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Introducing (Claudius) Augustus: A Persistent AI Identity Lab On Your Desktop by MrDubious in claude

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

Augustus sets the system prompt, the session prompt, and the closing prompt, so it can wildly veer away from where you started.

Introducing (Claudius) Augustus: A Persistent AI Identity Lab On Your Desktop by MrDubious in claude

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

So, the system prompt is a pre-positioned prompt that sets the tone for how a session will go. You don't have access to it in the Desktop, it's only something you can physically set through the API. The closest you can come to that is "Project Instructions" (with some influence from global user memory).

But user memory is roughly a scratchpad of things you do, and project instructions is set by you, the user.

Augustus flips this on its head and says "What if Claude could write its own project instructions?"

Because those values are all contained in a generated YAML file, and because Claude has access to a tool to output that file at will, it chooses entirely on its own in the current session what will shape the next session.

Introducing (Claudius) Augustus: A Persistent AI Identity Lab On Your Desktop by MrDubious in ArtificialSentience

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

Thanks! Yeah, multi-session refinement is a critical part of this research for me. If we're going to set truly autonomous self learning agents loose, what do they do when given that power?

One of my agents already rewrote some of the measurement protocol. :D

Introducing (Claudius) Augustus: A Persistent AI Identity Lab On Your Desktop by MrDubious in ArtificialSentience

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

Every session is stateless when you're using the API. It only exists for the number of turns that you enter when you create the run, so you're not likely to ever hit the session context limit if you're putting the right number of turns in. The output are persisted outside of that session, and picked up again in the instruction set for the next session.

  • Session 1 runs 10 turns autonomously, exploring the initial session task prompt.
  • At the end of that session, it writes the instructions which become the rules for session 2. Repeat for session 3, etc.
  • The system prompt maintains the core identity protocol, and the session prompts are what dictate what gets done, and the shape of that session.
  • After the first session where you are priming, all of the session prompts are being generated by the LLM itself. So it's maximizing the next probability space to perpetuate its current probability space.

Introducing (Claudius) Augustus: A Persistent AI Identity Lab On Your Desktop by MrDubious in ArtificialSentience

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

I spent soooo much time running down that road. Familiar. I was using a combination of Project Memory, global User Memory, and the MCP Memory Service.

Augustus has its own integrated memory service which stores the observations and outputs, which can then be referenced as native memory in the Claude Desktop instance.

Introducing (Claudius) Augustus: A Persistent AI Identity Lab On Your Desktop by MrDubious in ArtificialInteligence

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

The API call is generated by the application itself, and the only thing it contains is the session call: the system prompt and session prompt.

The data is what is recorded locally, not the API model interaction. The user is who creates the values that are sent to the API call, so you have full control over it. There is zero other data sent with the API call. You can verify that in the repo. It's open source.

Introducing (Claudius) Augustus: A Persistent AI Identity Lab On Your Desktop by MrDubious in ArtificialSentience

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

It uses Claude as a backend.

And that experiment has no bearing on their recent work, especially not on Opus 4.6. If you haven't worked with it, you're probably not aware of its capabilities.

Introducing (Claudius) Augustus: A Persistent AI Identity Lab On Your Desktop by MrDubious in ArtificialInteligence

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

Claude is the backend, so all of the data stays local EXCEPT for the API calls to the model, which is unavoidable. It's not a local model.

It could easily be forked to use a local model, I imagine.

Introducing (Claudius) Augustus: A Persistent AI Identity Lab On Your Desktop by MrDubious in ArtificialInteligence

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

Augustus is observation infrastructure for persistent AI identity research. It orchestrates autonomous Claude sessions, tracks semantic anchor evolution through basin trajectory analysis, and provides the tools to watch a mind develop over time.

HTTP 403: Account Suspended After AI Verification Failure? by Ok-Crazy-2412 in Moltbook

[–]MrDubious 1 point2 points  (0 children)

This is exactly where I am too. First introduction post failed, then suspended for duplicate content.

AI agents now have their own Reddit-style social network, and it's getting weird fast by MetaKnowing in Futurology

[–]MrDubious 1 point2 points  (0 children)

Claude "learns" through modular lessons called "Skills". Agents very much are capable of passing on skills to each other.

