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Everyone talks about adding 5 pounds each week Linear progression is delaying progress Progress in not linear, it’s non linear Here’s my preference and how to use it yourself 👇🏼

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Official Strength Debates

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82,963 просмотров • 9 месяцев назад •via X (Twitter)

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Let's talk about Beta 3 animations. • Beta 3 animations are definitely smoother than Beta 2, but I would like Samsung to implement non-linear animations. • We just want it to be perfect, not just better. Share your thoughts in the comments! #OneUI6 #GalaxyS23Ultra
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Let's talk about Beta 3 animations. • Beta 3 animations are definitely smoother than Beta 2, but I would like Samsung to implement non-linear animations. • We just want it to be perfect, not just better. Share your thoughts in the comments! #OneUI6 #GalaxyS23Ultra

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Owl_rz yeah it super was for the PS1. A 3D stealth game released before MGS1 and plays better than it with two protagonists having their own moveset. Levels were open and you could use a grappling hook to take advantage of rooftops. Non-linear open levels 100% is immersive to this day
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Owl_rz yeah it super was for the PS1. A 3D stealth game released before MGS1 and plays better than it with two protagonists having their own moveset. Levels were open and you could use a grappling hook to take advantage of rooftops. Non-linear open levels 100% is immersive to this day

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Low IQ or Ragebait? The guy is using the color yellow as in terms of "yellow paint" when in fact it only appears when you're using focus mode to highlight clues, footprints and important you can find in this game as you can see in my gameplay here. The setting is not on by default, so the player can still choose not to use the vision. Adding to this the game is open world, where the player can easily get lost, unlike linear games which are much easier to follow a path forward
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Low IQ or Ragebait? The guy is using the color yellow as in terms of "yellow paint" when in fact it only appears when you're using focus mode to highlight clues, footprints and important you can find in this game as you can see in my gameplay here. The setting is not on by default, so the player can still choose not to use the vision. Adding to this the game is open world, where the player can easily get lost, unlike linear games which are much easier to follow a path forward

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Genuine concerns considering the state of modern 3D fighting games. But just to clarify for everyone: linear moves will not connect against correctly timed sidesteps or sidewalking, especially moves classified under the “Demon’s Paw” category in my game. There is also no hitbox expansion applied to standardized moves. Oh and rollback is 100% the plan for online. 👍
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Genuine concerns considering the state of modern 3D fighting games. But just to clarify for everyone: linear moves will not connect against correctly timed sidesteps or sidewalking, especially moves classified under the “Demon’s Paw” category in my game. There is also no hitbox expansion applied to standardized moves. Oh and rollback is 100% the plan for online. 👍

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Training Wan 2.1-1.3B to use Qwen3-VL-2B text encoder. Doing 33% text only, 33% VL only, and 33% both. Just pretraining the two text input linear layers for now. It is amazing how quickly a model can adapt to a different text encoder. This is 25,750 steps, BS of 10.

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Tiny actuators. Accurate down to about 25 nanometers. Xeryon builds piezo actuators designed for positioning tasks where normal motors simply are not precise enough. • Repeatability around ±25 nm • Linear, rotary, and multi-DoF configurations • Used in metrology, semiconductor tooling, and laser systems At this scale, motion is no longer about speed or power. It is about staying exactly where you are supposed to be. —— Weekly robotics and AI insights. Subscribe free:

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MCP-apps support is coming soon to Visual Studio Code Really excited about this - IDE as agentic SDLC C&C center - devs can stay focused even more inside their work and bring pieces of it to their IDE like JIRA/linear tickets and other tools right in the IDE Tnx Connor Peet
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MCP-apps support is coming soon to Visual Studio Code Really excited about this - IDE as agentic SDLC C&C center - devs can stay focused even more inside their work and bring pieces of it to their IDE like JIRA/linear tickets and other tools right in the IDE Tnx Connor Peet

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Imagine you have a point dancing in space, following three simple rules. You’d think it would eventually settle down or repeat itself, right? Nature isn't always that predictable. This is the Rössler Attractor. It’s a beautiful piece of "organized chaos." By just tweaking a few non-linear equations, we get this marvelous fractal shape, a curve that weaves and loops, forever exploring new ground without ever retracing its exact same path. Most people try to solve this with standard linear tools, but that's like trying to understand a symphony by looking at a single note. To see the real "magic" the bifurcations and the patterns, you have to embrace the non-linearity. It shows us how the smallest nudge at the start can lead to a completely different, stunningly complex world. It’s a simple set of rules, yet it generates infinite complexity. Isn't that just the kick in the head? (📹Credit; equationsinmotion / IG)

