web dev skills are rusty, so I think the same thing would have taken 4-5 days and would not have looked as nice.

Basically my point of view is that if you don't feel comfortable reviewing your coworkers code, you shouldn't generate code with AI, because you will review it badly and then I will have to catch the bugs and fix it (happened 24 hours ago). If you generate code, you better understand where it can generate side effects.

New session: Fed the entire spec, asked to build generic scaffolding only. New session: Fed the entire spec, asked to build generic TEST scaffolding. New session: Extract features to implement out of spec doc into .md files New session: Perform research on codebase with the problem statement "in mind", write results to another .md. Performed manual review of every .md. New session(s): Fed research and feature .md and asked for ONE task at a time, ensuring tests were written as per spec and keep iterating until they passed. Code reviewed beginning with test assertions, and asked for modifications if required. Before commit, asked to update progress on .md.

Ended up with very solid large project including a technology I wasn't an expert on but familiar, that I would feel confident evolving without an agent if I had to, learned a lot in the process. It would've taken me at least 2 weeks to read docs about it and at least another 3 to implement by hand; I was done in 2 total.

Web framework (includes basic component library, optional bundler/optimizer, tutorial/docs, e2e tests, and demos): https://github.com/iwalton3/vdx-web Music player web app (supports large music libraries, pwa offline sync, parametric eq, crossfade, crossfeed, semantic feature-based music search/radio, milkdrop integration, and other interesting features): https://github.com/iwalton3/mrepo-web Documentation update script (also allows exporting Claude conversations to markdown): https://github.com/iwalton3/cl-pprint

Regarding QC these are side projects so I validate them based on code review of key components, e2e testing, and manual testing where applicable. I find having the agent be able to check its work is the single biggest factor to reducing rework, but I make no promises about these projects being completely free of bugs.

Long story short, since Claude 3.7 I haven’t written a single line of code and have had great success. I review it for cleanliness, anti-patterns, and good abstraction.

I was in charge of a couple full system projects and refactors and I put Claude Code on my work machine which no one seemed to care because the top down “you should use AI or else you aren’t a team player”. Before I left in November I basically didn’t work, was in meetings all the time while also being expected to deliver code, and I started moonlighting for the company I work at now.

My philosophy is, any tool can powerful if you learn how to use it effectively. Something something 10,000 hours, something something.

Edit: After leaving this post I came across this and it is spot on to my point about needing time. https://www.nibzard.com/agentic-handbook

The problem: there is no way, he verified the code in any way. The business logic behind the feature would take probably few days to check for correctness. But if it looks good -> done. Let the customer check it. Of course, he claims “he reviewed it”.

It feels to me, we just skip doing half the things proper senior devs did, and claim we’re faster.

Previously I tried to use Aider and openAI about 6 or 7 months ago and it was terrible mess. I went back to pasting snippets in the browser chat window until a few weeks ago and thought agents were mostly hype (was wrong).

I keep a browser chat window open to talk about the project at a higher level. I'll post command line output like `ls` and `cat` to the higher level chat and use Codex strictly for coding. I haven't tried to one shot anything. I just give it a smallish piece of work at a time and check as it goes in a separate terminal window. I make the commits and delete files (if needed) and anything administrative. I don't have any special agent instructions. I do give Codex good hints on where to look or how to handle things.

It's probably a bit slower than what some people are doing but it's still very fast and so far has worked well. I'm a bit cautious because of my previous experience with Aider which was like roller skating drunk while juggling open straight razors and which did nothing but make a huge mess (to be fair I didn't spend much time trying to tame it).

I'm not sold on Codex or openAI compared to other models and will likely try other agents later, but so far it's been good.

“Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down.”

If agentic coding worked as well as people claimed on large codebases I would be seeing a massive shift at my Job... Im really not seeing it.

We have access to pretty much all the latest and greatest internally at no cost and it still seems the majority of code is still written and reviewed by people.

AI assisted coding has been a huge help to everyone but straight up agentic coding seems like it does not scale to these very large codebases. You need to keep it on the rails ALL THE TIME.

I think it depends on your tooling, your code-base, your problem space, and your ability to intelligently inject context. If all four are aligned (in my case they are) it's the real deal.

Still takes much less time for me to review the plan and output than write the code myself.

Now in terms of using AI, the key is to view yourself as a technical lead, not a people manager. You don't stop coding completely or treat underlying frameworks as a black box, you just do less of it. But at some point fixing a bug yourself is faster than writing a page of text explaining exactly how you want it fixed. Although when you don't know the programming language, giving pseudocode or sample code in another language can be super handy.

Mostly Gemini Pro 2.5 (and now Gemini Pro 3) and mostly Clojure and/or Java, with some JavaScript/Python. I require Gemini's long context size because my approach leans heavily on in context learning to produce correct code.

I've recently found Claude Code with Opus 4.5 can relieve me of some of the "agent" stuff I've done, allowing the AI to work for 10-20 minutes at a time on its own. But for anything difficult, I still do it the old way, intervening every 1-3 minutes.

Each interaction with the AI costs at least a $1, usually more (except Claude Code, where I use the $200/month plan), so my workflow is not cheap. But it 100% works and I developed more high-quality code in 2025 than in any previous year.

> I personally can't accept shipping unreviewed code. It feels wrong. The product has to work, but the code must also be high-quality.

What’s the definition of high-quality? For the project I was working on, I just needed it to work without any obvious bugs. It’s not an app for an enterprise business critical purpose, life critical (it’s not a medical device or something), or regulated industry. It’s just a consumer app for convenience and novelty.

The app is fast, smaller than 50MB, doesn’t have any bugs that the AI couldn’t fix for my test users. Sounds like the code is high quality to me.

I literally don’t give a shit what the code looks like. You gotta remember that code is just one of many methods to implement business logic. If we didn’t have to write code to achieve the result of making apps and websites it would have no value and companies wouldn’t hire software engineers.

I don’t write all my apps this way, but in this specific case letting Jesus take the wheel made sense and saved me a ton of time.

1. It helps immensely if YOU take responsibility for the architecture. Just tell the agent not only what you want but also how you want it.

2. Refactoring with an agent is fast and cheap.

0. ...given you have good tests

---

Another thing: The agents are really good at understanding the context.

Here's an example of a prompt for a refactoring task that I gave to codex. it worked out great and took about 15 minutes. It mentions a lot of project specific concepts but codex could make sense of it.

""" we have just added a test backdoor prorogate to be used in the core module.

let's now extract it and move it into a self-contained entrypoint in the testing module (adjust the exports/esbuilds of the testing module as needed and in accordance with the existing patterns in the core and package-system modules).

this entrypoint should export the prorogate and also create its environment

refactor the core module to use it from there then also adjust the ui-prototype and package system modules to use this backdoor for cleanup """

In augment code (or any other IDE agent integration), I can just @powershell-advanced-function-design at the top so the agent references my rule file, and then I list requirements after.

Things like:

- Find any bugs or problems with this function and fix them.

- Optimize the function for performance and to reduce redundant operations.

- Add additional comments to the code in critical areas.

- Add debug and verbose output to the function.

- Add additional error handling to the function if necessary.

- Add additional validation to the function if necessary.

It was also essential for me to enable the "essential" MCP servers like sequential thinking, context7, fetch, filesystem, etc.

Powershell coding isn't particularly complex, so this might not work out exactly how you want if you're dealing with a larger codebase with very advanced logic.

Another tangent: Figma Make is actually extremely impressive. I tried it out yesterday to create a simple prompt manager application, and over a period of ~30min I had a working prototype with:

- An integrated markdown editor with HTML preview and syntax highlighting for code fences.

- A semi-polished UI with a nice category system for organizing prompts.

- All required modals / dialogs were automatically created and functioned properly.

I really think agentic coding DOES work. You just have to be very explicit with your instructions and planning.

YMMV.

In order to better research, I built (ironically, mostly vibe coded) a tool to run structured "self-experiments" on my own usage of AI. The idea is I've init a bunch of hypotheses I have around my own productivity/fulfillment/results with AI-assisted coding. The tool lets me establish those then run "blocks" where I test a particular strategy for a time period (default 2 weeks). So for example, I might have a "no AI" block followed by a "some AI" block followed by a "full agent all-in AI block".

The tool is there to make doing check-ins easier, basically a tiny CLI wrapper around journaling that stays out of my way. It also does some static analysis on commit frequency, code produced, etc. but I haven't fleshed out that part of it much and have been doing manual analysis at the end of blocks.

For me this kind of self-tracking has been more helpful than hearsay, since I can directly point to periods where it was working well and try to figure out why or what I was working on. It's not fool-proof, obviously, but for me the intentionality has helped me get clearer answers.

Whether those results translate beyond a single engineer isn't a question I'm interested in answering and feels like a variant of developer metrics-black-hole, but maybe we'll get more rigorous experiments in time.

The tool open source here (may be bugs, only been using it a few weeks): https://github.com/wellwright-labs/devex

The thing that people don't seem to understand is that these are two separate processes with separate goals. You don't do code reviews to validate behaviour, nor do you test to validate code.

Code reviews are for maintainability and non-functional requirements. Maintainability is something that every longer term software project has run into, to the point where applications have been rewritten from scratch because the old code was unmaintainable.

In theory you can say "let the LLM handle it", but how much do you trust it? It's practially equivalent to using a 3rd party library, most people treat them as a black box with an API - the code details don't matter. And it can work, I'm sure, but do you trust it?

Sadly review isn't enough. I just today I found some code that I reviewed 2 months where the developer clearly used an agent to generate the code and I completely missed some really dumb garbage the agent put in. The agent took a simple function that returns an object with some data and turned it into a mess that required multiple mocks in the tests (also generated by the agent).

