229 stories
Hindsight used to ship inside Hermes. It now installs from the plugin catalog. Your settings and memory are untouched, but one thing genuinely changed: where updates come from.
AI agent orchestration explained: the coordination patterns, how it works at production scale, and when to build on a framework versus buy a platform.
A Context Language Model is an existing model that rewrites its own context like a file. It manages working context inside a task better than summarisation, with less compute, and throws that context away when the task ends.
Hindsight 0.10.2 lets each observation scope consolidate under its own mission. The company-wide view can record trends while a user's own scope keeps the detail, from the same facts.
Learn AI engineering hands-on with SurrealDB, from vector search and RAG to hybrid retrieval, agent memory, Text-to-SurrealQL, and context-layer architecture.
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.
We shipped TypeSafe's Jev into our AI employee's memory five days after launch: measured latency, how we set a 0.7 threshold, where Jev fails, and what it costs.
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.
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