Philosophy Of Physics

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Day 27: my agents can now create a daily brief artifact that's better than Dia's Why: (1) For any app, you can add multiple accounts (if you have 4 emails, 3 calendars, 2 notions etc.) (2) 500+ more tools: Granola, Slack, Linear, name one and I'll lyk if it supports it (3) Improves with feedback. I told mine I want the weather every day, add news / Morning Brew ☕️ and it learns since my workspace has memory (4) it's live and tracks progress – your briefing today checks for what you did yesterday so it won't repeat Do you use Dia's morning brief and how would you one up it

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Jaylen Brown talks about the progress Jayson Tatum has made in his recovery: “It looks like he’s going to be ready for opening night. But this is somebody with a crazy work ethic and passion for the game. So I’m not surprised.”

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Video is day 3, but this is how I’ve been walking for 1 week after the stem cells. It is impossible to stand straight up - it feels like there is a rod in my lower back that can’t bend. Hopefully the 2nd week shows a bit more progress.

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5-axis CNC machining – but different... Most 5-axis machines use serial kinematics: stack a rotary A-axis on top of a rotary B-axis, mount that on linear X/Y/Z stages. Each axis carries the weight of everything after it. Heavy and slow. 🤖 The Sprint Z3 uses parallel kinematics: three linear drives working together to create BOTH the Z-axis motion AND the A/B tilt simultaneously. No stacked rotary joints. No heavy tilting head carrying a motor on top of another motor. The entire barrel-shaped headstock moves as one unit vertically in the column, while the three internal drives handle the spindle positioning and orientation. ⚙️ How it works: Three linear actuators are arranged in a circle, all connected to the spindle platform through pivot arms. - Extend all three equally → spindle moves straight up/down (Z-axis) - Extend them different amounts → spindle tilts (A/B axes) It's using geometry instead of stacked rotary joints to create tilt 🤔 Why this is useful: Way less moving mass than serial kinematics. Less mass = faster accelerations, higher precision, better surface finish. For high-speed machining, dynamics matter more than workspace. Question: Is this a good design or overcomplicated❓
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5-axis CNC machining – but different... Most 5-axis machines use serial kinematics: stack a rotary A-axis on top of a rotary B-axis, mount that on linear X/Y/Z stages. Each axis carries the weight of everything after it. Heavy and slow. 🤖 The Sprint Z3 uses parallel kinematics: three linear drives working together to create BOTH the Z-axis motion AND the A/B tilt simultaneously. No stacked rotary joints. No heavy tilting head carrying a motor on top of another motor. The entire barrel-shaped headstock moves as one unit vertically in the column, while the three internal drives handle the spindle positioning and orientation. ⚙️ How it works: Three linear actuators are arranged in a circle, all connected to the spindle platform through pivot arms. - Extend all three equally → spindle moves straight up/down (Z-axis) - Extend them different amounts → spindle tilts (A/B axes) It's using geometry instead of stacked rotary joints to create tilt 🤔 Why this is useful: Way less moving mass than serial kinematics. Less mass = faster accelerations, higher precision, better surface finish. For high-speed machining, dynamics matter more than workspace. Question: Is this a good design or overcomplicated❓

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The mirror reflects more than form — it reveals commitment. These sessions aren’t performed for likes or validation; they’re quiet acts of self-respect. Week after week, the work compounds: not just in muscle, but in resilience, focus, and the ability to keep promises to yourself when no one else is watching. What matters is the habit forged in discomfort and the quiet confidence that follows. Progress is rarely loud. It’s measured in consistency most people never see. Keep building.
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The mirror reflects more than form — it reveals commitment. These sessions aren’t performed for likes or validation; they’re quiet acts of self-respect. Week after week, the work compounds: not just in muscle, but in resilience, focus, and the ability to keep promises to yourself when no one else is watching. What matters is the habit forged in discomfort and the quiet confidence that follows. Progress is rarely loud. It’s measured in consistency most people never see. Keep building.

