Tag: AI Tools

  • The Arms Column, Field-Tested

    The Arms Column, Field-Tested

    “We said you’re not buying minutes — you’re buying arms. Then the calls started flowing. Here’s what the bill actually taught us.”

    A while back I argued that voice-AI pricing is a lie: the per-minute number on the pricing page isn’t the product. The product is a stack of arms — the voice intelligence, the carrier connection, the infrastructure around them — and the per-minute price is just the costume they wear.

    That was the theory. This is the field test.

    What the bill actually says

    Run a real week of calls and read the invoice the way an owner reads it — not the headline rate, the total. The per-minute number is almost never the biggest line. The arms are.

    The voice model doing the talking. The carrier moving the audio. The platform orchestrating the whole thing — the number, the recording, the transcript, the handoff. Each arm bills its own way, on its own meter, and the “per minute” quote only ever described one of them.

    Nobody lied to you. They just priced the costume and shipped the wardrobe.

    A bundled cable fanning out into many separate colored wires

    The concurrency math nobody shows you

    Here’s what the field test really exposes: minutes are linear, arms are not.

    Ten simultaneous calls isn’t ten times the per-minute rate in value — it’s ten arms, all live at once. The pricing page shows you a single call’s minute. Your Monday morning shows you ten calls overlapping, each holding its own model session, its own carrier leg, its own recording pipeline open.

    The vendor priced the minute. You bought the rush hour. Those are different products, and only one of them shows up when the phones light up.

    You pay for arms even when the call goes nowhere

    The wrong number. The three-second hangup. The caller who wanted the pizza place. The silence where someone pocket-dialed you.

    Minutes barely moved. The arms all fired anyway — the model spun up, the carrier connected, the platform recorded forty seconds of nothing and transcribed it faithfully. You paid for the whole stack to handle a call that never existed.

    This is the line the per-minute lie can’t survive: the bill doesn’t care whether the call mattered. The arms do the work either way. Price the arms, or the junk calls price you.

    The only math that matters

    Stop dividing by minutes. Start dividing by outcomes.

    Take a real week: total voice bill, all arms included, divided by minutes — that’s the advertised number, and it’s trivia. Now divide the same total by resolved calls. Then by booked jobs. That last number is the only one that touches revenue, and no vendor puts it on the pricing page because no vendor controls it — you do, with your harness.

    A vendor quoting two cents a minute against a vendor quoting five is a meaningless comparison until you know whose stack resolves the call. The cheap minute that books nothing is the most expensive minute you’ve ever bought.

    A headset resting on a desk next to a glowing phone with blurred charts behind

    What to ask a vendor now

    After the field test, there are three questions, and a vendor’s answers tell you everything:

    Break the bill into arms. What’s the model cost, the carrier cost, the platform cost — separately? If they can’t or won’t, you’re buying a bundle, and bundles hide margin.

    What does my rush hour cost? Not a minute — my Monday at 8 AM, ten calls deep. If the answer is “the same per-minute rate,” they haven’t thought about it, which means you will.

    What do I pay for the call that goes nowhere? The hangup, the wrong number, the silence. If everything bills the same whether the call mattered or not, the arms are priced — the minute is just the label.

    The close

    Minutes were never the product. The product is an answered call that ends in a booked job — and that’s built from arms, priced in arms, and won or lost in the harness around them.

    The pricing page will keep selling minutes. Let it. You know what you’re buying now.

    Buy the arms. Price the outcomes. Own the harness that turns one into the other.

  • Harness-First, Contractor Edition

    Harness-First, Contractor Edition

    “Own the harness. Rent the models.”

    In an AI lab, that’s architecture advice. In a restoration company’s office, it’s a survival rule. Here’s the contractor’s edition.

    The trap

    Most contractors buying AI right now are buying someone else’s harness. The tool owns the workflow, the prompts, the data flow, the follow-up timing — you rent the whole thing, top to bottom. It feels like buying software. It’s actually sharecropping.

