标签：#review

Google’s Pixel 11 Pro XL brings snappier cameras and genuinely useful AI features like Rambler, but its iterative upgrades may not be enough to tempt recent Pixel owners.

For uninterrupted service as you country-hop, the Saily Ultra eSIM works well and comes with plentiful perks. It’s pricey though.

Laptop prices are out of whack. $500 used to get you a tolerable laptop, and $900 got you a really good one. They often had similar CPU, RAM, and storage options because that stuff was comparatively cheap; the difference was often in build quality and screen rather than power. But RAMageddon has thrown everything off. […]

At LinkedIn's scale, relying solely on human reviewers or simply putting an off-the-shelf AI reviewer in front of GitHub is not an effective way to manage PRs. To address this, LinkedIn engineers built a multi-agent AI code review platform that understands the organization’s coding context, treats code review as production infrastructure, and minimizes hallucinations and low-signal feedback. By Sergio De Simone

The Nyrius Phoenix Home True 4K60 can wirelessly transmit games, TV shows, movies, and more across your home (even through walls).

RayNeo's latest AR smart glasses are going for very different audiences: The very discrete, and the very nerdy.

Incremental improvements fail to generate much excitement, but Google’s Pixel 11 is still an accomplished Android phone.

Originally published at tddbuddy.com . Related reading: Where the Review Point Moved is the direct predecessor; this post argues the industry's response to that shift is doubling down on the wrong surface at higher throughput. What "Senior" Means When Typing Is Free and The Test Pyramid Was an Economic Argument name where signal actually lives now. The review agent left fourteen comments on the pull request and none of them were the reason the PR should not have merged. That is the shape of the failure. The reviewer that shipped the review was a tool built to catch what a human reviewer no longer had time for. Three of the comments were genuine issues, unused imports, a typo in a log message, a dead branch. Eleven were style opinions, restatements of what the diff already made obvious, or false positives on patterns the codebase had chosen deliberately. The human on the PR spent more time filtering the review than reading the diff. The change that actually needed a second pair of eyes (a renamed field in a shared DTO that had already broken a downstream consumer twice this year) merged without a comment on it from either the human or the machine. The industry response to agent-generated pull-request volume has been to deploy more agents. The response is understandable. It is also empirically counterproductive. A 2026 study measured what happens when only a code-review agent reviews an agent-authored PR: 60.2% of closed pull requests sat in the 0 to 30 percent signal-ratio range, and twelve of the thirteen review agents evaluated averaged below a 60% signal ratio. Signal is what a human reviewer needs. The review agent produces less of it per unit of reviewer attention than the diff would have without a bot in the middle. The Volume Problem Is Real Four hundred thousand pull requests in two months from a single code-writing agent. One in five reviews on the largest hosting platform now involves an agent. Pickup time on agent-authored PRs is 5.3 times longer than on h

Is Amp Crash a guitar amp or a guitar pedal? Yes. Death By Audio's latest is, in fact, both. While it's in a guitar pedal enclosure, Amp Crash is actually a tiny 3-watt amp hooked up to a 2-inch speaker. You can even connect an external speaker if you want. This isn't a digital re-creation […]

Artificial intelligence (AI) makes all code write-only,. It’s too dense to read, and tests define the behaviour and become the documentation. Code is also disposable; it becomes easier to rewrite than to debug. Humans can't review AI-generated code at scale. Intent decouples from implementation; developers should focus on creativity. By Ben Linders

The Poolease X1 can collect leaves from the pool floor, but dirt, silt, walls, and steps are all beyond its pay grade.

Last year, Audi announced that it gave its all-EV-by-2033 plan the boot, and instead will offer a mix of gasoline, hybrid, and electrified propulsion. There's no doubt still some uncertainty among bigger automakers - especially those under the Volkswagen Group umbrella - when it comes to such a (now prior) commitment. Even so, what it's […]

With smarter gym tracking, new health alerts, and offline Gemini, the Pixel Watch 5 fine-tunes a winning formula—for a price.

Google's new Pixels make some compromises but still manage to be good phones.

Google’s new voice typing is pure magic, but subpar gaming performance and a useless rear LED keep these flagships from true greatness.

Google is trying to get you off your phone. The Pixel 11 Pro is "A Phone Designed to Help You Use It Less," the company promises. It can proactively help you book restaurant reservations, take the best frames from a video, and help you voice-text significantly faster. The pitch is that the new features bundled […]

Robot vacuums are great at keeping your floors clean, but there are a few areas they fall down on the job - stairs being one. Another is chairs and stools. You know the scenario: You have a row of stools at a kitchen table, or chairs tucked under a dining table, and the robovac just […]

Originally published at fathohm.dev . The term "comprehension debt" is Jason Gorman's, from September 2025, carried by Addy Osmani in March 2026 — this piece is about measuring it. There's a module in your codebase that shipped last month. It works. It has tests. It passed review. And if it breaks at 2am, nobody on your team can explain what it does. Ask "who understands this?" about any given file in an AI-native codebase and the honest answer, increasingly often, is no one — not because your engineers got worse, but because the code stopped passing through their heads on its way into production. The decoupling For seventy years, code getting written implied that somebody understood it. The implication was so reliable we never thought of it as an assumption: writing code was the act of understanding a problem precisely enough to express it. However bad the code, however absent the docs, there was at minimum one person — the author, at the moment of authorship — who knew what it did and why. Every practice we have for keeping teams oriented in a codebase quietly leans on that floor: review assumes the author can defend the change, onboarding assumes someone can explain the system, debugging assumes a colleague to ask. AI agents broke the implication. Code getting written and code getting understood are now separate events, and only one of them is scaling. An agent can produce in an afternoon what a team used to write in a month — and the afternoon does not come with a month's worth of understanding attached. The floor of "at least the author knows" is gone: for agent-authored code, the author isn't on your team. It isn't anyone. The gap between what a codebase does and what the humans responsible for it understand needs a name, because things without names don't get managed. It has one, and it has had one for a while. Jason Gorman named it comprehension debt in September 2025 — what happens "when teams produce code faster than they can understand it" — and Addy Osma

There's an argument to be made that people wouldn't be all that interested in Coyote vs. Acme if it weren't for the way David Zaslav tried to kill it. By trying to shelve the project, Warner Bros. Discovery only drew attention to its habit of disappearing nearly completed movies in order to cash in on […]

The Nimble SharePower is a versatile battery pack that gets even better when you're traveling with a friend.