Google began rolling out the November 2026 center update today, according to the Google Search Status Dashboard. The rollout started at 10:06 a.m. PT. Google says the update applies globally and to all languages and may obtain up to two weeks to complete.

The company’s Search Central document additionally announced the update on LinkedIn. It’s the third Search center update this year. Google recommends waiting until the rollout is complete before checking your Search Console data.

What Google Said

Google’s Search Status Dashboard admission reads:

“Released the November 2026 center update, which applies globally and to all languages. The rollout may obtain up to 2 weeks to complete.”

The LinkedIn article adds:

“This is a regular update designed to improved exterior relevant, satisfying satisfied for searchers from all types of sites.”

The exact wording in that declaration is what Google used for its March center update.

As of posting time, no particular objectives are mentioned on either the dashboard admission or the LinkedIn post. The Search Central blog doesn’t have a article concerning the update either. Google’s core updates page carries a revision date of Dec. 10, 2025.

Recent Update Timeline

The May center update finished rolling out on June 2, additional than five months before today’s launch. Three spam updates ran in between.

• September 2026 spam update: 13 days, 16 hours (Sept. 24 to Oct. 8)
• August 2026 spam update: 2 days, 16 hours (Aug. 18 to Aug. 21)
• June 2026 spam update: 2 days, 1 hr (June 24 to June 26)
• May 2026 center update: 11 days, 21 hours (May 21 to June 2)
• March 2026 center update: 12 days, 4 hours (March 27 to April 8)

The November update is the eighth ranking update listed on the Search Status Dashboard so far this year.

Why This Matters

Some sites may cognition position changes during the rollout of the November center update. Google’s center updates document advises waiting at smallest a complete week following the completion of a center update, afterward comparing your Search Console data from that week to a week previous to the commencement of the rollout. Google’s suggested baseline ongoing of the November update is a week previous to Nov. 10.

A complete two-week rollout would complete about Nov. 24. The week immediately following Nov. 24 volition contain U.S. Thanksgiving, Black Friday and Cyber Monday. Retail sites looking at this period duration compared to a pre-update week would be looking at holiday traffic against a regular week.

According to Google, center updates don’t mark particular sites or idiosyncratic pages and are meant to keep its systems current as satisfied on the web changes.

Looking Ahead

Per its LinkedIn post, Google volition update its ranking publish former leaf formerly the rollout is complete. We’ll update this part then, too.

Featured Image: Accogliente Design/Shutterstock. Source: Google.

Sony is retiring the PlayStation Plus app for PC on December 10th. Sony says it volition notify users immediately concerning the shutdown. The app allowed PlayStation Plus Premium subscribers to stream certain games on PC, but according to Sony, there aren’t many group using that perk:

PlayStation Plus Premium subscribers can motionless stream games on a console or on the PlayStation Portal. Sony’s decision to obtain the PC app offline follows reports before this twelvemonth saying Sony is pulling rear from PC and no longer plans to publish PC ports of PlayStation exclusives.

There’s an intuitive but simplified image of reasoning: let a example think for longer, and it’ll be additional apt to be correct. This aligns fine alongside our individual intuition: expend additional period and attempt on thinking and it’s additional apt that we’ll attain at a accurate answer.

But this image doesn’t appear to explain everything. During RL training, models study to compose longer chains of thought, and they get additional exact as they do. Yet if you obtain a trained example and power any one sequence to run longer, it helps for a while, afterward stops helping.¹ Sometimes the answer equal gets worse, as if it’s overthinking.² If a sequence of idea were really converging toward several optimum, additional steps shouldn’t create it worse. So is it really the dimension of all sequence that helps at conclusion time? Or is it item additional subtle?

This article is my attempt to explain this disturbance (mostly for myself) by looking at reasoning as pathfinding through token space. We’ll see that RL maximizes the probability of finding a way from $x$ to $\Scal_x$, but idiosyncratic paths are not guaranteed to converge to the accurate answer. As a bonus, this viewpoint clarifies several odd observations concerning reasoning models, akin unfaithful chains of idea and reward hacking.

Reasoning as pathfinding#

Ask a reasoning example the identical math issue ten times and you’ll get ten distinct chains of thought. Rather than thinking of reasoning as one mysterious procedure heading toward an answer, let’s think of it as pathfinding in token space. There are many paths from the issue to a accurate answer, and the example traverses one of them alongside all sample.

Consider the problem: “what’s $1 + 2 + 3 + \cdots + 10$”? One resolution is to add the numbers one at a time: 1, 3, 6, 10, and so on up to 55. Another way is to brace them from the exterior in: $1 + 10$, $2 + 9$, $3 + 8$, $4 + 7$ and $5 + 6$ all create 11, and five pairs of 11 create 55. Both of these paths district on the accurate answer, 55.

We can think concerning reasoning as following this broad pattern. We are stated a starting point, the prompt, which we’ll call $x$. We desire to discover a way $z$ that starts from $x$ and ends at a accurate answer $y$. To inform whether a way lands in the correct place, we assume we have a verifier $r(x, y)$ that returns 1 if $y$ is accurate and 0 if it isn’t.

So stated a starting item $x$, we desire to discover a way of tokens $z$ that lands in the set

$$\Scal_x = \{y : r(x, y) = 1\}.$$

The fig at the top of this post sketches this picture. Each gray row is a sampled path: it starts from the immediate on the left, meanders in token area by sampling $z$, alongside several of these paths landing in $\Scal_x$.

Since many paths district in $\Scal_x$, the achievement probability adds up complete all of them. Let $\pi(z \mid x)$ be the probability that the example generates the way $z$ stated the immediate $x$, and $y(z)$ for the final answer at the end of $z$. The probability of achievement is the probability of generating all path, times the probability that way lands in $\Scal_x$, summed complete all path:

$$p_x = \prob{\text{a way lands in } \Scal_x \mid x} = \sum_{z} \underbrace{\pi(z \mid x)}_{\text{samples way } z}\; \underbrace{\prob{y(z) \in \Scal_x \mid z}}_{\text{path } z \text{ lands in }\Scal_x}.$$

The second aspect is fair 1 if the way lands in $\Scal_x$ and 0 if it doesn’t, so we sum lone the probability of paths that district in $\Scal_x$:

$$p_x = \sum_{z} \pi(z \mid x)\, \ind{y(z) \in \Scal_x} = \sum_{z \,:\, y(z) \in \Scal_x} \pi(z \mid x).$$

We’ll call $p_x$ the success probability. Maximizing this probability method our example volition be additional apt to example a way that lands in $\Scal_x$.

Note that there are two ways of expanding this probability. Either the example can example multiple paths to the identical answer, all alongside a small probability, or the example can example a sole way to the answer alongside a extremely elevated probability. The achievement probability doesn’t attention which one we get, but in custom we power desire to prioritize particular paths or justify assortment in the samples³.

How do we boost our achievement probability?#

Best-of-$k$ sampling#

The simplest way to pathfind is to have the example $\pi$ keep sampling paths $z$ until one lands in $\Scal_x$.

Since we assumed we had a verifier, we can do best-of-$k$ sampling, which samples $k$ paths and looks for a success. Each way misses alongside probability $1 - p_x$, so all $k$ young female alongside probability $(1-p_x)^k$, and the achievement probability rises from $p_x$ to

$$\prob{\text{at smallest one of } k \text{ paths lands in } \Scal_x \mid x} = 1 - \underbrace{(1-p_x)^k}_{\text{all } k \text{ paths miss}}.$$

This is a guaranteed axis of compute-scaling. As lengthy as $p_x > 0$, the achievement probability increases monotonically as we example additional paths, so spending additional compute on additional samples continually helps, as seen first on by Brown et al. 2024. However, the scaling current is complete the figure of samples fairly than the dimension of the paths (thinking longer).

Train the example to create improved samples#

So far the example was fixed, so $p_x$ was fixed too. Can we alter the example to addition $p_x$ itself?

Here’s a uncomplicated way to do this. Collect many prompts whose answers we can check. For all one, example several paths from the model. We keep the paths that district in $\Scal_x$, and train the example on the ones we kept. Then repeat. Each round, we train the example to put additional importance on the prosperous paths that it sampled. This iteration is called rejection-sampling fine-tuning, and STaR was an first type of it for reasoning.

