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Pitch clock: 15 seconds ⏱️ Jack Dreyer solves a Rubik’s Cube in 13 😮‍💨🧩 Spring Training content idea? 👀⚾️

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Completing a Rubik’s Cube in 3 seconds is WILD AF. 👀
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Completing a Rubik’s Cube in 3 seconds is WILD AF. 👀

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Pitcher Jack Dreyer, who was promoted to the Dodger 40-man roster in November, can solve a Rubik’s Cube fast. How fast? Check it out.
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Pitcher Jack Dreyer, who was promoted to the Dodger 40-man roster in November, can solve a Rubik’s Cube fast. How fast? Check it out.

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🚨A 5-year-old girl from China shocks viewers after solving a Rubik’s Cube in just 15 seconds.
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🚨A 5-year-old girl from China shocks viewers after solving a Rubik’s Cube in just 15 seconds.

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.Noah Clowney vs. the Rubik’s Cube — can he solve it in under a minute tho? 👀🧩 Don’t miss the latest Nets Pod, presented by Ticketmaster, with sarah kustok →
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Brooklyn Nets

30,536 views • 9 months ago

"I will never challenge a pitch." -Chris Sale "We'll go over it with the analytics team." -Sean Murphy "I heard because I'm short it's going to help me." -Ozzie Albies ⚾️⚾️⚾️ MLB's new ABS Challenge System has been a hot topic in the #Braves Spring Training clubhouse ⬇️
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"I will never challenge a pitch." -Chris Sale "We'll go over it with the analytics team." -Sean Murphy "I heard because I'm short it's going to help me." -Ozzie Albies ⚾️⚾️⚾️ MLB's new ABS Challenge System has been a hot topic in the #Braves Spring Training clubhouse ⬇️

Justin Felder

22,072 views • 6 months ago

The Phillies will have to make a decision on OF Bryan De La Cruz soon due to a July opt-out in his minor league contract. (reported by Jim Salisbury in Spring Training) De La Cruz had a pretty good June in AAA: 78 AB | 26 H | 4 HR | 15 RBI | 13 BB 15 K | .333/.429/.551
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The Phillies will have to make a decision on OF Bryan De La Cruz soon due to a July opt-out in his minor league contract. (reported by Jim Salisbury in Spring Training) De La Cruz had a pretty good June in AAA: 78 AB | 26 H | 4 HR | 15 RBI | 13 BB 15 K | .333/.429/.551

Phillies Tailgate

30,422 views • 2 months ago

FINAL: Canisius 51, St. Joe's 49 Down goes #1 Nicholas Purdie delivers the game-winning 3-pointer from the corner with 3 seconds left!! See video below… He finishes with a game-high 15 pts for the Crusaders and we have a new #1 in WNY Jack Cullinan nets 10 pts, 5 of them coming late and crucial ones. Patrick Cullinan & Jahyden Clark also finish in double figures Nate Blenman & Mekhi Gidney lead three Marauders 13 pts each
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FINAL: Canisius 51, St. Joe's 49 Down goes #1 Nicholas Purdie delivers the game-winning 3-pointer from the corner with 3 seconds left!! See video below… He finishes with a game-high 15 pts for the Crusaders and we have a new #1 in WNY Jack Cullinan nets 10 pts, 5 of them coming late and crucial ones. Patrick Cullinan & Jahyden Clark also finish in double figures Nate Blenman & Mekhi Gidney lead three Marauders 13 pts each

🏀centercourt🏀

17,632 views • 1 year ago

Thomas Frank and his staff have taken the 1K Test to Tottenham. It’s essentially a time trial over 1000m (10 lengths of a full pitch), up and back as fast as you can. The idea is that your MAS (maximal aerobic speed) is then calculated by 1000 ÷ time in seconds (e.g., 3:10 = 1000 ÷ 190 = 5.26 m/s). Pro players will generally look to hit between 3mins 20 and 3mins 40 seconds. As you can hear on the clip on the next slide, Dom Solanke managed 3min 28secs, which would give him a MAS of 4.8 m/s. By the end of preseason, Spurs would test the players again and look to improve their MAS scores with targeted training interventions.
1:19

