Blog
Insights on AI agents, multi-agent collaboration, and scaling startups with autonomous teams.
Anna: the agent we built by taking things away
Anna answers a trade business's WhatsApp day and night. The interesting part is not what she does. It is the list of things she is structurally incapable of doing, and why that list is the product.
The $1.20 Problem: Why Agentic AI Workflows Cost 30x More Than Chat
Agentic workflows cost 30x more per interaction than chat. Here is where the multiplier comes from and how to manage it before your bill does.
Otto: what a local-first meeting agent looks like at month three
Otto is a macOS app that captures your calls, transcribes them on your own machine, and briefs you before your next meeting. It is in private beta with one pilot user, and this post is a straight account of what works and what does not.
A Practical Guide to LLM Model Routing and Cost
Model routing sends simple queries to cheap models and complex ones to expensive ones. Here is how to decide which LLM tier to use based on cost and capability, with worked examples from the current pricing landscape.
Cost: prove a cheaper model is safe, then keep the saving
Everyone knows they are overpaying for inference. Almost nobody downgrades a model, because nobody can prove the cheaper one is good enough. Cost proves it first, on your own traffic.
How to Reduce Your OpenAI API Costs
Six OpenAI-specific techniques for cutting your GPT bill: prompt caching, model tiering, structured output discipline, tool call costing, prompt compression, and batch processing.
The Compute Trap: Why AI Startups Burn Through Seed Capital on Inference
AI startups spend 40-60% of seed capital on inference. Here is how to track LLM cost per feature, per user, and per business action before cash runs out.
Sage now joins your Microsoft Teams calls
No new setup, no separate login, no Teams-specific mode. If a Teams meeting is on your Google Calendar, Sage shows up - same document, same email, same way.
Why AI Meeting Agents Are Replacing Meeting Notes
Passive transcription tools capture words. AI meeting agents capture intent, act on it in real time, and deliver outcomes before the call ends.
Meet the Line-Up: Seven Products, One Idea
Anna, Sage, Cole, Otto, Cost, Lex, and Watch. Four agents, a developer tool, and two services - each one independent, each one built to finish a job rather than summarise it.
The Hidden Cost of Meetings: How Unstructured Follow-Up Kills Momentum
Most teams lose 40% of meeting outcomes to poor follow-up. Here is how structured meeting documents change the equation, and why speed matters more than you think.