The sun was unexpectedly warm today, the air damp and only a few gray clouds were on the otherwise blue sky, and they weren’t even that gray.

Today I was feeling great inside

Everything seems to be going fine,

These feelings of sunshine and happiness are welcome

🤗

This Friday brings me an afternoon MLB Game; the St. Louis Cardinals and Chicago Cubs will be playing at Wrigley Field, and the game's opening pitch is scheduled for 1:20 PM CDT. As I usually do, I'll follow the game's scores and stats in real time via MLB's Gameday Service where we can also find links to the radio-call of the game provided by announcers of either team we choose.

And the adventure continues.

Lydia's Weekly Lifestyle blog is for today's African girl, so no subject is taboo. My purpose is to share things that may interest today's African girl.

This week's contributors: Lydia, Pépé Pépinière, Titi. This week's subjects: Styling Your Old Corporate Shirts the Ghanaian way, Poverty the mother of invention and fashion? Sport sport sport, Irregular flow, and +233 Jazz Bar & Grill

---

Styling Your Old Corporate Shirts the Ghanaian way. Refreshing your work wardrobe doesn't have to be expensive. By mixing classic corporate shirts with modern cuts, African prints, and timeless accessories, you can create countless outfits that look fresh, elegant, and uniquely Ghanaian. Add a Touch of African Elegance: Nothing refreshes a plain corporate shirt like African prints. Pair your white or blue shirt with a beautifully tailored Ankara pencil skirt or high-waisted trousers. The combination blends professionalism with cultural pride, making it perfect for the modern Ghanaian woman. Accessorise Like a Professional: A statement necklace, elegant earrings, a leather handbag, or a colourful silk scarf can completely transform your outfit. Keep accessories balanced so they complement your shirt without overpowering your professional image. Layer for the Rainy Season: Accra's rainy season can be unpredictable. Keep a lightweight blazer or tailored cardigan handy. It adds warmth in air-conditioned offices and protects your polished look when the weather changes. Roll Up Your Sleeves with Confidence: On warmer days, neatly rolled sleeves creates a relaxed yet sophisticated look. Pair them with loafers or block heels for comfort while moving around the office or attending meetings. Before you shop for something new, take another look at the shirts already hanging in your wardrobe. You may discover your next favourite office outfit is one you've owned all along.

Poverty the mother of invention and fashion? Every year about 200,000 Ghanaian girls finish secondary school, and many cannot continue because of lack of funds. Understandably, 1 year university comes to about 15,000 Cedis, if you add accommodation, fees and upkeep. Rather than idling many become seamstresses, some after getting a degree from a Roadside Academy, some going to a fashion school, some interning with a “Madam”. And then they offer their services to friends and neighbors and family. Competing against mountains and mountains of second hand stuff coming in from Europe. So about the only way to make more than 80 Cedis or so a dress is to be different. So we see all sort of trials, T-shirts turned into blouses with sleeves, skirts with a stripe of kente sewn onto it, what not. And some of it is quite original. I recently visited the Jaynal graduation day and really, a few of the pieces presented met all standards of a professional fashion designer. No wonder Europeans are evermore becoming interested in African designs, here we can still find really original things, and especially if traditional materials like Kente, Batakari, Adinkra and Bambolse are incorporated and traditional styles are followed.

Sport sport sport. Are you following the Women's Africa Cup of Nations (WAFCON)? Ghana may not win but I like to watch the ladies. They play better than the men, kicking, pushing, pulling, beating, bumping into each other at full speed, tackling, fouling, kicking the goal keeper in the face, and, with the men you hardly see that, hair pulling. This is sport at its best.

Irregular flow. This is unpleasant, you don't know again when is what, and especially if yours is “painful” you may want to plan around it and schedule that weekend in Elmina or Akosombo for better days. The main reasons for irregular flows mentioned are medical, hormones, medications, stress, sudden lifestyle changes, weight gain or loss, intense exercise, thyroid issues and even approaching perimenopause. A friend of mine had a fairly regular flow, until she started to gain weight. Often she was up to 10 days late. Then in January she cut carbs. Not entirely, that’s very difficult, but no sugar, no rice, no bread, instant noodles, pastries, sweets and no sodas. And since April, she’s regular again like a clock again, till today. All carbs eventually turn into sugar. First of all any form of cancer needs sugar, so the less you eat it, the less chance you have of getting cancer. But secondly too much sugar upsets your blood sugar balance, your pancreas has to work overtime and by the time you are 50 or so this turns into a full blown diabetes. And that means either a daily dose of insulin, at the cost of at least between GH₵3.50 and GH₵15.00 per day, or death between 10 and 20 years earlier. On top of that your intestines get confused and even if you eat the right food you’ll not digest it properly and will easily become deficient in all sort of things. So stay away from carbs as much as you can. Our typical diet is about 60% or more carbs, try to bring it down to 40% or less. And just for the record, a small bottle of coke contains about 35 grams of sugar, about 8 cubes or 8 teaspoons…Say it with Coke.

**+233 Jazz Bar & Grill **. On Tuesday is their jazz evening, the place is quiet and cool, the band played inside but we were at the bar where we could see and hear the band, with in the background the sound of the drizzle. We tried their spring rolls and immediately ordered a second portion, excellently fried. Their beef kebabs are still very good and juicy and spicy, and very big, almost like a steak at a restaurant, but then for 70 GHC only. And I like their burger at 160 GHC. They don't come with all the things most people like, just onion and tomato and beef and a bread bun, but the meat is tasty and tender. An excellent bite without the frequent sauce mess most beefburger unfortunately create. And their quarter grilled chicken is nicely juicy. All in all we had an excellent evening , nice food, nice music from Frank Kissi & The Electric Band, and the place outside empty with the drizzle of the rain, just the three of us. Could be a song.

Lydia...

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The Short Version

Between June and August 2026, PlantLab learned to diagnose several plants in one photo, started naming the family of a condition when it isn't sure of the specific name, made model upgrades visible to your code, and fixed an upload failure that took three tries to actually kill.

The middle two change what comes back in your JSON.

I don't write these often. Most of what happens on a project like this isn't worth a post, and I'd rather publish something useful about spider mites than a changelog with a bow on it. But enough landed this summer to be worth one place to see it, and the failures are more interesting than the features.

---

One Photo, Several Plants

The API used to assume your photo held one plant. Shoot a whole tent and it averaged everything into one answer for the room, which is the wrong answer for every plant in it.

It now finds each plant separately and returns a results array – one entry per plant, each with its own bounding box, health call, growth stage and conditions. Three plants, two fine and one yellowing, gets you exactly that, and the box tells you which pot to walk to.

This was a breaking change: the per-plant fields moved off the top level into results[]. A single-plant photo returns an array of one, so it's the same code path either way. There's a longer write-up if you're wiring it up.

---

It Admits When It's Unsure

The change I'm happiest with, and the least flashy.

Some plant problems look nearly identical in a photograph. Calcium and magnesium deficiencies need different fixes, and there are images where nothing in the RGB data cleanly separates them. The honest move there isn't to pick one and sound confident.

So every condition and pest now carries a coarse_group – the clinical family it belongs to, one of six. The family is often right when the specific name is shaky, so you can alert on “something in the mobile nutrient family is happening” and stand on much firmer ground than “it's definitely magnesium.”

A secondary finding can also come back marked suspected: true. That flags something for your attention without claiming it. Show those to a human. Don't dose on them.

Both fields are additive, so nothing broke when they appeared.

---

Model Upgrades Are Visible Now

Responses carry an engine_version naming the build and model iteration that served your call.

That matters because the models behind a diagnosis were replaced over the summer, and they'll be replaced again. If you cache results or tune thresholds against particular behavior, watch that field and invalidate when it moves. It used to be that a model upgrade quietly changed things underneath you. Now you can branch on it.

The honest numbers haven't moved much and I won't inflate them: cannabis verification sits at 99.96% balanced accuracy, health screening at 98.4%, both on plants held out from training. Naming the exact condition is still harder than noticing something's wrong. That gap is why the hedging fields exist.

---

The Upload Bug That Took Three Tries

My favorite failure of the summer.

Uploads from phones started failing. Not all of them, not reproducibly, and never on my machine. The pattern turned out to be full-resolution photos over slow upstream connections – which describes a lot of growers and almost no developers.

The server was hanging up while the phone was still sending. I raised the read timeout from 30 seconds to 120 and shipped it. Reports kept coming. Raised it to 300 and shipped that. Reports kept coming.

