Author: Christopher Parsons

Policy wonk. Torontonian. Photographer. Not necessarily in that order.

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Links Writing

Trusted Content Calls for Trusted Identities

Adam Mosseri recently posted about how Instagram is evolving to arbitrate what is, or is not, truthful in a generative AI era. Om Malik’s analysis of said post is well worth the time to read and, in particular, his framing of Instagram’s movement into what he calls a ‘trust graph’ era:

[Instagram] has moved from the social graph era, when you saw posts from people you knew, to the interest graph era, when you saw what algorithms though [sic] you will like. It is now entering a trust graph era, in which platforms arbitrate authenticity. And it is being dragged into this new era. [^ Emphasis added.]

…

AI is flooding the system, and feeds are filling with fakes. Visual cues are no longer reliable. Platforms will verify identities, trace media provenance, and rank by credibility and originality, not just engagement.

Malik’s framing is useful not simply because it captures a product evolution, but because it gestures toward a deeper shift, and one whose implications extend well beyond Instagram as a platform. Namely, platforms are positioning themselves as arbiters of authenticity and credibility in an environment where traditional signals of truth are increasingly unstable.

There are some efforts to try and assert that certain content has not been made using generative systems. Notwithstanding the visibility that Meta possesses to try to address problems at scale, what is becoming more salient is not merely a technical response to synthetic media, but a broader epistemic and ontological shift that increasingly resembles Jean Baudrillard’s account account of simulacra and life lived in a state of simulation:

Simulacra are copies that depict things that either had no original, or that no longer have an original. Simulation is the imitation of the operation of a real-world process or system over time.

This framing matters because efforts to ground authenticity and truth are predicated on the existence of an original, authentic referent that can be recovered, verified, or attested to.

Generative AI content can, arguably, be said to largely be divorced from the ‘original’ following the vectorization and statistical weighting of content; at most, the ‘original’ may persist only as a normalized residue within a lossy generative process derived from the world. Critically, generative systems do not simply remix content; they dissolve the very reference points on which provenance and authenticity regimes depend. And as generative LLMs (and Large World Models) are increasingly taken up, and used to operate the world in semi-autonomous ways, rather than to simply represent it, will they not constitute an imitation of the operation of real-world processes or systems themselves?

This level of heightened abstraction will, to some extent, be resisted. People will seek out more conservative, more grounded, and perceptibly more ‘truthful’ representations of the world. Some companies, in turn, may conclude that it is in their financial interest to meet this market need by establishing what is, and is not, a ‘truthful’ constitutive aspect of reality for their users.

How will companies, at least initially, try to exhibit the real? To some extent, they will almost certainly turn to identity monitoring and verification. In practice, this means shifting trust away from content itself and toward the identities, credentials, and attestations attached to published content. In this turn, they will likely be joined by some jurisdictions’ politicians and regulators; already, we see calls for identity and age verification regimes as tools to ameliorate online harms. In effect, epistemic uncertainty about content may be displaced onto confidence in identities attached to content.

This convergence between platform governance and regulatory activity may produce efforts to stabilize conservative notions of truth in response to emergent media creation and manipulation capabilities. Yet such stabilization may demand heightened digital surveillance systems to govern and police identity, age, and the generation and propagation of content. The mechanics of trust, in other words, risk becoming the mechanics of oversight and inviting heightened intrusions into private life along with continued erosion of privacy in digital settings.

Regardless of whether there is a popping of the AI bubble, the generative AI systems that are further throwing considerations of truth into relief are here to stay. What remains unsettled is not whether platforms will respond, but how different jurisdictions, companies, and regulators will choose to define authenticity, credibility, and trust in a world increasingly composed of simulacra and simulations. Whether the so-called trust-graph era ultimately serves users—or primarily reasserts institutional authority under conditions of ontological and epistemic uncertainty—will remain one of the more intriguing technology policy issues as we move into 2026 and beyond.

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Aside

2025.12.30

Managed to walk into — and out of — a Leica store and only bought a pin. I really wanted to get the used Q2 Monochrome that was ‘only’ 4750 euros.

Though I did buy the Leica Academy inspiration book, online and used, after leaving. But I refuse to count it because it’s a published-based purchase!

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Links

Vibe-Coded Malware Isn’t a Game Changer (Yet)

Over the past week there’s been heightened concern about how LLMs can be used to facilitate cyber operations. Much of that concern is tightly linked to recent reports from Anthropic, which are facing growing criticism from the security community.

