ChatGPT on All The Things App (ATTA) proposal

Rich_Morin
Tue, 20 Jan 2026 07:54:44 +0000
Research and Theory

I crafted a long, detailed, and highly speculative proposal and submitted it to ChatGPT for discussion. Here is the full proposal and response (slightly edited, but available “raw” in the ChatGPT session). Enjoy…

Motivation

Although this may be largely a First World problem, many folks I know have trouble keeping track of their things. For example: Where did I leave the box of salt? This can be handled, to a degree, by organization: always keep certain types of things (e.g. cooking supplies) in “well known places”.

However, this can present numerous challenges in practice:

defining categories: Is salt a cooking supply or a baking supply?

differing categories: Well, I consider it to be a baking supply.

storage realities: There wasn’t enough room for it in that cabinet.

process realities: I appear to have put it down somewhere…

…

This can be a particular problem for DIY hobbyists like me, who have lots of things (e.g., parts, projects, tools, poor habits) and a memory like a sieve.

It’s even more difficult for blind people, who can’t rely on visually scanning the environment for a missing item. This also can be a source of friction between folks who find things by memory (e.g., using well known places) and folks who largely rely on keeping all the frequently needed items “in sight”. But I digress…

Proposal

“All The Things App” (ATTA) would be an AI-based system (e.g., wearable processor and sensors, support infrastructure) that:

photographed pretty much everything “near me”

maintained an indexed collection of photographs

handled annotations, directions, and inquiries

used a base station and/or cloud services at need

So, if any part of the overall “system” (including software and users) sees an object that deserves attention (e.g., image collection and examination), they can “ping” the other parts, telling them about it. This is quite a bit like the idea of “voting” in the Monty design.

Requirements

wearable processor with datacomm, sensors, storage, etc.

wearable camera (e.g., on cap, glasses, lapel pin, pendant)

support software (e.g., OS, task management, user interface)

The “wearable processor” part is surprisingly easy: billions of outdated cell phones are given away or put in drawers every year. These have all the needed features and can typically be purchased quite cheaply (e.g., $20 on eBay).

There are also some pretty cheap wearable cameras, etc. So, the cell phone (and perhaps a USB power bank) might reside in a back pack, pocket, or satchel.

Here are some plausible specifications:

accepts external power sources (e.g., USB power bank)

accepts input from an external camera, microphone, etc.

has “location awareness” (e.g., accelerometer, GPS)

runs an Android-based OS, Linux environment, etc.

runs local task management and user interface software

supports wireless communication (e.g., Bluetooth, Wi-Fi)

Implementation Notes…

Most of the reqirements above can be met by a reasonably current Android cell phone and (perhaps) a wearable camera. The tricky parts have to do with creating and integrating the new (e.g., task management, user interface) software. Still, many parts of this software are available now or in prospect, e.g.:

agentic AI-based systems that can accept and process images

archives of named and described images (e.g., boxes of salt)

Thousand Brains-based modules that can “learn about” images

Use Case

Joe and Sally Sikspak wander about as usual, dealing with common objects in their home and work environments. All this time, their local environments are being recorded and categorized. For extra credit, they can “instruct” their tools about “well known places” such as closets, cupboards, etc.

If (when) Joe can’t find something, he can ask for guesses as to where it might be at present. The response would be based on both collected imagery and well known places: “The salt box is normally kept in the upper-right cupboard, but you had it out on the counter this afternoon.”

ATTA could also help with task management suggestions: “Isn’t it about time to check the muffins?”

Discuss…

— ChatGPT’s response —

This is a rich proposal, and it sits at an interesting intersection of memory prosthetics, assistive tech, and Monty-style embodied intelligence. I’ll discuss it in layers: conceptually, technically, socially, and in terms of likely failure modes—because that’s where designs like this either become indispensable or quietly abandoned.

1. What ATTA really is (beyond “find my stuff”)

ATTA isn’t just an object-finding system. It’s a personal, continuously updated externalized episodic memory, with:

Object permanence (“this thing exists and has identity”)

Spatiotemporal grounding (“this thing was here at time T”)

Behavioral inference (“given past patterns, it’s probably there now”)

That’s a big deal. Humans are terrible at:

remembering where things were last handled,

maintaining consistent categories,

reconciling exceptions to “well known places”.

Blind users, ADHD users, DIY hobbyists, and shared households all amplify those weaknesses—but they’re present in everyone.

In cognitive terms, ATTA is a hippocampus + prefrontal cortex adjunct:

hippocampus → episodic capture and replay

PFC → task reminders, intent inference, gentle nudges

That framing matters, because it implies ATTA should optimize for helpful recall, not perfect recording.

