21 stories
TL;DR Muse Wonder and Muse Wonder Full are a frontier model family from Meta built for agentic work, released on 12 October 2026 with a 1M-token context window and a thirteen-page model card published the same day.
A post this week called context-window compaction a "200k-token lobotomy" and patched it with external state files named after Memento. It is a good write-up, and the naming is honest about the stakes. But it also made the underlying problem easier to see than the fix does.
Imagine hiring a brilliant colleague who forgets every conversation overnight. Each morning you re-explain the project, your preferences, and what you decided yesterday. They are capable, but they never get better at working with you. That is an AI agent without memory.
Most chat apps forget you the moment the session ends. The usual fix is a backend with accounts, a vector database, and a privacy policy. If you ship a small iOS or macOS app, that is a lot to take on for "remember that I prefer short answers." I built AiPersona so the memory can live on the…
Most agent memory today is one of two failure modes. Either it's an append-only log that grows until the context window chokes on six-week-old trivia, or it's a lossy summary that quietly rewrites history and can't tell you what it dropped.
Short answer: Model Context Protocol documentation is versioned by date, not by number. Each revision has its own specification pages, a schema published beside them, and a changelog recording what moved since the previous revision.
I Built a Sales Assistant That Actually Remembers Customers What if your sales assistant could write the perfect email… …but had no idea who the customer was? That bothered me while building AI sales assistants. Today, an LLM can write a convincing email in seconds. It can summarize a meeting.
What Happened When DealMind Could Recall Past Objections Building persistent deal memory with Hindsight changed how I thought about context, retrieval, and the boundary between application state and agent memory. The first version of DealMind could answer questions about a deal.
I Built a Sales Assistant That Actually Remembers Customers What if your sales assistant could write the perfect email… …but had no idea who the customer was? That bothered me while building AI sales assistants. Today, an LLM can write a convincing email in seconds. It can summarize a meeting.
You know the feeling. Pager goes off at 2 AM. You stumble to your laptop, open the terminal, and stare at a PostgreSQL stack trace you've seen before — but can't remember exactly what fixed it last time. You dig through Slack threads, closed Jira tickets, and a half-finished runbook.
The Problem Competitive information can appear in many forms: Feature and product launches Pricing changes Marketing campaigns Promotional offers Product announcements Changes in messaging Other market signals When these activities are viewed independently, it can be difficult to understand the…
Memory-Enabled Customer Support Agent — Dev-Ready Specification Project Overview 1.1 Problem Current customer-support systems are commonly stateless at the interaction level.
I've watched LLMs confidently recommend approving an invoice for an amount that doesn't appear in the purchase order, the invoice, or any past decision. They do it with high confidence and well-structured reasoning. The reasoning sounds right. The number is invented.
The useful part of a deployment agent isn't telling me what a PostgreSQL upgrade is. It's remembering what happened the last time I tried one. That became the central idea behind DeployMind, a DevOps pipeline agent I built around Hindsight.
The worst call my voice agent ever made went like this: "Hi Imran, Aisha from TableTap. Our billing app starts at just nine hundred ninety nine rupees a month." Imran runs a cloud kitchen.
I Built an Invoice Agent That Remembers Why It Said Yes Most invoice-processing agents treat every invoice as a new problem. That becomes a problem when the same vendor keeps coming back with recurring exceptions.
I built an incident-diagnosis agent that remembers every outage we've resolved. The interesting part isn't the diagnosis. It is the persistence boundary I ended up drawing, and how much of the system's value lives on one side of it.
Building Stateful AI Workflows: Moving Past Raw Logs with Long-Term Agent Memory If you've ever watched an LLM-powered assistant forget a critical workflow decision three turns after you made it, you already know the wall that stateless agent architectures hit.
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