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South Korea Medical Coding Market register 7.18% CAGR, size, share, top companies, future trends, Growth analysis, technology and regional landscapes, forecast to 2035.

Reflection AI’s Beam open model targets coding and agentic workloads with fewer inference tokens, but independent benchmarks question whether its efficiency translates into lower real-world costs.

VRAM is the one spec that decides which local LLMs you can run. Anchored builds, a model-size × quantization VRAM matrix, and the best 16GB card setup for coding.

North America Computer Assisted Coding (cac) Software Market Size and Overview North America Computer Assisted Coding (cac) Software Market size was valued at USD 1.8 Billion in 2025 and is poised to ...

Recursive’s Flint is a 40-billion-parameter coding model designed for fast local laptop inference. Its AI-assisted development claims, hardware demands and mixed early results test whether smaller models can challenge cloud coding agents.

DeepReinforce AI's Ornith-1.0-9B and Ornith-1.0-35B models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models are part of the Ornith 1.0 family of open-source, self-scaffolding agentic coding models released under the MIT license, achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo, and OpenClaw. These models address different agentic coding challenges with specialized capabilities: Ornith-1.0-9B is a lightweight 9B-parameter dense model post-trained on Qwen 3.5 9B, designed for efficient single-GPU deployment. With a 256K-token context window and 32 decoder layers, it is well-suited for individual developers and small teams running agentic coding workflows on a single GPU — including laptops with consumer-grade hardware. Its self-improving training framework uses reinforcement learning to jointly optimize scaffold generation and solution rollouts, enabling the model to discover better search trajectories and generate higher-quality code solutions. Ornith-1.0-35B is a 35B-parameter Mixture-of-Experts (MoE) model post-trained on Qwen 3.5, delivering stronger agentic coding performance while remaining deployable on a single 24GB consumer GPU. Like its smaller sibling, it employs reinforcement learning to jointly optimize the scaffolds that drive coding tasks and the resulting solution rollouts, discovering superior code trajectories. Its MoE architecture activates only a subset of parameters per token, providing a strong balance of quality and efficiency for production agentic coding workloads. With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

Read-only disk analyser for Coding Challenge #145. Contribute to muneer-ahmed-khan/disk-analyser development by creating an account on GitHub.

DeepReinforce AI's Ornith-1.0-9B and Ornith-1.0-35B models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models are part of the Ornith 1.0 family of open-source, self-scaffolding agentic coding models released under the MIT license, achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo, and OpenClaw. These models address different agentic coding challenges with specialized capabilities: Ornith-1.0-9B is a lightweight 9B-parameter dense model post-trained on Qwen 3.5 9B, designed for efficient single-GPU deployment. With a 256K-token context window and 32 decoder layers, it is well-suited for individual developers and small teams running agentic coding workflows on a single GPU — including laptops with consumer-grade hardware. Its self-improving training framework uses reinforcement learning to jointly optimize scaffold generation and solution rollouts, enabling the model to discover better search trajectories and generate higher-quality code solutions. Ornith-1.0-35B is a 35B-parameter Mixture-of-Experts (MoE) model post-trained on Qwen 3.5, delivering stronger agentic coding performance while remaining deployable on a single 24GB consumer GPU. Like its smaller sibling, it employs reinforcement learning to jointly optimize the scaffolds that drive coding tasks and the resulting solution rollouts, discovering superior code trajectories. Its MoE architecture activates only a subset of parameters per token, providing a strong balance of quality and efficiency for production agentic coding workloads. With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

Google has released Gemini 4 Hydrogen, a fast model for coding and agentic work. Gemini 4 Hydrogen is a proprietary model from Google DeepMind built for everyday agent loops. It sits below the frontier models Gemini 4 Carbon and Gemini 4 Argon. Google calls it its “daily driver for agentic workflows.” TL;DR Size: 1,048,576-token unified window for input, thinking and output. Runs on: Google-hosted API only. No weights, no quantized variants, no self-hosting. Serving hardware not disclosed. Performance: Scores 51 on the Artificial Analysis Intelligence Index at high, above Argon’s 50. Best: 57% on Terminal-Bench 4.1, versus 18.2% for Gemini 3.8 Flash. Worst: Ranks only #5 on Agent Arena. Costs 2x Gemini 3.8 Flash’s current introductory price. Bottom line: Best: frontier-class agent scores at $1.50/$7.50 per 1M tokens. Worst: closed, and synchronous output caps at 65,536 tokens. What is Gemini 4 Hydrogen? Hydrogen is a closed-weights model served through the Gemini API and Google products. The model card says it is adapted from Gemini 4 Carbon. Google does not disclose parameter count, architecture or training hardware. The knowledge cutoff is March 2026. Input covers text, images, audio, video, PDFs, tables and charts, per the What’s New guide. Output is text...