{"version":1,"type":"rich","provider_name":"Libsyn","provider_url":"https:\/\/www.libsyn.com","height":90,"width":600,"title":"Microsoft Shipped Almost 700 Patches in One Patch Tuesday \u2014 Six Times a Normal Month","description":"This was the biggest Patch Tuesday on record: Microsoft pushed close to 700 patches in a single month, roughly six times the usual volume. Craig's follow-up question is the one that actually decides whether a machine is exposed. Microsoft is patching Microsoft. Who is patching everything else? Windows Update never touches third-party software \u2014 it barely updates Windows itself, Craig said, and it does nothing for the average of 62 other programs sitting on a typical computer, many of which are being actively exploited right now. He named Adobe Reader and WinZip specifically: the current versions are reasonably solid, but almost nobody can say for certain they are running the current version. His phrasing for the gap: vulnerabilities "like driving a truck into your living room and you don't even notice." The question came in from a listener \u2014 Andy in Sanford texted to ask what Craig thought of PC-matic. Craig gave a genuinely mixed answer rather than a pitch. PC-matic is whitelist-based rather than blacklist-based, meaning only software known to be good is allowed to run, verified by checksum. That is a real architectural advantage. But PCMag and others rate it in the 60s out of 100, and it is a nuisance to live with. He would reach for Bitdefender first, and then, separately, something that keeps the rest of the software on the machine current \u2014 which is the part the antivirus question tends to skip past. Most of the segment was about AI, and Craig's line on it is deflationary. Meta planned to cut about 100,000 employees in waves and replace them with AI agents \u2014 putting trackers on employees' computers to capture mouse movements and screen contents as training data \u2014 and the project came apart. About 80% of AI projects fail, Craig said, usually from overreach. What AI is genuinely good at is a first draft; what it cannot do is decide. Companies that laid off support staff and handed the queue to an agent have been hiring those people back. And the tell he keeps returning to: if the models have really reached general intelligence, why does every one of them still need a human supervising it? Also in this segment: Aaron's own experience upgrading ChatGPT to Astra \u2014 "blown away" by the speed and the tolerance for natural language over careful prompt engineering \u2014 and his question about how much more capable the unreleased models must already be Craig on why the newer models feel faster and smarter: built-in self-checks where the AI audits its own reasoning, plus multiple specialized sub-models tuned for law, for Python, and so on, with your question routed to whichever fits Aaron's McKinsey story, and the best analogy in the segment: briefing an AI is like sending a slide to an outsourcing team halfway around the world. Vague instructions come back wrong; precise ones come back close. "You've got to do the work." Craig's observation that AI tends to increase workload \u2014 staff stop doing the basic work and become supervisors of it, reviewing output from what amounts to a team of high schoolers A note on the market this creates: people paid $250,000 a year and up to go in and fix systems that were built with AI and don't work Free newsletter and Insider Session announcements: CraigPeterson.com ","author_name":"Craig Peterson - Secure Your Business, Your Privacy, and Save Your Sanity","author_url":"https:\/\/cptt.libsyn.com\/podcast","html":"