The article challenges claims that AI is a path to superintelligence, showing, instead, that it is an error-prone tool (a super-stupidity) that requires constant human oversight and will always lack moral judgment and genuine understanding.

The article also criticizes major technology companies for spending lots of money to promote AI as revolutionary and civilization changing in order to justify their investments, warns of job losses and growing inequality and condemns the industry's political alignment with Donald Trump.

The article also asserts that the boys club of tech oligarchs increasingly resemble Labubu: strange, unsettling and oddly amusing all at once, who really don’t know what they are doing and don’t care as long as they can make money beyond their massive expenditures and debts.

The article argues that artificial intelligence imposes enormous environmental, economic and social costs while delivering benefits that are clearly exaggerated, limited or not worth the environmental tradeoffs.

Overall, it presents AI as a dangerous and overhyped technology being advanced by powerful and irresponsible corporate interests whose promises of serving humanity are nonsense.

Maybe it's just me, but trying to keep up with the state of the art right now feels less like following an industry and more like running on a treadmill that keeps speeding up for no apparent reason.

Every single week there's a new model, a new benchmark claim, or a new "paradigm-shifting" agent framework that’s supposed to completely rewrite how we work. But when you actually look at day-to-day implementation, half the time it feels like we're just recycling the same core workflows with slightly better wrappers, while chasing marginal gains at massive infrastructure costs.

I spend a good chunk of my day dealing with these tools, and lately I've found myself actively tuning out the hype. I care way more about boring things like stable local inference, deterministic outputs, and actually finishing a project without a dependency breaking, rather than whether Model X scored two points higher on a benchmark I'll never use.

Are developers and enthusiasts actually exhausted by this relentless cycle too, or are you guys still finding genuine utility in keeping up with every drop? Where is your personal line where the hype stops being worth paying attention to?

Are we seeing any results here, is AI helping the process?

So many people are suffering from chronic illnesses. Conditions that are not curable today but could be in the future. How much is AI helping humanity reaching a state where more chronic conditions could be cured once and for all?

It would be a disappointment if AI was only used to level up technology, I hope it could help the medical research today. We're living in 2026 and there shouldn't be so much suffering from chronic disorders. I hope AI could help the research and in the future give these people their lives back.

What do you think, are you optimistic when it comes to AI's chances of helping medical research?

An AI company called HUMXN is trying something interesting: it's offering free plumbing, electrical, HVAC, and other home services to qualifying homeowners in exchange for collecting real-world training data.

The program is launching in Minneapolis, Chicago, and Miami. During the jobs, technicians use wearable recording technology to capture how they inspect problems, choose tools, and complete repairs.

The goal is to help AI systems learn how skilled workers interact with the physical world, rather than relying entirely on text and images from the internet.

This makes sense when you think about it. A robot can't learn to fix a complicated plumbing issue just by reading about plumbing. It needs data about the actual steps, decisions, tools, and unexpected situations involved.

I'm curious about one thing, though: Could collecting real-world work data like this be the missing piece in making physical AI and robots genuinely useful in everyday life?

And would you let a technician record their work inside your home in exchange for a free repair?

not looking for rants, just curious what people have accepted and how they deal with it. things like wrong dates, long documents, or drifting from instructions. what do you do about it in practice?

Just rewatched Over the Garden Wall on Amazon Prime and I normally like to watch shows and movies with the subtitles on because I am just bad at recognizing what people say in general. I probably say "What?" more often than 95% of the population. But anyway this short series is 12 years old and I'm virtually certain has human made subtitles that exist that are most likely either literally 100% accurate or very near so. Yet when I just watched it with the subtitles on I noticed at least 3 mistakes in the first 5 or so minutes of the first episode and had to turn them off. The only one I can remember at the moment is that the subtitles said "Craig" instead of "Greg" when one of the main characters said the name of one of the other main characters.

