AI technology — Maximem field notes
Agentic Context Management: Agent Memory Is Not Merely a Storage & Retrieval Problem, It Is an Architecture Problem
We argue in our latest paper, that agent memory and cost is a lifecycle and architecture problem
Why OpenAI shipped an Official Plugin for Claude Code
OpenAI ships Codex plugin for ClaudeCode harness
An Anthropic Leader Told a Room of Founders to Stop Worrying About Context Windows. Here's the catch
An Anthropic researcher told a room of founders to stop worrying about context windows. Here is the question I did not get to ask, and why a bigger window solves short-term memory with bad tradeoffs and does nothing for long-term memory.
Memory Is Now Table Stakes for AEO Tools. Profound's Launch Just Proved It.
Profound shipped conversation memory. Stateless AEO tools are now behind. Here is the category signal, the three layers they need, and why building in-house is the wrong bet.
Vity vs Obsidian for AI Agent Memory
Technical comparison of Vity and Obsidian for AI agent memory. Covers context rot, entity resolution, cross-AI coverage, setup complexity, and security.
I Spoke to 500+ Voice AI Builders in India Over 3 Months. Here Is What I Found.
Field notes from 500+ Voice AI builders in India. An exploration of outbound dominance, the "too good" TTS problem, and why memory is the final infrastructure hurdle for production agents.
The Real Cost of DIY Agent Memory
Building agent memory from scratch costs $40K-$120K and 6+ months of engineering time. See the real numbers behind DIY memory systems and when to build vs buy.
Why Skills Are The New Microservices
Skills are how modular AI agents scale. Discover why skills replace monolithic prompts, how to architect composable agents, and the context management patterns that make them work at scale.
What Is Agentic Context Management?
Agentic context management is how you orchestrate what information your AI agents see and when. Learn why it's critical infrastructure, not a feature, and how to architect it properly.
Most Agent Eval Frameworks Are Wrong. Here's What Actually Works
Your agent is silently degrading. Move beyond static benchmarks to master AI agent evaluation. This guide explores how to design frameworks that measure reasoning, tool-use, and reliability to bridge the gap between experimental prototypes and production-ready systems.
Voice Agent Stack: The Right Tools for Production Voice AI in 2026
Build production-ready voice agents with the right stack. Compare MCPs, frameworks (CrewAI, AutoGen, Swarms), APIs (Deepgram, ElevenLabs, Vapi), and learn cost-effective patterns for voice AI in 2026.
Image Processing for AI Agents: Embeddings & Vision Models & When to Use Each
Vision models or embeddings? You need both. Discover the hybrid approach that cuts image analysis costs 87% while maintaining quality—with pricing breakdowns for every tool.
Why We Built Synap
AI agents don’t fail from lack of memory, they fail because context doesn’t evolve. This article shows why current approaches break, introduces the Context Management Trilemma, and how Synap enables agents to learn, adapt, and stop forgetting over time.
Why AI Forgets: Why ChatGPT, Claude, and Gemini Don't Remember You Well
AI conversations are stateless by design. Each new chat starts with no knowledge of previous sessions. ChatGPT, Claude do have a built-in memory features that stores basic facts about you, but they only retain lightweight summaries of recent chats and don't carry over the detailed context from working sessions.
PDF Parsing for AI Agents: The Best MCPs and When to Use Each
Streamline your document workflows by integrating PDF parsing with the Model Context Protocol (MCP). This guide explores how to build a standardized interface that allows AI agents to extract and reason over complex PDF data with precision and ease.
$4K Courses Will Teach You Agent Evals. Here's a Free Guide.
Move beyond static benchmarks to master the art of AI agent evaluation. This guide explores how to design frameworks that measure reasoning, tool-use, and reliability to bridge the gap between experimental prototypes and production-ready systems.
Your AI Agent Is A Cash Guzzler. Here's a Framework for Thinking About It.
Most founders misjudge agent costs, focusing only on token price. In reality, stacked expenses from context accumulation and infrastructure can explode bills 10x at scale. Learn to identify the actual growth curve in your billing stack and why smart context management is the only viable path to sustainable unit economics.
MCP Servers Explained: What They Are and How AI Agents Use Them
Starting as a niche experiment, Model Context Protocol (MCP) is now the universal "USB-C" for AI agent integrations. Let's demystify MCP’s architecture across hosts, clients, and servers and understand how tools, resources, and prompts work together. Essential reading for engineers navigating the massive ecosystem of 18,000+ servers and 97 million monthly downloads.
A2A vs MCP: What Agent Builders Actually Need to Know
MCP and A2A are reshaping AI agent communication. MCP connects agents to tools and data (the toolkit), while A2A enables agents to talk to each other (coordination). They aren't competitors; they are complementary layers of the emerging agentic infrastructure stack essential for complex workflows by 2026.
The Memory Portability Problem: Why Your AI Still Doesn't Know You
We Looked at how 3 AI apps handle Memory. None of Them Solve the Real Problem.
How ChatGPT, Claude, and OpenClaw Remember You and Why it is Not Enough
9 Essential Claude Skills for AI Engineers Building Production Agents
Claude Skills are organized folders of instructions, scripts, and resources that Claude (both Claude Code CLI and Claude Cowork GUI) can discover and load dynamically to perform specialized tasks. Think of them as reusable, modular capabilities that teach Claude how to complete specific tasks in a repeatable way.
The Future of AI Memory Systems
Exploring the next generation of AI capabilities and how memory will shape the future of human-AI interaction.