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In today’s post, we get the low-down from Ali Alvi Q&A / featured snippet team lead at Bing.

Alvi’s official title is “Principal Lead Program Manager AI Products, Bing”

Read that twice and you’ll get a good idea of how much more this interview contains than “just” how to get a featured snippet.

During the podcast interview, I was looking to get an insight into how Bing generates the Q&A (featured snippet in Google-speak)…

That means I was asking how they extract the best answer to any question from the hundreds of billions of pages on the web.

I got that.

And way more.

Q&A / Featured Snippets

Firstly (and the raison d’etre) of this interview – I wanted to have an informative chat with someone from the team that works on the algorithm to generate the best possible answer. (Answer Engine Optimization is my thing.)

Descriptions (Snippets That Aren’t Featured)

Unexpectedly, I also got an insight into the algorithm that generates the descriptions used under the traditional blue links.

Turns out that the two are intimately linked.

Alvi (under) states that beautifully – for Q&A, Google uses the term “featured snippets.”

So those results right at the top, front and center are simply blue link snippets that are featured.

Blindingly obvious, once you fully digest the idea that the text below the blue links are not “glorified meta descriptions”, but summaries of the page adapted to address the search query.

Why Meta Descriptions Don’t Affect Ranking

Meta descriptions have no effect on rankings.


Because they moved that to a different algorithm years ago.

Possibly when they told us they no longer took them into consideration. Oh jeez.

SEO experts over-optimize meta descriptions.

Everyone else fails to provide one.

Either way, site owners are doing a bad job. 🙂

Bing and Google cannot rely on us to accurately summarise our own pages.

Now you know how Bing creates the ‘blue link descriptions’ when it doesn’t like your meta description.

Q&A / Featured Snippets Grew out of the System They Created to Generate Descriptions on the Fly

So, in short, the answer we see at the top of the results is simply taking a snippet Bing or Google pulled from our content and featuring it.

Alvi makes the point that they aren’t simply “taking a snippet and featuring it.” They do a lot more than that sometimes.

They can (and sometimes do) build summaries of the corpus of text and show that.

Extracting the Implied Question from a Document

And creating a summary of the document is part of the process by which they match the answer contained in a document to the question.

Bing’s user asks a question (in the form of a search query), then Q&A looks at the top blue link results (using Turing) and creates a summary.

That summary gives the question the document implicitly answers.

Identify the implicit question that is closest to the user’s question and, bingo, you have the “best” answer / Q&A / featured snippet.

According to Alvi, they are using high ambition AI that isn’t being used elsewhere, not even in academia. They are teaching machines how to read and understand.

Turing is key to Q&A, but much more than that…

Turing Drives Snippets, That Drive Q&A… & Every Microsoft Product

“Within Bing, we have a group of applied researchers who are working on high ambition natural language processing algorithms…” says Alvi.

The snippets team “are the hub for those algorithms for all of Microsoft.”

From what I understand that means that the team that drives these (seemingly innocuous) descriptions provides the algos to understand corpuses of text, and extract or create chunks of text for display – not only to any candidate set that needs it, but also any platform or software such as Word or Excel.

From an SEO point of view, that means the practice of using machine learning (in the form of Turing at Bing) to create the text that displays to users from titles to descriptions to summaries to answers to questions to… well, who knows?

From a wider perspective, it seems that the way this develops for descriptions of SERPs will give a window into where it is going elsewhere on the Microsoft ecosystem.

Once Alvi says it, it is blindingly obvious that there has to be heavy centralization for this type of technology (so we can use our imaginations and think up some other possible examples).

The interesting thing here is that something that covers (or will cover) all Microsoft products is being farmed out to them from the descriptions for the ten blue links.

Back to How the Search Algorithms Work Darwinism in Search Is a Thing – 100%

This interview is a lovely segue from the article I wrote after hearing how Google ranking works from Gary Illyes at Google.

