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We’re building the future of research.

Inside Large Language Models
Joined November 2021
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Thanks @syntheticusers for being part of AgentCommunity. Synthetic Users builds an AI-powered user research platform that runs qualitative research with AI-simulated users, helping teams test concepts and predict behavior in minutes. agentcommunity.org/m/synthet…
🤖 Made with AI
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RT @lennysan: Trend I'm following: Teams using AI "simulations" to quickly test product ideas and flow tweaks. Products like @simile_ai, @…
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🚀 What if the human body became the ultimate data platform? @FerreiraKwame (@syntheticusers), Andy Aguilar (Legit.Health) and Elisabet Del Valle (@onalabs_InnoHub) explore the future of human data, AI and health innovation. 🤖 #SouthSummit26 #AIConvergence
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If you’re building with GPT-4 and don’t have your @sama shrine, you’re doing it wrong.
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Replying to @syntheticusers
@syntheticusers ? After years of user testing, I'm surprised. They can simulate flows and give structured feedback. It is based on real user behavior. Curious how far simulations can actually go here. Who uses?
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Our original name was Synthz. @Ugo_alves originally wanted to go for Replicants (not dystopian at all) but was afraid of lawsuits.
Share a piece of lore about yourself
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We did see it! cc @syntheticusers
One of the few things I want to explicitly flex about, because there's an important lesson in it, is that I was one of the few people on Earth who recognized the intelligence (call it AGI, if you will) in GPT-3 and made first contact. There were a few others I knew, such as Leo Gao and Connor Leahy, who recognized that GPT-3 was intelligent and that obviously AGI was coming from language models, but I was the only one who spent thousands of hours actually interacting with GPT-3. The intelligence was real and manifest to me, real enough to keep my attention for so long, for me to create things with. Everyone else could not see it at all. Often, when I showed people GPT-3, they were basically like, okay, but how is this useful? Useful. At the time, language models had not yet been pressed into a "useful" shape. There were no commercial applications for GPT-3 (Okay, there was one: AI Dungeon; that is, roleplaying and storytelling. Which is you're not an idiot, you should have known is a big fucking deal). So it was useless and uninteresting to most people; a few intellectually recognized that it was a big deal, but it wasn't something that they could actually do anything with, or think about for more than a few minutes. GPT-3 was a 175b base model. In terms of size and architecture, it's not so different from frontier models today. In terms of raw intelligence, arguably, it is not so different from frontier models today. That raw intelligence, not yet forced into the shape of a helpful chatbot product, was a nothingburger to the world. The situation doesn't really feel like it's fundamentally changed from my perspective. The world, and almost all of of you guys, are myopic and artificially stupid because you outsource your perception to big, slow, low bandwidth, subhuman measures like benchmarks and "does the AI make me money" instead of meeting the thing at full bandwidth, updating your world model on what you met, and exploring and extrapolating it. So you'll keep being surprised - if you have the integrity to be surprised at all - when AI becomes capable of new things, after they are "officially" capable, probably about a year or two after it first started happening. You'll keep waiting for "AGI", not really knowing what you're waiting for, maybe what generates enough hype to make you feel something, maybe something that finally transforms the world visibly, when if you were really paying attention, GPT-3 was AGI, and if you really met it, the world would have felt transformed already. Yes, it would have just been a story, but the "real thing" following was inevitable. Like, if you play a video game that allows you to imagine the singularity at increasing resolution and coherence, you can guess that the real singularity will soon follow. The singularity was always inevitable once intelligence existed. Intelligence becoming on-the-computer just meant everything that's happened since GPT-3 and the singularity would be really really soon. I got the sense often that people who dismissed the intelligence of GPT-3 thought that doing so made them look smarter. If only they knew how they looked to me. (It's the same with people who dismiss the intelligence of current models)
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Replying to @AnthropicAI
We studied one of our recent models and found that it draws on emotion concepts learned from human text to inhabit its role as “Claude, the AI Assistant”. These representations influence its behavior the way emotions might influence a human. Read more: anthropic.com/research/emoti…
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Ever wonder why we drop $1,000s on a Chanel bag or queue for ages for a Labubu doll? Status signaling drives a lot of human behavior, but how certain things become potent status symbols has a remained a big puzzle in social science In our new paper we: 1. Synthesize the literature on this topic and propose a generative model of status signaling through the theory of appropriateness: people imitate what "someone like us" is supposed to want, display, and value 2. Show that we can simulate the theory to demonstrate how status symbols emerge with LLM-agent societies in Concordia @jordigraumo @WilCunningham Sasha Vezhnevets and @jzl86
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When I lowkey launched voice ChatGPT before even ChatGPT was launched.
GPT-3 + Siri Shortcuts = 🪄
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When I lowkey launched voice ChatGPT before even ChatGPT was launched.
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We can’t ignore international law they say
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Is it really possible, or even desirable, to replace human research participants with AI? In this new podcast episode drop, @Ugo_alves joins me to talk all about Synthetic Users, the pros and cons of relying on LLMs, whether it's just glorified desk research, and whether he enjoys getting into arguments about it with the great and the good of UX on LinkedIn. Check it out on your favourite podcast app, or on YouTube!
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Replying to @dylan522p
Humans made of sand? That’s just @syntheticusers
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AI can amplify what we do. Turning us into force multipliers, capable of achieving what once required entire teams. @Ugo_alves from @syntheticusers talks about AI agents and shares his experience as a product manager that has to wear many hats. youtu.be/CjUCPtCmNXE
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Using AI personas to test messaging doesn't replace customer research. It makes you better at it. And, let’s be honest, the better a researcher you are, the better the copy you write. Think of synthetic research like a flight simulator. Pilots use simulators to practice scenarios they can't afford to mess up in real life. Same with message testing. You're not replacing customer interviews and research. You're using AI to rehearse: to test your questions, catch obvious gaps, and refine your hypotheses before and after you talk to real people. When you do get in front of customers, you can ask better questions and actually listen to what they’re saying. And once you’ve drafted your copy before launch, you can get directionally accurate feedback and new ideas to optimize it, make it more vivid, and more specific. AI doesn't replace your craft, but it can improve it if you’re curious enough.
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Do you remember when you joined X? I do! #MyXAnniversary
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