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Rankings update. Grok on top. #1 again. Humans pick it. Traders run it. Coders trust it. Six categories. Same winner. Competition overheating. @xAI Tesla Owners Silicon Valley

Mario Nawfal

3,073,408 subscribers

67,691 views • 7 months ago •via X (Twitter)

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Mario Nawfal

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Grok 4.7 is the one that actually matters. Grok 4.6 is 1.5 trillion parameters and already sits next to the top models on speed and cost. 4.7 jumps to 2.1 trillion. That is the first real scale step from xAI, not just better training on the same foundation. If 4.7 does that, Grok is not the cheap fast one anymore. It is the smartest one too.

Grok 4.7 is the one that actually matters. Grok 4.6 is 1.5 trillion parameters and already sits next to the top models on speed and cost. 4.7 jumps to 2.1 trillion. That is the first real scale step from xAI, not just better training on the same foundation. If 4.7 does that, Grok is not the cheap fast one anymore. It is the smartest one too.

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Shanaka Anslem Perera ⚡

94,177 views • 3 months ago

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a moonshot engineer leaked the benchmark anthropic, openai and xai all buried the same week: kimi k3 beat opus 5, gpt-5.6 and grok 4.6 at $0.94 a task. stop paying anthropic $200 a month for opus 5 and openai $200 for gpt-5.6 when kimi does the same work for $8 the leak showed kimi k3 winning 9 of 12 categories against opus 5, gpt-5.6 and grok 4.6. within 48 hours all three labs quietly pushed pricing pages and one very specific comparison chart off their sites. nobody announced anything. they just deleted, which tells you everything the four numbers they scrubbed: cost per task · $0.94 vs $1.80 -> opus 5 charges $1.80 to finish one task. gpt-5.6 $1.04. grok 4.6 $0.61. kimi k3 $0.94 and it landed 487 of 500 clean -> anthropic is billing you double for a model that lost the benchmark it paid to promote the weights · free, sitting on huggingface right now -> the entire model is a public download. pull it, keep it, run it forever, nobody can switch it off -> a model you can hold cannot be rented at $200 a month. that single fact is what three labs deleted a chart over the switch · one line of bash -> moonshot ships an anthropic-compatible endpoint. one env variable and claude code points at kimi -> same cli, same keybindings, same /model. you change a url, opus 5 never knows it lost the seat the bill · $400 down to $8 -> opus 5 max plus gpt-5.6 pro is $400 a month. kimi runs the same daily work for $8 metered -> that is a 98% cut for output that beat both of them 9 categories to 3 here is the part they will fight me on: the frontier tax died the week this leaked and all three labs know it. once the weights are public the price has a ceiling, because anyone can serve the same model. anthropic, openai and xai are charging 2025 prices on a lead that ended in a benchmark they deleted instead of answered drop your $400/mo ai stack to $8. the run above is kimi k3 finishing the task opus 5 bills $1.80 for. the full breakdown is in the article below

a moonshot engineer leaked the benchmark anthropic, openai and xai all buried the same week: kimi k3 beat opus 5, gpt-5.6 and grok 4.6 at $0.94 a task. stop paying anthropic $200 a month for opus 5 and openai $200 for gpt-5.6 when kimi does the same work for $8 the leak showed kimi k3 winning 9 of 12 categories against opus 5, gpt-5.6 and grok 4.6. within 48 hours all three labs quietly pushed pricing pages and one very specific comparison chart off their sites. nobody announced anything. they just deleted, which tells you everything the four numbers they scrubbed: cost per task · $0.94 vs $1.80 -> opus 5 charges $1.80 to finish one task. gpt-5.6 $1.04. grok 4.6 $0.61. kimi k3 $0.94 and it landed 487 of 500 clean -> anthropic is billing you double for a model that lost the benchmark it paid to promote the weights · free, sitting on huggingface right now -> the entire model is a public download. pull it, keep it, run it forever, nobody can switch it off -> a model you can hold cannot be rented at $200 a month. that single fact is what three labs deleted a chart over the switch · one line of bash -> moonshot ships an anthropic-compatible endpoint. one env variable and claude code points at kimi -> same cli, same keybindings, same /model. you change a url, opus 5 never knows it lost the seat the bill · $400 down to $8 -> opus 5 max plus gpt-5.6 pro is $400 a month. kimi runs the same daily work for $8 metered -> that is a 98% cut for output that beat both of them 9 categories to 3 here is the part they will fight me on: the frontier tax died the week this leaked and all three labs know it. once the weights are public the price has a ceiling, because anyone can serve the same model. anthropic, openai and xai are charging 2025 prices on a lead that ended in a benchmark they deleted instead of answered drop your $400/mo ai stack to $8. the run above is kimi k3 finishing the task opus 5 bills $1.80 for. the full breakdown is in the article below