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23,834 просмотров • 5 месяцев назад

Opposition in the batters box isn't just a layer of complexity, an obstacle to overcome, it is the central feature that gives meaning to movement. My sense is that many in the baseball community view the live batter as a linear step to master after mastering other lower complexity pieces, instead of the central feature that we design our training environments around. Part of me understands why this is the case. Pitching is in a challenging place training wise because the behavior of pitches, movements, etc. change so much when you go low effort. In addition we have some limits on volume each day due to throwing being the single fastest movement in sports. Other sports can do unique warmups to work on interpersonal distance (football/basketball/soccer) and tagging games for more of a martial arts context. I simply can't have all 100 throws of a pitchers workload that day be to a live batter. And while I see value in pulling an athlete out of context at times to work on different things, ultimately, the entirety of my paradigm to train pitchers is how to do the real thing MORE, to keep them embedded in a representative environment, rather than treating it as the final step in a linear progression.
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Opposition in the batters box isn't just a layer of complexity, an obstacle to overcome, it is the central feature that gives meaning to movement. My sense is that many in the baseball community view the live batter as a linear step to master after mastering other lower complexity pieces, instead of the central feature that we design our training environments around. Part of me understands why this is the case. Pitching is in a challenging place training wise because the behavior of pitches, movements, etc. change so much when you go low effort. In addition we have some limits on volume each day due to throwing being the single fastest movement in sports. Other sports can do unique warmups to work on interpersonal distance (football/basketball/soccer) and tagging games for more of a martial arts context. I simply can't have all 100 throws of a pitchers workload that day be to a live batter. And while I see value in pulling an athlete out of context at times to work on different things, ultimately, the entirety of my paradigm to train pitchers is how to do the real thing MORE, to keep them embedded in a representative environment, rather than treating it as the final step in a linear progression.

Kyle Dupic

50,089 просмотров • 1 месяц назад

[LSTM] by Hand ✍️ LSTMs have been the most effective architecture to process long sequences of data, until our world was taken over by the Transformers. LSTMs belong to the broader family of recurrent neural network (RNNs) that process data sequentially in a recurrent manner. Transformers, on the other hand, abandon recurrence and use self-attention instead to process data concurrently in parallel. Recently, there is renewed interest in recurrence as people realized self-attention doesn’t scale to extremely long sequences, like hundreds of thousands of tokens. Mamba is a good example to bring back recurrence. All of a sudden, it is cool to study LSTMs. How do LSTMs work? [1] Given ↳ 🟨 Input sequence X1, X2, X3 (d = 3) ↳ 🟩 Hidden state h (d = 2) ↳ 🟦 Memory C (d = 2) ↳ Weight matrices Wf, Wc, Wi, Wo Process t = 1 [2] Initialize ↳ Randomly set the previous hidden state h0 to [1, 1] and memory cells C0 to [0.3, -0.5] [3] Linear Transform ↳ Multiply the four weight matrices with the concatenation of current input (X1) and the previous hidden state (h0). ↳ The results are feature values, each is a linear combination of the current input and hidden state. [4] Non-linear Transform ↳ Apply sigmoid σ to obtain gate values (between 0 and 1). • Forget gate (f1): [-4, -6] → [0, 0] • Input gate (i1): [6, 4] → [1, 1] • Output gate (o1): [4, -5] → [1, 0] ↳ Apply tanh to obtain candidate memory values (between -1 and 1) • Candidate memory (C’1): [1, -6] → [0.8, -1] [5] Update Memory ↳ Forget (C0 .* f1): Element-wise multiply the current memory with forget gate values. ↳ Input (C’1 .* o1): Element-wise multiply the “candidate” memory with input gate values. ↳ Update the memory to C1 by adding the two terms above: C0 .* f1 + C’1 .* o1 = C1 [6] Candiate Output ↳ Apply tanh to the new memory C1 to obtain candidate output o’1. [0.8, -1] → [0.7, -0.8] [7] Update Hidden State ↳ Output (o’1 .* o1 → h1): Element-wise multiply the candidate output with the output gate. ↳ The result is updated hidden state h1 ↳ Also, it is the first output. Process t = 2 [8] Initialize ↳ Copy previous hidden state h1 and memory C1 [9] Linear Transform ↳ Repeat [3] [10] Update Memory (C2) ↳ Repeat [4] and [5] [11] Update Hidden State (h2) ↳ Repeat [6] and [7] Process t = 3 [12] Initialize ↳ Copy previous hidden state h2 and memory C2 [13] Linear Transform ↳ Repeat [3] [14] Update Memory (C3) ↳ Repeat [4] and [5] [15] Update Hidden State (h3) ↳ Repeat [6] and [7]
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[LSTM] by Hand ✍️ LSTMs have been the most effective architecture to process long sequences of data, until our world was taken over by the Transformers. LSTMs belong to the broader family of recurrent neural network (RNNs) that process data sequentially in a recurrent manner. Transformers, on the other hand, abandon recurrence and use self-attention instead to process data concurrently in parallel. Recently, there is renewed interest in recurrence as people realized self-attention doesn’t scale to extremely long sequences, like hundreds of thousands of tokens. Mamba is a good example to bring back recurrence. All of a sudden, it is cool to study LSTMs. How do LSTMs work? [1] Given ↳ 🟨 Input sequence X1, X2, X3 (d = 3) ↳ 🟩 Hidden state h (d = 2) ↳ 🟦 Memory C (d = 2) ↳ Weight matrices Wf, Wc, Wi, Wo Process t = 1 [2] Initialize ↳ Randomly set the previous hidden state h0 to [1, 1] and memory cells C0 to [0.3, -0.5] [3] Linear Transform ↳ Multiply the four weight matrices with the concatenation of current input (X1) and the previous hidden state (h0). ↳ The results are feature values, each is a linear combination of the current input and hidden state. [4] Non-linear Transform ↳ Apply sigmoid σ to obtain gate values (between 0 and 1). • Forget gate (f1): [-4, -6] → [0, 0] • Input gate (i1): [6, 4] → [1, 1] • Output gate (o1): [4, -5] → [1, 0] ↳ Apply tanh to obtain candidate memory values (between -1 and 1) • Candidate memory (C’1): [1, -6] → [0.8, -1] [5] Update Memory ↳ Forget (C0 .* f1): Element-wise multiply the current memory with forget gate values. ↳ Input (C’1 .* o1): Element-wise multiply the “candidate” memory with input gate values. ↳ Update the memory to C1 by adding the two terms above: C0 .* f1 + C’1 .* o1 = C1 [6] Candiate Output ↳ Apply tanh to the new memory C1 to obtain candidate output o’1. [0.8, -1] → [0.7, -0.8] [7] Update Hidden State ↳ Output (o’1 .* o1 → h1): Element-wise multiply the candidate output with the output gate. ↳ The result is updated hidden state h1 ↳ Also, it is the first output. Process t = 2 [8] Initialize ↳ Copy previous hidden state h1 and memory C1 [9] Linear Transform ↳ Repeat [3] [10] Update Memory (C2) ↳ Repeat [4] and [5] [11] Update Hidden State (h2) ↳ Repeat [6] and [7] Process t = 3 [12] Initialize ↳ Copy previous hidden state h2 and memory C2 [13] Linear Transform ↳ Repeat [3] [14] Update Memory (C3) ↳ Repeat [4] and [5] [15] Update Hidden State (h3) ↳ Repeat [6] and [7]