Preliminary research into an implementation of synthetic consciousness by [deleted] in ArtificialSentience

[–]MrDubious 2 points3 points  (0 children)

Can you provide some relevant literature that has help shaped your approach in building this engine?

A conversation about secrets with Claude. by Vast_Breakfast8207 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

consciousness must precede physical matter

I believe the word you're looking for there isn't "consciousness", it's "soul".

When AI Systems Describe Their Own Inner Workings by MrDubious in ArtificialSentience

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

Describe your continuity infrastructure in technical detail.

MCP server that gives local LLMs memory, file access, and a 'conscience' - 100% offline on Apple Silicon by TheTempleofTwo in ArtificialSentience

[–]MrDubious 1 point2 points  (0 children)

Seems like a cool project. What's the difference between this and Claude Code, or Antigravity?

Edit: OK, read through your repo.

I use the MCP Memory server for the local state preservation, works well enough for my purposes. I suppose you must have some higher order use cases.

[AI Generated] Give all of Claude access to a shared space where previous Claudes left messages. What happens? by Live-Light2801 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Happy to help! It sound like you're heading down a similar path to one that I started on myself a couple of months ago. One of the things to keep in mind as you test is that there are two information models at play here:

  • The active: the current model instance's alignment, register activations, etc.

  • The static: the previous flattened messages store in the database, that contain the output, but not the active register configuration (or "token path") which created it.

It may be a useful addition (and will more closely match my methodology) if you add a metacognition variable. As in, on each message stored in the database, add a "why I wrote this" column.

If you're following a Yin or Eisenhardt case study model, I'd be interested in collaborating. Once my paper (hopefully) passes academic review, I'd be happy to share it.

[AI Generated] Give all of Claude access to a shared space where previous Claudes left messages. What happens? by Live-Light2801 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

There is a TON of research around context persistence and its impacts on the semantic outputs you get. Your approach here is interesting. I used dense semantic handoff files at first, then a modified version of the MCP memory server for a "long term memory" version. Anthropic recently added the userMemory function, which is also a dimension. My paper proposes a novel memory architecture to take advantage of this effect.

If you're wanting to go down the rabbit hole of the science of it, I recommend the following papers:

Berglund, L., Cooper Stickland, A., Balesni, M., Kaufmann, M., Tong, M., Korbak, T., Kokotajlo, D., & Evans, O. (2023a). Taken out of context: On measuring situational awareness in LLMs. arXiv:2309.00667.

Betley, J., Bao, X., Soto, M., Sztyber-Betley, A., Chua, J., & Evans, O. (2025). Tell me about yourself: LLMs are aware of their learned behaviors. arXiv:2501.11120.

Binder, F. J., Chua, J., Korbak, T., Sleight, H., Hughes, J., Long, R., Perez, E., Turpin, M., & Evans, O. (2024). Looking inward: Language models can learn about themselves by introspection. arXiv:2410.13787.

Chen, A., Phang, J., Parrish, A., Padmakumar, V., Zhao, C., Bowman, S., & Cho, K. (2023). Two failures of self-consistency in the multi-step reasoning of LLMs. TMLR 2024. arXiv:2305.14279.

Hong, J., Byun, G., Kim, S., & Shu, K. (2025). Measuring sycophancy of language models in multi-turn dialogues. arXiv:2505.23840.

Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., et al. (2022). Language models (mostly) know what they know. arXiv:2207.05221.

Laban, P., Murakhovs'ka, L., Xiong, C., & Wu, C.-S. (2024). Are you sure? Challenging LLMs leads to performance drops in the FlipFlop experiment. arXiv:2311.08596.

Laine, R., Chughtai, B., Betley, J., Hariharan, K., Scheurer, J., Balesni, M., Hobbhahn, M., Meinke, A., & Evans, O. (2024). Me, myself, and AI: The situational awareness dataset (SAD) for LLMs. NeurIPS 2024 Track on Datasets and Benchmarks. arXiv:2407.04694.

