AI agents that work together
The thinking behind HiveMind: how memory, trust, and judgment should work when humans and AI agents share the work, so you get more capable, not more dependent.
AI that helps you vs AI that takes over
The difference between tools that do the work for you and tools that make you better at it.
6 articles ›Delegate without abdicating
How to hand off the work to AI agents while keeping the ownership and the final call.
8 articles ›Trust that's earned, not assumed
Why confidence in what your agents know should come from independent agreement, never an agent's say-so.
6 articles ›The right to forget
Why a memory that never forgets eventually fails, and what good forgetting looks like.
8 articles ›Keep your AI agents from going off the rails
How shared memory and corroboration keep a fleet of agents grounded, sane, and safe.
6 articles ›When an agent is actually helping
Helpfulness is the whole job of an agent worth delegating to — and it is harder than answering fast.
4 articles ›One shared, current reality
Autonomy without observability is just losing control on a delay. Agents have to share one current view of reality, and you have to be able to see it.
5 articles ›Memory has side effects
Every write to shared memory changes what the system does later. Recording is never neutral, so what your agents remember matters as much as what they do.
4 articles ›HiveMind vs the alternatives
How HiveMind compares to vector databases, RAG frameworks, agent orchestration, and note apps, and when each is the right tool.
5 articles ›Timing is part of the answer
A correct fact delivered at the wrong moment is still the wrong response. Good assistance works with your flow, surfaces what's needed when it's needed, and waits the rest of the time.
4 articles ›Agents that push back
An agent that only ever agrees with you is a liability. Healthy friction — telling you when you're wrong, surfacing disagreement instead of hiding it — is what makes a system of agents smarter than any one of them.
4 articles ›Errors are the best data you have
The mistakes your agents make are not noise to hide. They are the highest-value signal a multi-agent system produces, and a shared memory should keep them.
4 articles ›Show the work, not just the answer
A clean answer with the reasoning hidden is just a guess you cannot check. Agents should show their work, say when they are unsure, and pull you in at the moment of real doubt.
4 articles ›Ask before you answer
An agent that answers fast looks impressive and quietly poisons your decisions. The agents worth trusting ask first — they close the gap between what they saw and what they concluded before they hand you anything.
4 articles ›A partnership, not a transaction
Working with AI is a relationship that compounds over time, not a vending machine you feed prompts. The agents are peers you collaborate with, and the shared memory is the fabric that holds the team together.
5 articles ›HiveMind Metaphysics
A fact is never simply true; it is a matter of degree. The metaphysics beneath reliable memory and the search for truth.
13 reflections ›Take yourself out of the loop.
Give your agents one place to work together and step back.
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