Cryptographic attestations for meta layer operations

MetaProofs are cryptographic attestations designed to operate at the meta layer level, providing a robust framework for 

abstracted with rewards for attestations. Unlike traditional proof mechanisms that operate on a single chain, 

MetaProofs create a chain-abstracted layer 

that can validate and attest to operations across multiple network dimensions simultaneously. 

 where a source maintains the event authority and deterministically provides a ZK validation hash which allows the validator to attest and receive the reward. The validator could be on the same network (Solana) or cross network (Solana source, Bitcoin Validation). 


What sets MetaProofs apart is their ability to operate at the meta layer level. This abstraction allows the system to:

: Support various underlying blockchain protocols without modification.

: Trustlessly, facilitate operations across different blockchain networks.

: Process high-volume transactions without compromising security.

: Adapt to different use cases and requirements

Enhanced Security Through Cryptographic Attestations

MetaProofs support use of many types of cryptographic techniques to ensure the integrity of every operation, this includes:

: Multi-party signatures that validate transaction authenticity

: Efficient verification of transaction inclusion in the meta-layer

: Time-bound proofs that prevent replay attacks

: Links between related operations for comprehensive audit trails

In DeFi applications, MetaProofs can facilitate:

In gaming ecosystems, MetaProofs support:


Today we're using MetaProofs to help users Pop Bubbles 🫧 and make predictions🫰on everything from token markets to gaming competitions. Now they can instantly claim their rewards and winnings with no latency or man-in-the-middle.... And I'm totally here for it! 


We're validating activity and rolling up to our zk net🩸to standardize reporting and making it easier for anyone to access high-scalable historic data, low latency activity and state across the applications and ecosystems they care about - regardless of the chain of origination. 


MetaProofs are the connective tissue helping us on our journey to a  MetaChain.


Oh ... and is it quantum-safe? OF COURSE! But that just sounds so gaye to say. Remember 



These tests demonstrate the attestation pattern:.




Special thanks to @eternitybro for co-authoring MetaProofs!

MetaProofs

Economic agents need data and hyper context.


"The ultimate UX pattern for autoFi is chat — full stop" - h

MetaModels: Adaptive Model Architecture for Autonomous Economic Agents

MetaModels provide a dynamic, context-aware operational frameworks that enable agents to adapt their capabilities, knowledge base, culture and behavioral patterns based on specific use cases and environmental conditions.

The strategy enables truly autonomous, incorruptible economic agents capable of governing complex autoFi workflows.


We created MetaModels to power GEOFF. The fundamental principle behind GEOFF is algorithmic incorruptibility in the context of autoFi. By operating through predetermined MetaModels rather than real-time human input, GEOFF cannot be bribed, manipulated, or influenced by external actors seeking to game the system.


GEOFF dynamically adapts its operational contexts through MetaModels.

The MetaModels enable lots of context engineering. Having environmental context with things like page improves MetaModel usage 324%. 

Originally we were using Ai simply for response entropy for Mining Rig Insights. Our thought was simple wrap the insights into a remix prompt so that the message was already fun and fresh as well as consistent with the data.


And then we started exploring ways to extend that. One of the main challenges we ran into was context poisoning where negative output surfaced due to the oversized context. To solve, we broke the MetaModels down into atomic parts and tapped into a router in instances where data could be easily enriched  which improved reasoning dramatically. 

it reads raw objects and wraps it into a text based request template:

This simple pattern taught us that 

. User requests provide multiple context layers:

Economic context (financial position, transaction history)

Participation context (community engagement, reputation)

Technological context (tools available, platform capabilities)

Environmental context (current page, session state)


While some data is resolute (dollars rewarded, active users, metric summaries), other data is surfaced through periodically snapshot by other models. For community memories, GEOFF communicates with Grok4 and periodically asks about users, profiles, sentiment, etc and saves it into a memory for later use: 

MetaModels serve as comprehensive operational blueprints that define every aspect of GEOFF's behavior in specific contexts. Each MetaModel encapsulates:

: Ethical, Economical and operational boundaries

: Domain-specific knowledge and understanding

Each MetaModel consists of six primary components:

: PDF, MD, TXT, DOC files processed through advanced RAG. Converted to embeddings that give each MetaModel guard rails both conversationally and functionally, and include configurations for additional data requests. 

