This issue covers a new AI-built SQL parser for PostHog that’s 70× faster, Linear’s move from styled-components to StyleX for better React performance, and a deep dive into misleading database benchmarks. It also highlights tools like Gemini 3.5 Flash’s computer use feature, the peerd automation extension, a breakdown of prompt injection vulnerabilities, and other AI and dev updates.

• PostHog built an AI-generated SQL parser that's 70x faster than their old C++ ANTLR version, using property-based tests to guide the model, without changing the query language for users
• Linear is replacing styled-components with StyleX to reduce runtime style recalculations and cut style-sheet overhead, since styled-components hasn't kept up with React
• LLMs can't reliably distinguish system/user role tags because they follow tone rather than tag hierarchy, letting attackers embed commands like "ignore previous" inside user-level text to bypass safeguards

This article argues that using Markdown prompts and LLMs as a runtime interpreter for business rules is slow, expensive, unreliable, and insecure. It shows how mixing regular code, human approvals, and LLMs without a solid framework leads teams to overuse LLMs instead of writing proper code and handling state continuations.

• Using LLMs to interpret Markdown business logic runs ~10,000x slower, costs more, and invites hallucinations, privacy leaks, and prompt-injection attacks than writing regular code.
• The real cause is engineering friction: without a framework to pause/resume code and store continuations, teams dump logic into prompts and let the LLM manage state via conversation history instead of building proper state machines.
• ~90% of support queries follow predictable flows that deterministic code handles better and near-instantly, so LLMs should be reserved for genuinely unstructured tasks like image recognition, sentiment scoring, or free-form-to-query translation.