The model represents a significant breakthrough in making powerful AI coding assistants accessible to individual developers without relying on expensive cloud APIs or subscriptions.

Identical pendulums, released almost together, that refuse to stay in step.

MicroDuck biped PPO policy running as an ArduPilot (ArduRover) task, MuJoCo as SITL plant, MAVProxy control.

Rust terminal UI for working with Git history from a topology-first point of view. It is built with ratatui, crossterm, and libgit2, and is designed for exploring, filtering, and operating on large repositories without leaving the terminal.

To remove a background, drop a photo here and the subject is cut out as a transparent PNG. Free, with no watermark, and the AI runs on your device, so the photo is never uploaded.

I asked this question to AI image generators for each country: Midjourney in 2023, and Grok Imagine, GPT Image and Gemini in 2026, separately for a man and a woman. Pick the model at the bottom of the map.

• AI-Generated: These images are generated by AI.
• Not Representative: They do not represent the diversity and complexity of each country.
• Just for Fun: This is a fun exploration of how AI sees the world.

Timestral, Mistral’s first family of time-series foundation models, delivers state-of-the-art probabilistic, multivariate forecasts with just 209M parameters, outperforming larger models on key benchmarks while supporting past and future covariates. Available in four sizes (5M to 209M parameters), it balances accuracy and efficiency, running efficiently on CPUs and excelling in zero-shot forecasting for diverse domains. The model combines synthetic pre-training, covariate-aware attention, and long-context learning, with fine-tuning options for domain adaptation via Mistral Forge.

How a broken pascal compiler, a 300 baud modem, and a 1970s terminal helped create the (in)famous text editor.

A lab releases a better model. The weights are public, the benchmarks look good, and developers start figuring out what they can build with it.

Then they check the memory requirements.

I think this is going to become one of the harder problems for the open-source AI community. Models can become more efficient at generating tokens while becoming harder for individuals and small teams to host. We could gain access to increasingly capable weights while losing the practical ability to run them ourselves.

The rapid integration of AI code agents tools like Claude, Codex, and various internal LLM deployments into our development workflow promises unparalleled productivity. These agents, capable of reading, analyzing, and modifying complex codebases, are revolutionizing how we build software.

However, this power comes with a critical security question: How do you prevent an autonomous, powerful AI agent from looking at your secrets, reading unrelated projects, or even accessing sensitive files outside the scope of the project you asked it to work on?

In his ground breaking 2011 book, “Thinking Fast and Slow”, Daniel Kahneman introduced the idea that your brain has two different modes of thinking, what he called System 1 and System 2. System 1 is the auto-pilot: fast, automatic, instinctive, and effortless. It runs continuously in the background and makes judgement calls: he looks angry, the road is blocked, this sounds off, etc.

System 2 is the opposite. Slow, deliberate, thoughtful, rational. It follows the rules, makes calculations, explains itself, and pays attention. Unfortunately, attention is limited and System 2 is lazy so it often follow the lead of System 1.

The current generation of LLMs, like Claude, ChatGPT, Gemini, Grok, etc fall cleanly into the System 2 category. They work through the prompt systematically to figure out what to output. They excel at producing long form text, work through complex problems, translate across multiple languages, etc. They fall neatly into the System 2 category.

Despite these strengths, many of the pipelines that leverage LLMs in production revolve around quick decision making. Which team should handle this ticket? Is this summary correct? What tool should be called? System 1 type problems.

“It’s just timestamps”: the decades-old mystery of the origins of one of the most popular cryptographic standards has been explained.

We pursued this long-standing open question of the cryptographic community to settle concerns about ECDSA, which is one of our most widely used cryptographic standards.

Readers should take this as a fun closing chapter and the end of years of speculation. There is no action to be taken or things to do differently. Most researchers trusted the curves already. Enjoy.

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