#jetbrains

JetBrains has announced JetBrains AI for Teams and Organizations, an initiative that promises to deliver a broad set of AI capabilities that connects AI tools developers already use with shared context, reusable agentic workflows, and organization-wide governance and cost control for software production. The intent is to move users from fragmented AI usage to coordinated software development, the company said. Unveiled July 7, JetBrains AI for Teams and Organizations will provide a unified system for agentic software development, according to the company. Vendor-agnostic by design, JetBrains AI for Teams and Organizations will connect external tools via Model Context Protocol (MCP) and external agents via Agent Client Protocol (ACP). Organizations will be able to evolve their AI stack without sacrificing governance or developer choice, the company said. Alongside new capabilities, JetBrains plans to evolve its commercial model to better support AI-powered software development. For bus

Developers looking to curb the cost of AI-powered coding tools have increasingly turned to the “Caveman” prompting style, which instructs coding assistants to communicate in blunt, telegraphic language and avoid conversational padding. The theory is simple: fewer words mean fewer tokens, translating into lower inference costs for organizations deploying AI agents at scale. A new test from IDE maker JetBrains confirms that terse prompting styles such as the viral open-source Caveman project can reduce token usage without hurting coding performance. However, the company found that the savings were far smaller than supporters claim. JetBrains used the Harbor open-source evaluation framework and tasks from SkillsBench for its test, and found that the Caveman technique reduced usage of output tokens by about 8.5%, far below its claimed 65%. The IDE-maker ran paired benchmarks across 86 real-world software engineering tasks in Claude Code, comparing coding sessions that used the Caveman pro