Hi, I'm Forrest

Category: Noticing

  • Why Time Matters More Than We Think

    March 19, 2025

    I had a realization yesterday that’s been nagging at me for weeks.

    Time isn’t just when something happens. Time is context.

    I know that sounds obvious, but hear me out because this changes everything.

    Time as Context

    When you walk into a 9am Monday meeting, your brain doesn’t just think “it’s 9am.” Your brain thinks:

    • “It’s Monday, so we’re probably reviewing last week”
    • “It’s 9am, so people might still be waking up”
    • “It’s the first meeting of the day, so we need to set the tone”

    Time triggers context. Context triggers beliefs. Beliefs trigger behavior.

    The AI Problem

    But AI agents? They treat time like a timestamp. Just a number. No meaning attached.

    This is why AI agents fail at professional work.

    A good professional knows:

    • Month-end is different than mid-month
    • Friday afternoon requires different communication than Tuesday morning
    • The week before a deadline has different priorities than a normal week

    None of this is explicitly taught. It’s learned through experience. And it’s all tied to time.

    Time-Based Context Activation

    Here’s what I’m working on: Time-based context activation.

    Instead of the AI waiting for you to tell it “hey, it’s month-end,” it knows:

    • What time of month it is
    • What that usually means for your work
    • Which beliefs and workflows are relevant right now

    The AI doesn’t just respond to your messages. It prepares for them.

    Example

    It’s 9:25am. You have a 9:30am meeting with Client A.

    A normal AI: Waits for you to start the meeting, then scrambles to load context.

    A time-aware AI: At 9:00am, it pre-loads:

    • Everything about Client A
    • The agenda for this meeting
    • Relevant past conversations
    • Your usual communication style with them

    When you walk into the meeting at 9:30, the AI is ready. Not because you told it to be ready, but because it understands that time triggers context.

    The Takeaway

    If your AI doesn’t understand time as context, it’s always going to be one step behind.

    Professionals don’t wait to be told what’s important. They know what’s coming based on the calendar.

    AI should work the same way.

    How does your AI handle time? Does it just timestamp events, or does it actually understand what time means?

  • Why AI Agents Can’t Learn

    January 8, 2025

    I’ve been thinking a lot about learning. Not the “read a book” kind—the real kind. The kind where you actually get better at something through experience.

    A few weeks ago, I watched my nephew learn to ride a bike. He fell. Got back up. Fell again. But here’s what fascinated me: every time he fell, he adjusted. Not because someone told him exactly what to do, but because his brain was building an internal model of “how bikes work.”

    Then I tested the latest AI agent everyone’s hyping up. I asked it to help with a workflow. It failed. I corrected it. Next day, same task—same exact failure.

    It forgot. Or more accurately, it never learned in the first place.

    The Missing Piece

    This is the problem nobody’s talking about. Current AI agents are like someone who takes notes during every meeting but never reads them. They have perfect memory of the conversation, but zero understanding of what actually matters.

    Here’s what I realized: AI agents don’t have beliefs.

    I don’t mean beliefs like motivational poster stuff. I mean the cognitive infrastructure humans use to navigate the world.

    How Humans Actually Learn

    When you meet a new client, your brain doesn’t store the entire conversation word-for-word. It extracts patterns:

    • “This person values directness”
    • “They get frustrated when I’m late”
    • “They trust me more when I show my work”

    These are beliefs. They’re built from assumptions, opinions, and real experiences.

    AI agents? They store everything and understand nothing.

    They can’t distinguish between:

    • A one-time exception
    • A pattern worth remembering
    • A core principle that should guide future decisions

    What Needs to Change

    If we want AI that actually learns, we need to stop building better chatbots and start building cognitive architectures.

    The question isn’t “Can AI remember more?”

    The question is “Can AI form beliefs that get stronger or weaker based on evidence?”

    That’s what I’m working on. More on this soon.

    How many times have you corrected the same AI agent for the same mistake?

  • Why Do Chatbots Forget Everything?

    January 22, 2025

    I’ve been obsessed with memory lately. Not the “where did I put my keys” kind—the “how does my brain actually work” kind.

    Last week, a client I’ve worked with for six months called me. The moment I heard their voice, my brain did something remarkable: it instantly loaded their communication style, their priorities, past projects we’ve done together, even that one meeting where they got frustrated about deadlines.

    All of this happened in milliseconds. I didn’t think “let me remember everything about this person.” My brain just knew.

    So I wondered: how does that work?

    Then I tried to build an AI agent to do the same thing.

    Complete disaster.

    The Problem

    The AI either forgot everything from our last conversation, or it dumped the entire conversation history into context and got overwhelmed. There was no middle ground. No intelligent selection of what actually matters.

    This is the memory problem.

    Current AI systems treat memory like a filing cabinet. Everything gets stored. Nothing gets prioritized. When you need something, you either search through everything or get nothing.

    But human memory doesn’t work like that.

    How Human Memory Actually Works

    Human memory is contextual. When you walk into a meeting, your brain doesn’t load your entire life history. It loads exactly what’s relevant:

    • Past interactions with the people in the room
    • Similar situations you’ve been in
    • Patterns about how these interactions typically go

    It’s selective. Smart. Efficient.

    AI agents need this capability. They need to know what to remember, what to forget, and what to load when.

    The Breakthrough

    I’m working on context-triggered memory activation. Instead of storing everything or nothing, the AI learns which memories are relevant for which contexts.

    Meeting with Client A? Load beliefs about Client A. Working on month-end close? Load accounting workflow patterns. Simple email? Keep it lightweight.

    It’s not about having more memory. It’s about having smarter memory.

    Moving Forward

    If you’re building AI agents and struggling with memory, you’re not alone. This is hard. But I think it’s solvable.

    The key is moving away from treating memory as a database problem and starting to think about it as a cognitive architecture problem.

    How do you currently handle memory in your AI systems?