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Controller vs mouse input latency duel. OP1 8K v2 vs MH5-Analog. New multi-MCU hardware tester (BETA).

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mariusheier

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37,528 görüntüleme • 3 ay önce •via X (Twitter)

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MH5-analog vs Gamesir G7 8K. Input lag battle on my new multi mcu hardware timer based tester in BETA
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MH5-analog vs Gamesir G7 8K. Input lag battle on my new multi mcu hardware timer based tester in BETA

mariusheier

105,009 görüntüleme • 3 ay önce

New mouse clip Endgame GearのOP1 8K v2神すぎる！ 相棒のマウスが見つかったかもしれん！！
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New mouse clip Endgame GearのOP1 8K v2神すぎる！ 相棒のマウスが見つかったかもしれん！！

MizuHAL

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The countdown begins! Pathfinder Batch 2 arrives Aug 28th at 2PM PT! - The most innovative gaming mouse of 2025 - A journey to the ultimate mouse shape…yours - Improved build quality, click feel & reliability - Web controller with advanced Esports features - New shapes to explore: Thumb Rests, Ergo Hump, and more! - Explore over 3,000+ configs to find the perfect fit for your grip & playstyle - Shapes refined and validated by elite FPS players - Flagship Nordic MCU, Pixart 3950, 8K polling, ultra-low click latency - Still 1:1 QC by Founders - Extremely limited stock Don’t miss out! join our Discord and sign up on the website to be first in line. Link in bio
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The countdown begins! Pathfinder Batch 2 arrives Aug 28th at 2PM PT! - The most innovative gaming mouse of 2025 - A journey to the ultimate mouse shape…yours - Improved build quality, click feel & reliability - Web controller with advanced Esports features - New shapes to explore: Thumb Rests, Ergo Hump, and more! - Explore over 3,000+ configs to find the perfect fit for your grip & playstyle - Shapes refined and validated by elite FPS players - Flagship Nordic MCU, Pixart 3950, 8K polling, ultra-low click latency - Still 1:1 QC by Founders - Extremely limited stock Don’t miss out! join our Discord and sign up on the website to be first in line. Link in bio

Orbital

12,068 görüntüleme • 1 yıl önce

TMR drifting 0% deadzone? 8bit vs 10bit vs 12 bit joysticks visualized. if you run your xinput on 12 bit, you need to hit a single center point at 4096 different value in order for your gamepad not to drift. thats why its drifting. its not drifting for the same reason your old pots did it.
0:46

TMR drifting 0% deadzone? 8bit vs 10bit vs 12 bit joysticks visualized. if you run your xinput on 12 bit, you need to hit a single center point at 4096 different value in order for your gamepad not to drift. thats why its drifting. its not drifting for the same reason your old pots did it.

mariusheier

47,476 görüntüleme • 8 gün önce

⚙️Update Quick Video Review: GameSir Connect V1.14.4⚙️ GameSir Connect has just been updated to version V1.14.4. Over the past few updates, we have introduced many practical features to enhance your experience. Here is the video detailing all the highlights of the recent updates. We highly recommend that players visit the Microsoft Store to download the latest version from here: For details on the updates, please refer to the text below. ✅Feature 1 - Professional Tips and Explanation for Controller Settings. You can now hover your mouse over the ⓘ icon to access detailed explanations of controller terminology. These tips have been verified by the GameSir product team to ensure you can quickly, conveniently, and accurately understand the information you need. ✅Feature 2 - Enhanced Stick Customization: Expanded Curve & Calibration Settings Now, you can adjust the stick center point to fix any potential center offset after the controller has already been calibrated. In addition, you can now achieve more precise fine-tuning of the stick input curve by manually entering the coordinates for each point within a 100x100 coordinate system. Please be aware that when Zero Deadzone is enabled, it is normal for the center point to not return to absolute zero or for the data to fluctuate repeatedly. ✅Feature 3 - Support for in-app software updates and halved input latency We've now set up a dedicated server for GameSir Connect software updates. Now, you can update directly within the software. And through optimizations, the end-to-end latency for both the G7 Pro 8K PC and Tarantula 8K PC has now reached 0.25ms with the latest firmware. And only by using the latest software will you be able to receive the latest firmware updates.
1:49

⚙️Update Quick Video Review: GameSir Connect V1.14.4⚙️ GameSir Connect has just been updated to version V1.14.4. Over the past few updates, we have introduced many practical features to enhance your experience. Here is the video detailing all the highlights of the recent updates. We highly recommend that players visit the Microsoft Store to download the latest version from here: For details on the updates, please refer to the text below. ✅Feature 1 - Professional Tips and Explanation for Controller Settings. You can now hover your mouse over the ⓘ icon to access detailed explanations of controller terminology. These tips have been verified by the GameSir product team to ensure you can quickly, conveniently, and accurately understand the information you need. ✅Feature 2 - Enhanced Stick Customization: Expanded Curve & Calibration Settings Now, you can adjust the stick center point to fix any potential center offset after the controller has already been calibrated. In addition, you can now achieve more precise fine-tuning of the stick input curve by manually entering the coordinates for each point within a 100x100 coordinate system. Please be aware that when Zero Deadzone is enabled, it is normal for the center point to not return to absolute zero or for the data to fluctuate repeatedly. ✅Feature 3 - Support for in-app software updates and halved input latency We've now set up a dedicated server for GameSir Connect software updates. Now, you can update directly within the software. And through optimizations, the end-to-end latency for both the G7 Pro 8K PC and Tarantula 8K PC has now reached 0.25ms with the latest firmware. And only by using the latest software will you be able to receive the latest firmware updates.

GameSir

29,311 görüntüleme • 1 ay önce

How Fast is Gemma 4 on a MacBook Pro M4? Benchmarking Google's new MoE (26B-A4B) > Model size: 26.1 GiB > Load time: ~4.2s Comparing single request VS > concurrent requests performance > 32k total context, 4 parallel slots single request behavior > TTFT: 5.68s > prompt: 3,701 tokens @ 652 tok/s > decode: 40.08 tok/s sequential (1 request at a time): > avg duration: 20.5s > p99: 22.1s > throughput: 40.11 tok/s > clean finishes: 100% concurrent (4 parallel requests): > aggregate throughput: 47.25 tok/s > total system throughput: 262.27 tok/s > avg duration: 65.1s > p95 latency: 68.8s > req/sec: 0.058 Head-to-Head: Sequential vs Concurrent throughput: > 40.11 tok/s → 47.25 tok/s (+17.8%) > small gain despite 4x parallelism latency per request: > 20.5s → 65.1s (~3.2x slower) > you pay heavily for concurrency system throughput (true utilization): > ~40 tok/s → 262 tok/s (~6.5x total output) > this is where concurrency wins tokens per second (decode ceiling): > ~40 tok/s steady in both modes > hardware-bound, not scheduler-bound TTFT impact: > ~5.7s baseline → buried under queueing in concurrent > “headers waittime” becomes the bottleneck What this actually means? - You don’t get linear scaling from parallel slots - You trade latency for total output - Mac Unified Memory setup is clearly saturating - Bandwidth + Scheduling overhead show up immediately This is exactly why GPUs dominate here Concurrency without killing latency
2:42

