Sin of Angels Is a Browser-Based Board Game Built for Sons of Anarchy Fans

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CRIME Digital has launched Sin of Angels, a browser-based turn-based strategy board game supporting two to six players across desktop, tablet, and phone with no installation required. The game draws inspiration from the out-of-print Sons of Anarchy: Men of Mayhem board game, featuring rival motorcycle clubs, area control, and cinematic 3D brawls. A standout feature called Party Mode lets the game board render on a TV while each player acts privately from their own phone. To prevent private player data from leaking to the shared TV screen, the developers built a server-side system that sends distinct state projections to each connected client rather than filtering data at the UI level. The team also ensured all game outcomes are computed server-side before animations play, preventing synchronization issues across multiple devices.

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IBM Engineer Proposes 'Outcome Reviews' to Shift Code Review Focus in AI Era

IBM Distinguished Engineer Grant Miller has introduced a framework called Outcome Reviews, arguing that as large language models take over routine code generation, human engineers should redirect their attention from line-by-line syntax checks to verifying business intent, technical trade-offs, and final outcomes. Major tech companies including Microsoft, Google, Amazon, and HubSpot have broadly endorsed this shift, with several already deploying AI-assisted pre-review tools to handle mechanical checks like formatting and simple bug detection. Chinese tech giants such as Alibaba, Meituan, and Kuaishou are similarly adopting a layered approach, using AI for baseline reviews while reserving human judgment for architecture, business logic, and security compliance. However, critics and practitioners warn that AI-generated code can contain subtle logic flaws and security vulnerabilities that pass business-level tests, meaning implementation-level review cannot be entirely abandoned. Regulated industries such as finance and healthcare, both globally and in China, maintain that compliance audits require code-level evidence, making a purely outcome-based review insufficient for high-risk modules.

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Google Releases AI Search Guide Urging Unique, Expert-Driven Content Over Volume

Google published new guidance on May 15, 2026, through Google Search Central, advising website owners, SEO professionals, and developers on optimizing content for generative AI-driven search experiences. The guide emphasizes that core SEO principles remain foundational even as AI features expand across Google Search. A central theme is the importance of non-commodity content — material that adds genuine expertise, perspective, or utility rather than simply restating widely available information. Google's guidance extends beyond traditional text pages to cover local, shopping, image, and video content surfaces. The company also addresses common misconceptions around AI-focused optimization and offers preliminary guidance on AI agents.

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Developers warned that old screenshots can silently store live credentials and secrets

Security-focused developers often overlook their camera rolls, which can accumulate screenshots containing API keys, recovery codes, environment variables, and other sensitive credentials. Unlike source repositories or password managers, photo libraries sync across devices and persist in backups, turning a temporary debugging screenshot into a long-lived security risk. Deleting an image from Slack, a ticket, or a support thread does not remove the local copy saved to Photos, and conversely, deleting the local image does not revoke the exposed credential. Experts recommend treating screenshots with the same lifecycle discipline as logs, using local OCR and pattern-matching tools to scan for sensitive text without uploading the library to external services. When a sensitive credential is found, the advised response is to rotate or revoke it first, then update dependent systems, redact or delete the image, and check recently deleted items — treating cleanup and credential rotation as distinct steps.

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Developer builds ML-powered Mancala bot to solve game's player-one bias problem

A developer has built a machine learning bot for Mancala as part of a personal traditional games website aimed at older players, a project previously shelved due to time and skill constraints. The site uses a custom variant of Mancala with randomised stone placement to reduce the game's inherent player-one bias, which caused roughly 75% of tournament matches to be decided before play began. To train the bot, a Python-based game simulator using NumPy was built, allowing a neural network to repeatedly play against itself and improve over time. A 15% randomiser was incorporated into training so the bot occasionally makes non-optimal moves, exposing it to a wider variety of board positions. The developer credits agentic coding tools with finally making the project feasible by combining improved personal skills with AI-assisted development.

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