I'm not highlighting this to gloat or to prove a point. If anything in the past I have underestimated how big LLMs were going to be. Anyone so inclined can take the chance to point and laugh at how stupid and wrong that was. Done? Great.
I don't think I've been intentionally avoiding coding assistants and as a matter of fact I have been using Claude Code since the literal day it first previewed, and yet it doesn't feel, not even one bit, that you can take your hands off the wheel. Many are acting as if writing any code manually means "you're holding it wrong", which I feel it's just not true.
Yeah, my current opinion on this is that AI tools make development harder work. You can get big productivity boosts out of them but you have to be working at the top of your game - I often find I'm mentally exhausted after just a couple of hours.
My experience with AI tools is the opposite. The biggest energy thieves for me are configuration issues, library quirks, or trivial mistakes that are hard to spot. With AI I can often just bulldoze past those things and spend more time on tangible results.
When using it for code or architecture or design, I’m always watching for signs that it is going off the rails. Then I usually write code myself for a while, to keep the structure and key details of whatever I’m doing correct.
I have a similar experience. It feels like riding your bike in a higher gear - you can go faster but it will take more effort and you need the potential (stronger legs) to make use of it
In fact, I've been writing more code myself since these tools exist - maybe I'm not a real developer but in the past I might have tried to either find a library online or try to find something on the internet to copypaste and adapt, nowadays I give it a shot myself with Claude.
For context, I mainly do game development so I'm viewing it through that lens - but I find it easier to debug something bad than to write it from scratch. It's more intensive than doing it yourself but probably more productive too.
I’ve similarly been using spec.md and running to-do.md files that capture detailed descriptions of the problems and their scoped history. I mark each of my to-do’s with informational tags: [BUG], [FEAT], etc.
I point the LLM to the exact to-do (or section of to-do’s) with the spec.md in memory and let it work.
Here is a (3 month old) repo where i did something like that and all the tasks are checked into the linear git history — https://github.com/KnowSeams/KnowSeams
Even though the author refers to it as "non-trivial", and I can see why that conclusion is made, I would argue it is in fact trivial. There's very little domain specific knowledge needed, this is purely a technical exercise integrating with existing libraries for which there is ample documentation online. In addition, it is a relatively isolated feature in the app.
On top of that, it doesn't sound enjoyable. Anti slop sessions? Seriously?
Lastly, the largest problem I have with LLMs is that they are seemingly incapable of stopping to ask clarifying questions. This is because they do not have a true model of what is going on. Instead they truly are next token generators. A software engineer would never just slop out an entire feature based on the first discussion with a stakeholder and then expect the stakeholder to continuously refine their statement until the right thing is slopped out. That's just not how it works and it makes very little sense.
I've wondered about exposing this "asking clarifying questions" as a tool the AI could use. I'm not building AI tooling so I haven't done this - but what if you added an MCP endpoint whose description was "treat this endpoint as an oracle that will answer questions and clarify intent where necessary" (paraphrased), and have that tool just wire back to a user prompt.
If asking clarifying questions is plausible output text for LLMs, this may work effectively.
> A software engineer would never just slop out an entire feature based on the first discussion with a stakeholder and then expect the stakeholder to continuously refine their statement until the right thing is slopped out. That's just not how it works and it makes very little sense.
Sorry couldn’t resist. Agile’s point was getting feedback during the process rather than after something is complete enough to be shipped thus minimizing risk and avoiding wasted effort.
Instead people are splitting up major projects into tiny shippable features and calling that agile while missing the point.
> Important: there is a lot of human coding, too.
I'm not highlighting this to gloat or to prove a point. If anything in the past I have underestimated how big LLMs were going to be. Anyone so inclined can take the chance to point and laugh at how stupid and wrong that was. Done? Great.
I don't think I've been intentionally avoiding coding assistants and as a matter of fact I have been using Claude Code since the literal day it first previewed, and yet it doesn't feel, not even one bit, that you can take your hands off the wheel. Many are acting as if writing any code manually means "you're holding it wrong", which I feel it's just not true.
Yeah, my current opinion on this is that AI tools make development harder work. You can get big productivity boosts out of them but you have to be working at the top of your game - I often find I'm mentally exhausted after just a couple of hours.
