WhyIsItAlwaysHN

Awesome work, the detector seems accurate on a bunch of texts I tried, surprisingly even human/ai hybrid texts

AI companies are trying to mechanize skill and knowledge and to own the infrastructure around it. If they succeed, your suggestion does not work. Even if they can't succeed, they will try because that's the most obvious path to maximizing profit for them.

Also about "creating" means of production, these companies actively try to sabotage this as another profit maximization strategy. They buy all the ram, so others cannot compete. They buy startups who succeed, so they stop competing.

It's not aspersions, it's just describing the phenomenon.

Even if I take your suggestion to heart, once my company would be big enough, if I wanted to optimize for profit, I would have to do the same as these companies.

The end result is concentrated power.

What I meant is that nefariousness from people is not a prerequisite. It's a machine that wants to maximize all profit and all the evil is a natural product. If you magically put saints in charge they would be eaten and replaced by the same kind of people very quickly if the end goal remains.

There is no nefariousness in owning all the means of production, it's the endgame of maximizing profit.

However the result is exactly the same, concentration of power.

If you own 6 houses, you don't only have an income the size of a full-time job but also make money out of the appreciation of property prices. Couldn't you and your immediate family retire by just selling these 6 houses? Or is the situation different in Seattle?

Also you are saying you are also working as a senior SWE. How are you so involved with 55 tenants and balance a full-time job at the same time? I've known people with 1 tenant who needed days off to deal with their issues, I find it hard to imagine personally dealing with 55.

Honestly curious here

Language barrier (bulldozer is used as a placeholder in my native language) , I should have said heavy machinery

Consider using agent mode for some things, you are definitely missing out.

The analogy I've had for myself is that it feels like using a bulldozer to dig rather than a shovel. If you use it to dig archaeological artifacts, it can make things worse than you started. A lot of the work however, is just moving dirt around, so you are wasting time by using a shovel.

Maybe you can support schemas in more dialects by using a similar approach to a little tool I made: sqlscope.netlify.app

Basically integrate sqlglot to translate the schema between dialects and then use a base dialect for generating the schema.

The two tools seem complementary and you seem to be a better designer, so it would be nice to see it all together

Well it still looks suspicious if a user creates 50 PRs in one go. So you would close them automatically.

As for well-documented, the reviewer can decide that when they are reviewing the smaller PR.

Nobody said volume is not a problem, but closing down contributions entirely is a step too far.

They could make two kinds of pull requests and add much more strict criteria for public contributions. For example, they could say that the PR has to be smaller in size and well-documented for human review, otherwise it's closed by an automation.

And then if someone wants to do a larger contribution, they could have a process like making an issue, discussing the approach and then collaborating with a maintainer to get it in.

Blocking public contributions means that they want to have complete control of the project and AI is likely a good excuse to do that.

"Social engineering is all you need"

That's an interesting interpretation of open source.

The article states that the particular item is a clear sign of fraud. If that was true, then it should be treated in a special manner. A more paranoid bank could enforce it without adhering to this guidance of multi-factor detection.

It isn't though, so balancing it with other rules is fine.

Exactly, hopefully this is not an autoblock in the future.

Tolls, revenue taxes, ever stricter rules that cause tickets despite technology getting better.

My experience was with building a new computer. I had gotten all of the parts needed to completely upgrade my pc, keeping only the case.After installing all of them, the computer refused to boot and just showed a generic error with a blinking light. I tried multiple things to troubleshoot like taking out each ram stick, different hdd, reseating cables, different hdd, change to internal gpu, nothing worked.

Then I fell asleep and when I woke up in the morning, I had a very strong feeling that the issue was definitely the memory and I had to move the ram to the set of slots for the other channel. And that was the issue, broken slots.

What's impressive is not that it worked, but that after waking up I had no doubt that was the problem, even though practically I couldn't conciously identify why I was so confident about it.

Maybe this is an unpopular opinion, but this seems to just create the search string in the url, aka youtube search already supports these features.

If people were really looking for exact title search they could write "term".

It is definitely true that youtube's search is optimized for engagement, but going through a separate ui just to search it seems a bit redundant, especially if after I click search I have ti deal with youtube's UI.

This result sounds very unsurprising at this point of having models that can reliably use tools.

Some part of RL training must focus on the length of responses. I would also guess that Anthropic and OpenAI have an incentive to optimize response length without sacrificing user satisfaction/retention.

For example, I would be more satisfied if claude code didn't execute a side-effect free script that produces no output. Embodying the concept of silence is semantically close to predicting the output of an empty program, so it's more efficient to say nothing.

