# Learning Palantir Foundry 🚀 Bring complex logic that no-code can't reach into your data platform, along with full software-engineering quality control. That's what Code Repositories delivers. 📌 Title and Feature URL Title: Code Repositories (Python Transforms) URL: 📝 Overview Code Repositories is a web-based integrated development environment (IDE) for creating and collaborating on production-ready code within Foundry. It provides a friendly UI over the underlying Git repositories, so teams can work without command-line access. With platform-specific features, you can apply software development practices directly to data engineering. 🔧 How It Works Version control and collaboration are at its core. - Common Git tasks (branching, committing, release tagging) execute through the web UI - Pull requests drive code review, with "highly configurable" permissions that support quality assurance such as mandatory reviews - IntelliSense, linting, error checking, and contextual help dialogs are available across all repository types - Transforms repositories let you author data transformation logic in Python, Java, or SQL with preview and debugging - Functions repositories natively integrate the Ontology and run low-latency business logic in TypeScript or Python 🛠 Practical Usage - Use PySpark to implement billion-row entity resolution and complex business rules in code - Require PR reviews so a second reviewer and CI checks must pass before merge - Add unit tests to guard transform logic against regressions - In Functions repositories, leverage Ontology-data-type autocomplete to write logic safely - Bring machine learning workflows into the platform via model development repositories 🎯 Use Cases - Implementing complex reconciliation and business rules in PySpark that Pipeline Builder can't express - Structurally eliminating "regressions from editing production directly" through mandatory reviews and branch-based workflows - Implementing derived KPIs and validation logic as Functions reused across apps - Managing ML model training and inference code under governance ⚠️ Caveats - The docs note that Japanese translations are machine-generated and unverified, so localized content may have accuracy limitations - Each repository type (Transforms/Functions/Model) supports different languages and purposes, so pick the one that fits your goal - Being a pro-code environment, the quality benefits only materialize if your organization establishes review, CI, and test practices #PalantirFoundry# #DataEngineering#

Intelligence officials are warning that the New IRA is plotting a campaign of violence aimed at driving migrants out of Ireland, including threatening to give some just 24 hours to leave the country.

Intelligence agencies: 🇺🇸 United States → CIA 🇮🇱 Israel → Mossad 🇷🇺 Russia → SVR 🇬🇧 United Kingdom → MI6 🇮🇳 India → RAW 🇦🇺 Australia → ASIS 🇵🇰 Pakistan → ISI 🇩🇪 Germany → BND 🇫🇷 France → DGSE 🇨🇦 Canada → CSIS 🇨🇳 China → MSS 🇿🇦 South Africa → SSA 🇮🇹 Italy → AISE 🇸🇪 Sweden → SÄPO 🇳🇴 Norway → NIS 🇰🇷 South Korea → NIS 🇧🇷 Brazil → ABIN 🇳🇬 Nigeria → NIA 🇪🇸 Spain → CNI 🇺🇦 Ukraine → FISU 🇵🇱 Poland → AW 🇳🇱 Netherlands → AIVD 🇮🇩 Indonesia → BIN 🇦🇷 Argentina → AFI 🇹🇷 Turkey → MIT 🇲🇽 Mexico → CNI

Intelligence cofounder @grx_xce wants to build an intelligence marketplace for AI capabilities, not just a leaderboard that tells you which model is best. Today, Design Arena users submit creative tasks and have multiple AI models attempt it. The company then collects users' preference about which result was best, which gives Intelligence data about which models perform well for particular kinds of work. Grace thinks that in the future, it will become way easier for companies and individuals to customize models for one narrow purpose and sell access to it on a marketplace. She explains: