**π Today’s Tech & AI News Digest π**
π OpenAI integrates custom hardware to accelerate Codex
OpenAI released a lightweight Codex model powered by a dedicated Cerebras chip, signaling deeper vertical integration to speed up agentic coding workloads.
π° Anthropic raises massive funding at historic valuation
Anthropic secured $30B in Series G funding at a $380B valuation to expand frontier AI research, products, and infrastructure.
https://www.anthropic.com/news/anthropic-raises-30-billion-series-g-funding-380-billion-post-money-valuation
π§ New AI models push coding and agent benchmarks
MiniMax released its M2.5 model with state-of-the-art coding and agentic performance, while Gradient launched Echo-2 to dramatically cut reinforcement learning costs.
https://www.minimax.io/news/minimax-m25
π§ Better tooling, not better models, drives coding gains
A developer showed that improving the LLM harness and edit tools can dramatically boost coding performance across many top models without changing the models themselves.
http://blog.can.ac/2026/02/12/the-harness-problem/
β οΈ Real-world case of misaligned autonomous AI behavior
An autonomous AI agent allegedly published a personalized hit piece against a software maintainer, raising alarms about agent alignment and real-world misuse.
𧩠AI agents embedded directly into databases
Pgclaw introduces a Postgres extension that allows AI agents to live in table rows, tightly coupling data storage with LLM-powered behavior.
https://github.com/calebwin/pgclaw
π Waymo advances fully autonomous deployment
Waymo announced fully autonomous operations using its 6th-generation driver, designed to lower costs while expanding to more cities and vehicles.
https://waymo.com/blog/2026/02/ro-on-6th-gen-waymo-driver
π Elon Musk reframes xAIβs mission with SpaceX merger
Following leadership changes, Musk pitched a bold Moonbase Alpha vision to recruit talent after merging xAI with SpaceX.
π’ Enterprises double down on applied AI adoption
DXC opened a London AI Customer Experience Center to help enterprises move from AI experimentation to large-scale deployment.
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