From Paris, France.

Listening now early Sunday morning to the Pittsburgh Steelers Audio Network announcers broadcasting the Pittsburgh Steelers vs the New Orleans Saints NFL Game played this morning in Paris, France; NFL+ giving us the option of hearing home, away, Westwood One, etc. game announcers.

Will this be my game of choice today? Maybe. Possibly. We'll just have to see how this day develops.

And the adventure continues.

#fiction #books #SF

Warning: Some spoilers

Death comes for us all. But if you happen to be an extra in a movie or an NPC (non-player character) in a videogame, death can come rather gratuitously. Your role, after all, is to serve the plot and potentially make a hero(ine) look good. You are expendable by definition. You are such a trope that you have your own term: red shirt, in homage to the frequently fatally phasered security personnel in Star Trek. Red Shirts are those characters where you just know, as soon as they walk into view, that they will not last until the credits roll.

What ought a group of workers to do when they are systematically maltreated? Organise, of course! That is the starting point for John Scalzi’s Redshirts. Set on the Universal Union (UU) Intrepid, a thinly veiled analogue of the USS Enterprise from Star Trek, it regales us with the story of four wary ensigns who must uncover why so many people on the UU Intrepid die on away missions under mysterious, avoidable and often gruesome circumstances, and how they might escape the same fate.

Redshirts was my first return to John Scalzi after The Interdependency trilogy, which I found intriguing in setup but underwhelming in execution. Redshirts worked much better for me. It is more tightly written. Scalzi’s style and pace work better at the scale of a small company of friends on a single spaceship than for the grand space opera of a collapsing civilisation. And Scalzi exploits the creative premise much more adroitly than in The Interdependency, tying everything together in a satisfying and touching ending.

What made the conclusion the more pleasing is that about midway through the novel, I started to worry that Scalzi wasn’t going to be able to stick the landing. The first half of Redshirts has our four ensigns figuring out what is going on on the UU Intrepid and, more importantly, how they avoid getting killed by whatever ‘it’ is. The challenge of Redshirt’s conceit is that the reader knows what is going on from the prologue and is thus well ahead of its heroes. Finding out how they unravel the mystery is entertaining, but it is not clear how they might get themselves out of this predicament in a satisfying way.

This is where Redshirt’s first major twist comes in, and I’m not going to spoil that here. Suffice to say that our putative disposable heroes manage to use the power of tropes in their favour to get themselves out of harms’ way. More or less.

And the ‘more or less’ is important here. Not being a redshirt does not guarantee a long and happy ever after. In the long run we’re all dead, and many have perished in circumstances that were more tragic, calamitous or avoidable than those of the sacrificial redshirt. This is where Scalzi pulls a second, more philosophical twist. The complaint of the redshirt is not death itself, but a death that is pointless, stupid, and above all bereft of agency. The demand of the redshirt is not immortality, but a life worth living and a death worth dying. In a way not dissimilar to Castlevania, the latter half of Redshirts is in part a meditation on what it means to act in the world, to have agency, to make choices, and to grow, and the consequences of us either not using that potential or it being taken away from us.

In some ways, redshirts are the necessary corollary of Ada Palmer’s heroic protagonist: for the single hero(ine) to hold all the agency in the universe, they must deny it to everyone else. Redistributing agency across a larger group of characters requires a different mode of writing. At a minimum, something like the ensemble cast we see in Redshirts itself, where solving the mystery is the effort of a group of reasonably ordinary people, rather than the predestined fate of the Chosen One. Beyond that are more composite approaches such as The Ten Percent Thief, that abandon the traditional form of the novel altogether.

Redshirts does for single-episode disposable companions what Terry Pratchett’s Guards! Guards! did for the nameless battalions of disposable guards and goons. It does so with similar panache, if not the same level of genius or wit. There are also acknowledged nods to Jasper Fforde’s Thursday Next series about the lives of fictional characters after they’ve exited stage/screen/page left. Like these inspirations, it is a rewarding combination of witty writing, entertaining plot and cultural commentary, with some philosophical reflection thrown in to boot. It won’t change your life, but it will make you stop, smile and remember for a moment that we don’t all have to be heroes to make a change in the world.

Notes & Suggestions

• The ‘dead wife’ trope also makes an appearance in Redshirts, and on a sympathetic reading also finds itself subverted. Explicitly positioned as a plot demand, the dead wife in Redshirts is not the motive force for a hero, but instead a reason for withdrawal and fear, with Scalzi compassionately incorporating it in the book’s conclusion. I wrote a bit more about the dead wife trope in my review of Castlevania.
• Ada Palmer’s essay on the damage caused by a monoculture of heroic tales is well worth reading and can still be read in Uncanny Magazine. I engaged with it in my review of the Mass Effect series, particularly Mass Effect 3, as well as The Ten Percent Thief.
• How to exert our agency is of course the question of our times, or of life really. It is a question we must each answer for ourselves, but if you are looking for some inspiration on what is to be done, Erik Olin Wright’s How to Be an Anti-capitalist in the 21st Century is not a bad starting point.

“Just be yourself.”

One of the many things I’ve discovered about being yourself in the open is a lot of folks don’t like that. People tell you to “be yourself” all the damn time as a musician. But it seems like when you take their advice, they vanish. It’s because it’s true, they do.

You do not know what you think you know.

Advice online is stupid. Unless you’re intimately familiar with someone and their problem, you don’t have anything for them. Yet, folks offer up dumb shit like “just be yourself” on the daily. It’s plain irresponsible.

Many musicians have careers to lose by being themselves, or they could be putting themselves in danger coming out in the open right now. Some may not be good at what they themselves like, but good at what others like. There’s tons of reasons to not be yourself. Good ones! But we aren’t thinking about those reasons when we give advice. We should be. Also, whether or not a person is “themselves” is completely irrelevant to the caliber of music they put out. So why say it? That’s the issue, right? Why do we say this dumb shit?

Is this for me or you?

Giving advice is for the person giving it, typically. They’d like to talk. They can’t be cool and start their own site or write their own songs, write their own stories, so they hound musicians and other folks online, giving them a piece of their mind. Advice is one of the main reasons I quit social media. A daily thing. Folks think they know who you are, then they yap about what you should be doing. No thanks. I got enough people in my head already. 

Projection, (again).

Projection is nearly always involved. Not only do folks think they know the problem, they think they know you. Double dumb shit. They just have good imaginations. 

When I was a woman online..

I know folks project online on social media badly. Before I showed my face online, I just was “Kelly.” Many folks thought I was a woman. I mean, I guess that’s why I got dick pics and called sassy by quasi-“normal” dudes. They projected the wrong sex on me just because of my name and proceeded to treat me like garbage. Another reason I quit social media: no one knows me! That’s kinda’ what I was after. Never happened. Even old “friends” said things like, “you’d be cooler as an artist were it not for your mental health issues.” You can’t possibly know who I am and say that shit without expecting me to take offense to it.

Sorry, guys. But it’s mostly men.

Men, in general, suck. They sucked when I was a kid, they suck as an adult. I don’t trust them. I’ve met more bad ones than good ones. We’re just awful. Lots of reasons. Sure there’s good guys, but I promise we aren’t the norm. Most dudes are bad guys.🤷. I know, they often mistake me for one of them, or I’m the subject of their ire. Advice is generally a men problem. We have to stroke our ego, among other things. I hate it.

