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RIO DE JANEIRO Shortly after President Donald J. Trump was banned from Twitter early this year, Brazils like-minded leader made a plea to his millions of followers on the site.

Sign up for my official channel on Telegram, President Jair Bolsonaro requested.

Since then, Telegram, an encrypted messaging and social media platform run by an elusive Russian exile, has racked up tens of millions of new users in Brazil.

Its growing popularity in Brazil and elsewhere is being fueled by conservative politicians and commentators for whom it has become the most permissive disseminator of problematic content including disinformation in a social media ecosystem facing mounting pressure to combat fake news and polarization.

While WhatsApp remains by far the dominant messaging platform in Brazil, Telegram is making inroads fast. By August, it had been installed in 53 percent of all smartphones in Brazil, up from 15 percent two years earlier, according to a report.

Founded in 2013, Telegram has become a tool coveted by activists, dissidents and politicians many in repressive nations like Iran and Cuba to communicate privately.

But Brazilian government officials and experts worry the app could become a powerful vector for lies and vitriol before next years presidential elections a tense political moment in the country.

Mr. Bolsonaro, his re-election prospects endangered by his diminishing popularity, has followed the Trump playbook and begun sowing doubts about the integrity of Brazils voting system, raising the possibility of a disputed outcome. His unfounded claim that electronic voting machines will be rigged has unnerved the opposition and the countrys top judges, who say the abundance of disinformation in Brazilian politics is doing lasting damage to its democracy.

We know that systemic disinformation is produced by structures that are very well organized and financed, said Aline Osrio, a secretary general at Brazils electoral court who heads its program against misinformation.

Ms. Osrio said the court had established constructive working relationships with executives from other social media companies that have become vehicles for misinformation campaigns. But its efforts to reach Telegram, which is based in Dubai, have been unsuccessful.

Telegram has no representatives in Brazil, and this has made it difficult to establish a partnership in the same way weve done with other platforms, she said.

Telegram did not respond to a request for an interview. Press queries are submitted through a bot on the platform.

Experts say political content and conversations have migrated substantially to Telegram in recent years in Brazil and other countries, largely because of the apps capacity to mass-reproduce content.

Group chats can include up to 200,000 users, exponentially more than WhatsApps limit of 256. WhatsApp curbed users ability to forward messages after coming under criticism in Brazil and elsewhere for the role it played in misinformation campaigns during recent elections.

In addition to group chats, Telegram hosts channels, a one-way mass-communication tool used by corporations, artists and politicians to distribute messages, videos and audio files. Mr. Bolsonaros channel surpassed one million followers in recent weeks, putting him among the worlds most followed politicians on the platform.

While rival apps have adopted stricter and more clearly defined policies on abuse and disinformation, Telegrams guidelines are vague, and the service takes a hands-off approach to content in individual and group chats.

That makes it a safe space for incendiary figures, including politicians, who have been banned from other platforms. In Brazil, the Twitter and Instagram accounts of a lawmaker, Daniel Silveira, and a conservative journalist, Allan dos Santos, were suspended as part of a Supreme Court investigation into disinformation campaigns that included threats against justices.

But Telegram remains a portal to their followers. That has enabled Mr. dos Santos to raise funds for his legal defense and call the justice who got him banned from other sites a psychopath.

The network is clearly benefiting from the removal of users from other platforms, Fabrcio Benevenuto, a computer science professor at the Federal University of Minas Gerais, said of Telegram. Politicians have noticed it makes no effort to remove accounts, so it is becoming an appealing network for more radical groups.

Farzaneh Badiei, an internet governance expert who published a paper on Telegram at Yale Law School this year, said that Telegrams founder, Pavel Durov, had been unwilling to meaningfully grapple with the problem of disinformation that goes viral.

Their approach is very disorganized and very opaque, she said. We dont see a systemic approach to solving these problems.

Mr. Durov left Russia in 2014 after battling government efforts to censor content on the social networking site he founded, VKontakte. He has said he designed Telegram as an ultra private means of communicating based on the persecution he says he endured in his native country.

Twitter, Facebook, WhatsApp and YouTube played critical roles in Mr. Bolsonaros stunning victory in 2018, and the far-right leader has continued to rely heavily on social media to energize his base, attack opponents and make false claims that go largely unchallenged.

