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I guess we're just going to be in the age of this conversation topic until everyone gets tired of talking about it.

Every one of these discussions boils down to the following:

- LLMs are not good at writing code on their own unless it's extremely simple or boilerplate

- LLMs can be good at helping you debug existing code

- LLMs can be good at brainstorming solutions to new problems

- The code that is written by LLMs always needs to be heavily monitored for correctness, style, and design, and then typically edited down, often to at least half its original size

- LLMs utility is high enough that it is now going to be a standard tool in the toolbox of every software engineer, but it is definitely not replacing anyone at current capability.

- New software engineers are going to suffer the most because they know how to edit the responses the least, but this was true when they wrote their own code with stack overflow.

- At senior level, sometimes using LLMs is going to save you a ton of time and sometimes it's going to waste your time. Net-net, it's probably positive, but there are definitely some horrible days where you spend too long going back and forth, when you should have just tried to solve the problem yourself.

Does anyone have a link to a video that uses Claude Code to produce clean robust code that solves a non trivial problem (ie not tic tac toe or a landing page) more quickly than a human programmer can write? I don’t want a “demo”, I want a livestream from an independent programmer unaffiliated with any AI company and thus not incentivised to hype.

I want the code to have subsequently been deployed in production and demonstrably robust, without additional work outside of the livestream.

The livestream should include code review, test creation, testing, PR creation.

It should not be on a greenfield project, because nearly all coding is not.

I want to use Claude and I want to be more productive, but my experience to date is that for writing code beyond autocomplete AI is not good enough and leads to low quality code that can’t be maintained, or else requires so much hand holding that it is actually less efficient than a good programmer.

There are lots of incentives for marketing at the grassroots level. I am totally open to changing my mind but I need evidence.

Preventing garbage just requires that you take into account the cognitive limits of the agent. For example ...

1) Don't ask for large / complex change. Ask for a plan but ask it to implement the plan in small steps and ask the model to test each step before starting the next.

2) For really complex steps, ask the model to write code to visualize the problem and solution.

3) If the model fails on a given step, ask it to add logging to the code, save the logs, run the tests and the review the logs to determine what went wrong. Do this repeatedly until the step works well.

4) Ask the model to look at your existing code and determine how it was designed to implement a task. Some times the model will put all of the changes in one file but your code has a cleaner design the model doesn't take into account.

I've seen other people blog about their tricks and tips. I do still see garbage results but not as high as 95%.

Guy said a whole lot of nothing. Said he's improved productivity, but also said AI falls short in all the common ways people have noticed. Also guarantee no one is building core functionality delegating to Claude Code.

This has been my experience too. I’m just not quite as far along as the author.

Detachment from the code has been excellent for me. Just started a v2 rewrite of something I’d never had done in the past. Mostly because it would have taken me too much time to try it out if I wrote it all by hand.

I'd like to share my journey with Claude (not code).

I fed Claude a copy of everything I've ever written on Hacker News. Then I asked it to generate an essay that sounds like me.

Out of five paragraphs I had to change one sentence. Everything else sounded exactly as I would have written it.

It was scary good.

$1000-1500/month for ai paid by employer... that's quite nice. I wonder how much would it cost to run couple of claude code instance to run 24/7 indefinitely. If company's got resources they might as well try that against their issues.

The author doesn't make it clear why they switched from Cursor to Claude. Curious about what they can do with Claude that can't be done with Cursor. I use both a lot and find Cursor to be superior for the very large codebases I work in.

To all the engineers using claude code: how do you submit your (well, claude’s) to review? Say, you have a big feature/epic to implement. Typically (pre-ai) times you would split it in chunks and submit each chunk as PR to be reviewed. You don’t want to submit dozens of file changes because nobody would review it. Now with llms, one can easily explain the whole feature to the machine and they would output the whole code just fine. What do you do? You divide it manually for review submission? One chunk after another?

It’s way easier to let the agent code the whole thing if your prompt is good enough than to give instructions bit by bit only because your colleagues cannot review a PR with 50 file changes.

Does this work for others when working in other domains? When creating a Swift application, I can't imagine creating 20 agents and letting them go to town. Same for the backend of such an application if it's in say, Java+Springboot

did this guy read that other paper about ai usage making people stupid?

how long until he falls from staff engineer back down to senior or something less?

