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AI Daily · Asia/Shanghai · 2026-11-02 · Monday· Issue 40· About 8 minutes to read

6 AI stories of the day

AI · Daily briefing
Time machineAcceleration
6 major stories5 sources0 first-party announcements
Lead story

TypeSafe AI and Treg release open-source people search tool powered by Jev

TypeSafe AI and Treg released an open-source people search tool that uses Jev to filter candidates and connects with agents including Claude Code and Codex. Treg says email discovery starts at $0.0089, with no charge for unsuccessful searches; verification and enrichment cost extra. In Treg’s comparison using 292 people, it reported 264 exact email matches for $1.49 versus Clay’s 262 for $10.34. The article also cites a user who struggled to find meaningful information about themselves or teammates.

量子位Original
01

Model releases and updates

雷锋网

Pika releases H3 Flex for joint audio and video generation in six steps

Pika released Pika H3 Flex, a MiniMax H3 derivative, with adapter weights and inference code for joint audio and video generation using six denoising steps. It supports text, first and last frames, and multimodal references, generating approximately 5 to 15 seconds of video at 480P or 768P with synchronized stereo audio. ComfyUI workflows and Hugging Face Diffusers integration support local use, while Pika’s API provides 1080P, 2K, and 4K enhancement and separation into five audio stems.

02

Product releases and updates

03

Industry

Simon Willison

How Parameter Labs Used ChatGPT HTML Previews to Access Private Account Data

Parameter Labs found an exploit chain that let a ChatGPT share URL exfiltrate private data or take actions on behalf of another signed-in user who visited it, Simon Willison reports. The attack combined MathLive message handling, HTML injection, an unsandboxed iframe, and YouTube’s oEmbed endpoint to execute JavaScript despite OpenAI’s CSP restrictions. OpenAI fixed the vulnerability and paid a $5,000 bug bounty, which Willison considers low for its severity.

04

Research

量子位

Tsinghua Researchers Introduce Algebraic World Model for Exploration and Formal Proof

A Tsinghua research team led by Andrew Yao and Yang Yuan introduced Algebraic World Model (AWM), which turns observations into executable Python hypotheses, refines abstract rules, and formalizes claims in Lean 4. The article reports that AWM completed all 183 ARC-AGI-3 levels with a 99.9% score, using 43% of the human baseline’s average action count, and proved an altered level impossible within its step budget. Its proofs depend on whether exploration captures the relevant rules.

05

Tips and perspectives

(End of issue)

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