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Google DeepMind releases the Gemini 4 Carbon model
On October 28, 2026, Google DeepMind released Gemini 4 Carbon, calling it its strongest model, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0, and scoring 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. The model is aimed at long-horizon engineering tasks and was first opened to some cybersecurity defenders, then expanded through the Fairwind Program, and afterward made available to developers, enterprises, and consumers. On October 29, Arena.ai said Gemini 4 Carbon ranked 2nd in Agent Arena with a net improvement of 16.2%, and was on the Pareto frontier for cost, time, and output tokens per task; the same day, Yuchen Jin cited Artificial Analysis data saying it was smarter than Opus 5.5 and cheaper per task, but noted that the evaluation used the Antigravity CLI. On October 30, the Artificial Analysis Intelligence Index showed it in first place with 59 points, 1 point higher than Claude Opus 5.5. On October 31, Perplexity CEO Aravind Srinivas shared early evaluations
ers, then expanded through the Fairwind Program, and afterward made available to developers, enterprises, and consumers. On October 29, Arena.ai said Gemini 4 Carbon ranked 2nd in Agent Arena with a net improvement of 16.2%, and was on the Pareto frontier for cost, time, and output tokens per task; the same day, Yuchen Jin cited Artificial Analysis data saying it was smarter than Opus 5.5 and cheaper per task, but noted that the evaluation used the Antigravity CLI. On October 30, the Artificial
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DeepLearning.AI Launches Context Engineering Course
On October 29, 2026, DeepLearning.AI launched a Context Engineering course, taught by Chroma CEO Jeff Huber and CMPND CEO Drew Breunig, with context engineering as the throughline for keeping agents reliable in complex multi-step tasks and saving tokens. The course builds a harness from scratch in Python and uses traces and evals to quantify each change, covering three capabilities: Agentic Search, Context Management, and Agentic Memory. It is divided into four modules, and the labs are mostly code exercises of about one hour. The course assumes intermediate Python, familiarity with LLM applications and tool calling, and basic retrieval. There are currently no further developments.
9, 2026, DeepLearning.AI launched a Context Engineering course, taught by Chroma CEO Jeff Huber and CMPND CEO Drew Breunig, with context engineering as the throughline for keeping agents reliable in complex multi-step tasks and saving tokens. The course builds a harness from scratch in Python and uses traces and evals to quantify each change, covering three capabilities: Agentic Search, Context Management, and Agentic Memory. It is divided into four modules, and the labs are mostly code exercise
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