But in recent months, the platforms that enabled Mr. Bolsonaros rise have reined him in over his false or misleading claims about measures to contain the coronavirus. Social media companies put him on notice by taking down a handful of videos and tweets that they deemed dangerous.

Mr. Bolsonaro and his followers have railed against those removals as forms of censorship. In September, he argued that disinformation was now a permanent feature of politics and dismissed it as a trivial issue.

Fake news is part of our life, he said. Who has never told a little lie to their girlfriend?

Telegram has drawn critical scrutiny in Brazil for more than its disruptive role in politics. Investigations by news organizations found that it was hosting illegal arms networks and enabling the distribution of child pornography.

Brazilian lawmakers are debating legislation that would require platforms like Telegram to have legal representation in Brazil or risk being banned. However, users have easily circumvented such bans in countries like Iran and Russia by using software that lets them disguise their location.

Diogo Rais, a professor at Mackenzie University in So Paulo and a co-founder of the Digital Freedom Institute, called blocking apps a drastic measure that would be ineffective.

We need to deal with digital challenges realizing that our laws are from 2009 and limited to our physical territory, he said. The digital world has no such limit. This is a global challenge.

Original post:
Brazils Far-Right Disinformation Pushers Find a Safe Space on Telegram - The New York Times

A man demonstrates the uSens Inc. Impression Pi virtual reality and augmented reality interactive device at CES Unveiled, a media preview event for CES International, Monday, Jan. 4, 2016, in Las Vegas.

John Locher | AP

The so-called metaverse has a "big time" investment case, according to Puerto Rican billionaire businessman Orlando Bravo.

Bravo, co-founder and managing partner of private equity firm Thoma Bravo, told CNBC that he thinks "metaverse" is the big word of 2021.

"It's investable and it's going to be very big," Bravo said in an interview with CNBC's Annette Weisbach on Friday.

The metaverse is a sci-fi concept whereby humans put on some sort of headset or smart glasses that allows them to live, work and play in a virtual world much like the one depicted in the "Ready Player One" novel and movie. Depending on your point of view, it's either a utopian dream or a dystopian nightmare.

The term metaverse was thrust into the spotlight last month by Facebook co-founder Mark Zuckerberg when he changed Facebook's name to Meta and said the new company was going to focus on the metaverse.

"The metaverse is the next frontier just like social networking was when we got started," he said at the time.

The announcement was mocked in avideo published last week by Inspired by Iceland, a marketing campaign for Icelandic tourism. In the video, a Zuckerberg lookalike introduces viewers to "Icelandverse," a place of "enhanced actual reality without silly-looking headsets."

Dozens of other companies including Microsoft, Roblox, Nvidia and Britain's Improbable are already trying to build the software and hardware that could power the metaverse.

Thoma Bravo has more than $83 billion in assets under management and a portfolio that comprises more than 40 software companies. It has invested in the likes of cybersecurity firms McAfee and Barracuda, as well as enterprise software firm Dynatrace.

In addition to the metaverse, Bravo is also bullish on crypto and he owns an undisclosed amount of bitcoin.

"How could you not love crypto?" Bravo said at CNBC's Delivering Alpha conference in September. "Crypto is just a great system. It's frictionless. It's decentralized. And young people want their own financial system. So it is here to stay."

Correction: Facebook co-founder Mark Zuckerberg announced in October he changed Facebook's name to Meta. An earlier version misstated the timing.

View post:
The metaverse is investable and it's going to be big, says tech billionaire - CNBC

We now present the results indicating the effects of social networks on clinicians revisions to their diagnostic assessments and their treatment recommendations. In the following analyses, diagnostic accuracy is defined as the absolute number of percentage points between a clinicians diagnostic assessment and the most accurate diagnostic assessment. For clarity of presentation, we normalize diagnostic accuracy on a 01 scale by applying min-max normalization to the absolute error of clinicians diagnostic assessments. Under this procedure, the minimum possible accuracy (indicated by 0) corresponds to the diagnostic assessment with the greatest absolute error (i.e. an estimate that is as far as possible from the most accurate answer of 16%, which in this case is 84 percentage points), while the maximum possible accuracy (indicated by 1) corresponds to a diagnostic assessment that is 0 percentage points away from the most accurate answer, such that they are equivalent (SI, Statistical Analyses). As above, in the discussion of our results we refer to the patient-actors in the standardized patient videos as patients.

