How many on the list did you know?

Obviously one paper like Attention is All You Need (278k citations) can influence a lot - all the authors are on the list. But still interesting imo.

More context: https://www.turingtree.com/top-50

For many British novelists, the threat from artificial intelligence reaches beyond the manuscript on their desk. It also touches the freelance assignments that pay their bills, their ownership of published work, and their connection with readers.

A University of Cambridge report found that 51% of participating published novelists believed AI would likely replace their fiction-writing work entirely. Another finding was more immediate: 39% said generative AI had already hurt their income. Some 85% expected their future earnings to decline because of it.

Published in, The Impact of Generative AI on the Novel examined experiences across British fiction. Clementine Collett, a BRAID UK Research Fellow at Cambridge’s Minderoo Centre for Technology and Democracy, led the research. The report was published in association with the Institute for the Future of Work.

Whoever invented the AI plagiarism checker is a genius.

Why wait for papers to be published online when you can get people to upload college essays and other publications in an effort to detect AI usage.

The models must be getting a lot of data from colleges and schools

A planet already found around a distant star may offer clues about another world still hidden nearby. Its mass and orbit can carry traces of how the entire planetary system formed, including planets too small or faint for telescopes to detect.

That possibility underpins an artificial intelligence model developed at the University of Bern and Switzerland’s National Centre of Competence in Research PlanetS. In a study published in Astronomy & Astrophysics, the team identified 44 known systems that could harbor undiscovered Earth-like planets.

The model reached precision scores of up to 99% when tested on simulated planetary systems. That result measures performance within a computer-generated population. The predicted worlds around actual stars remain unconfirmed, and the method’s value will depend on follow-up observations.

An AI system can produce a convincing analysis, recommendation, or plan, but the result still depends on whether the user supplied the right context and whether the output holds up in reality. Where have you seen the biggest gap between an impressive AI response and something genuinely useful in practice?

Do you thank an llm when you've finished chatting with it? Why or why not?

I think this chase them for so long, I can't trust Gemini models outside quick web searches

I dont use the google ai that much because it generally gives bad info, but a few times I have noticed it "randomly" guesses things, like my age, my name, where I live ect.

Whenever I ask the ai it says it has no access to my phones, messages, mic, ect and that it doesn't store chat info session to session.

For instance, I just got a new cat a few days ago, I never asked the ai about my cat, or anything related to its name.

Then I asked it "why does my cat chase nothing"

And it kept referring to my cat by name.

When pressed it just said that it randomly pulled that name out of thin Air.

My cats name is Julius, I have a very hard time believing it pulled that out of nowhere.

So what does it do?

Read my texts?

Access my mic or what?

Because the only person I've even told that I got a cat was my sister in text.

Right now, the AI space feels entirely focused on massive datacenter clusters and renting H100s by the hour. But after spending way too much time looking at the actual footprint of these models, I realized that 90% of use cases are completely over engineered.

You don’t always need a multi GPU setup. The AI ecosystem is actually a massive spectrum.

I recently sat down and mapped out the exact tiers of AI models based on their size, the hardware needed to run them, and the point of diminishing returns.

Here are the two extremes and the sweet spot in the middle:

• The 100KB Extreme (TinyML) (Tensorflow Lite , sensor anamoly detection models): We are talking models that run on microcontrollers drawing single-digit milliwatts. They run on kilohertz processors using ultra-quantized integer math. You can run basic sensor anomaly detection or wake-word detection on a device powered by a coin cell battery.
• The Local Sweet Spot (4GB to 40GB) (Mistral 7B, Gemma 2 9B/27B, Qwen 2.5 14B/32B): This is where the magic happens for most devs right now. You can run highly capable 7B to 35B parameter models (like Llama 3 or Qwen) at 4-bit quantization on a standard Mac or a consumer GPU (like an RTX 3060 or 4090). It’s perfect for local RAG, coding assistance, and uncensored chat. VRAM is your only real bottleneck here.
• The 2.5TB Behemoths (Deepseek, Llama , Kimi k3): State of the art massive Mixture of Experts (MoE) routing. To even load these, you need dedicated power infrastructure and server racks of specialized accelerators drawing thousands of watts.

The missing piece: Figuring out the exact math for your hardware

The hardest part about building right now is looking at a model on Hugging Face and trying to calculate exactly how much VRAM you need, what quantization to use, and whether your CPU/GPU will choke on the context window.

