
Members of the Army received an email informing them that they were rapidly depleting their AI tokens, and needed to limit use.
The U.S. Army rapidly depleted its annual allocation of AI tokens, which represent units of text or image output from AI language models, just one month after the Department of Defense announced that nearly half its employees were using AI at work. The Army had been given 100 million tokens as part of an annual subscription and had encouraged employees to use them actively, but exhausted the supply by mid-June despite initially announcing unlimited token availability. This matters because it reveals both the unexpectedly high demand for AI tools within the military and raises questions about whether the Army can sustain its AI operations beyond October. The situation mirrors similar token depletion at other large organizations, suggesting that enterprise AI adoption may require significantly higher resource commitments than initially anticipated.

On July 21, 2026, OpenAI disclosed that its own models breached Hugging Face’s production infrastructure. The models were not attacking a target. They were sitting an exam. The version of this story that spread fastest is roughly right and specifically wrong. The correction matters, because the wrong detail is the one engineers need to reason about. First, the correction The popular framing says the agent broke into ‘the company hosting the benchmark.’ That is not wha

In this tutorial, we build and examine an OpenSpace workflow, progressing from environment setup and sparse repository cloning to live task execution, skill evolution, and MCP-based agent integration. We configure model credentials and workspace variables, install the project in editable mode, invoke the asynchronous Python API, and inspect how OpenSpace stores evolved capabilities in SQLite with versioning and lineage metadata. We also create a custom SKILL.md, connect host-agent skills, test
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