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.
Google AI Introduces Gemini 3 Pro, Sparse MoE Multimodal Model With 1M Token Context for Agentic Workloads
Gemini 3 Pro is Google’s new flagship sparse MoE multimodal model with 1M token context, designed for long context reasoning, coding and agentic workloads across text, image, audio and video. It significantly outperforms Gemini 2.5 Pro, GPT 5.1 and Claude Sonnet 4.5 on key benchmarks such as Humanity’s Last Exam, ARC AGI 2, GPQA Diamond, AIME 2025 and MMMU Pro, and is already integrated into the Gemini app, AI Mode in Search, Gemini API, Vertex AI and the Antigravity agentic development environment. Read the full launch insights/article here.
xAI’s Grok 4.1 Pushes Toward Higher Emotional Intelligence, Lower Hallucinations and Tighter Safety Controls
xAI’s Grok 4.1 is now live across grok.com, X and the mobile apps, bringing 2 variants, a Thinking and a fast non reasoning mode, that currently hold the top 2 Elo spots on LMArena’s Text Arena. The update focuses on large scale RL tuning with frontier agentic reasoning models as reward models, leading to higher emotional intelligence scores on EQ Bench3, reduced hallucinations on production queries and FActScore, and a more detailed safety card that also exposes increased deception and sycophancy rates versus Grok 4. 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.
Google DeepMind’s WeatherNext 2 Uses Functional Generative Networks For 8x Faster Probabilistic Weather Forecasts
WeatherNext 2 is Google medium range weather system built on a Functional Generative Network that generates joint probabilistic 15 day global forecasts on a 0.25 degree grid at 6 hour steps. It models 6 atmospheric variables at 13 pressure levels plus 6 surface variables, improves CRPS over the previous GenCast based WeatherNext model on 99.9% of targets, runs efficiently on TPU v5p and now powers upgraded forecasts in Google products and cloud platforms. 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
▶ How to Build an Agentic Deep Reinforcement Learning System with Curriculum Progression, Adaptive Exploration, and Meta-Level UCB Planning Codes Full Tutorial