Pillar
Workflow Architecture
Designing the loops work happens inside — instruction design, migrations, test-driven cycles and the gates that keep a codebase coherent over time.
01
A DSL Fine-Tuning Pipeline: Validator First, Training Script Second
The QLoRA pipeline for a proprietary DSL once you have established the case: compiler-backed dataset validation, AST mutation for expansion, error-correction pairs, and evaluating on parse rate rather than loss.
02
CI Gating for Agent-Generated Pull Requests
Why agents pass the traditional gate more easily than humans, seven checks worth adding, diff coverage over total coverage, and why model-graded review should never be the gate.
Other pillars
- Context Architecture — How a codebase explains itself — context files, documentation and conventions structured so the right material is found at the right time, by a person or a tool.
- Security Engineering — Threat models, trust boundaries and controls for systems that hold credentials, execute code and call tools on your behalf.
- Tooling & Integration — How the pieces fit together: editors, build systems, CI, protocols and the day-to-day mechanics of shipping software.
- Codex vs Claude — Practical comparisons across execution, security, cost, speed, orchestration, planning, integrations and verification — each examined as a decision a working developer can test.
- Higgsfield AI — A practical field guide to Higgsfield's generation, camera, consistency, advertising, audio and editing workflows — including the limits hidden by feature names and the checks that prevent wasted renders.
- Flow vs Higgsfield — A beginner-focused comparison of Google Flow and Higgsfield across models, mobile use, story building, camera control, editing, integrations and the real cost of accepted video.
- Humanoid Robots — Practical buying guides for commercially offered humanoid robots, separating published specifications from configuration, integration, safety, privacy and procurement obligations.
- ChatGPT vs Grok vs Claude Code — A practical comparison of three increasingly broad AI products across identity, interfaces, files, autonomy, search, coding, media, memory, integration, safety and the work each is best equipped to own.
- Tesla Roadster — Evidence-aware engineering guides to the announced, proposed and prototype Roadster features — separating Tesla's published targets from executive claims, visible concepts, engineering inference and unsupported expectations.
- Tesla Optimus — Evidence-checked guides to Optimus hardware, perception, autonomy and safety — plus what Tesla's production statements do and do not say about public availability and price.
- Tesla Robotaxi — Evidence-checked guides to the live Model Y service and Cybercab — covering autonomy, fleet operations, passenger experience, economics and the future owner-network claim.
- Joby S4 vs Archer Midnight — A source-checked comparison of two piloted electric air taxis — propulsion, performance, charging, mass, manufacturing, operations and flight controls without treating design targets as certified specifications.
- Samsung Galaxy — Practical, source-checked comparisons of Samsung phones — the hardware differences that change daily use, the tradeoffs hidden by headline specifications and who should buy which.
- Walmart Technology — How Walmart turns stores, automation, forecasting, drones, connected television and last-mile delivery into a retail technology stack — and where its rivals remain stronger.