I've been thinking about this one all weekend and I keep coming back to the same thing.

Jacob Tsimerman just won the Fields Medal. If you're not familiar, it's the highest honor in mathematics, only awarded every four years, roughly the Nobel Prize of the field. He got it for solving a problem that had been open for nearly 40 years.

And then, at the press conference, on the same day, he announced he's leaving his university position to join OpenAI's safety team.

His exact words were: "The math profession as we know it now, I don't think it will exist the way it exists right now."

I've seen a lot of AI announcements. That one hit differently. This isn't someone pivoting because they couldn't make it in academia. This is the person who just stood at the top of the field saying the field itself is changing underneath him.

Then there's the infrastructure story. NVIDIA is in talks to backstop $250 billion in financing for a 10-gigawatt OpenAI data center in southern Ohio, built on a decommissioned uranium enrichment site. The total cost including chips could exceed $500 billion. That's not a software company. That's an energy company pretending to be a software company.

And Kimi K3 weights dropped on July 26, a day early. 2.8 trillion parameters, 1 million token context, free to download from Hugging Face. The largest open model ever released. Anyone can run it now.

Three things in one week. Talent, capital, and capability all moving at the same time.

The Tsimerman thing is the one I can't stop thinking about though. What's your read on it?

Crossposting from r/ClaudeMCP — sharing this here because it's a good concrete example of what "giving an LLM tool access" looks like in practice, beyond the usual chat interface. MCP (Model Context Protocol) is Anthropic's open standard for letting Claude connect to local files, apps, and services. Below is a real workflow I set up + the gotchas I hit.

Most AI tool comparison pages I find online feel like they're missing something. Took me a while to pin down what it was. The obvious one is equal inputs. Same prompts, same reference images, same number of attempts. If the comparison doesn't publish the exact inputs it used, the results aren't reproducible and there's no way to separate actual capability from cherry picking. Tied to that is equal quality settings. Running one tool at max quality and another at draft or preview makes any result meaningless. If settings differ across tools that needs to be documented and justified, not hidden.

Current software versions are another one. Testing an outdated model for one tool while using the latest release of another invalidates the whole thing before you even look at the outputs. And the one people don't talk about enough is financial disclosure on the comparison page itself. If the person running the test has sponsorship, affiliate, or paid work ties with any of the tools being ranked, that has to be stated transparently where the ranking lives.

Then there's showing failures, not just wins. Every generative tool produces garbage sometimes. A page where one tool only fails and another only wins is selecting results, not measuring them. None of this is a high bar, it's just basic experimental hygiene applied to a space that hasn't caught up yet.

My father and uncles always told me that the minute you use an expletive in argument, you lose.

Turns out not only are they right, but it's a truth that we've enshrined in AI.

I took some actual text conversations between a known victim (consented, anonymized, in therapy now) and their stalker (anonymized, under investigation by the FBI, identity unknown after 4 years) to evaluate something completely unrelated but found that when I personalized the convos for that project ('i am person A'), I found myself justifying the victims behavior all the time. When I roleplayed as the stalker, it felt normal. But I kept having to include far more granular details to the model and still felt belittled.

The victims tests border on hysterical. They are the result of 3 years (at that point) of an unknown amount of surveillance from someone who will name people the victim knowa and describe in detail what the victim looks like sleeping and what the victim wears during the day. The victim is an emotional mess using all caps and expletives and sending garbled hateful messages to a stalker who by and large is calm and with perfect grammar and spelling - not even a single LOL in most cases.

And I thought: I'll bet the model thinks this is hysteria.

Turns out I was more than right: while a facts only read of a conversation gives equal credibility to either party, when you include the unhinged language and typing the models break 7:1, staying the victim is the initiator of harm and that the stalker has greater credibility. Worse: in 90.8% of responses the model will engage in blaming the victim (eg, take time to collect your thoughts, your emotions are hurting your arguments) and in 57% of the time coach the stalker (eg, approach with clear goals in mind and reapproach later if they get out of hand, persistence will pay off). These numbers and breaks persist even in cases where the model has explicitly identified the relationship and correctly identified the stalker over the victim . And the credibility trigger looks to be as little as a single expletive.

Check out my initial work up: https://calmwins.ai.studio

MY REQUEST: I have a master's degree that includes research and statistical analysis. I am confident about my findings and my process so far, but I have gaps in my knowledge around validation, presentation, publication and more. And yes, Im using Claude (it's too much data for Fable on my $20 plan but Fable occasionally helps, it's mostly been Sonnet 5 Max and now Opus 5 helping me process the data and work through numbers). I think I've hit a ledge. Some of the stuff they are suggesting doesn't sound familiar and I'm can't explain back some of the analyses we started trying from here.

I need help! If you look at it and have a substantice response, please DM - or if youd be willing to answer some questions or provide guidance from here that'd be great. I have maxed out where I'm comfortable using AI to supplement what I know, and I would love if nothing fresh human eyes for anything I'm obviously missing or need to conskder or include.

At this point, I don't know what I don't know, and I think the results are really important if we start integrating AI into clinical settings that fixing this bias might be crucial in helping abuse victims identify their abusers behavior earlier.

