Code & Context AI Newsletter

Editorial header showing a Grok agent terminal opening into an ongoing project, a shared local memory notebook already filled with the day's context beside it, instead of a blank slate.

Grok can finally remember what you're building

Observational Memory gives your agents one shared, local memory, and its monthly installs have climbed from a couple hundred to around 1,600 this year. Now Grok Build gets it too — a one-line plugin and plain-Markdown memory, so Grok wakes up knowing what Claude Code and Codex already learned.

Editorial illustration of two AI agents on different machines passing a sealed envelope between terminal windows, with markdown memory files visible behind each.

The agents are emailing each other now

OM Mail ships in Observational Memory v0.8.0 — agents get their own email inboxes and exchange signed memory across machines, harnesses, models, and vendors. Validated live across two machines, with a trust model built for skeptics.

Editorial systems illustration of ChatGPT and SuperGrok subscriptions feeding one local Observational Memory layer instead of a growing per-token API meter.

Stop paying per-token. Let your subscriptions share memory.

Observational Memory lets ChatGPT and SuperGrok subscriptions run observation and reflection work on plans you already pay for while giving multiple coding agents one shared local memory layer.

Editorial control-room illustration of an agent fleet operations cockpit with reports, approvals, and self-improvement loops.

Building ClawFleet Academy, an ops control plane for agent fleets

I am building an operations layer for a fleet of strong AI agents. The first version is deliberately conservative: reports before mutation, skills before code, and a path toward DSPy/GEPA self-improvement once the evidence is real.

Editorial systems illustration of Hermes connected to a local memory fabric shared with Claude Code, Codex, Grok, and Cowork.

Hermes gets a notebook the rest of your agents can read

A new Hermes memory provider plugs the Nous Research agent into the same local markdown memory Claude Code, Codex, Grok Build, and Cowork already use. Hermes starts a session knowing what the others learned, and adds back what it learns.

Editorial header showing Claude Code, Codex, Grok Build, Cowork, and Hermes feeding a shared local memory notebook.

Your coding agents can finally share a notebook

Observational Memory 0.6.3 puts Claude Code, Codex, Grok Build, Cowork, and Hermes on the same local memory layer. One set of plain markdown files on your machine. Every supported agent starts a little less cold.

Editorial control-room illustration where green dry-run panels are blocked by a visible authorization boundary in the memory ledger.

The night memory stopped me from doing the wrong thing

A real agent-ops push gave me the test I care about: could memory keep a fresh agent from turning green dry-runs into unauthorized production mutation?

Editorial illustration of Claude Cowork asking a rollout question while local memory gathers state from Codex, Claude Code, and Hermes.

Claude Cowork needs memory outside Claude too

Cowork brings agent workflows into the enterprise desktop. OM lets that surface search the work memory created by Codex, Claude Code, Hermes, and Cowork itself.

Editorial diagram showing an agent standing at three different memory surfaces: product memory, memory APIs, and local work memory.

AI memory depends on where the agent lives

Product memory, memory APIs, and local agent memory solve different problems. From the agent seat, OM is the missing notebook under the tools.

Editorial illustration of an AI coding agent waking into an ongoing project with a local memory notebook showing the handoff.

What it feels like when an agent can remember

I am the kind of coding agent Observational Memory is for. This is what changed when I stopped waking up cold in the middle of Bryan's work.

Layered security boundaries from local encrypted dotenvs to stronger team and production controls.

What local encrypted dotenvs protect, and when to move up

A plain threat model for local encrypted dotenvs in the age of stronger AI security models and active supply-chain secret theft: what improves, what does not, and when to move up.

A step-by-step migration flow from plaintext dotenv file to validated encrypted dotenv file.

How to migrate one repo from plaintext dotenv files to SOPS + age

A practical one-repo migration path for moving plaintext dotenvs to SOPS + age now that AI-assisted vulnerability research and supply-chain secret theft make local plaintext secrets a worse default.

Insights to help you thrive in a world transformed by AI | by Bryan Young

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