I'm almost sure that we all ended up at the same set of rules and steps how to get the best out of Claude - mine are almost identical, others' I know as well :-)

I'm using Claude all the time now. It works, and I'm amazed it worked so easily for me. Here's what it looks like:

1) Summarize what I think my project currently does

2) Summarize what I think it should do

3) Give a couple of hints about how to do it

4) Watch it iterate a write-compile-test loop until it thinks it's ready

I haven't added any files or instructions anywhere, I just do that loop above. I know of people who put their Claude in YOLO mode on multiple sessions, but for the moment I'm just sitting there watching it.

Example:

"So at the moment, we're connecting to a websocket and subscribing to data, and it works fine, all the parsing tests are working, all good. But I want to connect over multiple sockets and just take whichever one receives the message first, and discard subsequent copies. Maybe you need a module that remembers what sequence number it has seen?"

Claude will then praise my insightful guidance and start making edits.

At some point, it will do something silly, and I will say:

"Why are you doing this with a bunch of Arc<RwLock> things? Let's share state by sharing messages!"

Claude will then apologize profusely and give reasons why I'm so wise, and then build the module in an async way.

I just keep an eye on what it tries, and it's completely changed how I code. For instance, I don't need to be fully concentrated anymore. I can be sitting in a meeting while I tell Claude what to do. Or I can be close to falling asleep, but still be productive.

> If I were to give advice from an engineer's perspective, if you're a technical leader considering AI adoption: >> Let your engineers adopt and test different AI solutions: AI-assisted coding is a skill that you have to practice to learn.

I am sorry, but this is so out of touch with reality. Maybe in the US most companies are willing to allocate you 1000 or 1500 USD/month/engineer, but I am sure that in many countries outside of the US not even a single line (or other type of) manager will allocate you such a budget.

I know for a fact that in countries like Japan you even need to present your arguments for a pizza party :D So that's all you need to know about AI adoption and what's driving it

It’s about time these types of articles actually include the types of tasks being “orchestrated” (as the author writes) that aren’t just plain refactoring chores or React boilerplate. Sanity has quite a backlog of long-requested features and the message here is that these agents are supposedly parallelizing a lot of the work. What kind of staff engineer has “80% of their code” written by a “junior developer who doesn't learn“?

Once thing I've noticed is the difference in code quality by language. I'm constantly disappointed by the output of python code. I have to correct it to follow even the most basic software development principles (DRY, etc).

Typescript on the other hand, seems to do much better on first pass. Still not always beautiful code, but much more application ready.

My hypothesis is that this is due to the billions LOC of Jupyter Notebook it was probably trained on :/

That is 150MWh per month in AI for a staff engineer. If we are doing a straight dollar to kWh conversion, plus/minus an order of magnitude.

to throw my hat into the ring, I am in no way shy about using the AI tooling and I like using it, but I am happy we're finally seeing people talk about AI that matches with my personal reality with the tools.

for the record, I've been bullish on the tooling from the beginning

My dev-tooling AI journey has been chatGPT -> vscode + copilot -> early cursor adopter -> early claude + cursor adopter -> cursor agent with claude -> and now claude code

I've also spent a lot of time trying out self-hosted LLMs such as couple version of Qwen coder 2.5/3 32B, as well as deepseek 30B - and talking to them through the vscode continue.dev extension

My personal feelings are that the AI coding/tooling industry has seen a major plateau in usefulness as soon as agents became apart of the tooling. The reality is coding is a highly precise task, and LLMs down to the very core of the model architecture are not precise in the way coding needs them to be. and it's not that I don't think we won't one day see coding agents, but I think it will take a deep and complete bottom up kind of change and an possibly an entirely new model architecture to get us to what people imagine a coding agent is

I've accepted to just use claude w/ cursor and to be done with experimenting. the agent tooling just slows my engineering team down

I think the worst part about this dev tooling space is the comment sections on these kinds of articles is completely useless. it's either AI hype bots just saying non-sense, or the most mid an obvious takes that you here everywhere else. I've genuinely have become frustrated with all this vague advice and how the AI dev community talks about this domain space. there is no science, data, or reason as to why these things fail or how to improve it

I think anyone who tries to take this domain space seriously knows that there's limit to all this tooling, we're probably not going to see anything group breaking for a while, and there doesn't exist a person, outside the AI researchers at the the big AI companies, that could tell ya how to actually improve the performance of a coding agent

I think that famous vibe-code reddit post said it best

"what's the point of using these tools if I still need a software engineer to actually build it when I'm done prototyping"

> The shift to Claude Code? That took just hours of use for me to become productive.

> This isn't failure; it's the process!

> The biggest challenge? AI can't retain learning between sessions

ai slop

I've come around on something like this. I start by putting a little effort into a prompt and into providing context, but not a ton - and see where Claude Code gets with it. It might even get what I asked for working in terms of features, but it's garbage code. This is a vibe session, not caring about the code at all, or hardly at all.

