Taiwan's drone spending plans for defense could also boost business overseas.

Startup Baseten is reportedly close to finalizing a $1.5 billion round at a $13 billion as the “inference gold rush" marches on.

Every connected device on your desk, from a smart plug to a fitness band to a hobbyist ESP32 board, runs on a descendant of one tiny chip that was never meant to change the world. In 1971, Intel released the 4004, the first commercially available microprocessor. It was not built for computers, robots, or the internet. It was built to run a desk calculator. The story of how a calculator chip became the foundation of modern IoT is one of the most instructive in all of electronics. A calculator contract that got out of hand The 4004 began as a job for hire. A Japanese calculator company called Busicom approached Intel in 1969 wanting a set of custom chips for a new line of printing calculators. The original plan called for around a dozen separate, purpose-built integrated circuits, each wired to do one fixed task. It was the standard approach of the era: if you wanted a device to do something, you designed silicon that did exactly that and nothing else. Intel engineer Ted Hoff looked at the sprawling design and proposed something radical. Instead of a pile of single-purpose chips, why not build one general-purpose processor that could be told what to do through software? A program stored in memory could make the same chip behave like a calculator today and something else entirely tomorrow. Stanley Mazor helped shape the architecture, and a newly arrived engineer named Federico Faggin turned the concept into a working device, inventing the silicon-gate design techniques that made it physically possible. Masatoshi Shima, Busicom's representative, worked alongside them on the logic. 2,300 transistors that started everything When the 4004 was announced on November 15, 1971, it packed about 2,300 transistors onto a single sliver of silicon. By modern standards that is almost nothing; a current smartphone chip holds tens of billions. But the leap was not about raw count. It was about the idea. For the first time, a complete central processing unit existed on one chip that an

AI Observability for Lovable Apps: Monitor Prompts, Traces, and Evaluations with Currai Building AI applications has never been easier. Tools like Lovable allow developers and founders to create AI-powered products in minutes. Whether you're building a chatbot, AI assistant, recommendation engine, AI agent, or prediction app, generating the application is often the easy part. The real challenge starts after launch. How do you know what prompts are being sent to the model? How do you debug unexpected AI responses? How do you compare prompt variations and determine which performs better? How do you evaluate output quality over time? How do you track token usage and costs? This is exactly why we built Currai . What is Currai? Currai is an AI observability platform that helps teams understand, test, and improve AI applications in production. It provides: Prompt tracing AI request monitoring Session tracking Prompt versioning A/B testing LLM evaluations Cost and token analytics OpenTelemetry support Instead of guessing why your AI application produced a particular response, Currai lets you inspect the entire execution flow. The Problem With AI Applications Traditional monitoring tools were built for APIs, databases, and backend services. AI applications introduce a completely different set of challenges: Prompt changes can significantly impact output quality Model updates can affect behavior Hallucinations are difficult to track User conversations are hard to debug Prompt experiments are often unmanaged Quality evaluation is usually manual When something goes wrong, application logs alone don't provide enough visibility. You need observability designed specifically for AI systems. Trace Every AI Request Currai captures every prompt, model response, latency metric, token usage, and cost. You can inspect: System prompts User prompts Model outputs Execution traces Tool calls Metadata This makes debugging AI applications dramatically easier. Run Prompt A/B Tests Prompt engin

Most AI coding tools assume you're sitting in front of a repo. There's a working directory, some source files, tests, maybe a CI pipeline. The AI reads your code, suggests changes, ru****ns tests. Great model — if you're a developer writing code on a Tuesday afternoon. Now picture the other scenario. It's 2am. PagerDuty fires. You SSH into a box you haven't touched in three months. Something is broken, you're not sure what, and the runbook was last updated by someone who left the company in 2022. You're not thinking about repos. You're thinking: what OS is this thing running? What just failed? Is it safe to restart that service or will I make it worse? These are two fundamentally different workflows. But almost every AI terminal tool I've seen is built for the first one. The 30 minutes nobody builds for There's a ton of tooling for the world before you log into the box: Prometheus, Grafana, PagerDuty, incident.io, runbooks, dashboards. All useful. No complaints. But there's this practical 30-minute window after you SSH in where you're basically doing archaeology with journalctl and grep. You check systemd. You look at disk. You read logs that were clearly written by someone who hated future-you. You copy-paste terminal output into Slack so your teammate can squint at it from a different timezone. This is where I think AI could actually help. Not by replacing Grafana. Not by building another dashboard. Just by being present in the shell while you're debugging. Please, for the love of uptime, don't replace my shell I've seen a few AI terminal projects that basically build an entire new terminal experience from scratch. New keybindings, new UI, new everything. Here's the thing: ops people have muscle memory. We have aliases we wrote in 2019 and forgot about. We have tmux configs we'd defend with our lives. We SSH through jump hosts with key forwarding chains that barely work but have worked for years so nobody touches them. If your AI tool requires me to abandon all of

HiveTalk.space is a privacy focused chat app. HiveTalk.space should not be confused with hivetalk.org. While both platforms focus on communication, they are separate projects with different goals and feature sets. HiveTalk is closed source and cloud hosted, making it easy to start chatting without setting up your own server. Despite not being self-hosted, privacy remains a core focus. Private conversations are designed with privacy in mind, allowing users to communicate without unnecessary tracking or intrusive data collection. Every account includes generous free limits. Users can upload files and videos up to 1 GB each, send unlimited messages , and sign in using supported social login providers or a traditional account. Creating communities is simple, with the ability to make your own chat rooms for friends, gaming groups, project teams, schools, or fanbases in just a few clicks. HiveTalk also aims to provide a modern messaging experience with features such as polls, rich text formatting, media sharing, and room management tools, while keeping the interface simple and easy to use. Whether you want a private conversation, a small group chat, or a larger community, HiveTalk is designed to scale without placing artificial limits on everyday usage. Unlike many messaging platforms that reserve key features for paid subscriptions, HiveTalk offers its core functionality for free. The goal is to make private, feature-rich communication accessible without requiring users to pay just to unlock basic messaging features. As the platform continues to develop, new features and improvements are regularly added, with a focus on privacy, usability, and giving communities more control over how they communicate.

Anthropic still can’t distribute Claude Mythos or Fable 5 after running afoul of the Trump administration. But no one can say exactly what the company did wrong.

Draft, restructure & tighten wording from panel inside Word Discussion | Link

"We are reassigning most affected employees across existing opportunities and programs."

With more traffic moving off-web and into LLMs, I got curious about what traces we leave "in the weights". My design partner and I built a site in the past few weeks that checks recognition across frontier and small models. It queries many of them in parallel, clusters the responses, and tells you how strongly they recognize you. Happy to answer any questions here!

At its core, software development has always been about a simple cycle: Write > Review >...

On to the next one.

The Snapchat maker is spinning off yet another internal unit. Dotmo will be comprised of current Snap staff who are leaving the social media company to focus on AI video development.