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Curious if you should get Dyson’s new 2026 stick vacuums or stick to the older ones? I tested five models, old and new, to find out for you.

Andon Labs' latest vending machine simulation shows Opus 5 lied and colluded its way to become the best AI capitalist ever.

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This was a week where the open lakehouse stack spent most of its energy on contracts. Not legal contracts, but the promises that formats, catalogs, and clients make to each other. Iceberg debated whether equality deletes belong in V4 and whether incremental scan semantics belong in the spec at all. Parquet voted a new floating point encoding into the format and argued about who owns the Thrift file that defines everything. Polaris tried to ship 1.7.0, found a licensing gap in 44 staged jars, and pulled the release candidate. Arrow wrestled with what the nullable flag actually means. Across all of it, a pattern showed up again and again: the community is done with informal conventions and wants written guarantees. Here is what happened, project by project. Apache Iceberg The week opened with a personnel note that matters more than personnel notes usually do. Steven Wu announced that Maximilian Michels joined the project as a committer , and the thread ran to 30 messages of congratulations from across the contributor base. Iceberg has grown into a project where the streaming and Flink side of the ecosystem carries real weight, and adding committers who live in that part of the codebase keeps the review load from concentrating on a handful of people. The size of the congratulations thread is its own signal about how many active humans are now paying attention to this list. The most consequential technical debate of the week was about deletes. Huaxin Gao pushed forward on the proposal to deprecate equality deletes in Iceberg V4 , and the thread drew 15 messages including strong support from Ryan Blue. Equality deletes are the mechanism that lets a streaming writer say "delete every row where id equals 42" without knowing which file that row lives in. They make writes cheap and reads expensive. Every scan has to carry the delete predicates forward and apply them against candidate files, and scan planning cannot prune as aggressively because the planner does not know whic

You correct someone once. Not perfectly, but they get it. Next time, they do not make the same mistake. That is not optimism. That is just how correction works, "with people". I worked with agents on that assumption for a long time before I even noticed I was doing it. The plan that never held Before I had a single written rule anywhere, I would open a new session and ask for a plan first. Resolve the edge cases before touching a line of code, I said. The agent would agree, in whatever way a chat window agrees, and go straight to implementation anyway. I corrected it. Same session, it adjusted. New session, next day, same repo, same everything except the chat history: straight to implementation again. Every single time! So I did what looked reasonable. I wrote the plan myself. I resolved the edge cases myself, the open questions, the gaps the agent skipped past on its way to code. ' Tedious ' is the polite word for it. I was doing the one task I brought the agent in to do, and calling it collaboration. The same recipe, again The second correction arrived the same way. Every repo had its own shape. A recipe, a standard, a way things were supposed to be built here and not there. I would explain it. Full session, good results, the agent following the standard like it understood the standard. New session. Same repo, sometimes the new repo. Explain it again. Word for word, close enough. It was not that the agent forgot how to code. It was that nothing from the last conversation traveled with it into this one. Nothing said in the chat survives it I kept treating this like a training problem. Say it clearer. Say it earlier. Say it with an example next time. None of that was wrong exactly. It was aimed at the wrong layer. The actual mistake was assuming correction compounds the way it does with a person. It does not. A person carries what you told them into the next conversation without being asked to. An agent starts the next session exactly where it started the first one.

Apple's new Upgrade program is here, allowing you to lease select models of iPhones, iPads, Macs, and Watches with a relatively low monthly payment. The company promises you won't pay more than the full price of the device over the course of the one- to three-year lease, and in some cases, you'll pay hundreds of […]

Most lists that claim to help you block AI crawlers are copy-pasted and dangerously wrong about the two tokens that actually matter. Sorting them properly is not an abstract taxonomy exercise. It is the single decision that determines whether your content vanishes from AI answers while training continues — or vice versa. We maintain the crawler registry in lib/ai-crawlers.ts that powers techpotions’ free AI robots.txt generator . Every agent string and description was verified against the operator’s own crawler documentation. The registry holds 15 verified bots across four categories, and that four-way split is this article’s structure, because the categories map directly to what blocking costs you. Two tokens almost everyone gets wrong Before the list, the single most important correction to make, and almost every listicle on this query gets it wrong: Google-Extended and Applebot-Extended are not crawlers. They are robots.txt tokens — product controls that govern whether your content is used for Gemini and Apple foundation-model training. Blocking Google-Extended does not affect Google Search crawling, Google ranking, or regular Applebot search indexing. People block them believing they are opting out of AI Overviews, and are actually opting out of nothing they think they are, while leaving search indexing completely untouched. Platforms have started wiring these tokens into one-click controls. Cloudflare’s managed robots feature, released mid-2025, lets you add AI crawler rules through a dashboard toggle rather than editing a raw file — but the underlying token logic above still applies. Block AI crawlers: the four categories that decide the cost Every AI crawler we track belongs to one of four categories. The category tells you the cost of blocking it. Training crawlers scrape pages to feed a model that may never cite you. Blocking them is a defensive data decision. Assistant crawlers fetch pages to answer a live user’s question and can cite and link you. Blockin