I had asked Illyes if there is a separate algorithm for the featured snippet and he said “No”…

There is a core algorithm for the blue links and all the candidate sets use that in a modular manner, applying different weightings to the factors (or more accurately, features) in a modular fashion.

Alvi states “the idea is exactly that.”

In the first episode, Frédéric Dubut confirms this, and in the fifth Nathan Chalmers (Whole Page Team Lead) also confirms, so we are now on very safe ground: Darwinism in search is a “thing.”

The Foundation Is Always the 10 Blue Links

Alvi makes a great point: search engines evolve (ooooh, Darwinism again).

Historically, for the first 15 years or so, search engines were just 10 blue links.

Then when new features like Q&A come along, they have to fit on top of the original system without disrupting the core.




Q&A: ‘The Best Answer From the Top Ranking Blue Links’

The Q&A algorithm is simply running through the top results from the blue links to see if it can pull content from one of the documents that accurately answer this question right on the spot.

So ranking in the top 20 or so is necessary (the exact number is unclear, and almost certainly varies on a case-by-case basis).

There is an interesting exception (see below).

Perhaps we tend to forget that people who use Bing and Google trust them.

As a user, we tend to trust the answer at the top. And that is crucial to understanding how both businesses function.

For both, their users are, in truth, their clients. Like any business, Google and Bing must serve their clients.

Those clients want and expect a simple answer to a question, or a quick solution to a problem.

Q&A / featured snippets are the simplest and quickest solution they can provide their clients.

Part of Alvi’s job is to ensure that the result Bing provides sits with the expectations of their clients, Microsoft’s corporate image, and Bing’s business model.

That is a delicate balance that all businesses face:

Satisfy users.

Maintain a corporate image.

Make money.

In the case of Q&A (or, any search result for that matter), that means providing the “best, most convenient answer” for the user without being perceived as wrong, biased, misleading, offensive, or whatever.

Quirk: To Get a Q&A Place You Don’t Necessarily Have to Rank in the Blue Links

Alvi states that most of the time, Q&A simply builds on top of the blue links.

But they memorize the results they show and sometimes show a result that isn’t currently in the blue links.

So, you have to rank to get the Q&A in the first place, but you don’t need to maintain that blue link ranking to be considered for the Q&A spot in the future since Q&A has a memory.

What Are the Ranking Factors for Q&A? Expertise, Authority & Trust. Simple.

Bing use the term “Relevancy” rather than expertise.

What they mean is accuracy, and is not a million miles away from the concept of expertise.

So Q&A is very much E-A-T-based.

Google and Bing are looking at our Expertise, Authority, and Trust because they want to show the “best” results – those that make them appear expert, authoritative and trustworthy to their users.

Now, doesn’t that make sense?

Here’s the Process to Find the ‘Best’ Answer

The algo starts with relevance.

Is the answer correct?

If so, it gets a chance.

The correctness of any document is based on whether it conforms to accepted opinion and the quality of the document.

Both of these are determined by the algorithms’ understanding of entities and their relationships (so entity-based search is a thing, too).

Once an entity is identified as key to the answer, neural networks figure out if that entity is present in this answer.

And if so, what is the context vis-a-vis other related entities also present and how closely does that mini knowledge graph correspond to the “accepted truth.”

Then, from those documents that are relevant (or accurate/correct /expert – choose your version), they will look at the authority and trust signals.

End-to-end neural networks evaluate the explicit and implicit authority and trust of the document, author, and publisher.

End-to-End Neural Networks

Alvi is insistent that Q&A is pretty much end-to-end neural networks / machine learning.

Like Dubut, he sees the algorithm as simply a measuring model…

It measures success and failure and adapts itself accordingly.

Measuring Success & Failure: User Feedback

With end-to-end neural networks, the control that humans have is the data they put in and the metrics they use to judge performance.

They feed what I would call “corrective data” to the machine on an ongoing basis.