The dev is a junior and a clear example of what is to come, inexperienced people thinking coding is getting an agent to get something to pass CI.

The tech debt is accelerating exponentially!

The first thing anyone should do is immediately understand that they are in a finite-sum adversarial relationship with all of their vendors. If you're getting repeatably and unambiguously food outcomes with Claude Code you're either in the bandit arm where high twitter follower count people go or you're counting cards in a twelve deck shoe and it'll be 24 soon. Switch to opencode today, the little thing in the corner is a clock, and a prominent clock is more or less proof you're not in a casino.

There is another key observation that took me a long time to internalize, which is that the loss surface of formalism is not convex: most of us have tried to lift the droids of of the untyped lambda calculus and into System F, and this does not as a general thing go well. But a droid in the untyped lambda calculus will with 100% likelihood eventually Superfund your codebase, no exceptions.

The droids are wildly "happy" for lack of a better term in CIC but the sweet spot is System F Omega. A web app that's not in Halogen is an unforced error now: pure vibecoders can do lastingly valuable work in PureScript, and they can't in React, which is irretrievably broken under any sane effects algebra.

So AI coding is kind of a mean reversion in a sense. Knuth and Lamport and Hoare and Djikstra had an LLM: it was an army of guys in short-sleeved dress shirts with half a page of fanfold system prompt on every page. That is automatable now, but there's a straightforward algorithm for remaining relevant: crank the ambition up until Opus 4.5 is whiffing.

Computer Scientist is still a high impact job. The cast of Office Space is redundant. But we kinda knew that the first time we saw Office Space.

Personally I'm working harder than ever on harder problems than ever with pretty extreme AI assist hygiene: you spend all your time on hard problems with serious impact. The bueden of understanding the code and being able to quit vim has gone up but down, and mathematics is absorbing computer programming.

The popular narrative about Claude Code 10x ing JavaScript projects is maskirovska

In these types of applications, there's already a lot of low hanging fruit to be had from working with an LLM.

If you're on a greenfield app where you get to make those decisions at the start, then I think I would still use the LLMs but I would be mindful of what you check into the code base. You would be better off setting up the project structure yourself and coding some things as examples of how you want the app to work. Once you have some examples in place, then you can use the LLMs to repeat the process for new screens/features.

I had 12 legacy node apps running node 4, with jQuery front ends. Outdated dependencies and best practices all over. No build pipeline. No tests. Express 3. All of it worked but it's aging to the point of no return. And the upgrade work is boring, with very little ROI.

In a month, without writing any code, I've got them all upgraded to Node 22, with updated dependencies, removed jQuery completely, newer version of express, better logging, improved UI.

It's work that would have taken me a year of my free time and been soul crushing and annoying.

Did it with codex as a way of better learning the tooling. It felt more like playing a resource sim game than coding. Pretty enjoyable. Was able to work on multiple tasks at once while doing some other work.

It worked really well for that.

I think a critical point is how well one can communicate/delegate. I have a background with systems thinking and communication, so figuring out how to prompt for what I’m after was smooth.

I was also an early adopter of LLMs so there’s good muscle memory there.

It’s important to see AI tools as accelerators - not replacements or solvers. Still do test-driven development. Still maintain robust documentation. Good practices + AI is where the value is; not just throwing AI at things.

I made the worlds fastest and most accurate JSON Schema validator.

https://github.com/sberan/tjs

With that in mind, a couple of comments - think of the coding agents as personalities with blind spots. A code review by all of them and a synthesis step is a good idea. In fact currently popular is the “rule of 5” which suggests you need the LLM to review five times, and to vary the level of review, e.g. bugs, architecture, structure, etc. Anecdotally, I find this is extremely effective.

Right now, Claude is in my opinion the best coding agent out there. With Claude code, the best harnesses are starting to automate the review / PR process a bit, but the hand holding around bugs is real.

I also really like Yegge’s beads for LLMs keeping state and track of what they’re doing — upshot, I suggest you install beads, load Claude, run ‘!bd prime’ and say “Give me a full, thorough code review for all sorts of bugs, architecture, incorrect tests, specification, usability, code bugs, plus anything else you see, and write out beads based on your findings.” Then you could have Claude (or codex) work through them. But you’ll probably find a fresh eye will save time, e.g. give Claude a try for a day.

Your ‘duplicated code’ complaint is likely an artifact of how codex interacts with your codebase - codex in particular likes to load smaller chunks of code in to do work, and sometimes it can get too little context. You can always just cat the relevant files right into the context, which can be helpful.

Finally, iOS is a tough target — I’d expect a few more bumps. The vast bulk of iOS apps are not up on GitHub, so there’s less facility in the coding models.

And any front end work doesn’t really have good native visual harnesses set up, (although Claude has the Claude chrome extension for web UIs). So there’s going to be more back and forth.

Anyway - if you’re a career engineer, I’d tell you - learn this stuff. It’s going to be how you work in very short order. If you’re a hobbyist, have a good time and do whatever you want.

Because the network of abstractions that is a human awareness (the ol' meat suit pilot model) is unique to all of us we cannot directly share components of our internal networks directly. Thus, we all interact through language and we all use language differently. While it's true that compute is fundamentally the same for all of us (we have to convert complex human abstractions into computable forms and computers don't vary that much), programming languages provide general mappings for diverse human abstractions back to basic compute features.

And so, just like with coding, the most natural path for interacting with a LLM is also unique to all of us. Your assumptions, your prior knowledge, and your world perspective all shape how you interact with the model. Remember you're not just getting code back though... LLMs represent a more comprehensive world of ideas.

So approach the process of learning about large language models the same way that you approach the process of learning a new language in general: pick a hello world project (something that's hello world for you) and walk through it with the model paying attention to what works and what doesn't. You'd do someone similar if you were handed a team of devs that you didn't know.

For general use, I start by having the model generate a req document that 1) I vet thoroughly. Then I have the model make TODO lists at all levels of abstraction (think procedural decomposition for the whole project) down to my code that 2) I vet thoroughly. Then I require the model to complete the TODO tasks. There are always hiccups same as when working with people. I know the places that I can count on solid, boiler plate results and require fewer details in the TODOs. I do not release changes to the TODO files without 3) review. It's not fire-and-forget but the process is modular and understandable and 4) errors finding from system design are mine to identify and address in the req and TODOs.

Good luck and have fun!

Did it in one go, WAY better than I ever could have. Creates directories, generates configs from templates, all secrets are encrypted and managed etc.

I've iterated on it a bit more to optimise some bits, mostly for ergonomics, but the basic structure is still there.

---

Did a similar thing with Ansible. I gave claude a way to access my local computer (Mac) and a server I have (Linux), told it to create an ansible setup that sets up whatever is installed on both machines + configurations.

Again, managed it faster and way better than I every could have.

I even added an Arch Linux VM to the mix just to complicate things, that went faster than I could've done it myself too.

The only positive antigenic coding experience I had was using it as a "translator" from some old unmaintained shell + C code to Go.

I gave it the old code, told it to translate to Go. I pre-installed a compiled C binary and told it to validate its work using interop tests.

It took about four hours of what the vibecoding lovers call "prompt engineering" but at the end I have to admit it did give me a pretty decent "translation".

However for everything else I have tried (and yes, vibecoders, "tried" means very tightly defined tasks) all I have ever got is over-engineered vibecoding slop.

The worst part of of it is that because the typical cut-off window is anywhere between 6–18 months prior, you get slop that is full of deprecated code because there is almost always a newer/more efficient way to do things. Even in languages like Go. The difference between an AI-slop answer for Go 1.20 and a human coded Go 1.24/1.25 one can be substantial.

My advice - embrace TDD. Work with AI on tests, not implementation - your implementation is disposable, to be regenerated, tests fully specify your system through contracts. This is more tricky for UI than for logic. Embracing architectures allowing to test view model in separation might help. I general anything reducing cognitive load during inference time is worth doing.

I call it "moonwalk" because, when throwing away the intermediate vibe-coded prototype code in the middle, it feels like walking backwards while looking forward.

- Check out a spike branch

- Vibe code until prototype feels right.

- Turn prototype into markdown specification

- Throw away vibe'd code, keep specification

- Rebase specification into main, check out main

- Feed specification to our XP/TDD agents

- Wait, review a few short iterations if any

- Ship to production

This allows me to get the best of vibe-coding (exploring, fast iterating and dialing-in on the product experience) and writing production-grade code (using our existing XP practices via dedicated CC sub-agents and skills.)

I am writing an automation software that interfaces with a legacy windows CAD program. Depending on the automation, I just need a picture of the part. Sometimes I need part thickness. Sometimes I need to delete parts. Etc... Its very much interacting with the CAD system and checking the CAD file or output for desired results.

I was considering something that would take screenshots and send it back for checks. Not sure what platforms can do this. I am stumped how Visual Studio works with this, there are a bunch of pieces like servers, agents, etc...

Even a how-to link would work for me. I imagine this would be extremely custom.

I just don't think theres enough Swift in the LLMs corpus and the "right way" to do things in Swift has changes a few times in the last few years which I imagine compounds the sparse signal.

For SwiftUI work I'd personally recommend using Opus 4.5 with Axiom. Anytime you're designing something refer to Axiom, Claude needs its skills and agents to steer designs.

Vibe coding is a farce, but their is real value if you have the experience to set up a decent workflow.

