10 articles on AI Memory — programs, tooling, and delivery on Petralian.
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10 articles on AI Memory
Best forBuilders connecting file memory, Obsidian, and agent loops in daily work
External memory is four tiers in practice — short-term, operational, evergreen, and a feedback loop hardened into rules and footers. The harness gates when each tier loads so you keep control without token bloat.
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Best forPower users optimizing token spend and context windows in Cursor
I ran Headroom, built a 300-line proxy, wired a Cloudflare tunnel, and added RTK. On my Cursor + OpenRouter workload the dollars did not move. Here is what is worth doing instead.
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Best forDevelopers comparing Copilot and Cursor in a real daily workflow
Copilot had the same footer spec but dropped it on long chats. Cursor keeps it with alwaysApply rules, optional hooks, and a v3.1 mode-based Response Footer Contract.
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Best forDevelopers standardizing on a single coding model and tighter agent bootstrap
I run Cursor on Composer 2.5 only—not to save money alone, but to get predictable rule compliance. A tighter session bootstrap beat chasing frontier models for my workflow.
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Best forAnyone adopting file-based memory for AI-assisted work — start here for the series map
Chat is not memory. This series explains a file-based external brain for builders and leaders—four layers, hooks, and why it beats hoping the model remembers.
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Best forKnowledge workers layering Obsidian memory beyond a single chat thread
The same three-layer memory stack used for shipping code works for strategic work, client engagements, and cross-tool AI—short chat, operational handoffs, evergreen notes, and explicit feedback.
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Best forBuilders implementing the three-layer external memory model for AI-first dev
Chat context is not memory. A three-layer file system—session, operational, evergreen—plus hooks and git automation is how I keep production codebases coherent across hundreds of agent sessions.
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Best forTeams adopting governance for file-based agent memory instead of hoping context sticks
Chat memory is opaque and ephemeral. Deliberate files give audit trails, solo-shipping continuity, team handoffs, and survival when models or tools change.
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Best forBuilders choosing pragmatic file memory over diagram-perfect architecture
The popular STM / LTM / feedback diagram optimizes in-model memory. A file-based external brain optimizes audit, handoff, and tool churn. Here is when each design wins—and why I chose files.
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Best forBuilders and product leads comparing durable memory approaches for agents
Every AI session starts from scratch. Four tools are racing to solve the AI memory problem - OpenClaw, Hermes, Karpathy's LLM wiki, and a plain Obsidian vault. Here's how they differ and which approach actually survives tool churn.
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