There were three timeouts in that path, not one, and they were all different: the API's own, the reverse proxy in front of it, and the client's. Fixing one just moved the failure to whichever was now shortest. The connection died at the tightest link no matter what I did to the others. Aligned all three at 300 seconds and the reports stopped.

I shouldn't have needed this lesson twice: when a timeout fix doesn't work, the timeout you fixed isn't the one that fired. Find every layer that can hang up before you touch any of them.

The mobile app shipped with the aligned values in July.

---

Quieter Things

• The API no longer dies at boot on a transient database hiccup. It used to try once and exit, so a DNS blip during a deploy could take the service down until someone noticed.
• There's a partners page now for hardware and platform companies who want plant health inside their own products.
• /usage returns your counts, limits and remaining quota, and doesn't itself consume quota.
• I measured what ruins a diagnosis photo. Tight close-ups and overexposure hurt far more than blur or purple light, which is the opposite of what most people assume.
• Infrastructure runs in Europe. That happened earlier in the year, and here's why.

---

What's Next

More work on naming the specific condition rather than the family, since that's the honest weak point and everything else is downstream of it. And better handling of the photos growers actually take, which are lit by purple LEDs at midnight rather than by a photographer.

Free tier is 3 diagnoses a day, no card. The full contract is at plantlab.ai/openapi.json, with a field-by-field walkthrough if you want it, plus guides for Home Assistant and Node-RED.

What It Does

The PlantLab API takes one image of a cannabis plant and returns structured JSON describing what's wrong with it: one of 30 conditions and pests (or healthy), a growth stage, a confidence score on every call it makes, and a bounding box per plant when the photo has more than one. It answers in about 18 milliseconds. Auth is a single X-API-Key header, and the free tier is 3 diagnoses a day without a card.

If you already have a camera pointed at your tent, this is the piece that turns a JPEG into something your automation can branch on.

Most grow stacks are blind in the same place. Temperature, humidity, VPD, EC, runoff pH, substrate moisture at three depths – all of it describes the room, none of it describes the plant. So the loop still ends at a person squinting at a phone.

Every response below came out of the live engine.

---

One Call

curl -X POST https://api.plantlab.ai/diagnose \
  -H "X-API-Key: $PLANTLAB_API_KEY" \
  -F "image=@canopy.jpg"

Multipart upload, one required field.

---

What Comes Back

A real response, from a plant with powdery mildew on it:

{
  "request_id": "8919a46e-a704-4a4e-a700-b754188165b5",
  "schema_version": "3.1.0",
  "engine_version": { "api": "1.0.166", "models": "v6" },
  "success": true,
  "is_cannabis": true,
  "cannabis_confidence": 0.95,
  "results": [
    {
      "bbox": { "x0": 0, "y0": 0, "x1": 1, "y1": 1, "normalized": true },
      "is_healthy": false,
      "health_confidence": 0.1,
      "growth_stage": "vegetative",
      "growth_stage_confidence": 0.9,
      "conditions": [
        {
          "class_id": "powdery_mildew",
          "display_name": "Powdery Mildew",
          "confidence": 0.8,
          "coarse_group": "fungal_disease"
        },
        {
          "class_id": "potassium_deficiency",
          "display_name": "Potassium Deficiency",
          "confidence": 0.6,
          "suspected": true,
          "coarse_group": "mobile_nutrient"
        }
      ]
    }
  ]
}

It returned two things. The mildew is the call it's making. The potassium deficiency is marked suspected – something else worth a look, without claiming it.

---

The Fields

Field	Level	What it means
is_cannabis	image	Whether the photo is cannabis at all. Decided first, so it sits at the top
cannabis_confidence	image	Probability the image is cannabis
results[]	image	One entry per detected plant. A single-plant photo returns an array of one
bbox	plant	Where this plant is, in normalized 0-1 coordinates. Multiply by width and height to draw it
is_healthy	plant	The health call for this plant
health_confidence	plant	Probability the plant is healthy. Read the next section before using it
growth_stage	plant	seedling, vegetative, or flowering
conditions[]	plant	Diseases and deficiencies, most confident first
pests[]	plant	Pests, same shape
schema_version	response	Contract version, currently 3.1.0
engine_version	response	The build and model iteration that served this call

The field people get wrong

health_confidence is the probability the plant is healthy. It isn't confidence in the verdict you were just handed.

In the response above, is_healthy is false and health_confidence is 0.1. That's not a shaky answer – it's a very confident sick one, because a 10% chance of healthy is a 90% chance of not. Write if health_confidence < 0.5: flag_uncertain() and you'll fire uncertainty warnings on the clearest sick plants you own while staying quiet on the genuinely ambiguous ones.

When is_healthy is false, low health_confidence means more certain, not less.

suspected and coarse_group

suspected: true marks a secondary finding the engine wants to flag but isn't asserting. Show it to a human; don't dose on it.

coarse_group is the clinical family a condition belongs to – one of mobile_nutrient, immobile_newgrowth, water, light, fungal_disease, pest. Some problems genuinely look alike in a photograph, and the family is often right when the specific name is shaky. If you're deciding whether to alert rather than what to dose, group on this instead.

Also in the response

Three more fields show up when they apply.

mulders_hypotheses names nutrient excesses that would explain the deficiency you're looking at. A calcium excess locking out nitrogen looks exactly like a nitrogen shortage, and feeding more nitrogen makes it worse.

progression_risks says what this turns into if nothing changes.

stage_advisories adds context that depends on the plant's stage. Lower-leaf yellowing in late flower is usually normal, and it says so rather than letting you chase it.

---

The Rest of the Surface

Endpoint	Method	Purpose
/diagnose	POST	The one you came for
/usage	GET	Current counts, limits, remaining quota. Read-only, doesn't consume quota
/health	GET	Liveness. No key required
/info	GET	Metadata and capabilities
/history	GET	Past diagnoses, newest first. Pro tier and above, with data sharing enabled
/feedback	POST	Report a wrong result

/usage is the one integrators forget exists and then rebuild badly. Poll it instead of counting your own calls.

The full machine-readable contract is at plantlab.ai/openapi.json, so generate a client rather than hand-rolling one.

---

Limits and Errors

Free tier is 3 diagnoses a day. No card, no trial clock, and nothing to cancel – paid plans aren't live yet, so right now that's the whole offer. Higher-volume tiers exist in the API for granted accounts, and paid access is coming.

429 means you hit a limit. 408 means inference timed out. 400 on upload usually means the image failed a sanity check rather than a malformed request.

---

What It Doesn't Do

It's cannabis-specific. Point it at a tomato and is_cannabis comes back false, which is correct and not useful.

It reads a photograph, so it's bounded by what a photograph contains. Root-zone problems only appear once they reach the leaves, and two conditions that look identical in RGB are hard to separate in RGB. That's why suspected and coarse_group exist instead of a single confident label.

It's sensitive to how you shoot. Overexposure and tight close-ups reliably produce wrong answers – I measured what breaks a diagnosis, and the results aren't what most people guess.

It doesn't replace looking at your plants. It notices things earlier and more consistently than you will at 11pm, and it never gets bored.

On accuracy: cannabis verification runs at 99.96% balanced accuracy and health screening at 98.4%, both on plants held out from training. Naming the specific condition is a harder problem than deciding something is wrong, which is the honest reason those hedging fields are in the response at all.

---

Getting Started

Sign up at plantlab.ai, copy your key from the dashboard, and run the curl command at the top of this post against one of your own plants.

Prefer wires to code? There are guides for Home Assistant and Node-RED.

How to Photograph a Cannabis Plant for Diagnosis

Frame the whole plant so it fills most of the shot, and use even light that isn't blown out. Don't zoom in on the damaged leaf, and don't shoot from across the tent. If your lights are blurple, that matters far less than whether the photo is overexposed.

The mistakes that cost you most aren't the ones people expect.

The One That Surprises Everyone

When a leaf looks wrong, your instinct is to get close and photograph the damage. It feels like helping.

It's the most reliable way to get a wrong answer.

I took four labeled images – powdery mildew, spider mites, a nitrogen deficiency and a calcium deficiency – cropped each one tight around the affected area, and ran them through. Not one survived. The mildew came back healthy. The spider mites came back as a nitrogen deficiency. The calcium deficiency came back as septoria. The nitrogen crop wasn't recognized as cannabis at all.