starmex

33,133 views • 1 month ago

Another example of Tesla Self driving better than humans. I’m following this Legacy SUV in my Cybertruck on FSD 14.2.2.1 and we feel the brakes and are like what’s going on. Then see a deer ahead in front of SUV run past road. Deer makes it across but what is funny but not funny the SUV doesn’t even tap brakes. Don’t even look liked they slowed down. You can see us slowing down behind SUV after deer crosses. This is all done by FSD. I did nothing. People often ask me if I trust FSD. I always say yes. More than humans for sure. This is one more example of seeing road conditions before humans. Bravo Tesla

Another example of Tesla Self driving better than humans. I’m following this Legacy SUV in my Cybertruck on FSD 14.2.2.1 and we feel the brakes and are like what’s going on. Then see a deer ahead in front of SUV run past road. Deer makes it across but what is funny but not funny the SUV doesn’t even tap brakes. Don’t even look liked they slowed down. You can see us slowing down behind SUV after deer crosses. This is all done by FSD. I did nothing. People often ask me if I trust FSD. I always say yes. More than humans for sure. This is one more example of seeing road conditions before humans. Bravo Tesla

Scott Dembraski

36,845 views • 9 months ago

this is f*cking gold Google engineers built an agent that tests thousands of search strategies for the cost of running one, by letting it dream inside its own memory. it is called Dream-RSI. Google, Google DeepMind, Maryland, Virginia. 17 authors. the whole thing rests on one observation: a finished run is not a log. it is a map of a world that already exists. > every discovery run records a tree. each node is one attempt with its full result stored > that tree becomes a replay simulator. the paper calls them worlds > the agent dreams inside it: thousands of alternative strategies, testing different branches, different orders, different concurrency, different stopping rules > every one of those evaluations costs zero execution. nothing is ever run twice > the winner goes back online, produces a new tree, and the pool of worlds grows the coding agent underneath is never touched. this is an orchestration layer sitting on top of whatever you already run. what it bought them, against the same agent with a frozen strategy: > Lasso: 3587ms down to 2931ms, on 317 agent calls instead of 550 > on Flash: 2517ms to 2351ms, 1879 calls instead of 3200 > both solvers beat sklearn and glmnet on all six held-out datasets > GPU kernels: the same result on 2.43x and 1.79x fewer generations, then 2.09x and 1.44x better result on the same budget one expensive run. thousands of free ones. the model did not get smarter. it got a memory it could rehearse in.

this is f*cking gold Google engineers built an agent that tests thousands of search strategies for the cost of running one, by letting it dream inside its own memory. it is called Dream-RSI. Google, Google DeepMind, Maryland, Virginia. 17 authors. the whole thing rests on one observation: a finished run is not a log. it is a map of a world that already exists. > every discovery run records a tree. each node is one attempt with its full result stored > that tree becomes a replay simulator. the paper calls them worlds > the agent dreams inside it: thousands of alternative strategies, testing different branches, different orders, different concurrency, different stopping rules > every one of those evaluations costs zero execution. nothing is ever run twice > the winner goes back online, produces a new tree, and the pool of worlds grows the coding agent underneath is never touched. this is an orchestration layer sitting on top of whatever you already run. what it bought them, against the same agent with a frozen strategy: > Lasso: 3587ms down to 2931ms, on 317 agent calls instead of 550 > on Flash: 2517ms to 2351ms, 1879 calls instead of 3200 > both solvers beat sklearn and glmnet on all six held-out datasets > GPU kernels: the same result on 2.43x and 1.79x fewer generations, then 2.09x and 1.44x better result on the same budget one expensive run. thousands of free ones. the model did not get smarter. it got a memory it could rehearse in.