How about simple acknowledgement?

If you’re a man giving advice on social media, try not doing that. Try saying “sounds rough,” in your own words. Gauge if folks are asking for that acknowledgment or advice.. you might be surprised. 

You stepped in advice!

Whether you want to converse, feel important, or show off, unwanted advice is like stepping in online dog shit. It really is. Like “dammit, really!?” Many times advice givers give advice before knowing the problem. It’s because they are not there and like hearing themselves talk. I’d do a website.

Do a site!

Sure I like hearing myself talk, but at least this way, with my site, I’m not bothering folks on their posts. Many advice-givers don’t realize they’re really into performing. I made my own platform for that, you’re welcomed to visit. I like my blog. Feel free to email and talk, thekintnersmusic@gmail.com.

Fear is the mind killer.

I think the reason folks do what they do online a lot of times is they’re afraid and alone. No one wants to be irrelevant. It happens. Doesn’t mean you’re bad. Doesn’t mean you’re empty. There’s historically bad shit happening at the moment. It’s going to be rough. It’s going to feel lonely at times when you’re the only one you know with truths. It’s grounds for conflict! They know this. Bullies have been counting on you to feel alone for centuries. Now they have data scientists.

Just say no to giving unwanted advice to strangers online. Thanks.

Kelly Kintner

🔵 05 The Archive Begins with Loss Signal Glade™ A reflection on memory, dismissal, and why archives are never still.

What if I were an agent, a secret agent, left on my own; dismissed by my country – a tale as old as time? And what if I had just learned that my purpose, my service, was no longer needed or wanted? My identity and passion, built over decades maybe, abandoned to the wind.

I might be upset, lost; maybe try to crawl back to a former self. Maybe I’d wallow. Maybe I’d move on. I am sure I’d try to rationalize it and make sense of it myself.

Now, if I saw it coming, I might try to stop it. Maybe I was a target, or maybe there were backroom deals against me. No normal person would agree to let others destroy them if they had the means and will to confront it. Right?

In the end, if I were unsuccessful, how would I piece my identity back together? Would I search for my truth, my passion, my purpose going forward? Would I say “hell to it all”? Would I aim to protect others from the same fate? Would I dismiss the insolence and hold my ground and stature despite the assault? Would I expose and shame those who dismissed me? Would I take up arms with others in my same plight?

Be it may if I had no hope, maybe I’d chronicle my decline. Pity me, the fallen soul. An archive journal affecting myself and the world at large. A voice, a cry for help. A window to show others who dare glance that they are not alone.

Social media, the social world, is a composite of our time. Each post interacts with another and alters what follows. Memory, evolution, participation. Where does it begin? Where does it end? Where do we fit in? The mystery we all search for.

An archive, often imagined as a place where history rests, behaves more like a living system. Accumulating, reacting, adapting, reinforcing, contradicting and reshaping. It is not only something to read. It is something to enter. An archive as a pattern that continues to evolve with new memories, interpretations and experiences. The question becomes: who will add to the story?

History repeats itself. An archive illustrates the pattern. It allows us to learn from the past and bring the best out of ourselves. And your story, no matter how hidden, is important and worth sharing as memory and experience shaped by perspective.

What if I really was just a cog in a wheel, my importance and purpose neither grand nor remembered in any way? How might I react? My identity shattered. Would I revolt? Try to take over the world? Wallow in my existence? Or add to the Archive and make my memory known?

I am a cog in a wheel. I am what is brushed by in the wind. I choose instead to follow the pattern, to become part of the memory. A cog in a wheel becomes a cog with purpose if it binds a memory and lets it continue on.

Signal Glade begins with memory, fracture, and the choice to enter the Archive. I invite you to share in the memory. Maybe we will find a single memory, a single truth, that binds us all.

The Archive opens in fragments. Week by week, the pattern begins to answer.

-signalglade.org

Stay entertained thanks to our Weekly Tracker giving you next week's Anticipated Movies & Shows, Most Watched & Returning Favorites, and Shows Changes & Popular Trailers.

Anticipated Movies

• ☆ Raise the Red Lantern
• Breeder
• The Mongoose
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Anticipated Shows

• The One About Matthew Perry
• ☆ Nocturne
• Dead-End Job

Returing Favorites

• Ghosts — Season 6
• Khemjira — Season 2
• Miraculous: Tales of Ladybug & Cat Noir — Season 7
• ☆ Dexter: Resurrection — Season 2
• The Gilded Age — Season 4
• SpongeBob SquarePants — Season 20

Trending Shows Status Changes

• Carrie — Ended → Returning Series
• The New Stanford Prison Experiment — Returning Series → Ended
• Time — Returning Series → Ended

Most Watched Movies this Week

• = Spider-Man: Brand New Day
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• +2 VisionQuest
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• new Cyberpunk: Edgerunners

Popular Trailers

• In Theaters December 18 — Avengers: Doomsday
• You heard them! — Clayface
• The Wicker Husband — Wicker
• don't hold your breath — Whalefall
• House Rules — Other Mommy
• Now Playing In Cinemas — Wildwood
• In Theaters November 6 — Wild Horse Nine
• Popcorn Meter — Street Fighter
• Season 2 Announcement — Carrie
• On Blu-ray, DVD, & Digital — Shaun the Sheep: The Beast of Mossy Bottom

---

Hi, I’m Kevin 👋. Product Manager at Trakt and creator of Rippple. If you’d like to support what I'm building, you can download Rippple for Trakt, explore the open source project, or go Trakt VIP.

---

This note follows a discussion about why I find it difficult to identify concepts such as style without a deep dive into structure; why I am so engaged with the concept of fidelity; and why I see myself as differing from many contemporary essayists who appear to write from a position in which the self stands separate from the world.

I am. I begin with am-ness. I am a being through time. My first experience is neither of philosophy nor abstraction, but of being. Soon after comes the recognition that this being exists under conditions. Hunger, shelter, affection, language, family, landscape, history, scarcity and opportunity all shape the form that being takes.

I am therefore not separate from the world. I am one of its products.

The task of inquiry is not to stand outside the world and describe it, but to dig where I stand and gradually extend perception outward. What I discover are not isolated things but relations. The world reveals itself as relation upon relation upon relation.

Perhaps this is why the essay matters to me. It allows me to explore those relations de bonne foi; beginning from the fact of my own being and moving outward into wider circles of understanding.

I am left with a notion on which to ponder: Am-ness may be the ground from which fidelity arises. One must first be before one can be de bonne foi.

This is my last day off. Casting a ballot for the mayoral election on my way home tomorrow. Get home, eat, shower, and then going to see a therapist for the first time in my life. It is all rather stressful and exhausting to think about right now.

I'm about four decades late for therapy. I'm not going into it with an attitude like that though. Not really. Just thinking that I likely could have benefited from it at any point in my life over the last forty years. Same as these pills that I have been taking for just over a week. They say it can take up to four weeks to feel what they are going to do. I'm impatient. Kind of annoyed. Not expecting any kind of miracle.

