alt Hacker NewsThis all looks fine for someone who can't code, but for anyone with even a moderate amount of experience as a developer all this planning and checking and prompting and orchestrating is far more work than just writing the code yourself.
There's no winner for "least amount of code written regardless of productivity outcomes.", except for maybe Anthropic's bank account.
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There is a miscommunication happening, this entire time we all had surprisingly different ideas about what quality of work is acceptable which seems to account for differences of opinion on this stuff.
Well it's less mental load. It's like Tesla's FSD. Am I a better driver than the FSD? For sure. But is it nice to just sit back and let it drive for a bit even if it's suboptimal and gets me there 10% slower, and maybe slightly pisses off the guy behind me? Yes, nice enough to shell out $99/mo. Code implementation takes a toll on you in the same way that driving does.
I think the method in TFA is overall less stressful for the dev. And you can always fix it up manually in the end; AI coding vs manual coding is not either-or.
Researching and planning a project is a generally usefully thing. This is something I've been doing for years, and have always had great results compared to just jumping in and coding. It makes perfect sense that this transfers to LLM use.
Since Opus 4.5, things have changed quite a lot. I find LLMs very useful for discussing new features or ideas, and Sonnet is great for executing your plan while you grab a coffee.
Most of these AI coding articles seem to be about greenfield development.
That said, if you're on a serious team writing professional software there is still tons of value in always telling AI to plan first, unless it's a small quick task. This post just takes it a few steps further and formalizes it.
I find Cursor works much more reliably using plan mode, reviewing/revising output in markdown, then pressing build. Which isn't a ton of overhead but often leads to lots of context switching as it definitely adds more time.
Surely Addy Osmani can code. Even he suggests plan first.
I partly agree with you. But once you have a codebase large enough, the changes become longer to even type in, once figured out.
I find the best way to use agents (and I don't use claude) is to hash it out like I'm about to write these changes and I make my own mental notes, and get the agent to execute on it.
Agents don't get tired, they don't start fat fingering stuff at 4pm, the quality doesn't suffer. And they can be parallelised.
Finally, this allows me to stay at a higher level and not get bogged down of "right oh did we do this simple thing again?" which wipes some of the context in my mind and gets tiring through the day.
Always, 100% review every line of code written by an agent though. I do not condone committing code you don't 'own'.
I'll never agree with a job that forces developers to use 'AI', I sometimes like to write everything by hand. But having this tool available is also very powerful.
> planning and checking and prompting and orchestrating is far more work than just writing the code yourself.
This! Once I'm familiar with the codebase (which I strive to do very quickly), for most tickets, I usually have a plan by the time I've read the description. I can have a couple of implementation questions, but I knew where the info is located in the codebase. For things, I only have a vague idea, the whiteboard is where I go.
The nice thing with such a mental plan, you can start with a rougher version (like a drawing sketch). Like if I'm starting a new UI screen, I can put a placeholder text like "Hello, world", then work on navigation. Once that done, I can start to pull data, then I add mapping functions to have a view model,...
Each step is a verifiable milestone. Describing them is more mentally taxing than just writing the code (which is a flow state for me). Why? Because English is not fit to describe how computer works (try describe a finite state machine like navigation flow in natural languages). My mental mental model is already aligned to code, writing the solution in natural language is asking me to be ambiguous and unclear on purpose.
I really don't understand why there are so many comments like this.
Yesterday I had Claude write an audit logging feature to track all changes made to entities in my app. Yeah you get this for free with many frameworks, but my company's custom setup doesn't have it.
It took maybe 5-10 minutes of wall-time to come up with a good plan, and then ~20-30 min for Claude implement, test, etc.
That would've taken me at least a day, maybe two. I had 4-5 other tasks going on in other tabs while I waited the 20-30 min for Claude to generate the feature.
After Claude generated, I needed to manually test that it worked, and it did. I then needed to review the code before making a PR. In all, maybe 30-45 minutes of my actual time to add a small feature.
All I can really say is... are you sure you're using it right? Have you _really_ invested time into learning how to use AI tools?