FrancescoBarbato

Context

Most development teams integrate hashing libraries without understanding the threat-model differences between algorithms. The result: legacy systems still relying on broken MD5 and SHA-1, silent data integrity failures, and no practical migration framework. The risk is especially severe in authentication flows and document verification pipelines.

Approach

Conducted a deep-dive analysis of cryptographic hashing properties — collision resistance, pre-image resistance and avalanche effect — across all three algorithms. Mapped each to real-world attack scenarios including rainbow tables, length-extension attacks and GPU-accelerated brute-force. Produced an architecture guide for integrity verification in modern API and storage layers with a step-by-step migration path.

Takeaway

The article gives engineering teams a reusable decision framework for algorithm selection — when SHA-256 is genuinely needed, when SHA-3 should be considered, and what to do with legacy MD5 in existing systems. Includes an actionable migration checklist.

Context

Most people interact with AI models the same way they'd type a Google search — vague, context-free, one-shot. The result is generic output that requires extensive editing, misses the actual need, or confidently produces plausible but wrong answers. Teams adopting AI tools without a prompting framework see low return on the technology investment and growing frustration with model reliability.

Approach

Researched and systematised the core principles behind effective prompt engineering: role framing, context injection, chain-of-thought elicitation, output format constraints, few-shot examples and negative prompting. Tested patterns against real workflows — content production, code generation, data analysis and document summarisation — iterating against measurable output quality benchmarks.

Takeaway

A practical playbook of 40+ reusable prompt patterns across six task categories. Each pattern includes the structure, when to use it, when not to, and concrete before/after examples. Designed as an onboarding guide for teams new to AI tools.

Context

Deepfake technology has outpaced the biometric authentication systems most organisations rely on. Face verification APIs, liveness detection layers and voice authentication endpoints — all originally designed for fraud prevention — now face adversarial inputs generated by consumer-grade tools in minutes. The gap between what organisations believe their biometric systems can detect and what they actually stop is growing faster than most security teams realise.

Approach

Analysed five major deepfake attack vectors against biometric systems: face-swap injection, replay attacks, voice cloning, GAN-generated synthetic identities and 3D mask spoofing. For each vector, mapped the specific failure modes of current detection approaches — passive liveness checks, texture analysis, frequency-domain anomaly detection — and evaluated the state of the art in adversarial-robust biometric pipelines.

Takeaway

A structured threat landscape view identifying which signal combinations (frequency artifacts, physiological micro-signals, temporal consistency) are hardest to simultaneously spoof — and therefore most worth investing in. Includes a prioritised mitigation framework for high-stakes deployments.

Context

Building a fully playable chess game from scratch — without a game engine or UI library — means implementing every rule correctly (castling, en passant, promotion, stalemate, threefold repetition), designing an AI opponent that is responsive and genuinely competitive, and doing all of it within a React/Next.js stack while maintaining a design quality that matches commercial chess applications.

Approach

Built a single Next.js 15 application with TypeScript end-to-end. chess.js handles rule enforcement; a custom Minimax algorithm with alpha-beta pruning (depth 3) and eight piece-square evaluation tables drives the AI bot. Zustand manages all game state — board position, timers, move history, captured pieces, promotion flow — as a single source of truth shared across every component.

Takeaway

A fully deployable, production-ready chess game live at royalchess-tau.vercel.app. Complete rule set, per-player 10-minute countdown timers with pause/resume, scrollable SAN move history, pawn promotion dialog, material advantage display, and a dark-gold luxury aesthetic built entirely in CSS with no animation libraries.