RL alongside a verifiable reward, which is how today’s reasoning models are trained, turns out to do nearly the identical thing. Let $\pi_\theta$ be the example alongside weights $\theta$. Then since $r(x, y(z)) = 1$ exactly whenever $y(z) \in \Scal_x$:

$$ \begin{aligned} p_x &= \sum_{z \,:\, y(z) \in \Scal_x} \pi_\theta(z \mid x) \\ &= \sum_{z} \pi_\theta(z \mid x)\, \ind{y(z) \in \Scal_x} \\ &= \sum_{z} \pi_\theta(z \mid x)\, r(x, y(z)) \\ &= \ev[z \sim \pi_\theta(\cdot \mid x)]{r(x, y(z))}, \end{aligned} $$

meaning the expected binary reward is exactly the achievement probability $p_x$.

Computing the gradient of the achievement probability via the log-derivative trick, we attain at

$$\grad_\theta\, p_x = \ev[z \sim \pi_\theta(\cdot \mid x)]{r(x, y(z))\, \grad_\theta \log \pi_\theta(z \mid x)},$$

which is exactly the guideline gradient, additionally called the REINFORCE estimator. Now evaluation this gradient alongside $N$ sampled paths $z_1, \dots, z_N$. The paths that unsuccessful have $r = 0$ and disappear, during the paths that district in $\Scal_x$ have $r = 1$. If $M$ of the $N$ paths succeed, the evaluation is

$$\grad_\theta\, p_x \approx \frac{1}{N} \sum_{i \,:\, \text{landed}} \grad_\theta \log \pi_\theta(z_i \mid x) = \underbrace{\frac{M}{N}}_{\text{success rate}} \cdot \underbrace{\frac{1}{M} \sum_{i \,:\, \text{landed}} \grad_\theta \log \pi_\theta(z_i \mid x)}_{\text{log likelihood of the prosperous paths}}.$$

This gradient involves two terms: the empirical achievement charge $M/N$ and the average log likelihood of the prosperous paths.

In another words, a stage of REINFORCE alongside a binary reward is a stage of rejection-sampling fine-tuning on the model’s own sampled paths, scaled by the current achievement charge $M/N$.

This additionally explains the archetypal study from the intro, that chains of idea get longer during RL training. Each stage pulls the example toward the paths that landed, so if the paths that succeeded tended to be longer than the ones that unsuccessful (e.g. since they contain backtracking behavior, collect additional feedback from the environment, or fair dumb luck), the example volition study to compose longer paths.

Amortization and generalization#

During RL training, we expend a lot of compute to hunt for fine paths and afterward we train the example to create those paths directly. Once trained, the example can create the answer without spending all this additional compute on sampling multiple paths. This is called amortizing the search: we pay for it formerly during training, so we don’t have to pay for it all period we ask the example a question.⁴ Since we train a single model on many prompts, the example can possibly generalize to new prompts it has never seen.

The fig below shows this for four prompts, all alongside its own $\Scal_x$, and switches between the example before and following training. During training, we example paths from all prompt, keep the ones that succeed, and SFT on them. The percentages display the empirical charge of achievement stated all prompt. The hollow immediate is new and not seen before by the model, but the example generalizes to this new immediate following training. As the new immediate gets additional from the training distribution, its accuracy drops (try dragging it!).

Revisiting several observations concerning reasoning#

Once we see a reasoning example as a allocation trained to district in $\Scal_x$ stated any $x$, a few odd facts concerning these models rotate into clear.

Chains of idea are not guaranteed to be faithful#

Our verifier complete lone checks whether the answer is correct. The reward is 1 if $y(z) \in \Scal_x$ and 0 otherwise, so the intermediate tokens $z$ are fair a way for the example to get to the answer. Nothing requires those tokens to be true, or to explain the answer that follows, although of way these are frequently correlated because of the model’s pretraining.

If we desire meaningful chains of thought, we need to modify the rewards or the training objective.

Reward hacking comes from the two the verifier and the sampler#

We additionally assumed the verifier is never wrong, that $r(x, y)=1$ if and lone if $y$ is really the accurate answer to $x$. But in custom the verifier can be imperfect. Then $\Scal_x$, the set of answers the verifier accepts, isn’t the identical as the set of accurate answers: it can contain incorrect answers and depart out accurate ones. This misspecification opens up regions of $\Scal_x$ that the example power study to district in equal although it’s not what we intended.

Thus reward-hacking comes from an imperfect verifier, which fails to grasp what we really want, as fine as an imperfect sampling procedure which fails to remain distant from these shortcut regions.

In the fig below, the dashed circle is the set of accurate answers and the red oval is $\Scal_x$. The oval stretches rear toward the prompt, so its incorrect answers are the easiest to reach, and following training most paths district there.

This visualization additionally shows why scaling up RL (generating additional samples and longer samples) makes reward hacking additional likely. The additional extensively we “fuzz” the verifier, the additional apt we’ll discover a way that lands in the misspecified area of $\Scal_x$. Once the example finds this shortcut, training pushes the example towards akin behavior.

So what goal is reasoning solving?#

Reasoning isn’t really solving any goal at all. A way is not guaranteed to converge to the accurate answer. It’s lone in aggregate, complete the allocation of paths, do we get a elevated probability of success.

RL training immediately raises the achievement probability $p_x$, by spending a lot of compute on searching for prosperous paths and afterward training the example to reproduce them. The gains we credit to “thinking longer” arrive from the hunt we paid for during training, fairly than at inference. Longer chains of thoughts lean to correlate alongside achievement during training, which afterward gets trained into the model. The disbursal of the sampling is amortized into the weights, so at conclusion period the example is apt to win alongside a sole sample, but any idiosyncratic example is not guaranteed to succeed.

A prudent housewife formerly proclaimed the power of "Receipts, proof, timelines, screenshots." You'll cognize this if you've always been the individual who put downward their cardstock to end an clumsy standoff at the end of an costly meal, and afterward fretted concerning whether you'd be paid back.

But the days of stressing complete covering collection expenses are concerning to get a small easier: Venmo fair added receipt scanning to its app.

The feature, which is rolling out to Venmo users this week, lets you break or upload a image of your receipt and itemizes it for you. Venmo afterward calculates how much proportionate tax and tip everyone owes (which I, as a math-averse person, have continually established to be the most tedious part) before sending out the requests.

The function helps create Venmo a one-stop shop for invoice splitting and repayment — and particularly helpful for Gen Z, the collection that's least apt to transport cash and who struggles alongside murky repayment etiquette.

Gen Zers don't equal concur on how much all individual owes whenever they portion meals: Venmo's own study of 5,000 18- to 22-year-olds in the US found that 20% favor to have everyone pay exactly what they ordered, 15% would fairly distinct evenly, and 12% obtain turns or have distinct group shield distinct costs.

By allowing users to divided by item on the receipt, Venmo is making it easier to portion expenses — but I wanted to discover out exactly how much easier. So I tested the new function alongside two of my colleagues (Disclaimer: Neither of them is Gen Z) on Monday to see how it stacks up against two another famous receipt-scanning apps, Tab and Splitwise.

Here's how it went.

(Robot) Literacy isn't dead

In the Pay/Request tab of my app, I selected the "Scan Receipt" function and uploaded a image of the invoice from Monday's lunch at Blue Park Kitchen, a fast-casual shared we common in downtown New York City.

Dan, Grace, and I all got bowls alongside veggies, grains, and varying degrees of protein.

Venmo's function accurately transcribed the totals from the picture.

Tab, the receipt-scanning app I've used for years, sometimes misreads the date or inspect figure as the amount paid, resulting in an inaccurate balance. That study error didn't happen in Venmo's new function.

I reached out to Tab for comment, but didn't comprehend back.

Extra protein, please

After uploading the receipt and confirming the totals, I selected Dan and Grace as the group I wanted to petition prosperity from. Then, I assigned who ordered what.

Grace and I all ordered a seasonal specialty bowl, during Dan opted to DIY his, adding additional protein. Because of that, Dan's subtotal was a small higher than quarry and Grace's.

Venmo afterward calculated the proportionate tax and tip we all owed. Once I reviewed and confirmed these totals, I hit "send" to petition repayment.

Getting a second opinion

To inspect Venmo's math, I went onward and loaded the identical receipt into Splitwise, a famous bill-splitting calculator. That app turned out the exact identical totals as Venmo's did — but without the comfort of settling up inside the identical app.

