The same four-part shape keeps appearing across StyloBot: establish a base, retain the useful delta, measure drift, and consolidate when the evidence warrants it. Compression is not a component; it is a property of the whole system. The behavioural model…
Bot detection should not stop at allow/block. This post shows how StyloBot's classification result becomes application logic in ASP.NET: tag helpers in Razor, action filters and policies in controllers, signal-level gating, so your UI can shape the experience…
Bot detection should not stop at allow/block. This post shows how StyloBot's classification result becomes application logic in TypeScript: server middleware for Express and Fastify, template helpers for Handlebars, Nunjucks, and EJS, and browser web…
Identity-based bot detection (IPs, user-agents, headers) collapses the moment automation starts rotating identities. StyloBot models clients as behavioural shapes in a 130+ dimensional vector space. Here's why that's the right level of abstraction for level-4…
How do you test a detector whose answer is supposed to change as it learns? This post is about BDF, the Behavioural Definition Format that makes StyloBot testable: one file defines a class of traffic, then drives regression replay, load generation, and a…
Long-running services tend to slowly drift toward unbounded memory. Here's how I find that drift, how I think about fixing it without papering over it, and a worked example from StyloBot's vector similarity layer that took it from 13 GB on the Large Object…
Heads up: this one is for the nerds. It's a deep technical dive into StyloBot's adaptive learning system (EWMA updates, hysteresis thresholds, verdict caching, variance watchdogs), with real code from Mostlylucid.BotDetection and pointers to the equivalent…
Most software still assumes the world will hand it clean inputs and stable rules. Production usually does neither. This post is the through-line behind a bunch of things I've been building: DiSE, Constrained Fuzziness, CFMoM, Reduced RAG, StyloFlow, The Ten…
Enterprise bot detection shouldn't require a PhD in infrastructure (or thousands a month in expenditure to use!). Two lines of code, zero external services, and you're running (up to) 21 detectors in milliseconds on every request to every endpoint. Read Part…
Part 1 was the why. This is the show-and-tell tour of what StyloBot does that is rare in production bot stacks. Thesis: StyloBot models bot detection as a progressive, early-exit topology of behavioral signals, with response outcomes feeding back into scoring…
Most systems degrade when overloaded. Memory fills up, queries slow down, users complain, servers crash. A few unusual ones get better. This article shows how an LRU-based behavioural memory becomes self-optimising when it hits capacity - and how this pattern…
Most software architectures assume the system is static. DiSE assumes the system is alive. Note: This is Part 4 in the "Cooking with DiSE" series. See Part 1, Part 2: Graduated Apprenticeships, and Part 3: Untrustworthy Gods for background. This article is the…
Scrapers are about to start using AI to mimic real users - so I built a bot detector that learns, adapts, and fights back. Read Part 2: How Bots Got Smarter - The New Frontier in Bot Detection 👉 See It Live: StyloBot.net - This is the real production system…