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Phase core ☆

8,725,916 просмотров • 11 месяцев назад •via X (Twitter)

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⚽ Football Farm Testnet Update & Gameplay Vision The Football Farm Testnet is progressing strongly 🚀 The community is active, the system is stable, and no major issues have been reported so far. This phase allows players to explore the game while contributing to the ecosystem through real feedback. ⚙️ How It Works • Every user receives a free football player upon registration • Players can be trained in 4 core attributes: → Speed ⚡ → Technique 🎯 → Intelligence 🧠 → Power 💪 • Each training session costs 25 Energy • Energy can be replenished by watching rewarded ads 📈 As players improve, their market value increases — calculated in GCM Coin, the ecosystem’s native currency ⭐ For faster progression: Pro Players are available (limited to 3 per user to maintain balance) 🏆 Future Vision After the KYC phase: • Highly trained players will be listed on the marketplace • Diamonds 💎 earned from referrals can be used to purchase teams 🎮 These teams will compete in the upcoming Goal Chain Manager 3D Metaverse leagues 📱 Launching April 4 on the App Store & Google Play! 🌱 In simple terms: Football Farm → Develops players Goal Chain → Builds the ecosystem Goal Chain Manager → Powers the competition ⚽⛓️ The future of blockchain football is being built step by step — with the community at its core. #GoalChain #FootballFarm #Web3Gaming #GameFi #BlockchainGaming #GCM

Goal Chain Manager

17,114 просмотров • 3 месяцев назад

$AISI | WEEK RECAP & WHAT’S NEXT 🚀 The past week was about locking foundations. This week is about showing the world what we’ve built. Last week - what’s done: • Worked closely with high-end production teams across Europe, filming premium advertising campaigns. Backstage moments & sneak peeks coming soon. • Banking partners gave the green light to all core app functions. Partner announcement is now being prepared. • Final touches on the AISI app are completed, app previews are ready to be shared. • Hosted a strong founders livestream with great energy and engagement. • Joined an excellent guest livestream at MCG thanks again for the warm welcome. • Produced multiple AISI app trailer videos, now circulating in advertising pipelines. • A lot of behind-the-scenes work you haven’t seen yet, but you’ll feel it very soon. This week - what’s coming: • Two livestreams – A cozy founders livestream with app sneak peeks and a deeper breakdown of the launch plan. – A special livestream with a guest from the banking side, sharing real-world perspective. • App preview drops, real screens, real flows, real features. • Multiple long-form articles, including a founders interview and potential guest podcast appearances. • Continued rollout of marketing content across channels as we enter the public launch phase. We’re moving from building quietly to speaking loudly and proudly, with substance. This is the phase where we tell the world what AISI is, before the app launches and redefines how Web3 banking feels. Help us spread the word. The next chapter is starting now.

AISI GROUP

18,484 просмотров • 5 месяцев 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

14,753 просмотров • 5 месяцев назад

Facial reconstruction of a Hun man from Kokel, Tuva, dated to 200–400 AD. The Kokel cemetery in Tuva is divided into two parts: Ulug-Khem (Xiongnu period) and Kokel (post-Xiongnu, up to the 4th century AD). The Ulug-Khem phase represents core Xiongnu migrants who swarmed into Tuva and replaced the local Saka culture, while the Kokel culture represents a post-Xiongnu Hunnic population to which this man belonged. The people of the Kokel culture had a custom of burying their dead in larch log tombs. According to Alekseev and Gokhman (1984), paleoanthropological materials from burials in such tombs, dating to the last two centuries BC and the first three centuries AD, belong to the Hunnic period. Predominantly, these remains show long-headed and mesocranial skulls with broad and medium-high faces, flat, sometimes mesognathic facial structures of the East Asian type, similar to those known from the Hunnic kurgans of Noin-Ula. A slight Europoid admixture is undoubtedly present. A minor admixture of Far Eastern racial elements is also possible, corresponding with information found in Chinese written sources. This provides grounds to assert that the Huns of Transbaikalia were not direct descendants of the Scythian population, although some elements of that population may have been absorbed into their composition. As for the Europoid component among the Huns, it may have appeared either through contacts with the Scythian or Saka populations of earlier times, or as a legacy inherited from the Neolithic inhabitants of the Cis-Baikal region.

Ancestral Whispers

26,792 просмотров • 9 месяцев назад

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

255,784 просмотров • 23 дней назад

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22,919 просмотров • 1 год назад