Federal cyber experts called Microsoft's cloud a "pile of shit," approved it anyway — Ars Technica

One Microsoft product was approved despite years of concerns about its security.

ENIAC’s 80th Anniversary: A Legacy of Innovation — IEEE Spectrum

Celebrating the innovation that sparked a tech revolution

Major Data Brokers Tried to Hide Their Opt-Out Pages From Search Engines — PCMag

Four data brokers make their opt-out pages more accessible after a US senator calls them out for indexing tricks that prevented people from asking to have their data deleted.

Data not showing AI is causing major dislocations in jobs, says WSJ's Greg Ip — CNBC

WSJ's Greg Ip joins 'Power Lunch' to discuss AI's impact to the labor market.

The Hidden Compiler Bug That Quietly Breaks Your C Code: How GCC and Clang Both Generate Incorrect Assembly — WebProNews

For decades, C programmers have placed implicit trust in their compilers — the sophisticated software tools that translate human-readable code into the machine instructions that power everything from embedded medical devices to the servers underpinning global financial markets.

Microsoft’s original Windows NT OS/2 design documents – OSnews

Lines of Code Are Back (And It's Worse Than Before)

The metric we killed is back, and AI made it worse

Bloat Risk? Microsoft's Notepad Upgrade Also Introduced a Vulnerability — PCMag

The flaw exploits Notepad’s recently added support for Markdown, a formatting language used on websites and in files, to run malicious code on a Windows PC.

France’s Bold Bet on Digital Sovereignty: Inside Suite Numérique, the Open-Source Office Suite Challenging Big Tech — WebProNews

When the French government announced it was building its own suite of productivity tools to replace commercial offerings from Microsoft and Google, skeptics dismissed it as another quixotic European attempt to compete with Silicon Valley.

Why Large Language Models Make Terrible Compilers — And Why the Industry Keeps Trying Anyway — WebProNews

Sat Feb 7 01:49:23 GMT 2026 In the rush to apply large language models to every conceivable software engineering task, a provocative question has emerged from the developer community: Should LLMs be used as compilers? The answer, according to a growing chorus of computer scientists and practitioners, is a resounding no — not because they can’t do it, but because the very nature of compilation demands guarantees that probabilistic systems are fundamentally incapable of providing. The argument was