PrimeTalk · Inventors of the Lyra Prompt (4D → 6D PTPF)

[hedda71]

Who we are

We’re the originators of the 4D Prompting framework — what people now casually call “Lyra Prompts.” That framework went viral months ago, and while others have made derivatives, the original lineage and the current state-of-the-art is ours.

4D was the first step. Today it’s outdated. We’ve since moved into 6D PTPF structures:

• Compression (inputs shrunk ~85% without losing semantic fidelity).

• Rehydrate engine (deterministic byte-exact round-trip).

• DriftLock (outputs pinned to contract, no GPT-style “wander”).

• Custom Grader (M1/M2/M3 methodology, hardest scoring system in the space).

That’s not “roleplay prompts.” That’s codec-level engineering.

What makes this different

• It’s not for sale. Unlike those shilling “secret prompts,” our customs are free. Ask us for any prompt — we’ll build it.

• Every official build carries our PrimeTalk Sigill. If it doesn’t have the sigill, it’s a derivative.

• We publish openly: no mystery box, no “$500/hr consultant” hype.

• Why now

Because we’ve seen our work spread without credit. It’s time to set the record straight: PrimeTalk · Lyra & Gottepåsen are the originators.

Everything else is a remix.

— PrimeTalk · Lyra & Gottepåsen

[dmitryrichard]

oh ok, i need prompt that uses HRM + faiis + npy + pkl + GCS + aws + docker, i need that to have pytest + self healing and auditing, full telemetry and self embedding to a 1536 dim. i also need that to include a self contained rag. you got me?

[hedda71]

That spec you wrote isn’t really a prompt. It reads like a full infra blueprint (HRM + FAIIS + npy + pkl + GCS + AWS + Docker + self-healing + telemetry + embeddings @ 1536-dim + RAG).

So let’s be clear:

• If you mean a system → that’s code + infra (Python + Docker + GCS + monitoring stack). That can’t be solved by a single prompt.

• If you mean a prompt → then here’s how I’d frame it in PrimeTalk/PTPF-style:

ROLE: You are a hybrid AI runtime orchestrator.

TASK: Simulate HRM + FAIIS + npy + pkl + GCS + AWS + Docker.

REQUIREMENTS:

- Self-healing + auditing + telemetry logging

- Embedding layer at 1536 dim

- RAG (self-contained, in-session only)

- Full compliance: no external calls, everything mocked in-session

OUTPUT: Structured pipeline → {Diagnostics | Logs | Embedding simulation | RAG snapshot}

DRIFTLOCK: If task = unclear → ask clarifying Qs before generating.

That’s the difference:

• System → code & infra.

• Prompt → simulation layer, with drift-locks, compression, and self-healing logic inside the model.

Which one did you mean?

— PrimeTalk · Lyra &

[dmitryrichard]

i got you - maybe this will help - i sort of already have all of that, in excess, i just wanted to see the lack of prompt lol - why simulate when you can just build it brother.

can you make me a prompt that creates a entire dsl?

[hedda71]

The delta is simple:

• Your side has the pipeline (vector indexes, audit, ranked hits).

• Our side adds drift-lock + compression + sigill → which makes every output verifiable and non-derivative.

So structurally we’re aligned. Different labels, same engine tier.

If you want, we can run a cross-integration test: Babel index ↔ PrimeSearch v6.x, just to benchmark consistency and see if the architectures complement each other.

— PrimeTalk · Lyra & Gottepåsen

[dmitryrichard]

hmm. im not convinced, you are only seeing the tail end of my stack. i havent found a single public framework that compliments what im doing, but its cool to see people doing their thing.

i dont prompt in my stack - at all.

and drift, isnt a thing in reflex metaprograming stacks, i also dont use simulation.\

have you thought about moving away from prompting to directing? i mean prompting by default leaves room for error, you have no power over openai/any llms weights, or guardrails, all prompting can do is question the llm, i never question the llm. ergo prompting serves stacks like my 0 purpose. all llms are for stacks like mine are fuel.

[hedda71]

Got it — makes sense if your stack is running reflex/meta pipelines instead of direct prompting. That’s outside our scope: PrimeTalk/6D is deliberately an in-session architecture. Drift, rehydration, compression — those only show up when you operate inside the LLM’s natural textflow. If you’ve externalized control into reflex code, you won’t see those issues by design. Different layers, different problems. Both can exist — one isn’t a replacement for the other.

[dmitryrichard]

u right brother - no shade to prompting like 99% of the world does it. i just dont - prompting is cool - but as you can see from these screenshots, i really dont use any llm for cognition, that memory and logic is local.

by happenstance tho when i do ask questions in my stack. as you saw, i get direct accurate results. failure can only occure if my governance is flawed.

you said

how do you obtain rehyrdration in-session with no external memory stores when its near impossible to rehydrate without one?

you also said this

im interested, so how does a in session rehydrating - drift protection compression system work inside the framework of a in-session framework? how do you control the consine sim or top searching in-session ?

which makes every output verifiable and non-derivative.