The aim is to indicate to the machine:

Where it is getting things right (Dubut talks about reinforcement in learning) .

When it gets it wrong (that pushes the machine to adjust).

Much of that data is based on user feedback in the form of:

Judges (the equivalent of quality raters at Google – Dubut talks about them here).


Feedback from the SERP.

Alvi suggests that this is key to how the machine is judged, but also how the team themselves are judged.

The relevant team members are required to respond internally.

The principal responsibility of the team behind the algo is creating a reliable algorithm that generates results that build trust in the search engine.

For me, that feeds back into the idea that people who search on Bing or Google are their clients.

Like any other business, their business model relies on satisfying those clients.

And like any other business, they have every interest in using client feedback to improve the product.

Ranking Factors Are Out, Metrics Are In

Since machine learning dominates the ranking process, the key question is not “what are the factors” but “what are the metrics.”

The actual calculation of the rankings has become pretty much end-to-end neural networks.

And what humans are tasked to do is to set the metrics, do quality control, and feed clean, labeled data to encourage the machine to correct itself.

The factors the machine uses to meet that measurement is something we (and they) cannot know.

The models Bing has in production have hundreds of millions of parameters.

There is no way anyone can actually go in and understand what is going on. The only way to measure it is give it input and measure the output.

We can give the machines a set of factors we think are relevant.

But once we let them loose on the data, they will identify factors we hadn’t thought of.

These implied / indirect factors are not known to the people at Bing or Google, so it is pointless asking them what they are.

Some of the factors they initially thought were important aren’t.

Some they thought weren’t a big deal are.

And some they hadn’t thought of are needed.

So the question to ask is “what are the metrics” because that is where the product teams have control. These are the measurements of success for the machine.

Importantly, the machine will latch onto whatever the metric is saying.

If the metric is not correct, the machine aims for the wrong targets, the corrective data (instructions) is misleading – and ultimately the machine will get everything wrong.

If the metric is correct, the whole process helps improve results in a virtuous circle, and the results improve for Bing’s clients.

And the Bing product is a success.

Filtering the Results / Guardrails

Since the team is judged on the quality of the results their algo produces and that quality is judged on the capacity of those results to improve Bing’s clients’ trust in the Bing product, they have a filtering algo to prevent “bad” results damaging the Bing brand.

That filter is itself an algorithm based on machine learning.

A filter that learns to identify and suppress anything unhelpful, offensive, or damaging to Bing’s reputation. For example:

Hate speech.

Adult content.

Fake news.

Offensive language.

The filter doesn’t change the chosen candidate, but simply suppresses the bid to the Whole Page algo.

Alvi interestingly points out that they simply exercise the prerogative to not answer a given question.

Annotations Are Key

“Fabrice and his team do some really amazing work that we actually absolutely rely on” Alvi says.

He goes on to say that they cannot build the algos to generate Q&A without Canel’s annotations.

And this series indicates that this is a common theme that applies to all the rich elements.

Specifically to Q&A, these annotations enable the algo to easily identify the relevant blocks and allow them to reach in and pull out the appropriate passage, wherever it appears in a document (Cindy Krum’s “Fraggles”).

They are also the handles the snippets algo use to pull out the most appropriate part of the document when rewriting meta descriptions for the blue links.

That’s already quite cool. But it seems that Canel’s annotations go way further than simply identifying the blocks.

They go so far as to suggest possible relationships between different blocks within the document that vastly facilitates the task of pulling together text from multiple parts of the document and stitching them together.

So, on top of everything else it does, Bingbot has a strong semantic labeling role, too.

And that brings back once again quite how fundamental it is that we structure our pages and give Bingbot (and Googlebot) as many clues as possible so that it can add the richest possible layer of annotation to our HTML, since that annotation vastly helps the algos extract and make the best use of our (wonderful) content.

