Testing
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Building LLMs: Evaluation & Deployment
Read Full Article: Building LLMs: Evaluation & Deployment![Part 4 (Finale): Building LLMs from Scratch – Evaluation & Deployment [Follow-up to Parts 1, thru 3]](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8167-300x171.png)
The final installment in the series on building language models from scratch focuses on the crucial phase of evaluation, testing, and deployment. It emphasizes the importance of validating trained models through a practical evaluation framework that includes both quick and comprehensive checks beyond just perplexity. Key tests include historical accuracy, linguistic checks, temporal consistency, and performance sanity checks. Deployment strategies involve using CI-like smoke checks on CPUs to ensure models are reliable and reproducible. This phase is essential because training a model is only half the battle; without thorough evaluation and a repeatable publishing workflow, models risk being unreliable and unusable.
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Introducing Syrin: Debugging and Testing MCP Servers
Read Full Article: Introducing Syrin: Debugging and Testing MCP Servers
Building MCP servers often presents challenges such as lack of visibility into LLM decisions, tool call issues, and the absence of deterministic testing methods. Syrin, a local-first CLI debugger and test runner, addresses these challenges by offering full MCP protocol support, multi-LLM compatibility, and safe execution features. It includes CLI commands for initialization, testing, and development, and supports YAML configuration with HTTP and stdio transport. Future developments aim to enhance deterministic unit tests, workflow testing, and runtime event assertions. This matters because it provides developers with essential tools to efficiently debug and test MCP servers, improving reliability and performance.
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5 Agentic Coding Tips & Tricks
Read Full Article: 5 Agentic Coding Tips & Tricks
Agentic coding becomes effective when it consistently delivers correct updates, passes tests, and maintains a reliable record. To achieve this, it's crucial to guide code agents with a structured workflow that emphasizes clarity, evidence, and containment. Key strategies include using a repo map to prevent broad refactors by helping agents understand the codebase's structure, enforcing a diff budget to keep changes manageable, and converting requirements into executable acceptance tests to provide clear targets. Additionally, incorporating a "rubber duck" step can reveal hidden assumptions, and requiring run recipes ensures the agent's output is reproducible and verifiable. These practices enhance the agent's precision and reliability, transforming it from a flashy tool into a dependable contributor to the development process. This matters because it enables more efficient and error-free coding, ultimately leading to higher quality software development.