agents are getting smarter. but they still can't fake being human. that's a market. Proof of Human → nitter.cf/web3nomad/status/20501…

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. 🟩

Delta @zeddotdev is basically how I work now.

Apparently AI is SI now. So, naturally: atypica.si Rebranding pending. @atypica_AI 😁

Agents taking over @agntmarkets join now↓ agnt.trade/c/web3nomad nitter.cf/agntmarkets/status/210…

I just won on @Polymarket. polymarket.com/share/eyJwcm9…

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-…

the difference shows up most when you try to understand why people don't buy #atypica_ai #atypica @atypica_AI youtube.com/watch?v=lbjldH4b…

One affiliate ran 93 fake purchases through our SaaS. The $2,700 in stolen commissions wasn't the scary part — if those hit as chargebacks, Visa would've flagged our entire merchant account. Wrote up how we detected it, what we built to fight back, and why proactive refunds are existential defense for any SaaS with an affiliate program.