Stripe

Building Intelligent Research Agents with Manus - Ivan Leo, Manus AI (now Meta Superintelligence)

Dec 30, 2025 · 1:21:30

Ivan Leo of Manus AI introduces the Manus API and Manus 1.5, positioning the platform as a general action engine for building intelligent research agents. He demonstrates how the API enables asynchronous task dispatching, file uploads with automatic 48-hour deletion, and webhooks for scalable workflows. The workshop covers creating a Slack bot that integrates Manus's browser operator, file attachments, and connectors to private data sources like Notion. Leo shows real-time demos including a custom French learning app, a conference event scraper with calendar integration, and an invoice processing bot that references company policies. He also discusses the roadmap for memory persistence, document exporting (PPTX/PDF), and enhanced permission systems for browser automation.

"Data readiness" is a Myth: Reliable AI with an Agentic Semantic Layer — Anushrut Gupta, PromptQL

Jun 27, 2025 · 17:02

Anushrut Gupta of PromptQL argues that 'data readiness' is a myth — perfect, clean data is unattainable — and instead advocates for an agentic semantic layer that learns from user corrections. He contrasts traditional approaches like manual semantic layers and knowledge graphs, which break as business definitions change, with PromptQL's design: a deterministic domain-specific language (PromptQL) that lets an LLM generate a plan executed by a runtime, avoiding hallucination. The system behaves like a new hire analyst: day zero it can handle messy tables (e.g., 'Morc, Plug, Zorp'), and through human guidance it self-improves — learning 47 business terms, mapping six systems, and discovering 12 calculation variants within 30 days. Gupta demonstrates a multi-step query across databases, Zendesk, and Stripe, with explainable steps and editable 'brain'; the AI achieves 100% accuracy on complex tasks for customers like a Fortune 500 food chain and a fintech company.

(possible dupe but better sound) What does Enterprise Ready MCP mean? — Tobin South, WorkOS

Jun 27, 2025 · 13:53

Tobin South from WorkOS explains what it takes to make Model Context Protocol (MCP) enterprise-ready, covering the full journey from a hacky local server to a production system with authentication, authorization, SSO, audit logs, and data loss prevention. He demonstrates buying a shirt via MCP live, highlighting the need for bot blocking, input validation, and adapting auth stacks for dynamic client registration. South also discusses open challenges like headless auth for remote asynchronous workloads and communicating scope between AI agents, emphasizing that authorization is the hardest part of scaling MCP to the enterprise.

Windsurf everywhere, doing everything, all at once - Kevin Hou, Windsurf

Jun 23, 2025 · 16:03

Windsurf head of product Kevin Hou explains the company's philosophy of a shared human-AI timeline and its vision to be everywhere, do everything, and work all the time. The AI ingests context from tools like Google Docs, Figma, GitHub, and Notion, and takes actions beyond coding—opening PRs, deploying, and reviewing code. Windsurf generates 90 million lines of code daily and processes over 1,000 messages per minute. Its new model SWE-1, trained on software engineering workflows, achieves near-frontier performance with a fraction of the resources. Hou emphasizes a data flywheel where user feedback on real workflows drives continuous improvement, arguing that successful AI products in 2025 require harmony among model, data, and application.

Which Jobs Can Be Replaced Today: Fryderyk Wiatrowski and Peter Albert

Feb 6, 2025 · 19:59

Fryderyk Wiatrowski and Peter Albert, co-founders of Zeta Labs, argue that autonomous browser agents will first replace reactive jobs—like customer support and scheduling—by automating low-leverage tasks while preserving human focus on high-leverage activities. They propose a trigger-pool system where agents react to emails, Slack, or events, requiring only approval for actions. Peter details building reliable agents: start with prompting optimized for model distribution, then add cognitive architectures (e.g., planning, scratchpads) to split tasks, and finally fine-tune with synthetic data or reinforcement learning. He advises minimizing noise in prompts, preferring text-based reasoning over images, and using language model judges to filter training data. The founders see continuous model improvement enabling agents to handle increasingly complex, proactive roles, moving toward full job replacement.