Q&A Is Leading the Way

Q&A is front and center right at the top in the results, it is a hub used by all the other Microsoft products and it is central to the task-based journey that Bing and Google are talking about as the future of search.

Q&A / featured snippets are the ones really pushing the boundaries and are at the fore and a focal point for us all: Bing, Google, their users, and us as search marketers – which inspires me to say this…

SEO Strategy in a Nutshell

As I listen back to the conversations to write this series of articles, it strikes me just how closely all this fits together.

For me, it is now crystal clear that the entire process of crawling, storing and ranking results (be they blue links or rich elements) is deeply inter-reliant.

And, given what Canel, Dubut, Alvi, Merchant, and Chalmers share in this series, our principal focuses can usefully be summarized as:

Structuring our content to make it easy to crawl, extract and annotate.

Making sure our content is valuable to the subset of their users that is our audience.

Building E-A-T at content, author and publisher levels.

And that is true whatever content we are asking Bing (or Google) to present to their users – whether for blue links or rich elements.

Read the Other Articles in the Bing Series

How Ranking Works at Bing – Frédéric Dubut, Senior Program Manager Lead, Bing

Discovering, Crawling, Extracting and Indexing at Bing – Fabrice Canel Principal Program Manager, Bing

How the Q&A / Featured Snippet Algorithm Works – Ali Alvi, Principal Lead Program Manager AI Products, Bing

How the Image and Video Algorithm Works – Meenaz Merchant, Principal Program Manager Lead, AI and Research, Bing

How the Whole Page Algorithm Works – Nathan Chalmers, Program Manager, Search Relevance Team, Bing

Image Credits

Featured & In-Post Images: Véronique Barnard,

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How To Change Tones In Bing Ai In Microsoft Edge

It’s only been a few weeks since Microsoft unveiled its ChatGPT-powered Bing AI and the tool has since then attracted quite a lot of users for its interesting responses to different types of queries. Being in its early stages, many testers have found that while Microsoft’s AI chatbot can give you accurate results for some occasions, you may also get responses that aren’t entirely correct or straight-up absurd. 

The company has been quick to acknowledge this issue by giving you to option to choose the AI’s personality. The new feature allows you to toggle responses from the AI to suit your needs by letting you set its creativity and accuracy values. In this post, we’ll explain what personality tones you can set for the Bing AI and how to choose a preferred tone on Microsoft Edge. 

What tones can you choose for Bing AI

With the latest update to Bing AI, you can set any of the following tones as your preferred conversation style depending on whether you want to chatbot to give you more accurate responses or something unique and creative. Here’s what you can choose from:

More Creative: Selecting this tone will help you get more original and creative responses from the AI. These responses may not be the most accurate but this setting is designed to generate unique responses that may end up being more descriptive, entertaining, or even surprising. This tone can be used in instances when you want Bing AI to write you essays, poems, and opinions. 

More Balanced: If you’re unclear about a subject you wish to enquire about, you can choose More Balanced to get an unbiased response from Bing. The results will not be as descriptive as the above option but will be powered to sound more neutral about the topic. When looking for a clear opinion with this setting, the AI chatbot will offer pros and cons for the topic in question or choose to ignore the question entirely.  

More Precise: You can choose this setting to get more accurate and on-point answers about topics you’re working on. The responses you get with this tone will be brief and may include links to more detailed info found on the search. 

How to change tones in Bing AI in Microsoft Edge

When the Chat tab opens, you should be able to view toggles to change the personality tone for Bing AI just under the “Welcome to the new Bing” banner. As explained above, you can choose your preferred tone from these options – More Creative, More Balanced, and More Precise to get the desired kind of responses.

You can hover over any of these options to check how they work or the type of responses you may get from each of them. 

When you change the AI’s personality, you should see the chat’s UI change colors to let you know which tone you’re currently using. Depending on the tone you choose, you would be greeted with purple, blue, and green UIs for creative, balanced, and precise styles respectively. 