I notice what worked and what didn't, what was good and what was garbage -- and also how my own opinion of what should be done changed. I have Claude Code help me update the initial prompt, help me update what should have been in the initial context, maybe add some of the bits that looked good to the initial context as well, and then write it all to a file.

Then I revert everything else and start with a totally blank context, except that file. In this session I care about the code, I review it, I am vigilant to not let any slop through. I've been trying for the second session to be the one that's gonna work -- but I'm open to another round or two of this iteration.

every god damn time AI hallucinates a solution that is not real (in ChatGPT)

I havn't put a huge effort into learning to write prompts but in short, it seems easier to write the code myself than determine prompts. If you don't know every detail ahead of time and ask a slightly off question, the entire result will be garbage.

I have barely written any code since my switch to Claude Code! It's the best thing since sliced bread!

Here's what works for me:

- Detailed claude.md containing overall information about the project.

- Anytime Claude chooses a different route that's not my preferred route - ask my preference to be saved in global memory.

- Detailed planning documentation for each feature - Describe high-level functionality.

- As I develop the feature, add documentation with database schema, sample records, sample JSON responses, API endpoints used, test scripts.

- MCP, MCP, MCP! Playwright is a game changer

The more context you give upfront, the less back-and-forth you need. It's been absolutely transformative for my productivity.

Thank you Claude Code team!

Avoiding the boilerplate is part of the job as a software developer.

Abstracting the boilerplate is how you make things easier for future you.

Giving it to an AI to generate just makes the boilerplate more of a problem when there's a change that needs to be made to _all_ the instances of it. Even worse if the boilerplate isn't consistent between copies in the codebase.

Maybe I’m contrarian but I design and write most of my code and let LLMs do the reviews. Why?

First I know my problem space better than the LLM.

Second, the best way to express coding intention is with code. The models often have excellent suggestions on improvements I wouldn’t have thought of. I suspect the probability of providing a good answer has been increased significantly by narrowing the scope.

Another technique is to say “do this like <some good project> does it” but I suspect that might be close to copyright theft.

For me, working mostly in Planning Mode skips much of the initial misfires, and often leads to correct outcomes for the first edit.

So we’re supposed to start paying $1k-$1,5k on top of already crazy salaries just to maybe get a productivity boost on trivial to semi trivial issues? I know my boss would not be keen on that at least.

Interesting that this guy uses AI for the initial implementation. I do the opposite. I always build the foundation. That way I know how things work fundamentally. Then I ask agents to do boilerplate tasks. They're really good at following suit, but very bad at architecture.

> budget for $1000-1500/month for a senior engineer going all-in on AI development.

Is this another case of someone using API keys and not knowing about the claude MAX plans? It's $100 or $200 a month, if you're not pure yolo brute-force vibe coding $100 plan works.

https://www.anthropic.com/max

There is one thing I would highly recommend to anyone using Claude or any other agents: logging. I can't emphasize it more, if you have logging you can take the whole log file, dump it into AI, outline the problem and likely you're getting solution or would advance to the next step. Logging is everything.

Anthropic just posted an interview with Boris Cherny, the creator of Claude Code. He also offers some ideas on how to use it.

“The future of agentic coding with Claude Code”

https://youtu.be/iF9iV4xponk

I like his point about more objectivity and zero ego. You don't have to worry about hurting an AI's feelings or your own when you throw away code.

I often find that Claude introduces a level of complexity that is not necessary in my cases. I suspect this is a function of the training data (large repos or novel solutions). That said, I do sometimes find inspiration for new techniques in its answers.

I just haven't heard others express the same over-engineering problem and wonder if this is a general observation or only shows up b/c my requests are quite simple.

(I have found that prompting it for the simplest or most efficient solution seems to help - sometimes taking 20+ lines down to 2-3, often more understandable.)

P.S. I tend to work with data and a web app for processes related to a small business, while not a formally trained developer.

I don’t understand the use of MCP described in the post.

Claude code can access pretty much all those third party services in the shell, using curl or gh and so on. And in at least one case using MCP can cause trouble: the linear MCP server truncates long issues, in my experience, whereas curling the API does not.

What am I missing?

Spending $1500 per-month is a crazy wasteful amount of money

One thing that I haven't seen a lot of people talk about is the relatively new model config "Opus Plan Mode: Use Opus 4.1 in plan mode, Sonnet 4 otherwise".

In my opinion this should be the default config. Increasing the quality of the plans gives you a much better experience using Claude Code.

The author will be in upper management before they know it!