The Stack Behind Ephemeral Interfaces

For AI to become truly useful in our daily lives, it requires more than just raw data — it needs high-quality, structured information that retains context while remaining deterministic and reliable. This isn’t simply about having more data; it’s about having the right kind of data organized in ways that enable contextual understanding.

As AI systems shift from simply generating content to curating, organizing, and presenting information in context-appropriate ways, the quality of their underlying data becomes increasingly critical. Current AI systems can produce vast amounts of content but often struggle to organize it in ways that mirror human cognitive processes. This creates a fundamental gap between AI’s capability to generate information and our ability to effectively use it.

The solution to this gap lies in high-quality data that embodies two complementary dimensions: semantic richness and deterministic structure. Semantic richness captures meaning, intent, and conceptual relationships, enabling systems to understand context and relevance beyond simple keyword matching. Deterministic structure provides clear, reliable organizational frameworks that allow systems to navigate relationships with confidence and consistency.

Together, these dimensions create a foundation where data can be flexible and context-aware, yet still structured enough to be reliable and actionable. This dual nature of quality data is what will enable the next generation of AI systems — whether they materialize as ephemeral interfaces or take other forms — to interact with information in ways that align with how we naturally think and work.

The stakes are high: as AI-generated content continues to proliferate, our ability to organize, filter, and make sense of this information becomes increasingly crucial. Without systems that can effectively manage this flood of content, we risk drowning in information while starving for insight. The universal data layer and its supporting technologies represent our response to this challenge — turning overwhelming data into contextual, meaningful information.

Among the most critical requirements for advanced AI systems is the ability to maintain context across interactions. Every time we interact with an AI system, we build up valuable contextual knowledge — clarifications we’ve made, preferences we’ve expressed, insights we’ve developed together. Yet in most current systems, this context is fleeting, lost when a session ends or a conversation grows too long for the model’s context window.

This limitation creates two significant challenges that must be addressed for AI to reach its full potential:

Continuity across sessions

Current AI interactions often feel disjointed and repetitive because systems lack persistent memory of previous conversations. Each new session starts nearly from scratch, forcing users to reestablish context, restate preferences, and rebuild the shared understanding that had previously developed. This creates significant friction, particularly for complex, ongoing tasks that naturally span multiple sessions.

What’s needed is state preservation — the ability for AI systems to remember not just what was explicitly said but the implicit context that developed through interaction. This includes understanding what the user already knows, what approaches have been tried, what terminology has been established, and even the emotional tone of previous exchanges.

Consider how frustrating it is when you’ve spent hours working with an AI on a complex project, only to have that shared context erased when you return the next day. True state preservation would allow you to pick up exactly where you left off, with all the nuance and understanding intact — just as you would with a human collaborator.

Relevance is personal

The second critical challenge is understanding what’s relevant to each specific user. As AI systems gain access to increasingly vast information sources, the challenge isn’t just finding information but finding the right information for this particular person in this specific context.

This requires intent recognition that goes beyond surface-level understanding of queries to grasp deeper patterns of interest, thinking styles, and current goals. The system needs to build a model of the user’s knowledge landscape — understanding what they already know, what they’re trying to learn, and how new information connects to their existing understanding.

Intent recognition must be personalized and contextual. The same query from different users should yield different responses based on their history, expertise, and current projects. Similarly, the same query from the same user in different contexts should be interpreted differently based on what they’re currently working on.

Together, state preservation and intent recognition form the foundation for truly personalized AI interactions. They allow systems to maintain the valuable context built through conversation and to surface information that’s specifically relevant to each user’s unique needs and interests. These capabilities are essential not just for ephemeral interfaces but for any AI system that aims to provide consistently valuable assistance across multiple interactions.

Addressing these challenges requires a technical architecture that can bridge the gap between siloed applications, maintain rich contextual information, and support fluid, personalized interactions. Drawing from concepts explored in auto-associative workspaces, the universal data layer architecture consists of several interconnected layers, each handling specific aspects of data management and contextual awareness.

Adapter layer

At the foundation of universal data layers is the adapter layer, which serves as a bridge between diverse information sources and the system. This layer monitors varied input streams — email, messaging apps, web content, notes, and more — standardizing their formats for consistent processing while maintaining connections to source platforms.

The adapter layer addresses the immediate challenge of information silos by creating standardized pathways for data to flow between previously isolated systems. Rather than forcing a complete reinvention of the digital ecosystem, it enables evolutionary change by connecting existing systems through common protocols and data formats.

