Clients ask me to bolt an AI chatbot onto their sites all the time, and those sites run on everything: Shopify, WordPress, Drupal, sometimes abandoned custom builds. But “which plugin?” is never the right question: an AI chatbot is two layers people almost always conflate, the widget delivery and the brain that answers. The entire decision sorts onto a single scale, from locked-in turnkey where you control nothing, all the way to a pipeline you fully own. For a single client, turnkey is plenty; for an agency banking reusable knowledge across a whole fleet, owning the brain becomes an asset. Here’s the framework I use to decide, CMS by CMS, with Shopify leading the way.
Most AI SVG generators cheat: they hand you a base64-encoded PNG dressed up with a .svg extension. The test takes ten seconds, open the file in a text editor. In 2026, Recraft V4 is the only consumer tool that reliably produces real native SVG from a prompt, with Adobe Firefly Text-to-Vector as a legally better-protected alternative. Three trades collapse along the way: junior UI icon designer, low-end freelance logotype, vectorizer. What gains value is the upstream (brand design, design-system architecture) and the downstream, the technical integration.
I have been loyal to Levi’s for decades, and yet I have never owned the same pair of jeans twice. Every time I replace a pair, something has changed: cut, colour, fabric composition, weight. This is not disorder. It’s a strategy. By making product loyalty impossible, the brand exploits precisely the loyalty to the icon: the red tab, the Far West mythology, a carefully maintained heritage designed to conceal a denim that has gone from 14 to 15 oz in the 1980s to 10 to 12 oz today. What is the point of being loyal to a brand that makes loyalty to its product impossible?
On 8 May 2026, Thariq Shihipar, an engineer on the Claude Code team at Anthropic, published a manifesto that’s been circulating ever since: drop markdown, switch to HTML for your specs, implementation plans and reports. The piece is clever, well written, and structurally an act of strategic marketing dressed up as personal experience. On a narrow subset (throwaway editors, interactive playgrounds, prototypes with sliders), the author is right; on everything else, his blanket enthusiasm conceals six blind spots. Token cost waved away, semantic HTML flipped into presentational HTML, loss of reviewability, the maintenance paradox that traps the user inside the production loop, reading that gets skimmed rather than scrutinised, an attack surface opened up by indirect prompt injection. An analysis of a poorly disguised strategic signal, plus a pragmatic decision matrix for sorting out where HTML deserves to be adopted and where markdown remains, by design, the right pivot format.
Every session, Claude Code reopens your codebase like a visitor who has never set foot in it. It unfolds the README, fires off its greps, opens whole files to reconstruct an architecture it had already pieced together the day before. Understand-Anything, an open-source project released in March 2026, offers another path: index the codebase as a local knowledge graph, versioned like a lock-file, queried on demand by the agent instead of being re-read on every turn. This isn’t a token optimisation, it’s an architectural shift that extends the “RAG is dead, long live the Agent” thesis right into the code itself. Multi-agent pipeline, comparative economics, the limits of a project just six months old: what this break changes for Claude Code and for your workflow.
For twenty years, WordPress has let every plugin name its options and tables however it likes. The add_option() function sets autoload = ‘yes’ by default, with no warning, no documentation urging caution. The register_uninstall_hook() hook exists but is mandatory for no one. The uninstall.php file is optional. And the API provides no discovery mechanism letting the administrator find out, after the fact, which plugin a given option or table belonged to.
Three days without bank transfers, two years to renovate a five-million-euro rural bridge, eleven months to repair three escalators at Châtelet, two weeks without internet in Saint-Marcellin. These situations are no longer accidents: they have become the cruising speed of a country that has lost its ability to do things fast and at a reasonable cost. At the root of this paralysis, four structural causes that reinforce one another; cascading subcontracting, administrative layering, the asymmetry of privatisation, devouring bureaucracy. A million euros has become the minimum unit at municipal scale, a billion at national scale, and the citizen ends up taking out a Starlink subscription to get what the State, the operators and the local authorities no longer know how to guarantee. A methodical slide and a way out, on condition of shortening decision chains and rehabilitating local doing.
The refrain is familiar: when the firm makes 200,000 euros in profit, it’s the workers’ labour that created the wealth. But when it loses 50,000 euros because the market turned, far fewer voices call for symmetry. The employee risks their job, their unpaid back wages, their pension. The shareholder risks their stake. Both take a risk, but not the same one, and it’s precisely this imbalance that has to be discussed honestly, figures in hand. A coherent answer does exist, by the way: the cooperative, where everyone is exposed. And when it breaks, as at Duralex, it’s the worker-shareholders who lose their savings. So, the real question: do you accept the symmetry, or do you just want the upsides of capitalism without the downsides?
For more than a decade on Sony A7R bodies, one observation has imposed itself with irritating regularity: skin tones drift towards green, and no amount of post-production correction really sets things right. This Sony green, as the photo community calls it, isn’t the whim of a Canon nostalgic. It’s a technical reality written into the colour matrix that Sony has been supplying to RAW-conversion software for years. The most paradoxical part? Sony bought Konica Minolta in 2006, inheriting along the way decades of expertise in the natural rendering of skin, a heritage Minolta had spent fifty years building, and that Sony seems to have filed away in a cupboard. While Fujifilm built its entire marketing around its Film Simulations and Canon kept flattering flesh tones by default, Sony was optimising its matrix for DxOMark test benches. Laboratory truth rather than human truth. Mathematical excellence comes at a price, and it’s your portraits that pay it.
Converting a technical PDF into RAG-ready markdown isn’t just a matter of running LlamaParse over it. On a 51-page chapter of the Funktionsrahmen MSS60, the Siemens documentation for the engine control unit of the BMW M3 E92 and M5 E60, I discovered that the interesting work begins after the automatic parsing. The semantic decisions that really condition retrieval quality, preserving the source language rather than translating, breaking dense tables into individual subsections, supplementing diagrams with French prose and pseudo-code, no parser will make them for you. This LLM-assisted work is done only once per document, its marginal cost per query is zero, and its upfront investment pays for itself in user-hours from the very first week of use. Field notes from two evenings of conversion that turned my understanding of what really makes a RAG pipeline worthwhile on its head.