A historian-turned-software engineer warns that "so little is ever written down" by professional programmers in a new article for Fast Company: Perhaps there's an early design doc, but then it turns out that everything was substantially revised before work began. Maybe there are a few wiki pages explaining known issues, some of which were solved a long time ago and others that have been left to molder in the codebase. Somebody might have left a comment in the code itself, but typically it's a warning not to change something or else something else will break... Software engineering has an ambivalent relationship with documentation. Everyone agrees documentation matters in theory, but in practice it's inconsistent, outdated, or missing entirely. Part of that is simple inertia. Writing documentation is usually less interesting than writing the code itself. But it's also ideological. The Agile movement emerged in part as a reaction against the heavily documented Waterfall methodology, and one of Agile's core values explicitly prioritizes "working software over comprehensive documentation." In escaping bureaucratic overdocumentation, the industry also normalized underdocumentation. High turnover at software jobs always brings "a constant drain of domain knowledge." And he's he's skeptical that generative AI will be able to fill in those gaps: [H]aving it generate documentation on the codebase itself might sound like a solution to the absence of other written information. LLMs can certainly summarize code back to you. But hold up with that idea. Beyond hallucinations, there's a deeper problem: Writing documentation is itself part of the thinking process. Whether I'm writing history or software, putting an approach into words helps refine it before I sink hours into implementation. Documentation also captures intent. An LLM may be able to summarize what a codebase does, but it cannot reliably explain why a developer chose one approach over another, or what trade-offs shaped that decision... An LLM can read code that I've written. It might even scan a large codebase and accurately summarize what it's doing. But it can't assess authorial intent. Thanks to long-time Slashdot reader smooth wombat for sharing the article.

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"In the future, AI agents will be able to find one another using the Domain Name System (DNS), instead of crawling about and probing ports or checking configured resources," writes The Register. InfoWorld writes that "numerous proprietary agent registries are on the market, but the Linux Foundation suggests we simply extend the distributed, open Domain Name System (DNS) infrastructure we already have." The foundation is now inviting contributions to the DNS-AID project, a standard way for AI agents to discover, verify, and communicate with one another over DNS that requires no new infrastructure. It enables agents and Model Context Protocol (MCP) servers to use DNS as a global, vendor-neutral directory. While many details remain to be worked out, the proposal suggests domain owners create a new well-known address that can provide a starting point for agents looking for one another: _index._agents.{domain}. This approach ensures that agent discovery remains scalable, secure, and compatible with the protocols that underly the internet, the Linux Foundation said. The Linux Foundation descrbes DNS-AID as enabling a standard way for AI agents to discover and communicate with one another. "By leveraging the internet's existing Domain Name System (DNS) infrastructure, DNS-AID provides a robust, decentralized alternative to the centralized registries and hardcoded URLs currently limiting AI interoperability." The standard was originally developed by Infoblox, their announcement notes, but "Because the protocol is implementation-agnostic, it functions across any DNS provider, ensuring that organizations maintain control over their agent infrastructure without relying on proprietary, centralized services."

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The state of Ohio — one of America's hot regions for data center construction — "is suspending a tax break that has been critical to its competition with other states," reports the Associated Press. The move "comes as tax breaks for energy-hungry AI data centers are increasingly playing a role in state budgets," the article points out. But they also note the expanding data center industry "is under pressure to pay the full costs" The size of Ohio's tax break skyrocketed, dwarfing previous projections, as opposition to data centers is sweeping through cities, suburbs and towns there and prompting lawmakers to form a committee to study the impact. In the meantime, residents are trying to bypass the GOP-controlled Legislature and get a referendum on November's midterm election ballot that's designed to permanently ban hyperscale data centers, likely the strictest such statewide ban under consideration in the U.S... The state, in 2024, had used previous history in projecting that the exemption would total $136 million in fiscal 2025 and $142 million in fiscal 2026. It was $554 million in 2024 and nearly $1.6 billion in 2025, the state reported... State tax breaks for the massive data center industry are facing growing criticism by governors and lawmakers... Thirty-eight states have some form of a sales tax break for data centers, according to the National Conference of State Legislatures... [Though many were passed before 2022, when data centers were smaller.] Ohio's exemption is fairly broad, applying not only to construction materials, but to the expensive equipment — such as server racks and cooling systems — used in data centers. Operators might buy new server racks every couple of years as the technology improves.

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The Zig programming language wants to be a modern alternative to C (including better memory safety features). It's maintained by as an open-source project by a 501(c)(3) nonprofit and a network of contributors. But Business Insider notes that Zig bans the submission of AI-assisted code: On the JetBrains podcast, Zig President Andrew Kelley called AI-assisted contributions "invariably garbage." "People are sending us contributions that have no value whatsoever," Kelley said. "They have negative value, because they take review time away from the team...." There are more pull requests than reviewers. At the time of the recording, Kelley said that Zig had 200 open pull requests. Those AI-generated "slop contributions" slow the whole team down even more, Kelley said. "We've wasted everybody's time...." Big Tech companies have projected lofty goals for the percentage of code that should be — and already is — written with AI. Zig doesn't have a mandate to be maximally efficient like these public companies. Instead, "mentorship" is part of its core mission, Kelley said, making AI contributions counterproductive. "We're all trying to get better at programming," Kelley said. "People who are sending AI pull requests, those people are not helping this goal."

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