15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T4c8,TL;DR: A lightweight TS library to parse natural-language dates and inject temporal context into LLM prompts. It turns vague phrases like "tomorrow" into precise, timezone-aware dates to reduce ambiguity in agents, schedulers, and chatbots.

Why you might care: - Fewer ambiguous instructions ("next Tuesday" -> 2025-09-23) - Works across timezones/locales - Choose formatting strategy: preserve, normalize, or hybrid

Quick example:

``` enhancePrompt("Schedule a demo next Tuesday and remind me tomorrow") → "Schedule a demo next Tuesday (2025-09-23) and remind me tomorrow (2025-09-16)" ```

Links: - Project: https://time-ai.blueprintlab.io - GitHub: https://github.com/BlueprintLabIO/time-ai - NPM: https://www.npmjs.com/package/@blueprintlabio/time-ai

Would love feedback on real-world prompts, tricky date phrases, and missing patterns.17:T573,Recent ["self-propagating NPM malware"](https://news.ycombinator.com/item?id=45260741) reminds us that the predominant security model is basically whack-a-mole: you gotta trust _every_ piece of software you run (including all the libraries, plugins, etc), unless you explicitly sandbox it.

Capability-based security might offer an alternative: software should not have access to things when it's not explicitly provided with access. I.e. "classic" desktop security is kind of a blacklist model (everything is possible unless explicitly restricted e.g. via sandbox) while capbility-based sec

Apertus: An open, transparent, multilingual language model