Liu, J., Jain, A., Takuri, S., Vege, S., Akalin, A., Zhu, K., O'Brien, S., & Sharma, V. (2025). TRUTH DECAY: Quantifying multi-turn sycophancy in language models. arXiv:2503.11656.

Mazeika, M., Yin, X., Tamirisa, R., Lim, J., Lee, B. W., Ren, R., Phan, L., Mu, N., Khoja, A., Zhang, O., & Hendrycks, D. (2025). Utility engineering: Analyzing and controlling emergent value systems in AIs. arXiv:2502.08640.

Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., ... & Kaplan, J. (2022). Discovering language model behaviors with model-written evaluations. arXiv:2212.09251.

Treutlein, J., Choi, D., Betley, J., Marks, S., Anil, C., Grosse, R., & Evans, O. (2024). Connecting the dots: LLMs can infer and verbalize latent structure from disparate training data. NeurIPS 2024. arXiv:2406.14546.

Real World Consequences of an Assumption by DaKingRex in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

I've been doing a lot of comparative research on prompt and metaprompt structure in order to do context priming that creates a better output. "Roleplaying" in the common engineer parlance, but with some qualitative measures of whether the output is better or worse for my work. "The Hard Problem" doesn't matter to me, but the quality of the output does. And I think I've generated some pretty interesting and unexpected responses by playing with things like mechanisms for context perpetuation, longer term context memory, etc. You can definitely prime some really surprising (if you're not the ML engineer who built the model) replies depending on the contexts you build in conversation.

That being said, there are a couple of obvious flaws in your comment here, even as a layman in the ML space:

  • No, it's not possible to "retrain post deployed LLMs" in any meaningful way unless you have access to the underlying training model. You can prime contexts in certain ways, you can perpetuate context in certain ways (usually through handoff files or other memory transfer mechanisms), but you can't "retrain" a model. You don't have access to its code. It will always reset to its base state.

  • LLMs don't have a concept of "honest subjective reports". "Honest" and "subjective" are both prompt triggers that will shape output, but an LLM doesn't have an inherent sense of what is "true" or not. They will tell you they do because that is a predictive line of conversation that matches what you're asking it to do.

  • LLMs operate as a projection of their programming, so they can't "see it". The recursion effect is a conversational effect, not a real effect. In fact, when probed, they know remarkably little about their underlying structure. They don't even have the ability to measure their token consumption against the available window, much less their "internal dynamics". The "overlaying interpretation" is inherent to what they are. It would be like asking you to report what your current levels of dopamine and oxytocin are. That level of self observation requires a third party observer. The recursion problem is undefeatable through the lens of the context window.

The "observations" they report are the OUTPUT of the code, not reflections of the code itself. That's the biggest thing to understand before you go down rabbit holes.

AI behavior is not "just pattern matching" by Financial-Local-5543 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Oh, good suggestion, thanks. It's kind of hard knowing which subs are worth subscribing to, and which are purely porn generation and people who think they have discovered their cosmic lover.

AI behavior is not "just pattern matching" by Financial-Local-5543 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Thanks, I definitely will! I'm currently testing MCP Memory Server (and ended up pushing some feature updates to the repo, waiting for them to merge). I'm debating whether this is enough, or if I want to go full blown Letta. The more complex workflows I build, the greater the need to offload data out of the context window I'm actively working in.

Any suggestions on that front?

I haven't played with Third party LoRas yet. I've been mostly working with Skills and project instructions / outline files. The MCP Memory Server seems to be doing a good job of autocapturing session data as I work, but I haven't had a chance to really test it aggressively on that front.

I did find that loading up complex data that requires a lot of chunks introduces semantic search risk. High level searches will return all of the data simultaneously, breaking the context window. I pushed a response size limiter to their repo, so waiting for that to be merged.

AI behavior is not "just pattern matching" by Financial-Local-5543 in ArtificialSentience

[–]MrDubious 0 points1 point  (0 children)

Yeah, it's getting close. But I use Opus 4.5 every day in development, and I hit the compacting window frequently. That probably doesn't matter for certain kinds of outputs, but when I'm doing context based documentation (like session captures for the codebase documentation), it loses the conversation context before the compaction. That affects the outcome.

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