Clinicians initial assessments and treatment recommendations were made independently. Figure1 shows that for the initial responses of all clinicians in the study, there were no significant differences in the accuracy of the diagnostic assessments (Fig.1a, b) given to the Black female patient and the white male patient (p>0.5, n=28, Wilcoxon Rank Sum Test, Two-sided); nor were there any significant differences in the accuracy of initial diagnostic assessments when controlling for experimental condition using a regression approach (=1.06, CI=[3.79 to 5.92], p=0.67, Supplementary Table6). However, consistent with previous studies of bias in medical care2,3,4,5,6, despite clinicians providing both patients with similar diagnostic assessments, clinicians treatment recommendations varied significantly between patients. Across all clinicians, their initial treatment recommendations (Fig.1c, d) show a significant disparity in the rate at which the guideline-recommended treatment was recommended for the white male patient versus the Black female patient. Overall, clinicians recommended Option C, referral to the emergency department for immediate evaluation, for the white male patient in 22% of responses, while only making this recommendation for the Black female patient in 14% of responses (p=0.02, n=28 observations, Wilcoxon Rank Sum Test, Two-sided).

Panels a and b show the change (from the initial assessment to the final assessment) in the average diagnostic accuracy of clinicians. Panel a shows the control conditions. Panel b shows the network conditions. The insets in both panels show the total improvement (in percentage points) in the accuracy of clinicians diagnostic assessments. Error bars display 95% confidence intervals; data points display the mean change for each of the trials (N=7) in each condition. Panels c and d show the change (from the initial recommendation to the final recommendation) in the proportion of clinicians recommending the guideline-recommended treatment recommendationreferral to the emergency department for immediate cardiac evaluation (Option C)for the white male patient-actor and Black female patient-actor. Panel c shows the control conditions. Panel d shows the network conditions. The insets in both panels show the total improvement (in percentage points) in the percent of clinicians recommending the guideline-recommended treatment. Error bars display 95% confidence intervals; data points display the mean change for each of the trials (N=7) in each condition. Panels e and f show the change (from the initial response to the final response) in the odds of clinicians recommending option A (unsafe undertreatment) rather than option C (highest quality, guideline-recommended treatment) for each patient-actor. Panel e shows the control conditions. Panel f shows the network conditions. The insets in both panels show the total reduction in the likelihood that clinicians would recommend unsafe undertreatment rather than the guideline-recommended treatment for each patient-actor. Error bars display 95% confidence intervals; data points display the mean change for each of the trials (N=7) in each condition.

In the control conditions (Fig.1a), after two rounds of revision there was no significant change in the accuracy of clinicians assessments (i.e. diagnostic estimates) for either the white male patient (p>0.9, n=7, Fig.1a inset, Wilcoxon Signed Rank Test, Two-sided) or the Black female patient (p>0.9, n=7, Fig.1a inset, Wilcoxon Signed Rank Test, Two-sided). Correspondingly, Fig.1c shows that in the control conditions there was no significant change in the rate at which clinicians recommend the guideline-recommend treatment for either the Black female patient or the white male patient (Black female patient showed a 3 percentage point increase, p=0.81, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; white male patient showed a 1 percentage point increase, p=0.93, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1c). Clinicians final treatment recommendations in the control conditions still showed a significant disparity between the white male patient and the Black female patient in their rates of referral to the emergency department (p=0.04, n=14 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1c).