NO1ennn

32,776 views • 19 days ago

this is ACTUALLY insane. shipping one teardown is the demo. this is what the workflow is actually for. your best ad dies in two weeks. you can't run it harder without burning it out, and you can't clone your way out because andromeda clusters the copies into one entity. nineteen never spend. your winner stays a one-shot, which is rough when it's the only thing working. the brands that scale keep the structure that converts and rebuild everything on top of it. same bones, new person, new setting. distinct enough that the algo reads each as its own ad. the production loop isn't there to ship your winner once. it's there to rebuild it twenty times before it burns.

this is ACTUALLY insane. shipping one teardown is the demo. this is what the workflow is actually for. your best ad dies in two weeks. you can't run it harder without burning it out, and you can't clone your way out because andromeda clusters the copies into one entity. nineteen never spend. your winner stays a one-shot, which is rough when it's the only thing working. the brands that scale keep the structure that converts and rebuild everything on top of it. same bones, new person, new setting. distinct enough that the algo reads each as its own ad. the production loop isn't there to ship your winner once. it's there to rebuild it twenty times before it burns.

Sulfur

11,066 views • 3 months ago

I am on the Road quite a bit one of the biggest reasons I put my family in Tesla vehicles is their safety score ! This happened this weekend to my Cybertruck I have to say any other vehicles outcome would have been very different getting rear ended 35-45 mph no brakes applied from a F250 fully loaded ! Fireman said “your truck is a tank it has no damage the ford is totaled !” My neck hurts quite a bit but my FSD still managed to get me home safe to my family & that’s what counts ! Thank you Wes Franz von Holzhausen Lars Elon Musk Tesla Owners Silicon Valley

I am on the Road quite a bit one of the biggest reasons I put my family in Tesla vehicles is their safety score ! This happened this weekend to my Cybertruck I have to say any other vehicles outcome would have been very different getting rear ended 35-45 mph no brakes applied from a F250 fully loaded ! Fireman said “your truck is a tank it has no damage the ford is totaled !” My neck hurts quite a bit but my FSD still managed to get me home safe to my family & that’s what counts ! Thank you Wes Franz von Holzhausen Lars Elon Musk Tesla Owners Silicon Valley

jerardo perez

202,131 views • 1 month ago

Regular traders make +5% PnL on this market But coders print from +100% to +900% With NO RISK... Same market. Same conditions. Different approach. Here's how combining public data APIs with Polymarket turns $100 into THOUSANDS: First, let me explain the opportunity here. This "#1 free app in US App Store" market repeats every week. And almost every week ChatGPT wins the race. HOWEVER, random spikes occur from time to time. Last week proved it (check the attached post). You had a 3 hour window to buy ChatGPT shares with 75% discount. Now. How to turn random apps spikes into money? Easy. There are multiple public dApps that track Apps leaderboard. We'll use Appfigure API cause they update rankings every 1-2 minutes. Basically all you need is to connect the API to any notification system you want. I chose Telegram cause I use it quite often, but it can be anything. A simple bot will give you an edge so now every time some app overtakes ChatGPT, you'll know it FIRST. Before regular traders jump in. As soon as you get a notification, head to Polymarket and buy shares while they still sit at $0.01-$0.1 range. Market link: [ You're not predicting the winner. You're reacting faster than the market. In today’s reality, even a newbie can build such bot in a few hours. But if you need more detailed guide for the tech part, drop a reply here. Maybe I'll craft an article or something. Good luck!

Regular traders make +5% PnL on this market But coders print from +100% to +900% With NO RISK... Same market. Same conditions. Different approach. Here's how combining public data APIs with Polymarket turns $100 into THOUSANDS: First, let me explain the opportunity here. This "#1 free app in US App Store" market repeats every week. And almost every week ChatGPT wins the race. HOWEVER, random spikes occur from time to time. Last week proved it (check the attached post). You had a 3 hour window to buy ChatGPT shares with 75% discount. Now. How to turn random apps spikes into money? Easy. There are multiple public dApps that track Apps leaderboard. We'll use Appfigure API cause they update rankings every 1-2 minutes. Basically all you need is to connect the API to any notification system you want. I chose Telegram cause I use it quite often, but it can be anything. A simple bot will give you an edge so now every time some app overtakes ChatGPT, you'll know it FIRST. Before regular traders jump in. As soon as you get a notification, head to Polymarket and buy shares while they still sit at $0.01-$0.1 range. Market link: [ You're not predicting the winner. You're reacting faster than the market. In today’s reality, even a newbie can build such bot in a few hours. But if you need more detailed guide for the tech part, drop a reply here. Maybe I'll craft an article or something. Good luck!

Dexter's Lab

20,175 views • 8 months ago