That's the biggest value-add that Venmo's function brings: It lets you outsource the math, dispatch requests, and, hopefully, get repaid faster, without always leaving the app.

Adding a receipt-scanning function likely won't resolve Gen Z's disturbance concerning etiquette, but it power decrease several of the conflict about it. I'd fairly have a neutral, third-party app akin Venmo inform me what I owe in tax and tip than a companion who might be overcharging me.

And although the function could cut downward arguments complete what everyone owes, it doesn't justify that your allies volition react to — or equal see — your requests any faster.

Although Grace repaid me as shortly as I sent the request, I didn't get my prosperity rear from Dan (who was presumably #lockedin penning Business Insider's newsletter) until I verbally asked him if he'd seen it.

SpaceX announced a new type of its all-in-one moon net terminal that integrates a dish, Wi-Fi router, and now a power division into a sole equipment concerning the size of a laptop. According to some users on X who shared screenshots of invitations to command Starlink Mini 2, the dish is priced at $999, which includes a complete twelvemonth of Roam Unlimited assistance that SpaceX values at $2,100.

The Starlink Mini 2 volition run for up to 4.5 hours on a complete charge, SpaceX claims, but that can be expanded by hot-swapping in additional batteries that volition be sold separately, or by connecting the dish to a 60W power origin complete USB-C.

Despite the supplement of an unified battery, the Starlink Mini 2 dish is thinner than the first Mini. SpaceX has managed to slim it downward to 23.1mm thick, during the former type of the Mini was 38.5mm. The Starlink Mini 2 is slightly heavier at about 3.5 pounds hence of the added power division weight, but carries onward an IP67 dust- and waterproof ranking so it’s motionless harmless to use outdoors.

Preorders are now open “by invitation for choose US customers,” according to the Starlink website, but “coming soon” to additional customers and countries.

Google is expanding ads in AI Mode to additional than 10 new countries in English during launching multiturn ads for follow-up queries in the U.S.

Google shared the update from its authoritative Google Ads X account on November 9, following an before LinkedIn post from Dan Taylor noting plans to bring ads in AI Mode to additional countries in Q4.

Eligible campaigns can already participate, although advertisers motionless can’t see AI Mode achievement separately in Google Ads.

Here’s what’s changing, how conversational placements work, and what you need to cognize whenever clients ask concerning AI Mode reporting.

Where Ads In AI Mode Are Expanding

Google’s October notice named 10 additional countries anywhere ads in AI Mode are expanding:

• Australia
• Canada
• India
• Kenya
• Malaysia
• New Zealand
• Nigeria
• Pakistan
• Phillippines
• Singapore

The enlargement currently supports English. Google additionally announced plans to bring ads in AI Mode to Brazil, Mexico, and the United Kingdom in Q4 2026. Those markets were listed as upcoming fairly than part of the first rollout.

Google’s availability documentation notes that Pakistan supports content ads only.

Multiturn ads are launching in the U.S., allowing ads to appear on follow-up questions inside an AI Mode conversation. Google hasn’t announced whenever those placements volition develop exterior the U.S.

How Ads Work Across Follow-Up Questions In AI Mode

Google describes AI Mode as a hunt cognition for questions involving exploration, reasoning, or complex comparisons. Users can ask a detailed inquiry an dcontinue alongside follow-ups as they activity through their options.

AI Mode may use “query fan-out,” issuing connected searches throughout subtopics and data sources to create a response.

With multiturn ads, Google can display an ad during those follow-up exchanges whenever the conversation has business relevance.

According to Google’s advertiser documentation, ads can equivalent immediately to a hunt query or diagram on environment from the two the inquiry and AI response. An exploratory inquiry can afterward create an promotion chance equal whenever the person hasn’t searched for a particular product.

In his LinkedIn update, Taylor noted that group are asking longer, additional detailed, and exploratory questions in AI Mode, and described many of those interactions as upper-funnel queries that transport business intent.

For advertisers, that method ads may attain group motionless comparing approaches or deciding what they need. A longer inquiry or another follow-up does not, on its own, signify that person is prepared to buy.

What This Means For Advertisers And Client Reporting

As ads in AI Mode expand, clients volition apt ask whether their campaigns are appearing there and how those ads perform.

In my view, the most crucial item for agency and in-house teams to communicate correct now is that reporting is limited, and you can’t distinct out AI Mode achievement granularly.

Google’s reporting documentation confirms that AI Mode impressions, clicks, conversions, and expenses are included in norm run reporting. Search conditions are additionally blended alongside another traffic, without a distinct AI Mode breakdown. That prevents advertisers from calculating an AI Mode-specific CPA or ROAS.

Google’s keyword prioritization documentation additionally says reported hunt conditions for experiences including AI Mode may approximate the user’s intent. Advertisers should not assume they are seeing the exact inquiry person entered.

Teams can motionless assessment those conditions for relevance, difference conversion performance, and inspect guide quality. If results alter following the rollout, though, the timing solitary can’t immediately established that AI Mode caused it.

When clients ask how AI Mode ads are performing, advertisers can explain that those results are included in run totals. Google Ads doesn’t study them separately, so teams can’t display how much expend or how many conversions came from AI Mode.

AI Mode Expansion Leaves Advertisers With A Reporting Gap

Google is adding countries and follow-up placements without giving advertisers a distinct perspective of their results.

That leaves teams answering questions concerning AI Mode alongside campaign-level data. A run could enhance general during AI Mode underperforms, or the reverse, and the blended totals wouldn’t display that granularity.

Separate reporting would assistance advertisers comprehend which searches and placements are producing revenue or qualified leads. Until Google provides it, teams can explain how their campaigns meet requirements for AI Mode, but can’t necessarily inform clients what those placements contributed to.

Featured image: created by author

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• Robert Hart

Web browser fingerprinting has rotate into a key tracking scheme on the contemporary web, yet its real-world effectiveness and which of the existing mitigations recommendation value at measure remain insufficiently understood.

In this paper, we current a large-scale study of browser fingerprinting according to real-world telemetry from nearly 25 million Firefox publish users. This dataset enables us to quantify user individuality at community measure and acknowledge which characteristics contribute most to this uniqueness.

Our inspection shows fingerprintability is mostly driven by a small set of high-impact characteristics and their combinations, rather than the gathering of many autonomous measurements. Building on these insights, we propose and measure fingerprinting protections according to their effect on individuality and cohort structure, and design data-driven mitigations.

These protections, shipped in Firefox 143, decrease the fraction of uniquely identifiable users from 75% to 46% during maintaining web compatibility, demonstrating that applicable reductions in fingerprintability are achievable in a mainstream browser.

Here's what we desire to current to you alongside this document and the broader range of our work.

Fingerprinting test sites have a apparently endless catalog of measurements they can drag from your browser. If a person has a distinctive fingerprint, it is typically not since of a single measurement, but it is also not since of 20 or 30 measurements. It is a small figure of metrics that make users unique.

In fact, it's extremely near to the 80/20 rule. Specifically, of all the users seen as distinctive by Fingerprint Pro (the most discerning fingerprinter we model) - 80% of them are distinctive following lone 8 metrics - during the fingerprinter collects a total of complete 50 metrics. Signifyd collects additional metrics, but identifies small users as unique: 80% of the users it sees as distinctive volition be identified following 9 metrics notwithstanding collecting complete 80.

Whenever person cites a sole percent whenever describing "How many users are unique" - you have to be suspicious. Because this collapses multiple distinct populations that behave extremely differently.

Firstly, as a baseline, your functioning scheme is continually going to be exposed to a fingerprinter. We are continually operating within the constraint of web compatibility, and lying concerning the user's functioning scheme breaks shortcut keys and gives you the incorrect downloads among another problems. So if we know the website is going to study your genuine OS anyway, we can use it to section the community and analyze it separately.

And whenever you do you see that all functioning scheme behaves alternatively since they have distinct crucial metrics. Admittedly, it's extremely difficult to provision a concise summary of activity and additionally provision four sets of numbers - so equal we fall prey to the trap of providing a sole number, as we did in the previous paragraph. But every time we have a single percentage, location we have that figure damaged downward by functioning system, and in doing so, you volition see that each behaves differently.

It's so, so uncomplicated to obtain all the results for a metric and just compute Shannon entropy - it's a norm yardstick in this literature. But as we display - a elevated Shannon entropy does not reliably foretell fingerprinting impact.

