This could be done on the device, for example the Android app ObscuraCam has automatic face detection.

This is true not just for AI, but also adding more engineers to a project that isn’t going well. No matter how many resources you throw at it, it will continue to be slowed down by the underlying problems with the team or company culture.

Good question, though wouldn’t that be a feature of the browser? It wouldn’t be a good user experience asking people to manually enter coordinates.

Today I stumbled upon Don’t paste the AI:

When someone asks you something, they want your answer. Not a wall of ChatGPT text. A short answer from a human will always be worth more than one from a robot, even if the robot is right.

Exactly this! If I am talking to someone who has expertise in an area, I want to know what they think, not what an LLM outputted.

This sums up my current bugbear with people using Claude, as its output is too verbose. I don’t mind the use of agentic tools, though I want a concise explanation written by a human.

But where it starts to get dicey is when you have both jargon and long text explanations being generated by machines, for people at different levels of understanding. “Bumped dependencies” is good. Everyone knows we are updating packages. “Performed a scheduled dependency refresh as part of ongoing maintenance practices. Minor and patch-level version bumps were applied across the dependency graph, including transitive dependencies where applicable” is bad.

My takeaway from the article was, in the author’s own words, “start with debt in code you’re about to change anyway”. That’s not to say you shouldn’t tackle technical debt in code that isn’t changing, rather it’s lower priority.

The cost of a piece of debt is its messiness multiplied by how often you go near it. A horrible module that hasn’t changed since 2023 costs almost nothing today. You’re not reading it or extending it. Refactoring it is paying down a loan that isn’t accruing interest. Meanwhile the mediocre little helper that 40 features lean on, the one edited every other week, is quietly the most expensive code you own. It’s usually not on anyone’s list because it doesn’t look scary.

I had not thought about tech debt in this way before. It makes perfect sense to focus on paying off the tech debt where you spend the majority of your time working, and hence accruing interest on.

It’s so when it links to local bookshops and libraries, you can be directly be taken to the book when ISBN search is available. Given the site’s purpose is to put “independent bookstores front and centre”, not all of those small business are going to be able to display stock levels online

The bookshop data is crowdsourced, so the project relies on people suggesting bookshops and libraries. You can even raise a pull request.

Well that depends on how quickly you believe that engineering skills atrophy without use.

Though, I’d argue programming is like riding a bike. Even after a long break, it’s not difficult to get back into it, as the fundamentals for programming haven’t changed.

Could you qualify what you mean by “idiots”? The author and the examples he gives - Kent Beck, David Heinemeier Hansson, and Garry Tan - are all very experienced software engineers.

Funnily enough, Lemmy does allow AI-assisted code contributions, though they’re not encouraged. This is mentioned in the code of conduct:

Use of so-called Artificial Intelligence (AI) is allowed only if it is explicitly mentioned. Additionally all LLM-generated text or code must be manually reviewed by the author before submission (no vibe coding allowed).

This article is not advocating AI driven development. It’s arguing a human understanding the problem, and how best to solve it, is the most important work.

I’m not anti-spec. Far from it. I think rigorous, well-structured specs are about to be the single most important artifact in software. Read the book. Adopt the format. Use the tools. The mechanics matter and I’m not waving them away.

But the spec is a vessel. The recipe is not the cooking. If we pour mediocre understanding into a perfectly-formatted spec, we get perfectly-formatted slop, faster and at scale. The format is necessary. It is nowhere near sufficient.

The real work - the part no tool will do for you - is the human work of digging in. Of actually understanding the problem and the person you’re solving it for, deeply enough to have an opinion about what right feels like. Of capturing not just the requirements but the taste. The why. The elegance. The exact sweet spot of how it should work.

I agree that the AI generated image is trashy, however the article is a cautionary tale about the pitfalls of relying on agentic coding, instead of collaborating with other developers.

But there is always a ceiling on how far a single-player game can take you, even with agents. Software that lasts, software that grows, software that people can actually depend on – that is built by groups of people exercising judgment together over time. By teams developing shared taste, shared mental models, shared sense of what their product should be. None of that happens through individual prompting, no matter how clever the prompts.

Agile came from toyota?

My understanding is that Kanban came from Toyota, which is an agile way of working.

This is a fascinating article about the history of software development. For me the key quotes are:

The thing that killed Waterfall was that discovering your spec was wrong months later, after lots of code had been written - and fixing it cost a fortune because writing code was the most expensive part of the process.

The key reason Agile was invented was to account for the high cost of writing code, so yes, that part of the Agile value proposition is no more.

The risk isn’t that AI development is inherently Waterfall. The risk is that organizations with latent Waterfall instincts will use spec-generation as license to do the bad thing they always wanted to do — front-load requirements, skip customer validation, equate a fancier document with a better outcome, and ship one massive thing every quarter.

This quote from the article really sums it up:

And to be clear, I don’t care whether you typed the code yourself. I care whether you understood it before you shipped it. I care whether you can explain why the bug happened, why this fix is the right fix, what the model might have missed, and what would make you roll it back.

Could you elaborate on this?

Thank you! I’ve updated the post with the TL;DR from the article.