This modularity ensures the system can evolve with changing information landscapes. New adapters can be developed as new platforms emerge, allowing the universal data layer to grow and adapt over time without requiring fundamental architectural changes.

Ingestion layer: provenance and history

Building on the connections established by the adapter layer, the ingestion layer manages information provenance and version control. It processes standardized input, maintains source attribution and authorship information, implements version control for tracking content evolution, and creates stable addressing mechanisms for content identification.

This layer’s focus on provenance is crucial for maintaining trust in a system that integrates information from multiple sources. By tracking where information came from, when it was created, and how it has changed over time, the ingestion layer ensures that users can verify the source and reliability of information even as it flows across traditional boundaries.

The stable addressing mechanisms this layer provides are equally important. They allow the system to maintain consistent references to information even as the underlying sources change or evolve, creating the foundation for persistent relationships between different pieces of information regardless of where they originated.

This focus on provenance tracking and diff chaining aligns with approaches being developed in blockchain projects, where merkle trees and similar cryptographic structures provide tamper-evident verification of data integrity and history. While the universal data layer may not require the full decentralization of blockchain systems, these proven approaches to maintaining data lineage and version history offer valuable patterns for ensuring trust and reliability in a fluid information landscape.

Memory layer: associative core

At the heart of the universal data layer architecture is the memory layer, which handles the core associative functions necessary for contextual understanding. This layer organizes information not just hierarchically or categorically, but associatively — mimicking how human memory processes data.

The memory layer uses a hybrid approach combining two complementary technologies:

1. Graph databases establish explicit connections between pieces of information, creating a network of relationships that can be traversed and explored. These connections might represent direct references, dependencies, or semantic relationships.

2. Vector stores capture the meaning and context of information in a way that can evolve over time. By converting text and other content into mathematical representations (vectors), these systems can identify semantic similarities and discover implicit connections.

Consider how this works in practice: When a researcher is exploring climate science data, the memory layer might connect a specific data point about ocean temperatures to:

• Related research papers (through explicit citations in the graph database)
• Similar temperature patterns from different regions (through semantic similarity in the vector store)
• Previous notes the researcher has taken (through both explicit links and semantic matching)
• Relevant visualizations and datasets (through contextual relationships)

This hybrid system allows the memory layer to learn and adapt as new information emerges, maintaining both the precision of explicit relationships and the flexibility of semantic understanding. The graph database ensures reliable navigation of known connections, while the vector store enables discovery of unexpected relationships and patterns.

This space is currently being addressed by various projects and technologies: vector databases like Pinecone and Weaviate provide the semantic search capabilities, while GraphRAG architectures (such as those developed by WhyHow.ai) combine these with knowledge graphs. Additionally, curated domain-specific knowledge graphs are emerging as valuable resources for specialized fields, demonstrating how structured knowledge can enhance AI capabilities in specific domains.

Context-aware retrieval

The retrieval layer orchestrates how information is discovered and presented based on user context and intent. Unlike traditional search engines that respond to explicit queries, or basic AI assistants that follow predefined patterns, this layer maintains continuous awareness of the user’s context and proactively surfaces relevant information.

Understanding Context-Aware Retrieval

Context-aware retrieval goes beyond simple keyword matching or semantic search. It considers multiple dimensions of context:

• The user’s current task or project
• Their recent interactions and explorations
• Their established preferences and patterns
• The broader context of their work or interests

For example, when a user is writing about climate change, the system doesn’t just look for documents containing “climate change” or related terms. It understands the specific aspect they’re focusing on, their level of expertise, and how this connects to their previous work.

How Retrieval Agents Work

Retrieval agents are specialized components that use this contextual understanding to find and present information. They:

1. Monitor user activities to understand current context
2. Generate targeted retrieval tasks based on this context
3. Combine graph traversal (following explicit connections) with semantic search (finding related content)
4. Balance focused search with serendipitous discovery

These agents don’t just find information — they understand how it relates to the user’s current needs and present it in ways that support their thinking process. When a user marks certain information as particularly helpful or unhelpful, the system learns from these interactions, refining its understanding of what’s relevant in different contexts.

This context-aware approach reduces cognitive load by bringing relevant information to users based on their current activities and interests, rather than requiring them to formulate precise queries or navigate complex information structures.

Integration layer: user experience

The integration layer serves as the crucial bridge between users and the underlying data infrastructure. Its primary responsibility is intent recognition — understanding what users are trying to accomplish and translating that into appropriate actions across the system. This layer deploys specialized agents that iteratively refine retrieval queries until they find information that sufficiently answers the user’s needs and can be linked to accurate sources.