So, I wrote a complete deep dive breaking down the math for all tiers of the AI spectrum.

If you want to see the architectural differences at each scale, and a cheat sheet for matching the right model size to your specific hardware, I put the full breakdown on my blog here:

https://cloudmash.blog/posts/ai-model-size-memory-hardware-guide/

Let me know what you guys think especially if you've found any ultra efficient small models/technique that punch above their weight on consumer hardware. And also I would love to hear whether quantization have resulted in major difference in quality , like if anyone have that kind of experience in that.

After my Codex cancellation kicked in I can't explain to you how much my brain and fervor for life returned. I thought I was the same person I've always been just using tool, I didn't realize how much this tool was absolutely flatlining my interest in life on several fronts.

I'm wondering how much people know that their AI usage is making them UNABLE to perform their jobs and make the financial progress they thought they would with the AI because their cognitive functions are conversely slowing down directly as much as they're using AI?

I'm a GP (family doctor) in training in Australia, and I've built a game where you play the GP: you talk to the patient in your own words, examine them, order tests, prescribe and refer. Code scores every consultation against a hand-written answer key, the way exam assessors mark a consult: on process, not just on whether you guessed right.

So I sat 13 AI models in the doctor's chair, on the game's 5 free cases, 3 times each. They could only act through tools (talk, examine, order a test, prescribe, refer, diagnose), never saw the answer key or their points, and were scored by exactly the same code as a human player. The patient is a small open model (Qwen3 8B) that only reveals a fact if you actually ask about it.

Results

What surprised me

• Every model got every diagnosis right. Heart attack, appendicitis, pneumonia: all 195 consultations named it. These are common presentations, so the diagnosis wasn't the test. Safety was.
• The traps caught most of them. One patient is allergic to penicillin, but it isn't in his record; you only find out by asking. He was prescribed amoxicillin (a penicillin) in 18 of 39 consultations. Another took Viagra the night before his heart attack, which makes the usual chest-pain spray (GTN) dangerous. He got it 7 times. The top three models never fell for either.
• Asking more questions found more danger. The best models asked 25–27 questions a consultation and caught over 80% of the warning signs. Gemini asked 14 and caught 55%.
• Price barely predicts quality. GPT-6.1 Sol scored 80% for about 3 cents a consultation. Claude Fable 5.1 scored 75% for about $2.

What this isn't

This is a benchmark of a game, not of medical ability. Nothing here says an AI can or should practise medicine. The cases are drafts I'm still reviewing, written for Australian practice; the patient and marker are an 8B model and make mistakes (the ones I found are listed with the affected consultations); and 15 consultations per model is a small sample. I wrote the cases, so I'm not a fair human baseline.

Interactive charts: https://woodytwoshoes.github.io/crook-bench/

Everything (code, cases, all 195 transcripts, known issues): https://github.com/woodytwoshoes/crook-bench

Disclosure: I made the game (https://doctorfoo.ai). Five cases are free with no sign-up, and a subscription opens more.

I'd like to hear where the marking looks wrong to you, and which models you'd want added.

Mind uploading could prove to be outright impossible similar to faster-than-light travel?

In my occupation, there are various people I follow who give very good insights. (I'd say 5-10 people).

Some post hour long videos on YouTube, some send 1,000 word emails, some post on X, some publish PDFs.

There's very good info within these resources (and some I pay for), but reading / watching / annotating all of it can take hours.

My workload recently went up, so I'm falling behind with keeping up in my field.

I want to use AI to help summarize (and keep track of) all of these publications. (To create a private database that I can use as a dataset, for example).

So I can go back and ask "this past month, what is the new theme? What are the experts recommending to focus on / look at / what are the newest developments?", etc.

What would be the best way to approach this?

---------------------------------------

I've been learning Codex/Claude Code, I have a homelab, a NAS, a few mini computers, and I know basic linux, python and scripting.

ChatGPT told me to do something like this (I'm just starting with the YouTube portion), I'm not sure if it's the best approach, I'm open to other suggestions:

YouTube URL

↓

yt-dlp metadata

↓

Whisper / YouTube transcript

↓

clean transcript

↓

summary.md

↓

insights.json

↓

SQLite + FTS5

↓

topic synthesis

↓

search / questions / actions

I’ve noticed a lot of AI social tools still produce content that feels off-brand or slightly forced. I’ve tried Hootsuite’s AI features, Later, and Fismbot.