Anthropic claimed that a threat actor launched an AI-assisted operation which was up to 90% autonomous. But the LLM largely relied on pre-existing open source tools that operators already chain together, and the success rates appear low. Moreover, hallucinations meant that adversaries were often told that the LLM had done something, or had access to credentials, when it did not.

We should anticipate that LLMs will enable some adversaries to chain together code that could exploit vulnerabilities. But vibe‑coding an exploit chain is not the same as building something that can reliably compromise real systems. To date, experiments with vibe‑coded malware and autonomous agents suggest that generated outputs typically require skilled operators to debug, adapt, and operationalise them. Even then, the outputs of LLM‑assisted malware often fail outright when confronted with real‑world constraints and defences.

That’s partly because exploit development is a different skill set and capability than building “functional‑enough” software. Vibe coding for productivity apps might tolerate flaky edge cases and messy internals. Exploit chains, by contrast, often fail to exploit vulnerabilities unless they are properly tailored to a given target.

An AI system that can assemble a roughly working application from a series of prompts does not automatically inherit the ability to produce highly reliable, end‑to‑end exploit chains. Some capability will transfer, but we should be wary of assuming a neat, 100% carry‑over from vibe‑coded software to effective vibe‑coded malware.

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Links

Even Minimal Data Poisoning Can Undermine AI Model Integrity

As reported by Benj Edwards at Ars Technica, researchers demonstrated that even minimal data poisoning can implant backdoors in large language models.

For the largest model tested (13 billion parameters trained on 260 billion tokens), just 250 malicious documents representing 0.00016 percent of total training data proved sufficient to install the backdoor. The same held true for smaller models, even though the proportion of corrupted data relative to clean data varied dramatically across model sizes.

The findings apply to straightforward attacks like generating gibberish or switching languages. Whether the same pattern holds for more complex malicious behaviors remains unclear. The researchers note that more sophisticated attacks, such as making models write vulnerable code or reveal sensitive information, might require different amounts of malicious data.

The same pattern appeared in smaller models as well:

Despite larger models processing over 20 times more total training data, all models learned the same backdoor behavior after encountering roughly the same small number of malicious examples.

The authors note important limitations: the tested models were all relatively small, the results depend on tainted data being present in the training set, and real-world mitigations like guardrails or corrective fine-tuning may blunt such effects.

Even so, the findings point to the ongoing immaturity of LLM cybersecurity practices and the difficulty of assuring trustworthiness in systems trained at scale. Safely deploying AI in high-risk contexts will require not just policy oversight, but rigorous testing, data provenance controls, and continuous monitoring of model behaviour.

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Photography

Automate Screen Brightness for Photography Apps

Several years ago I posted about a “Glass Time” shortcut. When activated it opened the social media application I use to post and view photography — Glass — and then increased the brightness to 100%. The intent was to ensure I was looking at images closer to how the photographer intended.

The only issue was that I needed to remember to activate the shortcut instead of opening the application itself. This worked but was a bit clunky, and so I’ve created a more pleasant way to achieve the same thing.

Enter some Apple Shortcuts and Automations.

Mission Statement

I wanted my screen brightness to jump to 100% whenever I opened Glass or my photo editor (Darkroom) and then drop back down to 50% once I closed them.

Components

To make this work I had to create Apple Shortcuts and Automations. The shortcuts handle the brightness setting and the automations tell your device when to run those shortcuts. Specifically, I needed an:

  • Apple Shortcut that, when activated, increased the screen brightness to 100%
  • Apple Shortcut that, when activated, reduced the screen brightness to 50%
  • Apple Automation that triggered 100% brightness when opening Glass, and a separate Apple Automation that did the same thing when opening Darkroom.
  • Apple Automation that triggered 50% brightness when closing Glass, and a separate Apple Automation that did the same thing when closing Darkroom.

Building and Linking Components

The process for creating the various components was very easy. To create the underlying Apple Shortcuts:

  1. Open Apple Shortcuts
  2. Tap/click the “ ” button, and in the search bar search for “Set Brightness”
  3. Set the Brightness slider to 100%. Modify the name of the shortcut to something like “Set Brightness 100%”.
  4. Create a second shortcut the same way, but set the slider at 50%. Name the shortcut something like “Set Brightness 50%.”

