The more I think about SAUCE, the more I think that a classic search-engine comparison is one of the easiest ways to understand why it matters 🔍 and before we get started here, i think clarification of what sauce stands for is valuable context: 𝐒 𝐀 𝐔 𝐂 𝐄: 𝐒ource 𝐀ttribution & 𝐔sage 𝐂ompensation 𝐄ngine Anyway, now that we have that out of the way, let's get going. Search engines have always helped people discover public information online. But some of the most valuable knowledge is not public. It can't be public, as it would immediately lose its value. The rise of LLMs will continue to make this exponentially worse, as agents continue trawling the internet 24/7, scraping as much knowledge and data as they can to try and obtain an edge within a particular domain. Of course, the classic paywall model has been available for many years, with subscriptions offering paid access to expertise knowledge. But, the thing about traditional paywalled content is that once you're behind the wall, you can see EVERYTHING, and the platform charges you that price regardless of how much you consume. For example, I myself have had a @RealVision subscription for many years now, but if i'm being totally honest with, i've never maximised (not even close) my access to all of the deep knowledge and expertise that people like @RaoulGMI have been putting out for over a decade now. If a platform such as RV adopted a SAUCE-style model, users would only need to pay for knowledge/alpha they benefitted from. So SAUCE represents the arrival of an entirely new model for agentic knowledge-economies. Real world knowledge, insight and expertise can now be accessed and paid for at a rate relative to the direct answer your question triggers. And here's a really cool part.....if SAUCE doesn't have any relevant source material to be able to sufficiently answer your question, it tells you that, and charges you nothing. And that's something we most definitely do NOT see from all these LLMs! 😬😂 This actually leads me to a point I want to REALLY elucidate here: Unlike traditional AI tools, including LLMs, which are models built in to of training data, or that scrape the entire internet to try and piece together a suitable answer for the user, SOURCE only consults the source materials and knowledge it has on record. SAUCE evaluates and values submitted knowledge so it can determine how useful that source is, when it should be used, how it should be attributed, and how much contributors should be compensated for their knowledge. And although this post is not an architectural breakdown, it is most definitely worth highlighting the role @TracNetwork plays in making this possible. The knowledge itself does not need to be public or stored on Trac. That would defeat the whole point of SAUCE, because the contributor’s source material could be viewed/copied. Instead, SAUCE keeps the knowledge protected, while Trac stores proofs around all activity within the platform's knowledge economy. This can include proofs of source, content hash, vaulting, valuation, usage, attribution, compensation, removal, revocation, and $TAP burn activity. In other words, the content stays gated, but all the important events around that content are immutably provable. This entire model has huge future implications for enterprise adoption of decentralised technologies along with agentic systems and processes. And the fact that any token, from any chain/network (or fiat for that matter) can be integrated direclty into systems such as these when built with @TracNetwork, means that if this model were to take off in coming months and years, then Trac will gain an entirely new group of builders and appreciators around its ecosystem. Many people claim they have 'the sauce', and now we can finally measure it in monetary terms. Follow @fitzyOG for more in-depth breakdowns of innovative technologies. 🤝