You can change the AI’s personality midway through a conversation or check for responses to your query with each of the tones to experiment with the AI tool. 

That’s all you need to know about changing tones in Bing AI in Microsoft Edge. 

Microsoft’s Bing Eyes The 2023 Presidential Election After Predicting Ncaa Brackets

Microsoft has already told you who will win the men’s NCAA basketball tournament. Now, the question is: why?

The Microsoft Bing executives riding herd on Microsoft’s mathematical model of March Madness, Bracket Builder, crunched several quintillion combinations Sunday night, tracking the 68 teams and the various scenarios as they progressed through the tournament. It’s a new level of complexity for Microsoft, and good training for one of its next projects—predicting the outcome of the 2023 presidential election. 

Microsoft’s bracket predictions aren’t over, either. Microsoft executives said they plan to expose what might be called the “keys to the game:” what a team must do to win, based on Microsoft’s mathematical model, so you may talk knowledgeably about which teams will win, and how they will do so.

What’s interesting, however, is Microsoft’s awareness of what March Madness represents: an emotional run for fans of the teams, complete with upsets, buzzer-beating shots, controversial calls, and gut feelings of which team will win, contrasted with the sheer mathematical logic of it all. 

Microsoft’s Bing Bracket Builder tool.

Bryan Saftler, the senior marketing manager in charge of the Bing Bracket Builder, says that Microsoft’s goal “is not to replace your answer” on which team will win. 

“At the end of the day, our job is to remove a little bit of the emotion from the bracket-building experience, and operate more from a place of logic, so you’re going to end up with a smarter, winning bracket,” Saftler said.

Bing’s smarts, behind the scenes

Saftler said that, behind the scenes, Bing also knows why a team will win a particular matchup—or, conversely, what an underdog needs to do to pull out an upset. “So for Hampton to beat Manhattan, they have to have blocks—more than five—or turnovers, less than ten,” he said. “They have to hold Manhattan to under 25 percent from the three-point line. They have to have more than ten defensive rebounds. And they have to hold their opponent to under 20 points in the paint. Being able to say, with that fidelity, what are the scenarios that need to happen for a predicted upset.”

Microsoft plans to expose that reasoning via social media posts prior to every game, Saftler said. Originally, those justifications were designed to be part of the Bracket Builder UI. It’s probable that as the tournament progresses—and as games wind down into the Sweet Sixteen or Elite Eight, where there’s more time between games—Microsoft will release a new version of the Bracket Builder user interface, where bracket players will be able to dig down through some of these keys to the game, Saftler said.

“The moment that we start exposing those statistics, and show how we got there, you’re going to trust [Bing] more and find more value more understanding… We’re helping you make smarter decisions,” Saftler said.

According to Walter Sun, the principal applied science manager overseeing the project, Microsoft also has the capability to adjust the predictions for a game as it progresses. If Kentucky opens its first game by scoring 25 unanswered points, for example, its win probability would climb to almost 100 percent, Sun said.

Microsoft tried real-time predictions for the World Cup, however, and users became confused, Sun noted. So that feature may be left out for now, he said.

A future in politics

Now that Bing’s addressed the NCAA men’s tournament, plus World Cup and NFL football predictions, you probably don’t need the browser’s powerful algorithms to guess what’s next. When asked whether Microsoft plans to call the 2023 presidential elections, both Sun and Saftler responded with a chorus of yeses. Microsoft already tracks elections and other social and political questions as a matter of course. Microsoft also says it’ll add the most popular vacation destinations, whether concert ticket prices are going to go up, and more to the list.

Elections, though, could allow Bing to make predictions on a level of granularity that no analyst firm has before. Microsoft already has access to a wealth of social data, from partnerships with Twitter, Facebook, and more. Typically, analysts like FiveThirtyEight analyze polls—essentially all of that social data, but abstracted. That means Gallup can poll a cross-section of Massachusetts voters, for example, and determine what it thinks might be the most likely candidate.