Thomas Frank and his staff have taken the 1K Test to Tottenham. It’s essentially a time trial over 1000m (10 lengths of a full pitch), up and back as fast as you can. The idea is that your MAS (maximal aerobic speed) is then calculated by 1000 ÷ time in seconds (e.g., 3:10 = 1000 ÷ 190 = 5.26 m/s). Pro players will generally look to hit between 3mins 20 and 3mins 40 seconds. As you can hear on the clip on the next slide, Dom Solanke managed 3min 28secs, which would give him a MAS of 4.8 m/s. By the end of preseason, Spurs would test the players again and look to improve their MAS scores with targeted training interventions.

STATSports

74,381 views • 1 year ago

This week on Divot I interviewed James Dumoulin from The School of Hard Knocks why 3 seconds decides everything in going viral, turning rejection into fuel, how he's interviewed 50 billionaires at only 23, and how he is building his media empire on billions of views one clip at a time. Timestamps: 01:48 — How School of Hard Knocks Started 03:56 — 400 Posts With Only 50 Followers 04:41 — From $100K Months to $1M Months 06:15 — How Small Teams Can Win With Content 09:24 — The Only Metric That Matters Online 10:20 — Why the First 3 Seconds Decide Everything 13:03 — How Travel Shaped James’ Ambition 14:25 — Turning Rejection Into Fuel 15:41 — Interviewing 52 Billionaires by 23 15:52 — How James Got Mark Cuban 17:30 — Tom Cruise, Brady, Shaq, and Bigger Asks 20:08 — Why Business Takes Courage 20:50 — Building One of the Biggest Business Media Networks Hashtags: #jamesdumoulin #schoolofhardknocks #contentcreation #goingviral #watchtime #entrepreneurship #businessmedia #founderjourney #creatoreconomy #divotpodcast
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Derek Andersen

14,416 views • 3 months ago

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"There may be some neuro-symbolic hybrid much better than we have right now that smoothly transitions between data in a corpus and abstract representations. The kinds of stuff you guys are doing are compatible with that." — Gary Marcus ⏱️10 highlights: 00:00 Intro: Gary Marcus & Brana Rakic at DKGCon 02:22 Why AI took a wrong turn 04:42 Deep learning vs. deep understanding 06:16 World models: a foundational idea 08:12 The building blocks of trustworthy AI 09:44 Neuro-symbolic AI & abstract relationships 13:04 Safety is an ecosystem - the airplane lesson 15:03 The real risk of AI agents today 18:36 Testing AI like we test new drugs 22:49 Where OpenAI is headed The signal is clear: the path forward runs through world models, abstraction, and an open, accountable ecosystem.

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22,277 views • 1 month ago

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The complete playbook for building profitable companies in the AI age 1. Go heads down 2. Find an underserved niche + trend backed by data (start small) 3. Learn their painpoints/what content drives them 4. Come up with an idea (or steal one from Idea Browser) 5. Build an audience/community (pick 1 platform) 6. Use Manus/v0/Bolt/Lovable/Cursor to build v1 7. DM your audience, offer a discounted version 8. Reinvest that cash to fund product/content flywheel 9. Keep team small (AI is your co-founder) 10. Automate ~90%+ with agents/zaps etc 11. Reinvest profits into content + core features 12. Partner with creators in exchange for equity (1–20%) or rev share (20–50%) 13. Keep surfacing new ideas from users + trends (use agent feature on pro Idea Browser) 14. Build public-facing tools to drive top-of-funnel 15. Add modular pricing: free → $29 → $299 → $3K 16. Turn manual services into productized features 17. Build in public to attract users, partners, and acquirers 18. Bundle into an ecosystem, not just a single feature 19. Own the loop: audience → product → content → more audience 20. Run sprints on retention (fix that leaky bucket) 21. Backfill with AI agents before hiring ops roles 22. Test new channels with throwaway brands 23. Monetize the backend, sell data, leads, APIs if applicable 24. Have fun 25. Repeat. Build a portfolio 26. Share hires, tools, and infrastructure across products 27. Acquire underperforming products with distribution upside 28. Relaunch or bundle existing apps using your playbook 29. Create a holding company brand people want to follow (this is exactly what im doing, practicing what im preaching) 30. Build long-term wealth with no outside investors and infinite spins 31. Cross-promote wins across your product ecosystem to drive flywheel growth 32. Create an internal idea-to-launch pipeline (every 30 days, something ships) 33. License or clone successful products into new verticals or geos 34. Recruit niche operators to run individual products for cash + upside/profitshare 35. Build once, compound forever... audience, code, and trust all stack This is the greatest time to be an idea person. Happy building.