How Fast is Gemma 4 on a MacBook Pro M4? Benchmarking Google's new MoE (26B-A4B) > Model size: 26.1 GiB > Load time: ~4.2s Comparing single request VS > concurrent requests performance > 32k total context, 4 parallel slots single request behavior > TTFT: 5.68s > prompt: 3,701 tokens @ 652 tok/s > decode: 40.08 tok/s sequential (1 request at a time): > avg duration: 20.5s > p99: 22.1s > throughput: 40.11 tok/s > clean finishes: 100% concurrent (4 parallel requests): > aggregate throughput: 47.25 tok/s > total system throughput: 262.27 tok/s > avg duration: 65.1s > p95 latency: 68.8s > req/sec: 0.058 Head-to-Head: Sequential vs Concurrent throughput: > 40.11 tok/s → 47.25 tok/s (+17.8%) > small gain despite 4x parallelism latency per request: > 20.5s → 65.1s (~3.2x slower) > you pay heavily for concurrency system throughput (true utilization): > ~40 tok/s → 262 tok/s (~6.5x total output) > this is where concurrency wins tokens per second (decode ceiling): > ~40 tok/s steady in both modes > hardware-bound, not scheduler-bound TTFT impact: > ~5.7s baseline → buried under queueing in concurrent > “headers waittime” becomes the bottleneck What this actually means? - You don’t get linear scaling from parallel slots - You trade latency for total output - Mac Unified Memory setup is clearly saturating - Bandwidth + Scheduling overhead show up immediately This is exactly why GPUs dominate here Concurrency without killing latency

Ahmad

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Marius board- DIY adding buttons
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Marius board- DIY adding buttons

mariusheier

62,445 görüntüleme • 4 ay önce

While reading the responses to jnmartin's #Doom 64 port to the Sega #Dreamcast, I stumbled upon the comments of a few haters who were stating that the DC version had absolutely NOTHING to offer over the Steam and Switch versions, claiming that we're just a community of wishful thinkers living in the past... OH. REALLY, NOW!? Actual hardware capture on the left of me playing the Steam-exclusive map, "Wretched Vats," on my DC, while the right is a recording of the same area from the Steam version... The Dreamcast version only offers up to 16 simultaneous dynamic lights onscreen, bump mapping on environments and weapons, controller (with dual analog) + mouse + keyboard support, VMU icons showing damage, quicker startup times, no forced online registration, supports all of the new Steam-exclusive levels, and now, for the first time ever, also runs mostly at 60fps... I do believe you've been Dreamcasted, haters. PS: When the algorithm inevitably destroys the footage due to compression, here's where you can download the original: #gamedev #retrogaming
0:40

While reading the responses to jnmartin's #Doom 64 port to the Sega #Dreamcast, I stumbled upon the comments of a few haters who were stating that the DC version had absolutely NOTHING to offer over the Steam and Switch versions, claiming that we're just a community of wishful thinkers living in the past... OH. REALLY, NOW!? Actual hardware capture on the left of me playing the Steam-exclusive map, "Wretched Vats," on my DC, while the right is a recording of the same area from the Steam version... The Dreamcast version only offers up to 16 simultaneous dynamic lights onscreen, bump mapping on environments and weapons, controller (with dual analog) + mouse + keyboard support, VMU icons showing damage, quicker startup times, no forced online registration, supports all of the new Steam-exclusive levels, and now, for the first time ever, also runs mostly at 60fps... I do believe you've been Dreamcasted, haters. PS: When the algorithm inevitably destroys the footage due to compression, here's where you can download the original: #gamedev #retrogaming

Falco Girgis

13,720 görüntüleme • 1 yıl önce

Smart Turn v2: open source, native audio turn detection in 14 languages. New checkpoint of the open source, open data, open training code, semantic VAD model on Hugging Face, fal, and Pipecat AI. - 3x faster inference (12ms on an L40) - 14 languages (13 more than v1, which was english-only) - New synthetic data set `chirp_3_all` with ~163k audio samples - 99% accuracy on held out `human_5_all` test data Good turn detection is critical for voice agents. This model "understands" both semantic and audio patterns, and mitigates the voice AI trade-off between unwanted turn latency vs the agent interrupting people before they are finished speaking. Training scripts for both Modal and local training are in the repo. We want to make it as easy as possible to contribute to or customize this model! Here's a demo running the smart-turn model with default settings, aimed at generally hitting 400ms total turn detection time. You can tune things to be faster, too. You can help by contributing data, doing architecture expermints, or cleaning open source data! Keep reading ...
2:12

Smart Turn v2: open source, native audio turn detection in 14 languages. New checkpoint of the open source, open data, open training code, semantic VAD model on Hugging Face, fal, and Pipecat AI. - 3x faster inference (12ms on an L40) - 14 languages (13 more than v1, which was english-only) - New synthetic data set `chirp_3_all` with ~163k audio samples - 99% accuracy on held out `human_5_all` test data Good turn detection is critical for voice agents. This model "understands" both semantic and audio patterns, and mitigates the voice AI trade-off between unwanted turn latency vs the agent interrupting people before they are finished speaking. Training scripts for both Modal and local training are in the repo. We want to make it as easy as possible to contribute to or customize this model! Here's a demo running the smart-turn model with default settings, aimed at generally hitting 400ms total turn detection time. You can tune things to be faster, too. You can help by contributing data, doing architecture expermints, or cleaning open source data! Keep reading ...