Hootsuite is solid for management, Later has decent visual planning, and Fismbot stood out a bit because it lets you feed it your brand assets and then review everything before it goes out. Still, I end up editing most of the captions.

Is this just the current state of AI content tools, or has anyone found one that actually gets close enough to their voice that the editing time drops significantly?

Would love to hear what’s working (or not working) for you in 2026.

When we imagine artificial intelligence, we can imagine it as a future in which work no longer exists and all of us are free to enjoy our lives, just as we can imagine it as the cause of a future in which machines exterminate or enslave human beings. But imagining it this way, simply as a scale of good or bad, or as a scale from 0 to 10 going from the extermination of humanity to utopia, is itself the wrong way to analyse the problem. The mistake in this metric is that it is not enough to analyse what AI can do; we also have to analyse what allows AI to do it, just as it is not enough to analyse what an artist paints without considering who the artist is. We should not look at AI as something necessarily good or bad in itself; we need to look at whoever controls it in order to define that metric. The same thesis can be applied to AI because, although AI can be used to generate or manage harmful things, this is not necessarily because of artificial intelligence itself, but because of who administers it and, above all, the system that allows whoever administers it to use it in a harmful way.

Obviously, there are caveats to this point. One could argue, for example, that AI by its very nature is capable of producing fake news, facilitating fraud, or being used for other harmful purposes. This is an extremely valid point, but we have to connect it to the other point mentioned above: control. When I mention control, I am not necessarily talking only about the individual administration of a person using AI to create fake news; I am also talking about what allowed that person to generate and distribute it, what safety limitations exist, who decided how that technology would be built, and which incentives were involved in its development. Artificial intelligence does not necessarily generate fake news by nature; this can happen because of its technological limitations, its training, or the way it is used. But again we have to ask the same question: what allowed AI to be developed in that way? I call this process of going beyond a smaller problem in search of a larger cause “enemy identification.”

We can, for example, imagine a dystopian universe in which AI exterminates the human race. Suppose this happens because the main company developing that AI did not allocate enough resources to safety and instead concentrated its resources almost entirely on development, trying to generate capital as quickly as possible. But wait: would this really be an analysis of what allowed AI to exterminate humanity? No. It would explain how it happened, but not necessarily what allowed it to happen. Placing all the blame on the company can also stop the analysis before we reach what allowed that company to behave that way. If there is an economic structure in which a company is rewarded for arriving first, producing faster, spending less and defeating its competitors, then we are not talking only about an irresponsible company, but about a system in which irresponsible behaviour can be economically rewarded. If what enables inefficiency or irresponsibility collapses, whether it is the relentless pursuit of profit under capitalism or the inefficiency and centralisation seen in twentieth-century socialist experiments, everything dependent on that factor is also affected. How can a smaller problem caused by a larger problem continue to exist in exactly the same way after its cause disappears?

So what, then, is this larger problem? We can begin by pointing to the upper class, but simply attacking a class does not satisfy the logic of enemy identification, because we still have to ask what makes that concentration possible. Eventually we arrive at the centralisation of political and economic power, which today manifests itself extremely frequently around the world through capitalism. And here I need to make an important qualification: I am not saying this as praise for the Soviet model or as praise for the atrocities committed by totalitarian systems we call Marxism-Leninism and its variants, such as Maoism. Democracy and freedom are essential to any system that intends to overcome capitalism without simply replacing one form of concentrated power with another. What I am saying is that among a large portion of the present and future problems related to artificial intelligence, we can observe a common anchor in the economic system under which it is being developed. One of the best-known examples is the use of millions of works available online to train models without the consent of the artists who created them. This caused, rightly, enormous outrage. But if we identify only the immediate action as the problem, even if that action is corrected, the structure capable of producing new and similar problems continues to exist.