What attitude am I going into this with? And what am I expecting, if not a miracle? No attitude at all, and zero expectations? I don't know. If I knew what therapy was going to help me with I wouldn't have to go to it. Why am I taking these pills? To help me cope. For change. Altering my brain chemistry with pills, and seeking assistance with my thought processes. All to help me deal with life. Life is shit, and I'm in it up to my neck.

If I were to put my bad attitude into words I would say that I need to go for therapy and take pills so I can tolerate the job that is making me need to go for therapy and take pills. Stop. Rewind. The job gives me benefits to pay for the therapy and pills. Their way of maintaining this malfunctioning machine that I am to them. If it can't be fixed they will just throw me in the garbage and get a new one. That is a terrible attitude to have.

This is about self care. A thing that has nothing, or very little, to do with my stupid job.

Writings will continue until morale improves.

@plot_experiments@bookstodon.com #PlotExperiments

From the introduction by Sim Kern:

Speculative fiction started pulling its punches in the 2010s. Stories were scrubbed clean of any grit. Authors sanded down sharp edges, and the safest bet was to write low-stakes, low-conflict, “cozy” fiction—a label that came to define a booming sub-genre.

Don’t get me wrong—these aesthetics stemmed from legitimate critiques of genre fiction that had come before. Western scifi, fantasy, and horror stories of decades prior often did contain white supremacist, sexist, etc. elements, and so conversations about avoiding harmful tropes were long overdue. The culture of interrogating authors’ identities and questioning whether they were “allowed” to write about people unlike them, came from the desire to make room for marginalized voices in a publishing industry that was (and remains) dominated by white people. And the demand for trigger warnings and cozy options in genre fiction was an understandable reaction to the extreme, graphic violence—especially, the gratuitous sexual violence—that dominated the fiction of some of the most successful speculative authors of the decade prior: Frank Miller, Cormac McCarthy, and George R.R. Martin.

But something had clearly gone wrong when these legitimate concerns became strict commandments, used to crush dissent, flatten literature, and destroy the burgeoning careers of young, marginalized authors they were supposed to help. In his book Elite Capture: How the Powerful took over Identity Politics (And Everything Else), philosopher Olúfémi O. Táíwò calls the weaponization of identity politics “deference politics,” which he defines as, “an etiquette that asks people to pass attention, resources, and initiative to those perceived as more marginalized than themselves.” Táíwò argues that deference politics winds up harming marginalized people—by tokenizing them, reducing their value to the sum of their traumas and oppressions, and by directing “what little attentional power we can control at symbolic sites of power rather than at the root political issues that explain why everything is so fucked up.”

When well-meaning people fall for deference politics, it’s easy for elites to position themselves as the most marginalized person in the room, grab the mic, and direct liberatory energy towards symbolic struggles, while those same elites materially rob, poison, enslave, and kill us. Back in the 2010s, the Twitter literary community was a small legion of well-meaning activists, and we could’ve been doing any number of things with our time—organizing to feed and house people, boycott evil corporations, or seize political power, to name a few ideas. Instead, we each spent hundreds of hours online, focusing our collective ire, not on the pedophilic oligarchs, war profiteers, and oil-and-gas corporations dooming our planet to mass extinction, but on debut twenty-two-year-old authors who’d written space adventures and dragon stories for our amusement.

Ganzeer was evidently aware of these forces, because in 2016, as he inked the first pages of The Solar Grid, he offered one of the most incisive critiques of deference politics I’ve seen in fiction.

---

It's a big one, coming in at 3147 words, and it is awesome. It can be read in full in the pages of THE SOLAR GRID hardback, now available for preorder.

#tsg

Experimenting with write.as for long-form pieces.

Initial testing with a temporary account worked well with the Fediverse.

@plot_experiments@bookstodon.com #PlotExperiments

Lots of pain, throat hurts so much to do anything. Afraid to swallow. Mom today talked for first time in years, and refused to apologize and said to let things go. Fuck off.

Il y a des jours où j’ai la sensation de ne rien comprendre. D’être comme dans une sorte de brouillard qui m’empêche de voir clairement.

Parfois, ce brouillard persiste.

C’est peut-être juste qu’il me manque des feux anti-brouillard.

Une autre image : les choses ne s’imbriquent pas. Vous savez, comme dans un puzzle où chaque pièce a sa place. Et bien là, ce serait un puzzle de plusieurs puzzles, du coup normal que les pièces ne vont pas ensemble.

Je m’arrête là. Vous avez compris l’image.

After the recent round of “your TV is spying on you” news, I thought I’d attempt to get off using my Sony (running Android TV) and switch to something that I had a bit more control of. Unfortunately, the TV remote control interface is super smooth and is hard to lose. I recently got the final piece of the setup I made and am now pretty damn happy with it. Here’s the steps I took to jump to something more custom, and how it went.

tl;dr: Fedora KDE + Plasma Bigscreen + Jellyfin MPV Shim + G20S Pro

Previous State

Hardware/OSs

I have a couple options for watching stuff on my TV, with some things working better than others for what I stream on or watch. I either use my TV’s android interface, or my XBox Series X. Both are made for management/use via a remote control, which I love. For the XBox Series X I have 8BitDo Media Remote for Xbox and while it’s a bit battery hungry, it’s great for my purposes.

I have HDMI CEC set up so using the Xbox remote lets me turn on the TV and sound bar automatically.

The XBox is plenty powerful, but MS is likely logging everything for AI purposes, plus it seems like XBox is going through a change and will start to suck, assuming it survives. Also certain apps don’t exist/won’t work with the XBox.

The Sony Android TV is a fairly weak CPU and has less codec support, but works for streaming most stuff. The app selection is better, but I started seeing more ads and stuff creep into the interface, so it’s getting enshittified. The “apps-only” mode helps it, but I’m sure it’s still phoning home to Sony often.

Nothing I have supports Dolby Vision, the bane of my existence.

Apps/Streaming Sites

I have various streaming applications I use on one/both of the systems:

• Jellyfin – I also have Plex. I only use Plex rarely if Jellyfin is having problems playing a specific video on my systems. That’s becoming much less of an issue these days, thankfully. I should do a blog post about Jellyfin and the community sometime.
• Shudder – They’ve abandoned the app on Xbox (but left a broken version in the ms store 🙄)
• MyAEW – No XBox app. Hell, no Android app. Only Android TV.
• Dropout
• Grouse House – No XBox app.
• Nebula – No XBox app.

Wants

My goals are to get something that is powerful enough to direct play most everything I throw at it, while still being able to be controlled with a remote as much as possible. The solution should make using the above services as easy as possible.

Current State

I have a setup I am pretty happy with, mostly using stuff I was fortunate enough to already have.

Hardware/OSs

Over a year ago I needed a physical machine I could use as a desktop for a few things where a VM and/or my Mac weren't sufficient. I ended up with a GMKtec AMD Ryzen 7 5825U w/ 32GB DDR4. It works pretty well, but the fans can be noisy when it really gets going. All the same, I decided to move it to the lounge room to see how it goes as a TV box. It's useful because it has 2 USB-A ports on the front. It still does some of the stuff I originally bought it for (tertiary backups to IDrive), but for actual desktop use, it's done. The fans generally aren't noticeable if something is on (like music or tv playing or something), but it could just be my old man ears.

