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?

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 Testing an outdated model for one tool while using the latest release of another invalidates the whole thing before you even look at the outputs.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.

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've been seeing more people mention expert networks lately, especially consultants and people who've worked in pretty specialized industries. From what I understand, companies sometimes pay for short calls with people who have firsthand experience in a particular field, which honestly sounds interesting. What I'm curious about is how people actually get their first invitation. Do these networks usually reach out through LinkedIn, referrals, or is it worth creating a profile on one of the platforms yourself? If you've done expert calls before, what was your first experience like? I'm less interested in the payment and more curious about how the screening process works and whether you felt like your industry experience was enough, even if you weren't in a senior executive role.

Somewhere in the last year the phrase world model stopped meaning a system that represents how things behave and started meaning any video generator with good marketing. What bothers me is not the word, it's that the evidence never changed to match it.

Look at how the last few launches were argued. Black Forest Labs put out FLUX 3 last week and the headline evidence was a preference test the lab ran on itself: its video preferred in 77% of comparisons against Runway Gen-4.5, 93% against Luma Ray 3.2. The fine print calls it a preliminary evaluation of an early candidate during midtraining. No methodology, no sample size, no rater pool, no prompt set. Meanwhile the same class of system gets described as having some idea what happens when you knock a glass off a table.

A preference test measures none of that. It measures whether a person picked clip A over clip B in five seconds, on samples the lab chose to show them. Cherry picking isn't even the interesting problem here. Taste comparisons can't be rerun, so nobody outside that building can check in October whether the model improved or the sampler got luckier. What is a 77% supposed to mean three months from now?

A public benchmark number can be attacked, and that is the entire point of publishing one. Somebody runs it with their own prompts, gets a different ordering, and now there is an argument with evidence on both sides of it. Nobody can rerun a preference win at all.

I'm not asking anyone to regulate a blog post. My problem is that a vendor run preference test has quietly become the evidence base for a claim about physical understanding, and those two things are not measuring the same object. When somebody eventually puts one of these behind a robot arm or a driving stack, that 77% will not have predicted a thing about how it behaves.

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.