I have had similar problems with colleagues who couldn't abide by others solving problems in ways that they disagreed with and would only be agreeable coworkers if they thought in the same way.

https://news.ycombinator.com/user?id=the_mitsuhiko

https://lucumr.pocoo.org/about/

He has an extensive and impressive body of work in Python and Rust pre LLMs. He's now working on his own startup and doing much of it with AI and documenting his journey. I trust his opinions even though I don't use LLMs as much as he does.

Write a good AGENTS.md (or CLAUDE.md) and you'll see that code is more idiomatic. Ask it to keep a changelog. Have the LLM write a plan before starting code. Ask it to ask you questions. Write abstraction layers it (along with the fellow humans of course) can use without messing with the low-level detail every time.

In a way you have to develop a framework to guide the LLM behavior. It takes time.

I find them most useful for making prototypes to show clients who are unimpressed with a presentation/document, but I end up doing most of the implementation myself. Which is fine.

- Cleaner code - Easily 5x speed minimum - Better docs, designs - Focus more on the product than than the mechanics - More time for family

Your job is to put them in constraints and give granular and clear tasks. Be aware that junior developer has very basic knowledge about architecture.

The good is that it does not simulate that part when developer tries shift blame or pin it on you. Because you’re to blame at all times.

In my personal experience (working as part of a team and not a solo dev), good documentation and well-documented/enforced practices can produce great results. That said, it’s not 100% perfect but neither are humans.

When I try to give agents broad architectural tasks, they flounder. When I constrain them to small, well-defined units of work within an existing architecture, they can produce clean, correct code surprisingly often.

One-shotting an application that is very bespoke and niche is not going to go well, and same goes for working on an existing codebase without a pile of background work on helping the model understand it piece by piece, and then restricting it to small changes in well-defined areas.

It's like teaching an intern.

I treat it like a little jump off platform, for my own initial velocity, any more and it goes off the rails like you describe

1. training a RAG on support questions for chat or documentation, w/good material

2. people doing GTM work in marketing, for things like email automation

3. people using a combination of expensive tools - Claude + Cursor + something else (maybe n8n, maybe a custom coding service) - to make greenfield apps

The article is a bit over the top, but several people on my company/team are doing exactly that approach, and have built similar orchestrators

I made the worlds fastest and most accurate JSON Schema parser.

https://github.com/sberan/tjs

But, nobody seems to care. The repo only has 18 stars and my Show HN post got no upvotes. I'm not sure what to take away from that.

But... tests and CI are also code. It may be buggy, it may not cover enough, etc. It is also likely written by an LLM in this scenario. So, it's more like a move from “validating architecture” to “LLM-based self-validating”

95% of the time the code doesn't even build but it gets all the jigsaw pieces in place and it's a million times easier to start deleting and moving pieces around than to start from scratch.

https://www.youtube.com/watch?v=4OmlGpVrVtM

You'll have your answer.

PS: don't try to use his website/apps, half of it is broken... and he has generated a 'jobs' page on the main app's website which made me laugh so hard I got a coughing fit.

I can get amazing results from a chatbot based workflow where I manually provide any context needed, if the agent can happily pull files into context on it's own it tends to pull in too much or completely irrelevant stuff and the quality of the output suffers.

It's significantly faster in many cases that having written the code by hand myself.

Since MCP came out the quality of code has improved since there is always context7 and fetch to look up syntax.

But yes at some point you need to look at the code yourself just to be sure

There are projects where throwing a dozen junior developers at the problem can work but they’re very basic CRUD type things.

Easiest way to get value is building tests. These don't ship.

You can get value from LLM as an additional layer of linting. Reviews don't ship either.

You can use LLM for planning. They can quickly scan across the codebases catch side effects of proposed changes or do gap analysis from the desired state.

Argumenting that agentic coding must be on or off seem very limiting.

Have had success at work, real value, real results.

Example: extracting a bunch of data from a tool we’re required to use at my company for getting a bunch of performance metrics. The data is useful but the interface is awful and it’s impossible to pull out trends and spot the real information I need from it. So, I threw Claude at it after months of dreaming of being able to better use the data. It generated for me in a few minutes all the data I could hope for in a CSV I was able to load into another tool that gave me deep insights almost immediately and allowed me to go make some different decisions I otherwise wouldn’t have.

What I did:

1. I have created and curated a set of sub-agents and commands/workflows for building things for me.

2. I used my build command, which details a workflow for refining, planning, implementing, code reviewing, testing, then conducting a final “product review” to determine if original requirements were met.

3. I then review the code myself before running it.

The code was solid (I’m also a very strong engineer and have tailored my agents and workflows to generate code I’d be comfortable with).

Another example: one of my teams went on a journey to convert one of our internal legacy frontend applications to a newer shared component library and eliminate old cruft that we inherited when we inherited the codebase.

The team was able to get this massive UI rewrite done in under two weeks, the updated code was better than the original code (it was all React to React, TypeScript to TypeScript), and we eliminated (literally) hundreds of thousands of lines of old hand-written over-abstracted code. Bundle sizes dramatically down, higher performance, more modern UX, and the thing is in production and working. Real value: faster product iteration in this now far smaller and easier-to-work-with codebase, far less technical debt, and faster builds, etc.

The team only used GitHub Copilot for this and it required a bunch of iteration and starting over with different instructions, but they got there and still managed to save a ridiculous amount of time; hand-writing the UI migration would have been one of those multi-month projects that went over schedule (I’ve seen and lived that movie many times before).

I’m still very skeptical of all the hype but I’ve seen very real, very valuable results out of this stuff.

edit: formatting

I've been working on wasm sandboxing and automatic verification that code doesn't have the lethal trifecta and got something working in a couple of days.

I'd like to do a clean rewrite at some point.

If you can express that in a form that can be easily tested, you can just instruct an agentic coding tool to do something about it. Most of my experience is with codex. Everytime I catch it doing something I don't like, I try to codify it in a skill or in my Agents.md or some test. I've been using codex specifically to work on addressing technical debt in my own code bases. There's a lot of stuff I never got around to fixing that I'm now actually addressing. Because it stopped being a monster project that would take weeks. You can actually nudge a code base in the right direction with agentic coding tools.

The same things that make it hard for people to iterate on code bases (complexity, technical debt, poor architectural decisions, etc.) also make it hard for LLMs to work on code bases. So, as soon as you start working on making those things better, you might get better results.

If you have a lot of regressions when iterating with an LLM, you don't have good enough regression tests. If code produces runtime type errors, maybe use something with a better type checker that can remove those bugs before they happen. If you see a lot of duplication, tell it to do something about it and/or use code quality tools that flag such issues and tell it to address those issues. This stuff requires a bit of discipline and skill. But they are fixable things. And the usual excuse that you can't be bothered doesn't apply here; just make the coding tools fix this for you.

As for evidence, the amount of dollars being spent by well respected people in the industry on these tools is increasing. That might not be the evidence you like but it's a clear indication that people are getting some value out of these tools.

I'm definitely getting more predictable results. I find myself merging most proposed changes after a few iterations. The percentage is trending up in the last months. I can only speak for myself. But essentially everybody I know and respect is using this stuff at this point. With very mixed results. But people are getting shit done. I think there are lots of things to improve with these tools. I'd like them to be faster and require less micro management. I'd like them to work across multiple repositories and issue trackers instead of suffering from perpetual tunnel vision. Mostly when I get bad results, it's a context problem. Some of these things are frustrating to fix. But in the end this is about good feedback loops, not about models magically getting what you want.

ive had more success with review tools, rather than the agent getting the code quality right the first time.

current workflow

1. specs/requirements/design, outputting tasks 2. implementation, outputting code and tests 3. run review scripts/debug loops, outputting tasks 4. implement tasks 5. go back to 3

the quality of specs, tasks, and review scripts make a big difference

one of the biggest things that gets the results better is if you can get a feedback loop in from what the app actually does back to the agent. good logs, being able to interact/take screenshots a la playwright etc

guidelines and guardrails are best if theyre tools that the agent runs, or that run automatically to give feedback.

Otherwise, they are bad.

I don’t know what I do differently, but I can get Cursor to do exactly what I want all the time.

Maybe it’s because it takes more time and effort, and I don’t connect to GitHub or actual databases, nor do I allow it to run terminal commands 99% of the time.

I have instructions for it to write up readme files of everything I need to know about what it has done. I’ve provided instructions and created an allow list of commands so it creates local backups of files before it touches them, and I always proceed through a plan process for any task that is slightly more complicated, followed by plan cleanup, and execution. I’m super specific about my tech stack and coding expectations too. Tests can be hard to prompt, I’ll sometimes just write those up by hand.

Also, I’ve never had to pay over my $60 a month pro plan price tag. I can’t figure out how others are even doing this.

At any rate, I think the problem appears to be the blind commands of “make this thing, make it good, no bugs” and “this broke. Fix!” I kid you not, I see this all the time with devs. Not at all saying this is what you do, just saying it’s out there.

And “high quality code” doesn’t actually mean anything. You have to define what that means to you. Good code to me may be slop to you, but who knows unless it is defined.

As in "Please write just this one for me". Even still, I take care to review each line produced. The key is making small changes at a time.

Otherwise, I type out and think about everything being done when in ‘Flow State’. I don't like the feeling of vibe coding for long periods. It completely changes the way work is done, it takes away agency.

On a bit of a tangent, I can't get in Flow State when using agents. At least not as we usually define it.

I don't have direct evidence of exactly what you're looking for (particularly the part about "someone responsible for the architecture to sign off"). Sticking strictly to that last caveat may prevent you from receiving some important signal.

> the claim that we should move from “validating architecture” to “validating behavior.”

I think these people are on the right track and the reason I think that is because of how I work with people right now.

