Monday, 29th June 2026
Ornith-1.0: Self-Scaffolding LLMs for Agentic Coding. This is an interesting new open weights (MIT licensed) model, the first model release from DeepReinforce.
[...] with variants including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. Built on top of pretrained Gemma 4 and Qwen 3.5, it achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks.
As far as I can tell the licenses of those underlying models is compatible with being used in this way - Gemma 4 is Apache 2.0 licensed (and not bound by the janky additional Gemma Terms of Use that afflicted the previous Gemma models) and Qwen 3.5 is Apache 2.0 licensed as well.
I've been running the model using LM Studio and the ornith-1.0-35b-Q4_K_M.gguf (20GB) GGUF, hooked up to Pi. Initial impressions are very good - it seems to be able to run the agent harness over many tool calls in a proficient way.
Here's a terminal session where I asked it to "find the code that decodes the actor cookie" and then "find the code that opens the insert dialog when thebutton is clicked" against a Datasette checkout, which it handled with ease.
I also had it draw this pelican, which came out at 103 tokens/second:
It's a little bit mangled but the pelican is clearly a pelican.
I couldn't find much information about DeepReinforce themselves. The earliest paper I could find from the was CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning from June 2025.
Tiniest TIL, using AppleScript to count the number of open browser tabs in Safari:
osascript -e 'tell application "Safari" to count tabs of every window'
Yet another in my growing collection of paste-conversion tools. This one accepts pasted rich text from browsers (with embedded HTML tables) and converts every detected table into HTML, Markdown, CSV, TSV, or JSON.
Try it out by selecting everything on the Wikipedia List of cities and towns in the San Francisco Bay Area page and pasting it directly into the tool:
On a similar note, I recently rebuilt my Rich text to markdown tool to add support for tables and generally improve the UI.
Update: It turns out Wikipedia has an open CORS API for retrieving the full rendered HTML content of any page - demo here - so I had Codex add the ability to search Wikipedia for a page and then automatically import and display any tables from that page.