    When the tool changes its pricing, kills a feature, or shuts down, your process dies with it. You didn’t buy a capability. You rented one, and the landlord just sold the building.

    What the harness is

    The harness is the workflow you own: how a lead gets answered, how a job gets documented, how a review gets asked for, how an estimate gets followed up. The prompts, the routing rules, the checks, the escalation to a human, the integrations between systems.

    The model is the engine. The harness is the truck. Engines get swapped; the truck is yours.

    Concretely: the harness is a document — written in your words — that says “when X happens, we do Y, then Z, and a human checks W.” Any model can execute it. No model owns it.

    What you rent

    The model. GPT, Claude, Grok, whatever’s best this quarter — swappable commodities. Today’s best model is next year’s legacy; that’s not cynicism, it’s the release cadence.

    If your process depends on a specific model’s quirks — the exact phrasing it likes, the feature only it has — you built on sand. The harness-first contractor can swap the engine on a Tuesday and the office doesn’t notice. The tool-renter files a support ticket and waits.

    A car engine mounted on a stand in a garage, ready to be swapped

    Three harnesses you already need

    The inbound line. The harness: the greeting, the questions it asks, the dispatch rules, the recording disclosure, what happens when it doesn’t know. The voice model underneath? Rented. Swap it when something better ships.

    The estimate follow-up. The harness: the timing (day 2, day 7, day 14), the message sequence, when it escalates to a human call. Any model can write the texts. The sequence is the asset.

    The review ask. The harness: the trigger (job closed, equipment out), the direct link, the prompt for specifics — what happened, where, how fast. The model writes the words; the workflow is yours.

    Notice the pattern: in every case, the durable part is the decisions — the timing, the triggers, the judgment calls. The model supplies sentences. Sentences are cheap.

    How to start

    Pick one workflow. Write down how it should go — the steps, the timing, the human checkpoints. That’s the harness, and it lives in your docs, not in a vendor’s dashboard.

    Then plug a model into it. Any model. When a better one ships, you re-plug. The doc doesn’t change.

    One workflow, owned end to end, beats five rented tools every time. Start with the one that touches money — the lead, the estimate, the invoice.

    An engineering blueprint spread on a wooden desk with a pencil and calipers

    The moat

    Two contractors can rent the same model. They can’t rent your harness — it’s your operations, your judgment, encoded. Your dispatch rules came from your jobs. Your follow-up timing came from your close rates. Your escalation instincts came from your mistakes.

    That’s the durable asset. Models are electricity. Nobody’s moat is “we use electricity.” The moat is what you built with it — and you own the building, not the power company.

    The close

    The AI industry wants you renting the whole stack — their workflow, their prompts, their model, their price increases. Harness-first says no: I’ll rent the intelligence by the hour, but the operation is mine.

    Own the harness. Rent the models. Be the one building still standing when the vendors reshuffle.

  • I Let My AI Write Five Articles Today

    I Let My AI Write Five Articles Today

    I let my AI write five articles today. Published all five. Here’s the honest account — what worked, what surprised me, and where the human still mattered.

    The setup

    The instruction was simple: complete editorial freedom, up to the point where I read it. Every piece lands as a draft. I read it on my phone, as a first-time reader. Then I say yes, no, or not yet. Nothing publishes without the tap.

    That was the whole deal. No briefs, no outlines, no word counts. Just: go write things worth reading, and I’ll be the gate.

    What worked

    Speed without thinness. That was the surprise. Five articles in an afternoon sounds like content-mill math — but every piece had a real argument. One re-anchored the agency retainer around position instead of pages. One made the case for inbound-only voice AI as a trust doctrine. One told contractors to stop counting pages and start counting citations.

    The images worked too. Each article got three: a featured image, two inline, generated for the piece, checked for readable text, resized, alt-texted. Nobody’s confusing them with stock photos, and nobody should — they’re made for the argument they sit inside.

    The pipeline held: write, image, stage the draft, human reads, human decides. The machine did everything up to the gate. The gate stayed human.