I manage the work of ~10 developers pretty closely and am called on for advice to another ~10, while also juggling demanding stakeholders. For a while now, I've only been able to do spot checks on PRs myself. I don't consider that a major part of my job anymore. The management that is most valuable is:

1) Teaching developers about quality so that they start with better code, and give better reviews to each other 2) Teaching people to focus and move in small steps 3) Investing in guardrails 4) Metrics, e.g. it doesn't matter what code is merged, it doesn't matter if a "task" is "shipped", what matters is if the metrics say that we've had the result we expected.

As I acknowledge how flimsy my review process is, my impulse is to worry about architecture and security. But metrics and guardrails exist for those things too. Opinionated stacks help, for instance SQL injection opportunities look different enough from "normal" Rails to mean that there are linters that can catch many problems, and the linters are better than I am at this job.

Some of these tools are available for agents just as they are for humans. Some of them are woefully bad or lack good ergonomics for agents, but I wouldn't bet against them becoming better.

I agree that agentic coding changes code review, but I don't think that has to inevitably / long-term mean worse.

> half of my time went into fixing the subtle mistakes it made or the duplication it introduced

A cold hard evaluation of the effectiveness of agentic coding doesn't care about what percentage of time went into fixing bad code; it cares about the total time.

That said, I find that making an agent move in many small steps (just how I would advise a human) creates far less rework.

> Is there evidence that agentic coding works?

Yes plenty, tons, and growing every single day, people are producing code and tooling that works for them and is providing them value. My linkedin is even starting to show me none-programmers knocking up web front ends for us, and my brother who is a builder is now drawing up requirement specs for software!

> Is the code high-quality

Only if you are really careful, and constantly have the human in the loop guiding the agent, and it's not easy. This is where domain expertise and experience come in.

Do I think it's possible, yes, do I think there are a ton of good examples out there, absolutely not.

https://www.linkedin.com/pulse/concrete-vibe-coding-jorge-va...

The bottom line is this:

* The developer stop been a developer, and becomes a product designer with high technical skills.

  * This is a different set of skills than than a developer or a product owner currently have. It is a mix of both, and the expectations of how agentic development works need to be adjusted.

  * The working with an agent feels more like mentoring the AI than ask and receive.

* Use TDD, ask the agent to create the tests, and then code to the test. Don't correct the bugs, make the agent correct them and explain why that is a bug, specially with code design decisions. Store those in AGENTS.md file at the root of the project.

There are more things that can be done to guide the agent, but I need to have clear in an articulable way the direction of the coding. On the other side, I don't worry about implementation details like how to use libraries and APIs that I am not familiar with, the agent just writes and I test.

Currently I am working on a product and I can tell you, working no more than 10 hours a week (2 hours here, 3 there, leave the agent working while I am having dinner with family) I am progressing at I would say 5 to 10 times faster than without it. So, yeah it works, but I had to adjust how I do my job.

Caveat: can't be pure vibes. Needs ownership, care, review and willingness to git reset and try again when needed. Needs a lot of tests.

Cavaet: Greenfield.

My thinking is that showcasing / interacting with products built by LLMs will tell you a lot about code quality AND maintenance.

I mean it’s easy to spin up static websites. It’s a whole another thing to create, maintain and iteratively edit/improve an actual digital product over time. That’s where the cracks will show up.

That also might be the core problem of agentic code: it’s fairly fresh so you won’t see products that have been maintained for long.

Thus, my current summary is: it’s great for prototyping and probably for specific tasks like test case generation but it’s not something you want to use when working on a multiyear product/project.

Feed it little tasks (30 s-5 min) and if you don't like this or that about the code it gives you either tell it something like

   Rewrite the selection so it uses const, ? and :

   I edited what you wrote to make it my own,  what do you think about my changes?

I understand and admire your commitment to code quality. I share similar ideals.

But it's 2026 and you're asking for evidence that agentic coding works. You're already behind. I don't think you're going to make it. Your competitors are going to outship you.

In most cases, your customers don't care about your code. They only want something that works right.

For me this has completely changed. The code needs to work. Bonus points when it is easy for a human to follow and has comments. But I don't do a full review anymore. I skim it and if I don't see an obvious flaw, it's lgtm. I also don't look into the bytecode / assembly to check whether the compiler did a good job.

I employ a few tricks:

1- I avoid auto-complete and always try to read what it does before committing. When it is doing something I don’t want, I course correct before it continues

2- I ask the LLM questions about the changes it is making and why. I even ask it to make me HTML schema diagrams of the changes.

3- I use my existing expertise. So I am an expert Swift developer, and I use my Swift knowledge to articulate the style of what I want to see in TypeScript, a language I have never worked in professionally.

4- I add the right testing and build infrastructure to put guardrails on its work.

5- I have an extensive library of good code for it to follow.

but the machine's keep going down, and we haven't cleaned enough of the bugs in the meta machine to open source it yet.

so...kinda?

Just start smaller. I'm not sure why people try to jump immediately to creating an entire app when they haven't even gotten any net-positive results at all yet. Just start using it for small time saving activities and then you will naturally figure out how to gradually expand the scope of what you can use it for.

AI has made it possible for me to build several one-off personal tools in the matter of a couple of hours and has improved my non-tech life as a result. Before, I wouldn't even have considered such small projects because of the effort needed. It's been relieving not to have to even look at code, assuming you can describe your needs in a good prompt. On the other hand, I've seen vibe coded codebases with excessive layers of abstraction and performance issues that came from a possibly lax engineering culture of not doing enough design work upfront before jumping into implementation. It's a classic mistake, that is amplified by AI.

Yes, average code itself has become cheap, but good code still costs, and amazing code, well, you might still have an edge there for now, but eventually, accept that you will have to move up the abstraction stack to remain valuable when pitted against an AI.

What does this mean? Focus on core software engineering principles, design patterns, and understanding what computer is doing at a low level. Just because you're writing TypeScript doesn't mean you shouldn't know what's happening at the CPU level.

I predict the rise in AI slop cleanup consultancies, but they'll be competing with smarter AIs who will clean up after themselves.

Also bear in mind that a lot of folks want to be seen as being on the bleeding edge, including famous people. They get money from people booking them for courses and consulting, buying their books, products and stuff. A "personal brand" can have a lot of value. They can't be seen as obsolete. They're likely to talk about what could or will be, more than about what currently is. Money isn't always the motive for sure, people also want to be considered useful, they want to genuinely play around and try and see where things are going.

All that said, I think your approach is fine. If you don't inspect what the agent is doing, you're down to faith. Is it the fastest way to get _something_ working? Probably not. Is it the best way to build an understanding of the capabilities and pit falls? I'd say so.

This stuff is relatively new, I don't think anyone has truly figured out how to best approach LLM assisted development yet. A lot of folks are on it, usually not exactly following the scientific method. We'll get evidence eventually.

> but the dissonance between what I'm seeing online and what I'm able to achieve is doing my head in.

... could be due to your choice to use Codex, which is OpenAI's coding agent. The stellar reports you're reading online are mostly about Claude Code. (People say so.)

Try your next project in Claude code. I am on the Max plan, which includes Claude code at the flat rate, and have had just as good an experience as the online reports I've read.

In my experience Copilots work expertly at CRUD'ing inside a well structured project, and for MVPs in languages you aren't an expert on (Rust, C/C++ in my case)

The biggest demerit is that agents are increasingly trying to be "smart" and using powershell search/replace or writing scripts to skimp on tokens, with results that make me unreasonably angry

I tried adding i18n to an old react project, and copilot used all my credits + 10 USD because it kept shitting everything up with its maddening, idiotic use if search replace

If it had simply ingested each file and modified them only once, it would have been cheaper

As you can tell, I am still salty about it

Recently there was this post which is largely generated by Claude Code. Read it.

This is very bad. A programmers output is code, when we begin to measure the output of code as the primary product of a programmers work we will fall into a death spiral of unmaintainable code. I still strongly believe we write code for other humans, not for machines. Yes, yes we need to solve problems and you may argue that is the true measure of code. However this argument ignores maintenance and continued developmental requirements.

One is a VSCode extension and has thousands of downloads across different flavors of the IDE -- won't plug it here to spare the downvotes ;)

Been a developer professionally for nearly 20 years. It is 100% replacing most of the things I used to code.

I spend most of my time while it's working testing what it's built to decide on what's next. I also spend way more time on DX of my own setup, improving orchestration, figuring out best practice guidance for the Agent(s), and building reusable tools for my Agents (MCP).

I think in most cases the speed at which AI can produce code outweighs technical debt, etc.

Technical debt, like financial debt, is a tool. The problem isn't its existence, it's unmanaged accumulation.

A few observations from my experience:

1. One-shotting - if you're prompting once and shipping, you're getting the "fast and working" version, not the "well-architected" version. Same as asking an experienced dev for a quick prototype.

2. AI can output excellent code - but it takes iteration, explicit architectural constraints, and often specialized tooling. The models have seen clean code too; they just need steering toward it.

3. The solution isn't debt-free commits. The solution is measuring, prioritizing, and reducing only the highest risk tech debt - the equivalent of focusing on bottlenecks with performance profiling. Which code is high-risk? Where's the debt concentrated? Poorly-factored code with good test coverage is low-risk. Poorly-tested code in critical execution paths is high-risk. Your CI pipeline needs to check the debt automatically for you just like it needs to lint and check your tests pass.

