made in hungary🇭🇺 phd student @oducs @webscidl💻 @odufootball @ccsffootball alum

Joined September 2016
Dominik Soós retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Presented our work at the 32nd IEEE International Conference on High-Performance Computing and Data Analytics (HiPC 2025) in Hyderabad, India🇮🇳 Trip report: ws-dl.blogspot.com/2025/12/2… @oducs @WebSciDL
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Dominik Soós retweeted
𝗦𝗧𝗔𝗨𝗡𝗖𝗛 𝗗𝗘𝗙𝗘𝗡𝗦𝗘. @KrisTrinidad43 led an @ODUFootball defense that conceded just 138 yards & recorded 9 sacks in a shutout win en route to the @SunBeltFB Defensive Player of the Week nod. ☀️🏈 📰 » sunbelt.me/4nWpSuz
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Dominik Soós retweeted
This is exciting; I expect we are going to see a lot more things like this and it will be one of the most important impacts of AI. Congrats to the Future House team. edisonscientific.com/article…
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𝗣𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗱𝗲𝗮𝗱. I know that sounds dramatic, but hear me out. Every developer building with LLMs eventually hits the same wall. The model is smart, sure. But it can't access your docs. It forgets yesterday's conversation. And it makes stuff up when it's not sure about something. You can't prompt your way out of these problems. The real skill now? 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - building the system 𝘢𝘳𝘰𝘶𝘯𝘥 the model that feeds it the right information at the right time. Think of it like this: the context window is basically the model's working memory. It's a whiteboard. Once it's full, old stuff gets erased to make room for new stuff. So you need architecture that manages what goes on that whiteboard and when. We just dropped a full guide on context engineering (free, obviously). It covers everything from chunking strategies to multi-agent systems to the new Model Context Protocol. And includes all these core components for building AI apps: • Agents - the brain that decides what to do when • Query Augmentation - turning messy requests into something useful • Retrieval - connecting the model to your actual data • Memory - so it doesn't forget everything between sessions • Tools - letting it interact with real systems • Prompting Techniques - yeah, this still matters, just not in isolation If you're building anything serious with AI, this is the shift you need to understand. Get your free copy here: weaviate.io/ebooks/the-conte…
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.@DomSoos from @WebSciDL and @oducs is presenting "Can LLMs Beat Humans on Discerning Human-written and LLM-generated Science News?" They explored whether LLMs can outperform humans for LLM-generated vs. human written news. 🔗doi: 10.1145/3720553.3746674 #LLM #NLP @fanchyna
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Excited to share that our paper with @Fermilab has been accepted to HiPC 2025🎉 We built an efficient GPU-based multidimensional global optimizer that combines PSO, BFGS, and Automatic Differentiation. The animation highlights the global search. @oducs @WebSciDL
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Imagine if ChatGPT highlighted every word it wasn't sure about. We built a streaming hallucination detector that flags hallucinations in real-time.
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Paper accepted to ACM Hypertext '25! 🎉 We show we can improve open-source LLMs to human level accuracy at distinguishing between human-written and LLM-generated science news. Thankful for my advisors for their support. @fanchyna @Meng_CS @WebSciDL @oducs
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Dominik Soós retweeted
LLMs Get Lost in Multi-turn Conversation The cat is out of the bag. Pay attention, devs. This is one of the most common issues when building with LLMs today. Glad there is now paper to share insights. Here are my notes:
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I Built Git to Avoid People!
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Dominik Soós retweeted
I almost got scammed yesterday! I'll post it all here to showcase the anatomy of an online scam in the time of LLMs. 1. I received this invitation to speak at a tech summit at a well known University in China.
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Dominik Soós retweeted
Pat Conroy is a FB prospect in the 2025 draft class. He scored a 9.98 RAS out of a possible 10.00. This ranked 2 out of 540 FB from 1987 to 2025. ras.football/ras-information…
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We knew very little about how LLMs actually work...until now. @AnthropicAI just dropped the most insane research paper, detailing some of the ways AI "thinks." And it's completely different than we thought. Here are their wild findings: 🧵
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Congratulations Dr. Wu! We’re proud of you!!🎉
Celebrating Dr. Wu’s well-deserved promotion to Associate Professor! 🎉 A wonderful gathering filled with joy, gratitude, and inspiration. Huge congratulations, Dr. Wu! 🥂@fanchyna @WebSciDL @rochanaro @DomSoos @Xin9Xin
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