Diagnosis is comparative. Which leaves are affected, old growth or new. Whether the pattern is uniform or between the veins. How the rest of the plant looks by comparison. A close-up throws all of that away and leaves a patch of discolored green that could be six different things.

Photograph the plant. The damage is already in the photo.

But Don't Go Too Far Back

The opposite mistake fails just as reliably. Shot from across the tent, all four went wrong: two returned the wrong condition, one came back healthy, and one wasn't recognized as cannabis at all.

There's also a hard floor on size. Scaled to 256 pixels wide, three of four failed the cannabis check outright and returned nothing. Any modern phone clears this easily, so it only bites when something in your pipeline resizes before upload. If you're automating, send the original.

One plant, filling most of the frame.

Light Level Beats Light Color

Overexposure is the dangerous failure, because it doesn't look like one.

Brightened until the highlights clipped, two of the four sick plants came back healthy with reasonable confidence. Blown-out highlights wash out the exact color and texture differences a diagnosis rests on, and what's left looks like an untroubled plant. You get an all-clear on a plant that needs attention, and nothing in the response tells you to doubt it.

Underexposure is gentler. Two of the four darkened images kept the exact right answer; of the other two, one dropped out of the cannabis check and one named the wrong pest. Neither returned a false all-clear. Wrong, or refused outright, you can recover from. Confidently-healthy you can't.

Blurple light did less damage than expected – three of four still returned the correct condition under a heavy magenta cast. That's smaller than the effect of exposure. It still costs some confidence, so shoot during a lights-off window or use your flash when that's easy. When it isn't, shoot under the grow lights anyway and watch the confidence score.

I simulated that color cast digitally rather than photographing under real fixtures, so it's directional rather than settled.

Blur Is More Forgiving Than You Think

A heavy blur kept the correct diagnosis on half the test images. On the other half it produced a wrong answer, with confidence collapsing to 0.10 and 0.15.

That's the system working. When the image doesn't support a call, confidence drops instead of the answer quietly getting worse. Treat anything under about 0.3 as “take another photo,” not as a diagnosis.

The reverse doesn't hold. A high score is not a guarantee: one tight crop returned the wrong condition at 0.70. Low confidence reliably means don't trust it. High confidence doesn't reliably mean you can.

Still focus your shots. But given the choice between a slightly soft photo of the whole plant and a razor-sharp photo of one leaf, take the soft one.

The Checklist

Do	Why
One plant, filling most of the frame	Diagnosis is comparative – it needs the whole plant
Even light, no blown highlights	Overexposure returns false healthy calls
Shoot lights-off, or use the flash	Costs less confidence than shooting under blurple
Send the original file	Aggressive downscaling breaks the cannabis check
Retake anything under 0.3 confidence	Low confidence often means the photo, not the plant

Avoid	What happens
Close-up of the damaged leaf	Wrong answer or a false healthy, every time
Whole-tent wide shots	Wrong answer, or not recognized as cannabis
Brightening a dark photo to “fix” it	Turns a diagnosable photo into a healthy verdict

How I Tested

Four labeled images – powdery mildew, spider mites, nitrogen deficiency, calcium deficiency. Eight versions of each: original, darkened, brightened, blurred, color-cast, tightly cropped, shot-from-distance, downscaled. Thirty-two diagnoses. All four originals returned the correct condition untouched.

The variants were made digitally rather than by re-photographing under each condition. And four plants is four plants: enough to show a pattern that held every time, not enough to put a percentage on it. Hence directions rather than numbers.

If You're Automating

Same rules, plus two. Don't resize before upload. And branch on confidence instead of treating every response as equally solid – under 0.3 usually means trigger another capture, not raise an alert.

The Home Assistant and Node-RED guides cover the wiring, and the API walkthrough covers what comes back.

Try it on your own plants at plantlab.ai – three diagnoses a day, free, no card.

– Det er viktig å skape nye historier, sa han. – Ikke bare leve på de gamle.

Det var kjøreskolelæreren min som skulle vise seg å bidra med den største innsikten jeg har blitt servert på en god stund. Han er 70 år, har kjørt motorsykkel lengre enn han kan huske, og har ingen planer om å gi seg før kroppen streiker. Det er hans måte å skape de historiene han vil leve. Det var dypt inspirerende der vi satt i salen og nøt svinger og landskap om hverandre.

Det er lett å bli hengende ved de gamle historiene, leve på dem hver dag. Man lar de definere en selv og ender ofte opp med å gjenfortelle gang på gang når man sitter rundt kaffekoppen med kamerater eller kollegaer.

Samtidig er det lett å tenke at alle nye historier må være store, må stadig overgå. At de må være grandiose og imponere. Det har ikke jeg tid eller mulighet til, kan man tenke.

Det er lett å tenke på livet som det som har skjedd, det man har opplevd. Men det er vel så mye det som gjenstår å oppleve. Historiene man ikke har skapt, som ligger der og venter. Ikke som en uoverkommelig bucket list, eller et jag etter å leve hver dag som den siste, men som en motivasjon og noe som kan skape glede og forventning. Noe å se frem mot heller enn å bare kikke i bakspeilet.

Det kan være vel så verdifullt med de må hverdagseventyrene som de store som bare skjer en gang i tiåret. Forsøke noen nye hobbyer. Gå nye stier i skogen – ta med middagen. Ta badstue med noen kamerater. Kjør motorsykkel. Spill frisbeegolf, eller vanlig golf. Eller begge deler. Lær deg å strikke. Hopp i fallskjerm.

Historier kan være så mangt og det viktige er å lage deg noen du kan leve, ikke bare fortelle de samme gamle om igjen.

Woke became an insult. What does it mean to be not woke? To oppose all the things that could bring humanity to a brighter future, like wisdom, common sense, empathy, acceptance, logic, education, science, etc.. Judge and hate people for the color of their skin, for who they fall in love with, for where they were born, or for no reason at all. Decide who should have and who should have not. Even when presented with solid evidence, deny that more of an iceberg exists beneath the surface than what can be seen. Life is all black or white with no in between. Don't listen to reason, and ignore any inclination to investigate anything. Cancel critical thinking. Don't care about how much suffering closed mindedness, greed and violence causes. Measure success by how much power and The Dollar Almighty a person has taken by manipulation, greed, or brute force. If you haven't achieved this (false) success for yourself, admire those who have, and elect them as leaders.

I have been getting a lot more matches on, and there’s this one person that I was talking to a lot today she really matches my energy. I also thought she was funny And she matched my freak. She also mentioned that she has had therapy in the past, and she lives pretty close by which I’m excited for. We have our first date on Saturday. I’m nervous but excited.

He did not settle upon my heart with the coziness of a warm blanket. He clawed his way through terror and rejection, roaring in silent defiance until I granted audience. Until I opened my eyes to see what stood before me.

It is said, that when the leaves blush in longing dance, air and water will unite to set flame to ways long rotting. What they forgot to mention is that the earth must tremble, cracking until mountains rise to spew fire into the heavens. Forever igniting our best minds to blaze in remorse. The unyielding storm holds no syllables in that passage.

Storms do not yield. They flood. They rupture. They clean the slate and leave a debris field of memories for us to learn from. But we haven't learned. Neither have I. Neither has he. We just are. Two victims of a cunning value. Little more. Little less.

We are taught that we must never gaze into the eyes of our heroes. One does not ascend to such great depths by doing what they are told. I dared to peer into the travesty of his gaze, and in that monumental moment of a not-so-insignificant contact—

the world tilted.

© 2026 AnOublietteofThought. All rights reserved.

WTF.

Today started out with the reminder that it may be my dogs last day. I am still not ready.

It was not quite as productive and busy yesterday. I can’t tell if that is a good thing or not, because I felt as if I had to fully acknowledge how I am feeling, which resulted in a lot of crying.

I could not eat this morning, the best I could do was a cup of coffee, which I hate taking on an empty stomach. I think I doomscrolled for a bit/started packing in short bursts of energy. It was weird. The energy was weird. The weirdest of all was my dog acting and looking completely fine and normal. It is disheartening and charming at the same time. Does she even know that I am just waiting for her to be ready? However, I have to leave in just a couple of days so I can’t wait any longer. My heart feels physically broken – like my chest physically hurts.

I am pulling every single string so I can have her for just one extra day.

It is too much. Everything happening right now is just too much.

To top it all of, I had an ATM machine malfunction on me on my last errand. Now I have to go to the bank tomorrow and get it sorted out because my deposit did not go through :/

Thank you for your message. I am currently out of office with no set return date. I will get back to you when the time is right.