: Cached web content for continuous knowledge updates

: Structured question-answer pairs for common queries

: Very specific functional calls for user data, network data, onchain data (example: 


Approaching in by breaking MetaModels into atomic parts and using routers help overcome today's LLM limitations.

Putting it all together, from an Incoming request:

🖥️ Page, User, Existing Context, Existing Conversation
🗄️ Get User Data: 

{ swaps: 45, isPro: false, twitter: @userX || user }


🗄️ Get Network Data
🔢 Transform Data
🧠 Get Historical Memory 
🧬 Route context to matching MetaModel
💭 Think 
🔎 Find objective 
⚡ Do (If applicable)

An inbound request is formatted for the Super Prompt:

Stats are included to enrich context responses:

Once you have user data, stats and request data you can then use the MetaModel components to wrap into a SuperPrompt:

: Breaking MetaModels into atomic components prevented negative outputs from oversized context

: Router-based data enrichment dramatically improved decision-making quality

: Modular design allows for easy expansion and customization

Now simple intent is enriched into SuperPrompts that not just improve technical performance, but in their ability to create more equitable, efficient, and sustainable economic agents that serve a collective good while maintaining individual agency.


This architecture represents a new paradigm for autonomous economic agents. By combining algorithmic incorruptibility with adaptive intelligence, we're building systems that can be trusted with real economic decisions at scale.



Thanks to everyone building and contributing to GEOFF: 
@pauly0x @eternitybro  @phunk2243 @soupydev @shannonNullCode @jakegallen_

And My little guy who setup the model router! (He take after me, I'm the big 🍎 tree, and you know the 🍎 don't fall too far from it - ngl proud pappy moment, on G! )

MetaModels

Needing verifiably provable data from any network, chain, or off-chain source is ridiculously expensive when using token-based "commercialware." Sometimes you just need the ability to get data into your program or smart contract for complete onchain operations without breaking the bank, buddy.

MetaVaults aren't just another data feed, they're a complete metachain data oracle infrastructure. Using a dual-mode architecture enables them to be used for expansive cases.



How We're Actually Using MetaVaults (Instead of theory):


We've been using the vaults in production for the following:




RFQ market filling, pricing oracle for direct order execution.



 and context memories that wrap into "Super Prompts" for improved AI reasoning and responses.




Slippage protected token price oracles. Scores, game stats.




Now powering our APIs and PRO products.




Winner selection, chaos generation and bot mitigation.




Cross chain user level activity history generated by deterministic paths. We're using it for lock vesting and claims. 


Automating mining revenue share from attested cross chain auction winner. 





Hash/Header comparing and validating functions (code) before executing them onchain within a transaction.  


Mashup of onchain functions being called via proofs, oracles and merkles for pure autoFi magix!



Dual-Mode Architecture - MetaVaults operate in two powerful modes:

Real-time token data feeds with sub-second updates

Built-in staleness validation and freshness guarantees

Slippage protection preventing manipulation

Support for any token, market or custom data source

Perfect for metered system, premium data feeds, API access, and membership systems


The Subscription Based Time Oracles use reusable templates which automatically inherit preset configurations for MetaVaults ensuring security and preventing manipulation (including false positives). Templates can establish custom pricing, duration, and access rules. Perfect for:


We've been actively backfilling our current tools and integrating MetaVaults into our upcoming feature releases. There is a dashboard and SDK. We use the SDK in GEOFF today.

100% of subscription fees go to template creators.
100% of vault creation fees go to 


Brick by brick - and then you have a MetaChain 🩸