A key aspect of the integration layer is its feedback loop with the memory layer. When users interact with retrieved information — for example, marking a source as unhelpful or particularly valuable — the system adjusts the weights on the corresponding relationships in the memory layer. This continuous learning process helps the system become more attuned to each user’s preferences and needs over time.

The challenge here is maintaining responsiveness while keeping the system’s behavior understandable. By making the connection between user actions and system responses clear, the integration layer helps users build an accurate mental model of how the system supports their thinking. It turns what could be overwhelming complexity into a clear, usable experience that feels like a natural extension of the user’s thought process.

Note: On top of these layers sits an orchestration layer where most “agentic AI systems” currently operate. This layer coordinates multiple agents, manages workflows, and handles complex multi-step tasks. While the layers below handle data infrastructure and retrieval, the orchestration layer is where agents make decisions, plan sequences of actions, and coordinate their activities. This is the domain of frameworks like LangChain, CrewAI, and AutoGPT — systems that orchestrate agent behavior but rely on the underlying memory and retrieval infrastructure to function effectively.

Together, these layers create an architecture capable of supporting truly contextual, personalized AI interactions. Each layer builds on the capabilities of those below it, turning raw data into context-aware, personalized experiences that adapt in real-time to changing user needs.

One of the most compelling aspects of universal data layers is their ability to preserve valuable information that would otherwise be lost between interactions. This includes both explicitly created content and what we might call “liminal” or “transient” data — the ephemeral insights, connections, and contexts that emerge during AI interactions but often disappear when a session ends.

Conversation value: beyond explicit artifacts

When we engage with AI systems, we create two distinct types of value: explicit artifacts (like documents, code, or images) and implicit context (the shared understanding that develops through conversation). Traditional systems focus almost exclusively on preserving explicit artifacts, but much of the value in AI interactions actually lies in this implicit context.

Consider a long conversation with an AI about a complex topic. Over the course of this exchange, you build up shared terminology, explore various approaches, discard some ideas and refine others. The AI comes to understand your specific interests and how to frame information in ways that connect to your existing knowledge. This emerging shared context represents significant intellectual value — often more valuable than any specific artifact produced.

Yet in current systems, this context is extremely fragile. It exists only within the temporary constraints of a session’s context window and disappears entirely when the session ends. The emotional impact of losing this context can be surprisingly strong — a feeling of valuable shared understanding simply evaporating, forcing you to start rebuilding it from scratch in your next interaction.

Liminal data

Liminal data exists in the spaces between explicit artifacts — the exploratory paths, the half-formed ideas, the contextual awareness that develops through conversation but isn’t explicitly captured in saved outputs. This includes:

• Clarifications and refinements made during conversation
• Terminology and concepts established through dialogue
• Abandoned approaches that inform current understanding
• Implicit user preferences revealed through interaction
• The trajectory of thought that led to current conclusions

Universal data layers provide mechanisms for preserving this liminal data across sessions. Rather than losing valuable context when a conversation ends, the system maintains a persistent record of both explicit artifacts and their surrounding context. This allows new conversations to pick up where previous ones left off, with full awareness of the shared context that had developed.

This liminal space represents a crucial learning environment where users often make their most significant discoveries with AI systems. It’s in these unstructured, exploratory moments — free from rigid goal orientation — that unexpected connections emerge and novel insights take shape. When users encounter surprising perspectives or alternative ways of thinking, it frequently happens in this liminal space where the AI can suggest unexpected connections that challenge existing assumptions and spark new understanding.

From ephemeral to persistent

The ability to preserve liminal data changes the emotional experience of working with AI. Instead of the frustration and loss that comes from constantly rebuilding context, users experience a sense of continuity and growth. The system remembers your journey — recognizing how your understanding has evolved and building on previous insights rather than starting anew each time.

This persistence creates practical benefits as well. Complex intellectual work often requires extended exploration across multiple sessions. By maintaining context across these sessions, universal data layers dramatically reduce the cognitive overhead of resuming work. You no longer need to spend the first part of each session rebuilding context — instead, you can immediately dive into new territory, building on the foundation already established.

Provenance and trust

As information flows more freely across traditional boundaries, tracking its origins becomes increasingly important. Universal data layers must incorporate reliable provenance tracking that maintains clear attribution and history as information moves through the system.

This goes beyond simple source attribution to include the context in which information was created or discovered, how it has evolved over time, and the relationships between different versions. By maintaining this rich provenance information, universal data layers enable users to verify the reliability and authenticity of information even as it flows across traditional application or platform boundaries.

