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⚡️Kaspa 11 #11 - BlockDAG Kaspa's BlockDAG data structure enables its unique GHOSTDAG consensus algorithm to solve the orphan blocks problem, allowing for high frequency block generation and high scalability. Learn all about this feature and others via the link in the thread. The 10BPS 𝆒 Crescendo update is...

32,642 просмотров • 1 год назад •via X (Twitter)

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What do $Kaspa and $Apple have in common? Apple wasn’t the first mover in the mobile phone business: Sony, Motorola, Nokia, and others were FAR ahead of them! Yet today, Apple is the market leader (> 20% market share) What makes Apple different? They think different! They built on the shoulders of giants and simplified things in a way their competitors couldn’t. You don’t have to be the first mover! You don’t have to reinvent the wheel. You just need to streamline the solution and customer behavior to the maximum, and you’ll win! The same principle that worked for Apple will now apply to Kaspa. Just as Apple took existing technology and made it more accessible, intuitive, and user-friendly, Kaspa is poised to do the same in its own field. By refining blockchain technology: focusing on speed, scalability and simplicity. Kaspa can take the groundwork laid by earlier cryptocurrencies and elevate it, meeting user needs more effectively than its predecessors. It’s not about being the first; it’s about being the BEST at delivering what people really want. 10bps countdown: ⏳ 34 days to go till 10bps GIGA Thread & Art from KASPArt_Qubicious_Analysis 💪 --> Check him out🫡 If you understand what you're investing in, short-term price fluctuations won't matter to you: • No insiders or VCs • Fair launch • Solves the blockchain trilemma • 2nd biggest Hash rate • Proof-of-Work • Worth $2B without Binance & Coinbase • T1 listings coming soon “Learn” comes before “earn” for a reason. The L stands for losses, and most people can’t stomach enough of them to EVER win. Don't be one of them! #kas #kasarmy #ghostdag #creszendo #kaspa #10bps #smartcontracts #btc

Jens Illgner - Road To Glory Jil

33,835 просмотров • 1 год назад

NASA ARTEMIS II MISSION UPDATE: DAY 7 The crew and the Orion spacecraft are approximately 57,157 km (35,518 mi) from the Moon. They are also about 380,631 km (236,512 mi) from Earth and are traveling at around 2,029 km/h (1,261 mph). The mission is currently 62.3% completed. Yesterday was a very big day for the mission as this culminated in the Lunar Flyby, which included the closest approach to the Moon at a distance of 4,067 km (6,545 mi), occurring at 11:02 UTC (MET+5/00:25). Following this, a record was set for the furthest distance humans had ever traveled from Earth, at a distance of 406,771 km (252,756 mi), which occurred at 23:02 UTC (MET+5/00:27). The crew also witnessed an Earthrise and solar eclipse during this time, before the lunar observation period concluded. Moving on to today's expected events, first up we have the crew wakeup, which is expected to occur at 15:35 UTC (MET+5/17:00). Following this, Orion will exit the lunar sphere of influence, which is expected to occur at 17:25 UTC (MET+5/18:50). Then, after that, will be the Crew Daily Planning Conference (PDC), which will occur at 18:05 UTC (MET+5/19:30). Moving on to the next event after that is PAO: Orion to ISS crew call (audio only), which is expected to happen at 18:40 UTC (MET+5/20:05). Then, following that, will be the Post Lunar flyby science debrief, which will occur at 19:00 UTC (MET+5/20:25). This will then be followed by a crew off-duty period that will start at 19:30 UTC (MET+5/20:55) and will last until 22:20 UTC (MET+5/23:45). Then, after that, will be the Return Trajectory Correction burn #1 (if needed) at 01:03 UTC (MET+6/02:28). And following that will be crew pre sleep at 04:35 UTC (MET+6/06:00), followed by the sleep period at 07:05 UTC (MET+6/08:30). And don't forget, you can tune into our Artemis II Real-Time Tracking 24/7 stream to watch the rest of the mission! NSF Artemis II Real Time Tracking 24/7 | NSF - NASASpaceflight.com