kwindla

42,246 görüntüleme • 1 yıl önce

Neuraxon 2.0 is out!!! Our new paper & code & demo by Jose Sánchez & David Vivancos - e/acc with Qubic #OpenScience setting the ground for #Aigarth #intelligenttissue by Come-from-Beyond The real path towards #TrueAI one step forward to #AGI & #ASI Read the preprint Paper: (All the details of our ongoing updated research) Explore the Code: (Please give us a⭐fork and build!) Play with the New Interactive Demo: (Build your own Neuraxon NetWork) 🚀Why Neuraxon 2.0 matters for AI? Because it replaces rigid binary perceptrons with bio-inspired trinary neurons that run continuously, compute at both synapse & neuron level, self-generate activity, rewire on the fly, and evolve via Aigarth hybridization, delivering real-time, energy-efficient, lifelong learning that actually adapts like a brain. 🔥Neuraxon 2.0 vs 1.0 — What’s New? - CTSN complemented trinary states → no more iterative info loss - Synaptic Time Warping (ChronoPlasticity) → adaptive long-horizon memory - 9-receptor neuromod system (DA/5HT/ACh/NA subtypes) with realistic tonic/phasic + crosstalk - Built-in oscillator bank + true phase-amplitude coupling (theta-gamma PAC etc.) - Nonlinear dendritic branch integration + input-conditioned dynamic decay (DSN-style parallel training) - Astrocyte-Gated Multi-Timescale Plasticity (AGMP) + multi-scale homeostasis - Watts-Strogatz small-world topology + deeper Aigarth evolutionary hybrid - Intrinsic energy tracking + differential DA-gated STDP + associative neighbor plasticity Brain-level fidelity in one release. v2.0 is the key for real continuous, lifelong AI. #EAGI #Neuraxon #Trinarystates #Continuousprocessing #Synapticdynamics #Neuralplasticity #Spontaneousactivity #Temporalsynchronisation #Bioinspiredcomputation #Artificiology
0:36

Neuraxon 2.0 is out!!! Our new paper & code & demo by Jose Sánchez & David Vivancos - e/acc with Qubic #OpenScience setting the ground for #Aigarth #intelligenttissue by Come-from-Beyond The real path towards #TrueAI one step forward to #AGI & #ASI Read the preprint Paper: (All the details of our ongoing updated research) Explore the Code: (Please give us a⭐fork and build!) Play with the New Interactive Demo: (Build your own Neuraxon NetWork) 🚀Why Neuraxon 2.0 matters for AI? Because it replaces rigid binary perceptrons with bio-inspired trinary neurons that run continuously, compute at both synapse & neuron level, self-generate activity, rewire on the fly, and evolve via Aigarth hybridization, delivering real-time, energy-efficient, lifelong learning that actually adapts like a brain. 🔥Neuraxon 2.0 vs 1.0 — What’s New? - CTSN complemented trinary states → no more iterative info loss - Synaptic Time Warping (ChronoPlasticity) → adaptive long-horizon memory - 9-receptor neuromod system (DA/5HT/ACh/NA subtypes) with realistic tonic/phasic + crosstalk - Built-in oscillator bank + true phase-amplitude coupling (theta-gamma PAC etc.) - Nonlinear dendritic branch integration + input-conditioned dynamic decay (DSN-style parallel training) - Astrocyte-Gated Multi-Timescale Plasticity (AGMP) + multi-scale homeostasis - Watts-Strogatz small-world topology + deeper Aigarth evolutionary hybrid - Intrinsic energy tracking + differential DA-gated STDP + associative neighbor plasticity Brain-level fidelity in one release. v2.0 is the key for real continuous, lifelong AI. #EAGI #Neuraxon #Trinarystates #Continuousprocessing #Synapticdynamics #Neuralplasticity #Spontaneousactivity #Temporalsynchronisation #Bioinspiredcomputation #Artificiology

David Vivancos - e/acc

34,940 görüntüleme • 6 ay önce

Hash-based signatures for Bitcoin's post-quantum future? with Jonas Nick | SLP713 Bitcoin researcher Blockstream and bitcoin/secp256k1 contributor, Jonas Nick joins me to discuss how quantum computing could impact Bitcoin’s security. We explore post-quantum cryptography, hash-based signatures, impact on hardware wallets, the controversial block size trade-offs and more. (00:00) - Intro (01:49) - How real is quantum risk to Bitcoin? (04:39) - When could quantum pose a threat to Bitcoin’s cryptography? (09:56) - Long range vs Short range attacks (12:37) - How many coins are vulnerable to Long range attacks? (14:12) - Different types of cryptography and exploring Hash-based signature schemes (17:00) - Categories of Hash-based signature scheme and their pros & cons (23:42) - How do Hash-based signatures work? (32:14) - Would Lightning, Multi-sig, Taproot, Silent Payments, Atomic swaps work in a post-quantum world? (38:50) - What are Adaptor signatures & how do they affect atomic swapping? (41:27) - Will we need new Bitcoin hardware wallets?; Signature production & verification (44:41) - Signature size and Bitcoin block capacity implications (46:52) - Should we revisit the block size conversation? (54:57) - Overview of SPHINCS+ & SHRINCS (59:49) - Transitioning to post-quantum signature schemes; Overview of BIP 360 (1:09:06) - Closing thoughts
1:12:29

Hash-based signatures for Bitcoin's post-quantum future? with Jonas Nick | SLP713 Bitcoin researcher Blockstream and bitcoin/secp256k1 contributor, Jonas Nick joins me to discuss how quantum computing could impact Bitcoin’s security. We explore post-quantum cryptography, hash-based signatures, impact on hardware wallets, the controversial block size trade-offs and more. (00:00) - Intro (01:49) - How real is quantum risk to Bitcoin? (04:39) - When could quantum pose a threat to Bitcoin’s cryptography? (09:56) - Long range vs Short range attacks (12:37) - How many coins are vulnerable to Long range attacks? (14:12) - Different types of cryptography and exploring Hash-based signature schemes (17:00) - Categories of Hash-based signature scheme and their pros & cons (23:42) - How do Hash-based signatures work? (32:14) - Would Lightning, Multi-sig, Taproot, Silent Payments, Atomic swaps work in a post-quantum world? (38:50) - What are Adaptor signatures & how do they affect atomic swapping? (41:27) - Will we need new Bitcoin hardware wallets?; Signature production & verification (44:41) - Signature size and Bitcoin block capacity implications (46:52) - Should we revisit the block size conversation? (54:57) - Overview of SPHINCS+ & SHRINCS (59:49) - Transitioning to post-quantum signature schemes; Overview of BIP 360 (1:09:06) - Closing thoughts