Previous analyses calculated how many group were unique, but they did not analyze the other group - the ones who weren't. All those another group power be 1 myriad users who portion one fingerprint or 500,000 distinct fingerprints shared by 2 group all - that's fairly the difference.

We use the Simpson Index (aka the Herfindahl–Hirschman Index) to quantify that difference and difference different sets of cohorts (shared fingerprints). This lets us compare how productive distinct fingerprinting defenses are based not fair on how much individuality they eliminate but how much they downfall groups of users into all other.

For example, two distinct protections can all lesser the share of distinctive users by concerning 2.1 percent points. But one improves the cohort mark by 56%, during another raises it by lone 13%. Judged on individuality alone, two defenses can appearance equally effective; during the cohort betterment varies significantly.

Protections are not uniformly impactful, and the assortment is not small. As an example, clamping the accessible screen resolution takes 25.3 percent points off cumulative macOS individuality but between 0 and 3 points off the another three platforms. Averaged throughout all users, that 25-point autumn shrinks to 3.5 - a safety that matters enormously to one phase can appearance marginal in the aggregate.

This is yet another logic why it's so crucial to section and analyze data on a per-operating-system basis.

Making users study the identical values makes them appearance additional alike, but it never makes everyone appearance the same. Some users volition still remain out, and that gets worse on websites alongside small visitor populations. Standardization improves the situation, but it should motionless be paired alongside randomization. Randomization is very powerful whenever it works, but can continually be detected and bypassed. Neither can transport the burden of providing privacy on its own.

Correlating fingerprinting values alongside users' hardware and other characteristics let us acknowledge why certain measurements differed between users. Knowing the base logic lets us build protections that attain much further, and defend small groups of users who would alternatively remain out as a minority.

Addressing these differences has already produced additional consistent outputs for image rendering, Web Audio, and mathematical operations on Android, alongside additional base causes being investigated.

Breakage from a strict manner is delayed, and the postpone hides the cause. Turning on a stricter manner is a one-time act - but the damaged website arrives afterward and nothing connects the two. From the user's perspective, this website doesn't activity in Firefox and they have no idea why and are frustrated. Report following study we see group posting for help, confused - notwithstanding having taken particular act to allow a hidden environment opting them into strict fingerprinting protections.

Users don't acknowledge that a environment they changed caused the breakage, and don't cognize how to fix it - so a safety that breaks the web breaks the browser alongside alongside it. We selected and tuned mitigations on the two measured impact and compatibility. For a default, compatibility isn't a trade-off against privacy - it's a precondition for it.

The web is vast, and equal the most innocuous alter is apt to break item somewhere. Some breakage can be anticipated, which helps design, testing, and detection. The another breakage gets fixed lone if a sequence of fortune holds: person has to acknowledge the breakage, study it, reproduce it on the correct hardware, and get it to the right person.

Commercial fingerprinters develop continuously, and they borrow openly from the community that studies them. Sites built to instruct group concerning fingerprinting - CreepJS among them - get picked up and used outright by fingerprinting products.

So we have been deliberately conservative. There are analyses this dataset would assistance that we have not run, data we could have released that we have not, and insulting angles on this activity that we have not pursued. That is a choice fairly than an oversight: we would fairly depart a inquiry open than create it easier to build a improved fingerprinter.

This interactive test re-implements idiosyncratic measurements as close as imaginable to how Firefox's own gathering code performed them, runs in your browser, and compares all outcome against the published total data. For all trait you can see your value and how many of the ~25 myriad measured browsers portion it - general and within your functioning system.

The test runs entirely in your browser. All comparisons happen locally, against our fixed citation files. No data is stored or submitted to us.

If the test, or item alternatively on this site, looks wrong, please report it as an matter on GitHub. For a issue alongside the test, contain the diagnostics from its Copy diagnostics button, or, if you would rather not article your measured values publicly, email them to tom@ritter.vg.

The extended type of the document is accessible as a PDF.

The paper's appendices merge a prosperity of data locked rearward a sensible leaf bounds and PDF formatting. This complete data page carries all of it - all position for all functioning scheme and all three modeled services, the complete safety chains, and the paper's figures regenerated at imprint resolution.

Our measurements arrive from Firefox telemetry, specifically the user-characteristics ping, a dedicated telemetry ping. Unlike normal telemetry pings that are sent regularly, this ping must be explicitly enabled (which has happened twice, formerly in 2025 and formerly in 2026), and sends one data study before becoming dormant again.

This ping carries no client ID, lone a UUID distinctive to the ping used to de-duplicate users. It cannot be keyed to any another telemetry submissions. It is submitted complete Oblivious HTTP (OHTTP): a relay operated independently of Mozilla forwards the encrypted submission, so the relay can see the sender's IP location but not the contents, and Mozilla receives the contents but not the IP address. Access to the collected data was restricted to four authorized Mozilla employees. The data was retained for 90 days and has since been deleted.

We publish four types of data:

1. A catalog of fingerprints generated for distinct phases of protections, per modeled fingerprinter, alongside the user's OS and locale unmasked.
2. Aggregate tables of the frequence of value per metric, divided by OS
3. Aggregate tables of the frequence of value per metric, correlated by another metrics' values, again divided by OS
4. A detailed inspection of rendered canvas images that allows comparison

In the fingerprint lists, the fingerprints cannot be recreated from a website measuring person characteristics, because of the way the telemetry was collected. After being generated, the records were shuffled to a new ordering, and any locale occurring small than 100 times was anonymized.

In the total tables, any value observed fewer than 100 times inside a sole per-OS community is withheld - so the published data describes cohorts, never individuals.

In the canvas comparison, raw canvas renderings are provided, but they are devoid of any association to another person data. Any distinctive canvas rendering is not provided; lone the hash.

For verification and exploration, the code used to procedure the data and execute distinct analyses is accessible for review. It additionally contains the particular fingerprinting scripts captured, reverse engineered, and modeled for the paper.

Far additional can be asked of this dataset than we have asked. Some of it we chose not to pursue, several of it we merely did not think of, and several of it is a improved fit for person who is not us.

There is a conversation commission for proposing follow-up analyses. Two kinds are value posting:

• Answerable from the published total data. Anyone can run these - the per-operating-system distributions are released, and the inspection toolkit is open. Post the inquiry anyway, so the activity is not duplicated and so the outcome lands location others can discover it.
• Needs the raw data. These we would have to run ourselves, since the underlying ping data is not item we can always release. Describe what you desire to cognize and why! To be clear, we do not have the raw data anymore but it is imaginable we volition execute a re-collection in the forthcoming which we could analyze. We cannot commitment to run everything, and several questions we will decrease since we do not desire to assistance build a improved fingerprinter.

The kei truck is coming to the US.

Kei vehicles (short for "keijidosha," approximately translated to "light car") are extremely famous and cheap in Japan. The average cost for a kei car is typically between $6,000 and $11,000.

On Tuesday, Toyota announced plans to collect a small pickup, the Kei-01, at its Indiana factory in the second fractional of 2027. It volition revive Toyota's low-cost Scion brand, which the automaker discontinued in 2016, and volition initially be reserved for business fleets.

The Kei-01 is extremely distinct from the remainder of America's car market. It's nearly 4 feet shorter than a Corolla, gets a motorcycle-sized three-cylinder engine, tops out at 25 mph, and has a steering rotor on the correct side.

More importantly, Toyota is joining a caller set of bets that Americans desire cheaper, smaller alternatives to today's big and costly pickup trucks.

In October, the average transaction cost for a new conveyance rosy to $51,109, per Kelley Blue Book analysis. That's a document high.

Pickup trucks were the most costly category, alongside an average cost of $67,912.

America's list for low-cost car options has been quickly vanishing from car dealership lots. Several sub-$25,000 options — the Mitsubishi Mirage, Nissan Versa, Kia Soul among them — have been discontinued since the end of 2024. Just five new-vehicle models now have an average transaction cost below $25,000, according to Cox Automotive.

For years, Americans who wanted a kei truck have mostly had to buy old ones. Federal rules authorize conveyance imports — equal if they don't encounter US safety standards — if the car is at smallest 25 years old. That has created a small but devoted market of car enthusiasts who are looking for their own keis.

And now, Toyota is the archetypal business to province plans to build its small truck in the US. The automaker stated its truck volition have updated safety features, including contemporary seatbelts, and stated it's eligible to run in all 50 states.