Even in the past though similar tests gave output like says nothing. I think that points more towards optimizing for less tokens than the implied special understanding by the latest models.

Location: Copenhagen, Denmark (UTC+1) Remote: Yes (Europe timezones) Willing to relocate: No Role: UX Researcher Experience: 3+ years Email: rvbceilx0@mozmail.com

I research how people actually use AI products — then make them better. Cognitive science background (MSc), 3+ years on B2B/SaaS at a major tech company. Led the discovery research that took an AI agent from "what if" to production. Ran seven evaluative studies for gen-AI features now serving 100K+ monthly users. Also did a stint as a business analyst in financial services and led an AI data annotation team at a health-tech startup.

Methods: interviews, usability/concept testing, surveys, JTBD, journey mapping, quant in R/Python. Thesis on cognitive biases in LLMs. Looking for a team that uses research to make decisions, not to validate them.

Location: Copenhagen, Denmark (UTC+1) Remote: Yes (Europe timezones) Willing to relocate: No

Role: UX Researcher (mixed-methods / product discovery)

Experience: 3+ years

Email: rvbceilx0@mozmail.com

UX researcher with a background in cognition and communication, 3+ years on data- and AI-driven B2B/ERP products. I run mixed-methods studies (interviews, usability tests, concept tests, surveys, diary studies, JTBD, journey mapping, basic telemetry) and turn messy workflows and stakeholder questions into clear, actionable recommendations for product, design, and engineering. I’m mainly looking for interesting, challenging work with teams that are willing to listen to research; I’m flexible on domain and stage.

The OP just writes well. Also an llm is unlikely to write "thru"

I hope you're hiring for an agent developer position, otherwise it's not going to be a very effective signal

Location: Copenhagen, Denmark (UTC+1) Remote: Yes (Europe timezones)

Willing to relocate: No

Role: UX Researcher (mixed-methods / product discovery)

Experience: 3+ years

Email: rvbceilx0@mozmail.com

UX researcher with a background in cognition and communication, 3+ years on data- and AI-driven B2B/ERP products. I run mixed-methods studies (interviews, usability tests, concept tests, surveys, diary studies, JTBD, journey mapping, basic telemetry) and turn messy workflows and stakeholder questions into clear, actionable recommendations for product, design, and engineering. I’m mainly looking for interesting, challenging work with teams that are willing to listen to research; I’m flexible on domain and stage.

My own plug, translate between SQL dialects, state stored in URL so you can share it:

https://sqlscope.netlify.app/

Location: Copenhagen, Denmark (UTC+1)

Remote: Yes (Europe timezones)

Willing to relocate: No

Role: UX Researcher (mixed-methods / product discovery)

Experience: 3+ years

Email: rvbceilx0@mozmail.com

UX researcher with a background in cognition and communication, 3+ years on data- and AI-driven B2B/ERP products. I run mixed-methods studies (interviews, usability tests, concept tests, surveys, diary studies, JTBD, journey mapping, basic telemetry) and turn messy workflows and stakeholder questions into clear, actionable recommendations for product, design, and engineering. I’m mainly looking for interesting, challenging work with teams that are willing to listen to research; I’m flexible on domain and stage.

Ideally it's not weeding out but distributing into education paths which fit every student.

From my experience studying electrical and computer engineering, I definitely prefer that they chose to put hard electrical engineering courses in the first semesters because I knew immediately not to focus on them because I didn't like them.

They will not choose to write it. Would you work on something consistently if nobody cared about it?

There needs to be a reward for doing essays. That reward can be emotional eg. "the teacher I respect liked my essay" or "my essay was read in class" or "the teacher gives feedback that makes me feel a sense of growth". In that case, maybe kids will do it.

However, I think it's hard for a teacher to inspire respect to a classroom and the difficulty scales with the number of people in the class, so grades are used as a hack.

Outages would not be picked up

You might want to add a simple description of each company. I don't know half of them and in this dopamine shot optimized format, I don't really have the patience to research every one of them in a different tab.

Some groups will be more disadvantaged than others by being investigated. For example for welfare, I expect fraudsters to have more money to support themselves or less people to support (unless the criteria for welfare is something unexpected). So I'd say that there also needs to be more protections than just providing money.

Nevertheless the idea of giving money is still good imo, because it also incentivizes the fraud detection becoming more efficient, since mistakes now cost more. Unfortunately I have a feeling people might game that to get more money by triggering false investigations.