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Sundar Pichai explains Google Search latency budgets: "I’ve always internalized speed as one of the distinguishing features of a great product. It almost always reflects the technical underpinnings of the product having been done well. It's easy to say you want low latency, but you're constantly adding capabilities. The...

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Stripe

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23,879 görüntüleme • 3 ay önce •via X (Twitter)

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solana's speed still amazes me this is a basic demo of helius enhanced websockets detecting a txn in literal milliseconds (before even the wallet itself can animate) it is crazy to have this low latency on a distributed system with thousands of global nodes
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solana's speed still amazes me this is a basic demo of helius enhanced websockets detecting a txn in literal milliseconds (before even the wallet itself can animate) it is crazy to have this low latency on a distributed system with thousands of global nodes

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In mobile gaming, milliseconds matter.⏱️ We put the #REDMAGIC10Air head-to-head in a robot-powered touch latency screen test against a competitor. Guess who won?
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In mobile gaming, milliseconds matter.⏱️ We put the #REDMAGIC10Air head-to-head in a robot-powered touch latency screen test against a competitor. Guess who won?

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In operations where milliseconds matter, routing data closer to where work happens improves speed. Edge Control enables low-latency performance and local data processing - and is centrally managed through T-Platform. More: T-Mobile Business Partner
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In operations where milliseconds matter, routing data closer to where work happens improves speed. Edge Control enables low-latency performance and local data processing - and is centrally managed through T-Platform. More: T-Mobile Business Partner

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I showed my Polymarket latency bot to a quant friend. He quit his job the next week. $800 → $5,000. Pure latency arbitrage. The bot doesn’t predict outcomes. It’s simply 200 milliseconds faster than the market. The formula is simple: P = Σ(ΔOdds × Size) / Latency_gap The bigger the gap, the better the entry. What’s running under the hood: Connected via API to live sports and political data feeds Receives match results and vote counts before they appear on Polymarket Enters using the Kelly criterion milliseconds before the spread closes Exits automatically once the market converges Win rate: 73% Sharpe ratio: 2.24 About 57 trades per hour Every trade exists inside a ~200 millisecond window. Most people analyze charts. The bot analyzes latency. Charts can be misleading. Milliseconds can’t. Copy trades → you were reading this, the bot likely already closed several more positions.
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I showed my Polymarket latency bot to a quant friend. He quit his job the next week. $800 → $5,000. Pure latency arbitrage. The bot doesn’t predict outcomes. It’s simply 200 milliseconds faster than the market. The formula is simple: P = Σ(ΔOdds × Size) / Latency_gap The bigger the gap, the better the entry. What’s running under the hood: Connected via API to live sports and political data feeds Receives match results and vote counts before they appear on Polymarket Enters using the Kelly criterion milliseconds before the spread closes Exits automatically once the market converges Win rate: 73% Sharpe ratio: 2.24 About 57 trades per hour Every trade exists inside a ~200 millisecond window. Most people analyze charts. The bot analyzes latency. Charts can be misleading. Milliseconds can’t. Copy trades → you were reading this, the bot likely already closed several more positions.

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It’s all in the details, and in this case, the milliseconds. ⏱ Quai Network’s hierarchical structure enables nodes + miners to topologically organize, resulting in decreased latency and a significant increase in TPS. 🚀 #quai #L1 #crypto #cryptocurrency #blockchain
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It’s all in the details, and in this case, the milliseconds. ⏱ Quai Network’s hierarchical structure enables nodes + miners to topologically organize, resulting in decreased latency and a significant increase in TPS. 🚀 #quai #L1 #crypto #cryptocurrency #blockchain

Quai Network ⚡️💵

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The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.
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The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.

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🚨 This is Warzone on the highest fps/ lowest latency settings 🚨 It is really hard to lose a straight up gunfight when you are on this low of latency. Feels similar to the "host advantage" we all used to want back in the console days.
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🚨 This is Warzone on the highest fps/ lowest latency settings 🚨 It is really hard to lose a straight up gunfight when you are on this low of latency. Feels similar to the "host advantage" we all used to want back in the console days.

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Today, we’re excited to introduce Miso One, the most emotive voice model in the world. Miso One is an 8-billion-parameter text-to-speech model for highly expressive speech generation. It emotes like a human and responds faster than a human, with just 110 milliseconds of latency. We’ve open-sourced the model weights, with API access coming soon. Hear how Miso One sounds in the thread below.
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Today, we’re excited to introduce Miso One, the most emotive voice model in the world. Miso One is an 8-billion-parameter text-to-speech model for highly expressive speech generation. It emotes like a human and responds faster than a human, with just 110 milliseconds of latency. We’ve open-sourced the model weights, with API access coming soon. Hear how Miso One sounds in the thread below.

