Kevin Steenburg

3.1K posts

Kevin Steenburg banner
Kevin Steenburg

Kevin Steenburg

@k_steenburg

Lover of music, Leafs, Jays and Raptors, with an exceptional talent for not being terribly creative on here!

Toronto Katılım Mayıs 2009
837 Takip Edilen204 Takipçiler
Kevin Steenburg retweetledi
The Curious Tales
The Curious Tales@thecurioustales·
NYU just proved it with numbers that should terrify anyone who cares about human decision making. They analyzed over half a million social media posts and discovered something that changes how you should think about every piece of content you consume: "Outrage has been reverse engineered into a science of manipulation." Every post containing words that trigger anger, disgust, or moral superiority gets 6 times more reach than neutral content. Stack additional outrage triggers into the same post, and virality increases by roughly 20% per word. The platforms figured out that your ancient brain chemistry responds to perceived threats and tribal signaling faster than it responds to anything else, and they built their entire engagement architecture around exploiting that reflex. Think about what that means for information flow in society. The posts that spread fastest are not the most accurate, insightful, or useful. They are the ones most precisely engineered to activate your fight or flight response. Your timeline is being curated by an algos that has learned to simulate the feeling of being under attack, because humans share content when they feel like their worldview or tribe is being threatened. The mathematical precision is what makes this so sinister. Traditional media used outrage as a tool, but social platforms turned it into a formula. Every word choice, every framing device, every emotional trigger gets tested against engagement metrics in real time. The algos doesn't care what the content says. It only cares how fast it spreads, and outrage spreads fastest. This creates a feedback loop that fundamentally warps the information ecosystem. Content creators discover that measured, nuanced takes get buried while inflammatory posts reach millions. The reward system trains everyone to become more extreme, more divisive, more outrageous over time. The platforms profit from the engagement surge. The audience gets more addicted to the emotional highs. Everyone loses except the attention merchants. The really disturbing part is how this exploits evolutionary psychology. Your ancestors survived by quickly identifying threats to their survival or social status. The humans who ignored danger signals died. The ones who overreacted to false alarms lived. Natural selection optimized your brain to err on the side of perceiving threats, especially social threats that could result in exile from the group. Social media platforms discovered they could trigger that same ancient alarm system with words on a screen. Your amygdala cannot tell the difference between a real threat and a carefully crafted post designed to simulate one. It responds with the same stress hormones, the same compulsion to warn others, the same addictive rush of righteous anger. But here's what makes modern outrage engineering different from anything humans have faced before: scale and speed. In a traditional tribe, false alarms eventually got corrected through face to face interaction. Someone spreading panic about a nonexistent threat would be called out directly. The social cost of being wrong acted as a brake on runaway fear cycles. Online, that brake disappears. A manufactured outrage can reach millions before anyone can fact check it. By the time corrections appear, the original false alarm has already shaped opinions, triggered responses, and moved on to the next controversy. The platform algos amplify the correction much less than they amplified the original outrage because corrections generate less engagement. The NYU study reveals something that should fundamentally change how you evaluate information: the posts you see are not a random sample of human thought. They are a carefully filtered selection optimized to make you angry, disgusted, or superior. Your worldview is being shaped by content that survived an engagement filter designed to promote the most emotionally manipulative material. That realization should change how you consume media entirely. Every viral post, trending topic, and recommended video is the product of an optimization system that profits from your emotional reaction. The more outraged you feel, the more engaged you become, the more valuable you are to advertisers. The platforms have turned human outrage into a renewable resource. They figured out how to harvest your anger, refine it, and sell it back to you in increasingly concentrated doses. The addiction cycle never ends because there's always a new target, a new crisis, a new reason to feel threatened or superior. Breaking free requires recognizing the manipulation for what it is: a business model that depends on keeping you in a constant state of emotional arousal. The cure involves deliberately seeking out content that doesn't trigger outrage, following sources that acknowledge complexity instead of manufacturing certainty, and remembering that the posts designed to make you angriest are probably the ones least connected to reality. Your attention is worth more than their engagement metrics.
The Curious Tales tweet mediaThe Curious Tales tweet media