Why this matters: If Microsoft can obtain all of that social information itself—and remember, all of that aggregated polling information correctly called the 2012 elections—Microsoft might be able to make even more detailed predictions. That’s extremely valuable information to any number of people —especially if Microsoft can track results on the fly. March Madness is one thing, but it pales in comparison to what will take place in November of 2023. 

Seo Link Building Q&A With An Ex

I recently caught up with an ex-member of Google’s webspam team, Andre Weyher. Andre worked directly on Matt Cutts’ team and agreed to offer some valuable insight into how Cutts’ team operates, what they look for with regard to inbound link profiles (and manipulation of them), and how SEOs and webmasters can conform to Google’s webmaster guidelines now and going forward.

What follows is my interview with Mr. Weyher.

1. What was your role on Matt Cutts’ team, and how long were you a part of it? 

The spam team is a pretty large organisation within Google. It consists of many people working towards one goal; keeping the organic search results free of poor quality sites and penalising the ones that got their ranking due to techniques that are against the Google guidelines. It’s often confused with the engineering team that’s responsible for the creation of the actual algorithm. These are two separate units within the organisation. It’s also not the external reviewers team that you often hear about. Within the spam team people usually get their own speciality. I was responsible for content quality and backlink profile. I’ve been with Google for 4.5 years, two of those in Matt Cutts’ team.

2. What’s Google’s process for determining when to apply a manual penalty to a website based on its inbound link profile?

Very good question, of course there are elements to it that are very secret internally but the process is in principle very straightforward. I often see people taking a very strict and mathematical approach to assessing a backlink profile. It’s good to do it in this way if you are doubting but it’s also important to use your intuition here. When reviewing a profile, the spam fighter would look at the quality of the pages where the links are hosted and the anchors used in the links. If the profile and anchors are not coherent with what a “natural” profile would look like, action would be taken. Lets take an example of a travel website – if there are 100,000 links coming in and 90,000 of them use an anchor like “cheap flights” or “book flight”, it would straight away arouse suspicion because this would never be the case if the links were natural. The quality of the pages linking in is of critical importance. Is it authentic? Or does it purely exist to host the link?

3. How does Google’s Penguin algorithm determine what domains to penalize? 

4. How does Google spot blog networks and/or bad neighborhoods?

5. What’s the best way to recover a website that has been sent a notification via Google Webmaster Tools of manual spam action? 

In the second case it’s a bit tougher. If you have been relying on poor quality link building, you have to get rid of as many bad links as you can. This used to be a very time consuming and difficult process but luckily the new disavow tool in WMT has made this much easier. You do have to be very careful with what you choose to disavow! Again, use your intuition here. Don’t just cut all the links below a certain PR, a low PR website is not necessarily bad, the relevance of the topic of the website and above all, its authenticity are much more important than just the PR.

6. What’s the best way to recover a website affected by Google Penguin? 

7. What are some of the biggest misconceptions or myths you’ve seen about “bad links” and link profile penalties in the SEO community? 

I think I could write a book about this topic! SEO is an unprotected title and anyone can call him or herself one. The result of this is that there are almost as many opinions as there are SEOs. Some of the biggest misconceptions that I have seen out there include; “directories are altogether bad” or “anything that is below a certain PR is considered spammy by Google”, I see a lot of people panicking and cutting off the head to cure the headache due to lack of knowledge. The most dangerous one of all I would consider to be the opinion that if an automated link building scheme is expensive, it must be good. Google has made it very clear that it wants links to be a sign of a real reason to link, an AUTHENTIC vote of confidence if you will. Anything that is paid for, is not considered quality by Google and participating in it puts your site at risk!