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'The narrative of the last few years is that Lewis Hamilton is washed, but if you actually look at the numbers and the context, I know that's tough for some, that falls apart real quickly.' 'This comes off of the Mexican Grand Prix qualifying where he stuck it in P3. Since the summer break, Hamilton's average qualifying gap to Leclerc, one of the best single lap drivers in Formula 1, is just 30 milliseconds. That's right, 30 milliseconds. And here's the kicker. Leclerc is in his prime.' 'Hamilton is 40, in a brand new team, learning an entirely different car philosophy, working with a completely new crew of engineers. He's really not supposed to be this close yet.' 'He's supposed to be learning still, but the data says otherwise. In Mexico, he was just 9 milliseconds off. In Singapore, he out-qualified Leclerc by a tenth. And he's quietly closing the gap while everyone else, including parts of the media, keep pushing this lazy narrative that he's lost it.' 'And you know what? It makes sense. Calling Lewis Hamilton washed is an easy one from a storytelling perspective. It avoids the complexity of what he's actually doing, adapting to this ground effect era and learning Ferrari's quirks, and still matching one of the great and quick drivers of his generation.' 'We talk a lot about greatness when it's obvious, wins, titles, domination, but real greatness looks like this, staying relevant when the odds say you shouldn't be. So here's the question. Are we witnessing Hamilton's decline? Or his reinvention.' 📹 tyrellformula1 / IG
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'The narrative of the last few years is that Lewis Hamilton is washed, but if you actually look at the numbers and the context, I know that's tough for some, that falls apart real quickly.' 'This comes off of the Mexican Grand Prix qualifying where he stuck it in P3. Since the summer break, Hamilton's average qualifying gap to Leclerc, one of the best single lap drivers in Formula 1, is just 30 milliseconds. That's right, 30 milliseconds. And here's the kicker. Leclerc is in his prime.' 'Hamilton is 40, in a brand new team, learning an entirely different car philosophy, working with a completely new crew of engineers. He's really not supposed to be this close yet.' 'He's supposed to be learning still, but the data says otherwise. In Mexico, he was just 9 milliseconds off. In Singapore, he out-qualified Leclerc by a tenth. And he's quietly closing the gap while everyone else, including parts of the media, keep pushing this lazy narrative that he's lost it.' 'And you know what? It makes sense. Calling Lewis Hamilton washed is an easy one from a storytelling perspective. It avoids the complexity of what he's actually doing, adapting to this ground effect era and learning Ferrari's quirks, and still matching one of the great and quick drivers of his generation.' 'We talk a lot about greatness when it's obvious, wins, titles, domination, but real greatness looks like this, staying relevant when the odds say you shouldn't be. So here's the question. Are we witnessing Hamilton's decline? Or his reinvention.' 📹 tyrellformula1 / IG

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A heartbeat: 800 milliseconds Chainlink Data Streams: 376 milliseconds
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A heartbeat: 800 milliseconds Chainlink Data Streams: 376 milliseconds

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BREAKING: Did Polymarket just silently nuke the 500ms taker delay? Rumor has it the speed bump on crypto markets is GONE. No announcement, no changelog, nothing. The crazy part? It was never officially documented in the first place. I literally only found proof of its existence buried in a 3rd-party GitHub repo code Why this matters: - Takers/aggressive bots: You're eating MUCH faster now, which is great... if your latency is good - Makers/liquidity providers: You're about to get picked off way more often. RIP to anyone not updating quotes constantly - Arbitrage: The game just got 500ms faster. May the lowest-latency chad win Traders are already claiming they got wrecked overnight. Unless you asked Claude to update your OpenClaw configs for zero-latency, you are probably getting farmed right now See the execution speed for yourself:

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The art of blasting, or the raw power of moving millions of tons of rock in milliseconds! [📹 realengineerings]
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The art of blasting, or the raw power of moving millions of tons of rock in milliseconds! [📹 realengineerings]

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Latency. Lag. Delay. No matter what you call it — when the timing is off, your performance suffers. Learn how our Xfinity 10G Network is being built to deliver an exceptional Internet experience with low latency, so you won't miss a beat.
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$GOOGL CEO Sundar Pichai on why Google didn't ship ChatGPT first, and the signal they missed from OpenAI: We even conceived the product, it was Lambda. There was an engineer inside who thought it was sentient. Think of it as an early version of ChatGPT. In the multiverse, Google probably shipped that 9 months earlier... but the version I saw was a lot more toxic at a level we couldn't have possibly put it out. As a company with this search quality bias, we had a higher bar for what we thought was acceptable product quality. I think they were lucky to also see it on the coding side with GitHub. Maybe there was a signal we were missing. The jumps between GPT-2 and 3 and later 4 were more pronounced if you were using it for coding.
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$GOOGL CEO Sundar Pichai on why Google didn't ship ChatGPT first, and the signal they missed from OpenAI: We even conceived the product, it was Lambda. There was an engineer inside who thought it was sentient. Think of it as an early version of ChatGPT. In the multiverse, Google probably shipped that 9 months earlier... but the version I saw was a lot more toxic at a level we couldn't have possibly put it out. As a company with this search quality bias, we had a higher bar for what we thought was acceptable product quality. I think they were lucky to also see it on the coding side with GitHub. Maybe there was a signal we were missing. The jumps between GPT-2 and 3 and later 4 were more pronounced if you were using it for coding.

Wall St Engine

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Let me try to explain what the big budget bill is, why it matters, and how you can think about budgets (yes, yawn! I get it). But if you get past yawning, you'll see that budgets really matter, and I want to give you a framework for how and why.
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Let me try to explain what the big budget bill is, why it matters, and how you can think about budgets (yes, yawn! I get it). But if you get past yawning, you'll see that budgets really matter, and I want to give you a framework for how and why.

Justin Wolfers

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The first milliseconds of a nuclear explosion (Operation Teapot – Turk, 43 kilotons, 1955)
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The first milliseconds of a nuclear explosion (Operation Teapot – Turk, 43 kilotons, 1955)

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What's next for Google Gemini? "I think 2.5 was the real breakthrough in terms of capabilities, it's at the frontier of where the models are." Google & Alphabet Sundar Pichai tells Emily Chang #BloombergTech
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What's next for Google Gemini? "I think 2.5 was the real breakthrough in terms of capabilities, it's at the frontier of where the models are." Google & Alphabet Sundar Pichai tells Emily Chang #BloombergTech

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