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Fi's latest dog tracker uses Starlink satellites for rural areas.

A new analysis from OpenAI reveals issues in SWE-Bench Pro, a popular coding benchmark, raising concerns about reliability and accuracy in evaluating AI models.

Fi Ultra is the first Starlink-enabled pet tracker you can buy. It expands on GPS and LTE trackers, adding automatic failover to T-Mobile's T-Satellite-branded direct-to-cell service when venturing into cellular dead zones. That lets owners tap into SpaceX's constellation of low Earth orbit Starlink satellites to track their pets anywhere in the US. But it […]

With funding from tennis star and angel investor Venus Williams, the French app WeWard says that it increases walking time by almost 25%.

The therapy unit at the hospital I work for had six treatment rooms. Room 1, Room 2, Room 3, and so on, each split by the kind of therapy it handled. And each room kept its own document to record patients. The problem wasn't that the documents existed. The problem was that no two of them looked alike. Same patient. Same information. But every room ordered the columns differently and named things differently. One put the date in the first column. Another put it last. One wrote "treatment time." The room next door wrote "minutes used." On their own, each form worked fine. Looked at one at a time, there was nothing wrong. The trouble showed up the moment anyone tried to combine them. The work that never ended Every so often, a request would come down from above: Can we see the overall numbers? That was when the real work began. I would open all six documents side by side. I would line up columns that didn't match, by eye, and move each value into one master table by hand. Days of this would get me a single sheet of statistics. Then the next quarter, the same request came down again. And I started over. The table I'd built last time was useless if the format had shifted even slightly. So I rebuilt it from scratch. Every time. I couldn't stand it. This was obviously a job you do right once and never touch again. We just weren't doing it right. So instead, we kept feeding people's evenings into it. The obvious answer The fix was simple. Make all six rooms use one form. Same columns. Same names. Same order, everywhere. Then there's nothing to move when you combine them. The statistics become a matter of stacking, not translating. The answer was so obvious I wondered why nobody had done it years ago. So I built a unified form in Excel and sent it around. And that's where I learned Excel has walls of its own. Where Excel broke down Once a file gets passed around, you lose track of which copy is the real one. The versions pile up. "Final." "Actually final." "Final, revised."

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Day 9 of the 30 Days of AWS Terraform series focuses on Terraform Lifecycle Rules — powerful controls that decide how Terraform creates, updates, replaces, and destroys resources. What Terraform LifeCycle meta arguments are Lifecycle meta arguments allow us to control how Terraform behaves when it creates, updates, or destroys resources. They help us: Avoid downtime Protect important resources Handle changes made outside Terraform Validate configurations before and after deployment Enforcing compliance Controlling replacement behavior Lifecycle rules allow us to override default behavior safely. Lifecycle rules are Terraform-native controls applied inside a resource block: lifecycle { ... } Lifecycle Rules Covered 1️⃣ create_before_destroy — Zero Downtime Updates Problem: Terraform destroys the old resource before creating the new one → downtime. Solution: lifecycle { create_before_destroy = true } Behavior: New resource is created first Old resource is destroyed only after Ensures zero downtime 2️⃣ prevent_destroy — Protect Critical Resources This setting prevents Terraform from deleting a resource. Example If Terraform tries to destroy this resource, it will fail with an error. This is useful for: Production databases State storage buckets Important data resources 3️⃣ ignore_changes — Allow External Modifications Problem: Terraform overwrites manual or automated external changes. Solution: lifecycle { ignore_changes = [desired_capacity] } Demo: Auto Scaling Group desired capacity modified manually in AWS Console terraform apply did not revert the change Behavior: Terraform ignores changes for specified attributes. ✅ Use for: Auto Scaling Groups Resources modified by external systems Ops-driven configurations 4️⃣ replace_triggered_by — Replace When Dependency Changes Problem: Changing a dependency doesn’t always recreate dependent resources. Solution: lifecycle { replace_triggered_by = [aws_security_group.main] } Behavior: When security group changes EC2 instance i

In the world of project management and team collaboration, the holy grail is reducing friction. Previously, we looked at how to make Trello cards talk back The Power of Gemini inside Trello and how to bring Gemini into your workspace Gemini in your Slack workspace . But what if you wanted a highly intelligent, stateful team assistant living directly in Slack that could answer complex, open-ended questions about your Trello boards? Questions like: "Which cards did I edit last week?" "Show me all comments made across my active boards in the last 7 days." "What is the current status of the card XYZ?" Answering these questions requires more than simple semantic search; it requires a tool that can dynamically write retrieval scripts, parse complex multi-board JSON payloads, filter dates, and compile elegant reports. In this article, we'll explore how to build exactly that using Google’s Antigravity Managed Agent (the "Agy" agent) , integrated into Slack's native Agent View , utilizing a secure, stateful, and sandboxed remote execution environment. 🚀 Prerequisites Before starting, make sure you have: A Google AI API Key with Gemini / Antigravity access A Trello Account (with an API Key and Token for read-only board queries) A Slack Workspace (with App Admin privileges to create a Socket Mode app) Node.js (v24+) and pnpm (v11+) installed 🔒 Security & Environment Controls: The Agy Sandbox Philosophy At the heart of this setup is Google's Antigravity (Agy) Managed Agent . Instead of running in a transient stateless environment, the Agy agent operates inside a persistent, secure, and remote Linux sandbox equipped with standard execution engines (Python, Node.js, bash, etc.). When a Slack user asks a question, the Agy agent dynamically writes a script, runs it in its isolated sandbox, inspects the Trello API output, self-corrects if any errors occur, and presents a formatted response. In this article, we focus on Trello, but the same principles can be applied to any other syst

Catch API drift before your customers do Discussion | Link

I initially wanted to **make a side-scrolling game like MapleStory**. YouTube was flooded with "I made a game with AI (Claude)," so I thought it would be easy. But when I tried it myself – it turned out that **people with existing game development knowledge were just using AI to improve quality and speed**, not that you could just "make it for me" without any knowledge. In the end, what I completed wasn't a playable game, but an **"auto-battle" spectator game** (like raising a mushroom) that you just watch. This post is about that **honest journey** – where I got stuck, why I pivoted, and what I learned. (And you can try out the completed version via the **🎮 Play Now** link below.) I'm a developer in Korea building an AI chatbot alone. I only write about things I've **actually tried and experienced**.## 1. The First Wall — AI-Generated Characters Can't 'Walk' **Moving characters** are essential for games like MapleStory. So, I first tried **AI image generation (gpt-image) to create chibi characters** and then generated walk cycles (4 frames of walking animation) for them. This is where I got stuck. **With each frame, the character subtly became a different character** – the color of the clothes, the proportions, the face all changed slightly between frames 1, 2, 3, and 4. When stitched together in a game, the character wouldn't walk; it would just **tremble erratically.** The Ceiling of Character Animation — AI Generation vs. Pre-made Sprites ❌ AI-Generated Characters (Re-imagined each frame) 🧍1 🧎2 🕴️3 🧍4 → Clothing/proportions wobble each frame = 'Trembling' instead of walking ✅ Pre-made CC0 Sprites (Hand-drawn sheet) 🏃1 🏃2 🏃3 🏃4 → Consistent frames = Smooth walk cycle This is **exactly the same ceiling** I hit in Making AI Videos (Dev Log #3) – AI image generation **cannot create consistent character animation (multi-frame movement).** The same wall in videos, the same wall in games. 2. Pivot ① — Abandoning AI Characters for Pre-made Sprites So, my first surrender