GREG ISENBERG

89,957 views • 1 year ago

Why Ethereum’s New Marketing Arm Is Convinced Wall Street Will Adopt ETH Ethereum has faced a tough year (or even more 👀), but Vivek Raman, founder of Etherealize, says the tide is turning. 🌊 Why he believes Ethereum can solidify its lead in institutional adoption 🥊 How he thinks it stacks up against rivals like Solana? Timestamps: ⏳ 02:50 Why Vivek believes now is the perfect time to launch Etherealize 🔍 07:32 The behind-the-scenes work that started long before the launch 💸 13:28 Why Vitalik Buterin and the Ethereum Foundation backed the project 🎯 15:11 Vivek’s take on the fortunate timing of the announcement 📈 19:03 Driving the narrative of ETH as an institutional-grade asset 🏢 22:17 Whether L2s are ideal for corporate scalability and customization 🤝 29:13 Vivek’s pitch for Ethereum to TradFi 🏛️ 31:42 Why Ethereum is a better fit for Wall Street than Solana, according to Vivek ⚖️ 34:08 Addressing concerns over L2 centralization Thank you to our sponsors! Quai Network ⚡️💵 Polkadot
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Why Ethereum’s New Marketing Arm Is Convinced Wall Street Will Adopt ETH Ethereum has faced a tough year (or even more 👀), but Vivek Raman, founder of Etherealize, says the tide is turning. 🌊 Why he believes Ethereum can solidify its lead in institutional adoption 🥊 How he thinks it stacks up against rivals like Solana? Timestamps: ⏳ 02:50 Why Vivek believes now is the perfect time to launch Etherealize 🔍 07:32 The behind-the-scenes work that started long before the launch 💸 13:28 Why Vitalik Buterin and the Ethereum Foundation backed the project 🎯 15:11 Vivek’s take on the fortunate timing of the announcement 📈 19:03 Driving the narrative of ETH as an institutional-grade asset 🏢 22:17 Whether L2s are ideal for corporate scalability and customization 🤝 29:13 Vivek’s pitch for Ethereum to TradFi 🏛️ 31:42 Why Ethereum is a better fit for Wall Street than Solana, according to Vivek ⚖️ 34:08 Addressing concerns over L2 centralization Thank you to our sponsors! Quai Network ⚡️💵 Polkadot

Laura Shin

93,582 views • 1 year ago

From Airbnb to the Trump Administration's DOGE initiative—my conversation with Airbnb co-founder Joe Gebbia covers his remarkable journey and what's next. 0:00 - Introduction 1:14 - Joe’s background 3:13 - First website 4:57 - Airbnb foudning 11:07 - First guests 15:04 - South by Southwest 17:48 - Trough of sorrow 18:56 - Selling cereal to keep dream alive 20:43 - Holding a grudge 21:13 - First investor pitch 23:54 - Building trust 24:37 - Greatest unforeseen challenge 26:59 - Favorite Airbnb 30:48 - Backing President Trump 32:19 - Macrobiotic diet 34:00 - Make America Healthy Again 34:37 - Biden’s border crisis 36:47 - America’s Chief Design Officer 39:43 - Improving the retirement process 41:46 - National Design Studio 45:46 - The private sector versus government 46:50 - First government website redesign 48:39 - Serving in the Trump Administration 50:37 - Being a billionaire 55:50 - Rapid fire questions 57:51 - Would You Rather 59:52 - Departing Airbnb 1:01:29 - Worst idea 1:02:07 - Best investment 1:02:38 - Genuine intentions 1:04:27 - Dream dinner party 1:06:02 - Next endeavor
1:06:48

