Blog
Deep dives into context infrastructure, agent architectures, and the systems that help AI reason over relationships.
Hypergraph Based Memory: Why Pairwise Graph Memory Isn't Enough for AI Agents
Most AI memory systems store knowledge as binary triples. Hypergraphs preserve the natural structure of multi-participant facts, setting a higher ceiling for what retrieval can recover.
What Ancient Sanskrit Solves in AI Memory
Pāṇini's 2,500-year-old kāraka roles provide a minimal, language-universal vocabulary for labeling how participants relate to actions — turning vague entity lookups into structured semantic search.
The Embedding Problem: Extracting Structure from Text
Text embeddings conflate structure — 'Alice gave Bob a book' and 'Bob gave Alice a book' have nearly identical embeddings. Structured extraction with Kāraka roles recovers what embeddings lose.
The Forgetting Problem: How Neuroscience Solves AI Memory
Not all memories are equal — episodic events should fade, semantic facts should persist, procedural skills should endure. Neuroscience-inspired memory types solve the forgetting problem for AI agents.
The Math Behind What Your Agent Remembers or Forgets
An ACT-R-inspired formula combining recency, frequency, salience, and confidence determines memory strength — governing what surfaces during retrieval and what fades into oblivion.
The Retrieval Problem: What Neuroscience Knows About Finding Memories
Embedding search alone misses structural connections. Dual-arm retrieval — combining hypergraph traversal with semantic search via Reciprocal Rank Fusion — covers both structure and similarity.
Lossless Claw: How DAG-Based Compression Stops AI Agents From Forgetting
How Martian Engineering's DAG-based compression replaces truncation with hierarchical summaries. Why lossless session memory changes everything for long-running AI agents.
Tobi Lütke's QMD: Why Shopify's CEO Built His Own AI Memory System
How Shopify's CEO built an on-device search engine that gives AI agents persistent memory across sessions. Why local-first retrieval is the missing infrastructure for AI-first workflows.
Garry Tan's GBrain: The Memex We Were Promised
How Y Combinator's President built a production AI memory system that reads before every response and writes after learning. The pattern every agent builder should understand.
Andrej Karpathy's LLM Wiki: Why the Future of AI Memory Isn't RAG
How Karpathy's wiki pattern replaces RAG with compiled knowledge that compounds. Why synthesis beats retrieval for AI memory systems.
LongMemEval Explained: The Benchmark That Tests Agent Memory
LongMemEval is the ICLR 2025 benchmark for evaluating long-term memory in conversational AI. Learn what it tests, why it's hard, and how to read benchmark claims critically.
Milla Jovovich's MemPalace: What a 100% LongMemEval Benchmark Score Reveals About AI Memory Design
MemPalace achieved the highest local-only retrieval score on LongMemEval by storing everything verbatim. We analyze what this reveals about the extraction vs. verbatim debate in AI memory design.
KAIROS and the End of Stateless Agents: What Anthropic's Architecture Reveals About Production Memory Systems
The leaked KAIROS system inside Claude Code reveals a paradigm shift in how coding agents handle memory. Learn what append-only logs, async consolidation, and cross-device sync mean for production agent architectures.
Persistent Memory for AI Agents - A Landscape
Every agent you've used has amnesia. Explore the six hard problems of persistent memory — extraction, retrieval, staleness, synthesis, forgetting, and abstention — and why flat fact stores can't solve them.
Why You Need a Context Layer for Your Agent
AI agents start fresh every conversation, forgetting your business context. Learn why context windows aren't the solution and what a proper context layer actually does.
5 Excel Alternatives for Business Analytics in 2025
Excel is a bottleneck for growing teams. Here are modern alternatives from traditional BI tools to Gen-AI powered analytics that can replace your spreadsheet workflows.
Building Reliable and Accurate Analytics Agents: Part 1
Why AI analytics tools don't always give accurate answers and one way to fix it. Learn how context injection improves accuracy for business queries.
The Data Platform Wars: Incumbents vs. Trailblazers in the AI Era
The Modern Data Stack is fracturing. Discover what trailblazers are doing differently and how to modernize your data infrastructure for maximum ROI.