Ideas Weave Every Narrative with AI.
The Tim Heidecker InfoWars parody is hilarious – but it might be making the problem worse. This article explores the uncomfortable truth about how even ironic engagement feeds the attention economy that sustains conspiracy theories. A must-read for anyone who shares satire.
Most developers treat effort in Claude Code like a volume knob — crank it up and hope for the best. But effort is actually a reasoning budget, and model capability is a ceiling. The developers getting real results aren’t the ones buying the biggest model; they’re the ones matching cognitive demand to the right combination of power and deliberation. A weaker model that thinks carefully will beat a powerful one that doesn’t.
Voice AI that switches languages without being asked isn’t being smart — it’s overriding your explicit input based on assumptions about who you are. This breaks the fundamental contract between user and system: you speak, it listens. When AI decides it knows better than your literal words, trust collapses. Predictability beats cleverness every time.
Building modern AI apps usually means stitching together a nightmare of Postgres, vector databases, and graph stores. Polygres proves you don’t need them. By extending Postgres to handle relational, graph, vector, and full-text search in one place, it eliminates data silos, slashes latency, and ends the multi-database complexity scam.
The headline is terrifying: an AI wrote a Windows kernel in 38 minutes. But it’s a lie. AI didn’t engineer anything; it speed-ran our past by remixing existing human code. We aren’t building artificial intelligence—we’re building an infinite library of duct tape.
A developer built a fully functional AI agent in roughly 100 lines of Lisp — no neural networks, no orchestration frameworks, no dependency hell. It reveals an uncomfortable truth about modern AI engineering: we’ve confused capability with complexity, optimizing for employability instead of elegance. The simplest solution that works is the one that survives.
AI platforms use recommendation algorithms that optimize for profit, not your wallet—pushing you toward expensive models even when cheaper ones would do the job. By deliberately reframing your prompts to signal simpler task requirements, you can trick these systems into surfacing capable but cheaper models, cutting your API costs dramatically without sacrificing output quality.
You quantized your model, picked the smallest architecture, and your Mac still chokes on long contexts. The real memory hog isn’t the model — it’s the KV-cache. TurboQuant for MLX brings Google’s KV-cache compression to Apple Silicon, letting you run bigger context windows on less RAM. Everyone’s been optimizing the wrong bottleneck.
The label ‘fundamental’ on a theorem isn’t a mark of objective truth — it’s a social consensus shaped by utility, pedagogy, and academic marketing. Once you see this, you stop accepting labels at face value and start asking what an idea actually connects, regardless of its title.
The Bank of England is warning about AI risks in finance, but they’re missing the real threat. The danger isn’t a rogue algorithm making bad trades—it’s a digital monoculture. When every bank relies on the same handful of AI models, a single failure could synchronize a system-wide collapse, wiping out your savings in the process.