Here is your today’s AI Dev Brief from Marktechpost, covering core research, models, infrastructure tools, and applied updates for AI developers and researchers.
Zhipu AI Releases GLM-4.6V: A 128K Context Vision Language Model with Native Tool Calling
GLM-4.6V is Z.ai’s new open source multimodal stack, combining a 106B cloud model and a 9B Flash variant with a 128K multimodal context window, native Function Calling on visual inputs and outputs, and interleaved image text generation, giving developers an execution capable vision language backend for long document analysis, UI to code workflows and tool driven agents running either via Z.ai’s API or locally on GPUs. Read the full insights/article here.
Jina AI Releases Jina-VLM: A 2.4B Multilingual Vision Language Model Focused on Token Efficient Visual QA
Jina-VLM is a 2.4B parameter multilingual vision language model that combines a SigLIP2 encoder with a Qwen3 backbone and an attention pooling connector to cut visual tokens by 4 times, delivering strong OCR, chart and document understanding, state of the art multilingual VQA scores on MMMB and Multilingual MMBench, and 72.3 average across 8 English VQA benchmarks, while staying deployable on modest GPUs with a 32,000 token context and support for 4K images. Read the full insights/article here.
[Time Sensitive] MiniMax - Developer Ambassador Program Application (Sponsored)
MiniMax has opened applications for its Developer Ambassador Program, aimed at independent ML and LLM developers who are already building with MiniMax models. Ambassadors get access to upgraded or free plans, early access to new releases, direct channels to the product and R&D teams, and visibility for their work through the MiniMax community and events Check out the details.
Google LiteRT NeuroPilot Stack Turns MediaTek Dimensity NPUs into First Class Targets for on Device LLMs
LiteRT NeuroPilot Accelerator is Google and MediaTek’s integrated stack for running open weight models such as Qwen3 0.6B, Gemma 3 270M, Gemma 3 1B, Gemma 3n E2B and EmbeddingGemma 300M directly on MediaTek Dimensity NPUs. It combines LiteRT’s Compiled Model API, AOT or on device compilation and Play for On device AI distribution, so developers can target Accelerator.NPU with a single code path, achieve up to 12 times throughput over CPU and 10 times over GPU and ship low latency on device generative workloads without custom vendor specific glue code. Read the full insights/article here.
Project Notebooks/Tutorials
▶ [Open Source] Rogue: An Open-Source AI Agent Evaluator worth trying Codes & Examples
▶ Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration Codes Tutorial
▶ How to Create a Bioinformatics AI Agent Using Biopython for DNA and Protein Analysis Codes Tutorial