Stephan Livera

45,684 görüntüleme • 7 ay önce

NEW RESEARCH: You can now create a new robot optimized for any given task! I love this new project by Huy Ha, Shuran Song, and others. Called "Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design", it generates a robot's physical design and its controller together from a task spec. DEFINITIONS: - Reward function: A scoring rule that assigns a number to how well a behavior achieves the task. Here, it is the objective the generated design is pushed to maximize (e.g., track the target motion with low error). - Tokenizing: dividing continuous or structured data (a robot's links, joints, motor specs, states, actions) into a discrete vocabulary of symbols a transformer can process, the same step that turned pixels and audio into "language" for these models. - Diffusion transformer (DiT): A transformer trained to turn random noise into structured output through iterative denoising. Here, it generates robot bodies and trajectories instead of images. - MuJoCo: The standard fast physics simulator for robotics research (DeepMind-maintained). The Menagerie is its curated zoo of ready-to-use robot models. - CMA-ES: Covariance Matrix Adaptation Evolution Strategy, the workhorse black-box optimizer: it evolves a population of candidate designs, keeps the best, and needs thousands of simulator rollouts. - Bimanual multi-trajectory optimization: Finding one design/controller that performs well across several target motions for a two-armed robot at once, harder than optimizing for a single arm and a single motion. - BERT/MAE masked-modeling trick: Train one model to fill in whatever parts of the input you hide (words for BERT, image patches for MAE); at inference, choosing what to mask chooses the task, so masking the body makes it a designer and masking the actions makes it a controller. In practice, you give it a target end-effector motion and a reward function, and it outputs a complete embodiment (link, joint, motor, and inertial property), as well as a controller to drive it. It works by tokenizing both the body (links/joints/motors) and the dynamics (states/actions) into a compact scheme called RoboTokens, training a diffusion transformer (DiT) over them. The same model predicts dynamics using those predictions ("Dynamics Self-Guidance") to push generated designs toward higher reward at inference time. Masking different token types (using the BERT/MAE masked-modeling trick) lets the one model do three jobs: generate an embodiment, control an arbitrary embodiment, or design one conditioned on a motion. It is trained on 11 robots from the MuJoCo Menagerie (0.65 kg hand to 67.5 kg quadruped, 6–35 joints), and validated in sim and on a physical ALOHA doing cloth flinging. I like the fact that this approach inverts the entire recent robotics ideas: designing a policy for a fixed robot -> designing the robot for a fixed task. Every other approach assumes the body is given and learns a controller. Transformer Transformer takes the task (target motion + reward), then generates the body and controller jointly. In practice, it is a ~180× speedup over the standard optimizer at equal-or-better quality. It reaches "CMA-ES-level quality in seconds" and finishes bimanual multi-trajectory optimization in that is worth underlining nowadays! Also worth mentioning: this is the lab behind UMI and Handroid, that I mentioned here previously! The team seems extremely creative, i love these out-of-the-box approaches. Enjoy watching the demo of robot optimization in 3D, data acquisition, then real-life testing:
0:28

NEW RESEARCH: You can now create a new robot optimized for any given task! I love this new project by Huy Ha, Shuran Song, and others. Called "Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design", it generates a robot's physical design and its controller together from a task spec. DEFINITIONS: - Reward function: A scoring rule that assigns a number to how well a behavior achieves the task. Here, it is the objective the generated design is pushed to maximize (e.g., track the target motion with low error). - Tokenizing: dividing continuous or structured data (a robot's links, joints, motor specs, states, actions) into a discrete vocabulary of symbols a transformer can process, the same step that turned pixels and audio into "language" for these models. - Diffusion transformer (DiT): A transformer trained to turn random noise into structured output through iterative denoising. Here, it generates robot bodies and trajectories instead of images. - MuJoCo: The standard fast physics simulator for robotics research (DeepMind-maintained). The Menagerie is its curated zoo of ready-to-use robot models. - CMA-ES: Covariance Matrix Adaptation Evolution Strategy, the workhorse black-box optimizer: it evolves a population of candidate designs, keeps the best, and needs thousands of simulator rollouts. - Bimanual multi-trajectory optimization: Finding one design/controller that performs well across several target motions for a two-armed robot at once, harder than optimizing for a single arm and a single motion. - BERT/MAE masked-modeling trick: Train one model to fill in whatever parts of the input you hide (words for BERT, image patches for MAE); at inference, choosing what to mask chooses the task, so masking the body makes it a designer and masking the actions makes it a controller. In practice, you give it a target end-effector motion and a reward function, and it outputs a complete embodiment (link, joint, motor, and inertial property), as well as a controller to drive it. It works by tokenizing both the body (links/joints/motors) and the dynamics (states/actions) into a compact scheme called RoboTokens, training a diffusion transformer (DiT) over them. The same model predicts dynamics using those predictions ("Dynamics Self-Guidance") to push generated designs toward higher reward at inference time. Masking different token types (using the BERT/MAE masked-modeling trick) lets the one model do three jobs: generate an embodiment, control an arbitrary embodiment, or design one conditioned on a motion. It is trained on 11 robots from the MuJoCo Menagerie (0.65 kg hand to 67.5 kg quadruped, 6–35 joints), and validated in sim and on a physical ALOHA doing cloth flinging. I like the fact that this approach inverts the entire recent robotics ideas: designing a policy for a fixed robot -> designing the robot for a fixed task. Every other approach assumes the body is given and learns a controller. Transformer Transformer takes the task (target motion + reward), then generates the body and controller jointly. In practice, it is a ~180× speedup over the standard optimizer at equal-or-better quality. It reaches "CMA-ES-level quality in seconds" and finishes bimanual multi-trajectory optimization in that is worth underlining nowadays! Also worth mentioning: this is the lab behind UMI and Handroid, that I mentioned here previously! The team seems extremely creative, i love these out-of-the-box approaches. Enjoy watching the demo of robot optimization in 3D, data acquisition, then real-life testing:

Léo

26,186 görüntüleme • 29 gün önce

grateful to spend a few hours with Captain Zwingli 🍌🎙️ this morning answering questions and speaking directly with members of our community. for anyone interested, the entire interview is below. have also provided timestamps if you'd like to hop from section to section. 00:00 Intro & Announcement Overview 08:58 OpenSea vs. OpenSea Foundation 11:45 Shipping Product Before TGE 17:03 Mobile & Perps Timeline 20:10 0% Fees for 60 days 21:35 Solana NFTs & Ordinals Question 24:45 Acquisitions Question 32:28 TGE Timing Question 42:20 Specific Waves Fee Refund Question 44:44 Discussion About Not Sharing New Date 50:42 Token Utility Conversation 52:52 Market Conditions Discussion 57:55 Community Q&A Begins (LoKi) 01:09:45 Decoding CT Feedback (Brad) 01:25:20 Beta Tester Experience (Chesus) 01:31:27 Creator Incentives (Grace) 01:36:20 How to Approach Current Rewards (Iron Shack) 01:40:47 Gemesis Question 01:42:02 Outro hope to have more opportunities like this in the coming days and weeks.
1:47:54