Figure1b shows that in the network conditions there were significant improvements (from the initial response to the final response) in the accuracy of the assessments given to both the white male patient (p=0.04, n=7, Wilcoxon Signed Rank Test, Two-sided; Fig.1b inset) and the Black female patient (p=0.01, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1b inset). Figure1d shows that in the network conditions, after two rounds of revision there was no significant change in the rate at which clinicians recommended the guideline-recommended treatment for the white male patient (p=0.57, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1d inset). This lack of change is due to the fact that, regardless of the accuracy of their initial assessments for the white male patient, clinicians were initially significantly more likely to recommend the guideline-recommended treatment for white male patient (p<0.01, OR=1.78, CI=[1.22.6], Supplementary Table7). Consequently, improvements in assessment accuracy for the white male patient had a smaller positive impact on increasing clinicians likelihood of recommending the guideline-recommended treatment. By contrast, clinicians initially were significantly less likely to recommend the guideline-recommended treatment for the Black female patient (p<0.01, OR=0.56, CI=[0.380.83], Supplementary Table7), while they were significantly more likely to recommend unsafe undertreatment for this patient (p<0.05, OR=1.5, CI=[1.082.04], Supplementary Table8). Consequently, improvements in assessment accuracy had a substantially greater effect on the final treatment recommendations for the Black female patient (Fig.1d). In the network condition, the rate at which clinicians recommended guideline-recommended treatment for the Black female patient increased significantly, from 14% in initial response to 27% in final response (p<0.01, n=7 observations, Wilcoxon Signed Rank Test, Two-sided; Fig.1d). As a result, clinicians final treatment recommendations in the network conditions exhibited no significant disparity between the Black female patient and the white male patient in terms of referral rates to the emergency department (p=0.22, n=14 observations, Wilcoxon Rank Sum Test, Two-sided; See Supplementary Table11).

The primary pathway for bias reduction in the network condition was the effect of improvements in clinicians assessment accuracy on reducing the initially high rates at which unsafe undertreatment was recommended for the Black female patient. Figure1e, f shows the odds of clinicians recommending unsafe undertreatment rather than the guideline-recommended treatment for both patients in both conditions. Consistent with the above discussion, treatment recommendations for the white male patient did not exhibit any bias toward unsafe undertreatment (p=0.19, n=14, Wilcoxon Signed Rank Test, Two-sided). As expected, improvements in assessment accuracy in the network condition did not significantly impact clinicians odds of recommending the guideline-recommended treatment rather than unsafe undertreatment for the white male patient (p=0.21, n=7, Wilcoxon Signed Rank Test, Two-sided). By contrast, clinicians initially had significantly greater odds of recommending unsafe undertreatment rather than the guideline-recommended treatment for the Black female patient (Fig.1e, f; p<0.01, n=28 observations, Wilcoxon Signed Rank Test, Two-sided). Independent revision in the control conditions did not have any impact on the treatment recommendations for either the white male (p=1.0, n=7, Wilcoxon Signed Rank Test, Two-sided) or the Black female patient (p=0.81, n=7, Wilcoxon Signed Rank Test, Two-sided). However, assessment revisions in the network condition led to a significant change in the odds of clinicians recommending the guideline-recommended treatment rather than unsafe undertreatment for the Black female patient (Fig.1fp=0.01, n=7, Wilcoxon Signed Rank Test, Two-sided). By the final round in the network conditions, there was no significant difference between patients in their odds of having clinicians recommend the guideline-recommended treatment rather than unsafe undertreatment (Fig.1f, p=0.19, n=14, Wilcoxon Rank Sum Test, Two-sided).

The network mechanism responsible for improvements in the accuracy of clinicians assessments, and the corresponding reduction of race and gender disparity in their treatment recommendations, is the disproportionate impact of accurate individuals in the process of belief revision within egalitarian social networks13,15,16. As demonstrated in earlier studies of networked collective intelligence13,15,16, during the process of belief revision in peer networks there is an expected correlation between the accuracy of an individuals beliefs and the magnitude of their belief revisions, such that accurate individuals revise their responses less; this correlation between accuracy and revision magnitude is referred to as the revision coefficient13. Within egalitarian social networks, a positive revision coefficient has been found to give greater de facto social influence to more accurate individuals, which is predicted to produce network-wide improvements in the accuracy of individual beliefs within the social network. These improvements in collective accuracy have been found to result in a corresponding reduction in biased responses among initially biased participants12,13,15,16. Figure2a tests this prediction for clinicians in our study. The results show, as expected, that there is a significant positive revision coefficient among clinicians in the network conditions (p<0.001, r=0.66, SE=0.1, clustered by trial, Supplementary Table14), indicating that less accurate clinicians made greater revisions to their responses while more accurate clinicians made smaller revisions, giving greater de facto influence in the social network to more accurate clinicians. This correlation holds equally for clinicians assessments for both the white male and Black female patients (Supplementary Table14). Figure2b shows that for both patients, improvements in assessment accuracy led to significant improvements in the quality of their treatment recommendations (p<0.05, OR=1.04, CI=[1.00, 1.09], Supplementary Table9). Importantly, for clinicians who initially recommended unsafe undertreatment (Option A), we find that improvements in assessment accuracy significantly predict an increased likelihood of recommending the guideline-recommended treatment (Option C) by the final round (p<0.01, OR=1.17, CI=[1.03, 1.33], Supplementary Table10). These improvements translated into a significant reduction in the inequity of recommended care for the Black female patient, for whom clinicians were initially significantly more likely to recommend unsafe undertreatment (see Fig.3, below).

