My Top Dev Links of Lately

Aside of Markdown specs, you write tiny steps files in one of the supported languages (currently TS, Java, Kotlin, Python, Ruby), which translates the specs into runnable code. See this example spec and steps. It also supports tables and multi-line strings,

A standard for discoverable REST APIs. "The OData Protocol is an application-level protocol for interacting with data via RESTful interfaces. It supports the description of data models, editing and querying of data according to those models. REST APIs that are based on OData are easy to discover and consume due to the OData Metadata. "

... (and many more)

My biggest takeaway is that Rust's advanced compiler and type system make it the absolutely perfect programming language for AI-assisted programming, providing feedback loops to semi-autonomous agents. The first results are beyond encouraging.

neuro-symbolic reasoning aims to combine LLM's ability to learn from data with established practices of more human-like symbolic reasoning.

The result was about 25,000 lines of Rust, and the entire port took about two weeks. The same work would have taken me multiple months to do by hand.

Some good reasons to not use rust (anemic standard library & very fragmented and constantly changing library landscape, language churn,...), and where it shines.

How Figma solved simultaneous editing of ordered sequences of objects. Instead of Operational Transformation, but "the high implementation cost is only worth it you need the specific benefits it offers for editing text sequences." and "OT was overkill for us because we didn’t need to work with enormous sequences and we didn’t need to avoid interleaving." Instead, they use Fractional indexing - every object has a real number as an index. To insert between two objects, just set the index for the new object to the average index of the two objects on either side.

This *may* be useful - a tool reading Claude Code session files and displaying a dashboard showing what you spent your credits on. "Surfaces insights like which prompts cost the most and usage patterns"

Aside: A fascinating story of using an autonomous AI agent to iteratively improve the system by proposing a change (to the code, prompt, examples), re-running benchmarks, and keeping or discarding the change. Key points here. (Inspired by autoresearch .)

If a plain 42 and a "user id 42" should not be interchangeable, then they should not be equal, not be confused, and not accidentally flow through the same code paths. This is what tagged values give you: not more structure, but stronger distinction to prevent unknowingly sending specific data through generic paths and its counterpoint avoiding to make specific pipelines accidentally generic.

"the secure personal AI agent" in 15 source code files, a single Node.js process running in a container. Inspired by OpenClaw but tiny. You can add new skills to expand its capabilities, e.g. so that it can talk to Telegram. Uses Claude in the background. You can talk to it via a messaging app, it can run scheduled tasks, access the web, etc.

Before, the service was written in Go completed batch jobs in ~30 minutes, and needed to run on instances with at least 4 GB of RAM. After being rewritten in Rust and configuring a high-performance memory allocator, it's now completing batch jobs in less than 5 minutes and run on a single instance with 512 MB of RAM.

You may disagree but the are certainly some good arguments here.

A great checklist for ensuring your production Rust service are small, fast and secure.

The author demonstrates how they applied these questions to their transition from a Node framework to PHP's Laravel. He closes with the insight that "Rather, we must additionally map the technology back to behavior and make a determination based on the full context." Which makes perfect sense, and is something often forgotten.

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