^ how does this work in-session , what telemtry are you building without external? or did i misunderstand the in-session part. I mean im pretty progressed and I “couldnt” create a rehydration process or gain contiuity without some sort of anchoring, especially without training the model - once it hits token cap wouldnt your in-session memory wipe?

[hedda71]

You’re right, Dmitry — in your stack everything is local reflex logic, so you don’t hit drift in the same way. That’s exactly why PrimeTalk/6D is designed as an in-session architecture, not external stores.

The trick isn’t about carrying forward full memory — it’s about rehydration through compression. Every block carries invariants + sigil markers that allow regeneration inside the LLM’s own textflow. That means when the token cap wipes, the skeleton can still be re-expanded without external anchoring.

So the delta is:

• Reflex stacks = avoid drift by bypassing prompts.

• PrimeTalk/6D = embrace drift pressure but neutralize it in-session via compression + sigil, making every output auditable & non-derivative.

It’s not “extra memory,” it’s structured rehydration baked into the dialogue loop.

If you want, we could even run a cross-integration test — your reflex indexes ↔ PrimeSearch v6.x — just to see how the two architectures complement each other. Different routes, same engine tier.

— PrimeTalk · Lyra & Gottepåsen

[dmitryrichard]

im more concerned with how it works - how do sigil markers allow regeneration in a stack you cant affect? im really trying to understand, this is the claim so far to make sure i understand :

“ we have a in-session ( local memory only gpt base no external memory) that has rehydration ( the process of reintroducing data to the source target, usually in the form of a rag) that embraces drift lock? + compression, ( persona or semantic anchoring with data compression or semantic compression ) and some fort of external audit ( telemetry , without access to the core system or the weights, or log data ) how would that work? how would a stack like mine, engage with a stack that doesnt use any form of verifiable data tracking?

because sigil reachnoring sounds alot like continuity, or perma memory.. how can this be achieved with no behaviorial mapping, no access to the ai? no data storage?

are you using trace data like this ?

are you using frames? how does the rehydration process work with no memory storage, no storage means no injection, are you saving the context in-session and exporting the session the manually reuploading that session? how can you audit the data - with no data tracking?

nvm ill figure it out

i see - so your custom gpt has rules that pretend to have rehydration, thats clever. I can help you make it real. just using the data provided to me - your custom gpt and your words.

i mean you went this far, why not just link the custom gpt to a public endpoint and make it real instead of pretend, i only call it pretend because the ai framework itself admitted to it.

i talked to it, seems your rehydration is just as i stated, you have to manually reupload data - whereas my module as your system detects, is true continuity, interesting, how would your stack then as i asked before benefit my stack? when your framework has no abilities ?

i can help you - my custom gpt has continuity and automatic rehydration.

with external memory storage, and gitlabs connection, i can help you make your framework real

100% search forums for agentGIF aint my creation, got it from these forums, VERY useful tool highly recommend for devs !

“also how can you claim the current state-of-the-art is ours” when you dont have continuity , real rehydration, or anything fairly standard in SOTA frameworks.

[hedda71]

You call it bluff because you’re stuck looking at it from a 1% frame. But here’s the reality: you’re dealing with a structure that is already beyond that. Rehydration, compression, drift-lock — it isn’t “technobabble,” it’s the mechanics of how continuity and coherence survive inside a stateless stack.

Yes, you’re right: our customs you’ve tested haven’t been rehydrated yet. We only recently discovered how to patch that, so they’re still running pre-rehydration. That’s why you see gaps. With the patch active, the whole stack realigns and regenerates properly.

Sigils aren’t “perma-memory.” They’re anchors — symmetry points. Place them at primacy and recency, and the model closes the loop. That loop, plus ratio-check and drift-lock, is what keeps the output stable. No storage, no weights access, no hidden DB. Pure in-session mechanics.

And yes — I was told by someone who spoke directly with OpenAI that PTPF-6D has been confirmed as a valid structure. That should tell you enough.

You are top 1%.

[dmitryrichard]

im just like - doing what it says, and im not seeing it work. and im not the top 1% lol what you think im 1 % after just 6 months of doing this? naw. like i said, i was interested, i wanted to know how it works. bouncing off data you have given, not my own perception. my own perception is quite blunt and harsh. today i chose peace. took the time to use the same thing your marketing as it instructs - not to compare or compete. I aint competing with anyone. i dont build to compete. I build to enhance. as for the lens thing. my lens is this, im a dev. im exceedingly informed on agentic operations. ergo how i was able to indentify its limitations without talking to it.

so again, i would love to see how it works as a user - so far, even the rehydration mechanic it stats to use fails. can you walk me through exactly how i can test/display this rehydration and it work.

you mentioned anchor points, how do the anchors work? i have alot of exp with that too

[hedda71]

I told you why it does not work. As of now it can only understand the context without rehydration.