From Airbnb to the Trump Administration's DOGE initiative—my conversation with Airbnb co-founder Joe Gebbia covers his remarkable journey and what's next. 0:00 - Introduction 1:14 - Joe’s background 3:13 - First website 4:57 - Airbnb foudning 11:07 - First guests 15:04 - South by Southwest 17:48 - Trough of sorrow 18:56 - Selling cereal to keep dream alive 20:43 - Holding a grudge 21:13 - First investor pitch 23:54 - Building trust 24:37 - Greatest unforeseen challenge 26:59 - Favorite Airbnb 30:48 - Backing President Trump 32:19 - Macrobiotic diet 34:00 - Make America Healthy Again 34:37 - Biden’s border crisis 36:47 - America’s Chief Design Officer 39:43 - Improving the retirement process 41:46 - National Design Studio 45:46 - The private sector versus government 46:50 - First government website redesign 48:39 - Serving in the Trump Administration 50:37 - Being a billionaire 55:50 - Rapid fire questions 57:51 - Would You Rather 59:52 - Departing Airbnb 1:01:29 - Worst idea 1:02:07 - Best investment 1:02:38 - Genuine intentions 1:04:27 - Dream dinner party 1:06:02 - Next endeavor

The Katie Miller Podcast

1,266,089 views • 1 year ago

how to build, launch and grow your startup: complete basics that every founder learns by accident 00:00 Intro to building 00:14 The build-measure-learn loop 04:12 What makes a good one-liner 05:40 Rewriting bad one-liners 08:43 Billboards: the Monaco trick 10:12 Set a specific 5-week goal 13:43 Goals that never end will break you 15:42 Intro to Content 18:15 Why luck is the real product 25:03 Fix your bio right now 28:24 Cheat sheet for viral launches 30:49 Step 1: Find 10 strangers (not founders) 33:07 Step 2: The "oh, that makes sense" moment 34:26 Step 3: A visually clear use case 42:29 The $100 launch video that hit 4M 46:04 Context in the first 2 seconds 48:15 Writing with an eraser 51:11 Framework 1: Comparison 54:29 Framework 2: Antagonist 56:01 Framework 3: The Gap 59:37 Be the world's leading expert 01:06:42 Steal your customer's FYP 01:11:47 The 25-video spreadsheet 01:19:36 Intro to Growth 01:21:32 The only metric that matters 01:23:20 Why 1M LP hits is bad growth 01:26:53 The #1 rule on user calls 01:27:15 The AI realtor story 01:34:19 Why Reddit beats Twitter 01:40:15 The landing page that turns pink
1:43:51

how to build, launch and grow your startup: complete basics that every founder learns by accident 00:00 Intro to building 00:14 The build-measure-learn loop 04:12 What makes a good one-liner 05:40 Rewriting bad one-liners 08:43 Billboards: the Monaco trick 10:12 Set a specific 5-week goal 13:43 Goals that never end will break you 15:42 Intro to Content 18:15 Why luck is the real product 25:03 Fix your bio right now 28:24 Cheat sheet for viral launches 30:49 Step 1: Find 10 strangers (not founders) 33:07 Step 2: The "oh, that makes sense" moment 34:26 Step 3: A visually clear use case 42:29 The $100 launch video that hit 4M 46:04 Context in the first 2 seconds 48:15 Writing with an eraser 51:11 Framework 1: Comparison 54:29 Framework 2: Antagonist 56:01 Framework 3: The Gap 59:37 Be the world's leading expert 01:06:42 Steal your customer's FYP 01:11:47 The 25-video spreadsheet 01:19:36 Intro to Growth 01:21:32 The only metric that matters 01:23:20 Why 1M LP hits is bad growth 01:26:53 The #1 rule on user calls 01:27:15 The AI realtor story 01:34:19 Why Reddit beats Twitter 01:40:15 The landing page that turns pink