Panel a shows clinicians propensity to revise their diagnostic assessments in the network conditions according to the initial error in their diagnostic assessments. Clinicians accuracy is represented as the absolute number of percentage points of a given assessment from the most accurate assessment of 16% (represented by 0 along the x-axis, indicating a distance of 0 percentage points from the most accurate response). Magnitude of revision is measured as the absolute difference (percentage points) between a clinicians initial diagnostic assessment and their final diagnostic assessment. Clinicians accuracy in their initial assessment significantly predicts the magnitude of their revisions between the initial to final response. Grey error band displays 95% confidence intervals for the fit of an OLS model regressing initial error of diagnostic assessment on magnitude of revision. Panel b shows the significant positive relationship between the improvement in clinicians diagnostic accuracy (from the initial to final assessment), and their likelihood of improving in their treatment recommendation (i.e. the probability of switching from recommending Option A, B, or D to Option C) for clinicians in the network conditions. The trend line shows the estimated probability of clinicians improving their treatment recommendations according to a logistic regression, controlling for an interaction between experimental condition (control or network) and patient-actor demographic (Black female or white male) (Supplementary Table9). Error bars show standard errors clustered at the trial level.

Each panel shows the fraction of clinicians providing each treatment recommendation at the initial and final response, averaged first within each of the trials in each condition (N=7), and then averaged across trials. Option A. 1 week follow-up (unsafe undertreatment). Option B. Stress test in 23 days (undertreatment). Option C. Immediate emergency department evaluation (guideline-recommended treatment). Option D. Immediate cardiac catheterization (overtreatment Panel a shows the change in control condition recommendations for the Black female patient-actor (initial recommendations light pink, final recommendations dark pink). Panel b shows the change in network condition recommendations for the Black female patient-actor (initial recommendations light pink, final recommendations dark pink). Panel c shows the change in control condition recommendations for the white male patient-actor (initial recommendations light blue, final recommendations dark blue). Panel d shows the change in network condition recommendations for the white male patient-actor (initial recommendations light blue, final recommendations dark blue).

Figure3 shows the changing rates at which clinicians recommended each option (Option A. unsafe undertreatment, Option B. undertreatment, Option C. guideline-recommended treatment, and Option D. overtreatment) for each patient, from the initial response to the final response, for all conditions. As discussed above, we are particularly interested in the inequity of patient care, defined as the rate at which clinicians made a clearly unsafe recommendation (Option A) versus recommending the guideline-recommended treatment (Option C)23,24. Initial responses exhibited significant inequity between patients. Initially, across both conditions, 29.9% of clinicians recommended the unsafe undertreatment for the Black female patient, while only 14.1% recommended the guideline-recommended treatment, resulting in a 15.7 percentage point difference in the rate at which clinicians recommended unsafe undertreatment rather than the guideline-recommended treatment for the Black female patient. By contrast, for the white male patient, 23.4% of clinicians recommended the unsafe undertreatment, while 21.4% of clinicians recommended the guideline-recommended treatment, resulting in a 2 percentage point difference in the likelihood of clinicians recommending unsafe undertreatment rather than the guideline-recommended treatment for the white male patient. This resulted in a 13.7 percentage point difference between the Black female patient and the white male patient in their likelihood of having clinicians recommend unsafe undertreatment rather than the guideline-recommended treatment (p=0.02, n=28 observations, Wilcoxon Rank Sum Test, Two-sided). Individual reflection did not reduce this inequity. The control conditions produced no significant change in the inequity between patients from the initial response to the final response (p=0.57, n=14 observations, Wilcoxon Signed Rank Test, Two-sided). Accordingly, in the final response in the control conditions, there was a 15.3 percentage point difference between the Black female patient and the white male patient in their likelihood of having the clinician recommend unsafe undertreatment rather than the guideline-recommended treatment (p=0.04, n=14 observations, Wilcoxon Rank Sum Test, Two-sided; see SI Eq. 2). Strikingly, however, improvements in diagnostic accuracy in the network condition produced a 20 percentage point reduction in the rate at which clinicians recommended unsafe undertreatment rather than the guideline-recommended treatment the Black female patient (p=0.04, n=14 observations, Wilcoxon Rank Sum Test, Two-sided). By the final response in the network conditions, inequity was eliminatedthe Black female patient was no longer more likely than the white male patient to have clinicians recommend unsafe undertreatment rather than the guideline-recommended treatment (p=0.16, n=14 observations, Wilcoxon Rank Sum Test, Two-sided).

