Article URL: https://en.wikipedia.org/wiki/Forestiere_Underground_Gardens Comments URL: https://news.ycombinator.com/item?id=48741359 Points: 3 # Comments: 0
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A developer has released a two-part tutorial series walking readers through building the classic game Pong from scratch using the LÖVE framework and Lua programming language. The first part covers the complete game across two short files, introducing core concepts such as the game loop and delta time. The second part expands on the project by implementing a computer-controlled opponent, enabling a single-player mode. The series is aimed at beginners who have little or no prior game development experience. Both parts are available on the author's personal blog at peculiarengineer.com.
Harness Engineering refers to the infrastructure layer that wraps large language models (LLMs) to handle failures, manage context, and enable reliable tool execution in agentic AI systems. Unlike the visible reasoning loop — where an AI thinks, calls a tool, reads the result, and repeats — the harness operates invisibly to verify tool outcomes, retry failed actions, and adapt strategies when things go wrong. A real-world example involved an AI agent attempting to submit a form on a Thai government property auction site, where the harness had to cycle through three different approaches before successfully bypassing a CAPTCHA race condition. Context window bloat is another core challenge the harness addresses: when an agent runs 50 or more tool calls, token counts can exceed 150,000, causing the LLM to lose track of earlier instructions and enter repetitive loops. To counter this, harnesses implement automatic context compression, trimming older tool results to restore model coherence without restarting the task.
Google has launched Antigravity 2.0, a next-generation agent development platform designed to help developers build and orchestrate multi-agent AI systems for enterprise use. The platform ships as a desktop application, a CLI tool, an SDK, and a managed agents tier within the Gemini API, all powered by Gemini 3.5 Flash optimized for agentic workflows. Unlike its predecessor, a coding assistant released in November 2025, version 2.0 centers on the agent itself, enabling parallel task execution through a manager-and-sub-agent architecture. It integrates with Model Context Protocol, which allows agents to access databases and APIs through modular, independently maintained MCP servers rather than custom-built connectors. Together with Google Cloud Data Services, the platform aims to provide a scalable, production-ready blueprint for building AI applications across complex enterprise environments.
A mobile development team running a React Native (Expo) app discovered their notification system was generating nearly 3,000 HTTP requests per day per user through a 30-second polling interval, draining battery and delivering nothing when the app was closed. The team initially considered using Expo's managed push service but opted instead for direct integration with Apple's APNs and Google's FCM to eliminate third-party runtime dependencies. This approach gave them full control over notification payloads, platform-specific settings, and device tokens without relying on Expo's token translation layer. The direct integration required implementing OAuth2 service account authentication and JWT signing separately for each platform, adding engineering complexity. The switch eliminated the self-inflicted API polling load and enabled true background push delivery for time-sensitive bid notifications.
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