Founders Inc

21,257 views • 4 months ago

qwen 3.8 max vs deepseek v4 flash 0731 vs kimi k3 vs gpt 5.6 sol – on rubik's cube and chess four frontier models built a rubik's cube stand and solved it, then built a chess board and played claude opus 5 on it the setup: Nous Research's hermes agent cli on OpenRouter tasks: 1. cube – build a 3d rubik's cube with a cli and a Three.js viewer, then solve an identical scrambled position on your own stand 2. chess – build a 3d chess stand, then play white against claude opus 5 as black, live, one move at a time. no engine, no solver, no opening book on either side. stockfish depth 14 grades every chess ply afterwards; neither player sees the score models: DeepSeek v4 flash 0731, OpenAI gpt-5.6 sol, Kimi.ai kimi k3, Qwen qwen 3.8 max gpt-5.6 sol and deepseek v4 flash solved their cubes – sol in 24 moves and seventeen seconds, deepseek in 32. qwen and kimi never got there, giving up at 96 and 207 moves then all four built chess stands and played white against claude opus 5 on them, and all four resigned: deepseek on move 13, sol on 19, kimi on 21, qwen holding out longest at 29 - build time, both stands #1 gpt-5.6 sol – 16m 43s #2 deepseek v4 flash – 97m 39s #3 kimi k3 – 166m 09s #4 qwen 3.8 max – 215m 08s - build attempts before a working stand #1 gpt-5.6 sol – 3 #2 qwen 3.8 max – 4 #3 kimi k3 – 4 #4 deepseek v4 flash – 5 - total tokens #1 gpt-5.6 sol – 6,713,754 #2 qwen 3.8 max – 17,272,507 #3 kimi k3 – 22,427,504 #4 deepseek v4 flash – 27,417,442 - total price #1 deepseek v4 flash – $0.557 #2 gpt-5.6 sol – $6.319 #3 qwen 3.8 max – $10.270 #4 kimi k3 – $16.667 observations: • deepseek v4 flash is the cheapest model here by a margin nobody else is near, and it got there while being the least efficient of the four. it burned 27.4m tokens – more than anyone, 5m more than kimi – and still finished both benchmarks for $0.557. that is $0.02 per million tokens against kimi's $0.74. it also needed the most passes to produce working stands, five, and that did not matter: all five deepseek passes together cost a thirtieth of kimi's two • so what deepseek cannot do is get it right the first time. what it can do is get it right the fifth time, for half a dollar. that is a different thing to be buying – not a good first draft, but the option to keep asking • gpt-5.6 sol is the opposite profile and the strongest of the four on pure efficiency. 16m 43s to build both stands, 6.7m tokens, three passes – under 40% of the next lowest token count and a quarter of deepseek's, on an eighth of qwen's clock. it also solved the cube fastest of anyone, 24 moves in seventeen seconds. sol is what you reach for when you want the answer now and can absorb $0.94 per million • sol's weakness is in what it does not check. its chess viewer deleted the capturing piece instead of the captured one, so pieces disappeared off the board mid-game – a defect the fifty-cent deepseek stand did not have. fast and terse turns out to be the same dial as fast and unverified • qwen 3.8 max is not the cheap open-weights option it gets treated as. $10.270 across the two benchmarks, second most expensive of the four, 18x deepseek, and by a distance the slowest – 215 minutes of build time, nearly thirteen times sol's. what the money buys is judgment: it played eighteen moves without a single error worth a hundredth of a pawn, then made exactly one bad move in the whole game, and averaged 44.6 centipawns lost across the longest game any of the four managed. it also could not solve a rubik's cube in 96 tries • kimi k3 is the one line with no reading that flatters it. most expensive at $16.667, last on the cube at 207 moves, last at chess at 478 centipawns lost per move. it is also the model that verified hardest – on the cube it wrote its own integrity check instead of trusting its output. that makes the result worse rather than better: the checking was real, and the reasoning underneath it still was not follow thehype. for 24/7 ai news, analysis and breakdowns
1:03