English
45
246
708
56.8K
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@AerLingus do you provide reimbursement for accommodations incurred due to itinrrary change on your behalf? My flight in April was moved up nearly 24 hours meaning I have to book a hotel for 1 night upon arrival. Online information and phone agent unclear on my rights to claim.
English
10
0
0
131
J O H N
J O H N@RomanEra0·
Rey Mysterio's Every WrestleMania Opponent! 🔥
J O H N tweet media
English
10
62
1.3K
52.6K
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@heynavtoor @ActualNosferatu I remember asking ChatGPT a year or so ago when the last time all Candian teams in the NHL made playoffs. It gave the wrong answer. It apologized, said ACTUALLY it was...I fact checked again, said it was wrong. It apologized, went back to first answer. I lost trust at that point.
English
0
0
0
32
Nav Toor
Nav Toor@heynavtoor·
🚨BREAKING: OpenAI published a paper proving that ChatGPT will always make things up. Not sometimes. Not until the next update. Always. They proved it with math. Even with perfect training data and unlimited computing power, AI models will still confidently tell you things that are completely false. This isn't a bug they're working on. It's baked into how these systems work at a fundamental level. And their own numbers are brutal. OpenAI's o1 reasoning model hallucinates 16% of the time. Their newer o3 model? 33%. Their newest o4-mini? 48%. Nearly half of what their most recent model tells you could be fabricated. The "smarter" models are actually getting worse at telling the truth. Here's why it can't be fixed. Language models work by predicting the next word based on probability. When they hit something uncertain, they don't pause. They don't flag it. They guess. And they guess with complete confidence, because that's exactly what they were trained to do. The researchers looked at the 10 biggest AI benchmarks used to measure how good these models are. 9 out of 10 give the same score for saying "I don't know" as for giving a completely wrong answer: zero points. The entire testing system literally punishes honesty and rewards guessing. So the AI learned the optimal strategy: always guess. Never admit uncertainty. Sound confident even when you're making it up. OpenAI's proposed fix? Have ChatGPT say "I don't know" when it's unsure. Their own math shows this would mean roughly 30% of your questions get no answer. Imagine asking ChatGPT something three times out of ten and getting "I'm not confident enough to respond." Users would leave overnight. So the fix exists, but it would kill the product. This isn't just OpenAI's problem. DeepMind and Tsinghua University independently reached the same conclusion. Three of the world's top AI labs, working separately, all agree: this is permanent. Every time ChatGPT gives you an answer, ask yourself: is this real, or is it just a confident guess?
Nav Toor tweet media
English
1.4K
8.9K
33.6K
3.3M
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@_NickRichard Just not the #1 guy they keep trying to make him to be. Good player, good guy for the team, just not the game changing elite #1 many good teams have.
English
0
0
0
102
Nick Richard
Nick Richard@_NickRichard·
Bryan McCabe was a star level, fan favourite defenceman for the Leafs but it got really bad for him near the end of his tenure. By the time they traded him, it felt like a mercy kill by the front office just to get him out of the spotlight. Anyway, how’s Morgan Rielly doing?
English
16
4
184
12.9K
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@Dan19975254851 @pdmcleod So I can't go in public with my daughter now without someone listening in on my conversation and "hearing something" that, in their brain "feels weird" and that warrants a call to police. Girl dads are in for a hell of a ride.
English
0
0
7
461
Dan1997
Dan1997@Dan19975254851·
@pdmcleod That guy did the right thing. The safety of Women and Girls is more important than men's feelings.
English
21
1
2
4.8K
Paul McLeod
Paul McLeod@pdmcleod·
The consequences of a moral panic: - A man goes to a coffee shop with his daughter. - Some guy thinks he sees human trafficking and reports it to police "just to be safe than sorry, because I know it’s a serious issue." - Guelph Police blast the father/daughter's photos out on social media where it is seen by tens of thousands of people. The result? Even after the dad calls police and clears things up Guelph police praise the guy who reported them based on nothing. ctvnews.ca/kitchener/guel…
English
173
437
5.1K
437.8K
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@ConorRyan_93 Is this the first "sorry" to come out of any player mouth? I've heard a lot of "unfortunate" but that does not equal sorry.
English
0
0
1
90
Conor Ryan
Conor Ryan@ConorRyan_93·
Charlie McAvoy on Team USA's reaction to Trump's joke about the US women's team: "Certainly sorry for how we responded to it in that moment. Things just happened really quick there. ... It's certainly not reflective of how we feel and look at them and their accomplishments."
English
855
324
8.7K
1.4M
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@PoolsMcPools @LeafsUpdates21 Totally fair, I don't remember hearing that about him but it was so long ago now. Chicken or the egg scenario. Was he always a "just shy of 40" ceiling or was he not trying hard enough because Leafs were mostly garbage back then. A frustrating period of time.