grateful to spend a few hours with Captain Zwingli 🍌🎙️ this morning answering questions and speaking directly with members of our community. for anyone interested, the entire interview is below. have also provided timestamps if you'd like to hop from section to section. 00:00 Intro & Announcement Overview 08:58 OpenSea vs. OpenSea Foundation 11:45 Shipping Product Before TGE 17:03 Mobile & Perps Timeline 20:10 0% Fees for 60 days 21:35 Solana NFTs & Ordinals Question 24:45 Acquisitions Question 32:28 TGE Timing Question 42:20 Specific Waves Fee Refund Question 44:44 Discussion About Not Sharing New Date 50:42 Token Utility Conversation 52:52 Market Conditions Discussion 57:55 Community Q&A Begins (LoKi) 01:09:45 Decoding CT Feedback (Brad) 01:25:20 Beta Tester Experience (Chesus) 01:31:27 Creator Incentives (Grace) 01:36:20 How to Approach Current Rewards (Iron Shack) 01:40:47 Gemesis Question 01:42:02 Outro hope to have more opportunities like this in the coming days and weeks.

Adam Hollander

29,882 görüntüleme • 5 ay önce

1. Essence of the Problem: Algorithm “Hesitation” and System “Jitter” The “clear → blurry → clear → blurry” cycle you see on the preview screen is essentially the AI algorithm dynamically switching between multiple image-processing paths. During 10× telephoto preview, Samsung’s multimodal imaging system makes decisions based on several concurrent signals: Scene Classifier (scene recognition) AI Detail Enhancer (texture-enhancement algorithm) Motion Estimation (motion detection) HDR Weight Selection (highlight suppression or shadow lift) The issue is that these modules lack a unified arbitration layer. When multiple modules give conflicting judgments about the same frame (for example, “static subject” vs. “slightly moving object”), the algorithm repeatedly enables and cancels enhancement strategies. The result is a visual oscillation of “pre-load → cancel → pre-load → cancel.” This reflects architectural uncertainty within Samsung’s image-processing framework. 2. Deeper Systemic Issue: Unstable Coordination Between ISP and AI In recent Galaxy generations, Samsung’s imaging stack consists of three main components: Exynos/Snapdragon ISP layer (hardware-level processing) Samsung Multi-Frame Engine (multi-frame fusion) Galaxy AI Pipeline (deep-learning post-processing) The core problem is that these modules do not operate within the same clock domain. The AI processing unit runs asynchronously on the NPU, while the ISP and multi-frame fusion run synchronously on the main SoC. In certain scenarios, when the AI result hasn’t returned yet, the ISP outputs the preview frame first—causing frame-to-frame style fluctuations. This isn’t a performance issue; it’s a scheduling bug in the system architecture. Apple avoids this by implementing a unified “Image Core” framework within the A17 Pro. All AI decisions, HDR merges, and white-balance calculations occur within one synchronized pipeline. As a result, the preview image already matches the final shot almost perfectly. 3. User-Level Impact: Inconsistent Output and Experience Fragmentation This “algorithm hesitation” leads to three direct consequences: Preview and final image mismatch — what users see is not what they get. Large variations between shots — even under identical conditions, different AI branches produce completely different looks. Loss of operational trust — users cannot predict results and hesitate to press the shutter. In imaging experience terms, this is actually more serious than sharpness or noise issues, because it breaks the user’s sense of stability and reliability with the device. 4. My View: Samsung’s AI Imaging Needs a “Referee System” The root cause isn’t insufficient power or hardware; it’s the absence of an orchestration layer. Samsung has too many independent sub-modules (super-resolution, noise reduction, detail enhancement, color reconstruction, depth recognition, AI HDR, etc.) but no master controller to decide when to activate them, how to prioritize, or how to manage latency. The ideal solution would be to: Establish a Central Scene Controller Manage all AI sub-modules with unified priority scheduling and decision memory Maintain temporal consistency of algorithmic states across consecutive frames Only then can Samsung truly fix its “algorithm instability” problem and move its Galaxy imaging pipeline toward maturity.
0:29

1. Essence of the Problem: Algorithm “Hesitation” and System “Jitter” The “clear → blurry → clear → blurry” cycle you see on the preview screen is essentially the AI algorithm dynamically switching between multiple image-processing paths. During 10× telephoto preview, Samsung’s multimodal imaging system makes decisions based on several concurrent signals: Scene Classifier (scene recognition) AI Detail Enhancer (texture-enhancement algorithm) Motion Estimation (motion detection) HDR Weight Selection (highlight suppression or shadow lift) The issue is that these modules lack a unified arbitration layer. When multiple modules give conflicting judgments about the same frame (for example, “static subject” vs. “slightly moving object”), the algorithm repeatedly enables and cancels enhancement strategies. The result is a visual oscillation of “pre-load → cancel → pre-load → cancel.” This reflects architectural uncertainty within Samsung’s image-processing framework. 2. Deeper Systemic Issue: Unstable Coordination Between ISP and AI In recent Galaxy generations, Samsung’s imaging stack consists of three main components: Exynos/Snapdragon ISP layer (hardware-level processing) Samsung Multi-Frame Engine (multi-frame fusion) Galaxy AI Pipeline (deep-learning post-processing) The core problem is that these modules do not operate within the same clock domain. The AI processing unit runs asynchronously on the NPU, while the ISP and multi-frame fusion run synchronously on the main SoC. In certain scenarios, when the AI result hasn’t returned yet, the ISP outputs the preview frame first—causing frame-to-frame style fluctuations. This isn’t a performance issue; it’s a scheduling bug in the system architecture. Apple avoids this by implementing a unified “Image Core” framework within the A17 Pro. All AI decisions, HDR merges, and white-balance calculations occur within one synchronized pipeline. As a result, the preview image already matches the final shot almost perfectly. 3. User-Level Impact: Inconsistent Output and Experience Fragmentation This “algorithm hesitation” leads to three direct consequences: Preview and final image mismatch — what users see is not what they get. Large variations between shots — even under identical conditions, different AI branches produce completely different looks. Loss of operational trust — users cannot predict results and hesitate to press the shutter. In imaging experience terms, this is actually more serious than sharpness or noise issues, because it breaks the user’s sense of stability and reliability with the device. 4. My View: Samsung’s AI Imaging Needs a “Referee System” The root cause isn’t insufficient power or hardware; it’s the absence of an orchestration layer. Samsung has too many independent sub-modules (super-resolution, noise reduction, detail enhancement, color reconstruction, depth recognition, AI HDR, etc.) but no master controller to decide when to activate them, how to prioritize, or how to manage latency. The ideal solution would be to: Establish a Central Scene Controller Manage all AI sub-modules with unified priority scheduling and decision memory Maintain temporal consistency of algorithmic states across consecutive frames Only then can Samsung truly fix its “algorithm instability” problem and move its Galaxy imaging pipeline toward maturity.

