Hey devs! The past week brought a flood of updates across the AI-developer ecosystem. From a massive study on how to structure context for agentic systems to ultra-fast coding models and new agentic tooling, here’s everything you need to know. ;)
Gemini 3 Deep Think
Google upgraded Deep Think, a reasoning-focused model built for science and engineering. Early use cases include detecting errors in research papers and optimizing semiconductor design. AI is no longer just generating text - it’s helping solve complex research problems.
Read more: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-deep-think/
GLM-5
ZAI introduced GLM-5, a new flagship model designed for agentic engineering. It can generate runnable code, plan tasks, call tools, and execute multi-step workflows with minimal supervision. Models are starting to execute, not just assist.
Read more: https://docs.z.ai/guides/llm/glm-5
Codex-Spark
OpenAI launched Codex-Spark, a research preview model built specifically for ultra-fast coding. Codex-Spark is a real-time coding model that delivers 1000+ tokens/sec with a 128k context window, optimized for fast collaboration. Speed is becoming a major differentiator in AI coding.
Read more: https://openai.com/index/introducing-gpt-5-3-codex-spark/
This AI No-Code Tool Builds REAL Apps, Not Just Prototypes (Sponsored)
Shows how you can build production-ready apps with real data, workflows, and permissions - without writing code.
Watch on YouTube: I Built Production-Ready Apps Without Writing Code, Here’s How
New Study: AI Assistance Helps Experts, Hurts Learning
A new paper by Damon McMillan examines how AI agents should structure system context when interacting with databases. The research included 9,649 experiments across 11 models, testing file formats such as YAML, Markdown, JSON, and TOON on schemas with 10 to 10,000 tables.
The results show that model capability matters more than architecture or format. Frontier models like Claude, GPT, and Gemini achieved around 86% accuracy, significantly outperforming open-source models at roughly 65%. File-based retrieval slightly improved performance for top-tier models (+2.7%) but actually reduced accuracy for open-source models by about 7.7%.
Read more: https://arxiv.org/abs/2602.05447
Conclusion
AI is getting smarter, faster, and more autonomous. 2026 might be the year AI stops assisting developers and starts working alongside them.
See you next week! Cheers, proflead! 😉