How-Tos
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Persistent Memory for Codex CLI with Clauder
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Clauder, an MCP server, now supports Codex CLI to provide persistent memory across sessions, addressing the issue of having to repeatedly explain codebases and architectural decisions in new Codex sessions. By storing context in a local SQLite database, Clauder automatically loads relevant information when a session starts, allowing users to store and recall facts, decisions, and conventions effortlessly. This setup, which also supports Claude Code, OpenCode, and Gemini CLI, enhances workflow efficiency by enabling cross-instance messaging for multi-terminal environments. The project is open source and MIT licensed, inviting feedback and contributions from the community. Why this matters: Persistent memory across sessions streamlines coding workflows by reducing repetitive explanations, enhancing productivity and collaboration.
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Real-Time Fall Detection with MediaPipe Pose
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Python is the dominant language for machine learning, favored for its simplicity, extensive libraries, and strong community support, making it ideal for interactive development and leveraging optimized C/C++ and GPU kernels. Other languages like C++, Java, Kotlin, R, Julia, Go, and Rust also play important roles depending on specific use cases; for instance, C++ is crucial for performance-critical tasks, Java and Kotlin are preferred in enterprise environments, R excels in statistical analysis and data visualization, Julia combines ease of use with performance, Go is noted for concurrency, and Rust offers memory safety. The choice of programming language in machine learning should align with the project's requirements and performance needs, highlighting the importance of understanding the strengths and weaknesses of each language. This matters because selecting the appropriate programming language can significantly impact the efficiency and success of machine learning projects.
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Automating ML Explainer Videos with AI
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A software engineer successfully automated the creation of machine learning explainer videos, focusing on LLM inference optimizations, using Claude Code and Opus 4.5. Despite having no prior video creation experience, the engineer developed a system that automatically generates video content, including the script, narration, audio effects, and background music, in just three days. The engineer did the voiceover manually due to the text-to-speech output being too robotic, but the rest of the process was automated. This achievement demonstrates the potential of AI to significantly accelerate and simplify complex content creation tasks.
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NextToken: Streamlining AI Engineering Workflows
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NextToken is an AI agent designed to alleviate the tedious aspects of AI and machine learning workflows, allowing engineers to focus more on model building rather than setup and debugging. It assists in environment setup, code debugging, data cleaning, and model training, providing explanations and real-time visualizations to enhance understanding and efficiency. By automating these grunt tasks, NextToken aims to make AI and ML more accessible, reducing the steep learning curve that often deters newcomers from completing projects. This matters because it democratizes AI/ML development, enabling more people to engage with and contribute to these fields.
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NextToken: Simplifying AI and ML Projects
Read Full Article: NextToken: Simplifying AI and ML Projects
NextToken is an AI agent designed to simplify the process of working on AI, ML, and data projects by handling tedious tasks such as environment setup, code debugging, and data cleaning. It assists users by configuring workspaces, fixing logic issues in code, explaining the math behind libraries, and automating data cleaning and model training processes. By reducing the time spent on these tasks, NextToken allows engineers to focus more on building models and less on troubleshooting, making AI and ML projects more accessible to beginners. This matters because it lowers the barrier to entry for those new to AI and ML, encouraging more people to engage with and complete their projects.
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Simple ML Digit Classifier in Vanilla Python
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A simple digit classifier has been developed as a toy project using vanilla Python, without relying on libraries like PyTorch. This project aims to provide a basic understanding of how a neural network functions. It includes a command line interface for training and predicting, allowing users to specify the number of training loops, or epochs, to observe the model's predictions over time. This matters because it offers an accessible way to learn the fundamentals of neural networks and machine learning through hands-on experience with basic Python coding.
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Enhance Prompts Without Libraries
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Enhancing prompts for ChatGPT can be achieved without relying on prompt libraries by using a method called Prompt Chain. This technique involves recursively building context by analyzing a prompt idea, rewriting it for clarity and effectiveness, identifying potential improvements, refining it, and then presenting the final optimized version. By using the Agentic Workers extension, this process can be automated, allowing for a streamlined approach to creating effective prompts. This matters because it empowers users to generate high-quality prompts efficiently, improving interactions with AI models like ChatGPT.
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MCP Chat Studio v2: New Features for MCP Servers
Read Full Article: MCP Chat Studio v2: New Features for MCP Servers
MCP Chat Studio v2 has been launched as a comprehensive tool for managing MCP servers, akin to Postman. The new version introduces a Workspace mode with an infinite canvas and features like draggable panels and a command palette, enhancing user interaction and organization. It also includes an Inspector for running tools and viewing protocol timelines, a visual Workflow builder with AI integration, and a Contracts feature for schema validation. Additionally, users can generate and connect mock servers, export workflows to Python and Node scripts, and utilize analytics for performance monitoring. This matters because it streamlines the development and testing of MCP servers, improving efficiency and collaboration for developers.
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VidaiMock: Local Mock Server for LLM APIs
Read Full Article: VidaiMock: Local Mock Server for LLM APIs
VidaiMock is a newly open-sourced local-first mock server designed to emulate the precise wire-format and latency of major LLM API providers, allowing developers to test streaming UIs and SDK resilience without incurring API costs. Unlike traditional mock servers that return static JSON, VidaiMock provides physics-accurate streaming by simulating the exact network protocols and per-token timing of providers like OpenAI and Anthropic. With features like chaos engineering for testing retry logic and dynamic response generation through Tera templates, VidaiMock offers a versatile and high-performance solution for developers needing realistic mock infrastructure. Built in Rust, it is easy to deploy with no external dependencies, making it accessible for developers to catch streaming bugs before they reach production. Why this matters: VidaiMock provides a cost-effective and realistic testing environment for developers working with LLM APIs, helping to ensure robust and reliable application performance in production.
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10 Tech Cleanup Tasks for New Year’s Day
Read Full Article: 10 Tech Cleanup Tasks for New Year’s Day
Starting the New Year by tackling tech cleanup tasks can significantly enhance your digital well-being. Simple chores like organizing files, updating passwords, and clearing out unused apps can streamline your digital environment and improve device performance. Regular maintenance such as backing up data and updating software ensures security and efficiency. Taking these steps not only refreshes your digital life but also sets a positive tone for the year ahead. This matters because maintaining an organized and secure digital space can reduce stress and increase productivity.