PhoneArt

28,588 görüntüleme • 10 ay önce

Voice used to be AI’s forgotten modality - now it's having its big moment: rapid innovation, big funding rounds, major agentic applications My conversation with Neil Zeghidour, top AI researcher in the field (Google DeepMind, Meta, kyutai) and now CEO of Gradium This is a reference episode on all things voice AI 🔥 00:00 Intro 01:21 Voice AI’s big moment, and why we’re still early 03:34 Why voice lagged behind text/image/video 06:06 The convergence era: transformers for every modality 07:40 Beyond Her: always-on assistants, wake words, voice-first devices 11:01 Voice vs text: where voice fits (even for coding) 12:56 Neil’s origin story: from finance to machine learning, with help from Yann LeCun and Soumith Chintala 18:35 Neural codecs (SoundStream): compression as the unlock 22:30 Kyutai: open research, small elite teams, moving fast 31:32 Why big labs haven’t “won” voice AI4 34:01 On-device voice: where it works, why compact models matter 46:37 The last mile: real-world robustness, pronunciation, uptime 41:35 Benchmarking voice: why metrics fail, how they actually test 47:03 Cascades vs speech-to-speech: trade-offs + what’s next 54:05 Hardest frontier: noisy rooms, factories, multi-speaker chaos 1:00:50 New languages + dialects: what transfers, what doesn’t 1:02:54 Hardware & compute: why voice isn’t a 10,000-GPU game 1:07:27 What data do you need to train voice models 1:09:02 Deepfakes + privacy: why watermarking isn’t a solution 1:12:30 Voice + vision: multimodality, screen awareness, video+audio 1:14:43 Voice cloning vs voice design: where the market goes 1:16:32 Paris/Europe AI: talent density, underdog energy, what’s next
1:22:49

Voice used to be AI’s forgotten modality - now it's having its big moment: rapid innovation, big funding rounds, major agentic applications My conversation with Neil Zeghidour, top AI researcher in the field (Google DeepMind, Meta, kyutai) and now CEO of Gradium This is a reference episode on all things voice AI 🔥 00:00 Intro 01:21 Voice AI’s big moment, and why we’re still early 03:34 Why voice lagged behind text/image/video 06:06 The convergence era: transformers for every modality 07:40 Beyond Her: always-on assistants, wake words, voice-first devices 11:01 Voice vs text: where voice fits (even for coding) 12:56 Neil’s origin story: from finance to machine learning, with help from Yann LeCun and Soumith Chintala 18:35 Neural codecs (SoundStream): compression as the unlock 22:30 Kyutai: open research, small elite teams, moving fast 31:32 Why big labs haven’t “won” voice AI4 34:01 On-device voice: where it works, why compact models matter 46:37 The last mile: real-world robustness, pronunciation, uptime 41:35 Benchmarking voice: why metrics fail, how they actually test 47:03 Cascades vs speech-to-speech: trade-offs + what’s next 54:05 Hardest frontier: noisy rooms, factories, multi-speaker chaos 1:00:50 New languages + dialects: what transfers, what doesn’t 1:02:54 Hardware & compute: why voice isn’t a 10,000-GPU game 1:07:27 What data do you need to train voice models 1:09:02 Deepfakes + privacy: why watermarking isn’t a solution 1:12:30 Voice + vision: multimodality, screen awareness, video+audio 1:14:43 Voice cloning vs voice design: where the market goes 1:16:32 Paris/Europe AI: talent density, underdog energy, what’s next

Matt Turck

22,980 görüntüleme • 6 ay önce

Heads up #Battlefield1 fans 😎 WW1 mobile shooter "Weltkrieg 1: Firestorm" @weltkrieg1fs is now recruting people for their upcoming April playtest on Android devices via Google Play. You can sing up here 👇 🔹️ Factions: United Kingdom vs German Empire. 🔹️ Map: Somme 🔹️ Game Mode: TDM (6v6) 🔹️ Units: Assault, Heavy, Medic and Sniper. 🔹️ Flamethrower and Grenadier. 🔹️ Battle Points System 🔹️ AI soldiers. Key features and future plans: 🔹️ No pay-to-win elements. 🔹️ There will be new playtests in May and June, including this summer beta. 🔹️ The beta is planned to launch first in Indonesia, the Philippines and Thailand — with more regions added over time. 🔹️ iOS testing will follow after the beta phase. 🔹️ It's being developed by a small team (3 people). 🔹️ Tanks (including the British Mark V and the German A7V), artillery (Morser M10), infantry battle...they're looking to add armored cars and planes in future updates - aircraft will appear as atmospheric elements only. 🔹️ Ultimate Conquest (10v10) includes tanks and artillery. 🔹️ Destruction System: You can knock down trees, break through barbed wire and clear various obstacles. According to devs, buildings are static and can’t be destroyed to keep performance stable across all devices. 🔹️ Controller support is planned for future updates. It will come after the beta. 🔹️ Players will be able to choose the game mode, but not the map — locations will be selected randomly. 🔹️ They’re planning a No HUD gameplay mode in future updates. 🔹️ Single-player modes and a full campaign are planned for stages after the beta. 🔹️ They're aiming for 60fps as the sweet spot for most players. 🔹️ The second multiplayer map Passchendaele "is all about chaos" — rain pouring down while fire still burns from recent shelling. The map also features gas effects which are currently used for visual atmosphere only - but gas will also impact gameplay in the future. 🔹️ Background music is on their radar.
0:36

Heads up #Battlefield1 fans 😎 WW1 mobile shooter "Weltkrieg 1: Firestorm" @weltkrieg1fs is now recruting people for their upcoming April playtest on Android devices via Google Play. You can sing up here 👇 🔹️ Factions: United Kingdom vs German Empire. 🔹️ Map: Somme 🔹️ Game Mode: TDM (6v6) 🔹️ Units: Assault, Heavy, Medic and Sniper. 🔹️ Flamethrower and Grenadier. 🔹️ Battle Points System 🔹️ AI soldiers. Key features and future plans: 🔹️ No pay-to-win elements. 🔹️ There will be new playtests in May and June, including this summer beta. 🔹️ The beta is planned to launch first in Indonesia, the Philippines and Thailand — with more regions added over time. 🔹️ iOS testing will follow after the beta phase. 🔹️ It's being developed by a small team (3 people). 🔹️ Tanks (including the British Mark V and the German A7V), artillery (Morser M10), infantry battle...they're looking to add armored cars and planes in future updates - aircraft will appear as atmospheric elements only. 🔹️ Ultimate Conquest (10v10) includes tanks and artillery. 🔹️ Destruction System: You can knock down trees, break through barbed wire and clear various obstacles. According to devs, buildings are static and can’t be destroyed to keep performance stable across all devices. 🔹️ Controller support is planned for future updates. It will come after the beta. 🔹️ Players will be able to choose the game mode, but not the map — locations will be selected randomly. 🔹️ They’re planning a No HUD gameplay mode in future updates. 🔹️ Single-player modes and a full campaign are planned for stages after the beta. 🔹️ They're aiming for 60fps as the sweet spot for most players. 🔹️ The second multiplayer map Passchendaele "is all about chaos" — rain pouring down while fire still burns from recent shelling. The map also features gas effects which are currently used for visual atmosphere only - but gas will also impact gameplay in the future. 🔹️ Background music is on their radar.