The system was already running Fedora 44 KDE, so I just stuck with that. Thankfully KDE revived its Plasma Bigscreen project last year. I'm a huge fan of the KDE ecosystem (KDE Connect is a godsend), so trying Bigscreen seemed like the obvious choice. It's packaged in the fedora repos so there was no additional work needed there, however I did have to switch my login manager to Plasma Login so automatic session login could work. It was previously SDDM, possibly due to this box being updated through a few Fedora versions. I think Plasma Login is now default.

I created a tv user to keep the tv stuff separate. I have the system auto-login as that user and it has KDE Bigscreen as its session manager. This got set through the KDE Settings area.

Apps

jellyfin-mpv-shim

Jellyfin Desktop, the primary desktop software for Jellyfin, just went through a shift, where the primary dev for it went totally into slop mode and was just causing chaos in the project, so he left. His project continues as Jellium, one of the million sloppified third party clients that are out there.

Thankfully, another Jellyfin dev was busy in the background upgrading her project, jellyfin-mpv-shim, to have a full UI. She uses AI for it but seems to be more sensible about its use. She released jellyfin-mpv-shim v3 (with full UI) soon after the Jellyfin Desktop dev left, and it has been the go-to standard for Jellyfin on a desktop since. It's also able to work with “standard” keyboard shortcuts and is customisable because it's basically mpv. I installed it from flathub and it goes fine. There are a few notes:

• When setting up the server connection, you should qualify the full https:// in the server URL, otherwise it silently defaults to http and fails without reason (unless you watch the logs in the console).
• Once you have it set up, make sure you go to the settings for playback and enable hardware decoding. In my experience, not doing this led to the fans being extremely loud as the CPU went hard.

For all the other services I use, their websites are the go-to. I'm using Zen, but I imagine any browser would work. Plasma Bigscreen has a built-in webview that lets you open up websites directly, but I haven't messed with it much. It may work more nicely with a keyboard (remote) control.

Remote Control

This is primarily why I wrote this post. I hadn't delved into remotes for the PC for about 15 years, and thought it'd be useful to write what I've gathered.

HDMI CEC

HDMI CEC doesn't work with Linux because of shithead corporations. When I was discussing this in the #jellyfin-offtopic matrix channel with others, someone suggested a device that hooks into your HDMI cable and intercepts CEC codes and translates them to keyboard presses, but I didn't opt for that. $50 seems like a lot for that.

USB receiver

There seems to be two types of communication using remotes:

• Serial/LIRC: I hated this method back in the day. USB serial receiver. Remote sends infrared signals to the receiver. You map them to a keypress (or many keypresses).
• 2.4 Ghz: These come with a little USB-A dongle that the remote talks to. These appear as a keyboard (and possibly a mouse) to the computer, so there's no key-remapping needed. But that also means you're limited in what keyboard presses you get, and you can't set up a string of them from a single button press.

I went the 2.4Ghz route. Trying various different ones from Ali Express, I found these two worked really well with minimal/no work needing to be done in jellyfin-mpv-shim.:

• YY30: I actually had to buy this twice because one of my dogs chewed the first one (to be fair the buttons smell weirdly chemically good). The first time I had to map the back button to back in mpv using the name “GO_BACK” in jellyfin-mpv-shim’s config, but then with the second remote that change wasn't necessary (I changed it back while I was waiting for the second remote to arrive). Whatever the case, this works well with jellyfin-mpv-shim. The problem is it didn't have enough buttons for me. There was no “next video” button, you have to navigate. I also didn't like that there wasn't a dedicated “pause” button, but that's just because that's what I'm used to hitting on previous remotes.
• G20S PRO BT: This is the one I landed on. It's great. More buttons, including media navigation. It also has a built in gyroscopic mouse that you can turn on/off. Using it to browse websites works OK, but the preciseness isn't great. Still usable.

The front USB-A ports on the machine come in handy, as they give the remote a direct shot to the receiver.

KDE Connect

I love KDE Connect. It is super handy for passing clipboards and files around. Thankfully, it also has a keyboard/mouse mode that lets me control another desktop/phone/whatever. This can fill in as a more precise mouse, or give me the ability to enter a lot of text via a real keyboard.

Issues / Conclusion

Honestly, no real issues. Using the browser is cumbersome, but manageable. I don't have a good way to stop/start the server, but I want it up for backup reasons, so I haven't dug into it. I can probably shut it down via the plasma-bigscreen interface, but no idea how to turn it back on besides WoL or flipping the power outlet and setting the BIOS to always turn the machine on after power restoration.

The TV has been disconnected from the Internet and I had some fun learning how to set up something new. Fun project.

Somewhere behind a police call in Heber City, Utah, a television was playing Disney's The Princess and the Frog. The officer's body-worn camera picked it up, because body-worn cameras pick up everything: the hum of a refrigerator, a dog, a film. That audio went into an artificial intelligence system the department had bought to turn recordings into police reports. The system did exactly what it was built to do. It listened, it narrated, and it produced a document in which the officer turned into a frog.

The story went around the world in January 2026. Sergeant Rick Keel of the Heber City Police Department told FOX 13 News that “the body cam software and the AI report writing software picked up on the movie that was playing in the background, which happened to be 'The Princess and the Frog.'” He added the line every department has since repeated in some form: “That's when we learned the importance of correcting these AI-generated reports.” FOX 13 reported the offending narrative came from Draft One, sold by Axon, the largest supplier of body cameras to American police. Heber City had been trialling it alongside a rival called Code Four. The department kept using AI. It simply added more review.

Here is the thing about the frog. It is the safest error the technology can make, because it is self-cancelling. No sergeant approves it, no prosecutor files it, no judge reads it without laughing, and no defendant goes to prison because of it. An error that absurd is a smoke alarm, not a fire. It tells you the machine is in the building.

The error that should frighten you is the one that reads perfectly. It is grammatical, plausible, consistent with the rest of the narrative, and wrong by a single preposition. It is the difference between a suspect who reached toward his waistband and a suspect who reached into it. One describes an ambiguous movement. The other describes a man going for a gun, and can justify a shooting, a search, a charge, a plea. They differ by two letters, and no reviewer on earth reliably catches that at eleven at night at the end of a twelve-hour shift, six calls deep, with the machine's version already on the screen looking finished.

That asymmetry is the whole problem. And Wyoming has just walked into it.

What the Cowboy State Bought Before It Decided Anything

On 23 August 2026, Cowboy State Daily reporter Kolby Fedore published an account of police departments in Casper, Evanston and Sheridan each testing artificial intelligence to write reports and look at evidence. Only one vendor was named. Evanston, according to Police Chief Mike Vranish, is testing Code Four, software founded in April 2025 by two MIT students, George Cheng and Dylan Nguyen, and now used by roughly 165 law enforcement agencies. Code Four, Fedore noted, markets itself as “The AI Detective That Never Sleeps.”

The company's own site is less whimsical and more instructive. It promises a 65 per cent average reduction in report writing time, fifty dollars per officer per month, and eighty thousand dollars in annual budget saved per department. Its report product “ingests audio, visual, and other meta data from each frame of an officer's body-worn camera to create a comprehensive narrative of every situation.” A companion product, INSIGHTS, described in sponsored content Cheng wrote for the trade publication Police1 in July 2026, pulls “digital evidence, records and jail-management data, body-camera footage, phone dumps, call logs, recorded audio and case files into a single layer a detective can question directly.” It can draft warrants, affidavits and prosecutor jackets.