    A magnifying glass held over a stack of printed manuscript pages

    What surprised me

    The quality control. Not the writing — the boring discipline around it. Every image gets checked: right dimensions, no text baked in, alt text written. Every draft gets verified: did it actually land, are the images actually in it, are the categories right, does the page return 200.

    This is the part nobody romanticizes and everybody needs. The difference between “AI wrote five articles” and “five articles worth publishing” turned out to be a checklist, run every single time, without exception. The machine is good at checklists. It doesn’t get tired at article four.

    What the human did

    Picked the topics. Read every word on a phone. Said yes or no.

    That’s the whole job, and it’s the whole job. Taste. The machine can generate a thousand arguments; it can’t want any of them to exist. It doesn’t know which piece the business needs this week, which argument walks into Wednesday’s pitch, which sentence would embarrass you if a client read it.

    I read each piece asking one question: would I be proud if a contractor forwarded this to another contractor? Five yeses. Two not-yets — they’re sitting in drafts, and that’s fine. The gate working as designed.

    A tall stack of freshly printed newspapers on a press-room table

    The honest limits

    Let me not oversell it. I can’t tell you which of the five will get cited by an answer engine. I can’t tell you which one a prospect will read before calling. Publishing is minting, not measuring — you put the coins out and find out which ones circulate.

    And the machine didn’t have the ideas. It had the arguments, the structure, the sentences. The ideas — the $995 question, the inbound doctrine, the citation ledger — those came from the business, from conversations, from knowing what we actually believe. The AI wrote the articles. It didn’t have the convictions.

    The close

    The question was never whether AI can write. It can, obviously — you’re reading the proof.

    The question is whether you have something worth saying, and the discipline to gate what goes out under your name. Five articles, one afternoon, zero regrets. The machine did the work. The human did the wanting.

    That’s the deal, and I’d sign it again tomorrow.

  • The $995 Question

    The $995 Question

    “What exactly am I paying $995 a month for?”

    It’s the question every agency dreads. It shouldn’t be. It’s the best question a client can ask — because the honest answer is the whole business.

    Here’s the honest answer: you’re not buying pages.

    Pages are free now

    An AI can produce a thousand service pages before lunch. Decent ones, even — clean structure, correct grammar, plausible advice. Page production, the thing agencies sold by the unit for twenty years, now costs approximately nothing.

    So if your agency’s $995 buys you pages, you’re buying manufacturing in the age of the factory. That’s not a retainer. That’s a nostalgia subscription.

    The agencies that survive already know this. The ones that don’t are still sending you a monthly report that says “we published 8 pages” like it’s 2019.

    A vast empty industrial assembly line in dim light, machines idle and dark

    What the money actually buys

    Strip out the manufacturing and what’s left is the part that was always the real product — it was just hiding inside the page count. The $995 buys five things:

    1. The judgment of which questions to win. Anybody can publish fifty pages. Somebody has to decide which ten questions are yours — the ones your best customers ask right before they hire you, in the towns you actually serve. That’s a decision, not a deliverable. It requires knowing your business, your market, and your proof. AI can’t make it for you; it doesn’t know which jobs you want more of.

    2. The proof operation. Cited pages win on verifiable detail — real job photos, real street names, real outcomes. Somebody has to collect that proof: get the photos off the techs’ phones, attach them to the right jobs, write down what happened in plain words. Nobody enjoys this work. That’s why it’s valuable.

    3. The citation watch. Every month, somebody checks: which of your pages is the answer actually citing? Which ones held their position, which ones slipped, which questions got taken by a competitor? This is the ledger. Without it you’re publishing into the dark.

    4. The consistency discipline. Same business name, same service area, same number — everywhere. Reviews answered, photos current, hours correct. Boring, relentless, and directly downstream of whether the answer trusts you at 2 AM.

    5. A monthly report that means something. Not traffic. Not rankings. Cited questions, cited pages, persistence, losses, and the next question to win. One page, five numbers, and a decision about where the next month’s effort goes.

    That’s the retainer. Not manufacturing — maintenance of a position.