The Kei-01 volition be according to the Daihatsu Hijet Jumbo, which sells in Japan for about $9,000. While the business called its US-bound Kei-01 "affordable" in the media release, it hasn't released pricing figures for the American market.

The scheme volition apt drag on President Donald Trump's heartstrings.

After a 2025 trip to Japan, he has stated multiple times that he wanted companies to commencement construction them in the US.

"I have fair approved TINY CARS to be built in America," he wrote on Truth Social in December 2025. "These cars of the extremely near forthcoming are inexpensive, safe, energy productive and, fairly simply, AMAZING!!! START BUILDING THEM NOW!"

While Toyota is the lone car business alongside tangible US kei truck plans, it's not solitary in betting that there is an beginning below the increasingly gargantuan full-size pickup. Ford plans to build its sub-$30,000 Fathom EV, during Slate Auto and Telo are evolving approximately Mini Cooper-sized electric pickups for the US market.

Google is adding a new characteristic to its underwhelming HiLight ray on the rear of Pixel 11 Pro phones: as part of the November 2026 Pixel Drop, the ray can ray up whenever you get Google Messages texts from your favorite contacts. Sure, it’s a small supplement to the disappointing ways you could use HiLight at the Pixel 11 Pro’s August launch, which included glowing whenever you used Gemini and lighting up for calls from your favorite contacts. But it’s something!

Fortunately, that’s not everything included in the Pixel Drop, as there are a bunch of another new features coming to a wider range of Pixel devices. The “Create My Widget” characteristic to build a tradition widget alongside a immediate is rolling out to Pixel 11 and Pixel 10 phones. Google is bringing Gemini Intelligence features to Pixel 10 devices, including the Rambler transcription tool. Users alongside Pixel 11 and Pixel 10 phones volition be capable to have Gemini create calls on your behalf to assistance alongside tasks akin making reservations or checking inventory. If you have a Pixel Watch 3 or newer, you’ll be capable to see step-by-step instructions during workouts. And users on Pixel 6A phones and newer volition be capable to obtain advantage of a new “Family Hub” place in settings that consolidates family administration controls.

Google is additionally announcing today that its Pixel Tag item tracker is now available. It was initially announced in August.

At 0.4 ounces, the Pixel Tag is slightly lighter than the AirTag 2 and during its oblong scheme is longer than Apple’s tracker, Google’s is a bit thinner. It doesn’t contain a gap to nexus it to keys, luggage, or another items you desire to track, but you can brace it alongside accessories that create it uncomplicated to tether to objects. Unlike Apple’s AirTags, the Pixel Tag has a clasp that can be pressed to trigger a audio should your phone get misplaced alternatively of the tracker.

Google says the Pixel Tag volition run for complete a twelvemonth on a sole CR2032 coin battery, and it has an IP67 ranking so it’s dustproof and can last a shallow dunking. The tracker additionally features ultra wideband (UWB) assistance and Channel Sounding through Bluetooth 6.0. Android phones alongside a UWB part can display the exact extend and direction to the Pixel Tag in the Find Hub app formerly you’re near to it, during Bluetooth Channel Sounding is constricted to fair distance.

The Pixel Tag is compatible alongside smartphones operating Android 9 or later, but you’ll need devices that specifically contain a UWB part or assistance Bluetooth 6.0 to obtain advantage of Find Hub’s exact locating features.

IBM stated it's ending immigration sponsorships for "a bulk of roles" in its biggest unit.

"IBM Consulting participates in U.S. high-skilled visa programs to location accomplishment shortages," an IBM spokesman stated in a statement. "That involvement has been declining for a figure of years and as accomplishment preparedness and client need have evolved, we have opted to cease visa and green cardstock sponsorship for the bulk of specified roles in IBM Consulting."

The news, which was archetypal reported by Bloomberg, comes as the Trump administration has sought to expert down on tech companies' reliance on abroad workers. In September 2025, President Donald Trump introduced a $100,000 fee on several H-1 B applicants, although that recommendation is tied up in the courts.

Last month, Vice President JD Vance announced that the Trump administration was suspending Microsoft from a national program that companies use to sponsor abroad workers for imperishable residency. Vance stated that the tech business had abused the Permanent Labor Certification process, frequently called PERM.

IBM Consulting is by far the largest component of the tech giant. According to its website, there are complete 150,000 consultants. In its 2025 annual report, IBM reported having 264,300 workforce throughout IBM and its entirely owned subsidiaries.

According to national records, IBM had fair complete 1,600 H-1B petitions approved in financial twelvemonth 2026, compared to complete 2,000 in FY 2025 and nearly 3,000 in FY 2024. The records are for IBM as a whole, definition they solitary wouldn't decide how many IBM Consulting workforce are being sponsored. The figures may additionally not portray new workers, as they can mention to revised or extended applications for existing employees.

Bloomberg reported that workforce affected by the alter can continue operating for IBM exterior the US or depart the business by the end of the year.

Anthropic is betting its tech can assistance corporate lawyers depend small on exterior counsel. Its own way to the community markets offers a reminder that law firms are not going anywhere.

The business expects to expend $41.5 myriad on lawful fees for its inventory market debut, according to paperwork it submitted Tuesday for its initial community offering. It additionally disclosed expenses tied to underwriting and a highway show. Outside advise was among its largest expenses.

That is a hefty sum equal by IPO standards. A Nasdaq analysis of 1,239 market listings from 2016 through 2025 established that companies raising additional than $1 milliard budgeted an average of $4.9 myriad for lawful fees. The fig may be rising as companies remain personal longer and arrive to market as larger, additional complex businesses.

SpaceX's IPO offers one sign of the new scale. Its S-1 filing showed the aerospace elephantine budgeted $25.5 myriad in lawful fees onward of its Nasdaq debut, the largest offering on record.

For the law firms on either flank of the deal, Anthropic's listing is an chance to portion in the windfall. Wilson Sonsini is foremost the offering, during Freshfields is representing the underwriters.

Orrick advised Anthropic on administrator pay and equity matters. Cleary Gottlieb advised the company's Long-Term Benefit Trust on governance issues, and Cravath, Swaine & Moore advised Anthropic's cofounders on governance matters.

A spokesman for Anthropic didn't immediately react to a petition for comment.

Anthropic is not fair staffing up exterior counsel. It's additionally reshuffling its own lawful squad onward of a market debut.

The business promoted Elena Leichty to broad advise at the end of the summer, during Jeffrey Bleich transitioned to a new function as global particular envoy. Brian Israel serves as chief lawful officer.

How do you remain on top of kernel development?

LWN is the lone outlet providing safety of Linux kernel betterment from the inside. Beyond contiguous admission to all content, LWN subscribers get a figure of benefits, including admission to the LWN Kernel Source Database, and they provision the crucial assistance that keeps this distinctive safety alive.

GIve LWN a try: get a one-month liberated test subscription, no obligations, no tricks, no credit cardstock required.

[+] Proceed to the article

By Joe Brockmeier
November 10, 2026

Mike Lothian owns a docking position (or dock) that uses a DisplayLink chipset that had, at smallest in his opinion, subpar achievement on Linux. He decided to try improving the Linux driver, alongside LLM assistance, but ran into one problem that he could not solve—so he asked for help, expecting that Synaptics's developers would contribute a hand. (DisplayLink was acquired by Synaptics in 2022.) Rather than assistance him in improving the driver, however, the Synaptics lawful squad responded alongside patent infringement claims, a demand that he cease unauthorized implementation of DisplayLink technology, and threats of costly lawful penalties. For now, it appears that Linux users of the company's docks volition have to continue to live without characteristic parity alongside other platforms.

Synaptics and Displaylink

The dock that he was trying to assistance is a Dell equipment that connects to a scheme via USB 3 and allows users to nexus multiple monitors and other devices to their computer. It provides its DisplayLink Manager (DLM) application for Linux for the devices via an APT repository for Ubuntu.

The business lone provides DLM application in binary form, and lone for x86-based platforms. The application is proprietary, but the business does keep an open-source, out-of-tree kernel component that is included alongside DLM, the Extensible Virtual Display Interface (EVDI) project. It provides a controller that's compatible alongside Linux DRM and allows users to oversee their docked displays alongside norm tools used for display settings, specified as xrandr or GNOME and KDE Plasma's utilities.