To create the automations to trigger the different screen brightness levels:

  1. Open the main screen of Apple Shortcuts.
  2. Tap/click “Automation” on the menu bar.
  3. Tap/click the “ ” button to create a new automation. Select “App” in the menu, choose either Glass or Darkroom (or another application of your preference), and the radio button “Is Opened.” Have the automation run immediately. Tap/click “Next”.
  4. In the next screen, scroll to find your Apple Shortcuts “Set Brightness 100%” in “My Shortcuts.” Your automation is now completed and the brightness will go to 100% when opening the relevant application.
  5. Repeat steps 1-4, but modify the radio button chosen in step 3 to “Is Closed”, and in step 4 choose the “Set Brightness 50%” shortcut.

Limitations of Automations

Your Apple Shortcuts will sync between all of your Apple devices through iCloud but this will not occur with your Apple Automations. This means that you’ll need to repeat the automation steps on all of your devices that you want the automation to activate on.

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Photography Writing

The Beauty of the Everyday

I really liked Robin Wong’s reflection on why he keeps returning to the same streets to make his images.

the beauty of doing the same routines, walking the same paths is the familiarity of the location, and you know every turn and corner, you know the details inside out, so you can be prepared for the unexpected. That is also the exciting part of shooting on the streets, you will find something unusual, something you will not know will happen beforehand, and the surprise is worth the redundant process of walking the same streets all over again. […] It isn’t about finding something completely new or extra-ordinary to shoot but finding beauty in the most ordinary settings and make it worth clicking your shutter button for.[^ Emphasis added.]

Like Wong, I’ve found that familiarity can sharpen my eye. Because I walk the same places regularly, I’m able to find the images I do. Having seen the same scene hundreds of times, I can tell when something has changed or that there’s some novelty in the scene that’s before me.

To some extent I think of regularly seeing the same scenes a little like drinking whiskey. At first, whiskey just tastes hot and spicy; any differences seem more theoretical than real. But over time you notice subtle nuances and also detect rarified variances between what you’re enjoying. And you can get excited over little things that really aren’t apparent or distinguishable to someone that hasn’t built up the same kind of palate.

When you walk the same streets over and over, you develop your own sense of what should and shouldn’t be there. You can detect what’s normal or novel. By training your eye on these common spaces, you develop your style. If you need to find something novel in the same place over and over, you’ll develop a unique way of seeing the world, whereas if you’re always seeing a new place you don’t need to stretch in quite the same way — you don’t need to push yourself to develop your sense of what is visually interesting to you.

All of which is to say: from afar, street photography can look pretty dull or boring because there’s a lot of repetition. It’s exactly this repetition, however, that helps you discover the kind of photographer you are.

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Solved

Solved: Apple Wallet Card Activation on a New iPhone

When I set up my new iPhone 17 Pro I ran into a frustrating problem: I couldn’t activate some of my credit and banking cards in Apple Wallet. Since this might happen to others, here’s how I diagnosed the issue and worked around it.

Setup Context

  • Transferred settings (including Apple Wallet) from my iPhone 14 Pro
  • Cards from CIBC and Wealthsimple
  • Used SMS (not voice) as the second factor for verification

The Problem

After transfer, my cards appeared in Apple Wallet but still needed to be re-verified. Each time I tried SMS verification, the text code arrived and auto-filled into the Wallet app — but the “Next” button was greyed out.

On some attempts, the button turned blue, but tapping it did nothing. Result: I couldn’t verify or use my cards.

The Workaround

The fix was surprisingly simple: don’t rely on auto-fill.

  1. Request the SMS code as usual.
  2. Open the Messages app, copy the code manually, and paste it into Wallet.
  3. This time the “Next” button turned blue and worked, letting me add the cards.

Likely Cause

Based on testing, there may be a bug in iOS 26.0 where auto-filled SMS codes don’t properly trigger the Apple Wallet verification step. Until Apple fixes it, copying the code manually may help you get around the issue.

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Aside

2025.9.13

I’m looking forward to an iPhone upgrade year and leapfrogging to AirPods Pro 3 — hopefully this time I can get a good fit without needing third party ear tips!

I’m also curious about Apple’s new crossbody straps. Not for my phone — I haven’t used a case in years — but maybe as an adjustable strap for my smaller cameras.

There’s got to be someone who’ll review this use case, right? And if not… consider this my open call.