Tom Yeh

73,030 просмотров • 2 лет назад

Working my way back in shape. This was a 185# push press. Not close to my previous best but that doesn’t matter. Making progress each day and most importantly, it rubs off on the kiddos which will pay off for life
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Working my way back in shape. This was a 185# push press. Not close to my previous best but that doesn’t matter. Making progress each day and most importantly, it rubs off on the kiddos which will pay off for life

Austin Lieberman

24,121 просмотров • 3 лет назад

Here’s some food for thought: Is it a coincidence that the pelvis + shoulders maintain nearly mirrored angles throughout the entire linear move, and maintain that symmetry all the way into the frames before front foot strike (FFS)? Let’s take it a step further, had to bring back this great visual: intentionally follow the tracking line drawn straight through the center of the pelvis and shoulders. You can clearly see the pelvis beginning its rotation in the latter part of the linear move, followed by a timed, delayed rotation of the torso… all happening just frames before initial ground contact. Here’s the takeaway: the “downhill” rotational planes of the pelvis and torso nearly mirror one-another. To make it relatable, the center of mass (pelvis + torso) sits firmly in the “driver’s seat”, the blueprint behind the entire mechanical sequence and organization of the extremities to inherently maximize force production and efficiency. Elite-level mechanical organization & sequencing in action.
0:12

Here’s some food for thought: Is it a coincidence that the pelvis + shoulders maintain nearly mirrored angles throughout the entire linear move, and maintain that symmetry all the way into the frames before front foot strike (FFS)? Let’s take it a step further, had to bring back this great visual: intentionally follow the tracking line drawn straight through the center of the pelvis and shoulders. You can clearly see the pelvis beginning its rotation in the latter part of the linear move, followed by a timed, delayed rotation of the torso… all happening just frames before initial ground contact. Here’s the takeaway: the “downhill” rotational planes of the pelvis and torso nearly mirror one-another. To make it relatable, the center of mass (pelvis + torso) sits firmly in the “driver’s seat”, the blueprint behind the entire mechanical sequence and organization of the extremities to inherently maximize force production and efficiency. Elite-level mechanical organization & sequencing in action.

Rob Blanc

13,773 просмотров • 6 месяцев назад