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3 SMG META!? CDL Meta Update Pre-Major 1. Learn when teams are pulling out that third sub! FULL 20+ min FLEX player guide now live on YT:

OpTic JP

38,735 subscribers

143,515 Aufrufe • vor 1 Jahr •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

6 Kommentare

Profilbild von RedDeer.Games

RedDeer.Gamesvor 2 Jahren

New is better Together with @mgpstudios we present you... 🟥 NECKBREAK 🟥 Stylish, #cyberpunk, #retro first-person shooter is available now! 🔥 Play it on ⤵️ #NintendoSwitch => #Xbox => #indiegames #release

Profilbild von Faded

Fadedvor 1 Jahr

When I got 3 smgs on p2 i say mainly watch cat and elevator, of course giving comms on where I see them I tell them hey watch here if I need help I rotate from watching kitchen to watching over piano usually unbreakable especially if you head glitch the rail near gulf/the tree

Profilbild von Cody

Codyvor 1 Jahr

What website is this?

Profilbild von OMGL DEV

OMGL DEVvor 1 Jahr

@morgiicod

Profilbild von LukeSkywalker

LukeSkywalkervor 1 Jahr

No such thing as roles in modern day call of duty ever play should be able to use all guns just aim and shoot lad no more to it

Profilbild von 🎩🎓

🎩🎓vor 1 Jahr

mfs do not look at the game like this straight the fuck up. Not who I play wit anyway 🤣

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Everybody is talking about recursive self-improvement (RSI) and meta learning. Here is my old 2020 talk about this [1]. It has aged well. Example: humans still define the starts & ends of trials of many modern meta learners. My RSI systems since 1994 LEARN to (re)define them [2]! [1] Meta Learning Machines in a Single Lifelong Trial (talk for workshops at ICML 2020 and NeurIPS 2021, based on earlier talks since 1994). Abstract: the most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987 [2]. Here I summarize our work on meta reinforcement learning with self-modifying policies in a single lifelong trial (since 1994), and mathematically optimal meta-learning through the self-referential Gödel Machine (since 2003). Many additional publications on meta-learning since 1987 can be found in the RSI overview [2]. [2] J. Schmidhuber (AI Blog, 2020-2025). 1/3 century anniversary of first publication on recursive self-improvement (RSI) and meta learning machines that learn to learn (1987). For its cover I drew a robot that bootstraps itself. 1992-: gradient descent-based neural meta learning. 1994-: meta reinforcement learning with self-modifying policies. 1997: meta RL plus artificial curiosity and intrinsic motivation. 2002-: asymptotically optimal meta learning for curriculum learning. 2003-: mathematically optimal Gödel Machine. 2020-: new stuff!

Everybody is talking about recursive self-improvement (RSI) and meta learning. Here is my old 2020 talk about this [1]. It has aged well. Example: humans still define the starts & ends of trials of many modern meta learners. My RSI systems since 1994 LEARN to (re)define them [2]! [1] Meta Learning Machines in a Single Lifelong Trial (talk for workshops at ICML 2020 and NeurIPS 2021, based on earlier talks since 1994). Abstract: the most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987 [2]. Here I summarize our work on meta reinforcement learning with self-modifying policies in a single lifelong trial (since 1994), and mathematically optimal meta-learning through the self-referential Gödel Machine (since 2003). Many additional publications on meta-learning since 1987 can be found in the RSI overview [2]. [2] J. Schmidhuber (AI Blog, 2020-2025). 1/3 century anniversary of first publication on recursive self-improvement (RSI) and meta learning machines that learn to learn (1987). For its cover I drew a robot that bootstraps itself. 1992-: gradient descent-based neural meta learning. 1994-: meta reinforcement learning with self-modifying policies. 1997: meta RL plus artificial curiosity and intrinsic motivation. 2002-: asymptotically optimal meta learning for curriculum learning. 2003-: mathematically optimal Gödel Machine. 2020-: new stuff!

Jürgen Schmidhuber

223,071 Aufrufe • vor 4 Monaten

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We called the bottom perfectly yesterday together. Expect a relief rally on the Magnificent 7 stocks now. It's going to be choppy for the first few weeks, but end-of-month will be strong. However, we are NOT out of the woods yet. 1. Amazon - $AMZN 2. Apple - $AAPL 3. Google - $GOOGL 4. Meta - $META 5. Microsoft - $MSFT 6. Nvidia - $NVDA 7. Tesla $TSLA I'll make sure to update you every single day.

StockWhale

84,907 Aufrufe • vor 4 Monaten

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284,206 Aufrufe • vor 10 Monaten

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A viral tweet claimed that Meta is now responsible for nearly a third of Anthropic's $30B ARR, based on reporting that Meta used 60.2T tokens over 30 days. This doesn't actually pencil out. It assumes all of Meta's spend is on output tokens, which are much more expensive than input tokens, and which would be extremely unusual — according to OpenRouter data, about 98.9% of all Opus 4.6 tokens are input. The actual number is maybe closer to $136M a month, or $1.6B a year. Tyler Cosgrove goes through the math:

TBPN

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Team Gullit

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53,750 Aufrufe • vor 11 Monaten

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Anttsinc

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dray

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16,146 Aufrufe • vor 1 Jahr

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Bearly AI

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Crazy that Claude can now autonomously code strategies and validate them on TradingView and it’s actually faster than my pipeline. Time to expand to a third platform for our code TradingView is next. 15+ strategies are waiting to be coded. And a full Meta EA TV version is coming. Probably only 0.001% of the industry is doing this. Huge thanks to Andrew and The Daily Profiler for this pipeline idea.

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⚠️Palantir, Microsoft, Amazon, Google, Oracle, Meta: Alex Jones Lays Out The Wide Spectrum Of AI Surveillance Control Grids That Are Threatening Humanity & Reveals The Ultimate Tool Of Tyranny: "Your Cell Phone Can Tell Your Heartbeat, It Can Listen Even When It's Off, It Has GPS Triangulation, It's Tyranny, It's Surveilling You, It's Selling Your Data, It's The Obelisk From 2001: A Space Odyssey!" 🔴WATCH ALEX JONES LIVE NOW:

Alex Jones

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3 Press Break Strategies That Flip the Script and Punish Full-Court Pressure They say pressure can bust pipes or make diamonds. It’s not different on the basketball court. -Mediocre teams panic when they get pressed. They lose their composure, leading to turnovers and bad shots. -Good teams are able to “handle” pressure. -The best teams are able to “beat” pressure. They see being pressed as an opportunity - an opportunity to attack and score. This video will help you become one of those teams! Whether you coach youth, high school, or college players, chances are you're going to see full-court pressure. When that moment comes, will your team panic… or attack? If you're looking to stop giving up live-ball turnovers and start scoring layups on the press, check out this video. 3 Game-Tested Press Breaks That Make The Press Pay! 1. Press Break #1: The “Bubble” Press Break From The Coaching System With Danny Miles 2. Press Break #2: 2-Up From The Youth Coaching System With Jim Huber 3. Press Break #3: Sideline Attack and the Power of the 45° Drive From Get Comfortable Playing At An Uncomfortable Pace With Rob Brost

Joe Haefner | Breakthrough Basketball

51,838 Aufrufe • vor 1 Jahr