8. What do SEOs need to know right now to prepare for future link profile-related algorithm updates? 

It’s hard to predict what the future will hold but you can be sure that Google will become more and more effective at fighting everything they are fighting currently. So if there are still people out there that are getting away with spammy techniques, it’s only matter of time before Google will find a new way of identifying it and penalizing the ones that do it. I think the best way of preparing yourself against future updates is to build an SEO strategy that depends on smart on-page techniques and internal linking on one side and relationship based linkbuilding on the other side. This means that the obtained links should come from a source that has a genuine reason to link to your site. The relevance of your linking partner to the topic of your site is the key!

9. You left your job in Google not long ago, what are your plans?

I have fulfilled a long dream and moved to Australia! Sydney is an amazing city with a great startup community. I have started my own company here and am very excited about it. It’s called chúng tôi The first intelligent website fingerprinting service on the net. After typing in a URL, we will show you, based on over 3000 factors, what other websites are owned or developed by the same owner. We’re in beta, though we’ve just finished crawling over 200 million websites and used elements like hosting, account IDs and even coding style to determine who owns what on the web… exciting times! This new version will be up in a few weeks.

Brave Search Cuts Ties With Bing & Goes Independent

Brave Search, the offspring of the privacy-focused browser Brave, has announced a significant leap forward in its evolution.

It now boasts complete independence from other search engines, a remarkable achievement in the world of search.

Brave Search: A Brief History

The journey towards independence for Brave Search began in 2023 following the acquisition of Tailcat’s search engine and development team.

To create a fully independent search engine, Brave set out on a path that culminated in the launch of Brave Search Beta in June 2023.

The search engine launched as a privacy-focused, transparent alternative to established search giants like Google.

Since its introduction, the Brave team has introduced numerous features to its search engine.

These include Discussions, allowing real-human conversations to appear in search results, Brave Search Goggles for customized results, and Brave Search Summarizer for AI-powered quick results.

Fast forward to today, Brave Search has reached a pivotal moment. The search engine now relies entirely on its index for all results.

Previously, about 7% of queries were sourced from Bing Search, but Brave has now cut all connections to the Bing API, cementing its independence.

The Web Discovery Project has been instrumental in reaching this milestone. The project has helped Brave Search’s index grow by allowing users to contribute browsing data anonymously.

Brave Search is handling 22 million daily queries, a testament to its rapidly expanding user base.

Why Independence Matters

The increasing costs and uncertainties around Bing’s API access were significant motivators for Brave in striving for independence.

Being independent means Brave has complete control over its search engine, and its index powers all the search results.

Looking towards the future, Brave plans to launch the Brave Search API, offering developers and companies access. However, the specifics regarding pricing and the availability of a free option are yet to be announced.

Brave intends to keep its search engine as the main search tool on its browser and keep it free for everyone.

In Summary

Brave Search relying on its own index and not on the big search engine companies is a big deal.

What makes Brave Search different is that the company behind it puts users in control and is open about how it works, which isn’t common in an industry run by huge companies.

As Brave keeps coming up with new ideas, it could change how we find information online.

Source: Brave

Featured Image: rafapress/Shutterstock

2023 Ford Fusion Sport Review: Blue Oval Q Ship Cancels Mid

2023 Ford Fusion Sport Review: Blue Oval Q ship cancels mid-size family sedan boredom

The Q-ship is a time-honored tradition in the world of family sedans, and the new Ford Fusion Sport is the latest effort from a mainstream automaker to present buyers with an enormous motor stuffed under the hood of an unassuming commuter. Only this being 2023, that motor isn’t so much ‘big’ in size as it is in output, thanks to the Blue Oval’s overwhelming desire to turbocharge absolutely every single drivetrain its engineers can get their hands on.

The term itself – Q-ship – dates back to World War 2, when convoys crossing the Atlantic would scattered armed escorts disguised as standard freighters amongst their number in a bid to fool the submarine wolf packs that hunted them from the depths. The firepower packed by the Ford Fusion Sport is equally stealthy, with the only overt indications of its 2.7-liter twin-turbo V6 being its black mesh grille and quad tailpipes.