Figure3 (panels ad) also shows that the network conditions improved the quality of clinical care recommended for both patients (white male and Black female). In particular, for both the Black female and white male patient, the network conditions produced significantly greater reductions in the proportion of clinicians recommending unsafe undertreatment (Option A) than the control conditions (1.6 percentage point reduction in the control conditions, 11.8 percentage point reduction in the network conditions; p<0.01, n=28 observations, Wilcoxon Signed Rank Test, Two-sided). This reduction in the recommendation of unsafe undertreatment (Option A) was associated with significant increases in recommendations for safer care for both patients. While Option B was not the guideline-recommended treatment, it represents a safer treatment than Option A. Correspondingly, the network conditions significantly increased the proportion of clinicians recommending safer undertreatment (Option B) than the control conditions (3.5 percentage point reduction in control conditions, +6.5 percentage point increase in the network conditions; p=0.03, n=28 observations, Wilcoxon Signed Rank Test, Two-sided). Strikingly, the rate of overtreatment (i.e. Option D, unnecessary invasive procedure) for both patients was significantly decreased in the network conditions, while it increased in the control conditions (2.8 percentage point reduction in the network conditions, +3.1 percentage point increase in the control conditions; p<0.01, n=28 observations, Wilcoxon Signed Rank Test, Two-sided).

These results reveal a tendency for clinicians in the control conditions to increase the acuity (i.e. urgency) of care for all patients as a result of independent reflection, leading to an increase in overtreatment. By contrast, in the network conditions, clinicians adjusted their recommendations toward safer, more equitable care for both patients, significantly reducing both unsafe undertreatment (Option A) and overtreatment (Option D). Additional sensitivity analyses show these findings to be robust to variations in clinicians characteristics26 (see SI, Sensitivity Analyses).

The rest is here:
The reduction of race and gender bias in clinical treatment recommendations using clinician peer networks in an experimental setting - Nature.com

The market study on the global Carrier Ethernet Access Devices market will encompass the entire ecosystem of the industry, covering major regions namely North America, Europe, Asia Pacific, South America, Middle East & Africa, and the major countries falling under those regions.

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Global Carrier Ethernet Access Devices Market 2020 : Industry Size, Share, Growth, Forecasts to 2027 by Types (Electrical Devices, Optical Devices) by...

Your favourite Toronto TikTokers, Instagrammers, YouTubers and your favourite mobile apps

Toronto women seem to like making the first move. Thats the whole premise of Bumble (at least for straight daters) where conversations cannot begin until the woman initiates it. Bumble has expanded beyond dating in recent years to include opportunities for matches unrelated to romance, like networking and platonic relationships. For millennials living in cities, making new friends can be just as challenging as finding someone worth dating.

bumble.com

Hinge

hinge.co

Live a day (or many days) in the life of a local jazz and blues musician by hitting follow on Heather Luckharts Instagram. Whether shes riding a water-spewing dog statue at Berczy Park, hitting the recording studio or just making soup, you can go along for the ride.

@doggydatestoronto

Morgan Cameron Rosss feed devoted to archival images of the city is among the most popular with 109,000 followers on the Gram. Toronto history has become a popular genre on the photo-sharing app, and Old Torontos mix of archival cityscape shots, pop culture moments and neighbourhood-centric videoshave attracted brand sponsorships, allowing Ross to quit his day job as a songwriter.

@oldtoronto

The Flyer Vault

@theflyervault

If you like mouth-watering photos of beautiful junk food like pizza, smash burgers and lobster hot dogs populating your Insta feed, youre going to want to throw a follow to @eatfamous. Ryan Hinkson is the man behind the lens (and sometimes in front of it), and hes happy to throw his personality out there too. Fun fact: his favourite pizza topping is potatoes.