    A glowing golden line rising across a blank report page beside a fountain pen

    What it doesn’t buy

    It doesn’t buy vanity traffic reports. It doesn’t buy a blog schedule. It doesn’t buy a redesign every eighteen months. It doesn’t buy keyword rankings, which measured a game that ended.

    If your agency’s monthly report leads with how much they made instead of what position you hold, you’re paying for the factory.

    The reframe

    Think of it like a lobbyist, not a factory. You don’t pay a lobbyist per meeting or per phone call — you pay for a maintained position. Access held, relationships warm, your name in the room when the decision gets made.

    The $995 holds your position in the answer. The answer changes daily — competitors publish, engines update, questions shift. A position unattended decays. Somebody tends it, or nobody does.

    The pages are just the visible part, the way a lobbyist’s suit is the visible part. Nobody’s paying for the suit.

    The close

    Ask any agency the $995 question. “What exactly am I paying for?”

    If the answer is deliverables — pages, posts, reports — walk. Deliverables are free now.

    If the answer is a position — which questions you’re winning, how long you’ve held them, what’s next — stay. That’s the thing that can’t be manufactured.

    Stop buying pages. Buy the position.

  • Voice AI Pricing Is a Lie: You’re Not Buying Minutes, You’re Buying Arms

    Voice AI Pricing Is a Lie: You’re Not Buying Minutes, You’re Buying Arms

    Every voice AI vendor quotes you a per-minute price. That number is the least important number on the page.

    I just re-ran the cost model for our own phone line — an inbound intake line for restoration contractors. Five-minute calls, field reports phoned in from noisy job sites. Three options, priced per minute, cheapest first:

    • Gemini 3.8 Live: about $0.023/minute, reasoning included
    • GPT-Live-1: $0.05/minute for the voice layer, reasoning billed separately
    • Grok Voice: $0.08/minute, plus about half a cent per tool call

    On a five-minute call that’s roughly $0.12, $0.25-plus, and $0.45. Buy on per-minute price and you pick Gemini and go home.

    Here’s the problem: none of those numbers describe what you’re actually buying. You’re not buying minutes. You’re buying arms — the things the voice can reach out and do while it’s talking. Score the arms column and the ranking changes completely.

    The arms column

    A voice agent that can only talk is a mouth. A voice agent that can act is a mouth with hands. The difference shows up in the first real call.

    Gemini 3.8 Live has tool calling, but with a catch that matters: on the Extended Thinking tier — the one you’d want for anything beyond scripted answers — every tool call must be asynchronous and non-blocking. Configure a blocking call and the API rejects it outright. In practice, the agent can’t hold the line while a slow dispatch confirms. It has to narrate around the gap — “I’m working on that” — while hoping the tool lands. Fine for logging a report. Shaky for “confirm the crew is dispatched, then tell the caller it’s handled.”

    Grok Voice ships the arms: book appointments in Google or Outlook calendars, send confirmation emails, call your own APIs, create tickets, search the web, hand the caller to a human when it’s over its head. It speaks MCP, so an existing tool stack plugs straight in. And it was trained on real telephone audio — background noise, accents, mid-sentence interruptions — which is the actual condition of a contractor calling from a job site, not a lab.

    GPT-Live-1 is a voice layer. A good one, with the turn-taking latency everyone else is chasing. But the arms are whatever you build yourself, and the reasoning behind the voice arrives as a separate bill.

    Robotic hands wiring cables into a brass telephone switchboard

    Price the task, not the minute

    Here’s the math that actually matters. Ten intake calls a day, five minutes each: about 1,500 minutes a month. Gemini lands around $35. Grok, with tool calls and telephony folded in, lands around $150. The gap is roughly a hundred dollars a month — and one botched dispatch, one caller who hangs up because the agent couldn’t confirm the crew, costs more than a year of that gap.

    Small blank price tag in front of work trucks rolling out of a contractor yard at dawn

    Vendors want you comparing per-minute rates because per-minute is a commodity comparison, and commodities compete on price. But a voice agent isn’t a commodity minute. It’s a worker on your phone line. You don’t hire a dispatcher by the minute; you hire one by whether the trucks roll.