This makes it the only car in its class to claim 325 horsepower and a thudding 380 lb-ft of torque while simultaneously going completely unnoticed. That is not to say that the Fusion is an anonymous car – the four-door’s pleasing lines place well alongside efforts like the Toyota Camry and the Honda Accord – but they don’t suggest to the casual viewer (read: almost every mid-size sedan shopper) that its all-wheel drive setup can wallop the quarter mile in a mere 13.7 seconds.

That’s muscle car performance in a vehicle that remains fundamentally unchanged from more frugal-minded Fusions in most of the important areas. The vehicle’s cabin, lightly updated, features a roomy rear seat and pleasingly stuffed cloth-wrapped front buckets (although not overly bolstered), while the SYNC3 infotainment interface reigns supreme on the center stack. A six-speed automatic transmission remains standard with the Ford, with its programming updated to offer quicker shifts while in S or Sport mode, and paddle shifters are present should one grow impatient with its algorithms.

The biggest deviation from the standard Ford Fusion playbook outside of the engine compartment is the presence of ‘continuously controlled damping,’ an active suspension technology that has trickled down to Ford by way of Lincoln. The system keeps a dozen watchful electronic eyes on the tarmac and adjusts shock absorber response every two milliseconds in a bid to firm up the ride without sacrificing comfort in the process. It also incorporates something the brand’s engineers have labeled ‘pothole detection’ that attempts to lock a strut in its stiffest setting and allow the wheel to ‘glide’ over a crater in the road with more grace than the expected up-and-down motion.

Montreal’s shattered urban infrastructure contains more potholes per square inch than there are chocolate chips in a tollhouse cookie, and I half expected the Fusion Sport’s trick suspension to throw in the towel and default into safe mode after I had traveled only a half-mile or so. It didn’t – but nor did it seem to provide any real-world improvement over a standard adaptive suspension over chunks of missing asphalt. A more positive result was obtained in the comfort department, where the Ford’s character remained smooth and quiet at a wide range of speeds, even in its most aggressive Sport setting.

Much has been made of the Ford Fusion Sport’s big power numbers as compared to imported four-door fare, and it’s certainly true that at a starting price of just over $34,000, it outguns similarly-priced BMWs – or even much more expensive BMWs – as well as Audis and other Euro cronies. It’s also no exaggeration to say that the Fusion Sport is extremely quick in a straight line, shooting past 60-mph in just a tick over five seconds and offering respectable throttle response and excellent highway passing capabilities thanks to its ample reserves of low-end torque.

In any scenario other than a drag race, however, the analogy begins to stretch thin. The Fusion’s front-wheel drive chassis, while strong in its family car class, makes use of its all-wheel drive system to mitigate the torque steer inherent in its twin-turbo design rather than to significantly boost handling past the limits of its more modest bones. The adaptive shocks help, but don’t fundamentally dial-out the Ford’s understeer at the limit or numb steering, and the fake-sound engine noise that’s piped into the cabin when you hammer the gas is an all-too-common plague on the modern automotive scene that’s almost as egregious as car lashes or fake HID headlights.

The 2023 Ford Fusion Sport is not a sport sedan, in the same way big, unassuming Galaxie 500s outfitted with elephantine 390 and 427 cubic inch V8s weren’t sport sedans back when they hunted state turnpikes back in the 1960s. But they were fast, and so is the Fusion Sport. Ultimately, this is what Ford was aiming to achieve with the car in order to give loyal buyers weary of its four-cylinder-only options list something to get the blood boiling on straight stretches of highway. There’s certainly no arguing with the price, either, which checks in at less than a fully-loaded Fusion Platinum, providing a welcome and affordable niche for undercover drag racers intent on heaping embarrassment on unsuspecting left-lane hogs and kraut-rockets alike.

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