🧙🏼 Anthropic makes it easier to link AI to data

Howdy, wizards.

Sam Altman’s favourite note taking tool is…

…pen & paper. In a recent interview, OpenAI's chief revealed his approach to note-taking: spiral notebooks (for writing, crumpling, and tossing pages with abandon) and cheap ball point pens.

The Verge’s reporter did a commentary on this where she fully endorses physically writing stuff down, but skewers Sam’s lack of sophistication and messy methods:

“This is a man who has not carefully considered his tools and expects someone else to pick up after him. That does explain a lot about OpenAI, doesn’t it?”

Key take-aways: Write stuff down without using ChatGPT—it helps clear thinking. Clean up the mess after yourself.

And now, what’s brewing in AI today.

DARIO’S PICKS

1. Anthropic’s standard for connecting AI to different data sources

Anthropic just launched the Model Context Protocol (MCP) – an open-source standard for connecting AI assistants to data sources, including content repositories, business tools, and development environments.

Even the best of models are constrained by their isolation from you or your company’s data. If you want it to access a data source directly it requires some kind of custom implementation, which makes it hard to build fully connected systems.

The new standard gives an easy way for developers to allow AIs to connect to their application’s data through MCP servers or build AI that connect to these applications. It’s also built with security in mind and no need to share API keys with LLM providers. Right now MCP servers have to run on a local machine, but there’ll be support for remote servers soon.

Reportedly, Claude 3.5 Sonnet is already very good at setting up MCP server implementations, and Anthropic has already shared pre-built servers for systems like Google Drive, Slack and GitHub.

‎ Why it matters‎ ‎ A challenge making many AI use cases cumbersome to implement is that they require custom integration with several data sources. Anthropic’s cleaner solution of replacing fragmented implementations of data sources with a single protocol/ecosystem is very alluring and might be a step towards truly context-aware AI.

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DARIO’S PICKS

2. Zoom’s rebrand as an AI-first company

Zoom had an insane growth trajectory during the pandemic, but has since seen strong decrease in growth driven by people returning to the office (lessening the needs for the product) as well as intense competition from collaboration platforms that companies already pay for offering similar features including Google Meet, Microsoft Teams and Slack.

Zoom’s new strategy is, in a nutshell, rebranding themselves into an “AI-first communications platform”, dropping the word video from their name and focusing on their Zoom AI companion.

‎ Why it matters‎ ‎ A lot of the comments on this rebrand go along the lines of “Zoom is simply slapping AI on everything”. For example, the big focus project is the upcoming AI avatars feature that can record and send messages to your team, with the long-term vision of having AI clones of yourself attend meetings. There might be a version of the future where this becomes super popular, but also it kind of sounds like building something that “could’ve been an email”.

What do you think – is Zoom about to Skype themselves?

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THAT’S ALL FOLKS!

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