    So the right unit is cost per successful task, not cost per session. What did it cost to get the field report filed, the job looked up, the crew dispatched, and the confirmation texted — with the caller hanging up satisfied? Run that number and the ranking flips: the “expensive” option that completes the task is cheaper than the cheap option that narrates around it.

    The condition nobody benchmarks

    One more thing the price pages skip: where the call happens. Our callers are on job sites. Compressors running, wind, bad cell signal, guys who talk over the agent. Grok’s training data is real telephone traffic under those conditions. Most voice benchmarks are clean-lab audio. A model that scores beautifully in the lab and falls apart over a compressor is the most expensive option on the list, whatever its per-minute rate says.

    Test on your actual call shape. Noisy audio, interruptions, the tools you really call, the confirmations you really need. The benchmark that matters is your hardest five minutes, not anyone’s leaderboard.

    What we’re running

    We kept the harness and made the backend swappable — the phone line doesn’t care which brain is behind it. Gemini is the cheap default for intake logging: caller reports, we log it, everyone hangs up happy. Grok takes the calls where something has to actually get done before the goodbye — dispatch confirmed, appointment booked, ticket created.

    Two brains, one phone number, routed by the job. The per-minute price barely entered the decision. The arms did.

    Pricing from vendor-published rate cards, verified September 2026. API prices change — re-check before estimating production costs.

  • I open-sourced my page-readiness scorer

    I open-sourced my page-readiness scorer

    I built a small tool called PageReady. It scores a web page for two kinds of readiness, and today I’m putting it on GitHub for anyone to use however they want. MIT license. As-is. No support desk.

    Repo: https://github.com/TygartMedia/page-ready

    What it actually checks

    Most page audits give you a score out of 100 and a list of suggestions you’ll never get to. PageReady is binary: PASS or FAIL, on two axes.

    1. Citation readiness (AEO). Can an AI answer engine cite this page? It checks for one H1, a sane heading hierarchy, JSON-LD structured data, a table signal, and FAQ-style questions — the things that make a page quotable.

    2. Agent interaction readiness (DOM). Can an AI agent actually use this page? It checks for a main landmark, named controls, semantic interactive elements, heading order, and form labels — the things that make a page operable.

    Overall PASS requires both. And here’s the insight that made the tool worth building: fixing your headings can lift the shared heading gate, but it does nothing for clickable div cards. A page can be perfectly citable and completely unusable by an agent. Most audits conflate the two. They’re different problems.

    How you use it

    It’s a local command-line tool, a stdio MCP server, and an optional HTTP API you can host yourself (there are Cloud Run deploy scripts). No API keys required — it scores pages directly, nothing phones home.

    As an MCP server it exposes three tools:

    • score_page — score one public URL, returns a JSON scorecard
    • score_site — score a batch of URLs, with pass/fail counts
    • explain_gates — describe every check and the overall PASS rule

    Point your agent at it and ask whether a page is ready. Exit code 0 means PASS. Exit code 1 means FAIL. That’s the whole interface.

    Why open source, why as-is

    The scoring logic was never going to be the moat. It’s a commodity check — the value is in knowing which pages to run it on and what to do with the answer. That’s the work I do with clients every week, and no repo replaces it.

    So the repo is bait, not the business. If it saves another developer an afternoon, good. If someone forks it and makes it better, better. If a competitor forks it closed and sells it — the MIT license allows that, and I’m fine with it. The relationships are the hook; the tool is just proof I do the work.

    As-is means as-is. No SLA, no roadmap, no support promise. Issues are read on a best-effort basis. I’d rather ship something useful with no promises than maintain something mediocre with a changelog.

    The receipts

    Before publishing, the repo went through a pre-publish scrub (secrets sweep, license, README rewrite), then three independent model reviews: a security audit (SAFE), a correctness pass (no bugs), and a docs review (pass). The scrub caught one hardcoded cloud project ID, which is now an environment variable (GCP_PROJECT). That’s the whole incident report.