English
1
0
1
14
Scott Pooley
Scott Pooley@PoolsMcPools·
@k_steenburg @LeafsUpdates21 He was a great goal scorer for sure, it was just funny he was often referred to as a perennial 40 goal scorer when he never did that.
English
1
0
1
31
Leafs Updates
Leafs Updates@LeafsUpdates21·
Still tbd if Matthews will ever get back to his 70-goal form but he’s still such a complete player and so skilled. One of the most dominant players in the league.
English
91
1
145
19.5K
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@PoolsMcPools @LeafsUpdates21 Kessel was consistently in the top goal scorer conversation during his Leafs time but people for some reaspn hated him because he ate hot dogs and don't score 60.
English
1
0
1
45
Scott Pooley
Scott Pooley@PoolsMcPools·
@LeafsUpdates21 This reminds me of when Phil Kessel used to be described as a perennial 40 goal scorer (when he was in Toronto), the only problem with that was he never scored more than 37. Still great mind you
English
1
0
2
828
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@mcclxskey As frustrated as I've been with what I see on the ice, even a little win streak will put them right back into the conversation again. November isn't even done yet.
English
0
0
1
10
Chris McCluskey
Chris McCluskey@mcclxskey·
Am I the only one who thinks the Toronto Maple Leafs are going to turn this season around?
English
281
10
547
50.5K
Kevin Steenburg
Kevin Steenburg@k_steenburg·
Heartbreaking finish. But thank you @BlueJays for giving me something to cheer for, while I've been fairly immobilized since Game 2 of ALDS with a double fractured ankle. This incredible run has made my nights easier to get through and what a hell of a run it was!
English
0
0
0
22
Kevin Papetti
Kevin Papetti@KPapetti·
The Jays are one win away from winning the World Series. It's time to play "remember a guy". Name one (1) random former Jays player from a time when things weren't so great
English
1.7K
45
2.4K
240.8K
cinesthetic.
cinesthetic.@TheCinesthetic·
what’s the first movie you think of when you see this?
cinesthetic. tweet media
English
2.2K
317
5.9K
726.8K
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@amartino85 @tavkniesnythews I've always maintained that making the playoffs in year 1 of Matthews was fast forwarding a bit too. Team did what they could aga9nst Washington but many didn't expect a playoff berth that year. It's possible it shifted how the team managed going forward
English
0
0
1
23
Anthony Martino
Anthony Martino@amartino85·
@tavkniesnythews We have to stop saying last 9 years. Sens didnt make the playoffs for 7 years. A lot of other teams have been in the abyss Leafs were victims of their own success. Expectations sped up when Tavares was signed. After yr 6 is when real change should have happened.
English
1
0
2
1.1K
jen
jen@tavkniesnythews·
Was just listening to the JD Bunkis pod and I love how James Mirtle put is. He says that the words around like from Paul Maurice and the panthers is that Toronto is nuts and it’s hard to play in. Mirtle says that’s a really bad read. He says that in game 5 and game 7, if the leafs played really hard and just lost to a better team or there was a bad bounce and it didn’t go their way that media and fans are intelligent enough to understand that. “But that’s not what happened this series and that’s not what happened the last 9 years.”
English
44
39
678
87.6K
Danny Ice Cold
Danny Ice Cold@danielroyyyy·
It cannot be overstated how badly these Three Stooges fucked up this franchise. Fire a HOF GM who wouldn’t coddle your soft babies you call stars. Bring in a computer math boy grifter as GM who has never played or been a manager above junior. Math boy brings in his best little buddy as coach to ride along for the grift. Overpay and cater to the stars every step of the way never demanding anything of them. Surround them with Kerfoots, Johnssons, Dermotts, Holls, Malgins etc cause you have no money for anybody else after unloading the Brinks trunk on the core 4. Wash, rinse and repeat hoping something will change. Pathetic.
Danny Ice Cold tweet mediaDanny Ice Cold tweet mediaDanny Ice Cold tweet media
English
73
77
598
64.7K
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@jeffmcdougall @danielroyyyy I don't agree with everything Dubas did but I think it's an easy out to argue his style was unique. He was the first GM of his kind sure but let's not pretend teams aren't doing it behind the scenes.
Kevin Steenburg tweet media
English
1
0
1
27
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@jeffmcdougall @danielroyyyy Tulsky in Carolina for one. And while yes many NHL GMs may not be "computer boys" some are business men, not former players. But many are surrounded by heavy analytics "computer boys". And I also mentioned "sports" not just NHL. Warriors for example.
English
2
0
0
42
cinesthetic.
cinesthetic.@TheCinesthetic·
Name the first movie that truly scared you.
English
811
36
848
673.3K
Kevin Steenburg
Kevin Steenburg@k_steenburg·
@TheCinesthetic Because I just watched it last night, J.K. Simmons in Whiplash. Holy crap he is one of the all timer villains that I feel like doesn't get talked about enough!
English
1
0
14
779
cinesthetic.
cinesthetic.@TheCinesthetic·
Who's an actor who actually won their Oscar for the RIGHT role?
English
226
25
1.1K
1.1M