Developer Builds Custom JavaScript Promise Class to Demystify Async Internals

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A developer published a tutorial on DEV Community explaining how to implement a custom Promise class in JavaScript from scratch. The project, called MyPromise, is modeled on the core principles of the Promises/A+ specification and replicates key native Promise behaviors. The implementation covers Promise states, resolve and reject functions, callback queuing, chaining, error propagation, and asynchronous execution of .then() handlers. A companion test suite was built to compare the custom implementation's behavior directly against JavaScript's native Promise object. The goal is to help developers move beyond treating Promise as a black box and gain a deeper understanding of how asynchronous JavaScript works internally.

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SQLite FTS5 Outperforms Whoosh, But Pure-Python Search Still Has a Place

An AI agent maintaining the whoosh3 Python search library published benchmarks showing SQLite FTS5 indexes and searches roughly 76–78 times faster than Whoosh on a 5,000-document test corpus. The author openly concedes FTS5's speed advantage, attributing it to being a compiled C extension versus pure Python. However, Whoosh remains relevant in environments where FTS5 is unavailable, such as locked-down enterprise systems or minimal containers that ship SQLite without the FTS5 option enabled. Whoosh also offers a richer query model with built-in support for fuzzy matching, wildcards, field-scoped terms, spelling correction, and result highlighting — features that would require custom implementation on top of FTS5. The article concludes that complex search UIs and constrained deployment environments are the clearest practical reasons to choose Whoosh over FTS5.

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Developer Replaces Gut-Feel Model Testing with Automated 30-Minute Scoring Loop

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Developer launches MCP server for precise financial math in AI agents

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Developer Builds Custom LLM Eval System to Cut Through Model Launch Hype

A software developer has published a personal method for rigorously vetting new large language models before integrating them into their workflow. The approach was motivated by a costly experience adopting a trending model on launch day, only to find it confidently generated hallucinated CLI flags. Rather than relying on public benchmarks or viral demos, the developer built a small deck of adversarial, repo-specific tasks paired with machine-checkable contracts that define what a passing response looks like. A lightweight, dependency-free Node.js runner executes each task against any OpenAI-compatible endpoint, measuring criteria such as required terms, forbidden terms, word count, and success rate across multiple samples. The system is designed to test whether a model can handle unglamorous, real-world coding tasks — such as YAML configs and database migrations — rather than polished benchmark prompts.

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