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Laguna M.1, a 225B parameter model with 256k context for agentic coding, is now available for free in Cline.
A career advice thread for the age of AI, arguing that valuable work involves problems that can't be graded within model training, and emphasizing the importance of time, relationships, reputation, and problem-finding skills over rote problem-solving.
Omar highlights the growing value of building LLM verifiers and judges for agentic coding, while Mira Murati shares that Bridgewater partnered with TinkerAPI to fine-tune a model for financial analysis.
Poolside releases Laguna S 2.1, a 117.6B parameter Mixture-of-Experts model with 8.5B activated parameters designed for agentic coding, featuring sliding window attention, native reasoning support, and local deployment capability.
Introduced MTP speculative decoding to the Ornith 35B coding model in FP8 precision, achieving approximately 18% faster inference with minimal extra VRAM.
ZCode is now available on macOS, Windows, and Linux, integrating AI agents for planning, coding, reviewing, and deploying, with deep GLM-5.2 integration and tiered pricing plans.
ZCode is a programming harness optimized for GLM-5.2, offering agentic coding with features like long-running tasks, bot control via messaging apps, and multiple pricing tiers.
Meituan launched LongCat-2.0, a 1.6 trillion-parameter Mixture-of-Experts model with a 1 million-token context window, available via API for agentic coding, tool use, and complex workflows.
Nathan Lambert shares his visit to Meituan and highlights their open model development approach. Meituan's LongCat team announces LongCat-2.0, a 1.6T parameter MoE model with 48B active parameters and 1M context, built for agentic coding and available on OpenRouter.
Meituan released LongCat-2.0, a 1.6T-parameter MoE model with 1M context, claimed as the first to train on a 50,000-GPU Chinese cluster, now available on OpenRouter for agentic coding.
Meituan introduces LongCat-2.0, a 1.6T parameter MoE model with ~48B active parameters and 1M context, featuring novel architectures like LongCat Sparse Attention and Zero-Compute Experts, achieving strong benchmark scores on coding and reasoning tasks.
Cognition introduces Devin Fusion, an adaptive model router that reduces cost by 35% while maintaining real frontier intelligence for agentic coding tasks.
Ornith-1.0 is a family of open-source, self-improving models for agentic coding, achieving state-of-the-art performance on coding benchmarks via reinforcement learning that jointly optimizes scaffold and solution rollouts.
DeepReinforce releases Ornith-1.0, an open-weight MIT-licensed LLM family built on Gemma 4 and Qwen 3.5, achieving state-of-the-art coding performance among comparable open-source models.
The tweet describes using LLM wikis for agentic coding, calling it extremely powerful, and gives an example of developing a coding harness by ingesting multiple repositories.
Deep Reinforce releases Ornith-1.0, a family of open-source self-improving LLMs for agentic coding, spanning 9B to 397B parameters and achieving state-of-the-art performance on benchmarks like SWE-Bench Verified and Terminal-Bench 2.1, surpassing Claude Opus 4.7 and other leading open-source models.
Ornith-1.0 is a new family of open-source agentic coding models from deepreinforce-ai, trained with reinforcement learning that jointly optimizes both the solution and the scaffolding. The 35B MoE version achieves state-of-the-art on coding benchmarks and supports efficient single-GPU deployment.
The tweet describes a test where Ornith-1.0 resisted a false premise about using Redis, highlighting its honesty in autonomous coding. The linked Hugging Face page announces Ornith-1.0, a family of open-source coding agent models with state-of-the-art benchmarks.
Ornith-1.0-9B is a new 9B parameter AI model optimized for 8-12GB GPUs, achieving strong performance on agentic coding benchmarks, matching or surpassing models 2-3x its size.
A new 35B coding model, Ornith-1.0, is compared against Qwen3.6-35B on custom tests. The user finds Ornith-1.0 to be genuinely stronger for long-horizon agentic coding, resisting bad context and finishing large tasks, but it is more cautious and verbose, sometimes over-gating simple requests.