标签：#ci

OpenAI leaders think the company’s next generation model, which excels at computer use and coding, may mark a major milestone in AI development.

It's also letting people upload multiple photos at once when reacting to a post.

The Circular Ring 3 series announcement comes amid a year of heightened competition in the smart ring market, following the launches of the Oura Ring 5 and RingConn Gen 3.

OpenAI recently estimated its Cursor partnership would make more than $1 billion in revenue a year, WIRED has learned. It still walked away after Elon Musk’s SpaceX acquired the AI coding startup.

A green required check is not a merge decision. It is a signal that one job graph finished without a red X. If a pre-push hook was skipped, or a snapshot fixture was regenerated without a reason, you can still ship a lie. This article walks through a merge packet: a small JSON artifact your CI publishes next to the check. The packet records hook results, fixture drift, and required-job status. A model may write the eight-line brief. It does not get a vote. Why green still lies CI dashboards collapse many facts into one glyph. You see green. You click merge. You miss three common failures. First, someone pushed with --no-verify and skipped the hook that keeps fixture hashes honest. Second, a test helper rewrote golden files because a serializer added a field. Third, a retry job went green on the second attempt and nobody recorded that the first attempt failed. You do not need a platform rewrite to catch this. You need a contract the merge button cannot ignore. Cheap code generation makes the second failure more common. When it is easy to regenerate tests, it is easy to regenerate the fixtures those tests pin. The pin becomes a moving target. Treat unexplained fixture diffs as merge blockers, the same way you treat a failed unit job. What the merge packet contains Keep the packet boring. One file. One schema. Commit it as a CI artifact, not as a comment that can be edited after the fact. { "commit" : "REPLACE_WITH_SHA" , "generated_at" : "2026-09-03T00:00:00Z" , "hooks" : { "pre_push_fixture_guard" : "passed" }, "fixtures" : { "manifest_path" : "tests/fixtures.sha256" , "changed_paths" : [], "unexplained_paths" : [] }, "required_jobs" : [ { "name" : "unit" , "conclusion" : "success" }, { "name" : "contract" , "conclusion" : "success" } ], "merge_ready" : false , "brief" : null } merge_ready is computed by a script you own. Not by a prompt. The brief is optional prose for humans who will not open the JSON. Step 1: Pin fixtures with a manifest Pick a directory you alrea

Google is rolling out an updated AI weather model that's supposed to be more accurate, especially when it comes to predicting rain and snowfall. In the announcement today, the company says it's now able to make forecasts with "unprecedented resolution" using its new WeatherNext 3 AI model. It can produce a global picture that's five […]

TikTok’s comments section is getting an upgrade. The social network announced on Thursday that it’s rolling out new features for its comment sections, including voice comments, comment polls, photo carousel comments, and Live Photo comments. With the additions, the app is borrowing features from messaging apps as it looks to deepen engagement on its platform. […]

Every backtest has to answer a boring question: when the strategy says "buy," what price does it actually get? Most backtesting frameworks answer this question badly by default, and the badness is almost always in the strategy's favor. Here are the four assumptions that do the most damage, roughly in order of how often they show up. Mid-price fills If your backtest fills orders at the midpoint of the bid-ask spread, you are assuming you trade for free. You don't. A market order pays at least half the spread to cross it; a marketable limit order pays something close to that too, once you're honest about how often it actually gets hit versus sitting unfilled while the market moves away. Mid-price fills are the single most common way a backtest manufactures edge that doesn't exist, because the effect compounds with trade frequency — a strategy that trades often looks great on mid-price fills and mediocre-to-negative once it pays the spread on every round trip. Zero slippage Slippage is the gap between the price your signal fired at and the price your order actually executed at, and it's not just a queuing artifact — it's partly information. If your strategy is buying because something changed, other participants are reacting to the same thing, and the price you wanted is often gone by the time your order reaches the book. A backtest with zero slippage is quietly assuming the market waits for you. Unlimited size at the touch Backtests routinely assume you can execute your full position size at the best bid or ask, no matter how large the order is relative to the visible size there. In practice, a large order walks the book, and the average fill price is worse than the touch price by an amount that depends on how thin the book is. This one is invisible until you try to size up, which is exactly when a strategy that looked fine in testing starts bleeding. Commissions omitted or averaged Commissions and fees are usually small per trade and therefore easy to skip or fold in

The long-rumored deal will give the chip giant access to—and help it promote—a huge repository of open-source AI models and data sets.

