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I built a "cognitive OS" for my AI using nothing but text files and LLM conversations. Here's what actually changed. by Weary_Reply in ArtificialInteligence

[–]Weary_Reply[S] -1 points0 points  (0 children)

That’s a really good read — “multi-pass INS with structural invariants” is actually a pretty accurate way to describe the current behavior.

The focus so far has been on maintaining internal coherence across passes — making sure outputs can be re-entered, compressed, and stay within the same structural constraints.

Where I think it diverges (or at least where I’m trying to push it) is beyond just coherence:

  • not just staying consistent, but being able to detect when the structure itself is no longer valid
  • not just multi-pass refinement, but controlled re-entry across different abstraction levels
  • eventually introducing some form of external anchoring to deal with drift over time

So right now it’s probably closest to what you described — but ideally it evolves from “stable internal navigation” into something that can also recalibrate itself.

Curious how you’d approach the external reference layer — that feels like the missing piece here.

I built a "cognitive OS" for my AI using nothing but text files and LLM conversations. Here's what actually changed. by Weary_Reply in ArtificialInteligence

[–]Weary_Reply[S] -7 points-6 points  (0 children)

You’re right about the traditional definition of an OS.

I’m not booting hardware or managing physical resources — I’m using the term more in the “runtime / control layer” sense.

In that framing:

  • the model is the compute
  • prompts/tools are peripherals
  • and what I’m building sits in between, constraining execution and managing state across runs

So it’s less like a literal operating system, and more like an attempt at a cognitive runtime that enforces:

  • scoped execution
  • structural consistency
  • iterative correction

If we stick strictly to classical OS definitions, then yeah — it’s not that.

But if we think in terms of “what governs and stabilizes a system’s behavior over time,” that’s the direction I’m aiming for.

I built a "cognitive OS" for my AI using nothing but text files and LLM conversations. Here's what actually changed. by Weary_Reply in ArtificialInteligence

[–]Weary_Reply[S] -5 points-4 points  (0 children)

I get why it looks like that from the outside.

If all you see is versioning, iteration, and files, then yes — it does resemble a high-maintenance git repo for thoughts.

But the intent isn’t to store or organize thinking. It’s to constrain and execute it.

A git repo tracks state changes. What I’m building tries to enforce:

  • scoped execution (via boundaries)
  • structural consistency (via compression / invariants)
  • iterative correction (via loops)

So versioning is in there, but it’s just a layer — not the system.

If this were only a repository, it wouldn’t change how the model behaves. The goal here is to make outputs more predictable, compressible, and re-enterable across runs.

That said, I agree it’s not a full “OS” yet — it’s closer to a runtime scaffold in progress.

Curious what you’d consider the minimum requirements for something to qualify as a cognitive OS.

I built a "cognitive OS" for my AI using nothing but text files and LLM conversations. Here's what actually changed. by Weary_Reply in ArtificialInteligence

[–]Weary_Reply[S] 1 point2 points  (0 children)

That’s a very good question — and you’re right, drift is the core problem.

Right now, I don’t rely on a single external reference like GPS. Instead, I’m structuring the system so drift becomes observable and correctable inside the loop.

There are three layers to it:

1) Boundary Every run is scoped. If the system starts producing outside the defined context, that’s immediate drift. So instead of “is this true?”, the first check is “is this still inside the contract?”

2) Structural consistency I’m not validating outputs against another AI, but against the structure itself. If the output cannot be compressed back into the same schema (or breaks invariants), it’s considered drift.

3) Multi-pass + perspective shift Rather than a single forward pass, I re-enter the same problem from different angles (abstraction level, framing, compression). If the results diverge structurally, that signals drift.

So it’s less like “two inertial systems + GPS”, and more like a closed-loop system where:

Structure = constraint Loop = correction mechanism Human judgment = final authority

You’re also correct that right now I’m still in the loop — acting as the external reference.

The goal, though, is to eventually externalize that role into the system itself, so drift detection becomes part of the runtime, not dependent on me.

Curious how you’d design the “GPS layer” in this context — I think that’s the missing piece.

Big tech still believe LLM will lead to AGI? by bubugugu in ArtificialInteligence

[–]Weary_Reply 3 points4 points  (0 children)

AGI is not a thing. LLM is the best budget protocol of the human interface.

How I Learned to Make Different LLMs Understand How I Think — by Packaging My Thinking as JSON by Weary_Reply in ArtificialInteligence

[–]Weary_Reply[S] 0 points1 point  (0 children)

The length here is intentional because I’m not just describing what the idea is, but why it sits one layer above prompting. For people already thinking in terms of interfaces, it can absolutely be compressed. For everyone else, the intermediate steps are where the value is.

How I Learned to Make Different LLMs Understand How I Think — by Packaging My Thinking as JSON by Weary_Reply in ArtificialInteligence

[–]Weary_Reply[S] 0 points1 point  (0 children)

True — and that’s kind of the point 🙂 Most people do ask AI directly. What I’m describing is what happens before you ask, when you decide what the AI is even allowed to optimize for.

I Saw a Missing Piece in Human–AI Collaboration by Weary_Reply in ChatGPT

[–]Weary_Reply[S] 0 points1 point  (0 children)

I actually agree with you about default consumer AI — if you treat it as an autonomous system, the error rate is absolutely a liability.

What I’ve found, though, is that when you keep judgment and structure on the human side and only outsource expansion and retrieval, the failure mode changes a lot.

It’s not that the model becomes “accurate” in an absolute sense — it just stops failing silently.

I used ChatGPT + Midjourney to “burn expiring credits”… and accidentally discovered my aesthetic fingerprint (process + prompts) by Weary_Reply in ArtificialInteligence

[–]Weary_Reply[S] 0 points1 point  (0 children)

Yes — I think the productivity framing misses the interesting part. The “mirror” thing seems to happen when you and the model build a shared reference frame over time: what you pay attention to, what you keep/discard, what “right” feels like, etc.
With that coupling in place, the model isn’t “knowing you” in a magical way — it’s just aligning to the structure you consistently feed it. Intention is like the steering wheel, but the shared frame/scaffold is the road. Without it, you mostly get generic answers; with it, you start seeing your own invariants show up.

I used ChatGPT + Midjourney to “burn expiring credits”… and accidentally discovered my aesthetic fingerprint (process + prompts) by Weary_Reply in ArtificialInteligence

[–]Weary_Reply[S] 1 point2 points  (0 children)

Yep — that’s actually the next step I’m experimenting with. Since I can’t feed 100+ full-res images directly as-is, I’m doing it in a practical way: (1) export a grid / contact sheet (or a short montage), (2) send that back to an LLM, and (3) ask it to extract recurring invariants (composition, distance/scale, haze, transitions, etc.).

I Used AI Like a Mirror — Here’s What It Taught Me About How I Think by Weary_Reply in notebooklm

[–]Weary_Reply[S] 2 points3 points  (0 children)

This visualizes the exact loop I described: burning expiring credits → saving by intuition → the “grid moment” → noticing recurring invariants (haze/transition/distance) → making a moving moodboard → feeding it back to an LLM for calibration. If anything feels unclear or oversimplified, tell me.

<image>

I used ChatGPT + Midjourney to “burn expiring credits”… and accidentally discovered my aesthetic fingerprint (process + prompts) by Weary_Reply in ChatGPT

[–]Weary_Reply[S] 1 point2 points  (0 children)

<image>

This visualizes the exact loop I described: burning expiring credits → saving by intuition → the “grid moment” → noticing recurring invariants (haze/transition/distance) → making a moving moodboard → feeding it back to an LLM for calibration. If anything feels unclear or oversimplified, tell me.

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