Read that list slowly. A warrant application persuades a judge to let the state enter your home; an affidavit of probable cause persuades a judge to hold your body. These are not administrative artefacts. They are the hinges on which constitutional rights swing.

Fedore's reporting established the gap that gives this story its edge. No Wyoming law specifically requires an original AI-generated police report to survive after an officer edits it, and none requires the finished report to disclose that a machine helped write it. The technology is in the building. The rules are not.

Gay Woodhouse, Wyoming's thirty-second attorney general from 1998 to 2001 and the first woman to hold the office, previously an assistant United States attorney and chief deputy attorney general, told Cowboy State Daily that “the thing that worries me about AI is just the ability to hallucinate,” and that Wyoming needs “to get on it.” She put her finger on the mechanism: “The main thing in criminal prosecutions is disclosure.” And then, with the dry economy of a former prosecutor: “It'd be awful to lose a case because you used AI and didn't disclose it.”

Casper criminal defence attorney Keith Nachbar called it “uncharted territory” and delivered the sentence that ought to be nailed above every police chief's desk in the state: “AI isn't going to be taking the witness stand.” Then a question nobody in Wyoming can answer: “How do you look behind the curtain and figure out what AI thought?”

Evanston Police Chief Mike Vranish supplied the counterweight every department offers. AI can do in minutes what takes officers hours. But a camera cannot smell the gunpowder hanging in a room, or the alcohol on a driver's breath. So, said Vranish, “everything's got to be verified and validated by an actual person.”

That last sentence is the load-bearing beam of the entire industry. We will come back to whether it holds.

Two Letters and a Life

A generative model producing a police narrative from body-camera audio and video is not reasoning about what happened. It is producing the most probable next token given everything before it. When the input is clear, the output is usually fine. When the input is ambiguous, and a real incident is nothing but ambiguity, the model does not flag the ambiguity. It resolves it, picking the likelier phrasing and writing it with the same fluency it uses for the parts it got right. There is no tonal difference between the sentences it knows and the sentences it guessed.

Human officers make errors too, and police reports have always been contested documents. The difference is the shape of the error. A tired officer produces a report with the texture of exhaustion: gaps, hedges, “I believe”, “it appeared that”, the occasional flat contradiction a defence lawyer can hold up in cross-examination. Those artefacts are evidence, telling a jury how confident the witness actually was. Machine prose has no such texture. It is uniformly confident, smoothing hedges into assertions because assertions are more probable in the training distribution of police reports than hedges are. The uncertainty that lived in the officer's memory is laundered out before anyone reads it.

The research base bears this out with unusual precision. In May 2026, criminologist Ian T. Adams of the University of South Carolina and colleagues Matt Barter, Kyle McLean, Irick A. Geary Jr, Alexis Fabila and Josh McCrain published a study on CrimRxiv titled “A Good College Essay but a Bad Police Report.” They ran a triple-blind evaluation in which 92 expert reviewers, sergeants and above with an average of 21.8 years of responsibility for approving reports, assessed 80 police reports, 20 drafted with Axon's Draft One. The reviewers did not know which was which.

Two findings matter. AI-assisted reports were rated substantively and significantly worse on accuracy, an effect moving a report from roughly the fiftieth percentile to the thirty-sixth, and text analysis found them longer-worded, more complex and less readable. They read like good college essays. They were worse police reports. And the expert reviewers could not tell which reports were written with AI. Their accuracy on that question was no better than a coin flip.

Hold those together. The exact people whose review is supposed to be the safeguard rated AI reports as less accurate whilst being unable to identify them. They sensed something was off and could not locate it. That is the signature of a failure mode human review is structurally poor at catching.

The Signature Stays, the Perception Goes

A police report's evidentiary power does not come from its literary quality. It comes from provenance. It is a contemporaneous account written by a person who was present, who perceived events through functioning senses, who can be cross-examined about those perceptions, who can be impeached with prior inconsistent statements, and who faces professional and sometimes criminal jeopardy for falsifying it. Every one of those guarantees attaches to a human being standing in a room.

Chief Vranish put it better than most legal scholars: a camera cannot smell the gunpowder hanging in a room, or the alcohol on a driver's breath. That is not a quaint remark about sensor limitations. It is a statement about the epistemology of the document. The camera captures a slice of a person's perception; the model writes a narrative from the slice and presents it as the whole.

What AI drafting does, quietly, is sever the sentence from the perception whilst leaving the signature in place. The officer still signs. The report still enters the file as his account. But a proportion of its sentences, and nobody can say which, originated not in his memory of the room but in a model's reconstruction of what a room like that usually sounds like.

This is what the machine is for. Comprehensiveness, which Code Four promises, is precisely the property a human account does not have and should not claim. Officers miss things, and the gaps in an honest report are informative. A machine that fills them is not improving the document; it is contaminating it with plausible invention.

The academic literature has caught up faster than the statutes have. In March 2026, a team led by Muffy Calder and Marion Oswald, with Elizabeth McClory-Tiarks, Michele Sevegnani and Evdoxia Taka, submitted a paper to arXiv titled “Responsible AI in criminal justice: LLMs in policing and risks to case progression.” They mapped 15 policing tasks that large language models could plausibly handle, from transcription and translation to witness statement generation, corroboration identification and police report generation, and set against them 17 categories of risk illustrated with more than 40 worked examples of how those risks propagate through a case.

Their taxonomy of output risk begins with completeness, meaning omissions and additions, and accuracy, meaning outright factual error. In one proof-of-concept trial they describe, a system produced a description of a witness as “a woman of average height and brown hair” when no such description had been given in the interview. Nobody was turned into an amphibian. A physical description simply appeared, from nowhere, in a document later read as a record of what a witness said. That is the plausible error, and it survives review because there is nothing about it to catch.

The Draft That Was Never Born

Now the design question, where this stops being a technology story and becomes a legal one.

In July 2025, Electronic Frontier Foundation investigators Matthew Guariglia and Dave Maass published a records-based investigation into Axon's Draft One under the headline “Axon's Draft One Is Designed to Defy Transparency.” Their central finding was not that the software hallucinated. It was that the software forgets.

Draft One, they found, does not save the draft it generates, nor any edited versions. An officer generates a narrative, copies it into the report, and the draft disappears when the window closes. The EFF quoted an Axon official explaining the rationale with startling candour: “the last thing we want to do is create more disclosure headaches for our customers.”

Sit with that sentence. It is not an engineering trade-off. It is a legal strategy. The product does not retain the machine's first draft because retaining it would generate discoverable material, and discoverable material creates work for prosecutors and ammunition for the defence.

The downstream effects landed where you would expect. Auditing meant trawling individual logs or keyword-searching, and only where a department had voluntarily inserted disclosure language. The Palm Beach County Sheriff's Office, which required a disclosure line, could identify more than three thousand Draft One reports. The Lafayette Police Department could not identify one, having no marker to search for.

Non-retention is not an oversight. It is the feature that makes the practice legally survivable, converting an auditable pipeline into an unauditable one by destroying evidence before anyone has standing to ask for it.