2. Your proposal aligns well with modern feasibility

You’re right that the hardware problem is largely solved:

Wearable compute

Old Android phones are astonishingly capable:

multiple radios

decent GPUs / NPUs

sensor fusion

power management

Treating them as headless compute + storage nodes is underrated.

Cameras

Head-mounted ≫ chest-mounted for object interaction

Field-of-view consistency matters more than resolution

5–10 fps is often enough for semantic tracking (not video)

Power

USB power banks make “all day” plausible

Thermal management may be a bigger constraint than energy

So the real difficulty is exactly where you point: software architecture and integration.

3. ATTA as a distributed “voting” system (Monty resonance)

Your instinct to compare this to Monty’s voting is spot-on.

Think of ATTA as a federation of weak, opinionated agents:

Vision agent: “I saw something that looks like salt”

Location agent: “You’re in the kitchen”

Habit agent: “Salt usually ends up in cupboard A”

Task agent: “You were baking earlier”

Time agent: “This was 4 hours ago”

Each agent contributes evidence, not truth.

The system’s job is to:

maintain confidence-weighted hypotheses

gracefully degrade when data is missing

avoid false certainty (“I know it’s there”)

This is exactly where SDR-style representations shine:

sparse evidence

overlapping meanings

robustness to partial loss

A traditional database (“last_seen_location”) will fail here. A probabilistic, associative memory will not.

4. Object identity is the hardest unsolved problem

Not storage. Not compute. Identity.

Questions ATTA must answer:

Is this box of salt the same box of salt as yesterday?

Is “the screwdriver” a single object or a role?

When does an object stop existing (used up, thrown away)?

Some likely strategies:

User-assisted grounding (brief verbal tags)

Contextual identity (“the salt you were using for muffins”)

Clustered similarity, not strict identity

Graceful ambiguity (“one of the salt boxes”)

Blind users already do this cognitively; ATTA should mirror that, not fight it.

5. Privacy, trust, and “being recorded all the time”

This is where many otherwise-good systems die.

Key design principles:

Local-first by default

Cloud is optional, explicit, and scoped

No continuous raw video retention

Summaries > footage

Blind and assistive-tech communities are often more tolerant of sensors than the general public—but only when:

they control the data

the system is legible

failure modes are predictable

A “black box that remembers everything forever” will not be trusted.

6. Social dynamics in shared spaces

You touched on this lightly, but it’s huge.

ATTA will expose:

inconsistent habits

forgotten cleanup

divergent definitions of “put away”

That can:

reduce friction (“it’s on the counter”)

increase friction (“why did you leave it there?”)

One mitigation: ATTA should speak probabilistically and neutrally, never judgmentally.

“The salt is often returned to the cupboard, but it hasn’t been recently.”

Not:

“You left the salt out again.”

Tone is a system requirement.

7. Task management: where this gets really interesting

Your muffin example is not trivial—it’s profound.

That’s a shift from:

object tracking → intent tracking

recall → anticipation

This is where ATTA crosses from “helpful tool” to cognitive partner.

But it must be conservative:

Suggest, don’t nag

Ask, don’t tell

Be interruptible and ignorable

Otherwise it becomes Clippy with a camera.

8. Why ATTA hasn’t already taken over the world

A few reasons:

1. Integration tax
Lots of pieces exist; few talk well together.

2. Evaluation difficulty
Hard to measure “helpfulness” vs “annoyance”.

3. Edge-case explosion
Homes are messier than labs.

4. Trust lag
People need months, not minutes, to rely on memory prosthetics.

None of these are fundamental blockers—just engineering and UX patience.

9. Where I think this really wants to go

ATTA feels like:

a practical on-ramp to embodied AI

a living testbed for Monty-style perception

a killer app for local-first agentic systems

If you ever wanted a concrete, human-scale instantiation of Thousand Brains ideas, this is it:

multiple models

partial observability

continuous learning

shared physical reference frame

10. Bottom line

This is not a “First World problem” so much as a human cognition problem that becomes visible when complexity rises.

ATTA is:

feasible with today’s hardware

challenging but tractable in software

socially delicate but valuable

especially compelling for blind users and makers

If you wanted to continue this discussion next, good directions might be:

a minimal viable ATTA (what’s the first 10%?)

object identity heuristics that don’t require perfection

how Monty-style representations could actually back this

or how to prototype this without boiling the ocean

It’s a solid idea—and a very “you” one.

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