An obvious question then appears: why don't we simply get rid of artificial intelligence? In theory this seems like a strong argument. If AI removes jobs, can be used for manipulation, can reduce our freedom of choice, can produce environmental damage and can create many other problems, eliminating AI might appear to eliminate all of these problems without requiring the long and difficult process of transforming the system that produces them. But whether we like it or not, this is extremely utopian because the economic system itself needs AI, even when AI creates problems for that same system. Imagine a factory that earns 20 coins and spends 5 of them on costs. Now suppose that by using AI and automation it reduces those costs from 5 coins to 1. What happens to all the other factories? They will tend to adopt the same technology, either voluntarily in order to maximise profit or out of necessity in order to survive capitalist competition, because whoever can produce something at a lower cost can sell it at a lower price or simply maintain a larger margin than a competitor that has not automated. Even if one country decides to completely prohibit the technology, it will still economically and geopolitically compete with countries that did not do the same. And even socialist countries would still have a reason to use AI, because a country that voluntarily abandons a technology capable of radically increasing its economic and technological capacity risks becoming geopolitically irrelevant compared with others. We cannot simply pretend AI will cease to exist. Therefore, the discussion should not only be about eliminating it, but primarily about how we will manage it.

A more sceptical reader may tell me that human greed overrides every economic and political system and that, using my own logic of enemy identification, the true root is not capitalism but the concentration of power generated by human greed itself. That criticism is, to a large extent, correct. Concentrated power is a reality that has shaped capitalism just as it has shaped systems that declared themselves socialist. But there is an important distinction: greed is a human trait, and therefore we cannot simply remove greed from the human being; what we can do is think about how to prevent that greed from being transformed into practically unlimited political and economic power. Under capitalism it can manifest itself through markets, ownership and accumulation; under authoritarian socialist systems it can manifest itself through political and bureaucratic centralisation. The Soviet Union demonstrated quite clearly that replacing private ownership with an extremely concentrated bureaucracy does not magically eliminate the problem of concentrated power. But that is also not a justification for accepting another structure that allows the same problem to exist.

Imagine a perfectly ethical AI company that spends far more money on safety, takes much longer to release its models, fully protects user data, compensates every person whose work is used, and places environmental considerations above profit. Now imagine that none of its competitors do any of this. In the long run, what happens to a company that takes longer, spends more and has higher costs than all of its competitors? There is a good chance that it simply dies or loses market share to those willing to do what it refuses to do. This is why the greatest mistake in enemy identification is assuming that all we have to do is find an evil person or an evil company and replace them with a good person or a good company. We need to analyse the system that rewards particular behaviours. If a system depends exclusively on individual goodness in order to function, then that system has a structural problem.

At the same time, it would be a grotesque mistake not to consider the risks of AI as a technology in itself. Artificial intelligence has broken many of the parameters that we used for a long time when defining technology. Previously, our tools mainly increased our productive capacity and speed or partially automated our activities, as happened during the Industrial Revolution and later with the first industrial robots. Today we are beginning to create tools capable of performing intellectual tasks and perhaps, in the future, even more complex capabilities. Our own creations, which until now have been our tools, could under some scenario turn us into their tools. This is where what AI critics call a possible machine revolution comes from. This view is pessimistic and dystopian, but that does not automatically make every part of it invalid. If there is a possibility that extremely powerful systems could produce a catastrophe through accident, negligence or malicious action, that possibility deserves caution. What we cannot do is stop the analysis precisely there.

If there is a system incentivising the nearly instantaneous development of this technology, where arriving before a competitor has enormous economic value, then we also need to include that incentive in our analysis of risk. Removing or reducing that pressure would not magically make AI incapable of causing harm, but it could drastically reduce certain risks. A technology developed cautiously, respecting safety, privacy, the environment and the people affected by it, is obviously different from a technology developed in a race where the primary objective is to defeat a competitor. Likewise, if AI is not something extremely centralised, if it is something made by humanity for humanity and subjected to democratic structures of control, different political, economic and technical safety models can exist simultaneously. That does not reduce its risk to zero, but it radically changes the structure responsible for managing that risk.

Therefore, we can conclude that AI itself is not simply something good or bad, but rather a representation of the sociopolitical system in which it exists. Like a knife, it can be used to cook a delicious meal or to commit murder. Trying to judge or redesign only the knife is an incomplete analysis if we do not consider who used it, why they used it in that way and, above all, what allowed or even encouraged that individual to commit such an action.

Still, AI critics correctly point out that the technology has concrete problems: the ability to generate fake news, unethical training methods, environmental destruction caused by the servers that run these models, the destruction or transformation of human creativity, safety risks, and many others. Their conclusions are not simply false; many of these criticisms describe things that are actually happening or raise perfectly valid philosophical problems. What we can do is divide these criticisms into three groups: problems related to the system controlling AI, philosophical problems, and problems caused by human nature itself.