Well, today was kind of busy ~ kind of sad.

Started with a bang at an oil change appointment. It was kind of annoying (another expense while having no paycheck), but also productive because I finally started writing my speech for my brother’s wedding!

Went to urgent care (finally) for my cough/congestion… only to be told it may be really bad allergies and to just continue taking allergy medicine.

Then I went to pottery and got quite a bit of work done with the trimming and stamping of a couple of pieces. All before heading to therapy for an hour. It was helpful, but man was it a lot to unpack this time.

Ran home, finished some laundry, and saw the dogs before going to a workout class! Continued on by another short pottery session of glazing a few other pieces with my mom. Proceeded to meet up with my dad and dogs at the dog park for what I have to assume is the last time (?!)

And finally, came home for the day.

Thank you for your message. I am currently out of office with no set return date. I will get back to you when the time is right.

I enjoy alt-media news much more than I ever really enjoyed mainstream media news. But something so strange happened during the whole Covid-19 thing. So many people got shadow banned during that event who were just being inquisitive and trying to find out what was going on. Legitimate doctors who were having successful results in treating Covid in many ways were harassed and even lost their licenses. As I watched all of this happen many alt-media lost a lot of their viewers because of being shadow banned or banned on Youtube, Facebook, Twitter, and probably more that I’m forgetting. Then some of them built back up on either their own platforms or other platforms. Now after all the dust has settled many are going back to their corrupt masters at Google and posting on YouTube. They talk about how they were banned and how lame the company is for doing that but still use it and post on it and funnel traffic to that beastly company. Isn’t this pretty much the definition of “selling out?” And all of us who disliked that whole thing, aren’t we selling out by still using the platform who thwarted free speech? I had Covid twice (untested cause I don’t trust the PCR test as viral diagnostics, neither did the inventor Kary Mullis. See a snippet article on the topic here and an open forum interview with him here skip to 48:30 to 52:11 for the reference about PCR testing, but the whole thing is interesting) and it wasn’t all that bad by taking vitamin C and D with zinc. Some doctors were harassed for saying such a thing, and that is known to help your immune system fight against just about any virus. I later learned that nicotine can help with long lasting Covid symptoms (and here). I did have decreased smelling for quite a while and got used to it. But after I heard about that I used a few nicotine patches cut up into small strips and nicotine gum. That gave me back a lot of my smell! Why won’t the main outlets give us at least the basics. Go get fresh air and sunlight for vitamin D, boost your vitamin C intake and make sure you have some zinc. That alone can keep many viruses from taking hold, and when they do they often will not be too debilitating.

Personally I have tried to avoid YouTube for many years now. There are some rare occasions that I do but I use it through third party platforms that block ads and hide my identity. I use a VPN, FreeTube, and/or PipePipe. I’ll switch to other things if/when those stop working. There is a constant little battle going on with YouTube trying to block those services and then those services get patched and they start working again. If I’m going to go use that platform which I dislike I’m not going to support them by watching their ads and letting them profile me. I’d rather not watch the video if I cannot do it on my own terms.

One of the biggest reasons I still ever go to YouTube is for the DIY and tutorial type videos. There are some decent alternative platforms for news, politics, and rantings of sorts but there isn’t much out there in the DIY and tutorial world besides YouTube that I have found. I would love a simple video site or video blog site to come along focused on that theme. Keep the bulk of the nonsense videos, politics, and compilations of silly animal events out, there are already plenty of platforms for that. If I had the ability and a bit more care that might be a project I’d put effort into. A simple clean DIY and tutorial video platform with no ads, just passion driven sharing of ideas. I wouldn’t block free speech with people posting a DIY video on something while rambling about their political views, but if it was just about politics I’d rather they go to Bitchute or something. A donation option would be cool and other payment options for content creators, but no ads and no paywall. That would be awesome!

Doesn’t this Proverbs 26:11 verse sort of fit with the people running back to YouTube after being banned?

Like a dog that returns to his vomit is a fool who repeats his folly

Proverbs is great.

Resources:

Books:

mRNA Vaccine Toxicity – by Dr. Michael Palmer M.D.

The Indoctrinated Brain: How to Successfully Fend Off the Global Attack on Your Mental Freedom – by Michael Nehls

Lies My Government Told Me: And The Better Future Coming – by Robert Malone, MD, MS

Apps and websites:

BitChute and Old BitChute — YouTube alternative focused more on freedom of speech.

Odyssey — YouTube alternative focused more on freedom of speech.

PipePipe — YouTube interface for Android and GrapheneOS that blocks your identity and ads.

FreeTube — YouTube interface for Linux, Mac, and Windows that blocks your identity and ads.

On Learning To Write

Current Reading: Adler and How To Read A Book have officially been shelved. Saint Michaels Lent has started and I'll be reading The Spiritual Combat by Lawrence Scupoli as part of my devotion. I won't come back to Adler right away. I want to move on to something else, but I'll keep it close by for reference.

---

I've noticed my reading decline as of late. Im trying to become a better writer and it's really taking up a lot of my time just focusing on that. And I know that reading helps you become a better writer. But trying to write as much as possible to really get a feel for it, to try and find my voice is hard.

I was doing a lot of research on how people write differently. A lot of people have an idea in their head, plan it out and then write it down in the most elegant way they can. Some people write very analytical, almost like a very precise scientific explanation of something. Some write in beautiful pros, leading you down a beautiful literary narrative to a deeper understanding.

I write different I think.

Usually when I start I have no idea where I'm going to end up. That's what the Current Reading sections is for. It's something concrete I can start with, get my mind and hands working, then a thought will usually come up, and I start writing towards it.

I noticed this a couple years ago. I struggled to keep a steady journaling habit. I would make it two or three days then completely fall off. No real reason, just that I didn't have anything to write about. I think it's because I had an idea of what I was supposed to be doing. I thought I had to have a complete, fully formed narrative in my head before I started writing. I mean, that's what I was taught in school. Figure out what you're going to write about, make an outline, construct the essay. And that's a completely valid way of writing, but just not what I was wanting to do.

I'm Catholic. And every first Friday of the month my parish has overnight adoration. You can sign up to make a Holy Hour during the overnight hours into Saturday morning. I started going at 3 o'clock AM, and I really wanted to keep an adoration journal. But the same thing started happening, I'd get there, pray, open the journal and...crickets. Nothing coming to mind. No mystical messages from Our Lord beamed straight into my head. No big inspiration of anything I just had to get out.

So I started doodling, just because the pen and paper were right there in front of me. And then to my surprise, just by the mechanics of my hand moving, something loosened up, and a thought would just pop into my head. Something I was dealing with at work, something one of my kids had asked me, some problem I had been struggling with, some family dynamic that was needing to be worked out. And my hand started moving, but making letters and words this time not just drawing. Not in some flashy “a-ha” moment. Just natural.

I've now developed a pretty robust journaling habit. I've learned that I need to externalize my thoughts onto the page, to see them, so I can really understand them. I have also started a bullet journal and I still doodle.

And I know this may seem trivial to some people, but it was monumental for me. People think “Why are you making mountains out of mole hills Stoney. Just write something down”. And yeah for some that might work. I needed a little more understanding first. For years I wanted to journal steadily but it always evaded me. I just had to figure out the thing in my head holding me back, the way I was conceptualizing writing, was what I was taught in school. But it had to make sense in my own head for it to finally click.

And that brings me to the next big step. Trying to learn to write for others to read. It's something I've been drawn to my whole life. I never had enough confidence to actually do it, and I kinda can't believe I'm doing it now. I'm practicing in public(TERRIFYING). These short essays aren't going to be heavily edited. They are, at their core, my learning process. If you're looking for a completely polished, 3,000 word, journalistic masterpiece you probably won't find that here. At least not at first. The goal is to practice getting better. And I hope, eventually, that I'll write things that people enjoy reading.

Chapter 1: The Thoughts That Get Louder After Everyone Else Is Asleep

There are nights when nothing dramatic is happening in the room, yet your mind feels like it is standing in the middle of a storm. The lights are off. The house is quiet. Everyone else seems to be sleeping. But you are thinking about the world, your family, money, the future, and all the things you cannot control. You tell yourself to stop thinking, but your thoughts keep reaching for one more possibility. That is when a quiet 2 Thessalonians 2 message for people who feel shaken inside can meet you differently, not as a lesson to study, but as a place to put down what you have been carrying.

