Additionally the "fix" fixes more than 40 separate issues in a single mega-commit touching 68 files which like. This is really beyond "you are trusting the slop machine too much". This is "the moment the slop machine got in your hands, a switch flipped in your head and you were like 'I never have to follow any engineering standards, any at all, ever again'". To repeat once again: "AI" is not a technology, it is a permission structure.

The good engineering practices are ignored by AI and the people seems to not care about it.

A quick-reference list of tools and guides for naming just about… well, anything, in one place:

• Naming buttons → NN/g on UI copy
• Naming classes → classnames.paulrobertlloyd.com
• Naming colors → parrot.color.pizza
• Naming design tokens → Intuit’s design token taxonomy
• Naming features → Shopify Polaris naming guide
• Naming icons → Iconography guide
• Naming layers → goodpractices.design
• Naming products → guide.onym.co
• Naming UI components → component.gallery
• Naming UI behavior → namethatui.com
• Naming variables → Vodafone UK Variables Taxonomy Map and naming-cheatsheet on GitHub
• UI terminology → Moccu’s naming convention framework

Which AGENTS.md to use for students

A carefully written piece

Josh Comeau notes that with LLMs, "there’s no incentive for people to make high-quality free content."

[Rachel Andrew] reports that GenAI has broken the relationship between subject matter experts – the authors of the original draft – and editors who proof-read, fact-check and polish the article. LLMs output needs to be reviewed by people who deeply care about the end result.

Today, we often hear the word “democratize” from GenAI promoters, but it means the exact opposite. AI centralizes power and commodifies knowledge, instead of teaching skills and deep understanding.

The survey of this year is concerning.

The most innovative and productive companies I’ve worked with had something in common: employees used the company’s products because they wanted to.

That willingness to use the product tells me more about a company than the size of its budget.

A company can hire excellent people and have a convincing plan for the future. But neither guarantees that anyone there will choose the product when they have a choice.

So when I think about what helps a company come up with useful improvements, one of the first things I’d look at is whether its employees choose to use what they’re building.

The AI websites are actually pretty slow, so does SPAs and the instability of big actors recently (GitHub)

Minimalistic tools (plain file or SQLite databases) focused on one core job well done, stability and privacy. They build an ecosystem.

Good advices

Where AI Actually Helps (With Clear Constraints)
I’m not saying never use AI. As I’ve written before, AI excels at automating the boring parts—and there’s nothing wrong with that.
But the key word is constraints. The successful uses I’ve seen have something in common:
The human sets the theory, and the AI executes within it.

Partie 9 de https://shaarli.lyokolux.space/shaare/m7Dh5g

Cette fois ci, le confinement n'a pas aidé.

La partie 7 de https://shaarli.lyokolux.space/shaare/m7Dh5g

Partie 6 de https://shaarli.lyokolux.space/shaare/m7Dh5g

Partie 5 de https://shaarli.lyokolux.space/shaare/m7Dh5g

Les SSII ne donnent toujours pas envie.

Answer:

But I think in general you see what you're paying attention to, and being on the constant look-out for bad things is a bad mental habit. Thinking back 15 years ago, did we use to do this? I don't know if it's the loss of common cultural touchstones but it feels like there's so much tribalism everywhere now.