made @atypica_ai · subjective world model · hippyghosts · eth. rust.
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Joined March 2010
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web3nomad.eth | atypica.ai retweeted
Atypica is featured in USA TODAY!
Our dynamic AI personas are grounded in in-depth interviews with real people and are available whenever teams need to ask questions, follow up, or explore consumer preferences.
We help brands test ideas while products are still being designed, before committing to expensive development. Findings are checked against real human responses, giving teams evidence they can examine and challenge.
As our CPO Angela Xu puts it: “Every finding still has to be checkable and validated against real people.”
Read the full feature here → usatoday.com/story/special/c…
web3nomad.eth | atypica.ai retweeted
A puzzled look or a first reaction to a product can change the next interview question.
@atypica_AI, with builders @ling_fan_tezign and @web3nomad, introduced Atypica AI Moderator for live video interviews with real participants.
The company says it reads visual cues, rephrases questions, and follows unexpected details. Participants can interrupt and try products on camera, with follow-ups while the experience is fresh. Transcripts and a report follow.
In a company-reported study of 40 participants across five countries, none told beforehand it was AI, 60% thought the interviewer was human; 95% called it their most natural, least stressful interview.
Coming soon; waitlist open.
atypica.ai/ai-moderator
#BackchannelSignals
Introducing Atypica AI Moderator
The first AI Moderator to pass the Turing test.
Built for live, face-to-face consumer research.
In a test with 40 participants from 5 different countries who weren’t told the interviewer was AI, 60% believed they were speaking with a human.
95% said it was the most relaxed and enjoyable interview they had ever taken part in.
It listens. It follows up. It rephrases when a question isn’t clear.
Up to an hour. Live. On camera.
When participants try a product, it explores how it feels in the moment.
Face-to-face INTERVIEW. At scale.
Coming soon. Join the waitlist now!
→ atypica.ai/ai-moderator
Human moderators don’t scale.
This does.
Introducing Atypica AI Moderator
The first AI Moderator to pass the Turing test.
Built for live, face-to-face consumer research.
In a test with 40 participants from 5 different countries who weren’t told the interviewer was AI, 60% believed they were speaking with a human.
95% said it was the most relaxed and enjoyable interview they had ever taken part in.
It listens. It follows up. It rephrases when a question isn’t clear.
Up to an hour. Live. On camera.
When participants try a product, it explores how it feels in the moment.
Face-to-face INTERVIEW. At scale.
Coming soon. Join the waitlist now!
→ atypica.ai/ai-moderator
web3nomad.eth | atypica.ai retweeted
Introducing Atypica AI Moderator
The first AI Moderator to pass the Turing test.
Built for live, face-to-face consumer research.
In a test with 40 participants from 5 different countries who weren’t told the interviewer was AI, 60% believed they were speaking with a human.
95% said it was the most relaxed and enjoyable interview they had ever taken part in.
It listens. It follows up. It rephrases when a question isn’t clear.
Up to an hour. Live. On camera.
When participants try a product, it explores how it feels in the moment.
Face-to-face INTERVIEW. At scale.
Coming soon. Join the waitlist now!
→ atypica.ai/ai-moderator
Lyrics: Claude → Song: ElevenLabs → Shots: GPT Image + MiniMax H3 → Edit: Claude writing Remotion, beat-synced to the song.
I gave Claude one song idea and a green paper ghost mask.
It wrote the lyrics, generated the song, cast the crowd, directed 15 shots, and cut every frame to the beat.
No editor. No timeline. Just code.
This is BLANK CARD. 🟩
Apparently AI is SI now.
So, naturally: atypica.si
Rebranding pending. @atypica_AI 😁
web3nomad.eth | atypica.ai retweeted
This is NOT three.js.
This is Reality.js.
The original graphics engine.
I’m just trying to observe closely enough to imitate a little of what it does.
web3nomad.eth | atypica.ai retweeted
We’ve acquired @guardrails_AI, an AI security platform for agents.
Together, we’ll strengthen how Harvey tests, simulates, and evaluates agent behavior across our platform.
Welcome to Harvey, Guardrails.
web3nomad.eth | atypica.ai retweeted
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
web3nomad.eth | atypica.ai retweeted
GPT-Live-1 is now available in the API.
Bring ChatGPT’s natural back-and-forth to your app, with voice agents that listen while they speak and work with the models and harness you choose.
Identity Economics in ABG CMOs: Personal Brand as Credential, Cage, and Currency in Modern Market... @atypica_AI atypica.ai/r/JtAUsp2z76PeHgm…
AI is leaving the screen.
We built for what comes after.
Coming soon — atypica.ai
I just reserved my Cloudflare Wallet tag: web3nomad.cloudflare.pay.
Reserve yours now at cloudflare.pay
I’ve been thinking about Agent Evals for AI user research systems.
The hardest part is not choosing a stronger model to grade your agent.
It’s realizing that, at the beginning, you may not even know what to evaluate.
At atypica, we found failures that were never on our original checklist:
- Eight AI participants with different jobs and life stages gave almost identical answers.
- A report included a polished “participant quote” that nobody had actually said.
Those are not generic quality issues. They point to specific failures in a research workflow: persona construction, interviewing, evidence synthesis, or report generation.
Our approach became:
- Let domain experts review real runs and describe what feels wrong.
- Group recurring feedback into concrete failure modes.
- Use LLMs to monitor those known failures at scale.
- Validate the Judge against expert-labelled examples.
- Use fixed inputs to compare agent versions over time.
- Keep collecting expert feedback, because new failure modes will still emerge.
One distinction that helped us:
> People discover and define problems. LLMs repeatedly check the problems we already understand.
And another:
> A golden set validates the Judge. Fixed inputs compare the Agent.
The goal is not to create a perfect, permanent checklist on day one.
Start with a real run. Find the sentence, decision, or step that makes an expert hesitate. Ask what exactly went wrong—and how you could catch it earlier next time.
atypica.ai/research/ai-user-…
web3nomad.eth | atypica.ai retweeted
⢎・singapore week ﴾0x﴿ ♪｀･ﾟ✧
⍻・from crypto native to AI native｀-҉･
web3nomad.eth | atypica.ai retweeted
Surge 支持 Tailscale 了
kb.nssurge.com/surge-knowled…