Sometimes the hardest fear is the kind you do not know how to explain. You are not in immediate danger. Nothing terrible happened today. You may even feel embarrassed that you are this unsettled. Still, your chest feels tight and your mind will not rest. You have read enough headlines, watched enough arguments, and heard enough warnings that the future has started to feel personal. You are not only thinking about what is happening in the world. You are wondering what it could eventually mean for your home, your job, the people you love, and the life you are trying to hold together. That is why Christian encouragement for the hidden fear that follows you into the night matters. Sometimes you need more than information. You need somewhere safe to be honest.

Paul’s words in 2 Thessalonians 2 speak directly into this kind of inner shaking. He tells believers not to be quickly shaken in mind or alarmed. I keep coming back to that phrase because it does not shame the person who feels unsettled. It recognizes that a mind can be moved. It can be pushed around by fear, confusion, rumors, and pressure. But it also tells us that being shaken does not have to become our permanent condition.

Maybe you know what this looks like when the day is finally over. You have answered everyone’s questions. You have handled what needed to be handled. You have been the dependable one at work, at home, or in your family. Then you lie down and realize you have been holding yourself together all day. The silence gives your fears room to speak. You wonder whether the economy will get worse, whether your children will be okay, whether someone you love is drifting away, whether your health will hold, or whether you are prepared for whatever comes next.

In those moments, I do not think the answer is to force yourself into a better mood. I do not think faith requires you to pretend you are not scared. Sometimes the most faithful thing you can do is tell God exactly where your mind has gone. Not the polished version you would say in front of other people, but the real version: you are afraid of what you cannot see, tired of feeling responsible for everything, and worn down from imagining the worst. There is relief in becoming honest enough to stop performing calmness.

2 Thessalonians 2 does not promise that believers will understand every confusing event around them. It does something more useful. It reminds them that they do not have to be ruled by confusion. Deception can exist without becoming your identity. Trouble can be real without becoming your master. You can admit that the world is unstable without deciding that God must be unstable too.

That distinction matters late at night because fear likes to erase proportion. A difficult possibility starts feeling inevitable. One frightening headline begins representing the entire future. A problem you cannot solve before morning suddenly feels like something you must solve before you are allowed to sleep. But you are still human, and you are allowed to reach the end of what you can carry today.

There is no spiritual failure in saying, “I cannot hold this tonight.” In fact, surrender often begins there. You stop trying to manage ten imaginary tomorrows and return to the one room you are actually sitting in. You notice the clock and your breathing. You notice that the people you love are still here. You remember that God is not waiting for you to predict the future correctly before He agrees to remain faithful.

This is where standing firm becomes deeply personal. It may mean turning the phone facedown even though part of you wants one more update. It may mean refusing to search for reassurance until two in the morning. It may mean opening your Bible without demanding that every question be answered. It may mean whispering one simple prayer because you do not have the energy for anything more. God is not measuring the beauty of your prayer; He is meeting you in the truth of it.

Paul later reminds these believers that they are loved by the Lord, and I think that belongs beside the warning not to be shaken. When your mind is tired, you can start believing that God must be disappointed in you for struggling. But tiredness does not cancel love. Fear does not erase belonging. An unsettled night does not move you outside the reach of grace.

You may not wake tomorrow with every concern resolved. The world may still be noisy, and your personal questions may still be waiting. But tonight does not have to become a courtroom where you put your faith on trial because you are afraid. You can simply be a tired person talking honestly to God, and sometimes that is where steadiness starts.

Chapter 2: The Morning After the Fear

Morning has a way of making some fears look different. You wake up, see a thin line of light around the curtains, and realize the world did not end while you were sleeping. The problem you were worried about may still be there. The headlines may still be difficult. The unanswered message may still be unanswered. But you are here, and the fear that felt enormous at 1:30 in the morning has lost some of its power in daylight.

That does not mean the fear was fake. It means fear changes shape when we stop feeding it.

Imagine walking into the kitchen and seeing the same unpaid bill you were thinking about the night before. The amount has not changed. Your bank account has not changed. But now you can make coffee, sit down, look at the numbers, and decide what the next step actually is. Maybe you need to move money, call someone, change a plan, or ask for help. Faith does not make the bill disappear. It helps you face the real problem instead of suffering every possible consequence before anything has happened.

That is one of the things I hear in Paul’s instruction to stand firm. Standing firm is not emotional numbness. It is refusing to let fear push you into a life of imagined emergencies. It is returning to what is true, what is in front of you, and what God is asking of you today.

There is also something deeply freeing in remembering that you are loved before you are steady. You do not have to calm yourself perfectly before you are welcome with God. You do not have to prove that your faith is strong enough by pretending you never get overwhelmed. You can come to Him tired, uncertain, and still asking the same question you asked last night.

Then you can take one faithful step.

Make the call. Eat breakfast. Go to work. Sit with the person who needs you. Pray for the part of the world that has been weighing on your heart. Turn off the noise when it stops helping. Tell the truth. Do what is yours to do and leave what is not yours with God.

The world may remain unstable. Your life may still contain uncertainty. But you do not have to become unstable with it.

Sometimes peace is not the feeling that nothing bad can happen. Sometimes it is the quieter confidence that whatever happens, you will not be alone in it.

Your friend, Douglas Vandergraph

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There is a particular kind of betrayal in being charged more for staying. It runs against every instinct a customer brings to a long relationship with a shop, a bank, an insurer. You assume, reasonably, that years of repeat business buy you something: goodwill, a better deal, the warmth of being a known quantity. The opposite is closer to the truth. In a growing number of markets, the longer you remain a customer, the more a pricing system has learned about you, and the higher the figure it concludes you will tolerate. Loyalty is not rewarded. It is metered, modelled and monetised. The system reads your steadfastness as a signal of your inability to leave, and prices the difference.

This is the strange logic at the heart of what regulators have started calling surveillance pricing, and it is worth pausing on how counter-intuitive it is. Surge pricing, the kind that lifts a taxi fare when it rains, at least responds to something visible and shared. Everyone standing on the same wet pavement faces the same multiplier. Surveillance pricing does something else entirely. It responds to you: to your browsing history, your device, your postcode, your past purchases, the financial circumstances it has inferred about you and, crucially, to how trapped you appear to be. By 2026 this is no longer a thought experiment kept warm in economics seminars. It is law in three American states, the subject of formal orders from the United States Federal Trade Commission to eight companies, and the documented practice of firms from grocery delivery platforms to airlines. The question it forces is not whether the technology works. It plainly does. The question is what happens to the idea of a fair public price, the same number for anyone who wants the same thing, when that idea quietly stops being true.

The Difference Between a Crowd and a Person

The vocabulary matters here, because the industry has a strong interest in blurring it. Dynamic pricing is old, familiar and broadly understood. Airlines have flexed fares with demand since deregulation. Hotels lift rates around conferences. Energy tariffs move with wholesale markets. What unites these practices is that the price, however much it jumps, is a fact about the market at a given moment. It is aimed at a condition, not a customer. Two strangers booking the same seat at the same second see the same number.

Surveillance pricing breaks that symmetry. It is the calculation turned inward, away from the conditions of supply and demand and towards the individual standing in front of the screen. The Federal Trade Commission, in the issue spotlight it published in January 2025, drew the line precisely: instead of a price being a static feature of a product, the same product could carry a different price for different people based on consumer data, behaviours, location, time and purchase channel. The agency described an opaque market of third-party intermediaries that use advanced algorithms, artificial intelligence and personal information to categorise individuals and set a targeted price for each. The signals it documented were granular to the point of intimacy: not merely what you bought, but the movement of your cursor across a page, the products you placed in a basket and then abandoned. One example named in the FTC's findings was a cosmetics company tailoring promotions to a customer's skin type and skin tone.

The distinction is not pedantry. It is the whole moral architecture of the thing. A dynamic price is a message the market sends to everyone. A surveillance price is a private message addressed to you alone, written from a profile you cannot read, by a system that knows things about you that you have not agreed to share and may not even know about yourself. The crowd has become a person, and the person has become a target.

The Loyalty Penalty, and Why Britain Saw It First

If you want to understand surveillance pricing before the algorithms arrived, look at British insurance. For years the home and motor insurance markets ran on a practice the industry euphemistically called price walking, and which everyone else called the loyalty penalty. The mechanism was simple and cynical. Insurers offered cheap, often loss-making premiums to new customers to win them, then ratcheted the price upward at each annual renewal, year after year, betting that inertia, busyness or simple trust would stop the customer from shopping around. The longer you stayed, the more you paid, not because your risk had risen but because your loyalty had been identified as exploitable.