Battlefield Bulletin

14,461 görüntüleme • 1 yıl önce

12 hours ago I made the decision to fly out to El Salvador to be the first ever to make an AI generated short film for a country. I’m now 4 hours away from arriving there. (Talk about executing fast) I will take my time writing this message because the reason why has layers. A few months ago I founded ARQ and bootstrapped a platform that lets anyone create high-quality AI productions from start to finish. the best models a multi-agent creative assistant a built-in video editor All in one place. Alongside the product, I’ve built a strong community where I livestream daily, teaching creators how to use AI to tell better stories and get paid for their work. Shortly after we got dozens of offers to make commercials for companies. This was fun, until we came to the conclusion that we were wasting a lot of potential by focussing on companies, while we could also tell the story of nations, movements, communities and narratives that the world needs to hear. So we’re starting with El Salvador. Why? Because Nayib Bukele is one of the most inspiring leaders in the world, not only embracing, but embodying change, turning the country from one of the most dangerous places on earth, to one of the safest and innovation friendly nations. Humanity is experiencing a shift. New technology, new philosophies, new artists, new story tellers, new builders. A new world. So the argument is not AI vs. Non AI. The argument is: Soul Vs. Soulless Pro Human vs. Anti Human Building Vs. Gambling Effort Vs. Effortless This is what ARQ stands for. Not just a SaaS but a safe haven for the soul. It feels like my moral responsibility to speak not only for the AI community but for every human creator. So I decided to do something that requires so much effort and human involvement; no one can deny that AI serves only as a tool in the process. Instead of making 100 commercials for companies, I want to prove what 2 young humans can do with balls and the tools available today. “You can just do things” Yesterday evening me and Ya-Sirr D left our comfortable office in Dubai and are now on our way to El Salvador. The goal: to create a 3-5 Minute AI powered short film telling THEIR story. We will spend every day live streaming, showcasing the process of creating the video while pushing the boundaries of every tool available on the market. Think Google DeepMind , Hailuo AI (MiniMax), Kling AI, Reve, Midjourney, xAI All through ARQ. Who knows who we inspire? Who knows who we meet? We might as well end up meeting the president himself. We want to get to know the country, its people and imagine its future. We want to visit innovative projects in El Salvador, hear the stories of it’s citizens and include them in the video: MURPHSLIFE Stacy Herbert 🇸🇻🚀 @BuildSalvador The Bitcoin Office Bitcoin Beach Anyone who can help us tell the story. Not just any story, one of the most inspiring stories of the past 10 years. Like I said. You can just do things. We have to stop thinking in steps. I know where I will be in 30 days if I stay in the office. More followers. More beta testers. More live streams. More revenue. But by taking this leap of faith. We can be a case study. An inspiration. An ARQ. I remove expectations and increase chances of special things happening. If anyone in El Salvador has any recommendations on who and what places I should visit, send me a DM. We will post updates on X and create an entire “vlog/documentary” for youtube. Join the discord for daily live streams and lessons on how to improve your creation skills. We expect that it will take 7-9 days to finish the entire short film. It will be posted on all social platforms. ARQ. A safe haven for the soul.
2:33

12 hours ago I made the decision to fly out to El Salvador to be the first ever to make an AI generated short film for a country. I’m now 4 hours away from arriving there. (Talk about executing fast) I will take my time writing this message because the reason why has layers. A few months ago I founded ARQ and bootstrapped a platform that lets anyone create high-quality AI productions from start to finish. the best models a multi-agent creative assistant a built-in video editor All in one place. Alongside the product, I’ve built a strong community where I livestream daily, teaching creators how to use AI to tell better stories and get paid for their work. Shortly after we got dozens of offers to make commercials for companies. This was fun, until we came to the conclusion that we were wasting a lot of potential by focussing on companies, while we could also tell the story of nations, movements, communities and narratives that the world needs to hear. So we’re starting with El Salvador. Why? Because Nayib Bukele is one of the most inspiring leaders in the world, not only embracing, but embodying change, turning the country from one of the most dangerous places on earth, to one of the safest and innovation friendly nations. Humanity is experiencing a shift. New technology, new philosophies, new artists, new story tellers, new builders. A new world. So the argument is not AI vs. Non AI. The argument is: Soul Vs. Soulless Pro Human vs. Anti Human Building Vs. Gambling Effort Vs. Effortless This is what ARQ stands for. Not just a SaaS but a safe haven for the soul. It feels like my moral responsibility to speak not only for the AI community but for every human creator. So I decided to do something that requires so much effort and human involvement; no one can deny that AI serves only as a tool in the process. Instead of making 100 commercials for companies, I want to prove what 2 young humans can do with balls and the tools available today. “You can just do things” Yesterday evening me and Ya-Sirr D left our comfortable office in Dubai and are now on our way to El Salvador. The goal: to create a 3-5 Minute AI powered short film telling THEIR story. We will spend every day live streaming, showcasing the process of creating the video while pushing the boundaries of every tool available on the market. Think Google DeepMind , Hailuo AI (MiniMax), Kling AI, Reve, Midjourney, xAI All through ARQ. Who knows who we inspire? Who knows who we meet? We might as well end up meeting the president himself. We want to get to know the country, its people and imagine its future. We want to visit innovative projects in El Salvador, hear the stories of it’s citizens and include them in the video: MURPHSLIFE Stacy Herbert 🇸🇻🚀 @BuildSalvador The Bitcoin Office Bitcoin Beach Anyone who can help us tell the story. Not just any story, one of the most inspiring stories of the past 10 years. Like I said. You can just do things. We have to stop thinking in steps. I know where I will be in 30 days if I stay in the office. More followers. More beta testers. More live streams. More revenue. But by taking this leap of faith. We can be a case study. An inspiration. An ARQ. I remove expectations and increase chances of special things happening. If anyone in El Salvador has any recommendations on who and what places I should visit, send me a DM. We will post updates on X and create an entire “vlog/documentary” for youtube. Join the discord for daily live streams and lessons on how to improve your creation skills. We expect that it will take 7-9 days to finish the entire short film. It will be posted on all social platforms. ARQ. A safe haven for the soul.

