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My old agentic stack was one agent per session with me in the loop for every task.
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Three viral posts agree that durable state is what makes agent loops work, then hand the job to a markdown file.
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Agent-led evaluation tells a user whether Neotoma fits.
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The first problem with running multiple agents is that they forget.
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Specialists at the ends produce deep artifacts in their own vocabularies; the old org used the execution middle to translate in real time.
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Humans at the ends, AI in the middle is a clean diagram.
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Startup hiring advice fights over speed: hire slow versus hire ahead of the curve.
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Software teams used to spend most of their time in execution—the middle between foundation and review.
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Async specialists can each be right inside their own domain and still ship something incoherent across domains.
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On the PM role, AI, and the strange decision to define your job around the skill that just got commoditized.
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When many agents write to one memory, you need proof on every row: who signed, what they were allowed to do, and no shared bearer secrets.
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Fourteen years ago I wrote a postmortem for Plancast.
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OpenClaw stores memory in markdown files.
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Three independent AI agent platforms worth billions converged on plain text files for memory.
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Most teams bolt agent memory onto whatever database they already have.
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Sarah Wooders argues memory is the harness.
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BEAM tests retrieval at 10 million tokens.
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The metrics that drive adoption in AI memory are almost all retrieval metrics.
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Eighteen human product evaluators ran the same evaluation prompt through their AI tools.
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I shipped eleven Neotoma releases in the first five weeks after the developer release.
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About eighteen of twenty-six people I reached ran the same evaluation prompt through their AI tools.
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The debate over AI maps onto a question the Enlightenment never resolved.
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I accidentally overwrote my production Neotoma database and dropped from 6,174 observations to 84.
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Narrative FOMO followed me from crypto to AI.
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The people who embrace AI encounter its problems more directly than anyone.
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I overhauled the Neotoma site.
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The structural pressures that underpin my work, and what would invalidate them as the AI industry evolves.
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Creation, whether a startup or a blog post, is a continuous habit of revealing your inner world and tuning into what comes back.
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I joined Blockstack in 2018 for the off-chain developer tools.
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Claude and ChatGPT now offer memory for free, but what they store are profile snippets, not the details of what you actually worked on.
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I built an agentic stack to dogfood Neotoma and accelerate my work.
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A new open-source memory agent from Google ditches vector databases for LLM consolidation, trading determinism for active pattern discovery.
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Structured agent memory you can inspect, replay, and trust.
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Retrieval fits coding and is cheap; it breaks on completeness, consistency, and provenance.
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Command centers for agents need a single, durable state layer for tasks and visibility.
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The humanness isn't in typing every word.
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The future of crypto wallets is agentic.
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Retrieval for agent memory should be driven by structure, not similarity.
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You don't protect it through secrecy or patents.
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Your AI finds things.
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Your own floor in the heart of Barcelona.
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OpenClaw runs on your machine, clears your inbox, and builds skills.
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Agent memory is forgetful.
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Building sovereign systems at the intersection of crypto and AI
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Should Facebook really be responsible for policing the misuse of user datahttps://newsroom.fb.com/news/2018/03/forensic-audits-cambridge-analytica/...
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One my highest priorities over the past several years has been to establish a more frequent state of flowhttps://en.wikipedia.org/wiki/Flowpsychology...
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Any recommendations on the best snowboarding destinations in the Alps with easy access from Barcelona and London?
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This is the most impressive media website I've seen in a while, from a creative design and construction quality point of view:...