I built https://github.com/iepathos/debtmap to solve this systematically for my projects. It measures technical debt density to prioritize risk, but more importantly for this discussion: it identifies the right context for an LLM to understand a problem without looking through the whole codebase. The output is designed to be used with an LLM for automated technical debt reduction. And because we're measuring debt before and after, we have a feedback loop - enabling the LLM to iterate effectively and see whether its refactoring had a positive impact or made things worse. That's the missing piece in most agentic workflows: measurement that closes the loop.

To your specific concern about shipping unreviewed code: I agree it's risky, but the review focus should shift from "is every line perfect" to "where are the structural risks, and are those paths well-tested?" If your code has low complexity everywhere, is well tested (always review tests), and passing everything, then ask yourself what you actually gain at that point from further investing your time over-engineering the lesser tech debt away? You can't eliminate all tech debt, but you can keep it from compounding in the places that matter.

I don't even care about abstract code quality. To me code quality means maintainability. If the agents are able to maintain the mess they are spewing out, that's quality code to me. We are decidedly not there yet though.

I had no idea so many people still clung to these lines!

Personally, I see huge value. I’ve built more in the past year than I ever have before. Ideas that used to go on a list, now get implemented in a weekend. The ones that are good, I’ve shipped. Others get played with, tested, and set aside. Not due to lack of quality, but the idea wasn’t right. Some need more thought so I’ll keep testing, and tweaking over time.

Evidence? Those projects would not exist if not for these tools. Not because I couldn’t write them, but because I WOULDN’T have written them.

But I’ve given up trying to convince people. If you don’t think it works, great. Don’t use it. That’s fine with me!

I’d urge you to keep trying though. Hopefully you have the moment as well. And if you think, “it’s ok for some things, but not what I do”, well…just don’t wait a year before trying again. Keep an open mind, and integrate the tools into your workflow everyday. Not as a one off “ok fine, I’ll test this stuff” grumpy kind of way, but in an honest iterative way where you are using the tools on a daily basis, even in a small way. For docs. Whatever. Just keep using them. Eventually you’ll see. Eventually you’ll have the moment.

One thing I think is lost in a lot of these conversations is how much FUN I am having. I’ve been coding for…well if you count copying BASIC from books into a Timex Sinclair, about 40+ years. Maybe professionally for 30+? I haven’t had this much FUN building since back when I copied those games from that little red book of BASIC programs. These tools make even mundane work fun for me, cause you need to have your workflow right. So coding becomes more like Factorio or something. Keep optimizing your setup, then simplify, the optimize, then simplify.

Syntax was always something that felt like a hurdle. I always rolled my eyes at people that get lost in the minutia of a language. Like I remember the Perl neck beards from way back. lol.

I learned what I needed to get the task done, then moved on. Likely forgetting 90% of the details anyway, so I could keep an eye on what I considered important. Architecture. Functionality. Separation of Concerns. Could I dive back in and figure out the details again if needed, sure. No problem.

NOW these tools let me work at this abstraction level even more.

And I’m here for it.

Just amazing this is where we are at as an industry.

If you know the field you want it to work in, then it can augment what you do very well.

Without that they all tend to create hot garbage that looks cool to a layperson.

I would also avoid getting it to write the whole thing up front. Creating a project plan and requirements can help ground them somewhat.

The simplest metric you should be tracking is; has it generated income.

In this sense my use of agentic coding has performed very well. People asking for evidence and repos are a little naive to how capitalism works in the real world. If I’m making money on something I’m not going to let you copy it, and I’m sure as hell not going to devalue it by publicising that it was built by AI.

The main library (rubygem) has 3,662 code lines and 9,199 comment lines of production Ruby and 4,933 code lines and 710 comment lines of Rust. There are a further 6,986 code lines and 2,304 comment lines of example applications code using the library as documentation, and 4,031 lines of markdown documentation. Plus, 11,902 code lines and 2,164 comment lines of automated tests. Oh, and 4,250 lines in bin/ and tasks/ but those are lower-quality "internal" automation scripts and apps.

The library is good enough that Sidekiq is using it to build their TUI. https://github.com/sidekiq/sidekiq/issues/6898

But that's not all I've built over this timeframe. I'm also a significant chunk of the way through an MVU framework, https://rooibos.run, built on top of it. That codebase is 1,163 code lines and 1,420 comment lines of production Ruby, 4,749 code lines and 521 comment lines of automated tests. I need to add to the 821 code lines 221 comment lines of example application code using the framework as documentation, and to the 2,326 lines of markdown documentation.

It's been going so well that the plan is to build out an ecosystem: the core library, an OOP and an FP library, and a set of UI widgets. There are 6,192 lines of markdown in the Wik about it: mailing list archives, AI chat archives, current design & architecture, etc.

For context, I am a long-time hobbyist Rubyist but I cannot write Rust. I have very little idea of the quality of the Rust code beyond what static analyzers and my test suite can tell me.

It's all been done very much in public. You can see every commit going back to December 22 in the git repos linked from the "Sources" tab here: https://sr.ht/~kerrick/ratatui_ruby/ If you look at the timestamps you'll even notice the wild difference between my Christmas vacation days, and when I went back to work and progress slowed. You can also see when I slowed down to work on distractions like https://git.sr.ht/~kerrick/ramforge/tree and https://git.sr.ht/~kerrick/semantic_syntax/tree.

If it keeps going as well as it has, I may be able to rival Charm's BubbleTea and Bubbles by summertime. I'm doing this to give Rubyists the opportunity to participate in the TUI renaissance... but my ultimate goal is to give folks who want to make a TUI a reason to learn Ruby instead of Go or Rust.

## Architecture Overview

This solution deploys auto-scaling GitHub Actions runners on EC2 instances that can trigger your existing AWS CodeBuild pipelines. Runners are managed via Auto Scaling Groups with automatic registration and health monitoring.

## Prerequisites

- AWS CLI configured with appropriate credentials - GitHub Enterprise Cloud organization admin access - Existing CodeBuild project(s) - VPC with public/private subnets

## Solution Components

### 1. CloudFormation Template### 2. GitHub Workflow for CodeBuild Integration## Deployment Steps

### Step 1: Create GitHub Personal Access Token

1. Navigate to GitHub → Settings → Developer settings → Personal access tokens → Fine-grained tokens 2. Create token with these permissions: - *Repository permissions:* - Actions: Read and write - Metadata: Read - *Organization permissions:* - Self-hosted runners: Read and write

```bash # Store token securely export GITHUB_PAT="ghp_xxxxxxxxxxxxxxxxxxxx" export GITHUB_ORG="your-org-name" ```

### Step 2: Deploy CloudFormation Stack

```bash # Set variables export AWS_REGION=us-east-1 export STACK_NAME=github-runner-ec2 export VPC_ID=vpc-xxxxxxxx export SUBNET_IDS="subnet-xxxxxxxx,subnet-yyyyyyyy"

# Deploy stack aws cloudformation create-stack \ --stack-name $STACK_NAME \ --template-body file://github-runner-ec2-asg.yaml \ --parameters \ ParameterKey=VpcId,ParameterValue=$VPC_ID \ ParameterKey=PrivateSubnetIds,ParameterValue=\"$SUBNET_IDS\" \ ParameterKey=GitHubOrganization,ParameterValue=$GITHUB_ORG \ ParameterKey=GitHubPAT,ParameterValue=$GITHUB_PAT \ ParameterKey=InstanceType,ParameterValue=t3.medium \ ParameterKey=MinSize,ParameterValue=2 \ ParameterKey=MaxSize,ParameterValue=10 \ ParameterKey=DesiredCapacity,ParameterValue=2 \ ParameterKey=RunnerLabels,ParameterValue="self-hosted,linux,x64,ec2,aws,codebuild" \ ParameterKey=CodeBuildProjectNames,ParameterValue="" \ --capabilities CAPABILITY_NAMED_IAM \ --region $AWS_REGION

# Wait for completion (5-10 minutes) aws cloudformation wait stack-create-complete \ --stack-name $STACK_NAME \ --region $AWS_REGION

# Get stack outputs aws cloudformation describe-stacks \ --stack-name $STACK_NAME \ --query 'Stacks[0].Outputs' \ --region $AWS_REGION ```

### Step 3: Verify Runners

```bash # Check Auto Scaling Group ASG_NAME=$(aws cloudformation describe-stacks \ --stack-name $STACK_NAME \ --query 'Stacks[0].Outputs[?OutputKey==`AutoScalingGroupName`].OutputValue' \ --output text)

aws autoscaling describe-auto-scaling-groups \ --auto-scaling-group-names $ASG_NAME \ --region $AWS_REGION

# List running instances aws ec2 describe-instances \ --filters "Name=tag:aws:autoscaling:groupName,Values=$ASG_NAME" \ --query 'Reservations[].Instances[].[InstanceId,State.Name,PrivateIpAddress]' \ --output table

# Check CloudWatch logs aws logs tail /github-runner/instances --follow ```

### Step 4: Verify in GitHub

Navigate to: `https://github.com/organizations/YOUR_ORG/settings/actions/r...`

You should see your EC2 runners listed as "Idle" with labels: `self-hosted, linux, x64, ec2, aws, codebuild`

## Using One Runner for Multiple Repos & Pipelines

### Organization-Level Runners (Recommended)

EC2 runners registered at the organization level can serve all repositories automatically.

*Benefits:* - Centralized management - Cost-efficient resource sharing - Simplified scaling - Single point of monitoring

*Configuration in CloudFormation:* The template already configures organization-level runners via the UserData script: ```bash ./config.sh --url "https://github.com/${GitHubOrganization}" ... ```

### Multi-Repository Workflow Examples### Advanced: Runner Groups for Access Control### Label-Based Runner Selection Strategy

*Create different runner pools with specific labels:*