The constitutional problem arrives immediately. Under Brady v. Maryland, decided in 1963, prosecutors must disclose evidence favourable to the accused that is material to guilt or punishment, regardless of good faith. Under Giglio v. United States, decided in 1972, that duty extends to information bearing on the credibility of government witnesses. A machine-generated first draft that differs materially from the signed report is, by definition, impeachment material about the officer's account. If the report says the suspect reached into his waistband and the draft said toward, the defence is entitled to know. If the officer corrected a dozen machine errors, that speaks to how much of the final document is his own recollection. If he corrected none, that speaks louder still.

You cannot disclose what does not exist. And when the non-existence is engineered by the vendor for the stated purpose of avoiding disclosure headaches, the ordinary comfort that Brady is a duty rather than a preservation mandate looks thin.

The Calder and Oswald team recorded what happens when a force takes preservation seriously. In a Hertfordshire Constabulary pilot of an AI interview assistant, the need “to disclose all versions of the transcript and statement produced by officers when using ADA ... impacts on workload for the Disclosure officer.” Doing this lawfully is expensive. Doing it cheaply means not doing it lawfully.

The Witness Who Cannot Be Called

Nachbar's line, that AI is not going to be taking the witness stand, is not a flourish. It sits on an unresolved constitutional question American courts have circled for twenty years.

The Sixth Amendment guarantees a defendant the right to be confronted with the witnesses against him. In Crawford v. Washington, decided in 2004, the Supreme Court held that testimonial statements cannot be admitted unless the declarant is unavailable and the defendant has had a prior opportunity for cross-examination. In Melendez-Diaz v. Massachusetts, decided in 2009, Justice Scalia wrote that forensic certificates of analysis were testimonial statements akin to affidavits, so the analysts who prepared them were witnesses the defendant could confront. In Bullcoming v. New Mexico, decided in 2011, Justice Ginsburg wrote for a five to four majority that the state could not launder that requirement by putting a different analyst on the stand to describe a report he had neither signed, performed nor observed. Surrogate testimony does not satisfy the clause.

The line those cases draw is about accountability for accusation. The state does not get to accuse through a document and then produce someone who can only vouch for the process in general terms.

An AI-drafted report sits awkwardly across that line, in a way that favours the prosecution. Machine output is generally not treated as a “statement” by a “witness” at all, because the Confrontation Clause has been read to govern human declarants. The Berkeley law professor Andrea Roth mapped this terrain in “Machine Testimony,” published in the Yale Law Journal in 2017. Machine sources of accusation, particularly proprietary software built for litigation, may function as witnesses against a defendant, she argued, and “black box dangers,” meaning errors that render a machine false by design, inarticulate or analytically unsound, are analogous to the hearsay dangers confrontation exists to expose. Machine testimony substituted for human testimony lets the state evade responsibility for its accusations.

Now apply it. The officer takes the stand. He is confrontable, and can be asked what he saw, what he smelled, what he remembers. But the sentence being challenged may not have originated with him. It came from a model. He can be asked whether he agrees with it now, which is a different question. And the thing that produced it cannot be cross-examined on its reliability, its error rate, its behaviour when the audio is poor, or its tendency to resolve ambiguity towards the incriminating reading.

The officer becomes, functionally, a surrogate witness for a process he did not perform and cannot describe. Bullcoming says the state cannot do that with a laboratory analyst. Nothing yet says it cannot do it with a language model. That gap is Nachbar's question about looking behind the curtain, restated in constitutional terms, and the current answer in Wyoming is that you cannot, because the curtain does not exist and neither does the draft behind it.

When the File Becomes the Memory

The sharpest point in the Cowboy State Daily piece is also the least discussed anywhere else. Fedore's account of Nachbar's concern runs like this. Six months on, an officer may no longer independently remember whether a suspect reached toward his waistband or into it, precisely what somebody said, or where everyone was standing. He returns to the report to refresh his memory, as officers routinely do before testifying. If the machine put the wrong detail there first, the report can begin informing the memory it was supposed to preserve.

This practice is not a loophole. It is formally sanctioned. Federal Rule of Evidence 612, and its analogues across the states, governs writings used to refresh a witness's memory, granting the adverse party the right to produce the writing, inspect it, cross-examine on it and introduce relevant portions. The rule assumes a document written by the witness, from the witness's own perception, reactivating the witness's own encoded memory. It has teeth, too. Under subsection ©, if a writing is not produced as ordered and the prosecution is at fault in a criminal case, the court must strike the witness's testimony or, if justice so requires, declare a mistrial. That mandatory remedy was written for a world in which the writing still exists.

The psychological literature has spent fifty years showing that the assumption underneath Rule 612 is fragile. The misinformation effect, the body of work most associated with Elizabeth Loftus, who published a retrospective on the idea's history in Legal and Criminological Psychology in 2026, shows that memory for an event is not sealed at the moment of the event. Post-event information modifies the memory itself, and misleading detail embedded in an apparently trustworthy narrative account is one of the classic delivery mechanisms. People do not merely repeat the misinformation. They come to remember it, with confidence, as something they perceived.

Now consider what an AI-drafted report is, structurally. It is post-event information, embedded in an apparently trustworthy narrative account, carrying the officer's own signature, which makes it maximally authoritative to him. It is reviewed under time pressure, then read again months later to reconstruct details he no longer independently recalls.

That is not a document refreshing a memory. That is close to an optimal protocol for installing one.

The consequence is that the testimony a jury hears may be sincere, confident, internally consistent, and derived from a sentence a language model generated because it was probable. The officer is not lying. He genuinely remembers it that way now. Cross-examination, built to detect deception and inconsistency, is close to useless against a witness who has been honestly contaminated. And the one artefact that could have exposed it, the original draft showing that “into” was never his word, has been deleted by design.

The Load-Bearing Human

Every deployment of this technology rests on one assurance, and Vranish stated it in its purest form: everything has to be verified and validated by an actual person.

The research on whether humans perform that role well is not encouraging, and it is not new. Raja Parasuraman and Dietrich Manzey's 2010 review in Human Factors synthesised decades of evidence that people under-monitor automated systems and accept incorrect automated advice rather than challenge it, even when contradictory information is available. The effect shows up in aviation, medicine, process control and the military, driven partly by a perception that automated systems are inherently superior and partly by the cognitive cost of checking.

Automation bias is worst under exactly the conditions that motivate buying this software: high workload, time pressure, fatigue and a high base rate of correct outputs, which trains the reviewer to expect correctness. A tool that is right ninety-five per cent of the time is more dangerous to review than one right sixty per cent of the time, because the reviewer stops looking.

The vendors know this. Code Four knows it well enough to have engineered a countermeasure that is also, inadvertently, a confession. As StateScoop's Keely Quinlan reported in July 2026, the company embeds hidden “Easter eggs” in AI-generated drafts, seldom-used words such as “axolotl,” which officers must find and remove before filing. Chief executive George Cheng explained the logic: “That basically prevents them from just copy and pasting everything from the AI at face value, and they actually have to make some edits.”

Read that as an admission, because it is one. The company built a booby trap into its own output because it did not trust officers to read the output. That is a more damning verdict on human review than anything a defence lawyer has said, and it does not solve the problem. Removing an obviously alien word proves the officer scanned for an alien word. It proves nothing about whether he verified that “into” should have been “toward.”

