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Cake day: June 14th, 2023

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  • I am making a time-trial race timer so my club can keep track of their practice times. Someone has to manually do it, taking them out of havinga yarn; and for online tournaments usually someone smashes their best time without it being recorded so doesn’t count.

    There’s

    • an esp32 powered LIDAR “gate” to start/stop a lap
    • an esp32 powering a display (giant 7 segment display made out of neopixels)
    • a raspberry pi that acts as an mqtt broker, and relay to the internet
    • a website to track and manage all the times

    When motivation strikes I’m planning on adding:

    • a camera system to the Pi to record and evidence times
    • a RFID system to track riders and vehicles, so it can be fully automated.













  • Not crazy, just sad.

    Middle of the day, sitting at our desks working. This middle aged guy who was usually happy as Larry gets up and leaves the office leaving his stuff behind. Not a word said. I just assumed he was getting a coffee or something.

    End of the day rolls around, stuff still there. Same thing the next day. Still there the next week.

    People start asking what happened to him, but the agency he was working through kept telling us he’s coming back soon.

    Over a month later, someone packs up his stuff and puts it in the bin. The guy was never coming back, turns out he went left and ended his own life the day he walked out. Never made it home.

    The agency apparently only found out he was dead a few weeks after the incident, then strung us along so they could find a replacement. We terminated their contract and offered the handful of other employees jobs.

    ———

    Another job, we had a new guy start. Very conventionally attractive and he seemed normal enough.

    A few weeks later one of the women complained to HR that someone was stalking her. She was getting ‘flattering’ letters, emails, notes etc and they often contained information and photos in/about/around her work. Flattering, but not something she was comfortable with

    Few weeks later, we’re told new guy won’t be coming back due to inappropriate behaviour.

    Woman had to get a restraining order against the guy. In a twist of irony, she said that if the guy had just talked to her, she would have gone on a date with him in a heartbeat.



  • Social/Mobile games. So an already predatory industry. Let’s get people addicted to a game, and then suck as much money from them as possible.

    In the industry, we definitely weren’t the only ones doing it. And really we were only doing basic stuff (it was all in house developed middleware, so effort vs reward didn’t make much sense to go hard) I wouldn’t be surprised if others were going deep.

    • the hardest part is getting someone to part with their money. But once they’ve done it once, even for the smallest amount, the second purchase will be easier.
    • conversions that stopped playing got emails with discounts.
    • whales got freebies when they lost to keep them happy.
    • everything else was just finding the customers perfect price.
    • ultimately we were selling noting. So any sale is better than no sale. You can’t make a loss on a number in a database.

    Everything was broken down into campaigns (we’d have multiple running at any one time) targeting different segments. Then we’d track the conversion, sale, and retention numbers of those campaigns against each other. Sometimes one campaign might flop for one segment but not another, so we’d retarget with a new one.

    I don’t think it’s used much in other markets. I know Twilio has Segment, that could be used to do segmented pricing but I’ve never really seen it done in other industries.

    I wouldn’t say it’s jaded me. It has made me conscious of my data footprint. I don’t play mobile or f2p games. But I am weary. The COVID greed-flation showed the mindset of businesses. It might not be long until targeted pricing becomes worthwhile to make number go up (still), and hidden under the guise of “lowering prices”.


  • You don’t need a monopoly for this to be a problem.

    Databrokers can offer data sets of “customer price elasticity”. Tables of “how much we think X would spend on these generic item categories”. Eg “booly would pay $15 for a burger, vs $10 average”

    Point of Sale systems could start offering integrations to these data sets.

    All shops have to do now is set a list price, a minimum price, a category, and leave it up to the PoS to (not) give discounts.

    You want a burger, you’re fed a single-use short lived discount “$5 off a $20 burger. Today only” While someone else gets “buy one get one free”.

    It’s then a ‘fair’ market. Shops have and ‘compete’ with their (high) list prices, data brokers compete with “excess profit” statistics (ie, how much more money above the minimum price they made). Nobody is colluding, they’re just basing discounts off external arbitrary signals.

    It slowly becomes the norm to get just-in-time discounts, and the consumer gets shafted. If you’re not in the system, you’re paying more than everyone else.

    (And all of this has been happening in some markets for over a decade)


  • In a past life I wrote the software that did this.

    It’s not just about charging more when you’re desperate. It’s also things like charging you less to keep you addicted, or getting you hooked. Exploiting your emotions and behaviour to make it effective. A small loss on you now could be a long time gain for them.

    Some more scenarios:

    • you’ve decided to quit alcohol. Your social media accounts are used to identify you’re looking for advice. They advertise more, and send you heavy, heavy discounts a few days in to keep you on the wagon.
    • Your cars insurance tracker has picked up your erratic driving. Your phone has tracked more forceful interactions, your works email provider has revealed you’ve been in a minimum of three meetings all day; You’re having a shit, stressful, day. They can’t give you discounts on your cigarettes but they do know they can get you to buy two packs instead of one by serving you ads that suggest stock levels are low. You buy two and chain smoke all day, your daily average goes from 0.5 to 0.7 packs a day.
    • You go to a chain restaurant often. They know they can get you to buy more in the long run if they increase the volume you eat gradually. Every visit they goad you into buying more. Didn’t do it last time? Steeper discounts next time. Until one day you buy the extra side. That’s now your new baseline. A few weeks of that and back onto the stair climb. A little by little. You’re spending more and more.
    • you’re on holiday. everyone knows you’re not coming back anytime soon so they charge full price. But move to a new city? Everyone has discounts for you to get you in the door.

    The data available back then was pretty minimal, effectively only the data we generated. But it was still enough to prey on your lizard brain. With data brokerage I’ve got no idea what level of evils we could have done.