标签：#gde

If you've ever wanted your AI coding agent to do more than just write code — to actually plan , validate , and cost-estimate real cloud infrastructure — the new Terraform Ops Kit for Docker Sandbox (sbx) is here to make that happen. This community-contributed kit, submitted to the docker/sbx-kits-contrib repository, brings a production-ready Infrastructure-as-Code (IaC) toolkit straight into the sandbox environment where agents like Claude, Gemini, GitHub Copilot, and Shell already run. What Is a Sandbox Kit? Docker Sandbox ( sbx ) is a runtime environment where AI agents operate. Kits are modular add-ons that extend the capabilities of those agents — think of them as pre-configured toolboxes that get installed automatically when a sandbox is created. The Terraform Ops Kit is a mixin kit , meaning it can be layered on top of any existing agent setup without replacing or conflicting with other kits. What's Inside the Kit? When the Terraform Ops Kit is activated, six tools are pre-installed and ready to use inside the sandbox: Tool Purpose Terraform Core IaC engine — plan, apply, and destroy infrastructure Terragrunt Terraform wrapper for DRY configurations and multi-account workflows tflint Linter for catching Terraform misconfigurations before they're applied Checkov Static analysis security scanner for IaC files Infracost Cost estimation — know the price tag before you deploy AWS CLI Interact with AWS services directly from the sandbox Together, these tools enable AI agents to autonomously carry out the full infrastructure development lifecycle: write Terraform code, lint it, scan it for security issues, estimate its cost, and plan the deployment — all without leaving the sandbox. Why This Matters Infrastructure work has traditionally required a human-in-the-loop at every step. You'd write the config, then manually run terraform plan , then check the security scan, then get a cost estimate — context switching across multiple tools. With the Terraform Ops Kit, an AI

I recently built GeoPrizm , a free and open-source dashboard for tracking bilateral relations through global news event signals. The idea is simple: instead of reading dozens of headlines every day and trying to guess whether a relationship is improving or worsening, can we turn public news event data into a readable trend signal? GeoPrizm is my attempt at that. Website: https://www.geoprizm.com/en GitHub: https://github.com/Haullk/relationship-temperature The problem International relations are usually discussed through headlines, speeches, official statements, and expert commentary. That is valuable, but it creates a few practical problems: It is hard to compare country pairs on the same scale. A single headline can feel more important than it really is. Readers often see conclusions before they see the underlying signals. Most non-specialists do not have time to follow every event in detail. I wanted a lightweight way to answer one question: Based on public news event signals, is this bilateral relationship trending more cooperative, neutral, or tense? Data source: GDELT GeoPrizm uses the GDELT global news event database. GDELT monitors global news coverage and converts news reports into structured event records. These records include fields such as: actor countries event date CAMEO event category GoldsteinScale value number of mentions number of articles source information For GeoPrizm, the key idea is to focus on events where two countries appear as actors, then aggregate the cooperation or conflict signals over time. From event signals to an index Each bilateral pair is converted into a 0-100 relationship index. The midpoint is 50. Above 50 means the recent signal is more cooperative or favorable. Around 50 means the signal is relatively neutral or mixed. Below 50 means the recent signal is more tense or conflict-heavy. The rough process is: Select recent GDELT events for a country pair. Keep events where both actors are present and the GoldsteinScale value is