Life in the Lower Mainland (BC, Canada)

The Economics of Hiding Bad Posts: It’s About Inventory Quality, Not Quantity A little while ago, I wrote about The Moderation Problem —the technical and social friction of closing the loop on problematic content before it spirals out of control. After it went live, I heard a really fascinating counterargument. In fact, it’s probably the most logical, common-sense objection you could raise to the idea of platforms aggressively hiding bad posts. It goes something like this: "The gap isn't technical, it's economic. Closing the loop means some posts never go live, which kills impressions and ad inventory. Platforms won't build what hurts their metrics, even if it helps users. The incentive structure is the blocker." If you look at how social media has operated for the last decade, this makes total sense. Platforms are engagement machines. Every time a user scrolls, an ad is served. Every time a controversial post sparks a 500-reply argument, that’s hundreds o...

Beyond Regex: Building a Cost-Effective, AI-Powered Username Moderation System If a user tries to register the username "fuq-u-2", your traditional profanity filter will probably let it through. Standard filters rely on exact string matching. They are fast, but fundamentally fragile. Human creativity—specifically leetspeak, homophone substitution, and cross-linguistic workarounds (like using Pinyin to write Chinese slurs in the English alphabet)—renders them obsolete. Right now, the industry standard fallback is user-generated reports. But this is a terrible experience for everyone. It creates "Report Fatigue" for your community, and it creates a massive, expensive manual workload for your Customer Support team. Every time a user reports an offensive name, a support agent has to gather context, investigate the account, and action it. We need to shift moderation upstream. Here is a proposed architecture for an AI-driven username system that satisfies both the...

The Moderation Problem Nobody Is Solving [ 5 min read · Hub post for a series on next-generation AI moderation ] By the time a moderation report gets filed, the damage is already done. The post has been seen, reacted to, and replied to. Those replies have generated their own replies. Good-faith users have been drawn into something they didn't start. The person who lit the match is still posting. And somewhere in a queue, a moderator is about to open a ticket that describes the fire — with no information about what caused it. This is how every major community platform moderates in 2026. Not because better tools don't exist. Because nobody has connected the tools that do exist into a system that actually closes the loop. The match, not the fire Two types of content generate a disproportionate share of moderation work on every platform, and neither looks like a rule violation on the surface. The first is the ragebait topic. A user opens a thread des...

The Cost of Doing Nothing Has Been Quietly Compounding [ 8 min read · Part of a series on AI-powered moderation — start here ] Every platform running a reactive moderation system is making a choice. Not a considered choice with a cost-benefit analysis attached — a default choice, made by inertia, to keep doing what was done before because changing it requires a decision and not changing it doesn't. That choice has a cost. It just doesn't show up in a single line on a budget report. It accumulates in user churn, community trust erosion, reputational incidents, and the quiet departure of exactly the kind of users who make a platform worth being on. By the time it's visible it's already been compounding for a while. What's being proposed — and why it's different from what exists Three major platforms have already demonstrated that AI assessment of posts before they go live works at scale. Instagram has been doing it since 2019. Reddit...

The Intervention Point Is in the Wrong Place [ 8 min read · Part of a series on AI-powered moderation — start here ] Current forum moderation is architecturally a logging system. Something happens, it gets reported, it enters a queue, someone reviews it. The entire pipeline runs after the fact. The damage — the angry replies, the derailed thread, the good-faith users who got drawn in and penalised — has already propagated by the time any of it is actioned. The technical problem isn't that platforms lack the tools to do better. It's that the intervention point has been placed at the wrong end of the pipeline. This post describes a two-pass architecture that moves it. The data model everything else builds on Before getting into the passes, one structural observation that makes the whole system simpler than it might appear: every post in a forum already has exactly one of two states. It is a root post — it replies to nothing. It is the origin of a ...

Your Moderation Team Is Solving the Wrong Problem [ 7 min read · Part of a series on AI-powered moderation — start here ] A report lands in the queue. A moderator opens it, sees two users in a heated argument, applies the closest matching rule, and closes the ticket. The metric moves. The queue shortens slightly. And somewhere in the forum, the same problem that generated that report is already generating the next one. This is the moderation loop most platforms are stuck in. Not because the people running it aren't good at their jobs — they are. But because by the time a report exists, it's already a record of damage that has been propagating for minutes, sometimes hours. The report isn't the beginning of the problem. It's evidence that the problem has been running for a while. Why your queue never really empties Every second between a harmful post going live and a moderator actioning it is a second where other users are reading it and react...

When "No" Doesn't Mean No: The Hidden Language Problem in AI Imagine you've just hired a personal assistant. On their first day, you hand them a list of tasks and say clearly: "I'd like you to research some big screen TVs for me — but do not buy anything yet. Just look." You come back an hour later. There's a 75-inch television on its way to your door. Frustrated, you go to your manager and explain what happened. Instead of acknowledging the mistake, the manager says: "Oh, you should know — they don't respond well to instructions phrased as negatives. Next time try saying 'compile a research list of TVs, and wait for my go-ahead before purchasing.' That works better." Would you find that acceptable? Probably not. And you'd be right not to. This is not a hypothetical. This is, in essence, what is happening right now with AI — and most people have no idea. A Real Example From the Internet A Reddit thread recently s...