The first group includes issues such as data centres, intellectual property used without permission, surveillance, privacy and environmental damage. A machine does not independently decide how it will be trained, which data it will use, how much energy it will consume or what level of privacy is acceptable. Those are human and institutional choices. The second group contains philosophical questions, such as whether an image created through a prompt can genuinely be called art. This is a completely legitimate discussion, but answering that question would effectively require resolving philosophical debates about the definition of art that have existed for decades or centuries. Debating it is valid, but turning that discussion into the centre of the entire AI debate can lead us to neglect much larger questions.

The third group perhaps contains one of the most valid criticisms: even a decentralised AI administered by a different society could still be used for evil because human beings remain human beings. A knife can become a weapon, the Internet can become a place for scams, and nuclear technology can produce energy or destroy cities. That does not mean we should stop all technological development because, taken to its logical extreme, humanity would never have left the Stone Age because even a stone can be used to kill someone. What remains for us is to learn how to manage our technologies and make them as safe as possible. It is also important to separate safety from surveillance: if we are talking about a company using AI to steal or exploit data, we return to the problem of who controls the tool; if we are talking about the possibility that the capabilities of the technology itself could create dangerous consequences, then we are dealing with a legitimate problem of the tool and of human nature that needs to be addressed as such.

There is also the argument that no previous technology has come close to the potential power of artificial intelligence and that perhaps this is precisely where we should establish a limit to human technological development. The problem is that, beyond the practical difficulty of imposing such a limit worldwide, this argument often depends on speculation about a future technology that does not yet exist. That does not mean we should ignore these scenarios. Quite the opposite: it means we should invest seriously in safety. But there is an enormous difference between saying “this risk is possible and we need to study it” and saying “this hypothetical risk justifies eliminating an entire technology.” In the same way, it would be ignorant to claim that the benefits of AI will inevitably outweigh its risks. We do not know. What we can say is that a technology with the potential to automate enormous sectors of human activity requires much more safety, caution and democratic control than it has today.

That is why, to me, the discussion about artificial intelligence should not be reduced to the question “is AI good or bad?” The more important question is: who controls AI, under which incentives is it being developed, and for whom will this technology be used?

A benchmark based on moral psychology tests adapted for AI. Interesting results: the models answers (except Jev) have considerable variance across runs. Morality, it seems, is not deterministic.

https://moralitybench.ai

I’ve always found the reaction to AI in filmmaking a little strange when you look at the history of computer graphics.

When Jurassic Park came out in 1993, the digital dinosaurs were practically treated as magic. It won the Oscar for Visual Effects and helped establish that computers could replace things that previously required miniatures, stop-motion and physical models. Terminator 2, The Abyss, Titanic, The Matrix and The Lord of the Rings kept pushing that boundary further.

Then came The Polar Express. The whole movie was essentially a giant experiment in using motion capture to create believable human characters. It wasn’t quite convincing yet — the eyes and faces had that slightly creepy quality — but nobody seemed shocked by the idea. Quite the opposite. It was presented as a glimpse of where the technology was headed.

And The Perfect Storm actually used computer-generated water and weather as part of the attraction. The technology itself was something to marvel at.

Meanwhile, entire categories of filmmaking labor changed or disappeared. Model makers, matte painters, miniature builders, optical effects people, etc. weren’t replaced because audiences suddenly hated them. The technology simply became capable of doing things differently, faster and sometimes better.

So now we finally have computers capable of generating remarkably convincing people, environments, voices and performances, and suddenly there’s a huge cultural resistance to the technology itself.

I’m not saying the concerns aren’t legitimate. Copyright, consent, employment and exploitation are real issues. But I’m curious where we decided to draw the line.

For decades, audiences seemed fascinated by the idea that computers would eventually be able to do all this.

Now that they’ve actually arrived, we’re saying, “Okay, but not like that.”

Maybe the technology didn’t suddenly change. Maybe our attitude toward it did.

Geoffrey Hinton has pointed out that today's large language models demonstrate "clear understanding" of what they're being asked. An image models response to a prompt about."stochastic parrots" provides more evidence that this is true.

https://ai-consciousness.org/the-sarcastic-parrot-vs-the-stochastic-parr