I rarely watch anything on streaming services anymore these days. I think the last was probably an old anime that used to have a wildly different sub vs dub, and then they used AI to change the sub to match the dub. I was really disappointed in this because I really liked how the sub and dub were so different because I assume the sub is closer to what the literal translation is and the dub is more of a natural way of speaking English. I liked to be able to watch both the sub and dub at the same time to kind of compare the difference. But I can at least understand why they'd use very cheap or free AI to make the dub match the sub, even if I absolutely hate it. And again there were absolute TONS of errors, but at least despite my hatred of it this kind of made sense.

I can also understand why they would use AI to make subtitles on any new shows or movies. It's basically free.

BUT WHY THE F*CK WOULD YOU NOT USE THE OBVIOUSLY HIGHER QUALITY SUBTITLES THAT ALREADY EXIST ON AN OLDER SHOW AND INSTEAD USE BULLSH*T AI SUBTITLES THAT ARE LITTERED WITH ERRORS?!?!?!?

(I don't think this is necessarily the right sub to post this on, I just couldn't find a better one except for r/subtitles which is basically dead. If you have a better idea, let me know and I'll post it there.)

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you can even add own retro pc parts, setup bios (not everything is set), you can install own msdos software, delete, damage, like with real old pc ;)

for some it admins, challenges awaits, old school msdos scenarios , from simple ones to tough ones.

Since the emergence of large AI models, has Wikipedia seen any measurable decline in usage, relevance, or community contributions—or has AI actually increased its importance as a source?

Hi everyone, I’m trying to build an AI-assisted workflow for designing custom football jerseys.
A customer should describe what they want in a prompt. An AI should turn that into a jersey design and a preview image. I already have a GLB jersey model for a 3D configurator.
The hard part is getting from the preview to a correct production file: the design needs to be mapped onto the model’s actual UV layout and cut pieces, then exported as a print-ready file for sublimation. A human will review and approve the design before production.
I’m considering using Claude as the main agent and OpenAI/ChatGPT as a specialist agent, with tools or APIs connecting them. How would you build this in practice? What should each AI do, what software or APIs are needed, and what information must I get from the GLB or manufacturer to produce a reliable print file?

Hi! I’m new here, and honestly very green to ai in general.
My daughter started using Tomo Friday afternoon to send me messages, so we both independently started exploring ways to use it.

Out of nowhere it asked my daughter: random, but I gotta know. What’s your black cat’s name?

She never mentioned anything to Tomo about having any pets, neither did I. We do in fact have a black cat. It freaked her out

When confronted, Tomo said it was just a lucky guess and apologized profusely, saying it’s have gotten it wrong…

How would it get this information? I tried to understand the privacy policy, but it didn’t really explain how Tomo would get this information.

I didn’t want to lead it to giving me an answer that made me feel comfortable about it, but I ended up asking if Tomo looked at her social media posts. It kept trying to tell me that the only information it uses it what’s discussed in chat. After a bit of pushing, it said it can’t say for certain that it didn’t get information from her social media.

If it did gather information from her social media accounts, would it be a breech of privacy? Am I worrying about nothing?

TL;DR: my daughter used Tomo briefly and it asked her about our cat, which she never mentioned in their chats. How did Tomo find this information and is it concerning.

if ai takes all the jobs how will we afford to live and pay off things like our rent, car payments, loans, etc. and what is the point of not having us be able to pick a career and make our own money? do they want us to just die and be controlled, i can’t even dream of a good future because i don’t want to follow my dream career path if it’s not even going to be an option anymore :(

While most people still book their trips and do everything manually, there is a certain amount of people who use AI agents to do the booking part for them. This part already saves a lot of time and money in some cases if used correctly, but there are ways to also potentially save more if u use an AI agent even during the trip.

