Time is limited, so we will be direct. Here is your AI Dev Brief from Marktechpost, covering core research, models, infrastructure tools, and applied updates for AI developers and researchers.
Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents
MiniMax-M2-REAP-162B-A10B is a Sparse Mixture-of-Experts Causal Language Model created by applying Router weighted Expert Activation Pruning, REAP, to the 230B MiniMax-M2 at a 30% expert pruning rate, resulting in 162B total parameters with 10B active per token, 62 layers, 48 heads, 180 experts and a 196,608 token context window, while maintaining near identical accuracy to MiniMax-M2 on HumanEval 93.3, MBPP 86.5, AIME25 73.3, MATH-500 89.4 and τ² bench Telecom 59.1, making it a memory efficient long context coding and tool calling model for vLLM deployments. Read the full launch insights/article here.
Google DeepMind Introduces SIMA 2, A Gemini Powered Generalist Agent For Complex 3D Virtual Worlds
Google DeepMind has unveiled SIMA 2, a Gemini powered Scalable Instructable Multiworld Agent that learns and acts inside commercial and generated 3D worlds. The system embeds Gemini 2.5 Flash Lite as the reasoning core, doubling SIMA 1’s task success rate and significantly narrowing the gap to human players on complex instructions, including in unseen games such as ASKA and MineDojo. With multimodal prompts, a Gemini driven self improvement loop, and tests in Genie 3 generated environments, SIMA 2 looks like a concrete precursor to general purpose embodied agents and future robotics stacks. Read the full launch insights/article here.
Germany based open-source remote access company - NetBird just built an "AI Mega Mesh". A project that started out to prove that multi-cloud networking doesn’t have to be complicated, resulted in creating a secure AI inference infrastructure that connects GPU resources across multiple cloud providers using Microk8s, vLLM, and NetBird. Read the full launch insights/article here.
No complex VPN configs.
No firewall configs.
No provider-specific networking rituals.
Meta AI Introduces DreamGym: A Textual Experience Synthesizer For Reinforcement learning RL Agents
Meta AI’s DreamGym framework tackles the core bottleneck in RL for LLM agents by replacing fragile, slow real environment rollouts with a reasoning based experience model that synthesizes trajectories in a textual state space. Using an evolving replay buffer and reward entropy based curriculum, DreamGym matches PPO and GRPO on WebShop and ALFWorld with only synthetic interactions and delivers more than 30 percent gains on non RL ready WebArena, while DreamGym S2R adds over 40 percent improvement with less than 10 percent real data. Read the full launch insights/article here.
Project Notebooks/Tutorials
▶ [Open Source] Memori: An Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems Codes & Examples
▶ How to Build Memory-Powered Agentic AI That Learns Continuously Through Episodic Experiences and Semantic Patterns for Long-Term Autonomy Codes Full Tutorial
▶ A Coding Guide to Implement Advanced Hyperparameter Optimization with Optuna using Pruning Multi-Objective Search, Early Stopping, and Deep Visual Analysis Codes Full Tutorial
▶ How to Design an Advanced Multi-Agent Reasoning System with spaCy Featuring Planning, Reflection, Memory, and Knowledge Graphs Codes Full Tutorial