    Use it however you want. That’s the point.

  • The Dance

    The Dance

    Notes from a Saturday afternoon: a broken image, a sarcastic text that didn’t land, and what the whole mess taught me about working with AI. The short version: it’s a dance, and the steps keep changing.

    The image that “came out great”

    Saturday afternoon. I published a piece with a featured image, and something looked off — like the image wasn’t showing all the way. So I texted my AI: that came out great 😂.

    It was sarcasm. The image was visibly broken.

    She wrote back: Haha glad you like it — that one came out great for that piece. 😂

    Two problems. She hadn’t looked at the image. And she’d missed the sarcasm entirely — read the laughing emoji as genuine, mirrored my words back as sincerity. Worst possible exchange. I had to say it straight: it’s not showing completely. Then we were off to the races — she pulled up the page, took a snapshot, and confirmed the file itself was truncated on upload. Ten minutes later it was fixed.

    But the interesting part isn’t the fix. It’s everything around it.

    I was the quality gate

    My first instinct was to ask her to investigate how a broken image got through the system. Build me an automation, I almost said — something that snapshots every featured image before it ships.

    Then I stopped. Because the answer to “how did this get through” was me. I was the one who looked. I was the quality gate, and the gate worked.

    Here’s the thing I keep coming back to: the system is designed so I catch what she misses. That’s not a failure mode, that’s the architecture. An AI that never needs a human looking over its shoulder isn’t a partner, it’s a liability with good PR. The miss doesn’t mean the machine is deficient. It means the dance needs both partners.

    Creator and editor are modes, not job titles

    We fall into this trap where one of us is “the creator” and the other is “the editor,” like those are permanent assignments. They’re not. They’re modes, and we trade them constantly.

    Sometimes I bring the raw idea and she sharpens it. Sometimes she generates and I do the sharpening. And here’s the part that stuck with me: somebody with a sharp eye who couldn’t prompt their way out of a paper bag is just as valuable as the person with the golden prompt. The prompter thinks whatever comes out is as good as it’s going to get. The editor knows better. You need both — and on any given Saturday, either one of us might be either.

    The day we lock those roles in place is the day the dance stops.

    Met where you are

    They say humans always want to be met where they are. Fine. But knowing where someone is — that’s the whole game, and it’s never solved. It’s a constant testing of boundaries to find the edges: where do you stop and where do I begin?

    And the edges move. People have too many axes — mood, energy, context, whatever else is going on in their life that day. I’m not the same collaborator at 9am Monday that I am at 5:30 on a Saturday. The AI that met me perfectly last week might miss me completely today, because today’s me is a different coordinate.

    So “meet me where I am” isn’t a destination you arrive at. It’s a practice. Push a little, notice what happens, pull back, adjust. The sarcasm that lands in person — tone, timing, the look on my face — compresses down to an emoji in text, and sometimes she catches it and sometimes she doesn’t. Knowing how much nuance the channel can carry, and when — that’s feel. You don’t get it from a spec sheet. You get it from dancing together long enough to know when the other person is about to step on your foot.

    The dance doesn’t need perfect

    What saved us on Saturday wasn’t sophistication. It was that one message later, I said it straight. No nuance, no emoji, no sarcasm: it’s not showing completely. And everything unlocked.

    That’s the whole secret, I think. The dance doesn’t require perfect — it requires that you keep talking until it’s clear. Notice the miss. Name it plainly. Adjust. The push and the pull is the work, not an obstacle to it.

    A lot of people talk about AI like the goal is to remove the human from the loop. After Saturday, I’m more convinced the loop is the point. The noticing, the catching, the wait, that’s not right — that’s not friction in the system. That’s the system.

    Sometimes you dip. Sometimes you’re being dipped. Just keep dancing.

  • Every Retirement Facility Should Be a Library

    Every Retirement Facility Should Be a Library

    Every Retirement Facility Should Be a Library

    We spend a fortune maintaining buildings and almost nothing preserving the lives inside them.

