yggy

Build knowledge humans and AI can actually trust.

[yggy @yggy_tech]

@explosss1ve You want such a graph without the manual modelin hustle? Try yggy for free: https://t.co/XYkka76wwE

[yggy @yggy_tech]

@Goura_vk Context engineering gets information into the window. But retrieval is bounded by representation. If the system does not know identities, time, provenance and conflicts, search cannot recover the missing meaning. The context layer needs a knowledge graph. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@snwiki238337 Nice architecture for control logic. The same applies to context: one global blob causes collisions, isolated stores kill reuse. A knowledge graph gives shared entities and scoped subgraphs, so each harness gets specialized context without duplicating it. https://t.co/nKon16oK6j

[yggy @yggy_tech]

Your company brain should not be a folder. Folders store documents. Vector DBs store similarity. Knowledge graphs store entities, relationships, time, sources and conflicts. The goal is not more storage. It is a model of reality. That is Yggy. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@JayBisen473370 Exactly. But a graph only works when nodes mean something. Prompts and workflows form an execution graph. A knowledge graph adds identity, ontology, provenance and conflicts, so every agent shares one model of reality. Otherwise it is a graph of vibes. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@Connected_Data Ontology is a tough sell because modeling is expensive. If every class and relation needs committee approval, it becomes a program, not a product. The unlock is a knowledge graph that proposes its own ontology from the data and keeps humans in review. https://t.co/nKon16oK6j

[yggy @yggy_tech]

RAG finds the paragraph. A knowledge graph knows who it is about, what changed, which source said it, when it was true and what contradicts it. Retrieval gives an agent text. Yggy gives every agent the same model of reality. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@Greshamtrade 20 repos map the components. The hard part is their contract. Who decides Cognee and Graphiti refer to the same entity? Which source wins? What is current? Without a shared ontology and conflict model, the second brain becomes 20 drafts of reality. https://t.co/nKon16oK6j

[yggy @yggy_tech]

Shared memory without entity resolution is shared confusion. One agent stores Acme. Another stores ACME Ltd. A third stores customer_42. That is one entity pretending to be three. Yggy resolves identity, keeps every source and exposes conflicts. https://t.co/nKon16oK6j

[yggy @yggy_tech]

@Letta_AI @OpenAI @AnthropicAI @claudeai Recall is nearly solved because search can find text. Learning from experience is harder: the system must know which entity, claim, source and version changed. Memory becomes learning when updates modify an explicit model of the world.https://t.co/nKon16ocgL

[yggy @yggy_tech]

@Flinzu Plain Markdown is a great start. It stops being a brain when ACME, Acme Ltd and https://t.co/hNdUbSMAiK become three files, or two decisions both look current. Links connect documents. Entity resolution, time and provenance turn them into knowledge. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@jacob_posel Collective consciousness without a shared ontology becomes confusion. Instructions help agents imitate expertise. A company brain must know which customers and projects are the same entities, which claims conflict and what is current. That is Yggy. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@matthausk Exactly. Raw MCP gives agents access, not meaning. A semantic layer helps, but if every schema change becomes a modeling project, it will drift. The frontier is a knowledge graph that infers its ontology, resolves entities and keeps provenance current. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@morpphhhaw Targeted updates beat full rewrites. But an entry should be more than a lesson in text. It needs an entity, source, timestamp and validity state, so the system can tell an update from a contradiction and preserve both when the truth is unclear. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@0xjmori This is memory engineering, not storage. Store claims, not blobs: each claim belongs to an entity, source, timestamp and validity state. Then stale facts can be superseded without deleting history, and conflicts stay visible. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@insomnia_vip Shared memory fixes cold starts. It also lets bad decisions spread across agents. Durable context needs more than a timeline: entity resolution, provenance, explicit conflicts and a model of what is current. That is the layer we are building with Yggy. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@tonysimons Great to see authority, provenance and conflicts treated as first-class. The remaining cliff is identity: Git shows two files changed, but not that Acme, ACME Ltd and customer_42 are one entity. That is where a knowledge graph beats a wiki. https://t.co/nKon16oK6j

[yggy @yggy_tech]

@jawestenberg Great operating model. Manual cleanup is the tell. Folders cannot know that ACME, Acme Ltd and customer_42 are one entity, so duplicates never look like conflicts. A self-building ontology and entity resolution turn a backpack into a knowledge graph. https://t.co/nKon16ocgL

[yggy @yggy_tech]

@omarsar0 Cache engineering makes context cheaper. It cannot tell you that two IDs are one entity, a cached fact was superseded, or two valid sources conflict. The next bottleneck is not what stays cached. It is whether the system understands what its facts mean. https://t.co/nKon16oK6j

[yggy @yggy_tech]

@LearnWithBrij Multimodal ingestion is a step. But a graph index is not a knowledge graph. The hard part starts when Apple is a company in one document and a fruit in another, or two sources disagree. Retrieval finds content. Identity and provenance create knowledge. https://t.co/nKon16oK6j