```bash # Production runners RunnerLabels: "self-hosted,linux,ec2,production,high-performance"

# Development runners RunnerLabels: "self-hosted,linux,ec2,development,general"

# Team-specific runners RunnerLabels: "self-hosted,linux,ec2,team-platform,specialized" ```

*Use in workflows:*

```yaml jobs: prod-deploy: runs-on: [self-hosted, linux, ec2, production]

  dev-test:
    runs-on: [self-hosted, linux, ec2, development]
  
  platform-build:
    runs-on: [self-hosted, linux, ec2, team-platform]

```bash # Check Auto Scaling Group health aws autoscaling describe-auto-scaling-groups \ --auto-scaling-group-names $ASG_NAME \ --query 'AutoScalingGroups[0].[DesiredCapacity,MinSize,MaxSize,Instances[].[InstanceId,HealthStatus,LifecycleState]]'

# View instance system logs INSTANCE_ID=$(aws autoscaling describe-auto-scaling-groups \ --auto-scaling-group-names $ASG_NAME \ --query 'AutoScalingGroups[0].Instances[0].InstanceId' \ --output text)

aws ec2 get-console-output --instance-id $INSTANCE_ID

# Check CloudWatch logs aws logs get-log-events \ --log-group-name /github-runner/instances \ --log-stream-name $INSTANCE_ID/runner \ --limit 50 ```

### Connect to Runner Instance (via SSM)

```bash # List instances aws autoscaling describe-auto-scaling-groups \ --auto-scaling-group-names $ASG_NAME \ --query 'AutoScalingGroups[0].Instances[].[InstanceId,HealthStatus]' \ --output table

# Connect via Session Manager (no SSH key needed) aws ssm start-session --target $INSTANCE_ID

# Once connected, check runner status sudo systemctl status actions.runner. sudo journalctl -u actions.runner.* -f ```

### Troubleshooting Common Issues## Advanced Scaling Configuration

### Lambda-Based Dynamic Scaling

For more sophisticated scaling based on GitHub Actions queue depth:### Deploy Scaling Lambda

```bash # Create Lambda function zip function.zip github-queue-scaler.py

aws lambda create-function \ --function-name github-runner-scaler \ --runtime python3.11 \ --role arn:aws:iam::ACCOUNT_ID:role/lambda-execution-role \ --handler github-queue-scaler.lambda_handler \ --zip-file fileb://function.zip \ --timeout 30 \ --environment Variables="{ ASG_NAME=$ASG_NAME, GITHUB_ORG=$GITHUB_ORG, GITHUB_TOKEN=$GITHUB_PAT, MAX_RUNNERS=10, MIN_RUNNERS=2 }"

# Create CloudWatch Events rule to trigger every 2 minutes aws events put-rule \ --name github-runner-scaling \ --schedule-expression 'rate(2 minutes)'

aws events put-targets \ --rule github-runner-scaling \ --targets "Id"="1","Arn"="arn:aws:lambda:REGION:ACCOUNT:function:github-runner-scaler" ```

## Cost Optimization

### 1. Use Spot Instances

Add to Launch Template in CloudFormation:

```yaml LaunchTemplateData: InstanceMarketOptions: MarketType: spot SpotOptions: MaxPrice: "0.05" # Set max price SpotInstanceType: one-time ```

### 2. Scheduled Scaling

Scale down during off-hours:

```bash # Scale down at night (9 PM) aws autoscaling put-scheduled-action \ --auto-scaling-group-name $ASG_NAME \ --scheduled-action-name scale-down-night \ --recurrence "0 21 * * " \ --desired-capacity 1

# Scale up in morning (7 AM) aws autoscaling put-scheduled-action \ --auto-scaling-group-name $ASG_NAME \ --scheduled-action-name scale-up-morning \ --recurrence "0 7 * MON-FRI" \ --desired-capacity 3 ```

### 3. Instance Type Mix

Use multiple instance types for better availability and cost:

```yaml MixedInstancesPolicy: InstancesDistribution: OnDemandBaseCapacity: 1 OnDemandPercentageAboveBaseCapacity: 25 SpotAllocationStrategy: price-capacity-optimized LaunchTemplate: Overrides: - InstanceType: t3.medium - InstanceType: t3a.medium - InstanceType: t2.medium ```

## Security Best Practices

1. *No hardcoded credentials* - Using Secrets Manager for GitHub PAT 2. *IMDSv2 enforced* - Prevents SSRF attacks 3. *Minimal IAM permissions* - Scoped to specific CodeBuild projects 4. *Private subnets* - Runners not directly accessible from internet 5. *SSM for access* - No SSH keys needed 6. *Encrypted secrets* - Secrets Manager encryption at rest 7. *CloudWatch logging* - All runner activity logged

## References

- [GitHub Self-hosted Runners Documentation](https://docs.github.com/en/actions/hosting-your-own-runners/...) - [GitHub Runner Registration API](https://docs.github.com/en/rest/actions/self-hosted-runners) - [AWS Auto Scaling Documentation](https://docs.aws.amazon.com/autoscaling/ec2/userguide/what-i...) - [AWS CodeBuild API Reference](https://docs.aws.amazon.com/codebuild/latest/APIReference/We...) - [GitHub Actions Runner Releases](https://github.com/actions/runner/releases) - [AWS Systems Manager Session Manager](https://docs.aws.amazon.com/systems-manager/latest/userguide...)

This solution provides a production-ready, cost-effective EC2-based runner infrastructure with automatic scaling, comprehensive monitoring, and multi-repository support for triggering CodeBuild pipelines.

2. Then you can't see the AI slop in mountains of existing spaghetti

3. Profit

_For more life hacks like and subscribe_

numerous attempts are being made by edge researchers to fix it but their just throwing stuff at the wall

I expect gradual reasoning improvements... not agi to pop out of a box and surprise everyone

and... CEO means "liar"

## Architecture Overview

This solution deploys auto-scaling GitHub Actions runners on EC2 instances that can trigger your existing AWS CodeBuild pipelines. Runners are managed via Auto Scaling Groups with automatic registration and health monitoring.

## Prerequisites

- AWS CLI configured with appropriate credentials - GitHub Enterprise Cloud organization admin access - Existing CodeBuild project(s) - VPC with public/private subnets

## Solution Components

### 1. CloudFormation Template### 2. GitHub Workflow for CodeBuild Integration## Deployment Steps

### Step 1: Create GitHub Personal Access Token

1. Navigate to GitHub → Settings → Developer settings → Personal access tokens → Fine-grained tokens 2. Create token with these permissions: - *Repository permissions:* - Actions: Read and write - Metadata: Read - *Organization permissions:* - Self-hosted runners: Read and write

```bash # Store token securely export GITHUB_PAT="ghp_xxxxxxxxxxxxxxxxxxxx" export GITHUB_ORG="your-org-name" ```

### Step 2: Deploy CloudFormation Stack

```bash # Set variables export AWS_REGION=us-east-1 export STACK_NAME=github-runner-ec2 export VPC_ID=vpc-xxxxxxxx export SUBNET_IDS="subnet-xxxxxxxx,subnet-yyyyyyyy"

# Deploy stack aws cloudformation create-stack \ --stack-name $STACK_NAME \ --template-body file://github-runner-ec2-asg.yaml \ --parameters \ ParameterKey=VpcId,ParameterValue=$VPC_ID \ ParameterKey=PrivateSubnetIds,ParameterValue=\"$SUBNET_IDS\" \ ParameterKey=GitHubOrganization,ParameterValue=$GITHUB_ORG \ ParameterKey=GitHubPAT,ParameterValue=$GITHUB_PAT \ ParameterKey=InstanceType,ParameterValue=t3.medium \ ParameterKey=MinSize,ParameterValue=2 \ ParameterKey=MaxSize,ParameterValue=10 \ ParameterKey=DesiredCapacity,ParameterValue=2 \ ParameterKey=RunnerLabels,ParameterValue="self-hosted,linux,x64,ec2,aws,codebuild" \ ParameterKey=CodeBuildProjectNames,ParameterValue="" \ --capabilities CAPABILITY_NAMED_IAM \ --region $AWS_REGION

# Wait for completion (5-10 minutes) aws cloudformation wait stack-create-complete \ --stack-name $STACK_NAME \ --region $AWS_REGION

# Get stack outputs aws cloudformation describe-stacks \ --stack-name $STACK_NAME \ --query 'Stacks[0].Outputs' \ --region $AWS_REGION ```

### Step 3: Verify Runners

```bash # Check Auto Scaling Group ASG_NAME=$(aws cloudformation describe-stacks \ --stack-name $STACK_NAME \ --query 'Stacks[0].Outputs[?OutputKey==`AutoScalingGroupName`].OutputValue' \ --output text)

aws autoscaling describe-auto-scaling-groups \ --auto-scaling-group-names $ASG_NAME \ --region $AWS_REGION

# List running instances aws ec2 describe-instances \ --filters "Name=tag:aws:autoscaling:groupName,Values=$ASG_NAME" \ --query 'Reservations[].Instances[].[InstanceId,State.Name,PrivateIpAddress]' \ --output table

# Check CloudWatch logs aws logs tail /github-runner/instances --follow ```

### Step 4: Verify in GitHub

Navigate to: `https://github.com/organizations/YOUR_ORG/settings/actions/r...`

You should see your EC2 runners listed as "Idle" with labels: `self-hosted, linux, x64, ec2, aws, codebuild`

## Using One Runner for Multiple Repos & Pipelines

### Organization-Level Runners (Recommended)

EC2 runners registered at the organization level can serve all repositories automatically.

