Shane Vithana unveils AskSLIP, an AI platform for neurodivergent thinking

By AI, Created 10:40 AM UTC, May 20, 2026, /AGP/ – Shane Vithana has introduced AskSLIP, a state-aware AI platform built to turn unstructured thoughts and real-world context into structured action. The early-stage system is designed for neurodivergent users and aims to improve continuity, memory, and decision-making across daily tasks.

Why it matters: - AskSLIP is aimed at people who think in bursts, fragments, and patterns, including users with ADHD and AuDHD. - The platform tries to reduce the friction of translating unstructured thoughts into next steps. - The system is designed to preserve context across time, which could help users avoid restarting work from scratch.

What happened: - Shane Vithana, a systems developer and co-founder of Tergado, introduced AskSLIP, short for Situational Lifestyle Intelligence Platform. - The Wichita-based product was announced on May 7, 2026. - AskSLIP is described as a state-aware AI platform built to convert memory dumps, shifting clarity, and real-world context into usable outcomes. - Vithana said, “Most systems assume clarity before action. AskSLIP™ is designed to work from wherever the user actually is and help translate that into something actionable.”

The details: - AskSLIP is built on a neurodivergent operating system framework and powered by ERAGIN, which stands for Encrypted Relational Adaptive Graph Intelligence Node. - The platform accepts unstructured input without requiring users to organize thoughts first. - AskSLIP extracts ideas, tasks, and contextual signals, identifies recurring patterns, and connects related concepts across time. - The system can supplement gaps with external information and turn raw input into structured outputs. - AskSLIP is designed to respond to changes in cognition in real time. - When input is fragmented, the platform groups and organizes it. - When focus increases, AskSLIP expands detail and supports execution. - When clarity declines, the system simplifies output and highlights the next relevant step. - ERAGIN uses a node-based memory structure that converts each user input into interconnected nodes in a dynamic relational graph. - Those nodes represent ideas, tasks, context, and time signals. - Relationships between nodes are formed using context, similarity, and usage patterns. - The graph is meant to support contextual linking across past and present inputs instead of keyword retrieval alone. - ERAGIN incorporates encrypted data handling so personal data is not used for external model training without user control. - The adaptive layer strengthens useful connections over time and reduces less useful ones. - The platform supports memory-to-memory retrieval by matching new inputs against existing nodes in the graph. - AskSLIP also uses Situational Lifestyle Intelligence to factor in location, time constraints, budget, interests such as cars, travel, food, and technology, and the user’s current cognitive condition. - The platform builds a Contextual Thought Graph that surfaces earlier ideas, links related concepts, and identifies overlapping patterns across sessions. - AskSLIP is positioned as a State-Aware Cognitive System rather than a chatbot or traditional productivity app. - Initial prototypes focus on memory processing, adaptive interaction, relational graph architecture, and context-aware decision modeling. - Future development is expected to expand into adaptive scheduling, lifestyle optimization, and deeper integration with external systems and data sources. - Vithana has spent more than a decade working on analytical systems, simulation models, and human-machine interaction.

Between the lines: - AskSLIP is part of a broader effort to move AI away from generic prompt-and-response tools and toward systems that adapt to the user’s cognitive state. - The emphasis on memory, continuity, and context suggests the platform is trying to solve a real workflow problem: keeping track of unfinished ideas and decisions across sessions. - The architecture also reflects a bet that structured memory can be as important as language generation in practical AI use.

What’s next: - The platform remains in early-stage development. - The team is expected to keep working on prototypes tied to memory processing and decision modeling. - Future versions may add scheduling, lifestyle optimization, and more external data integrations. - The AskSLIP and ERAGIN names are being introduced as identifiers for the platform and its architecture.