![\"Stone](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2026\/07\/1782967872_662_0x0.jpg\")
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Stone Crab Claws Ready to Eat<\/p>\n
getty<\/p>\n
When it became available a few months ago, OpenClaw was a big mystery to most of us. There was the common understanding that you have to be cautious with an agentic technology like this, that any missteps could cause calamity.<\/p>\n
In China, people were lining up to get OpenClaw. In American communities, you didn\u2019t see that as much. But behind the scenes, the early adopters were at work.<\/p>\n
Now, in the summer of \u201926, OpenClaw is becoming, not old hat exactly, but more commonly implemented. Here\u2019s what Copilot has to say about it, citing the Linux Journal<\/a> and other sources:<\/p>\n \u201cBy June 2026, OpenClaw \u2014 an open-source AI agent framework \u2014 has matured from a niche tool into a widely adopted local-first assistant that can execute real-world tasks rather than just generate responses. Its ability to connect to APIs, files, browsers, and messaging apps makes it valuable across productivity, dev\/ops, business, and personal workflows.\u201d<\/p>\n Still, you don\u2019t want this autonomous AI to run amok in your systems, throwing your sensitive data around like a bull in a china shop. So how do you move forward with confidence?<\/p>\n Boston\u2019s IIA Conference in April<\/p>\n I sat in on a panel discussion in April, here at MIT, as part of the Imagination in Action event, where so many of our best and brightest tackle the big questions around AI. (Disclaimer: I help to run this event annually.)<\/p>\n This particular segment featured a group including Maria Gorskikh of Maritime, Andrew Mead of Vector Lab and others, discussing how to move forward with OpenClaw, without getting pinched.<\/p>\n Early on, there was a general focus on quality of process, on testing and verification of systems, and on corralling smart AI agents, to make sure they are oriented toward the user\u2019s goals, also considering the utility, in general, of the AI approach.<\/p>\n \u201cI think all software developers are using AI coding agents,\u201d one panelist said. \u201cYou can basically build much more, and it’s robust. \u2026 AI can do it faster and experiment much more than a human, (although) maybe a very experienced human can do it much better than AI. But if you don’t need perfect quality and you are good with good enough quality, AI can do much more and much faster, much cheaper, especially for startups, because startups, usually, they don’t have large reputational risks. \u2026 startups, I think, can take more risks in using AI agents.\u201d<\/p>\n Metrics for Progress<\/p>\n The panel also talked about using the eschatologically titled \u2018Humanitys Last Exam\u2019<\/a> or HLE, and how it succeeded metrics like Massive Multitask Language Understanding or MMLU. With around 2,500 questions covering mathematics, physics, biology, chemistry, computer science, engineering, humanities and more, with an estimated 14% of them multi-modal, the HLE is a good benchmark, and useful in quantifying what\u2019s happening with our models and their prowess.<\/p>\n Another topic of discussion was cost of credits. Obviously, these things cost money.<\/p>\n