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Exploring AI that helps in the moment

We’re building an AI system focused on real-time mental fitness.

The goal is to support people during actual moments of stress or focus, not just track things after the fact.
It’s still early, but a few challenges are already clear:

Figuring out what “context” really means from messy signals
(One thing we’re realizing is that context is not just activity data, it is patterns over time, interruption cost, and user intent)
Getting the timing right without being annoying
Making something useful from day one

Curious how others have approached problems like this, especially around real time systems or behavioral modeling.
Always open to connecting.

on April 7, 2026
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    Hey Im building something similar with friends, but my role is product manager with AI engineering background, would you like to talk?

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    Interesting. This maps closely to what we’ve seen with call handling. The challenge isn’t just understanding context, it’s reacting at the right moment without adding friction.

    A useful shift for us was thinking in terms of interruption cost vs response timing rather than just intent detection. Also found that patterns over time (not single events) make interactions feel much more natural.

    Curious how you’re balancing real-time signals vs learned behavior over time, that tradeoff seems to define the experience.

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    This is a thoughtful direction — “help in the moment” is actually the hardest and most meaningful version of AI assistance, because timing and context matter more than the model itself. Your point about context being patterns over time (not just raw activity signals) is especially important — that’s where most real-time systems usually break down.

    This feels like something worth testing early with narrow, high-frequency scenarios before expanding too broadly. There’s a competition where you can submit it — entry is $19 and winner gets a Tokyo trip. Prize pool just opened at $0. Your odds are the best right now.

    If you're working on something new, this could help 👀
    $19 entry → idea competition
    🏆 Tokyo trip
    💰 $500 guaranteed
    Round open 👉 tokyolore.com

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      This comment was deleted 4 months ago

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    "Faris, your definition of context including 'interruption cost' and 'user intent' is a much deeper approach than most 2026 AI wrappers. Solving for timing is the holy grail of behavioral systems.
    I'm currently running a Tokyo-based competition (Tokyo Lore) that is exploring how localized business ideas can use similar real-time context to improve traveler experiences. I’d love to get your eyes on our model—I have a specific question about your 'Day 1' utility theory."

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    Hi Faris, I found your idea interesting, especially around real-time context and user intent. I’m currently focused on full-stack development (React/Node), and while I’m still growing in this area, I’d be interested in learning more about your approach.

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      This comment was deleted 5 months ago

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    This comment was deleted 5 months ago