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⭐️STAAR data shows significant growth worth celebrating; district earns B rating. Read more below:

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Dallas ISD

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19,825 просмотров • 11 месяцев назад •via X (Twitter)

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Driven by a UK Government push to open up transport data and boost public transport use nationwide, Google Maps has officially integrated live public transport tracking. By mandating that operators share their live GPS data, tech apps can now let you literally watch your specific bus moving along the map in real-time. As seen in the screenshot above, you can tap on your route (like the 207 towards White City) and see exactly where the bus is on the road, how many minutes away it is, and when the data was last updated. No more relying on broken countdown boards. No more running for a “ghost bus” that never actually shows up. No more standing in the rain guessing if you have time to grab a coffee. Have you noticed this government-backed feature on your phone yet? And more importantly... which West London bus route is the most unreliable and needs this the most? Let us know below #UB1UB2 #London #Southall #Hayes #Ealing

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🚨 THE BIGGEST BOTTLENECK IN AI ISN'T COMPUTING POWER ANYMORE IT'S MOVING DATA. Instead of laying new cables, Chinese researchers have upgraded existing fiber infrastructure by doing two things at once: Using three wavelength bands (C + L + S) instead of the usual two. Using four cores inside each fiber instead of one. Each core acts like an independent highway, and each band acts like an extra lane on that highway. Together, they’ve reportedly increased transmission capacity per core by nearly 50% and overall data throughput by up to 5×. This matters enormously for AI. Modern AI clusters move terabits of data per second between thousands of GPUs. The biggest bottleneck is often not the chips themselves, but moving data fast enough between them. If you can push 5× more data through the same physical cables, you can train bigger models faster and reduce network congestion. Why this is significant: • It shows multi-core + extended spectrum technology moving from labs into real-world commercial use • The system has already run over 35 km of existing telecom network • It could be especially useful for submarine cables and large-scale data center interconnects • China is also eyeing it for its “Eastern Data, Western Computing” project The deeper implication: We’re reaching the physical limits of how much data we can push through single-core fibers using traditional methods. By combining spatial multiplexing (multiple cores) with spectral multiplexing (more wavelength bands), engineers are finding new ways to keep scaling bandwidth without having to dig up the planet to lay new cables. This kind of breakthrough is quiet but foundational it’s the kind of infrastructure upgrade that will determine how fast AI and cloud computing can actually grow in the coming years. The future of data movement might not require more cables. It might just require smarter ones. How important do you think multi-core and multi-band fiber will be for keeping up with AI’s exploding data demands? Follow for more frontier networking, photonics, and infrastructure technology.

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I’m so excited to share that Cribl has closed an oversubscribed $319M Series E round at a new $3.5B valuation, up 40% from our Series D just two years ago. The round was led by GV in one of their largest ever investments, with participation from GIC, CapitalG , IVP , and CRV . But that’s not all. GV partner and former GitLab CRO Michael McBride (Michael McBride) is joining our board of directors. This is an incredible milestone for us and it goes to show that solving real problems for real users works really well! Since day 1, Cribl was built to help you unlock the value of all your IT and security data. We’re taking a different approach to data management, with vendor-agnostic products that resolve the tension between data growth (28% CAGR and growing!) and budget growth while giving you choice, control, and flexibility. We’re here for the critically important but often underserved IT and security practitioners who keep businesses running. And we’ll continue to innovate to meet your needs. Thank you to our customers, partners, investors, and all of the amazing goats who have supported and trusted us during this journey. Here’s to more incredible moments as we continue to succeed together! Read more about the announcement in my blog post: ​​
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I’m so excited to share that Cribl has closed an oversubscribed $319M Series E round at a new $3.5B valuation, up 40% from our Series D just two years ago. The round was led by GV in one of their largest ever investments, with participation from GIC, CapitalG , IVP , and CRV . But that’s not all. GV partner and former GitLab CRO Michael McBride (Michael McBride) is joining our board of directors. This is an incredible milestone for us and it goes to show that solving real problems for real users works really well! Since day 1, Cribl was built to help you unlock the value of all your IT and security data. We’re taking a different approach to data management, with vendor-agnostic products that resolve the tension between data growth (28% CAGR and growing!) and budget growth while giving you choice, control, and flexibility. We’re here for the critically important but often underserved IT and security practitioners who keep businesses running. And we’ll continue to innovate to meet your needs. Thank you to our customers, partners, investors, and all of the amazing goats who have supported and trusted us during this journey. Here’s to more incredible moments as we continue to succeed together! Read more about the announcement in my blog post: ​​