The scale of it was extraordinary. In September 2018, Citizens Advice lodged a super-complaint with the Competition and Markets Authority arguing that across five essential markets, mobile, broadband, home insurance, mortgages and savings, British consumers were losing around 4.1 billion pounds a year to the loyalty penalty. That worked out at roughly 877 pounds per affected household, equal to about three per cent of the average household's annual spending. Eight in ten people, the charity found, were paying a significantly higher price in at least one of those markets simply for staying with their existing supplier. The CMA accepted the substance of the complaint when it responded that December.

The Financial Conduct Authority went furthest in the insurance sector. Its market study concluded that six million existing customers would have saved 1.2 billion pounds in a single year had they paid the average price for their actual risk rather than the inflated renewal figure their loyalty had earned them. From January 2022 the FCA banned the practice outright, requiring insurers to offer renewing customers a price no higher than they would quote a new customer for the equivalent policy. It was, in effect, a legal insistence that loyalty must not be priced as a weakness.

What makes this history so relevant is that it was the analogue rehearsal for the digital performance now underway. Price walking was the loyalty penalty done crudely, by hand, with renewal letters and call-centre scripts. It worked because insurers could make a rough guess about who was unlikely to switch. Surveillance pricing is the same instinct supercharged. Where the old insurer guessed at your inertia from your renewal record, the modern pricing engine infers it from hundreds of signals in real time, and applies the penalty not once a year but at the moment of every transaction. The loyalty penalty was never really about insurance. It was about the seller's ability to read a customer's captivity and charge for it, and that ability has grown beyond all recognition. Even the FCA's ban proved porous. Research by the consumer group Which? in 2024, two years after the rules took effect, found that only around one in five car insurance customers had thought to test their renewal quote against what their own insurer would offer them as a new customer, and that just over half of those who did were quoted less for identical cover. Nor was the leakage always accidental: Direct Line Group, having misapplied the new rules, agreed to hand back an estimated 30 million pounds to renewing customers it had overcharged. The penalty, banned in one form, leaks back in others.

The Oldest Dream, Finally Buildable

Economists have a clinical name for the destination all of this is travelling towards. They call it first-degree, or perfect, price discrimination: the seller's fantasy of charging every buyer the exact maximum they are willing to pay, capturing for the company every last scrap of value that, under a single posted price, would have stayed in the customer's pocket. The theory has sat in textbooks for a century. What it always lacked was a mechanism, because individual human beings are historically rather good at concealing their personal ceiling. The single posted price, the same number on the same shelf for everyone, emerged in part because sellers simply could not do better. It was a technological limit that hardened, over time, into something we mistook for a moral norm.

That limit has now dissolved. Lina Khan, who chaired the FTC from 2021 to 2025, put the shift plainly. The idea of charging every individual person according to their individual willingness to pay, she said, had for the most part been a thought experiment. Through the enormous quantity of behavioural and individualised data that brokers and other firms now collect, she went on, the environment had changed: technologically it had become much more possible to serve every individual person an individual price based on everything they knew about you. Her framing of the consequence is the one that has stuck to the debate. We are moving, she warned, from a transparent market with public prices to an opaque world in which we are alone against secret algorithms.

The first public glimpse of the dream came in September 2000, when an Amazon shopper noticed that deleting the cookies from his browser dropped the price of a DVD. The company had been varying prices according to what it could infer about each customer, and in some cases loyal returning buyers were quoted more than newcomers. The backlash was fierce, Amazon retreated, calling it a random test, and the industry drew a careful lesson. Not that personalised pricing was wrong, but that it must never again be visible. For two decades the dream advanced quietly, dressed in the respectable language of demand and yield, until the data and the machine learning matured enough to make the old fantasy operational.

A TV That Costs More in the Car Park

The clearest way to feel surveillance pricing is to watch a price move not because the market shifted, but because you did. In 2019 investigative journalists at the Minneapolis television station KARE 11 documented exactly this on the Target retailer's app. A Samsung 55-inch smart television was listed at 499.99 dollars when a shopper browsed it from home. When the same shopper drove into the car park of a Target store, the app's price for the identical set leapt to 599.99 dollars. The variable was not supply, demand or time of day. It was geolocation: the app could tell the customer was now physically present, less likely to walk away empty-handed, and it priced that captivity at a hundred dollars. The television was not a fluke. Reporters picked ten products at random, from toys to bottled water to vacuum cleaners, and four of them rose in price once they were inside the shop: an Apple Watch band by two dollars, a Shark vacuum cleaner by forty, a Graco child car seat by seventy-two and a Dyson vacuum cleaner by a hundred and forty-eight. The spread is the instructive part. A system that moves a watch strap by two dollars and a vacuum cleaner by a hundred and forty-eight is not applying a blunt in-store surcharge; it is judging, item by item, what the fact of your presence is worth. Target adjusted the app after the investigation, but the episode remains a near-perfect demonstration of the mechanism, because the only thing that changed about the customer was how committed they appeared.

The principle scales far beyond a single retailer's app. The same season the FTC published its findings, the practice was visibly spreading into one of the most price-sensitive markets there is: air travel. In July 2025, the airline Delta confirmed it was expanding the use of artificial intelligence to set fares, working with an Israeli startup called Fetcherr, with the stated ambition of using AI to price a fifth of its domestic network by the end of the year. Three United States senators, Mark Warner, Ruben Gallego and Richard Blumenthal, wrote to the airline demanding answers. Their letter, dated 22 July 2025, warned of prices tailored to an individual consumer's willingness to pay, and cited Fetcherr's chief executive describing the company's use of all the data it could get its hands on. The senators borrowed Khan's most vivid illustration of the danger: a system that charges a higher fare because it knows the traveller just had a death in the family and needs to fly across the country at any cost. Pricing, in other words, calibrated not to the seat but to the customer's pain point, the precise moment of maximum desperation. Delta denied that it used personal data to set individual fares, insisting the AI forecast demand and market conditions rather than identifying any one passenger's willingness to pay. The denial is instructive, and we will return to it, because the more telling fact is not whether Delta is doing it today but that the infrastructure to do it now exists and is being marketed as a route to extraordinary profit.

Who Actually Said Yes

Strip the practice to its foundation and the question that will not go away is one of consent. Somewhere in the architecture of surveillance pricing there is supposed to be a moment at which the customer agreed to be priced as an individual, to have their financial circumstances inferred and held against them, to be charged according to a secret model of who they are. Find that moment. It does not exist.

What exists instead is a chain of consents to other things entirely. You agreed, in some buried clause, to let an app use your location, ostensibly to find your nearest store. You agreed to cookies so the site would remember your basket. You agreed to a loyalty card so you could collect points. You agreed to terms of service no one reads, in exchange for a service you actually wanted. At no point in any of this did a screen ask whether you would consent to those signals being fused into an estimate of your maximum tolerable price, and at no point would a rational person have said yes if it had. The Federal Trade Commission, examining the third-party intermediaries that build these systems, found a market drawing on data both volunteered by consumers and, more troublingly, inferred about them from first and third-party sources. Inference is the crucial word. You cannot meaningfully consent to a conclusion drawn about you that you never disclosed and may not even know is true.

This is what consumer advocates and privacy lawyers mean when they describe surveillance pricing as a black box. The customer cannot see what data the company holds, cannot see how the price was reached, and cannot see what other shoppers are paying for the same item at the same moment. The ordinary apparatus of fairness, the ability to know the reason for a decision and to contest it, simply never engages, because the reason is locked inside a proprietary model and the decision arrives disguised as a fact of nature. A price, to the person looking at it, appears to be something the world has handed down. It does not look like a profile, an accusation or a bet. But that, increasingly, is what it is.

The asymmetry is total. The seller knows the product's cost, the price it is showing you, the prices it is showing everyone else, the model that produced your figure and the data that fed the model. You know one number. You cannot tell whether personalisation is even happening, because a personalised price and a public one look identical: both are simply digits on a screen. The market was supposed to be an information system, aggregating dispersed knowledge into a public signal that let strangers coordinate their behaviour. Surveillance pricing inverts that function. It turns the price from a signal the market sends to you into a signal the seller secretly extracts from you, while you carry on reading the number as though it still carried its old, shared meaning.

