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A developer's name is on a pull request written mostly by an AI agent, but they still own the code. The agent may write clean code, but lacks context about the company's system and decisions. This means the developer is ultimately responsible for ensuring the code is correct for their company, not just the AI. To mitigate this, developers should review the code and consider having a second AI agent review it as well.
Source →This article showcases a tiny language model built in pure Node.js, without relying on frameworks like TensorFlow or PyTorch. The model is trained on a simple text scenario and demonstrates its learning process. The code is simplified to focus on the main idea, and every scalar is visible in the terminal. The model produces correct answers after training, with a stable acceptance criterion reached 11 times in a row. Engineers can use this example as a starting point for building their own language models.
Source →To improve the accuracy of automated UI testing, hwatu is a browser designed to verify UI changes in the agent verification loop. It runs in headless mode, takes a screenshot, and performs pixel-level checks to report a match percentage. This makes 'pixel-perfect' a measurable claim. Engineers can install hwatu and integrate it with their existing testing frameworks.
Source →This article discusses advanced Terraform patterns for managing complex and scalable infrastructure in corporate environments. It covers deep merge of configurations, managing multiple YAML files, and integrating with CI/CD pipelines. To achieve deep merge, a custom function is required to merge maps hierarchically, where specific values override or complement generic values. This is necessary because Terraform's native merge function only performs a superficial merge, which is insufficient for nested maps.
Source →Brainfuck is an esoteric programming language that is Turing complete despite having only eight simple commands. It's a great way to understand low-level computing and memory management. The language operates on a memory tape with a data pointer that can move and manipulate cell values. The eight commands are: >, <, +, -, ., , [ and ].
Source →Sanctuary is a local-first reflection app that keeps user data in-browser, without syncing to cloud servers. This approach protects user privacy and reduces the risk of data exposure. The app features dynamic visualizations and offline PDF export capabilities. It's built with a focus on speed and security. Check out the live app for more information.
Source →Automate Playwright API tests from OpenAPI/Swagger spec using swagger-to-playwright.vercel.app. The tool generates four tests for each endpoint: happy path, auth check, input validation, and contract validation. Paste generated code into your Playwright project and set baseURL in playwright.config.ts. This does not test business logic or mock API calls.
Source →Samba file sharing on Linux allows for seamless file transfer between Linux and Windows machines. This article guides setting up shares with and without authentication. To set up, update packages, install Samba, back up the config file, and use testparm to check for mistakes. Start with a simple share that's open to the network, then move to a locked-down share with authentication.
Source →LeetCode-style practice for System Design interviews is now available on Codemia.io, a platform that offers interactive, structured, and guided system design problems with expert feedback. This is a game-changer for engineers preparing for MAANG interviews, as it fills the gap in system design practice. Codemia.io's free System Design course is also a great resource to learn essential concepts.
Source →This article explains the essential mathematical concepts powering modern AI and Large Language Models (LLMs). Understanding these concepts is crucial for grasping how AI systems think, learn, and generate responses. The guide covers the math behind text encoding, prediction, and model improvement. It provides a high-level overview of the text flow through a Large Language Model, highlighting key math concepts at each step. Engineers should take the time to understand these mathematical foundations to move beyond using AI as a black box.
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