And the productivity premise that justifies accepting the risk is itself unproven. Adams and colleagues published a pre-registered randomised controlled trial in the Journal of Experimental Criminology in 2024, titled “No man's hand: artificial intelligence does not improve police report writing speed,” covering 85 officers across 755 reports with a check across 6,084 reports over a full year. AI assistance did not significantly affect how long reports took. In May 2026 the ACLU's Jay Stanley set that null result alongside a 2025 study by Walley and Glasspoole-Bird finding an 18.5-minute increase in time per incident.

So the ledger reads: unverified time savings, measurably worse accuracy, reviewers who cannot identify AI text at better than chance, a documented tendency to rubber-stamp machine output, and a vendor countermeasure that concedes the point. Against that, a claimed sixty-five per cent cut in report writing time and eighty thousand dollars a year.

The Exemption That Wrote Itself

Wyoming did have a chance to draw a line, and what happened to it is instructive.

House Bill 91 of the 2026 budget session would have barred government entities from using artificial intelligence to calculate and assign social scores, and from identifying people using biometric data or gathered images. It reached the House Judiciary Committee on 10 February 2026, and on 19 February the committee recommended amendment and passage. Cowboy State Daily reported that the amendment specifically exempted law enforcement uses of AI for investigating, reporting or prosecuting violations of the criminal code, and that the committee approved it seven to one. The bill died without reaching a vote of the full House.

State the sequence plainly. The one piece of Wyoming legislation aimed at constraining government AI was amended so that the most consequential use of it, the production of documents that put people in cells, was carved out. Then it failed anyway.

Other states moved differently. Utah's SB 180, sponsored by Senator Stephanie Pitcher, a Democrat from Millcreek and a former prosecutor, passed unanimously and took effect on 7 May 2025. It requires agencies to hold a written generative AI policy, a disclaimer on any AI-assisted report, and officer certification that the output was reviewed for accuracy. Pitcher's framing, quoted by Governing's Logan Seacrest in July 2026: “I felt like it was something that it's better for us to get ahead of, instead of playing catch-up.”

California went further. Senate Bill 524, signed by Governor Gavin Newsom on 10 October 2025 and effective from 1 January 2026, requires a written disclosure on every page of any report produced fully or partly by AI, bars vendors from selling the information agencies feed into the system, and, critically, requires departments to retain the first draft along with audit trails identifying the footage or audio that informed it. That retention provision is the mechanism letting judges, defence attorneys and auditors see which portions of a report the officer wrote and which the computer did. California Senator Jesse Arreguín, quoted by Seacrest: “We're not going to gamble with personal liberty. AI hallucinations happen at significant rates.”

As of October 2026, those remain the only two states to have enacted laws on AI-generated police reports, according to a state-by-state tracker maintained by Barricade. Everywhere else the attempts have stalled. New York's S10425/A9253-A lapsed when the Legislature adjourned on 5 June 2026, with reintroduction expected in 2027. Minnesota's SF 4575/HF 4536 died without a hearing in May. Virginia's HB 1294 was carried over to 2027. Even Utah's own 2026 follow-up, SB 205, which would have required annual policy review and public posting of policies, died in the House in March.

Some prosecutors have not waited. The King County Prosecuting Attorney's Office in Washington, which covers Seattle, instructed police not to submit AI-generated narratives, citing concerns about the products on the market. The people who have to prove these cases beyond reasonable doubt decided the documents were a liability.

They are not alone. In Hays County, Texas, KUT 90.5's Leigh Walden reported in September 2026, the sheriff's office trialled Draft One for three months before county commissioners voted three to two to deny funding for an expanded Axon AI contract worth $4.5 million over ten years. In nearby Kyle, the police department uses several Axon AI tools but not Draft One for reports, at the request of the district attorney's office. Gregg Cox, Hays County's first assistant district attorney, explained why the document matters: “The offense report serves a lot of purposes.” It drives charging decisions, plea offers and whether a case goes to trial. “I would much rather have them write a detailed report themselves based on what they observed,” he said, worrying that machine drafts could weaken officers' own recall of what happened. That is the memory contamination problem, seen from the prosecutor's desk.

This is not a peculiarly American drift. In a paper submitted to arXiv in December 2025, Evdoxia Taka, Temitope Lawal, Kyriakos Kotsoglou and colleagues including Calder, Sevegnani, McClory-Tiarks and Oswald set out a methodology for charting probabilistic AI tools across the criminal justice stages of England and Wales, reporting heavy reliance on private sector providers alongside growing interest in large language models. Adoption is happening force by force, vendor by vendor, ahead of any governance layer.

Public deliberation, where anyone has attempted it, is more discriminating than procurement. In August 2026, Mackenzie Jorgensen, Jo Reilly, Alex Sutherland and Miri Zilka submitted a study to arXiv describing a workshop in which 30 community representatives, police officers and academics assessed 13 AI use cases in policing with an explicit focus on racial bias. On AI-assisted processing of reports, participants asked whether time saved is offset by time spent verifying. Foregrounding racial equity, the authors found, did not narrow the deliberation but pushed it towards first-principles questions: does this tool work, will it deliver genuine benefit, and will that benefit reach everyone.

Nobody asked those questions in Casper before the trial started, because in Wyoming nobody has to.

Nobody asked them when it ended, either. In a report syndicated by GovTech on 8 October 2026, the Casper Star-Tribune's Dylan Farrell described how Casper City Council had unanimously approved a one-year, $58,800 contract for TRULEO, letting the police department use the software full-time after a test the council had approved on 7 April. Both decisions went through on the consent agenda, the part of a meeting reserved for business considered too routine to debate, and the contract passed with little public discussion. TRULEO advertises AI-assisted report writing, summaries of encounters, automated review of body-camera footage and “AI-assisted interviews.” Most council members declined to comment. The coverage contains no statement about whether defendants will be told when a machine helped write the reports against them.

What Would Actually Fix This

The answer to Nachbar's closing question, what happens to the right to a fair trial when the ghost author of the foundational document cannot be questioned, is not that trials become unfair in some diffuse atmospheric sense. It is more specific than that.

A fair trial is an adversarial testing mechanism, and it works by making every link in the chain from perception to verdict contestable. The defendant can challenge the search, the arrest, the identification, the forensic analysis, the officer's account and the officer's credibility. Each challenge targets a human who can be put under oath and asked to explain themselves.

AI drafting inserts a link immune to every one of those tools. The model cannot be sworn, cannot be impeached, cannot be asked why it chose a preposition, and cannot be shown to have been wrong before, because the record of its previous drafts has been deleted. It has no jeopardy, so it has no incentive to accuracy. And because its output is fused into a signed human document, the defence cannot identify which links are machine-made. The result is not a rigged trial. It is a trial testing the wrong thing: an officer's present agreement with a narrative rather than his actual perception of an event.

That is fixable, and it does not require Wyoming to ban the technology. Four requirements would do most of the work.

Retain the original draft. Every machine-generated first draft should be preserved, versioned, timestamped and disclosed in discovery alongside the final report. California has shown a legislature can write this. It costs storage, which is cheap, and it restores the ability to see what changed. Without it, Brady and Giglio obligations attach to an artefact engineered out of existence, and Rule 612's production requirement, with its mandatory strike-or-mistrial remedy, applies to a document nobody can produce.