*Benefits:* - Centralized management - Cost-efficient resource sharing - Simplified scaling - Single point of monitoring

*Configuration in CloudFormation:* The template already configures organization-level runners via the UserData script: ```bash ./config.sh --url "https://github.com/${GitHubOrganization}" ... ```

### Multi-Repository Workflow Examples### Advanced: Runner Groups for Access Control### Label-Based Runner Selection Strategy

*Create different runner pools with specific labels:*

```bash # Production runners RunnerLabels: "self-hosted,linux,ec2,production,high-performance"

# Development runners RunnerLabels: "self-hosted,linux,ec2,development,general"

# Team-specific runners RunnerLabels: "self-hosted,linux,ec2,team-platform,specialized" ```

*Use in workflows:*

```yaml jobs: prod-deploy: runs-on: [self-hosted, linux, ec2, production]

  dev-test:
    runs-on: [self-hosted, linux, ec2, development]
  
  platform-build:
    runs-on: [self-hosted, linux, ec2, team-platform]

```bash # Check Auto Scaling Group health aws autoscaling describe-auto-scaling-groups \ --auto-scaling-group-names $ASG_NAME \ --query 'AutoScalingGroups[0].[DesiredCapacity,MinSize,MaxSize,Instances[].[InstanceId,HealthStatus,LifecycleState]]'

# View instance system logs INSTANCE_ID=$(aws autoscaling describe-auto-scaling-groups \ --auto-scaling-group-names $ASG_NAME \ --query 'AutoScalingGroups[0].Instances[0].InstanceId' \ --output text)

aws ec2 get-console-output --instance-id $INSTANCE_ID

# Check CloudWatch logs aws logs get-log-events \ --log-group-name /github-runner/instances \ --log-stream-name $INSTANCE_ID/runner \ --limit 50 ```

### Connect to Runner Instance (via SSM)

```bash # List instances aws autoscaling describe-auto-scaling-groups \ --auto-scaling-group-names $ASG_NAME \ --query 'AutoScalingGroups[0].Instances[].[InstanceId,HealthStatus]' \ --output table

# Connect via Session Manager (no SSH key needed) aws ssm start-session --target $INSTANCE_ID

# Once connected, check runner status sudo systemctl status actions.runner. sudo journalctl -u actions.runner.* -f ```

### Troubleshooting Common Issues## Advanced Scaling Configuration

### Lambda-Based Dynamic Scaling

For more sophisticated scaling based on GitHub Actions queue depth:### Deploy Scaling Lambda

```bash # Create Lambda function zip function.zip github-queue-scaler.py

aws lambda create-function \ --function-name github-runner-scaler \ --runtime python3.11 \ --role arn:aws:iam::ACCOUNT_ID:role/lambda-execution-role \ --handler github-queue-scaler.lambda_handler \ --zip-file fileb://function.zip \ --timeout 30 \ --environment Variables="{ ASG_NAME=$ASG_NAME, GITHUB_ORG=$GITHUB_ORG, GITHUB_TOKEN=$GITHUB_PAT, MAX_RUNNERS=10, MIN_RUNNERS=2 }"

# Create CloudWatch Events rule to trigger every 2 minutes aws events put-rule \ --name github-runner-scaling \ --schedule-expression 'rate(2 minutes)'

aws events put-targets \ --rule github-runner-scaling \ --targets "Id"="1","Arn"="arn:aws:lambda:REGION:ACCOUNT:function:github-runner-scaler" ```

## Cost Optimization

### 1. Use Spot Instances

Add to Launch Template in CloudFormation:

```yaml LaunchTemplateData: InstanceMarketOptions: MarketType: spot SpotOptions: MaxPrice: "0.05" # Set max price SpotInstanceType: one-time ```

### 2. Scheduled Scaling

Scale down during off-hours:

```bash # Scale down at night (9 PM) aws autoscaling put-scheduled-action \ --auto-scaling-group-name $ASG_NAME \ --scheduled-action-name scale-down-night \ --recurrence "0 21 * * " \ --desired-capacity 1

# Scale up in morning (7 AM) aws autoscaling put-scheduled-action \ --auto-scaling-group-name $ASG_NAME \ --scheduled-action-name scale-up-morning \ --recurrence "0 7 * MON-FRI" \ --desired-capacity 3 ```

### 3. Instance Type Mix

Use multiple instance types for better availability and cost:

```yaml MixedInstancesPolicy: InstancesDistribution: OnDemandBaseCapacity: 1 OnDemandPercentageAboveBaseCapacity: 25 SpotAllocationStrategy: price-capacity-optimized LaunchTemplate: Overrides: - InstanceType: t3.medium - InstanceType: t3a.medium - InstanceType: t2.medium ```

## Security Best Practices

1. *No hardcoded credentials* - Using Secrets Manager for GitHub PAT 2. *IMDSv2 enforced* - Prevents SSRF attacks 3. *Minimal IAM permissions* - Scoped to specific CodeBuild projects 4. *Private subnets* - Runners not directly accessible from internet 5. *SSM for access* - No SSH keys needed 6. *Encrypted secrets* - Secrets Manager encryption at rest 7. *CloudWatch logging* - All runner activity logged

## References

- [GitHub Self-hosted Runners Documentation](https://docs.github.com/en/actions/hosting-your-own-runners/...) - [GitHub Runner Registration API](https://docs.github.com/en/rest/actions/self-hosted-runners) - [AWS Auto Scaling Documentation](https://docs.aws.amazon.com/autoscaling/ec2/userguide/what-i...) - [AWS CodeBuild API Reference](https://docs.aws.amazon.com/codebuild/latest/APIReference/We...) - [GitHub Actions Runner Releases](https://github.com/actions/runner/releases) - [AWS Systems Manager Session Manager](https://docs.aws.amazon.com/systems-manager/latest/userguide...)

This solution provides a production-ready, cost-effective EC2-based runner infrastructure with automatic scaling, comprehensive monitoring, and multi-repository support for triggering CodeBuild pipelines.

I'm using Claude Code. I've been building software as a solo freelancer for the last 20+ years.

My latest workflow

- I work on "regular" web apps, C#/.NET on backend, React on web.

- I'm using 3-8 sessions in parallel, depending on the tasks and the mental bandwidth I have, all visible on external display.

- I've markdown rule files & documentation, 30k lines in total. Some of them describes how I want the agent to work (rule files), some of them describes the features/systems of the app.

- Depending on what I'm working on, I load relevant rule files selectively into the context via commands. I have a /fullstack command that loads @backend.md, @frontend.md and a few more. I have similar /frontend, /backend, /test commands with a few variants. These are the load bearing columns of my workflow. Agents takes a lot more time and produces more slop without these. Each one is written by agents also, with my guidance. They evolve based on what we encounter.

- Every feature in the app, and every system, has a markdown document that's created by the implementing agent, describing how it works, what it does, where it's used, why it's created, main entry points, main logic, gotchas specific to this feature/system etc. After every session, I have /write-system, /write-feature commands that I use to make the agent create/update those, with specific guidance on verbosity, complexity, length.

- Each session I select a specific task for a single system. I reference the relevant rule files and feature/system doc, and describe what I want it to achieve and start plan mode. If there are existing similar features, I ask the agent to explore and build something similar.

- Each task is specifically tuned to be planned/worked in a single session. This is the most crucial role of mine.

- For work that would span multiple sessions, I use a single session to create the initial plan, then plan each phase in depth in separate sessions.

- After it creates the plan, I examine, do a bit of back and forth, then approve.

- I watch it while it builds. Usually I have 1-2 main tasks and a few subtasks going in parallel. I pay close attention to main tasks and intervene when required. Subtasks rarely requires intervention due to their scope.

- After the building part is done, I go through the code via editor, test manually via UI, while the agent creates tests for the thing we built, again with specific guidance on what needs to be tested and how. Since the plan is pre-approved by me, this step usually goes without a hitch.

- Then I make the agent create/update the relevant documents.

- Last week I built another system to enhance that flow. I created a /devlog command. With the assist of some CLI tools and cladude log parsing, it creates a devlog file with some metadata (tokens, length, files updated, docs updated etc) and agent fills it with a title, summary of work, key decisions, lessons learned. First prompt is also copied there. These also get added to the relevant feature/system document automatically as changelog entries. So, for every session, I've a clear document about what got done, how long it took, what was the gotchas, what went right, what went wrong etc. This proved to be invaluable even with a week worth of develops, and allows me to further refine my workflows.

This looks convoluted at a first glance, but it's evolved over the months and works great. The code quality is almost the same with what I would have written by myself. All because of existing code to use as examples, and the rule files guiding the agents. I was already a fast builder before, but with agents it's a whole new level.

And this flow really unlocked with Opus 4.5. Sonnet 3.5/4/4.5 was also working OK, but required a lot more handholding and steering and correction. Parallel sessions wasn't really possible without producing slop. Opus 4.5 is significantly better.

More technical/close-to-hardware work will most likely require a different set of guidance & flow to create non-slop code. I don't have any experience there.

You need to invest in improving the workflow. The capacity is there in the models. The results all depends on how you use them.

I pointed Claude Code at it, and a few hours later, it had done all of the hard work.

I babysat it, but I was doing other things while it worked. I didn't verify all the code changes (although I did skim the resultant PR, especially for security concerns) but it worked. It rewrote my extensive hand-rolled Coffeescript into modern JavaScript, which was also nice; it did it perfectly. The tests passed, and it even uncovered some issues that I had it fix afterwards. (Places where my security settings weren't as good as they should have been, or edge cases I hadn't thought of ten years ago.)