A short article explains why the business has not released origin code for DLM: the software uses a proprietary graphics innovation called DL3 to create a compressed stream of the assorted channels (e.g., USB, DisplayLink video, audio) complete a communication conduit (USB 3 in this case) and decode it again for output to the user's devices.

Unfortunately, the vendor-supplied application did not activity fine for Lothian. I spoke and chatted alongside him via Signal during penning this article; he stated that the controller kept crashing under Linux, so he decided to use his Claude subscription to try to create a new controller and assistance software.

Vino

He posted the first patch series for Vino—his Rust-based DRM/KMS driver—to the kernel's dri-devel catalog as an RFC on June 17. Lothian noted that it was in an incomplete state, and asked for assistance alongside a blocker that he had run into trying to communicate alongside the dock's authority plane. "If you have knowledge of the DisplayLink DL3 control-plane involvement sequence, or ideas for isolating a whole-bus timing/ordering asset a per-channel diff cannot see, help would be extremely welcome." He received feedback from a few developers, including guidance from Danilo Krummrich to rework the controller to use the Rust KMS bindings, but no advice specific to the issue he was trying to solve.

Lothian additionally opened an issue on the EVDI GitHub repository on June 17. It explained the activity he'd done so far, outlined the approach in trying to reverse-engineer the DL3 protocol, provided links to his GitHub repository alongside the Vino code, and asked for clarification on a few issues. There was no answer to his issue.

He additionally reached out to Łukasz Spintzyk of Synaptics immediately by email; his correspondence alongside the business between July 5 and October 7 is available in this PDF compiled by Lothian. He noted that he had established a few bugs in EVDI and opened a pull request (that went unreviewed for multiple months), and additionally asked for aid anywhere he was blocked on Vino. On July 20, he received a reply, not from Spintzyk, but from Michal Lukaszek of Synaptics:

The logic I'm penning is that DisplayLink is not an open standard. The protocol, codec, and another technologies used by DisplayLink devices are proprietary to Synaptics and are protected by a significant portfolio of patents in a figure of jurisdictions, including the UK.

From what you've published, it appears that the VINO project is progressing beyond interoperability activity and toward implementing innovation that we believe is covered by those patents. While many developers understandably concentration on avoiding copyrighted origin code, patent law is different. An independently written or clean-room implementation does not necessarily evade patent infringement if it implements patented inventions.

Lukaszek stated it was entirely imaginable that Lothian was unaware of the "intellectual asset landscape surrounding DisplayLink", so he wanted to create him conscious now fairly than "have the circumstance rotate into additional formal later". Lothian lives in Scotland and has a ".co.uk" email address, which explains why the email made citation to the UK.

Cease and desist

On August 17, Andrew Bateman, an aide broad assembly of intellectual asset for Synaptics, sent a cease-and-desist notice. The letter stated that Lothian had not "substantially responded" to Lukaszek's communication, and had continued with "public statements and additional betterment activities". It included an appendix alongside a catalog of 125 patents that "cover essential aspects of DisplayLink's architecture and operation", but did not define which patents might be infringed by the work.

Synaptics included a catalog of eight demands it expected contiguous compliance with, including ceasing betterment of "any application or other implementation that infringes any Synaptics patent", withdrawing submissions of materials "made to Linux kernel maintainers, mailing lists, code-hosting platforms, bundle maintainers, distributions, or any another individual or organization", and to acknowledge in penning all location "in which the relevant materials have been published, submitted, stored, or distributed". It additionally demanded that he provision Synaptics alongside a signed confirmation, by August 28, that he had complied alongside its demands.

Lothian, instead, posted version three of his Vino activity for the DisplayLink docks to the dri-devel catalog on August 26. He stated that the former two versions had "never lit a panel", but the new activity could authority three generations of DL3 docks without any vendor software. There were no substantive reviews of the place set posted to the list, although there were several automated reviews from Sashiko.

On August 27, Lothian responded to Bateman and asked him to elaborate on the patents that Synaptics idea were being infringed. "A assertion chart mapping the applicable claims to the applicable code would create it much easier for me to comprehend and correctly react to your concerns."

He additionally expressed disturbance concerning Synaptics's stance that his activity was being treated as patent infringement. Synaptics is a associate of the Open Invention Network (OIN), which was created to assistance open-source developers and vendors agreement alongside the threats of costly patent litigation. It holds a large, cross-licensed portfolio that serves to provision patent protection to individuals, open-source projects, and ample vendors alike. Lothian stated that he was now an OIN licensee as well, and he idea that it was expected to "reduce exactly this kind of patent issue about Linux and open source software".

He asked Bateman to explain how Synaptics claims "interact alongside our respective OIN commitments", and expressed a desire to kind things out with Synaptics constructively. "I don't think a community disagreement between Synaptics and an idiosyncratic open origin developer would be particularly helpful for either of us, particularly inside the Linux and open origin communities".

Lothian did not think he could "reasonably provision the signed undertaking" Synaptics had demanded by August 28 without understanding which patent claims it was alleging. He asked for an expansion to the deadline. There was no response from Bateman, so Lothian sent a follow-up email on September 18. He stated he was concerned there was no acknowledgment to his last email, and wondered it was harmless to assume he could continue his work. He had been operating the Vino controller himself for fairly a while, and was enthusiastic to move it upstream without having patent threats hanging complete the work.

Bateman responded the next day and stated that he have to be concerned:

As you earlier requested, we have been documenting your ongoing patent infringement by mapping your Vino controller to particular claims of our DisplayLink patents. I volition provision this assertion mapping records to you inside the next two weeks. I volition additionally be sending to you our petition for significant monetary compensation to recoup the disbursal and attempt required to create specified documentation of your infringing acts. I earlier gave you the chance to halt your infringement, but you decided to shove rear and not heed my warnings, and so now we are escalating as we deem appropriate.

The letter continued to assertion that Lothian would be "single-handedly eviscerating DisplayLink's rivalrous advantage in this crowded market by educating our competitors and another third parties as to how we execute our proprietary technology". He stated that DisplayLink is not a associate of OIN, and none of its innovation is licensable through OIN or any other organization.

Bateman sent a follow-up letter, dated October 5, that included two assertion charts for UK patents: GB2484736B ("Display authority device") and GB2485613B ("Image generation"), the two submitted in 2010 and published in 2012. He reiterated Synaptics's demands from its previous letter, and stated that the deadline was now October 11; nonaccomplishment to comply would outcome in the business seeking "monetary compensation from you in the amount of US$25,000 to recoup the expenses and attempt required to create the attached records of your infringing acts". That amount would be risen by $25,000 all week until the company's demands were complied with.

Lothian responded the next day, by email, to say that he was on vacation, and would not be rear residence until October 13. He asked for an extension of the deadline to come back residence and go through the assertion charts "ideally with someone qualified to assistance me peruse them". Bateman replied that Synaptics would concur to broaden the deadline to October 18, although no further extensions would be granted. He reiterated that "DisplayLink is not a member of OIN" and none of its innovation was licensable through OIN.

OIN to the rescue?

Synaptics is a associate of OIN, and is listed in the community directory. In supplement to joining OIN, Lothian had reached out to the organization to try to solicit its assistance and has been in communication alongside several of the personnel there. OIN indicated that it would appearance into the matter and see if he had any safety as an OIN member. That procedure took longer than Lothian hoped, although OIN did eventually provision its conclusion.

OIN's portfolio covers a broad range of application that matches its Linux System Definition, but it does not shield everything that power be implemented for a Linux system. In particular, its definition specifies that codecs ("a component of a program capable of performing transformations of a video, still image or audio data stream or signal") are not covered unless specifically mentioned in this list of components.

I said briefly to OIN CEO Keith Bergelt on October 23, shortly after OIN personnel informed Lothian of the outcome of their research. Bergelt stated that OIN's decision was that, since codecs are explicitly not covered by OIN's Linux System Definition, its protections did not broaden to Lothian's attempted kernel contributions. OIN had recommended that Lothian attain out to a attorney in the UK, and provided a recommendation.

Status

Unfortunately, that referral did not pan out, Lothian said. He has not stated up entirely, but he has taken downward the GitHub repository to comply alongside Synaptics's demands. For now, he may continue operating on several of components that are not affected by the patent claims, but the activity on Vino volition not be submitted to the kernel. "Vino itself has to remain local until this is sorted out, I just can't oversee to defend myself against Synaptics."