An underground detector recorded a strange interaction pointing to a particle with some properties that signify dark matter. The detection is small but promising.

Restaurant menus with AI-generated food images are going viral for their off-putting aesthetic. But AI’s impact on modern menus runs even deeper.

Common sense is of no help in studying reality at the atomic scale.

While companies promise they can help users monitor gut health, experts suggest that smart toilets provide little in the way of benefits to the average person.

Brian Martin discusses the real-world performance costs of metrics libraries and shares strategies for low-overhead, "fearless" instrumentation. Drawing from his work at IOP Systems, he explores atomic primitives, per-CPU sharding, lock-free histograms, and eBPF integration to help software architects and engineering leaders maintain full system visibility without sacrificing performance. By Brian Martin

Anker's SleepLab speaker promises to track and enhance sleep without a wearable. It uses millimeter-wave radar (60GHz) blasting from your nightstand to track micro-movements in your chest and body to continuously monitor your heart rate and breathing to enhance whatever sleep stage you're in. It also embeds lots of hippie shit. If I'm understanding things […]

The World Meteorological Organization is warning that the climate phenomenon that has the Pacific running a fever will last until at least February, with dire consequences for weather worldwide.

Two weeks after launching Buildside, 130 people had joined. I did not have an ad budget or a huge email list. There was no secret growth trick. I grew Buildside by sharing the journey on X and LinkedIn, speaking with users, and letting their feedback guide the app. The biggest lesson I learned was: You have to market your product long before you launch it. I started marketing before the app was ready Many founders wait until their product feels finished before they talk about it. Sharing early work can feel risky. You may worry that people will judge it, copy it, or ignore it. I chose to share anyway. Before Buildside launched, I posted about the problem I wanted to solve, why I cared about it, and what I was learning. I showed small bits of progress and asked people what they thought. That gave people a reason to follow the journey. On launch day, I was not asking strangers to care about a new app. I was inviting people who had already watched the idea grow. Y Combinator tells founders to release early. Its guide says waiting too long is often driven by fear and the wish to make everything perfect. An early version lets you ask, “What do you think?” That advice matched my experience . I shared the fails as well as the wins It is easy to post a new user count or a kind comment. It is harder to share what went wrong. Yet the honest posts often led to the best talks. When something failed, I said so. When I changed my mind, I explained why. I shared the good days too. The goal was not to make every post look impressive. It was to show the real work. Honesty builds trust. Buffer has shared parts of its business in public for years, through both strong and hard moments. The company says being open builds trust and keeps it accountable. Its open company page shows this in action . People do not need a founder to look perfect. They want to know there is a real person listening and trying to make something useful. I let users shape Buildside Building in public was not only

The company is reducing pressure on workers to use artificial intelligence tools while encouraging them to experiment with Hatch, its most advanced AI project yet.

Is a blank cell signal, or just missing? Sometimes an empty cell is the most informative thing in the row. The trouble is that you usually only know which case you're in by reading the data dictionary — and that doesn't scale to 800 columns named f_0347 . So we measure it instead, then check the answer against the literature. Ames housing · 1,460 sales · 79 columns · 19 of them contain blanks "Drop any column that's more than 70% missing." I've written that line into more pipelines than I can count. On Ames it deletes four columns — and three of them have real price signal sitting in the gap. The blanks in this dataset are structural . A blank GarageQual doesn't mean the value was lost; it means the house has no garage. A blank Alley means no alley access. The emptiness is the measurement. That's easy to see here because the columns have English names and a published data dictionary. It is not easy to see on a vendor feed of anonymised features, which is what most real projects look like. So the question worth answering isn't "does missingness carry signal" — it's can you tell, without knowing what the column means? 0.41 R² from the blank/not-blank pattern alone — every value discarded 1.00 AUC recovering the garage blanks from other columns' values ±0.9% Total spread across five strategies — inside a ±1.5% CV noise band 1 · A blank cell has a price tag Start with the crude check: does sale price differ between rows where a column is blank and rows where it isn't? Columns that go blank on the same rows describe one fact, so the five garage columns collapse into one. Fig 1. Median sale price, blank rows vs. valued rows. No garage is a $68k median discount on a $163k median house. Note the sign flip: houses that have an alley or fence are the cheaper ones — those features mark older, denser blocks. "Blank = worse" is not a rule you can assume. Then the harder test. Throw away every value in the table and keep only a 19-column matrix of True / False — was this cell emp

On our new Real World AI stage, we’ll be focusing on the intersection between the digital and physical, and all the ways we’ll continue to see a blending of the two.