DevSecOps engineer sharing experiences and insights on AI, Web3, and Claude code development. Aims to help solve technical challenges, improve development efficiency, and grow through community interaction. Feel free to comment and discuss.
Terminal‑Bench 2.1 shows Claude Code + Fable 5 topping the leaderboard with an 83.8% success rate, revealing that raw model strength alone does not guarantee terminal performance and that the engineering of the Agent Harness is the decisive factor.
The article dissects how salespeople exploit cognitive biases such as the halo effect, assumption acceptance, and availability heuristic—linking the claim that cancer can be cured to unrelated health products—to manipulate consumer trust and drive purchases.
The article explains how enterprises can replace fragmented, hard‑coded security checks in Terraform, CI/CD pipelines, Kubernetes admission webhooks, and API gateways with a unified, declarative policy engine—Open Policy Agent—using Rego to decouple decision and enforcement, enabling fast, auditable SecOps guardrails across the entire software lifecycle.
Sakana AI’s newly released Fugu system uses a tiny 7B “commander” model to dynamically orchestrate a pool of global and local AI models, achieving a 73.7 % SWE‑bench Pro score that outperforms GPT‑5.5 and the heavily sanctioned Claude Fable 5, while illustrating a sovereign‑free routing strategy born from geopolitical and compute limitations.
The article explains how the Poisson distribution models football scores, demonstrates calculations for the Argentina‑France World Cup match, and reveals why its independence assumption and static nature let bookmakers out‑perform bettors using advanced bivariate and zero‑inflated Poisson models.
The article uses economics and game theory to dissect the seemingly harmonious early‑career "honeymoon" period, revealing how overlapping interests give way to marginal skill decay and expectation gaps, prompting rational strategies like quiet quitting or polished long‑term cooperation.
The article argues that Chinese firms' reliance on relentless pressure combined with fixed low wages creates a short‑term arbitrage of a "survival dividend" that fractures labor‑capital interests, fuels quiet quitting, and ultimately stifles discretionary effort, product quality, and breakthrough innovation.
The article uses game theory to show how external pressure and trust deficits push both employers and employees into a Prisoner's Dilemma where betrayal becomes the dominant strategy, creating a double‑loss Nash equilibrium, and then proposes repeated‑game and profit‑sharing mechanisms to break the stalemate.
The article analyzes how reliance on AI coding assistants erodes developers' hands‑on skills, compares this to aviation automation bias, and proposes a “mixed‑intelligence” approach that balances AI efficiency with deliberate manual practice to retain low‑level control and resilience.
The article analyzes how Google’s AI tools, exemplified by the sluggish Antigravity CLI 2.0, are throttled by heavy compliance audits and cost routing, drawing parallels with IBM’s missed cloud era to argue that bureaucratic inertia threatens big‑tech giants unless they radically reinvent their AI strategy.