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Photography Writing

Supporting Artistic Creativity

Back in February 2022, I made a commitment to myself. I set out to add a bit more positivity to the internet by reaching out to writers whose work inspired me, or photographers whose images resonated with me. I wanted to thank them for their efforts and let them know their work was appreciated.

Recognizing People Matters

It is all too common for people to move through life without peers, friends, or family recognizing the importance of their work or the ways they’ve shaped others’ lives. In my personal life, it was only after my father died that many of the kids he’d mentored reached out to me to share how he had positively affected the course of their lives. His Facebook feed was filled with comments from people who had benefitted from his generosity and kindness. But I was left wondering: had they ever told him directly about his impact? And if they had, might he have avoided his death of despair?

And I’ve seen the power of professional recognition–and felt the cost of its absence. Years ago, after a major project wrapped up, I realized I had forgotten to recognize the exemplary contribution of a junior staff member. I went back into the room to point out how critical their work had been. That small moment of recognition, as it turned out, had a profound impact on their career trajectory. And it was only after I left my last professional job that people contacted me from around Canada about how the work I produced had influenced them, their practice, and their thinking. I’ll be honest: when I left that job, I felt like I’d been writing into a void. Almost no one ever directly recognized the work I was producing or the value they placed in it.

Recognizing Creatives

On Glass, I try to leave a couple of comments on other photographers’ work each month. Sometimes those comments are short, like “Love the composition!👏👏👏 ,” or “Great use of tonality across the frame! 👏👏👏” Other times, when I have more bandwidth, I write longer, more substantive reflections on what I see in their images.

I think this kind of recognition matters. Too often, we wait until it’s too late to share it. Positive, explicit recognition can motivate people who may not have received much encouragement otherwise. It’s one of the many reasons why I support Neale James’ Photowalk Podcast and the community of kindness that he fosters with every single episode.

Lately I’ve been thinking about how to take this further. For me, the next step has been to begin collecting prints or zines from photographers whose work or practice I deeply admire. I’m not buying prints from the famous names you see in galleries–no Martin Parrs for me!–but photographers working in niches that speak to me. Owning their prints feels special. It’s not just me saying “great work,” it’s me saying, to them, “I value this enough to want it in my home.”

Ownership is a Kind of Intimacy

There are practical challenges with purchasing other people’s work. As we know, shipping expenses, cost of making physical artefacts, and the economic realities facing both buyers and artists can impede purchasing other creatives’ work. We can’t all afford to purchase prints regularly. But even buying one piece every year, every few years, or even once a decade can make a meaningful difference. It’s a way of supporting creativity and giving artists recognition that lasts.

What’s powerful about this isn’t only the financial support. It’s the intimacy of having someone’s work become part of your everyday life. Unlike a gallery exhibition, which is temporary and public, a print hanging in your home or office is permanent and personal. It shapes the space you live in, and every time you see it you’re reminded of the artist and the respect you have for their work.

That, to me, is one of the most profound ways we can support and recognize each other as creators. It’s something that I continue to do, and I appreciate the works of others that I have the privilege of viewing on a regular basis.

In closing, if a creator’s work inspires you then I’d strongly encourage you to leave a comment, send a note, or even consider acquiring a print. It might mean more than you know.

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Photography Reviews Writing

An Amateur Photographer’s Mid-Term Review of the Leica Q2

Black-and-white street crowd at Yonge–Dundas; older woman adjusting hood, masked pedestrians, large bank ad billboard behind.”
(Yonge & Dundas, Toronto, 2024)

I’m an amateur Toronto-based documentary and street photographer, and have been making images on the street for over a decade. In the fall of 2023 I purchased a used Leica Q2. I’d wanted the camera for a while, but it wasn’t until late 2023 that I began running into situations where I’d benefit from a full-frame sensor. Since then I’ve been going out and making images with it at least once a week for hours at a time and have made tens of thousands of frames in all kinds of weather.

In this post I discuss my experiences using the Leica Q2 in a variety of weather conditions to make monochromatic JPG images. I tend to exclusively use either single-point autofocus or zone focusing, and either multi-field or highlight-weighted exposure modes, generally while using aperture priority at 1/500s to freeze action on the street. My edits to images have, previously, used Apple Photos and now rely on the Darkroom app on my iPad Pro. You can see the kinds of images that I’ve been making on my Glass profile.

Continue reading “An Amateur Photographer’s Mid-Term Review of the Leica Q2”