If AI wearables could help them navigate unfamiliar places, identify and read signs or answer questions about their surroundings I think they would leap at it.

But you want to focus on one end of the spectrum? Yes, it's the truth but not the whole truth.

So the elite users are the ones who choose. Funny how our communities mirror the social structures we supposedly hate.

But yes, the original poster with their allusions to lost limbs and "pointless swinging in a vast, unquantifiable emptiness.." is overreacting, depressed or both.

The slop will continue until it is no longer profit-making for someone.

I ask, not to condemn, but to find out what your process was for developing the requirements. Clearly it was done with LLM help but what was the refinement process?

Generally its feature a, feature b and a refactoring branch of some kind.

My workflow is:

1. Add ticket in gitlab describing bug or feature in as much detail as possible along with acceptance criteria like expected unit tests or browser based tests.

2. In a work tree create a branch based on the id of that ticket in gitlab.

3. Start Claude, tell it to use a skill to pull that ticket, research and make a plan.

4. Review the plan, ask questions, refine.

5. Approve plan and let cluade cook.

6. Have Claude run a set of linters/tests/code quality checks and ground until done.

7. Start a new Claude instance, ask it to review changes made. Provide feedback to first Claude instance for changes.

8. Commit and push, creating a draft mr/pr in gitlab.

9. Review the actual code changes myself using gitlab. Comment on things not right.

10. Get Claude to use another skill to pull comments and work to resolve them. Also feed back any CI failures.

11. Manually close comments and push again. Repeat until done and ready for co-worker review.

I can only keep 3 threads like this going at once. Sometimes it’s only 1 or 2, depending on complexity. Smaller is better. Try to stay atomic and avoid feature creep in each mr.

Username checks out, but you might want to check with your mother about how she feels about this comparison.

TFA brings up abuse not stndef.

An analogy is "a thing which is comparable to something else in significant respects" and stndef is right to point out that microsoft behavior, while abusive, is not comparable to domestic abuse "in significant respects". Not even close.

The TFA title is sensational for effect and in very poor taste.

And it's a spectrum, at one end you got the full-on AI psychosis and at the other "its a machine, I owe it nothing".

Conversational AI is going to be sticky to the extent that you see a switch to a different provider as dropping a relationship.

Given the crazy money and vying for supremacy among AI companies right now it does seem naive to belive that no attempt at better pelicans on bicycles is being made. You can argue "but I will know because of the quality of ocelots on skateboards" but without a back catalog of ocelots on skateboards to publish its one datapoint and leaves the AI companies with too much plausible deniability.

The pelicans-on-bicycles is a bit of fun for you (and us!) but it has become a measure of the quality of models so its serious business for them.

There is an assymetry of incentives and high risk you are being their useful idiot. Sorry to be blunt.

So when someone wants to know something about the topic that my website is focused on, chances are it will not be the material from the website they see directly, but a summary of what the LLM learned from my website.

Ergo, if I want to get my message across I have to write for the LLM. It's the only reader that really matters and it is going to have its stylistic preferences (I suspect bland, corporate, factual, authoritative, avoiding controversy but this will be the new SEO).

We meatbags are not the audience.

https://ifs.org.uk/publications/health-related-benefit-claim...

You can argue that nobody is systematically abusing the system but numbers go up. To those paying the taxes to support these benefits that sort of growth looks like abuse.