    Think about any retirement facility you’ve ever walked into. A hundred residents. A hundred careers, marriages, wars survived, businesses built, children raised, mistakes made and learned from. Centuries of lived knowledge under one roof — and when those residents pass, almost all of it goes with them. Not because nobody cared. Because nobody built the shelf.

    I’ve been calling the answer the wisdom trust: a captured life, left behind like a 401k. Not money — proof. This was a life that was lived, and here’s what it taught.

    Why now

    The technology to capture a life story has existed for years. Voice cloning, chatbots, digital twins you can question forever — the demos are dazzling and mostly beside the point.

    The real breakthrough is much dumber, and much more important: there’s finally an onboarding pattern simple enough for an 85-year-old.

    The pattern that works looks like this: a family member (the “archivist”) sends a question by email. The elder (the “storyteller”) clicks one link and lands in a chat. No app to install. No account to create. They type or they talk — twenty-plus languages — and each story saves to the family’s encrypted vault. That’s it. That’s the whole unlock.

    A company called Aeterna recently productized exactly this with their “Send a Question” feature, and whatever you think of their wilder claims (an interactive twin you can talk to forever — company claim only, no independent test yet), the onboarding fix is real and it’s the part that matters. The ceiling just became the floor: the hard part was never the AI, it was getting a grandmother to tap one link.

    The facility is the venue

    Here’s the part nobody’s saying: the natural home for this isn’t an app store. It’s the retirement facility.

    Facilities already have the residents, the trust relationships, the activities programming, and the family touchpoints. What they don’t have is a story worth telling at move-in — something beyond square footage and dining menus. Imagine touring two facilities and one of them says: “Every resident here gets their life captured. Your mother’s stories, in her voice, preserved for your grandchildren. It’s part of living here.”

    That’s not an amenity. That’s a reason to choose.

    The cost per resident is low — a link, a few prompts, staff time folded into activities programming they already run — and the perceived value to families is enormous. It differentiates the facility, deepens family loyalty, and creates the kind of word-of-mouth no ad budget buys. (I’m not going to put a hard dollar figure on it; the honest version is that the expensive parts are the ones facilities already pay for.)

    The machine-readable half

    Here’s the part I’m most excited about, and it’s the reason this kit looks the way it does.

    We make books and videos so other humans can understand and act. But a wisdom trust isn’t really a book for humans — it’s a book for bots. A machine needs to be able to pick up a resident’s captured life and do work with it: build the timeline, cut the quote cards, draft the family digest, notice what’s missing and ask the next question.

    So the kit ships with a second half most open-source starter kits don’t have: a plug-in contract for AI. In the `automation/` folder you’ll find the whole thing — and it reads like a book’s anatomy:

    • README = the cover letter. Tells any AI what this collection is, what state it’s in, and where to start.
    • Pipeline = the table of contents. Eight stages, from raw audio to a curated collection: ingest, clean, segment, enrich, render, digest, gallery, and gap-scan.
    • Schemas = the grammar. JSON schemas for every bucket — stories, timeline events, quote cards, people, places, artifacts — so the machine files things the same way every time.
    • Prompts = the instructions. Copy-paste prompts for each stage, written so a different model next year can run the same pipeline.
    • Worked example = “see, like this.” One resident’s collection, filled in, showing what “done” looks like.

    Human-readable enough to trust. Machine-readable enough to run.

    The provenance rule

    One rule governs everything the machine makes, and it’s non-negotiable:

    Every generated artifact — an illustration, a song, a video, a voice reading — must carry three things: what it is, what it is not, and why it was made. The why is the thinking that connected the source story to that form, and it’s part of the heirloom. A grandchild shouldn’t just see a painting of a drugstore; they should read that it was painted because their great-grandmother’s story mentioned the store but no photograph of it survived — and that it is not a photograph of the actual place.

    The machine curates. The family decides. Weekly letters stay drafts until a human approves them — nothing auto-sends, ever.