It ensures that users can always trace information back to its origin, understand its context, and make informed judgments about its reliability and relevance.

As universal data layers evolve, the retrieval mechanisms that power them are becoming more capable. This evolution can be traced from basic Retrieval Augmented Generation (RAG) through GraphRAG to emerging approaches that address the limitations of current systems. Each advancement brings new capabilities for supporting contextual, personalized interactions.

RAG basics

Traditional RAG systems aims at advancing an AI systems capabilities by connecting large language models with external knowledge sources. These systems break documents (or past conversations) into chunks, create vector embeddings for each chunk, store these embeddings in a vector database, and retrieve relevant chunks based on query similarity to augment the AI’s prompts with this retrieved information.

This approach enables AI to access and use information beyond its training data, improving its ability to provide accurate, up-to-date responses. However, traditional RAG systems have significant limitations when it comes to supporting persistent context and personalized interactions.

The primary issue is that standard RAG treats documents as flat collections of independent chunks, with little consideration for their structural relationships or contextual connections. This results in several critical limitations:

• Loss of document hierarchy and structure
• Limited contextual awareness beyond the chunk level
• Inability to navigate information at different levels of detail
• Difficulty maintaining coherence across related but separate chunks
• Challenges in supporting complex, multidimensional queries

For systems that need to maintain rich contextual awareness across multiple interactions, these limitations create significant obstacles. A system that can only retrieve flat chunks of information lacks the nuanced understanding needed to preserve and navigate complex contextual relationships.

GraphRAG adds structure

GraphRAG emerged as a response to these limitations, combining the semantic power of vector embeddings with the structured precision of knowledge graphs. Rather than treating information as isolated chunks, GraphRAG systems maintain explicit relationships between entities and concepts, creating a more nuanced representation of knowledge.

This hybrid approach offers several important advantages over traditional RAG:

• Preservation of explicit relationships between entities
• Ability to traverse connected information through defined paths
• Support for structured queries that follow relationship patterns
• Maintenance of hierarchical relationships within information
• Greater contextual awareness through relationship networks

By adding this structural dimension, GraphRAG systems can better support persistent context by understanding not just what information is relevant but how it relates to other information. This enables more structured information retrieval and presentation, allowing systems to maintain and navigate complex contextual relationships across multiple interactions.

However, even GraphRAG systems have important limitations when it comes to supporting truly adaptive, personalized interactions:

• Many implementations still treat documents as relatively flat collections, even if they maintain some relationships between chunks
• The granularity of information retrieval remains relatively coarse
• Navigating between different levels of detail (from overview to specific details) remains challenging
• Associative relationships based on usage patterns and implicit connections are often overlooked
• Multidimensional traversal across different relationship types is limited

These limitations mean that while GraphRAG represents a significant improvement over traditional RAG, it still doesn’t fully capture the fluid, associative nature of human thought that universal data layers aim to support.

Beyond GraphRAG

Addressing these remaining challenges requires a more structured approach to information retrieval and organization. Recent research has identified several key innovations that could better align with the needs of persistent, context-aware systems.

The key insight behind these emerging approaches is that information exists naturally in multiple dimensions and at multiple levels of detail. Rather than treating documents as collections of chunks, these systems process them recursively, creating a hierarchical representation that maintains both structure and semantics across multiple levels of granularity.

This recursive processing enables several critical capabilities:

• Hierarchical understanding of information, from broad concepts to specific details
• Multi-level semantic networks that connect information at appropriate levels of abstraction
• In practice, this means enabling agents to navigate levels of content granularity — to “spiral” between overview and detail (see How We May Think We Think)
• Preservation of context across different levels of detail
• Connections between concepts and specific sentences or paragraphs within source materials

This is a practical method for handling zoom levels in content.

In plain terms: the system can generate a tighter summary of an idea (zoom out) and a more specific/expanded version (zoom in), while keeping both versions linked to the same source. Instead of forcing users (or agents) to read an entire document or rely on one fixed chunking strategy, you can move up and down the “detail ladder” depending on what you need in the moment.

This bidirectional generation process is conceptually similar to how some foundation model distillation strategies work, like those employed in DeepSeek, where models learn to generate both detailed and abstract versions of content while maintaining semantic coherence. The result is simpler navigation between overview and detail while preserving the underlying meaning and relationships.

This approach to information organization closely mirrors how human cognition works. We don’t think of information as existing at a single level of granularity — we fluidly move between broad concepts and specific details, between abstract principles and concrete examples, creating mental models that connect information across multiple dimensions.

