Better Code Merging with Less Compute: Meet Osmosis-Apply-1.7B from Osmosis AI
Osmosis AI has open-sourced Osmosis-Apply-1.7B, a fine-tuned variant of Qwen3-1.7B, designed to perform highly accurate and structured code merge tasks. Drawing inspiration from IDE agents like Cursor’s “instant apply,” Osmosis-Apply-1.7B…
ByteDance Just Released Trae Agent: An LLM-based Agent for General Purpose Software Engineering Tasks
ByteDance, the Chinese tech giant behind TikTok and other global platforms, has officially released Trae Agent, a general-purpose software engineering agent powered by large language models (LLMs). Designed to execute…
Getting Started with Agent Communication Protocol (ACP): Build a Weather Agent with Python
The Agent Communication Protocol (ACP) is an open standard designed to enable seamless communication between AI agents, applications, and humans. As AI systems are often developed using diverse frameworks and…
SynPref-40M and Skywork-Reward-V2: Scalable Human-AI Alignment for State-of-the-Art Reward Models
Understanding Limitations of Current Reward Models Although reward models play a crucial role in Reinforcement Learning from Human Feedback (RLHF), many of today’s top-performing open models still struggle to reflect…
New AI Method From Meta and NYU Boosts LLM Alignment Using Semi-Online Reinforcement Learning
Optimizing LLMs for Human Alignment Using Reinforcement Learning Large language models often require a further alignment phase to optimize them for human use. In this phase, reinforcement learning plays a…
What Is Context Engineering in AI? Techniques, Use Cases, and Why It Matters
Introduction: What is Context Engineering? Context engineering refers to the discipline of designing, organizing, and manipulating the context that is fed into large language models (LLMs) to optimize their performance.…
A Coding Guide to Build Modular and Self-Correcting QA Systems with DSPy
In this tutorial, we explore how to build an intelligent and self-correcting question-answering system using the DSPy framework, integrated with Google’s Gemini 1.5 Flash model. We begin by defining structured…
Chai Discovery Team Releases Chai-2: AI Model Achieves 16% Hit Rate in De Novo Antibody Design
TLDR: Chai Discovery Team introduces Chai-2, a multimodal AI model that enables zero-shot de novo antibody design. Achieving a 16% hit rate across 52 novel targets using ≤20 candidates per…
AbstRaL: Teaching LLMs Abstract Reasoning via Reinforcement to Boost Robustness on GSM Benchmarks
Recent research indicates that LLMs, particularly smaller ones, frequently struggle with robust reasoning. They tend to perform well on familiar questions but falter when those same problems are slightly altered,…
Kyutai Releases 2B Parameter Streaming Text-to-Speech TTS with 220ms Latency and 2.5M Hours of Training
Kyutai, an open AI research lab, has released a groundbreaking streaming Text-to-Speech (TTS) model with ~2 billion parameters. Designed for real-time responsiveness, this model delivers ultra-low latency audio generation (220…
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