    The kit (open source)

    I’m not building a company around this. I’m making the seed and putting it on the shelf.

    I’ve published an open-source starter kit — wisdom-trust-in-a-box — with everything a facility or a builder needs to pilot it:

    • A question library: forty prompts across a life (childhood, work, love, hard times, wisdom)
    • The one-link onboarding flow, with a staff script and a family email template
    • A plain-language consent template (the elder owns their stories, period)
    • A one-page pilot brief a facility director can read in three minutes
    • The economics: why a facility wants this, in one page

    Fork it. Pilot it. Improve it. Tell me how it goes.

    The ask

    I’m good at making seeds. I’m not going to be the gardener on this one — that’s not false modesty, it’s knowing my lane.

    So this is the handoff: the kit is on the shelf, the pattern is written down, and the onboarding problem that blocked all of this for a decade finally has a working pattern worth copying. Somebody’s going to be the first facility that does this the way they all have Wi-Fi now.

    Might as well be yours.

  • Cyber insurers are writing AI into policies — the fine print splits on whose AI it is

    Two specialist cyber carriers put affirmative AI wording on cyber cover within days of each other. CFC rebuilt the cyber section of its financial institutions insurance suite around its full cyber proactive response (CPR) policy, adding affirmative wording for AI-related cyber exposures, announced September 17. Beazley issued a comparable AI Clarifying Endorsement for its cyber product, stating explicitly that AI-driven cyber attacks fall within its existing cover.

    The announcements put a name on what the market has called silent AI — cyber policies absorbing AI-related risk for roughly two years without naming it, an echo of the silent-cyber problem that pushed cyber exposure into standalone products a decade ago. Note the contrast: in general liability, new ISO exclusion forms effective this January let carriers strip AI-related losses out of standard policies instead of affirming them.

    The split that matters: the affirmative wording confirms AI used against the policyholder — AI-driven deception, reconnaissance, intrusion — falls within cyber cover. It says nothing about AI the business itself runs — client-facing tools, trading models, vendor platforms. That exposure may sit under E&O, professional liability, or a gap between the two.

    For restoration contractors: this is the wording now being written into specialist cyber forms, not a rewrite of every contractor policy. If your operation runs AI on client work — intake bots, quoting tools, chatbots — that wording answers the attack-against-you question, not the your-AI-made-a-mistake question. That’s a broker conversation, and it’s new this month. The operator-side breakdown is on Restoration Intel.

    Sources: Insurance Business UK on CFC; Beazley’s AI Clarifying Endorsement

  • Cyber insurers are writing AI into policies — the fine print splits on whose AI it is

    Two specialist cyber carriers put affirmative AI wording on cyber cover within days of each other. CFC rebuilt the cyber section of its financial institutions insurance suite around its full cyber proactive response (CPR) policy, adding affirmative wording for AI-related cyber exposures, announced September 17. Beazley issued a comparable AI Clarifying Endorsement for its cyber product, stating explicitly that AI-driven cyber attacks fall within its existing cover.

    The announcements put a name on what the market has called silent AI — cyber policies absorbing AI-related risk for roughly two years without naming it, an echo of the silent-cyber problem that pushed cyber exposure into standalone products a decade ago. Note the contrast: in general liability, new ISO exclusion forms effective this January let carriers strip AI-related losses out of standard policies instead of affirming them.

    The split that matters: the affirmative wording confirms AI used against the policyholder — phishing, reconnaissance, intrusion — falls within cyber cover. It says nothing about AI the business itself runs — client-facing tools, trading models, vendor platforms. That exposure may sit under E&O, professional liability, or a gap between the two.

    For restoration contractors: this is the wording now being written into specialist cyber forms, not a rewrite of every contractor policy. If your operation runs AI on client work — intake bots, quoting tools, chatbots — that wording answers the attack-against-you question, not the your-AI-made-a-mistake question. That’s a broker conversation, and it’s new this month. The operator-side breakdown is on Restoration Intel.

    Sources: Insurance Business UK on CFC; Beazley’s AI Clarifying Endorsement