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Thinking Machines Lab just released Inkling, their first model trained from scratch, weights are open, fine-tunable on Tinker. The lab pitches it as a base for customization. Inkling is a Mixture-of-Experts transformer with 975B total parameters and 41B active. It supports a context window of up to 1M tokens. Pretraining covered 45 trillion tokens of text, images, audio, and video. Inputs accept text, images, and audio; output is UTF-8 text only.

A German research consortium has published the pretraining report for Soofi S 30B-A3B. It is an open base model for German and English. Training ran end to end on Deutsche Telekom’s Industrial AI Cloud in Munich. Preview weights are on Hugging Face. It is worth noting that among some of the fully open base models tested, Soofi S records the highest English and German aggregate scores. Soofi S is a Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model.

In this tutorial, we implement a Gin Config–controlled PyTorch experiment pipeline in which the executable training code remains stable. At the same time, the experimental degrees of freedom are moved into declarative configuration files. We construct a nonlinear spiral binary classification task, define a configurable MLP with scoped architectural variants, and expose parameters for the optimizer, scheduler, loss, batching, seeding, and training loop via @gin.configurable bindings.

Google released LiteRT.js, a JavaScript binding of LiteRT. LiteRT is Google’s on-device inference library, previously called TensorFlow Lite. LiteRT.js runs .tflite models directly inside the browser. Because inference stays local, Google cites enhanced user privacy, zero server costs, and ultra-low latency. It is not a new model format. Rather, Google compiled its existing native runtime to WebAssembly and exposed it to JavaScript.

PrismML just released Bonsai 27B. It is a low-bit representation of Qwen3.6-27B, not a new pretrain. The architecture is unchanged. Two variants ship under Apache 2.0. Ternary Bonsai 27B uses {−1, 0, +1} weights at a true 1.71 bits per weight. Its ideal size is 5.9GB. 1-bit Bonsai 27B uses binary {−1, +1} weights at 1.125 bits per weight, for 3.9GB. Both are multimodal. The split is ~24.8B language weights, a 0.46B vision tower, and 2.5B in embeddings and the LM head.

Coding agents are the most contested category in developer tooling right now. Four names dominate the shortlist: Mistral Vibe for Code, Claude Code, Cursor, and OpenAI Codex. Each claims to take a feature from prompt to pull request. This comparison runs all four against one practical workflow. Not a toy script. A real unit of engineering work: scaffold a feature across multiple files, generate and run tests, then open a pull request.

Custom domains are a standard SaaS feature. Yet every hosting platform exposes a different API for them. OpenCoreDev has published Domain SDK, a TypeScript client that normalizes that work. Version 0.2.0 reached npm a day after the first release. Domain SDK covers Vercel, Cloudflare for SaaS, Railway, Render, and Netlify. You add a hostname, show the exact DNS records, then track provider state until it is ready. It does not register domains, host DNS, deploy apps, proxy traffic, or store tenant data.

Hayden Bleasel, an expert developer from OpenAI, released Blume, an open-source documentation framework. Blume shipped to npm as version 1.0.3 the same day. It is as simple as Drop Markdown into a folder and ship a docs site. No app boilerplate is written or maintained afterward. The project is MIT-licensed and open sourced. Blume is a command-line tool paired with a component library for docs. It reads a folder of Markdown or MDX files.

Mistral AI has released Robostral Navigate, its first model built for embodied navigation. The 8B model takes RGB images and a plain-language instruction, then moves a robot. Notably, it reaches 76.6% success on R2R-CE validation unseen using only a single RGB camera. Robostral Navigate is an 8B model for robotic navigation through complex environments. These environments include offices, residential buildings, commercial buildings, and outdoor settings.

Most reinforcement learning benchmarks reset the world after every episode. Real operations never reset. Skyfall AI’s MORPHEUS targets that gap. It is a persistent enterprise simulation platform for continual reinforcement learning (CRL). MORPHEUS is grounded in the Big World Hypothesis (Javed & Sutton, 2024). It says the world’s complexity exceeds any agent’s representational capacity. As a result, the environment looks non-stationary even under fixed dynamics.