When initially experimenting with LLMs and agentic AI, software engineers at Notion AI applied advanced code generation, complex schemas, and heavy instructioning. Quickly, though, trial and error taught the team that it could get rid of all of that complicated data modeling. Notion’s AI engineering lead Ryan Nystrom and his team pivoted to simple prompts, human-readable representations, minimal abstraction, and familiar markdown formats. The result was dramatically improved model performance. Applying this re-wired approach, the AI-native company released V3 of its productivity software in September. Its notable feature: Cutomizable AI agents — which have quickly become Notion’s most successful AI tool to date. Based on usage patterns compared to previous versions, Nystrom calls i [...]
A few years ago, I gave up on my Gmail inbox. I used to be meticulous. I would assign labels to every new email that came in, starring those that I wanted to find later easily. But between a job in jo [...]
Notion is coming for Otter.ai. On Tuesday, the company announced an update for Notion AI, the suite of generative AI features available through its popular note-taking app. Among the new tools include [...]
Alembic Technologies has raised $145 million in Series B and growth funding at a valuation 13 times higher than its previous round, betting that the next competitive advantage in artificial intelligen [...]
Notion has unveiled version 3.0 of its software, highlighted by the launch of AI "agents" that can take over entire tasks on their own - from creating documents and databases to running mult [...]
It didn’t take long for Notion 3.0’s new AI agents to show a serious weakness: they can be tricked into leaking sensitive data through something as simple as a malicious PDF.<br /> The artic [...]
OpenAI is adding plugins to Codex that integrate with popular work tools like Slack, Figma, Notion, Gmail, and Google Drive.<br /> The article OpenAI's Codex gets a plugin marketplace for S [...]