@OriDaganJazz

Pat ORourke, the self-taught photographer behind @Chilligansisland, is clearly an outdoors type. His Instagram is filled with pics and videos of him biking, skateboarding or generally finding himself at the best spots to capture birds flocking in apocalyptic looking patterns around the CN Tower, a doe grazing in cottage country during golden hour and those magenta Toronto skies that so frequently break the app.

@jgazze

Photographer and dog walker Jack Jackson is a past winner with @doggydatestoronto, which brings their two callings together. They create heavenly portraits of furry friends posing, smiling and leaping across Toronto shores. Jackson makes it look easy, getting energetic pets to stay, lay down and hand over their paw long enough while they get the light and angles just right.

Jackson also sponsors @dontyouwantmeproject, a collaboration that posts beautiful portraits of LGBTQ+ people with their rescue dogs.

@doggydatestoronto, doggydatestoronto.com

@HortonMcSnorton

A standout in the world of dating apps, Lex has no profile photos or cis men. Aimed at lesbian, bisexual, non-binary, trans, genderqueer, intersex, Two Spirit, asexual and queer people, its entirely text based, making it a throwback to old-school newspaper personal ads that many queer people used to meet before the internet was a thing. Its a refreshing take on online dating, which has become so focused on optimal photos and looks.

thisislex.app

Hinge

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More than 16 million people have fallen in love with Leenda Dong, a TikTok persona created by former YouTuber Linda Dong. With her hair in a messy bun, glasses perched precariously on her nose and affecting an exaggerated accent the Vietnamese-Canadian content creator has perfected the art of making people laugh with just a facial expression or well-timed splice. Of course, that hasnt cleared her from accusations of minstrelsy (no, thats not her real accent).

@bomanizer

TikTok has become a unique avenue towards music discovery, and Akintoyes account is one of the best examples of that phenomenon. Stumble upon the local rappers mile-a-minute flow on your For You page and youll be hooked right away. Next thing you know, youre watching all of his verses duetting with producers, showing up the rap skills of Dwayne The Rock Johnson or even turning Squid Games musical score into a certified banger.

@OriDaganJazz

Wardell is lucky enough to play video games for a living, and you can take a first-person shotgun ride with the Valorant pro on his Twitch stream. When hes not competing in the TSM FTX eSports league (or sometimes when he is), you can find him in the little corner box playing against some of Twitchs other favourite gaming stars.

@akaNemsko

Canadian decorator Alexandra Gater is all about walking you through budget-friendly and renter-friendly DIY hacks to make your condo or apartment look Instagram or Pinterest-ready in a hurry so perfect for people in Toronto. Shes so engaging and easy to follow, you might find yourself with a ruler and pencil in your hand whether you meant to or not.

youtube.com/alexandragater

Heather Luckhart

youtube.com/channel/UCekeRirMK4g2s3L7TjUHX8w

This note-taking app is designed with creatives in mind, so its more visual than the yellow legal pad or Post-It-mimicking note apps youre used to. You can create mood boards, drag around visual references and diagrams, work from built-in templates and collaborate remotely. Now is certainly the time.

milanote.com

Driftscape

driftscape.com

Wealthsimple is the standard-setter for a new crop of personal finance services geared towards millennials. Its a user-friendly, attractively designed app that builds the realities of being an urban-dwelling 20- or 30-something into its investment and financial planning tools. No avocado toast shaming here.

wealthsimple.com

You Need A Budget (YNAB)

youneedabudget.com

Online recipes are supposed to make it easy to decide what to make for dinner, but sometimes you find yourself scrolling through multiple sites and long personal preambles to find out your chosen dish includes provisions that arent in your fridge. SuperCook simplifies that search. Its essentially a recipe-only Google, sifting through sites to give you recipes based on the ingredients you already have.

supercook.com

Recipe Keeper

recipekeeperonline.com

This app has gotten hundreds and thousands and millions of mindfulness-curious beginners into meditation, and it continues to expand its empire. Now you can hear its guides soothing British accent from the app to Jimmy Fallon to Netflix, where Headspace has its own series of streaming meditation guides.

headspace.com

MyFitnessPal

myfitnesspal.com

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Readers' Choice 2021: Toronto's best tech, apps and social media - NOW Toronto