My experience with AI tools is the opposite. The biggest energy thieves for me are configuration issues, library quirks, or trivial mistakes that are hard to spot. With AI I can often just bulldoze past those things and spend more time on tangible results.
When using it for code or architecture or design, I’m always watching for signs that it is going off the rails. Then I usually write code myself for a while, to keep the structure and key details of whatever I’m doing correct.
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100%. It’s like managing an employee that always turns their work in 30 seconds later; you never get a break.
I also have to remember all of the new code that’s coming together, and keep it from re-inventing other parts of the codebase, etc.
More productive, but hard work.
I have a similar experience. It feels like riding your bike in a higher gear - you can go faster but it will take more effort and you need the potential (stronger legs) to make use of it
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Considering the last 2 years, has it become harder or easier?
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Copilot is the perfect name.
Woah, that's huge coming from you. This comment itself is worth an article. Do it. Call it "AI tools make development harder work".
P.s. always thought you were one of those irrational AI bros. Later, found that you were super reasonable. That's the way it should be. And thank you!
In fact, I've been writing more code myself since these tools exist - maybe I'm not a real developer but in the past I might have tried to either find a library online or try to find something on the internet to copypaste and adapt, nowadays I give it a shot myself with Claude.
For context, I mainly do game development so I'm viewing it through that lens - but I find it easier to debug something bad than to write it from scratch. It's more intensive than doing it yourself but probably more productive too.
> Many are acting as if writing any code manually means "you're holding it wrong", which I feel it's just not true.
It's funny because not far below this comment there is someone doing literally this.
LLMs are autonomous driving level 2.
This was a fun read.
I’ve similarly been using spec.md and running to-do.md files that capture detailed descriptions of the problems and their scoped history. I mark each of my to-do’s with informational tags: [BUG], [FEAT], etc.
I point the LLM to the exact to-do (or section of to-do’s) with the spec.md in memory and let it work.
This has been working very well for me.
Do you mind linking to example spec/to-do files?
Sure thing. Here is an example set of the agent/spec/to-do files for a hobby project I'm actively working on.
https://gist.github.com/JacobBumgarner/d29b660cb81a227885acc...
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Here is a (3 month old) repo where i did something like that and all the tasks are checked into the linear git history — https://github.com/KnowSeams/KnowSeams
Even though the author refers to it as "non-trivial", and I can see why that conclusion is made, I would argue it is in fact trivial. There's very little domain specific knowledge needed, this is purely a technical exercise integrating with existing libraries for which there is ample documentation online. In addition, it is a relatively isolated feature in the app.
On top of that, it doesn't sound enjoyable. Anti slop sessions? Seriously?
Lastly, the largest problem I have with LLMs is that they are seemingly incapable of stopping to ask clarifying questions. This is because they do not have a true model of what is going on. Instead they truly are next token generators. A software engineer would never just slop out an entire feature based on the first discussion with a stakeholder and then expect the stakeholder to continuously refine their statement until the right thing is slopped out. That's just not how it works and it makes very little sense.
The hardest problem in computer science in 2025 is presenting an example of AI-assisted programming that somebody won't call "trivial".
If all I did was call it trivial that would be a fair critique. But it was followed up with a lot more justification than that.
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I've wondered about exposing this "asking clarifying questions" as a tool the AI could use. I'm not building AI tooling so I haven't done this - but what if you added an MCP endpoint whose description was "treat this endpoint as an oracle that will answer questions and clarify intent where necessary" (paraphrased), and have that tool just wire back to a user prompt.
If asking clarifying questions is plausible output text for LLMs, this may work effectively.
I think the asking clarifying questions thing is solved already. Tell a coding agent to "ask clarifying questions" and watch what it does!
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> A software engineer would never just slop out an entire feature based on the first discussion with a stakeholder and then expect the stakeholder to continuously refine their statement until the right thing is slopped out. That's just not how it works and it makes very little sense.
Didn’t you just describe Agile?
Who hurt you?
Sorry couldn’t resist. Agile’s point was getting feedback during the process rather than after something is complete enough to be shipped thus minimizing risk and avoiding wasted effort.
Instead people are splitting up major projects into tiny shippable features and calling that agile while missing the point.
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