What the Models Choose to Notice

There is a comforting story the industry tells about all this, and it deserves a fair hearing before it is dismantled. In theory, the argument runs, the ability to price each customer individually can expand a market rather than merely milk it. A seller who can identify price-sensitive buyers can profitably offer them a discount that brings them into the market, while charging more to those who can comfortably pay. On a whiteboard this looks almost progressive, a kind of automated means-testing that funds cheaper goods for the poor out of the wallets of the rich.

The world runs the logic in reverse. The signals a machine-learning system finds most useful for estimating willingness to pay are, with grim reliability, the same signals that track vulnerability and constraint. A shopper in an area with no rival supermarket within reach has fewer alternatives, and an algorithm can learn to read that absence of competition and charge for it. A household ordering nappies and repeat prescription items has predictable, inelastic demand, and inelasticity is precisely what a pricing model is built to detect and exploit. The customer with limited mobility, least able to drive between shops to compare, is the one least able to escape and therefore, on the model's own cold accounting, the one most worth charging extra. The system does not optimise for fairness. It optimises for revenue. The people with the least room to push back are exactly the people from whom there is most to extract.

A price built from inferred willingness to pay is, in the end, a price built from a model of who you are, and the characteristics that feed such a model are chosen for their predictive power, not their moral acceptability. If income predicts what you will pay, the model uses income, and if it can infer your income from your postcode, your phone and the brands you buy, it is charging you according to your wealth without ever asking your salary. If household size predicts inelastic demand for essentials, the model uses household size, which can mean that a larger and often poorer family faces systematically higher prices on the very goods it cannot do without. Some of these are characteristics that anti-discrimination law has spent a century learning to treat as illegitimate grounds for differential treatment. None is one that an ordinary shopper would knowingly hand over as a reason to be charged more for milk. And here the loyalty penalty completes its circle, for the most predictive signal of all is your own history. The longer the relationship, the richer the profile, the more confident the model becomes about exactly how much you will bear. Devotion, fed into the machine, comes out as leverage.

The Year the Law Began to Notice

For most of this story the law was simply absent. There is still no comprehensive federal statute in the United States governing surveillance pricing, and enforcement has leaned on the general prohibition of unfair or deceptive practices in Section 5 of the FTC Act, an authority written long before anyone imagined a retailer inferring your income from your shopping habits. That gap has begun to narrow at its edges rather than its centre. On 14 April 2026 the Commission issued an advance notice of proposed rulemaking on unfair and deceptive fee practices in online food and grocery delivery, taking comments until 18 May, and among its questions was whether platforms tell customers when the price in front of them has been personalised; the agency has also signalled work towards a policy statement on when failing to disclose the use of personal data to set an individual price becomes a Section 5 problem. That is rulemaking about disclosure in a single sector, not a prohibition, and it leaves the federal position roughly where it has always been.

The states moved faster, and the first of them moved earlier than the headlines of 2026 suggest. New York's Algorithmic Pricing Disclosure Act took effect on 10 November 2025, obliging a seller that uses personal data to set a price to say so, clearly and at the moment of the price, in a notice reading THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA, with civil penalties of up to a thousand dollars for each violation. The same Act bars the use of protected-class data where the effect is a price different from the one offered to others, and Attorney General Letitia James issued a public warning to New Yorkers as it came into force. What changed in 2026, then, was not that the law noticed surveillance pricing for the first time. It was what the law began to ask of it: the move from compelling a seller to confess the practice to forbidding the practice outright. And the centre of gravity is the food on your table.

Maryland moved first on prohibition. On 28 April 2026, Governor Wes Moore signed House Bill 895, the Protection From Predatory Pricing Act, the first law in the country to restrict surveillance pricing in the grocery sector. It takes effect on 1 October 2026. The statute prohibits food retailers above a certain size and third-party delivery services from using a consumer's personal data to set individualised prices, and pairs this with a rule requiring that displayed prices remain fixed for at least one business day, a direct check on the prospect of electronic shelf labels being used to surge the price of essentials minute by minute. Violations are treated as unfair or deceptive trade practices, carrying civil penalties of up to 10,000 dollars for a first offence and 25,000 dollars for repeat conduct. Enforcement rests solely with the state attorney general, who must give a company 45 days to cure a violation before acting, and consumers have no right to sue on their own behalf.

Those limits are not incidental, and Consumer Reports, while welcoming the law, criticised what it called weak enforcement provisions. Analysts at the International Association of Privacy Professionals catalogued the loopholes plainly: the ban bites only on prices customised for an individual, not on prices set for narrow segments of consumers; it establishes no baseline or standard price against which a personalised figure could be measured; and it explicitly exempts loyalty programmes, the very mechanism through which, in the British insurance saga, the loyalty penalty was built. A law that targets surveillance pricing while carving out loyalty schemes is a law with a gap shaped exactly like the original problem.

Connecticut went broader in scope and slower in delivery. Its Senate Bill 4 passed by overwhelming margins, 141 to 6 in the House and 31 to 4 in the Senate, Governor Ned Lamont signed it on 27 May 2026, and it is now Public Act 26-64. Where Maryland confined itself to groceries, Connecticut's law defines surveillance pricing as setting a customised price for a consumer or group of consumers based on personal data collected through any technology, and bans it across retail sellers and third-party delivery services. Businesses outside those categories that price this way are not forbidden to do it, but must label it, in wording substantially similar to THIS PRICE WAS INCREASED USING YOUR PERSONAL DATA, which is New York's device sharpened by a verb. The act also reaches into the machinery that makes surveillance pricing possible, establishing a state registry of data brokers, building towards a single universal mechanism by which a consumer can demand deletion of their records across the whole industry, and prohibiting the sale of precise geolocation data, the very signal that pushed the price of a television up in a Target car park. By attacking the data supply rather than only the pricing output, Connecticut aimed at the root as well as the fruit. Its timetable, though, is staggered, and the pricing ban is the slowest-arriving piece of it: the privacy act amendments, including the prohibition on selling precise geolocation, begin on 1 October 2026, data brokers must register from 1 January 2027, the surveillance pricing restrictions do not bite until 1 July 2027, and the universal deletion mechanism is not in working use until late 2028. The practice was outlawed in Connecticut with more than a year's notice.

Connecticut also carved out much of what Maryland carved out. Loyalty and rewards programmes are permitted provided their terms are clearly posted and available to every eligible consumer, as are discounts offered to retain a customer, and price differences traceable to legitimate factors: delivery cost, timing, supply and demand, pricing errors, network outages. Insurance-licensed entities sit outside the pricing provisions altogether, as do credit decisions governed by the Fair Credit Reporting Act and the Equal Credit Opportunity Act. The loyalty carve-out, then, is not a Maryland eccentricity. It is a defining feature of this first generation of statutes, and it is the most awkward thing about them, because the loyalty penalty is where the story started. Britain spent the better part of a decade establishing that a rewards relationship is precisely the instrument through which a seller learns to price your captivity, and the first American laws against surveillance pricing have written that instrument into the list of things they do not cover.

New Jersey came third, and went at the thing the other two left alone. Governor Mikie Sherrill signed the Fair Price Protection Act on 23 July 2026, banning personalised algorithmic pricing based on personal data, browsing history, location or protected-class status in the grocery sector, while preserving, as its predecessors did, loyalty programmes and bona fide group discounts. It takes effect on 1 August 2027. What makes it the most consequential of the three is not its reach but its enforcement. Maryland and Connecticut hand the whole job to a state attorney general; New Jersey is the first jurisdiction in the country to give the shopper a weapon of their own. A consumer who believes they were charged more than others for identical groceries because of their personal data may sue under the New Jersey Consumer Fraud Act, individually or as a class, and recover treble damages where the conduct was wilful. The attorney general may still sue as well, for actual damages or fifty thousand dollars for each violation, whichever is the greater. That alters the arithmetic of compliance in a way a cure period and a capped penalty do not, because it turns an invisible practice into a litigable one.

These three are the leading edge of a wave rather than its conclusion, and the wave is now large enough to measure. More than forty bills across at least twenty-four states were introduced in 2026 to regulate personalised algorithmic pricing, already outpacing the whole of 2025. New York, having gone first on disclosure, is poised to go further: its One Fair Price Act, which would ban the practice outright rather than merely oblige a seller to admit to it, passed the legislature on 10 June 2026 and awaits the governor's signature, carrying penalties of five thousand dollars for a first offence and twenty thousand for those that follow. Vermont has taken a narrower route, permitting electronic shelf labels but barring price increases within the day except to correct a documented error, which is Maryland's one-business-day freeze rewritten as a rule about the shelf rather than the shopper. Not all of it will land. The wider point is that the law is responding at the level of state lines while the technology operates at national, indeed global, scale. The result, for the time being, is a map in which the legality of being charged a personalised price for a tin of beans depends substantially on which side of a state border you happen to be standing, and across most of the country the practice remains lawful, undisclosed and unmeasured.

