Story 01stripeeconomics.com
- TLDR
- Quick Links
- ai_models
An empirical agenda for AI
How AI will transform society will depend on how businesses reorganize, markets adjust, and workers respond.
Release 01
6 source stories
Story 02nathanbenaich.substack.com- TLDR
- Miscellaneous
- ai_models
The State of AI Report 2026
Agents are now doing valuable work in software and science. Physical AI is learning how to act in the world, and access and control are becoming more essential. The frontier is now a three-lab race between Anthropic, OpenAI, and Google. Anthropic leads on Artificial Analysis' Intelligence Index, while Google leads on Arena's ranking of the answers people prefer.
Story 03blog.ravi-mehta.com- TLDR Product
- Resources & Tools
- business
Product definition
Product thinking and "Product Definition" are essential in the AI era, where agents execute code rapidly given clear direction. Formerly known as feature specification, Product Definition shapes requirements and context into actionable artifacts like prototypes and PRDs. This competency evolves from tactical execution to building systems that align teams and AI agents.
Story 04stack72.dev- TLDR Product
- Opinions & Tutorials
- ai_models
Why Agent-Written Code Needs Product Verification
When AI agents write both code and tests, they encode identical misunderstandings, producing green builds that fail real user needs. Teams must establish independent verification based on user requirements. Lower maintenance costs and faster repair cycles make comprehensive end-to-end product verification feasible.
Story 05engineering.atspotify.com- TLDR Product
- Miscellaneous
- tools_self
Introducing: Spotify Technology. Proven at Spotify, now yours
Spotify Technology is a unified suite of developer and media tools including Portal, Confidence, Xirp, and Spotify for Backstage. Tested across Spotify's internal engineering teams to cover the entire development lifecycle, the offering aims to help external R&D organizations balance team autonomy with architectural alignment.
Story 06towardsdatascience.com- TLDR Marketing
- Miscellaneous
- design_growth
How Wrong Is Your Marketing Mix Model (MMM)?
MMM becomes more reliable when media budgets are deliberately phased, so channels move independently and spend reaches different levels. Build variation into the media plan by using tactics like weekly budget changes, dark periods, and peak spend periods instead of relying on model changes to solve weak data. In the simulation, the combined approach cut variance by 70% and bias by 47% while keeping each channel's annual budget unchanged. The tradeoff was a 3.42% loss in channel-driven revenue, so use lighter phasing when that cost is too high.