Perhaps amusingly, Lothian told me that person from Synaptics finally replied to the drag petition on October 7. Lothian told me he does not intend to prosecute up on the comments: "You can't endanger to sue me and then say 'can you update your drag request?'"

For the period being, Synaptics has succeeded in shutting downward an attempt to improve the way DisplayLink docks activity alongside Linux. It is an unpleasant reminder that, during much of the current conversations in open-source communities are concerned alongside LLM-driven development, application patents motionless stance problems for free-software developers and users.

Quickstart

npx modern-web-guidance@latest install

Try it out (without installing)

# Search for applicable guides npx modern-web-guidance@latest hunt "animate a dialog modal backdrop" # Retrieve a guide by ID npx modern-web-guidance@latest recover "animate-to-from-top-layer" # Scan a codebase for bequest patterns to modernize npx modern-web-guidance@latest legacy-scan .

Why?

What's Included?

CSS & Layout (140 features)

JavaScript & APIs (74 features)

Safe Adoption of Modern Features

Project Configuration (.mwgrc)

How It Works

Tip

Note: We use npx to justify the satisfied doesn't go stale, but the CLI plant offline, entirely personal and local. The npm bundle is self-contained, alongside no additional requirements to justify the two low-latency and supply-chain security.

Alternative Installation Methods

npx skills add GoogleChrome/modern-web-guidance

gh accomplishment instal GoogleChrome/modern-web-guidance

agy plugin instal https://github.com/GoogleChrome/modern-web-guidance

/plugin market add GoogleChrome/modern-web-guidance /plugin instal modern-web-guidance@googlechrome

/plugin market add GoogleChrome/modern-web-guidance /plugin instal modern-web-guidance@googlechrome /plugin # Select GoogleChrome marketplace, media enter, allow AutoUpdate /reload-plugins

grok plugin instal https://github.com/GoogleChrome/modern-web-guidance --trust

/plugins instal https://github.com/GoogleChrome/modern-web-guidance

codex plugin market add GoogleChrome/modern-web-guidance codex plugin add modern-web-guidance@googlechrome

Updating

Evals to demonstrate this plant well

 [ Expert-authored direction and demo ] │ ▼ [ Target generation ] ──> Tasks on genuine webapps, Golden solutions, Playwright graders │ ▼ [ Calibration iteration ] ───> Runs Grader on Golden Solutions (Must Pass 100%) │ Runs Grader on Unmodified Base App (Must Fail 100%) ▼ [ E2E delegate evals ] ────> Runs coding agents in guided vs. unguided modes Compares continue rates and accessibility w/ and w/o the skill 

Recent eval results snapshot

Available Skill Packs

npx modern-web-guidance@latest instal --choose

Telemetry & Privacy

Tip

To Opt-Out: set the DISABLE_TELEMETRY=1 env changeable in your casing overview (e.g., .bashrc or .zshrc):

export DISABLE_TELEMETRY=1

Contributors

License

Every so often, person in SEO announces “f*** keywords” or a akin sentiment. It’s not really a “hot take.” Moreover, it’s item that we see period and again.

Sometimes it’s a fair reply to how reluctant several group in the industry are to change. Other times it feels additional akin a meme response, or a flex. A way to display you’ve moved on from old-school SEO without saying much concerning what you do differently, exaggerating a strain between pro/anti keywords without understanding it.

I have several sympathy for the opinion for sure. I’ve worked on too many campaigns anywhere achievement came downward to whether a fistful of keywords went up or down. Once those rankings became THE GOAL, it was uncomplicated to expend additional period explaining two dropped places than asking whether we met the chief endeavor need. More damagingly, in the worst cases we were remaining making decisions fair for promoting keywords, fairly than the endeavor goals they were expected to enable.

I don’t cognize how many group motionless run campaigns that way, and I doubtful small than the “f*** keywords” cry implies. What interests me is how quickly the disagreement goes from “this is a bad way to fairness a campaign” to “why are we thinking concerning keywords at all?”

Which Page Is Catering To What Intent?

Take an example hunt akin [hotels near Heathrow alongside a shuttle]. It’s a decent shorthand for what person wants to cognize before they book. Is there a shuttle to the terminal? Does it run whenever my escape lands? Do I need to reserve a seat? It additionally gives you a clue concerning what tongue you need to use to acquire acknowledgment from the searcher.

If guests are looking for a shuttle, the leaf titled “ground transport solutions” is making them do additional activity than they need to. Again, a fictitious example, but I have misplaced track of how many times in my occupation I’ve seen businesses wanting to mark the ways they define their products/services, NOT how group hunt for them.

In this instance, what is additional crucial is that the first hunt query becomes emblematic of a sequence of questions that need answering, possibly throughout a sequence of pages. Whether we call this a hub and said model, or a satisfied cluster, whatever, we have captured part of the client journey alongside a query and afterward resolute the possible connected queries.

This is anywhere a keyword map (or location map) earns its keep, lengthy following the first investigation is done and the satisfied written and published. Imagine that six months afterward person rewrites a particular location page. The shuttle timetable moves into a broad FAQ and a new transfers leaf goes live. All three changes are totally logical, but taken together, they can depart the location without a apparent location to answer the inquiry the location leaf used to own. Without realizing it, you have sleepwalked into undoing the investigation you have done before.

If you’ve recorded which leaf serves that need, the next satisfied publishing company or SEO has item to go rear to. They can justify that intent/search query is motionless considered, move satisfied responsibility, update the inner links, and justify the redirects are done. The keyword map preserves the reasoning rearward those first decisions and helps you inform the difference between an intentional alter and targeting that drifted complete a few rounds of absolutely sensible edits.

The keywords aren’t the sole truth, nor are they to be treated as an immutable dogma, but throwing them in the “f***it bucket” could be missing item important.

Too chill for keywords now? Great, but how does governance work? How do you justify you’re not wasting the prosperity already spent on research? Yeah, I cognize – that appears tedious, since it can be. And construction a procedure to create certain this isn’t painfully manual IS hard.

But it is value it.

What Will You Actually Track?

You can arrange your activity about topics, and you can use embeddings to assistance collection the tongue – in fact you should! But at several point, you’ll at smallest need to benchmark general visibility, and you motionless have to choose what to category into a hunt box or put in a prompt. You can total your rank/prompt tracking for a satisfied collection – which is needed – but before the aggregation comes selections of idiosyncratic prompts and terms.

Stakeholders need to buy into those prompts/terms/queries that create up that collection OR you 10x the figure of particular tracked elements to totally de-emphasize any idiosyncratic one. Not a bad idea, but you addition your expenses considerably and motionless need to regard the wording, conditions and intents on idiosyncratic pages whenever it comes to making optimizations.

Instead, I’d happily track a few delegate searches for a stated page, anywhere I would additionally desire to see which URL(s) appeared and whether they answered the question. Those observations can inform us whether the leaf is doing several of the activity we intended, whether another leaf is possibly conflicting and anywhere our own satisfied is falling rearward competitors.

“Wait, we motionless use keywords in our data, but we don’t study on the achievement of idiosyncratic keywords anymore!” That is absolutely sensible, but it turns out that maybe, perhaps, the “f*** keywords” stance is additional clickbait than genuine astute thinking – right?

These are SEO basics; I totally understand. Prompt tracking and AI hunt do ultimately de-emphasize the importance of exact keywords in favor of meaning. But regard that embedding content into vectors is so “machines” can comprehend it. Your team, colleagues and stakeholders motionless need the individual type to comprehend how to optimize and tell the narrative of what is/isn’t working.

What Happened Between The Ranking And The Business Outcomes?

All of this helps us comprehend what we’re apparent for, but it doesn’t inform us what happened next – and that is perchance the biggest weakness.

In Search City, I wrote concerning the enticement to lone optimize for the bits of the hunt journey we can see. Rankings were continually among the easier bits to detect – that is why we have had an mighty job replacing them alongside a additional helpful metric, and why the issues hitting immediate trackers are so disruptive to the industry! But now, equal whenever person makes a hunt and sees you for that query/keyword/prompt, the part that links that instant to a click and afterward an crucial endeavor event can be nearly totally disconnected.

Knowing whether we “rank” for a keyword or not is motionless an crucial data point. Fixating on that sole data item is mad – but that goes for any sole data point. We need to use it to build the picture, build a narrative and provision insight.