By enabling this kind of multidimensional navigation, these emerging approaches provide the information retrieval capabilities needed for truly adaptive, personalized interactions. A user exploring a complex topic can start with a high-level overview, then dive deeper into specific aspects that interest them, moving between different levels of detail and different types of relationships based on their current needs and interests.

The universal data layers and retrieval technologies we’ve explored provide the essential foundation for ephemeral interfaces — the dynamic, context-sensitive experiences we explored in Part 2 of this series. But how exactly does this technical infrastructure translate into adaptive interfaces that materialize when needed and dissolve when their purpose is fulfilled?

The key lies in how these technologies enable interfaces to respond to specific user needs and contexts. With access to information across traditional boundaries, awareness of relationships between different pieces of information, and the ability to navigate these relationships at multiple levels of detail, AI systems can generate interfaces that present exactly the right information in exactly the right way for the current task.

As we saw in Part 2, consider how a researcher exploring climate science data might interact with such a system. Rather than opening multiple applications and manually coordinating information between them, they simply express an intention to understand the relationship between ocean temperatures and weather patterns. The universal data layer makes this possible by breaking down the boundaries between different information sources, maintaining rich contextual relationships, and enabling retrieval based on meaning rather than location. The ephemeral interface materializes as a temporary manifestation of this underlying data infrastructure, shaped by the user’s current intent and dissolving when no longer needed.

While the vision of universal data layers and ephemeral interfaces is compelling, significant technical challenges remain in fully realizing this vision. Understanding these challenges — and the emerging solutions addressing them — gives us insight into how these systems will evolve in the coming years.

Performance and scale

The information processing required for universal data layers creates substantial computational demands. Vector similarity searches, graph traversals, recursive document processing, and real-time interface generation all require significant computational resources, especially at scale.

Early implementations have demonstrated the feasibility of these approaches, but scaling them to handle the full breadth of a user’s digital life remains challenging. Processing massive personal knowledge bases while maintaining responsiveness requires innovations in indexing structures, query optimization, distributed processing, and intelligent caching.

Privacy and agency

The universal data layer’s ability to access information across traditional boundaries raises important questions about privacy, security, and user agency. For users to embrace these systems, they need confidence that their personal information is protected while still being available for legitimate use.

This challenge requires privacy models that go beyond simple binary permissions to understand context, intent, and appropriate information flows. Granular privacy controls need to operate at the level of specific intents and contexts rather than just at the application level, giving users precise control over how their information is used while avoiding the cognitive burden of micromanaging permissions.

Emerging approaches include contextual integrity frameworks that respect appropriate information flows based on established norms and user preferences, privacy-preserving computation techniques that enable useful processing without exposing sensitive data, and intent-based permission models that align access with specific user goals rather than broad categories.

Adoption and integration

Perhaps the most significant challenge for universal data layers is integrating with the existing digital ecosystem. The current landscape of siloed applications and platforms reflects not just technical limitations but also business models built around controlling access to user data.

Overcoming these barriers requires both technical standards that enable interoperability and business models that incentivize participation in a more open ecosystem. Several trends suggest progress in this direction:

Operating system providers are increasingly implementing cross-app awareness and integration features, recognizing that enhanced user experiences depend on contextual understanding that spans traditional application boundaries. Middleware solutions like browser extensions and integration platforms are demonstrating the value of cross-service access while working within the constraints of current systems. And user-controlled data vaults are emerging as a way to give individuals more control over their information while still enabling integration across services.

Regulatory frameworks like GDPR and CCPA, which include data portability requirements, are also pushing the ecosystem toward greater interoperability. As users come to expect continuous experiences that span different services and platforms, market pressures will likely accelerate this trend, creating incentives for platforms to participate in more open data ecosystems.

The technical foundation we’ve explored — universal data layers with their layered architectures and retrieval systems — represents a fundamental shift in human-computer interaction. By breaking down traditional boundaries between applications and enabling truly context-aware computing, these technologies create the conditions for a new paradigm of computing that adapts to humans rather than forcing humans to adapt to technology.

The key innovations driving this shift are:

• Semantic understanding that captures meaning beyond keywords
• Structured knowledge representation that maintains relationships and context
• Cross-service access that transcends application boundaries
• Persistent memory that preserves valuable context across interactions

While significant technical challenges remain — particularly around privacy, performance, and ecosystem integration — the foundation is taking shape. In Part 4, we’ll explore how this technical infrastructure enables ambient computing systems — where technology is woven into our environments and responds naturally to our needs without requiring explicit commands or attention.