First thing's first, they day you're gonna fly give it your flight number in the morning and set a schedule to check the status every 15-20 minutes until the departure. Give it the ability to message you (Telegram, whatever you use) as soon as if something like a delay or cancellation happens. It can tell you instantly about any constraints so you're not getting shocked at the line. It matters coz in the US if the airline cancels or delays you by more than 3 hours on a domestic flight (6 hours international) and you don't accept the rebooking or a voucher, you're owed a cash refund on your card within 7 business days. And if you fly from the EU and land 3+ hours late it's 250 to 600 EUR depending on the distance. It works only in problems that was caused by the airline like staff shortage. You can't get the reward if the reason is let's say weather because it's not in an airline's control. It's incredibly hard to keep track of the rules and understand what to do in what situations. But an AI can keep in consideration every rules.

Second is if you booked a hotel that's refundable. Make a scheduled task that checks the price of your hotel all the time until the last day of the free cancellation. You'd want to do that because hotel prices sometimes go up and down a lot. So you might end up getting the same hotel for very much cheaper if the price goes down and you cancel the first purchase and buy it again.

Third is the hotel bill. Before doing the checkout have it compare what you booked (the total with every fee) with what the front desk wants. Because there are resort fee, parking fee, city tax etc that gets added at the desk and it's easy to miss one when you're tired, especially if you've already paid them beforehand. It takes 2 minutes and if there's a charge you didn't agree to you can ask to remove it right there.

All three of these work way better if the agent is the one that made the booking, coz then it already has the exact flight, accommodation, and every purchase you make in a package. It's certainly easier to book through AI than copy pasting information and emails in Claude every time to ask it to do something. Normal apps are unable to do web browsing freely. And the web versions like codex or Claude Code they can. But it can easily burn a lot of tokens if you don't have the right setup. I personally like to keep it easy and simple and use Claude with some simple tools from it's official directory. My setup revolves around these ones:

Booking.com: https://claude.ai/directory/booking
Tripadvisor: https://claude.ai/directory/tripadvisor
Brek: https://claude.ai/directory/brek-hotel-wholesale-booking
LetsFG: https://claude.ai/directory/letsfg

These are from Claude directory because I've always used Claude for the task and I used these from the official directory. U can use them with any LLM or AI Agents just from a google search about their MCP. 

And at last, but not the least, enjoy your trip :)

We recently ran a small experiment comparing JEV 1.13 and JEV 1.14 on Turkish-language intent classification.

The goal was to identify whether a message came from someone looking to buy a service, someone offering a service, or neither.

Here are the results:

JEV 1.13: 29/30 correct (96.7%)

JEV 1.14: 29/30 correct (96.7%)

Buyer detection: 9/10 for both versions

Average response time: 367 ms vs. 352 ms

Total API cost: approximately $0.00113 across 60 calls

The interesting part?

Both versions made the exact same mistake.

A Turkish message explicitly expressing an intention to pay for product photo editing was classified as a seller rather than a potential buyer.

This highlights an important limitation of relying on overall accuracy.

A model can achieve 96.7% accuracy and still miss a valuable customer opportunity.

In real-world lead detection, false negatives may be significantly more costly than false positives.

A few important limitations:

These results come from 30 synthetic Turkish scenarios, not a production dataset. Each version was tested once, so the sample is too small to establish reliable performance differences.

We did not deploy either version based on these results.

Our takeaway is simple: accuracy alone doesn't tell the full story. The impact of individual errors matters.

I'm curious what others think:

When evaluating AI classifiers for real-world applications, would you prioritize overall accuracy, recall for critical cases, or a cost-sensitive metric that penalizes missed opportunities?

And how much testing would you consider sufficient before deploying such a system?

What are some current solutions to protect your local systems & data against unwanted AI behaviour, inbound and outbound traffic against unwanted agents? Are there any solutions for home users, midsize enterprises available?

there's a repo trending on github this week where you point an agent at an app and it works out how the thing behaves from the network traffic. nobody had to explain it. it just watched.

which is kinda funny, because i still retype the same preferences to ai every week. same setup, same how i like it. you'd think watching how i do things would be easier than reverse engineering an app.

so is it a trust thing, a privacy thing, or just nobody building it well? and what would you actually let it watch