Clint Sharp

13,981 просмотров • 1 год назад

I dunno, I think it's actually reasonable enough to say that data centers "use" water colloquially, at least in the context of aquifers. If data centers (and the similarly water-intensive combined-cycle gas turbines favored to power them) pump water from an aquifer at a rate that exceeds the rate at which the aquifer is recharged, the water table drops. If you depend upon a well for your water supply, and the water table drops below the depth your well, for all intents and purposes, it's "used up" until you pay $30-50k to drill a new well. Maybe this isn't the most sophisticated language, but we're communicating with the public and it's functional. Anyway, this is a significant problem in regions where people frequently rely upon shallow residential wells because public water supplies aren't available - like much of rural Texas, a prime target for data centers - particularly when many of these same regions are so prone to severe droughts and experiencing explosive population growth. Video related: my well going dry last year due to the severe 5y drought, and coincidentally, the same day I discovered the local data center developer bragging about their permit that allows them to pump millions of gallons of water per day.
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I dunno, I think it's actually reasonable enough to say that data centers "use" water colloquially, at least in the context of aquifers. If data centers (and the similarly water-intensive combined-cycle gas turbines favored to power them) pump water from an aquifer at a rate that exceeds the rate at which the aquifer is recharged, the water table drops. If you depend upon a well for your water supply, and the water table drops below the depth your well, for all intents and purposes, it's "used up" until you pay $30-50k to drill a new well. Maybe this isn't the most sophisticated language, but we're communicating with the public and it's functional. Anyway, this is a significant problem in regions where people frequently rely upon shallow residential wells because public water supplies aren't available - like much of rural Texas, a prime target for data centers - particularly when many of these same regions are so prone to severe droughts and experiencing explosive population growth. Video related: my well going dry last year due to the severe 5y drought, and coincidentally, the same day I discovered the local data center developer bragging about their permit that allows them to pump millions of gallons of water per day.

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> 8 GPUs in one server rig > dude went homeless to build it > electrical bill costs more than rent now > while everyone else pays $400/month to openai > a 2 GPU desktop kills the api bill forever > rtx 4080 super + rtx 5060 ti = 32gb vram > runs qwen 3.6 with 100k context locally > no rate limits, no api keys, no data leaving the room > agents loop 400 times for free > claude opus still wins on hard reasoning > but local handles 90% of daily work > $1,200 setup pays itself off in 4 months > bookmark this and read the article below
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> 8 GPUs in one server rig > dude went homeless to build it > electrical bill costs more than rent now > while everyone else pays $400/month to openai > a 2 GPU desktop kills the api bill forever > rtx 4080 super + rtx 5060 ti = 32gb vram > runs qwen 3.6 with 100k context locally > no rate limits, no api keys, no data leaving the room > agents loop 400 times for free > claude opus still wins on hard reasoning > but local handles 90% of daily work > $1,200 setup pays itself off in 4 months > bookmark this and read the article below

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Our first spacecraft, Ray, has completed its mission on-orbit – serving as an extremely successful testbed for validating key technologies despite not attempting re-entry due to an on-orbit short circuit in a component preventing our deorbit engine from igniting. Throughout 1,600+ orbits around Earth, our team collected invaluable technical data and operational experience. Notably, Ray executed two major orbital maneuvers, repeated on-orbit software updates, and proved out our in-house designed separation system. Read the full mission recap below and stay tuned! More insights on Ray to come and big updates ahead around its successor: Arc.
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Our first spacecraft, Ray, has completed its mission on-orbit – serving as an extremely successful testbed for validating key technologies despite not attempting re-entry due to an on-orbit short circuit in a component preventing our deorbit engine from igniting. Throughout 1,600+ orbits around Earth, our team collected invaluable technical data and operational experience. Notably, Ray executed two major orbital maneuvers, repeated on-orbit software updates, and proved out our in-house designed separation system. Read the full mission recap below and stay tuned! More insights on Ray to come and big updates ahead around its successor: Arc.

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The best Polymarket Quant bot for copy-trading with a 99.3% win rate. backtested strategy on 72M Polymarket/Kalshi trades to hit +$805K PnL on 27,000 predictions. bot doesn't gamble - it uses math and statistics in its algo to consistently hit 99% win rate. his algo decoded: 1. Mispricing formula based on 72M trades data, traders constantly overpay for cheap contracts (0.1¢–50¢) most of the edge sits in (80¢-99¢) contracts - that's the range where the bot mostly trades • formula: δ = actual win rate - implied probability bot applies this to every trade to find the edge. // 2. Expected value calculation EV tells you whether a bet is worth taking, regardless of the outcome of any single trade. • formula: EV = (P win × Payout) - (P lose × Cost) bot calculates it to understand if the trade is worth the risk. // 3. Kelly Criterion sizing most powerful position sizing formula ever discovered for gambling, trading and prediction markets it tells the algo what % of your portfolio to size into each bet to win long term. • formula: f* = (p * b - q) / b mispricing found → EV calced → kelly sizing → enter profile: start copy trading the bot with as little as $10 using Ares: 2 more formulas behind its algo revealed in the article below ↓
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The best Polymarket Quant bot for copy-trading with a 99.3% win rate. backtested strategy on 72M Polymarket/Kalshi trades to hit +$805K PnL on 27,000 predictions. bot doesn't gamble - it uses math and statistics in its algo to consistently hit 99% win rate. his algo decoded: 1. Mispricing formula based on 72M trades data, traders constantly overpay for cheap contracts (0.1¢–50¢) most of the edge sits in (80¢-99¢) contracts - that's the range where the bot mostly trades • formula: δ = actual win rate - implied probability bot applies this to every trade to find the edge. // 2. Expected value calculation EV tells you whether a bet is worth taking, regardless of the outcome of any single trade. • formula: EV = (P win × Payout) - (P lose × Cost) bot calculates it to understand if the trade is worth the risk. // 3. Kelly Criterion sizing most powerful position sizing formula ever discovered for gambling, trading and prediction markets it tells the algo what % of your portfolio to size into each bet to win long term. • formula: f* = (p * b - q) / b mispricing found → EV calced → kelly sizing → enter profile: start copy trading the bot with as little as $10 using Ares: 2 more formulas behind its algo revealed in the article below ↓

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1,797,612 просмотров • 3 месяцев назад