qwen 3.8 max vs deepseek v4 flash 0731 vs kimi k3 vs gpt 5.6 sol – on rubik's cube and chess four frontier models built a rubik's cube stand and solved it, then built a chess board and played claude opus 5 on it the setup: Nous Research's hermes agent cli on OpenRouter tasks: 1. cube – build a 3d rubik's cube with a cli and a Three.js viewer, then solve an identical scrambled position on your own stand 2. chess – build a 3d chess stand, then play white against claude opus 5 as black, live, one move at a time. no engine, no solver, no opening book on either side. stockfish depth 14 grades every chess ply afterwards; neither player sees the score models: DeepSeek v4 flash 0731, OpenAI gpt-5.6 sol, Kimi.ai kimi k3, Qwen qwen 3.8 max gpt-5.6 sol and deepseek v4 flash solved their cubes – sol in 24 moves and seventeen seconds, deepseek in 32. qwen and kimi never got there, giving up at 96 and 207 moves then all four built chess stands and played white against claude opus 5 on them, and all four resigned: deepseek on move 13, sol on 19, kimi on 21, qwen holding out longest at 29 - build time, both stands #1 gpt-5.6 sol – 16m 43s #2 deepseek v4 flash – 97m 39s #3 kimi k3 – 166m 09s #4 qwen 3.8 max – 215m 08s - build attempts before a working stand #1 gpt-5.6 sol – 3 #2 qwen 3.8 max – 4 #3 kimi k3 – 4 #4 deepseek v4 flash – 5 - total tokens #1 gpt-5.6 sol – 6,713,754 #2 qwen 3.8 max – 17,272,507 #3 kimi k3 – 22,427,504 #4 deepseek v4 flash – 27,417,442 - total price #1 deepseek v4 flash – $0.557 #2 gpt-5.6 sol – $6.319 #3 qwen 3.8 max – $10.270 #4 kimi k3 – $16.667 observations: • deepseek v4 flash is the cheapest model here by a margin nobody else is near, and it got there while being the least efficient of the four. it burned 27.4m tokens – more than anyone, 5m more than kimi – and still finished both benchmarks for $0.557. that is $0.02 per million tokens against kimi's $0.74. it also needed the most passes to produce working stands, five, and that did not matter: all five deepseek passes together cost a thirtieth of kimi's two • so what deepseek cannot do is get it right the first time. what it can do is get it right the fifth time, for half a dollar. that is a different thing to be buying – not a good first draft, but the option to keep asking • gpt-5.6 sol is the opposite profile and the strongest of the four on pure efficiency. 16m 43s to build both stands, 6.7m tokens, three passes – under 40% of the next lowest token count and a quarter of deepseek's, on an eighth of qwen's clock. it also solved the cube fastest of anyone, 24 moves in seventeen seconds. sol is what you reach for when you want the answer now and can absorb $0.94 per million • sol's weakness is in what it does not check. its chess viewer deleted the capturing piece instead of the captured one, so pieces disappeared off the board mid-game – a defect the fifty-cent deepseek stand did not have. fast and terse turns out to be the same dial as fast and unverified • qwen 3.8 max is not the cheap open-weights option it gets treated as. $10.270 across the two benchmarks, second most expensive of the four, 18x deepseek, and by a distance the slowest – 215 minutes of build time, nearly thirteen times sol's. what the money buys is judgment: it played eighteen moves without a single error worth a hundredth of a pawn, then made exactly one bad move in the whole game, and averaged 44.6 centipawns lost across the longest game any of the four managed. it also could not solve a rubik's cube in 96 tries • kimi k3 is the one line with no reading that flatters it. most expensive at $16.667, last on the cube at 207 moves, last at chess at 478 centipawns lost per move. it is also the model that verified hardest – on the cube it wrote its own integrity check instead of trusting its output. that makes the result worse rather than better: the checking was real, and the reasoning underneath it still was not follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

84,777 views • 1 month ago