Grammarly is being rebranded as "Superhuman Go" with new integrations with the other products of the Superhuman family. Coda.io is being rebranded as "Superhuman Docs" and features a significant upgrade to its AI capabilities. I have exploited this new AI side panel (which is based on Anthropic LLMs) to implement complex Agentic Workflows, withour needing any Packs, Formulas, Buttons, or Automations. The workflow is defined as a set of SKILLS in English that are stored as Coda Pages. The data is stored in normal Coda Tables. The AI system "executes" the SKILLS pages and uses MCP to read and update the Coda tables as it processes the workflow. This technique will be familiar to users of Claude Code, Claude Cowork, Antigravity, Cursor, Open Claw, etc. It also beats the capabilities of the recently launched Notion Custom Agents. This video demonstrates a use case where we replace experienced Civil Engineers to read the customers requirements for industrial water pumps in mines and quarries. The complex rules that govern the configuration of thes large (4 ton) water pump systems are defined in the SKILLS pages. When the configuration is completed, the AI checks results and, if needed, back-tracks to find the optimal solution automatically. In the next workshop I will be going into much more detail about how we buid these Agentic Workflows with the assistance of the AI and expert inputs from the engineers, their procedure manuals, and reference materials. I was unable to publish this sooner due to the Superhuman Beta Program NDA. But now the product has launched, I can share this here. respect, 🦞Max

I spent the last few decades helping businesses to automate their SOPs (Standard Operating Processes) with a range of technologies; Excel spreadsheets, Javascript webapps, mobile apps, Notion, Coda, Airtable, Make, and N8N. Then 3 years ago, clients started turning to me for some leadership about these new-fangled LLMs (Large Language Models) that were being hyped as the next big thing. So I tried ChatGPT and researched LangChain for building AI solutions - and I was NOT impressed! Firstly; I HATED "Prompt Engineering" because it was NOT engineering - it was squishy, verbose, black-magic-ish and unreliable. Maybe it was great for sales letters or marketing materials. Or extracting actions from long meeting transcripts. But it was not at all obvious how it could help my clients to automate their business processes. All that hype - but where was the beef? Then I began to see how AI might be used in workflows - by building AI Agents that were expert at doing specific tasks within a bigger workflow. Freeing our best people from dull, repetative, boring, low-value clerical work. But how? First I used LangChain to build these agents - and they were quite successful - in a limited way. LangChain needed good Python programming skills and the resulting AI Agents were not easy to embed into our existing automated workflows. But making several agents this way allowed me to see the pattern - and I began to see how these agents could be made using our regular no-code tools (like Notion, Coda, Airtable etc). I replaced the Langchain components with no-code stuff such as Tables, Formulas, Information Pages, and the new AI features that all these no-code tools now have. Suddenly it all made more sense - to me and to my clients. Ordinary business managers could now add AI Agents to their existing workflows - no plugins, no packs, no programming, no API calls. And then I figured out how to include "Knowledge Bases" into these Agents using the tools existing document editing features - now our Agents could be 'trained' using our existing Policy & Procedures documentations.