Grounding AI in memory is moving from research to product. Treyspace is an AI-native canvas that gives LLMs spatial awareness by building a knowledge graph instead of re-explaining context from scattered notes. It’s a fork of Excalidraw with a retrieval-augmented engine designed to keep answers grounded in your own context [1].
On the tooling side, grounding tech is slipping into everyday apps. Grapes Studio blends an HTML-first editor with an LLM assistant on top of GrapesJS. You can edit visually or request scoped changes, and the editor stays rooted in straight HTML/CSS. The team notes this hybrid model helps avoid the fragility of AI-only builders like Lovable or traditional WordPress-style workflows [2].
Ticket categorization chatter leans into embedding-driven RAG for the final pass and asks how to keep it cheap and fast as categories grow.
• Embedding + simple MLP — fast way to test and surface category issues; quality hinges on embeddings and data [3]. • 9k categories with dynamic evolution raises maintenance and speed questions; a vector DB may become expensive [3]. • Gemini 2.0 flash and embedding-model choices affect latency and cost [3]. • adaptive-classifier and simpler models can beat a heavyweight LLMS path when data is scarce [3]. • Some advocate using RAG at the final stage to improve nuance in multi-layer categorization [3].
Grounding is becoming a real product feature, not a lab trick—watch how memory graphs land in consumer and enterprise tools.
References
Show HN: Treyspace – A canvas that gives your LLM spatial awareness
Founder unveils Treyspace, an AI-native canvas with a knowledge graph for RAG, grounding LLMs in personal notes and context relevance.
View sourceShow HN: Grapes Studio – HTML-first WYSIWYG website editor with LLM assistant
HTML-first editor combines LLM assistant with drag-and-drop; contrasts with AI-only builders; discusses pricing, portability, and hybrid editing.
View sourceTicket categorization. Classifying tickets into around 9k categories.
Discussion of using LLMs for ticket categorization, embeddings, RAG, latency, cost, and language constraints for low-resource languages in practice today.
View source