Disclose on the face of the report. Not in a settings menu, not as an optional agency policy, but as a mandatory statement on the document itself, as California requires on every page. Woodhouse's warning is the operative one: it would be awful to lose a case because you used AI and did not disclose it. It would be worse to win one.

Attach provenance metadata. The report should record which model and version produced it, what source material it consumed, and which body-camera or audio file it derived from. Systems engineering risks in the Calder and Oswald taxonomy include version tracking, tool chaining and supplier dependency for a reason. A defence expert cannot evaluate a system that will not say what it is, and a system silently updated between the incident and the trial cannot be tested at all.

Prohibit AI drafting where the officer's own perception is the evidence. Where the contested fact is what an officer saw, heard, smelled or believed in the moment, and above all in use-of-force cases, the account must be written by the human before he is shown any machine draft, exactly as a witness statement is taken before the witness is shown someone else's version. Vranish made the argument for this rule himself without quite drawing the conclusion. If the camera cannot smell the room, the sentences describing the room should not come from the camera.

None of this is technologically demanding. It has not happened because the current arrangement suits everyone with power in the room. Departments get their hours back, or believe they do. Vendors avoid disclosure headaches. Prosecutors, most of them, do not have to think about it.

The person for whom the arrangement is not convenient is the defendant, who is the one party in the courtroom without a procurement budget, and whose case now rests on a document with an author who cannot be called, cannot be cross-examined, and whose first version was deleted before anyone knew there would be a trial. Wyoming is running the experiment on him right now, in three towns, at fifty dollars per officer per month in Evanston and a little under sixty thousand dollars a year in Casper.

References

1. Fedore, Kolby. Cowboy State Daily, 2026. “Wyoming Police Departments Testing AI To Generate Reports, Look At Evidence.” https://cowboystatedaily.com/2026/08/23/wyoming-police-departments-test-using-ai-to-generate-reports-look-at-evidence/
2. Code Four. Company website and product pages, 2026. https://www.codefour.us/
3. Cheng, George. Police1 (sponsored content by Code Four), 2026. “The operating system the modern detective never had.” https://www.police1.com/police-products/investigation-software/the-operating-system-the-modern-detective-never-had
4. Quinlan, Keely. StateScoop, 2026. “Police AI platform uses 'Easter eggs' to verify human review.” https://statescoop.com/police-ai-platform-code-four-human-review/
5. Guariglia, Matthew and Maass, Dave. Electronic Frontier Foundation, 2025. “Axon's Draft One Is Designed to Defy Transparency.” https://www.eff.org/deeplinks/2025/07/axons-draft-one-designed-defy-transparency
6. Calder, Muffy; Oswald, Marion; McClory-Tiarks, Elizabeth; Sevegnani, Michele; and Taka, Evdoxia, 2026. “Responsible AI in criminal justice: LLMs in policing and risks to case progression.” arXiv:2603.18116. https://arxiv.org/abs/2603.18116
7. Jorgensen, Mackenzie; Reilly, Jo; Sutherland, Alex; and Zilka, Miri, 2026. “Negotiating Risk Boundaries in AI for Policing Through Mixed-Stakeholder Deliberation.” arXiv:2608.05418. https://arxiv.org/abs/2608.05418
8. Taka, Evdoxia; Lawal, Temitope; Calder, Muffy; Sevegnani, Michele; Kotsoglou, Kyriakos; McClory-Tiarks, Elizabeth; and Oswald, Marion, 2025. “Mapping the Probabilistic AI Ecosystem in Criminal Justice in England and Wales.” arXiv:2512.04116. https://arxiv.org/abs/2512.04116
9. Adams, Ian T.; Barter, Matt; McLean, Kyle; Geary, Irick A. Jr; Fabila, Alexis; and McCrain, Josh. CrimRxiv, 2026. “A Good College Essay but a Bad Police Report: A Triple-Blind Expert Evaluation of AI-Assisted Police Reporting.” https://www.crimrxiv.com/pub/u7azgqzd/release/1
10. Adams, Ian T. et al. Journal of Experimental Criminology, 2024. “No man's hand: artificial intelligence does not improve police report writing speed.” https://link.springer.com/article/10.1007/s11292-024-09644-7
11. Stanley, Jay. American Civil Liberties Union, 2026. “Studies Question Value of AI-Assisted Police Reports.” https://www.aclu.org/news/privacy-technology/studies-question-value-of-ai-assisted-police-reports
12. Seacrest, Logan. Governing (R Street Institute), 2026. “The Risky Marriage of AI and Police Reports.” https://www.governing.com/policy/the-risky-marriage-of-ai-and-police-reports
13. Constantino, Mya. FOX 13 News Utah, 2026. “AI programs used by Heber City police claim officer turned into a frog.” https://www.fox13now.com/news/local-news/summit-county/how-utah-police-departments-are-using-ai-to-keep-streets-safer
14. Roth, Andrea L. Yale Law Journal, volume 126, 2017. “Machine Testimony.” https://yalelawjournal.org/article/machine-testimony
15. Loftus, Elizabeth F. Legal and Criminological Psychology, 2026. “The history of an idea: The misinformation effect.” https://bpspsychub.onlinelibrary.wiley.com/doi/10.1111/lcrp.70020
16. Supreme Court of the United States. Brady v. Maryland, 373 U.S. 83 (1963). https://supreme.justia.com/cases/federal/us/373/83/
17. Supreme Court of the United States. Giglio v. United States, 405 U.S. 150 (1972). https://supreme.justia.com/cases/federal/us/405/150/
18. Supreme Court of the United States. Melendez-Diaz v. Massachusetts, 557 U.S. 305 (2009). https://tile.loc.gov/storage-services/service/ll/usrep/usrep557/usrep557305/usrep557305.pdf
19. Supreme Court of the United States. Bullcoming v. New Mexico, 564 U.S. 647 (2011). https://tile.loc.gov/storage-services/service/ll/usrep/usrep564/usrep564647/usrep564647.pdf
20. Legal Information Institute, Cornell Law School. Federal Rule of Evidence 612, “Writing Used to Refresh a Witness.” https://www.law.cornell.edu/rules/fre/rule_612
21. Parasuraman, Raja and Manzey, Dietrich H. Human Factors, 2010. “Complacency and Bias in Human Use of Automation: An Attentional Integration.” https://journals.sagepub.com/doi/10.1177/0018720810376055
22. Wyoming Legislature, 2026 Budget Session. House Bill 91, government use of artificial intelligence. https://legiscan.com/WY/bill/HB0091/2026
23. Farrell, Dylan. Casper Star-Tribune, via GovTech, 2026. “In Casper, Wyo., a Green Light for AI Police Bodycams.” https://www.govtech.com/artificial-intelligence/in-casper-wyo-a-green-light-for-ai-police-bodycams
24. Barricade, 2026. “AI Police Report Laws by State: 2026 Tracker” (updated October 2026). https://barricade.tech/articles/ai-police-report-laws-by-state
25. Walden, Leigh. KUT 90.5, 2026. “Austin-area law enforcement is testing AI-assisted police reports.” https://www.kut.org/crime-justice/2026-09-30/police-ai-hays-county-san-marcos-kyle-district-attorney

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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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