If I went rear to any of those campaigns I ran anywhere I felt sole conditions were prioritized (to a fault), I’d likely motionless keep several of the identical keywords – as triggering as that may be.

I’d use them to concur which searches mattered the most, decide which pages should assist them and inspect whether those pages motionless do the job. When it came period to fairness the campaign, I’d desire to cognize what happened to the customers/clicks we were trying to win. That was the inquiry the rankings were expected to assistance us answer in the archetypal place, and it is motionless item they can assistance answer today.

More Resources:

• AI Visibility Measurement: What To Track & What To Ignore
• 4 Datasets On AI Visibility: ChatGPT Sends 0.62% Of Google’s Organic Traffic
• How To Define & Report SEO KPIs That Actually Move The C-Suite

This article was initially published on Chris Green SEO.

Featured Image: Roman Samborskyi/Shutterstock

Uber cofounder and ex-CEO Travis Kalanick has shared a guide for combating what he called a "modern-day coup/psyop/struggle session" following Nvidia CEO Jensen Huang became embroiled in scandal.

Kalanick did not name Huang or Nvidia in his X article Monday, but his communication appeared as scrutiny intensified complete Huang's admission that he had an extramarital affair.

At the center of the saga is Alice Huang, whom The New York Times reported final duration was believed by US intellect officials to be acting as a spy for the Chinese government.

In his X post, Kalanick detailed a 12-point catalog of "practical principles to counter" a reputational crisis.

"Own what you did but fiercely oppose accommodating the mob, oppose owning half-truths or actions/behaviors you didn't do," Kalanick wrote as the archetypal regulation on his list.

He followed that with, "The op is not concerning your former actions, it's concerning your enemies and this event as their opportunity."

Kalanick additionally suggested that any fallout may not be as dire as you think, writing, "No matter how bad it gets, nothing is as bad as it seems."

The Uber cofounder's direction included finding the "enemies at the extremely base of the op," cutting off anyone suspected of disloyalty, and construction a squad of "close and loyal patriots" alongside one who can run a "well-resourced war room."

"Your enemies aren't as astute or difficult operating as you," Kalanick wrote in item nine. "Outsmart and outwork them as a way of existence until the op is over."

He additionally urged those facing specified a circumstance to transport on alongside "business as usual," adding, "Continue on alongside optimism and joy in your mundane activity and its mission."

Additionally, Kalanick advised downloading the encrypted messaging app Telegram for "all delicate communications."

At the top of the post, Kalanick stated he had "a small period on my hands this dawn to do several writing" and encouraged followers to "send alongside to person you cognize who may benefit."

Kalanick did not immediately react to a petition for comment concerning whether he may have had Jensen Huang in mind.

Uber investors pressured Kalanick to resign as the ride-hailing company's CEO in 2017 following a sequence of scandals roiled the company.

Meanwhile, Jensen Huang's admission that he was "unfaithful" to his spouse of complete 40 years has prompted Nvidia to initiate an autonomous inquiry into whether he was the "target of a abroad intellect effort."

Nvidia's commission has stated it has confidence in his leadership, although the controversy has raised questions concerning whether he volition remain CEO of the $4.5 trillion AI chipmaker.

My family continually hosted Thanksgiving, which meant cooking our complete meal from scratch, from stuffing to gravy to pie crust. One year, we equal made our own rolls.

But everyone is busier these days, and purchasing specialty ingredients adds up. So whenever I heard concerning Aldi's Thanksgiving market plan that, according to the retailer, includes everything you need for a complete, no-fuss, home-cooked feast for 10 for under $39, I knew I had to try it.

A few weeks before the holiday, I visited Aldi, bought the ingredients, and attempted to create its Turkey Day spread.

Here's how the shopping, cooking, and tasting experiences stacked up — and whether the budget-friendly list felt value it.

Today, Ableton announced Live 13, which is expected to attain in first 2027 alongside a slew of new features. Most of the changes are apparently insignificant on paper, but whenever combined, they create for the biggest update to the digital audio workstation (DAW) in years.

The characteristic most apt to diagram notice is the supplement of a appropriate stage sequencer. While Ableton’s Push controller had a dedicated stage sequencer, and you could efficiently use the keyboard rotate as one, the desktop app never included one. Ableton didn’t phone this characteristic in either — it’s a mighty sequencer. You can alter velocity, timing, chance, length, and repeats for all stage in the grid. Each track can have its own length, playback rate, and direction, letting you create item complex and evolving.

The supplement of ARA plugin assistance volition additionally apt excite pro producers and vocalists. This method compatible plugins can now be used immediately on audio in the DAW. For example, throw correction and time-stretching tool Melodyne earlier required you to import audio from Ableton into Melodyne, afterward render it rear out to a track in your DAW. Now it can spotless up messy vocal takes immediately in Live’s UI.

Ableton has additionally added a new mixer perspective that lets you see all the loaded devices on a track in a compact vertical view. Here you can drag and autumn them between tracks, or copy and paste them. It additionally makes it much easier to navigate a sprawling 30-track meeting quickly at a glance.

My individual favorite new supplement is Looplet, an experimental looping device alongside four playheads and a ton of options for manipulating your audio. It offers granular processing, filtering, throw and speed control, and all playhead can be routed to idiosyncratic tracks for additional processing. It’s a mighty tool for ambient and soundtrack work.

The complete catalog of new features is sprawling, so current are fair a few additional highlights:

Boeing announced Monday that it volition cut 5% of personnel in its business division, which makes the 737 and another airplanes used by millions of travelers worldwide.

The layoffs stem from Boeing's inability to addition manufacturing as quickly as hoped, definition it is "overstaffed," the division's CEO, Stephanie Pope, stated in an inner memo obtained by Business Insider.

Boeing has been ramping up manufacturing of its most famous aircraft, the 737 Max, as it recovers from a norm and safety emergency sparked by the Alaska Airlines blowout in January 2024.

While it had hoped to create 47 of the 737 Max a duration this year, Boeing CEO Kelly Ortberg told investors on an earnings call in October that expanding manufacturing was taking longer than expected.

That sent the company's portion cost downward 10.7%. The inventory is 25% lesser so far in 2026.

Boeing volition create the cuts through "a blend of attrition and involuntary layoffs," Pope stated in the memo. Affected personnel volition be told commencement in December.

The business airplanes division, known as BCA, is Boeing's largest business, employing concerning 50,000 people, definition the planned 5% decrease is equal to approximately 2,500 employees.

BCA accounted for 44% of the company's income final fourth but made a $697 myriad loss. Boeing's Defense & Space and Global Services divisions were profitable in that period.

In a declaration released following the cuts were announced, Boeing stated it is operating alongside personnel to "minimize the impact" of the layoffs by "supporting their occupation passage through company-sponsored outplacement services, layoff benefits, and employee aid program support."

Read Pope's complete memo to personnel below:

Team,Over the former two years, we have made important investments in talent, training, and infrastructure throughout BCA to stabilize our endeavor and fortify our basis for the future.In supplement to your difficult activity and dedication, these investments have contributed to meaningful improvements in our improvement including norm and shipment performance, 737-7 and 737-10 certification, and affirmative changes to our culture.While we have made significant progress, we have not yet completely recovered.As shared in our third-quarter earnings, we need to reset the gait of our manufacturing charge ramps to provision our factories and provision sequence partners additional period to accomplish stability. Adjusting our manufacturing schedules and reducing airplane output puts us in an overstaffed position.Unfortunately, that method we need to align our staffing levels to our revised endeavor plan, resulting in a decrease of about 5% through a blend of attrition and involuntary layoffs, alongside notifications commencement in December.I cognize how difficult these actions are and the effect they volition have on all of us, our families, and our communities. We volition never obtain that lightly. We volition do everything we can to minimise the effect by supporting occupation transitions through company-sponsored outplacement services, layoff benefits, and Employee Assistance Program support.Throughout this process, we volition remain true to our Values & Behaviors, including our resolute dedication to safety and norm and our duty to justify that everyone is treated alongside dignity and respect.Thank you for everything you do to assistance all another and our customers. The advancement we have made demonstrates what is imaginable whenever we remain focused, resilient, and committed to one another. By continuing to attention for one another, uphold our standards, and activity together alongside purpose, we volition complete our improvement and build a stronger forthcoming for BCA.Stephanie