Jake (Max-Q) 🏴󠁧󠁢󠁷󠁬󠁳󠁿

23,025 просмотров • 6 месяцев назад

he turned $4k into 15x on weather markets. i reverse engineered his strategy. every day Polymarket runs the daily high temperature for 48 cities, 11 positions each: will be 14°, will be 15°, and so on. from morning to early afternoon the temperature only goes up. the moment the airport station prints 15, the "will be 14" position is dead. the high is already above it, no way back. but the market doesn't react instantly. there are still other people's YES bids sitting in the book, people who placed them in the morning at 30-80c and went to get coffee. that second, he sells them YES, meaning he buys NO at 20-85c. a few seconds later he dumps that NO at 99c to the bots that buy up the last cent. that's how he arbs the weather market. since june 1 he has done this 2,575 times. one loss, $34. a typical entry: $170 in, $5 profit, money free again 9 seconds later. rarely more than $1.5k working at once, $4k at the peak. on that $4k he made $63k in 98 days. the biggest profit comes from dying favorites: the position is cheap (60c), there are hundreds of dollars in bids in the book, and one station reading turns them into trash. then the bot buys out the whole book cheap at once and seconds later sells the positions to the bots at 99c. these are 18% of his trades and 44% of his total profit. NYC, june 25. the 82-83°F position was at 54c, the station printed 84. he took $1,470 of NO at 66c, sold it 9 seconds later at 99, +$732 from one trade. how to repeat it? the description of every temperature market has a link to the station it resolves on, like the same report comes from the api, free, no key: /api/data/metar?ids=UUWW the day's high on the station moved above a position = the position is dead. you sell into its YES bids and immediately dump the NO at 99. reports come out at :20 and :50 but get published with a 15 second to 5 minute delay, so you have to poll every second. you don't watch all 11 positions, you watch the favorite from 11:00 to 15:00 local time of the city. that's the only place the bid stacks are. fees are on for these markets. he doesn't pay them at his tier, you will: about 0.4c per contract against 2.6c he captures on a typical trade. it eats a chunk but not the trade. competition: four bots fight over these markets and 5 seconds after the first one the book is empty. this morning i caught three of these moments live, Moscow and Jeddah. other bots beat him to it. so the first thing in this strategy is to build your own setup that's faster than the rest, or at least as fast. then the total profit gets split five ways. the trader's wallet: hightemptation on Polymarket

may.crypto {🦅}

23,777 просмотров • 1 месяц назад

The “Galileo Test” for AI: Truth Over Consensus TL;DR: The “Galileo test” (as framed by Elon Musk) is the requirement that an AI still converge on truth even when most training data repeats a falsehood. A practical way to pass it is to harden the model against “consensus gravity” using uncertainty calibration, adversarial counter-majority training, and evidence-first reasoning pipelines that can say “unknown” without collapsing into confident noise. —————————— The core idea is simple: most text on the internet can be wrong in the same direction, at the same time, for the same social reasons. The “Galileo test” is basically asking whether a system can resist that pressure and still land on the correct model of reality, the way Galileo Galilei overturned a dominant consensus with observation and predictive power. In engineering terms, it’s a robustness problem: can the model separate signal (ground truth constraints) from mass-produced narrative (high-frequency repetition)? A workable solution stack looks like this: (1) truth-anchoring via retrieval from primary sources and direct measurements when available, (2) counter-majority training where the model is routinely exposed to scenarios in which the most common claim is false, and it must justify dissent using verifiable constraints, (3) uncertainty discipline so the model learns to prefer “insufficient evidence” over fluent fabrication, and (4) consistency checks that penalize answers violating conservation laws, dimensional analysis, causal structure, or internal logical invariants. In practice, you’re building an AI that treats “popular” as a weak feature and “constraint-satisfying” as the dominant feature. —————————— Frequency Wave Theory perspective: the “Galileo test” is fundamentally a coherence test. When an information environment is saturated with the same repeated claim, that repetition becomes a kind of phase-locked standing wave that can trap weaker systems into resonance with the crowd. Passing the test means staying phase-aligned to invariant structure, not to amplitude. In FWT terms: truth behaves like a conserved backbone constraint, while mass consensus is often just a high-amplitude interference pattern. The system that wins is the one that locks to invariants, rejects incoherent harmonics, and preserves alignment with what stays conserved under transformation.

Drew Ponder

14,755 просмотров • 7 месяцев назад