Amir D

54,566 görüntüleme • 9 ay önce

The new Huberman Lab episode is out: How to Speak Clearly & With Confidence | Matt Abrahams (Think Fast, Talk Smart: The Podcast) 0:00 Matt Abrahams 3:21 Public Speaking Fear, Status; Speech Delivery 5:36 Speech, Connection, Credibility; Authenticity 9:05 Monitoring, Self-Judgement; Memorization, Tool: Object Relabeling Exercise 13:13 Sponsors: Eight Sleep & BetterHelp 15:40 Cadence & Speech Patterns; Lego Manuals, Storytelling & Emotion 19:18 Visual vs Audio Content, Length, Detail 23:19 Understanding Audience's Needs, Tool: Recon – Reflection – Research 24:25 Judgement in Communication, Heuristics 27:33 Questions, Responding to the Audience, Tool: Structuring Information 31:34 Feedback & Observation; Tools: Three-Pass Speech Review; Communication Reflection Journal 39:09 Movement, Stage Fright, Content Expertise 42:54 Sponsors: AGZ by AG1 & Joovv 45:34 Multi-Generation Communication Styles & Trust; Curiosity, Conversation Turns 50:32 Linear vs Non-Linear Speech, Tool: Tour Guide Expectations 53:21 Develop Communication Skills, Audience Size, Tools: Distancing; Practicing 1:01:43 Tool: Improv & Agility; Great Communication Examples; Divided Attention 1:09:36 One-on-One Communication vs Public Speaking 1:11:00 Sponsor: Mateína 1:12:00 Neurodiversity, Introverts, Communication Styles; Writing & Editing 1:16:30 Calculating Risk, Tool: Violating Expectations & Engaging Audience 1:21:20 Authenticity, Strengths, Growth & Improv 1:23:23 Damage Control, Tools: Avoid Blanking Out; Contingency Planning, Silence 1:30:32 Nerves, Tool: Breathwork; Spontaneous Communication; Beta-Blockers 1:34:29 Communication Hygiene, Caffeine, Tools: NSDR/Yoga Nidra; Vestibular System & Sleep 1:40:08 Conversation Before Speaking; Delivering Engaging Speeches 1:42:56 Sponsor: Function 1:44:43 Anticipation, Tool: Introduce Yourself; Connect to Environment, Phones 1:51:30 Customer Service & Kids Jobs; Tool: Role Model Communication; COVID Pandemic 1:56:04 Quiet But Not Shy, Extroverts; Social Media Presence 2:00:25 Martial Arts, Sport, Running, Presence & Connection 2:04:16 Apologizing; Communication Across Accents & Cultures 2:07:36 Interruptions, Tools: Paraphrasing; Speech Preparation 2:10:57 Public Speaking Fear, Tool: Envision Positive Outcome; Arguments & Mediation 2:13:19 Omit Filler Words, Tool: Landing Phrases; Time & Storytelling 2:16:52 Asking For a Raise; Poor Communicators & Curiosity; Memorization 2:19:49 Pre-Talk Anxiety Management; Acknowledgements 2:23:47 Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter Includes paid partnerships.
2:26:20

The new Huberman Lab episode is out: How to Speak Clearly & With Confidence | Matt Abrahams (Think Fast, Talk Smart: The Podcast) 0:00 Matt Abrahams 3:21 Public Speaking Fear, Status; Speech Delivery 5:36 Speech, Connection, Credibility; Authenticity 9:05 Monitoring, Self-Judgement; Memorization, Tool: Object Relabeling Exercise 13:13 Sponsors: Eight Sleep & BetterHelp 15:40 Cadence & Speech Patterns; Lego Manuals, Storytelling & Emotion 19:18 Visual vs Audio Content, Length, Detail 23:19 Understanding Audience's Needs, Tool: Recon – Reflection – Research 24:25 Judgement in Communication, Heuristics 27:33 Questions, Responding to the Audience, Tool: Structuring Information 31:34 Feedback & Observation; Tools: Three-Pass Speech Review; Communication Reflection Journal 39:09 Movement, Stage Fright, Content Expertise 42:54 Sponsors: AGZ by AG1 & Joovv 45:34 Multi-Generation Communication Styles & Trust; Curiosity, Conversation Turns 50:32 Linear vs Non-Linear Speech, Tool: Tour Guide Expectations 53:21 Develop Communication Skills, Audience Size, Tools: Distancing; Practicing 1:01:43 Tool: Improv & Agility; Great Communication Examples; Divided Attention 1:09:36 One-on-One Communication vs Public Speaking 1:11:00 Sponsor: Mateína 1:12:00 Neurodiversity, Introverts, Communication Styles; Writing & Editing 1:16:30 Calculating Risk, Tool: Violating Expectations & Engaging Audience 1:21:20 Authenticity, Strengths, Growth & Improv 1:23:23 Damage Control, Tools: Avoid Blanking Out; Contingency Planning, Silence 1:30:32 Nerves, Tool: Breathwork; Spontaneous Communication; Beta-Blockers 1:34:29 Communication Hygiene, Caffeine, Tools: NSDR/Yoga Nidra; Vestibular System & Sleep 1:40:08 Conversation Before Speaking; Delivering Engaging Speeches 1:42:56 Sponsor: Function 1:44:43 Anticipation, Tool: Introduce Yourself; Connect to Environment, Phones 1:51:30 Customer Service & Kids Jobs; Tool: Role Model Communication; COVID Pandemic 1:56:04 Quiet But Not Shy, Extroverts; Social Media Presence 2:00:25 Martial Arts, Sport, Running, Presence & Connection 2:04:16 Apologizing; Communication Across Accents & Cultures 2:07:36 Interruptions, Tools: Paraphrasing; Speech Preparation 2:10:57 Public Speaking Fear, Tool: Envision Positive Outcome; Arguments & Mediation 2:13:19 Omit Filler Words, Tool: Landing Phrases; Time & Storytelling 2:16:52 Asking For a Raise; Poor Communicators & Curiosity; Memorization 2:19:49 Pre-Talk Anxiety Management; Acknowledgements 2:23:47 Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter Includes paid partnerships.

Andrew D. Huberman, Ph.D.

702,104 görüntüleme • 9 ay önce