Eight Companies and the Industrial Scale of Knowing

The clearest measure of how far this has already gone came when the FTC stopped theorising and started compelling. In July 2024, using its 6(b) authority, which lets it order companies to hand over internal documents whether or not it suspects wrongdoing, the agency issued orders to eight firms that sit, mostly unseen, in the machinery between retailers and shoppers: the payments network Mastercard, the consultancies Accenture and McKinsey and Company, the banking group JPMorgan Chase, and the pricing and personalisation specialists Revionics, Bloomreach, PROS and Task Software. The orders sought to map an opaque market in which intermediaries claim to use advanced algorithms, artificial intelligence and personal information, location, demographics, credit history, browsing and shopping behaviour, to categorise individuals and set a targeted price for each. The Commission voted unanimously to issue them.

When the preliminary findings landed in January 2025, the picture was not of a fringe experiment but of an established industry. The intermediaries examined were, between them, working with at least 250 clients, selling everything from groceries and clothing to health and beauty products and hardware. The capability to price an individual, in other words, is not waiting to be invented. It has been built, sold and installed at scale, wired into the systems of hundreds of well-known sellers. This is why the denials of any particular company, Delta insisting it does not use personal data for fares, the grocery platform Instacart insisting it merely assigns customers to random pricing cohorts, miss the point that industry analysts keep returning to. Instacart's own year makes the point for them. On 18 December 2025 it agreed to pay sixty million dollars in consumer refunds to settle an FTC lawsuit alleging deceptive delivery-fee and subscription practices, and was reported at the same time to be winding up its AI-based price testing. Neither fact is a finding that it ever charged anyone a surveillance price. Both establish that it had the apparatus and the appetite, and that a federal action was what changed its behaviour. Once the machinery is in place, the difference between not profiling you today and profiling you tomorrow is a single configuration change. The capacity is the danger. A loaded weapon does not need to be aimed at you to have altered the room you are standing in.

When a Price Stops Being a Public Fact

It is tempting to frame all of this as a story about money, about whether you personally end up paying a few pounds more or less. That framing is too small. The deeper casualty of surveillance pricing is something most of us have never had to think about because we have never lived without it: the public price, the single shared number that anyone can see, compare, refuse and undercut.

Consider what that shared number does. When a price is public, a shopper who thinks it too high can walk to a competitor. A rival firm that spots an inflated price can undercut it. A journalist can report it, a regulator can investigate it, and a neighbour can compare notes over the fence. The discipline of the market, the pressure that is supposed to keep prices honest, depends entirely on the price being a fact that more than one person can see. Surveillance pricing dissolves that fact. When your price is calculated for you alone, invisible to everyone else including the competitors, journalists and regulators who might otherwise discipline it, the price becomes a private transaction between you and a model. There is nothing for a rival to undercut, because they cannot see it. There is nothing for you to refuse on principle, because you cannot tell whether anyone else would have been charged the same. The very mechanism that makes markets self-correcting is switched off, quietly, one personalised quote at a time.

This is why disclosure remedies, useful as they are, feel inadequate to the scale of the thing. The European Union, through its Omnibus Directive, already requires a trader to tell a consumer when a price has been personalised on the basis of automated decision-making, and since November 2025 New York has required much the same in blunter language. That requirement has already survived the challenge you would expect. The National Retail Federation sued to have the New York label struck down on First Amendment grounds, arguing the state was compelling retailers to recite a misleading, government-scripted opinion about their own conduct. On 8 October 2025 the United States District Court for the Southern District of New York dismissed the case, holding the compelled disclosure plainly factual and not rendered controversial merely because the regulated business would rather not make it. That is a more important precedent than its narrow subject suggests, because it settles the proposition that a company has no constitutional right to conceal that it priced you from your own data. But being told that a price has been tailored to you, without being told from what data, by what logic, or to what end, restores only a sliver of the lost information. It is like being informed that a stranger has formed an opinion of your character, without being told what the opinion is or what evidence it rests on. The grievance is not merely that the price was personalised. It is that it was built from a portrait of you that you did not sit for, that you cannot see, that you cannot correct, and that may be wrong, unfair, or assembled from precisely the characteristics you would have refused to be judged by.

The ordinary intuition that there is something improper here is not economic naivety. It is an accurate perception that a hard-won feature of how markets are supposed to work is being removed, with nothing adequate put in its place. People do not, on the whole, object to a shop making a profit. They object to being individually appraised by a machine and charged according to a secret estimate of how much they can be made to bear, on the basis of data they did not knowingly surrender, with no way to see the number anyone else is paying. That objection is close to universal, and it is the bedrock on which any durable response will be built.

Switching the Lights On

So what is the customer at the invisible checkout actually to do? Honesty requires conceding that individual self-defence is mostly futile. Clearing your cookies, browsing in a private window, comparing prices across two devices: these are the folk remedies of a cruder era. The man who deleted his cookies on Amazon in 2000 found a cheaper DVD because the discrimination then was primitive. It is not primitive now, and a system that fuses hundreds of inferred signals cannot be reliably evaded by a shopper toggling settings. A person should not have to conduct counter-surveillance against their own grocer to be charged a fair price for bread, and the burden of evasion cannot honestly be placed on them.

The more truthful answer is that this is a collective problem requiring collective tools, and the encouraging part of the story is that those tools are beginning, unevenly, to appear. They arrive in three reinforcing layers. The first is sunlight: the dogged work of investigators, researchers and regulators in dragging an invisible practice into view, because surveillance pricing is a practice that struggles to survive being seen. The journalists who watched a television's price climb in a car park, the FTC compelling eight companies to open their books, the senators demanding answers from an airline, are all performing the same act of making the hidden visible so that it can be argued about. The second layer is disclosure as a legal default, the European and now the New York requirement to declare when a price has been personalised, imperfect but better than silence, and now tested and upheld in court. The third, on which the others depend, is substantive law of the kind Maryland, Connecticut and New Jersey enacted in 2026: rules that do not merely require a disclosure to be ignored but forbid the use of certain data and inferences to price the essentials of life, and give an enforcer real teeth. The loopholes in those first laws, the loyalty-scheme carve-out, the silence about consumer segments, the long lead times before the bans actually bite, show how much remains to be built. One of them has already been closed. The most cited weakness of the Maryland and Connecticut statutes was that they left the shopper with nowhere to go, and New Jersey's private right of action, treble damages and all, answers that objection directly. Everywhere else the enforcement gap remains what it was: one attorney general, a cure period, and a civil penalty a national retailer can treat as a rounding error.

What sets the direction of travel is a fact no amount of optimisation can engineer away. People do not want to be charged according to a secret guess about their desperation, and they do not want their loyalty read as a vulnerability to be priced. The British loyalty penalty was banned not because the maths was wrong but because the public found the principle intolerable once it was named. The same naming is now happening to its digital successor, in legislatures, in regulators and in newsrooms, and it is happening because the practice depends, in the end, on staying unnamed.

That naming is no longer a forecast. On 4 August 2026, two days before this piece went to press, the Senate Judiciary Subcommittee on Crime and Counterterrorism held a hearing titled Your Data, Their Profit: The Consumer Cost of AI Surveillance Pricing. Senator Josh Hawley chaired it, Senator Dick Durbin sat as ranking member, Lindsay Owens of the Groundwork Collaborative gave evidence, and Hawley opened by naming companies out loud: Staples, Target, Lyft, Amazon. Durbin, on a day when the two parties agreed about very little else, said he had little to add to it. A hearing is not a law, and nothing said in that room obliges anyone to change a single price. But the practice has now been described in public, on the record, by people who disagree about nearly everything, which is the condition under which a thing that survives on obscurity begins to lose.

The next time you confirm an order and the total looks about right, hold for a second the thought that you can no longer verify it is right, because right has quietly stopped meaning the same thing for everyone. The price you see may be the price everyone sees. It may be the price reserved for you alone, the loyal customer the system has finally finished learning. That you can no longer tell the difference is the whole of the problem. Reclaiming the ability to tell, the right to a price that is a public fact rather than a private verdict, is the whole of the answer.

References

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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