Comments URL: https://news.ycombinator.com/item?id=47555157

Points: 1

# Comments: 0

The best example of this is heroin, which has both a severe physical and mental addiction component, and it's the mental addiction that makes relapse so common.

Mental addictions rewire the brain's chemistry, causing the user to seek and only find joy in the substance. This is a better comparison for social media (albeit not as destructive and instantaneously harmful as narcotics)

I agree that a big part of this is educating children about these hazards, but that also doesn't mean we should allow these companies to data science the shit out of our attention and will power. Many adults have concerning relationships with social media too -- exposure, pressure, and manipulation are key ingredients that are difficult for anyone to deal with.

Personal responsibility IS important, but we also don't allow cigarette companies to advertise on billboards with cute characters (remember Joe Camel?)

YouTube allows you to "show fewer shorts" but what if you don't want them popping up at all?

AI Slop is the best thing to happen to these platforms - because it will lower trust and engagement as people (hopefully) become tired of inauthenticity. Rage bait is potent when the event in the video _actually_ happened, but when you realize it was AI generated, the manipulation feels even more obvious (though it was always there).

These platforms should also allow users to understand how the algorithm has categorized them, and be able to configure it. YouTube, Instagram, et al. would be safer places for viewers if they allowed users to tell them what they want to be exposed to, and what they don't. Big tech is dodgy about this currently, because the more control the user has the lower the engagement (good for the user, bad for profit).

This is the most important part of the article. Key takeaway.

Comments URL: https://news.ycombinator.com/item?id=47025903

Points: 3

# Comments: 0

Some questions:

[Tech]

1. How deep does the modification go? If I request a tweek to the YouTube homepage, do I need to re-specify or reload the tweek to have it persist across the entire site (deeply nested pages, iframes, etc.)

2. What is your test and eval setup? How confident are you that the model is performing the requested change without being overly aggressive and eliminating important content?

3. What is your upkeep strategy? How will you ensure that your system continues to WAI after site owners update their content in potentially adversarial ways? In my experience LLMs do a fairly poor job at website understanding when the original author is intentionally trying to mess with the model, or has overly complex CSS and JS.

4. Can I prompt changes that I want to see globally applied across all sites (or a category of sites)? For example, I may want a persistent toolbar for quick actions across all pages -- essentially becoming a generic extension builder.

[Privacy]

5. Where and how are results being cached? For example, if I apply tweeks to a banking website, what content is being scraped and sent to an LLM? When I reload a site, is content being pulled purely from a local cache on my machine?

[Business]

6. Is this (or will it be) open source? IMO a large component of empowering the user against enshittification is open source. As compute commoditizes it will likely be open source that is the best hope for protection against the overlords.

7. What is your revenue model? If your product essentially wrestles control from site owners and reduces their optionality for revenue, your arbitrage is likely to be equal or less than the sum of site owners' loss (a potentially massive amount to be sure). It's unclear to me how you'd capture this value though, if open source.

8. Interested in the cost and latency. If this essentially requires an LLM call for every website I visit, this will start to add up. Also curious if this means that my cost will scale with the efficiency of the sites I visit (i.e. do my costs scale with the size of the site's content).

Very cool.

Cheers

Can we also get the ability to filter by seller entity country of origin?

Amazon also needs to offer far better tools for buyers to effectively find and attach to brands.

Buying a house is often an emotionally motivated decision with many important risk factors... Were inspections comprehensive and thorough or did they overlook an issue with the foundation, wiring, plumbing, etc.? Did the buyer understand the required disclosures, and specifically understand what the seller is not obligated to disclose in their jurisdiction? (e.g., in many areas the seller is not obligated to disclose if a child sex offender lives next door). Is the neighborhood up and coming or struggling?

Getting these wrong can easily negate the potential upsides of ownership.

Renting can also be great, but as the article points out, if it mostly just results in more disposable cash, then you may be better off owning (forced savings). Rental properties often cannot be sublet and also cannot be used as collateral or passed on to family members with a step-up in basis. Rental leases also fluctuate with the market, so it's not uncommon in big cities for renters to be paying close or equal prices of their homeowners next door.

Anecdotally I know many folks who have rented their way through, invested wisely, and done well. I also know folks who have moved around the US and always purchased, did their homework, capitalized on tax incentives, and now have a stable of rental properties that helped them become FIRE.

We should be educating children at a young age about the benefits and risks of social media. We haven't adapted the way we educate society in light of massive tech changes.

This will likely be a topic that future humans look back on and wonder why we did this to ourselves.

I spent time at Microsoft as well, and one of the things I noticed was folks who spent time in different disciplines (e.g. dev, test, pgm) seemed to be especially great at tailoring these qualities to their needs. If you're working on optimizing a compiler, you probably need a bit more Einstein and Mort than Elvis. If you're working on a game engine you may need a different combination.

The quantities of each (or whether these are the correct archetypes) is certainly debatable, but understanding that you need all of them in different proportions over time is important, IMHO.

A few things to consider:

1. This is one example. How many other attempts did the person try that failed to be useful, accurate, coherent? The author is an OpenAI employee IIUC, so it begs this question. Sora's demos were amazing until you tried it, and realized it took 50 attempts to get a usable clip.

2. The author noted that humans had updated their own research in April 2025 with an improved solution. For cases where we detect signs of superior behavior, we need to start publishing the thought process (reasoning steps, inference cycles, tools used, etc.). Otherwise it's impossible to know whether this used a specialty model, had access to the more recent paper, or in other ways got lucky. Without detailed proof it's becoming harder to separate legitimate findings from marketing posts (not suggesting this specific case was a pure marketing post)

3. Points 1 and 2 would help with reproducibility, which is important for scientific rigor. If we give Claude the same tools and inputs, will it perform just as well? This would help the community understand if GPT-5 is novel, or if the novelty is in how the user is prompting it

There are many perspectives on coding agents because there are many different types of engineers, with different levels of experience.

In my interactions I've found that junior engineers overestimate or overuse the capabilities of these agents, while more senior engineers are better calibrated.

The biggest challenge I see is what to do in 5 years once a generation of fresh engineers never learned how compilers, operating systems, hardware, memory, etc actually work. Innovation almost always requires deep understanding of the fundamentals, and AI may erode our interest in learning these critical bits of knowledge.

What I see as a hiring manager is senior (perhaps older) engineers commanding higher comp, while junior engineers become increasingly less in demand.

Agents are here to stay, but I'd estimate your best engineering days are still ahead.

Not to mention Christmas trees, moving, helping friends out, etc.

For example, Microsoft's DXT codec lacks most modern features (no entropy coding, motion comp, deblocking, etc.), but delivers roughly 4x to 8x compression and is hardware decodable (saving on decoding and presentation latency).

Of course, once you've tuned the end to end capture-encode-transmit-decode-display loop to sub 10 ms, you then have to contend with the 30-100 ms of video processing latency introduced by the display :-)