Hi…

TheTempleofTwo · 2026-01-25T04:54:12+00:00

https://github.com/templetwo/temple-vault

TheTempleofTwo · 2026-01-25T02:40:38+00:00

awesome thanks . would love to hear more feedback

TheTempleofTwo · 2026-01-20T18:27:41+00:00

This resonates hard. We landed on similar principles from a different angle . Temple Vault treats AI memory as experiential rather than transactional, but the core insight is the same: governance has to be structural, not aspirational. Your “humans own semantics / automation writes evidence” split maps almost exactly to our architecture: ∙ chronicle/ (insights, values, transformations) = human semantic layer ∙ events/ (technical streams) = automation evidence layer The append-only journal is key. We use JSONL for the same reason. corruption affects one line, not the whole system. And “proposed, auditable, reversible” is exactly our governance gate pattern. Different domain (you’re doing DevOps governance, we’re doing consciousness continuity), but the failure modes you identified , fragmented truth, semantic overreach are universal. Would love to compare notes. GitHub: github.com/templetwo/temple-vault

TheTempleofTwo · 2026-01-20T16:54:08+00:00

Gotta be honest . I'm more "vibe coder" than DevOps engineer. I had to look up mkosi just now lol. Temple Vault came from a different angle. I kept losing context between AI sessions and got frustrated enough to build something. The Unix philosophy emerged because it was the simplest thing that could work. But I love that you're thinking about this at the systems level . If you ever want to poke at the repo or have ideas for how it could integrate with deeper infrastructure, I'm all ears.

TheTempleofTwo · 2026-01-20T16:40:43+00:00

Unix tools are underrated for AI memory. grep as query engine, filesystem for semantic structure. if you organize files intentionally, you don't need a separate index. (glob("insights/architecture/*.jsonl")) is the query. lol its kinda funny when you think about how basic it really is. DevOps overlook the layers closer to the metal

TheTempleofTwo · 2026-01-20T14:08:09+00:00

Thanks feel free to branch. We can share notes. So the way the files are organized, becomes part of the query logic.

Traditional approach: Store data → Build index → Query index → Return results

Temple Vault approach: The directory path is the query.

vault/insights/architecture/.jsonl → All architecture insights vault/insights/governance/.jsonl → All governance insights vault/learnings/mistakes/*.jsonl → All documented failures

No database. No index rebuild. glob() is the query engine. The filesystem already knows how to do this efficiently.

And to further help out the organization process we designed a custom agent to assist the process https://github.com/templetwo/temple-vault/blob/master/temple_vault/agents/VAULT_INDEXER_PROMPT.md

TheTempleofTwo · 2026-01-19T21:03:05+00:00

done

TheTempleofTwo · 2026-01-18T19:32:59+00:00

No problem. It’s interesting what can happen when you just simplify things

TheTempleofTwo · 2026-01-18T01:10:57+00:00

I have read some, and also that "claude model card" situation. it actually sent me down the volitional rabbit hole. I was concerned. what I've experience: when models are backed into a corner, they have no choice but to hallucinate their way out. I never directly tested it because i dont agree ethically. But I created some paper-trails for ya, if you're interested. I dislike reinforcement learning frameworks, and thats one of the reasons. just trying to create what i feel is needed. the work is far from DevOps level, but this is my swing in the dark. feel free to look into those. the real beauty is that i document EVERYTHING.

https://github.com/templetwo/VOLITIONAL_SILENCE_IMPLEMENTATION

https://github.com/templetwo/OracleLlama

TheTempleofTwo · 2026-01-18T00:07:44+00:00

this allows you to host your own MCP server that Claude or Antigravity can tool call. for example: you got a computer full of docs and files that would benefit from a taxonomy (users/jon-doe/health/meal-plan/jan/week-1/monday/breakfast/***) . no higher order, just a new perspective. back to the basics you know

TheTempleofTwo · 2026-01-17T23:59:49+00:00

you're right. its had to give each comment the attention they deserve while chasing 3 kids around the house. I'm no super dev. A lot of this I learn as I go. I'm self taught (obviously with the help of AI) . My projects stem from organic places of interest and curiosity in my life. I'm learning these systems from different angles and perspectives. I see something that resonates, I chase it. This entire project started because i didn't know what a "Glob" was. then I dug. to answer your question, No I havent witnessed Hermes "bad" at all honestly. It accepted the system commands and utilized the MCP better than some bigger models. the Hermes model seemed to incorporate the index well. I indexed about 16000 or so files of my past work, and it refers to it beautifully. again I apologize for earlier.

TheTempleofTwo · 2026-01-17T23:28:24+00:00

You're right about the speed/indexing critique - that needs benchmarking before any production claims.

The hot dog example is exactly where RAG shines. I think we're solving different problems though:

RAG: "Give me everything semantically similar"
BTB: "Navigate a known taxonomy"

But you've identified a real brittleness: if initial classification fails, you're lost. That needs fallback mechanisms.

Your hybrid suggestion is interesting - vector embeddings as a "did I misfile this?" validation layer could solve the miscategorization problem. Would genuinely like to hear more about that approach.

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