Now could I have done this? Yes, of course. I've done it before with other projects. But it *SUCKS* to do manually. Some folks suggest that you should only use these tools for tasks you COULD do, but would be annoyed to do. I kind of like that metric, but I bet my bar for annoyance will go down over time.

My experience with these systems is that they aren't significantly faster, ultimately, but I hate the sucky parts of my job VASTLY less. And there are a lot of sucky parts to even the code-creation side of programming. I *love* my career and have been doing it for 36 years, but like anything that you're very experienced in, you know the parts that suck.

Like some others, it helps that my most recent role was Staff Software Engineer, and so I was delegating and looking over the results of other folks work more than hand-rolling code. So the 'suggest and review' pattern is one that I'm very comfortable with, along with clearly separate small-scale plan and execute steps.

Ultimately I find these tools reduce cognitive load, which makes me happier when I'm building systems, so I don't care as much if I'm strictly faster. If at the end of the day I made progress and am not exhausted, that's a win. And the LLM coding tools deliver that for me, at least.

One of the things I've also had to come to terms with _in large companies_ is that the code is __never__ high quality. If you drill into almost any part of a huge codebase, you're going to start questioning your sanity (obligatory 'Programming Sucks' reference). Whether it's a single complex 750 line C++ function at the heart of a billion dollar payment system, or 2,000 lines in a single authentication function in a major CRM tool, or a microservice with complex deployment rules that just exists to unwrap a JWT, or 13 not-quite-identical date time picker libraries in one codebase, the code in any major system is not universally high quality. But it works. And there are always *very good reasons* why it was built that way. Those are the forces that were on the development team when it was built, and you don't usually know them, and you mustn't be a jerk about it. Many folks new to a team don't get that, and create a lot of friction, only to learn Chesterton's Fence all over again.

Coming to terms with this over the course of my career has also made coming to terms with the output of LLMs being functional, but not high quality, easier. I'm sure some folks will call this 'accepting mediocrity' and that's okay. I'd rather ship working code. (_And to be clear, this is excepting security vulnerabilities and things that will lose data. You always review for those kinds of errors, but even for those, reviews are made somewhat easier with LLMs._)

N.b. I pay for Claude Code, but I regularly test local coding models on my ML server in my homelab. The local models and tooling is getting surprisingly good...but not there yet.

I’m not exactly a typical SWE at the moment. The role I’m in is a lot of meeting with customers, understand their issues, and whip up demos to show how they might apply my company’s products to their problem.

So I’m not writing production code, but I am writing code that I want to to be maintainable and changeable so I can stash a demo for a year and then spin it up quickly when someone wants to see if or update/adapt it as products/problems change. Most of my career has been spent writing aircraft SW so I am heavily biased toward code quality and assurance. The demos I am building are not trivial or common in the training data. They’re highly domain specific and pretty niche, performance is very important, and usually span low level systems code all the way up to a decent looking gui. As a made up example, it wouldn’t be unusual for me to have a project to write a medical imaging pipeline from scratch that employs modern techniques from recent papers, etc.

Up until very recently, I only thought coding agents were useful for basic crud apps, etc. I said the same things a lot of people on this thread are saying, eg. people on twitter are all hype, their experience doesn’t match mine, they must be working on easy problems or be really bad at writing code

I recently decided to give into the hype and really try to use the tooling and… it’s kind of blown my mind.

Cursor + opus 4.5 high are my main tools and their ability to one shot major changes across many files and hundreds of lines of code, encompassing low level systems stuff, GOU accelerated stuff, networking, etc.

It’s seriously altering my perception of what software engineering is and will be and frankly I’m still kind of recoiling from it.

Don’t get me wrong, I don’t believe it fundamentally eliminates the need for SWEs, it still takes a lot of work on my part to come up with a spec (though I do have it help me with that part), correct things that I don’t like in its planning or catch it doing the wrong thing in real time in and re direct it. And it will make strange choices that I need to correct on the back end sometimes. But it has legitimately allowed me to build 10x faster than I probably could on my own.

Maybe the most important thing about it is what it enables you to build that would have been not worth the trouble before, Stuff like wrapping tools in really nice flexible TUIs, creating visualizations/dashboards/benchmark, slightly altering how an application works to cover a use case you hadn’t thought of before, wrapping an interface so it’s easy to swap libs/APIs later, etc.

If you are still skeptical, I would highly encourage you to immerse yourself in the SOTS tools right now and just give in to the hype for a bit, because I do think we’re rapidly going to reach a point here where if you aren’t using these tools you won’t be employable.

I agree with a lot of your instincts. Shipping unreviewed code is wrong. “Validate behavior not architecture” as a blanket rule is reckless. Tests passing is not the same thing as having a system you can reason about six months later. On that we’re aligned.

Where I diverge is the conclusion that agentic coding doesn’t produce net-positive results. For me it very clearly does, but perhaps it's very situation or condition dependent?

For me, I don’t treat the agent as a junior engineer I can hand work to and walk away from. I treat it more like an extremely fast, extremely literal staff member who will happily do exactly what you asked, including the wrong thing, unless you actively steer it. I sit there and watch it work (usually have 2-3 agents working at the same time, ideally on different codebases but sometimes they overlap). I interrupt it. I redirect it. I tell it when it is about to do something dumb. I almost never write code anymore, but I am constantly making architectural calls.

Second, tooling and context quality matter enormously. I’m using Claude Code. The MCP tools I have installed make a huge different: laravel-boost, context7, and figma (which in particular feels borderline magical at converting designs into code!).

I often have to tell the agent to visit GitHub READMEs and official docs instead of letting it hallucinate “best practices”, the agent will oftentimes guess and get stack, so if it's doing that, you’ve already lost.

Third, I wonder if perhaps starting from scratch is actually harder than migrating something real. Right now I’m migrating a backend from Java to Laravel and rebuilding native apps into KMP and Compose Multiplatform. So the domain and data is real and I can validate against a previous (if buggy) implimentation). In that environment, the agent is phenomenal. It understands patterns, ports logic faithfully, flags inconsistencies, and does a frankly ridiculous amount of correct work per hour.

Does it make mistakes? Of course. But they’re few and far between, and they’re usually obvious at the architectural or semantic level, not subtle landmines buried in the code. When something is wrong, it’s wrong in a way that’s easy to spot if you’re paying attention.

That’s the part I think gets missed. If you ask the agent to design, implement, review, and validate itself, then yes, you’re going to get spaghetti with a test suite that lies to you. If instead you keep architecture and taste firmly in human hands and use the agent as an execution engine, the leverage is enormous.

My strong suspicion is that a lot of the negative experiences come from a mismatch between expectations and operating model. If you expect the agent to be autonomous, it will disappoint you. If you expect it to be an amplifier for someone who already knows what “good” looks like, it’s transformative.

So while I guess plenty of hype exists, for me at least, they hype is justified. I’m shipping way (WAY!) more, with better consistency, and with less cognitive exhaustion than ever before in my 20+ years of doing dev work.

Some background, I'm a "working manager" in that I have some IC responsibilities as well as my management duties, and I'm pretty good at written communication of requirements and expectations. I've also spent a number of years, reading more code than I write, and have a pretty high tolerance for code review at this point. Finally, I'm comfortable with the shift from my value being what I create, to what I help others create.

TLDR: Agentic coding is working very well for me, and allows me to automate things I would have never spent the time on before, and to build things that the team doesn't really have time to build.

Personally, I started testing the waters seriously with agentic coding last June, and it took probably 1-2 months of explicitly only using it with the goal of figuring out how to use it well. Over that time, I went from a high success rate on simple tasks, but mid-to-low success rate on complex tasks to generally a high success rate overall. That said, my process evolved a LOT. I went from simple prompts that lacked context, to large prompts that had a ton of context where I was trying to one-shot the results, to simple prompts, with a lot of questions and answers to build a prompt to build a plan to execute on.

My current process is basically, state a goal or a current problem, and ask for questions to clarify requirements and the goal. Work through those questions and answers which often makes me examine my assumptions, and tweak my overall goal. Eventually have enough clarity to have the agent generate a prompt to build a plan.

Clear out context and feed in that prompt, and have it ask additional questions if I have a strong feeling about direction and what I would personally build, if there's still some uncertainty that usually means I don't understand the space well enough to get a good plan, so I have it build instead with the intention of learning through building and throwing it away once I have more clarity.

Once we have a plan, have the agent break it down into prioritized user stories with individual tasks, tests, and implementation details. Read through those user stories to get a good idea of how I think I would build it so I have a good mental model for my expectations.

Clear out context and have the agent read in the user stories and start implementing. Early on in the implementation, I'll read 100% of the code generated to understand the foundation it's building. I'll often learn a few things, tweak the user stories and implementation plans, delete the generated code and try again. Once I have a solid foundation, I stop reading all the code, and start skimming the boilerplate code and focus only on the business rules / high complexity code.

I focus heavily on strong barriers between modules, and keeping things as stupidly simple as I can get away with. This helps the models produce good results because it requires less context.

Different models prompt differently. While the Opus/Sonnet family of models drive me nuts with their "personality", I'm generally better at getting good results out of them. The GPT series of models, I like the personality more, but kinda suck at getting good results out of them at this point. It takes some time to develop good intuition about how to prompt different models well. Some require more steering as to which files/directories to look in, others are great at discovering context on their own.

If the agent is going down a wrong path, it's usually better to clear context and reset than to try and steer your way out of screwed up context.

Get comfortable throwing away code, you'll get better results if you don't think of the generated code as precious.

2. wait

3. post on LinkedIn about how amazing AI now is

4. throw away the slop and write proper code

5. go home, to repeat this again tomorrow