I made my first Claude Code plugin today to quickly build an agentic application I wanted to test and I realized that it’s a great platform to quickly test new ideas and agentic architectures, especially for multi-agent type of things. It does have it’s issues and limitations, e.g. you can’t really use a non anthropic model right? That’s exactly the reason I find it a great way to quickly put MVPs together to test ideas!
Keystatic is pretty cool. It turns your github repo into a CMS in a very elegant way. The experience of setting up my own GitHub app for oAuth was miserable though, but the keystatic.cloud service they offer is free and it works. Happy that I found this.
Just tried Anthropic’s Cowork and my first impression is that it indeed brings the experience of Claude Code on the desktop environment, especially if you combine it with the Claude extension for Chrome. It feels more and more that Anthropic is winning over OpenAI when it comes to building products and not only models. (Make sure you understand the security implications before you try Cowork!)
I've always loved building things from zero. The kind where you're not just writing code but you are figuring out everything: why it matters, who it's for, how to bring a team together and how to turn it into a product and a company. Two years ago I started working on something new based on a strong conviction that data, although already critical, was about to become exponentially more important.
Snowflake announced it would offer dbt Core as a native Snowflake feature. This prompted dbt Labs to modify dbt Fusion's licensing to gain more leverage over its IP. The partnership between these companies has been symbiotic. Snowflake benefits from tools like dbt because they drive workloads inside Snowflake, while dbt Labs gained customer access and revenue through Snowflake's ecosystem.
Snowflake's Cortex AISQL announcement got me excited, and I want to explain why. It represents a paradigm shift in integrating large language models into data systems as structured, composable functions rather than opaque tools. Distilling LLM capabilities into five well-defined operators that address 80% of use cases signals meaningful progress. This approach prioritizes reproducibility and composability over raw prompt flexibility.
Robust, maintainable, expressive and composable pipelines are critical for scaling synthetic data generation. This post advocates for abstractions that reduce boilerplate, avoiding ad-hoc scripts, and leveraging dataframe APIs with structured document representations. The concrete example involves fine-tuning a smaller model using synthetic QA pairs generated from SEC corporate reports by a frontier model, maintaining quality while reducing inference costs.
In this post, I explore how synthetic data is used to train and fine-tune large language models. I'll focus on Meta's open-source **synthetic-data-kit**, a tool built for exactly this purpose. LLMs owe their success to two factors: human ingenuity and the vast, annotated text of the internet.
Meta's **synthetic-data-kit** is a toolkit designed to generate high-quality synthetic datasets for fine-tuning Large Language Models. The tool streamlines the process of creating training data through an ETL-like pipeline with four key operations. The toolkit exposes a simple CLI interface with these commands:
User Experience is primarily concerned about guiding the user to a desired outcome in the most optimal way, optimizing for time and margin of error. That's why the term journey is heavily used within the context of UX design. Developer Experience on the other hand, is not about guiding the user, but designing the right abstractions and choosing what part of the system complexity to expose to the developer.