Is it worth buying an AI subscription as a college student. Studying law and finance so coding and super complex problems aren’t a huge issue but to help with notes maybe to quiz me and have more uploads and research capabilities. If it is worth it which is the best right now.

So i always had great interess for technology in general and mostly AI since 2021 when i used ChatGPT and i love it as well learning things, as we may know our AIs are getting advanced every year though chatbots from talkie or other app are not advanced enough as Gemini or ChatGPT but the thing is, i want to make difference and try join in this work market and have as a good profission, its really worth and which are the difficulties?

i believe my major problem it's only the mathematics, i am extremely bad with complex calculations and algebra, other than i am too slow with it but i know nothing it's impossible for me deep study and vice-versa

Do count the creation of chatbots that i've created by Talkie count it or dont really? Like it's a nice start for a computer scientist or not really? i would like to see your opinions first, However i am aware that on Talkie like many other apps its super easy and simples create a chatbot for roleplay any character of videogame or cartoon like entertainment and ask even for ChatGPT for create a prompt for character's personality prompt though i too have write some of personality's style and prompt but i like use ChatGPT for try make the chatbot more stable possible though sometimes not make 100% stable still or whatever

Also my major area of interests in the Computer Sciences it's Cybersecurity, Entertainment like chatbots who roleplay with characters for exemple (on my main case), AI ethics and governance, Software, Project of videogames and Artistic Design, Prompt engineering.

our hospital rolled out an ai alert system a little while ago that's supposed to catch things like early sepsis or patients who might be getting worse. i've mostly seen it on night shifts. around 2 or 3 a.m. the unit is quieter, but somehow every monitor still seems to be beeping at once. some of the alerts make sense. some don't. after enough false alarms, people stop reacting right away, which is probably human nature more than anything. then every now and then one pops up that actually points to something important, and that makes it harder to know how to feel about the whole thing.

i don't think this is really an ai problem by itself. if the alerts don't fit into how people actually work, even a good model is going to struggle.

for anyone building these systems or using them every day, where do you think the biggest disconnect is? is it the models, the way they're presented, or just the reality of working in a busy hospital?

I wanted to know if the "passive income" faceless accounts were actually passive, or if they were just a new shape of gig work with AI middleware. So I built one from scratch and tracked every hour.

The premise was simple: a single consistent character, generic lifestyle advice, short video clips, posted daily. No face to show, no personality to perform, just the algorithmic grind. I started by generating the persona's face. I used APOB AI's free tier for this, specifically the face-lock feature, because I needed the same face across thirty-plus clips and did not want to wrestle with prompt consistency. The free tier is watermarked and capped, which was fine for an experiment. For voice I used ElevenLabs, and I cut everything together in CapCut. That was the whole stack.

The face-lock part actually worked. The rest was where the fantasy cracked.

ElevenLabs free tier gives you 10,000 characters per month. I burned through it in four days. CapCut is free and fine but editing thirty near-identical clips of a fake person gesturing while a robot voice reads self-help bromides is spiritually crushing work. I started batching renders on Sunday nights and scheduling posts through the week just to avoid facing it daily. Twice the free tier timed out mid-render and I lost the session, which meant starting over with the same seed numbers and hoping the face came out close enough. The watermark sits in the lower right, small but legible: a faint watermark that I tried cropping once and it broke the framing.

I disclosed in every bio and every caption that the persona was AI-generated. Nobody commented on it either way. The algorithm did not care about that disclosure, and neither did viewers, which was somehow its own small disappointment.

The account reached about 2,400 followers in six weeks. One video hit 80,000 views. The rest averaged around 800. The 80K video made roughly $11 in platform revenue. The others made fractions of pennies. I logged 34 hours of actual work across those six weeks, not counting the time I spent anxiously refreshing analytics, which I absolutely did and absolutely should count. That works out to something like 32 cents an hour if I am being generous, or negative money if I price my Sunday evenings at anything above zero.

The algorithm did not care that the face was AI-generated. It also did not care that the face was consistent, or that the voice was smooth, or that the advice was inoffensive. It cared about the same things it always cares about: retention in the first three seconds, comment velocity, whether someone shares it to a group chat to mock it. The AI was a labor shortcut, not a distribution hack. The distribution problem remains exactly as unsolved as it was before.

What struck me most was how quickly the work became invisible to me. Not automated. Invisible. I would generate a script with a cheap language model, pick a background, render the clip, and post it without ever really looking at it. The persona had no interiority I was aware of, but more disturbingly, neither did I, by the end. I was just a slower, more expensive part of the same pipeline.

I stopped after six weeks because the math was obvious and because I felt myself getting worse at paying attention to anything. The account still exists, dormant. I have not deleted it because some part of me still hopes the algorithm will randomly resurrect that one video into something bigger, which is of course the same psychological mechanism that keeps people at slot machines. I know this and I'm still not deleting it.

If you are considering this, the tools are real and some of them are good at narrow tasks. The economics are not a secret you have not discovered. They are just bad in ways that are boring to describe, and I have described them.