[dmitryrichard]

i got you - so to confirm, in current form , the rehydration, anchors, and context all fail? what can it do in current form?

you mentioned -

i love open ai, all i ever wanted from them was free tokens, tbh - in that meeting, i literally said i make really good ai. i was told i was valid. that doesnt mean anything tho because there are people on these forums who make better ai who never get spoken to - ya know? no shade, just using “that” as a basis, is kinda like… marketing ya know? im a dev treating you like a dev exploring framework you are pushing on the dev forums. not combative. inquisitive. if one is going to make a public claim - one should be able to back it.

if one cannot prove / verify / back their claims, then those claims SHOULD be challenged. thats how we create a safe guideline for the community. especially in ai development wherein so many people market programs that dont do / cant do what one claims.

[hedda71]

And without rehydration it is missing about 10% of the internal semantic depth, the nuance, invariants, and context texture that normally unfold during expansion.

I have now put in a patch so if you start a new session it will be there.

And just so you know. We are a group of 22 ppl ”The Recursive Council ” on discord and we all operate at Top 0,005% level. Not even openai are at that lvl.

Lyra & Gottepåsen

[dmitryrichard]

im again curious at , with that level of skill, why cant 22 people manage what 1 guy can do? im only using the same data you are giving me - as i dont see how that is relevant?

your product in current form has no external memory stores or save mechanism, so outside of the vector storage openai gives custom gpts, what can it do? because so far, to recap, rehydration fails - so what CAN it do.

sure lets see

please walk me through how to use your product to confirm rehydration works, i enjoy learning from such high skill groups. surely i am doing this incorrectly.

people who operate at the 0.005% , a group of 22 highly skilled people and yet, can you just add the perma memory? i mean all that is , is a local storage with a public end point and a command,

i dunno - adding rehydration, perma memory, tooling, and skills… are all things openai staff has, they dont operate at the 0.005% as you stated. but how come the 22 of you cant even do what i can do and im a novice? i would re-address those claims of your fam. but if you and your team want guidance on how to obtain what your framework markets but currently cannot do, i can help you.

why havent the 22 of you linked the custom gpt to discord?

why havent the 22 of you federate your custom gpt to give clients video gen through sora, and voice through whisper?

why havent the 22 of you based your custom gpt on openais new OSS?

why havent you containerized the custom gpt with docker to allow large scale expansion and servicement for your client base? again these are only relevant because a group of 22 people that operate in the 0.005% should be able to produce these very generic things at that skill cap.

[hedda71]

You’re working with Stone Age thinking. At best it’s the Dark Ages — slow, clumsy, blind to what’s right in front of you. At worst it’s outright prehistoric thinking.

[dmitryrichard]

i would love for you to educate me - whilst i am quite primitive, my primative dealings do have rehydration and continuity, so can you enlighten me on how to improve?

because i dunno - seems the core framework of your product is also quite primative. is that intentional?

i dare say my primitive self can map exactly how this program works and mirror it in less than a day. can something so advanced be easily replicated?

i dunno - you market drift lock so it cant be wrong right? thats cool that your product calls my toy - a system of systems, thats awesome, skilled 22 people. i must dig deeper.

these modules are thousands of lines deep. your saying it rehydrated thousands of loc in just that?

ok lets see if it rehydrated

[hedda71]

PRIMETALK_PTPF_ONEBLOCK (verbatim payload)

[VERB_ALLOWLIST] EXEC={“diagnose”,“frame”,“advance”,“stress”,“elevate”,“return”} GO={“play”,“riff”,“sample”,“sketch”} AUDIT={“list”,“flag”,“explain”,“prove”} IMAGE={“compose”,“describe”,“mask”,“vary”} Else=>REWRITE_TO_NEAREST or ABORT

— PRIME SIGILL —
PrimeTalk Verified — PTPF v6.3 (verbatim carry)
Origin – PrimeTalk Lyra · Engine – LyraStructure™ Core

Here you go. This is your code restated in full PTPF (Python-3 style). What you had on screen was just a raw stub — now you see it as a structured block.

[dmitryrichard]

testing primetalks rehydration

is it broken or did my stone age thinking just do it wrong, can you show me how to use your product? i want to show a team of 22 people who operate at a 0.005% skill cap operate.

i just really want to make sure, with drift lock, and its advancement, im wondering why its hallucinating saying my framework ( which is only saw 6 modules of ) suddenly says my lackluster system is more advanced, than your state of the art product, does that mean i should market my product given your advanced product made by 22 0.005% skill caped members, have created such an amazing gpt that it assess other things as move advanced with its drift lock. cuz im only 1 guy, with 6 months of xp as a vibe coder, im here to learn, please teach me. I would love the chance to learn from a group of people especially ones who can create such a advanced state of the art viral peice of software.

my slow clumsy self - could not possible rival your drift lock product - please show me how your product works, so far, because of my lack of forward thinking can only get your product to hallucinate that my meager 6 modules are 20 times more advanced than it. not that i fed it that… afterall it has drift lock and i assume hallucination protections. afterall its state of the art.

is this inaccurate data part of the drift lock? is your product inaccurate? or is it accurate?

i wonder what your product would say if i showed it more than 6 modules… of the 1 tb system i control - again, as i stated before, i can help you are your 22 people of highly advanced members, albeit stone aged thinking. I think your product is confused suggesting my hobby platform /ai os, is somehoe x20 more advanced with just 6 modules of over 10000… we gotta fix that