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not all crypto rewards are created equal. when a platform advertises yield, i think the first thing to check is what holders actually receive and where that value comes from. @RebateLaunch has an interesting approach to this. instead of paying holder rewards in newly issued $rebate, eligible holders receive payouts in the pair asset. on robinhood chain, that's usdg. the platform reports: → $28.5k distributed to holders. → 120,403 individual payouts. → rewards sent several times throughout the day. → no staking or manual claiming required. but the reward distribution is only one part of the model. rebate pad also uses a declining launch tax to address early sniping. → 99% during the initial stage. → 66% in the next stage. → 33% in the following stage. these penalties go into a locked treasury that supports the buyer bonus mechanism. separately, the 0.7% platform fee is used to buy $rebate from the market and burn it. the reported cumulative burn has reached 35.3m tokens, representing 3.53% of the original 1 billion supply. what makes this model worth examining is how several mechanisms connect: ✦ early launch activity contributes to the treasury. ✦ the treasury supports buyer bonuses. ✦ trading fees fund holder payouts and the $rebate buyback-and-burn mechanism. my perspective is that paying rewards in the pair asset is an interesting distinction because holders aren't simply receiving more of the same token. however, attractive payout numbers alone don't prove sustainable yield. trading volume, fee generation, treasury health and long-term user activity still matter. i'd rather evaluate the source of rewards than focus only on the advertised percentage. also, round 3 of the creator campaign closes november 17 at 22:00 utc. the real question is whether rebate pad can maintain this economic cycle as adoption grows. $rebate

Safety evaluations are critical for understanding whether a model is ready to deploy, which safeguards to build for it, and where to focus training and research. On our Engineering Blog, we offer 10 recommendations for how to design, run and report them.

50 Sites That Show What Billionaires Buy and Read for Free🤯 1.) → Current portfolios of famous investors 2.) → Quarterly fund trades 3.) → Stock transactions of US politicians 4.) → Executives' own company stocks 5.) → Making fund filings readable 6.) → Companies' official reports 7.) → Buffett's letters since 1977 8.) → Video archive of Buffett meetings 9.) → Howard Marks' investor memos 10.) → Research from Ray Dalio's firm 11.) → Dalio's principles for free 12.) → Jeremy Grantham's market notes 13.) → Quantitative fund research 14.) → Damodaran's valuation data 15.) → Valuation professor's blog 16.) → Morgan Housel's money writings 17.) → Mental models for decision-making 18.) → JPMorgan's free market guide 19.) → Innovation-focused fund research 20.) → Venture capital essays 21.) → Value investing lecture notes 22.) → Columbia's value investing archive 23.) → Fed's economic data archive 24.) → Fed decisions and statements 25.) → European Central Bank data 26.) → Reports from the central bank of central banks 27.) → Countries' economic indicators 28.) → Companies' long-term charts 29.) → Company financials for free 30.) → Companies' market cap rankings 31.) → Stock screener and heat map 32.) → Portfolio backtesting 33.) → Charts and market analysis 34.) → Professional market screener 35.) → Company activity reports archive 36.) → SEC's investor education site 37.) → Passive investing encyclopedia 38.) → Financial terms dictionary 39.) → Free finance guides 40.) → CFA research publications 41.) → Fund and stock evaluations 42.) → Companies' visual analysis 43.) → Archive of selected investment theses 44.) → Seeing what's inside funds 45.) → European fund backtests 46.) → Investment writings with data 47.) → Market history notes 48.) → Investor psychology writings 49.) → Explaining the economy with charts 50.) ourfiniteworld? → (see note) The rich don't hoard information. They just don't tell you where it is. Save it, follow @Liyana_Coder you'll need it. Not investment advice. @Liyana_Coder

🚨 A 19-YEAR-OLD STUDENT TURNED $68 INTO $750K USING AI. No trading experience. No emotional decisions. No endless chart-watching. Just Claude Code, GPT-6 Astra, and 6 AI agents working together. I built a self-learning AI trading system that generated +$1,840.82 in just 48 hours. [ 𝐍𝐨𝐭𝐞: 𝐅𝐨𝐥𝐥𝐨𝐰 𝐌𝐞: @EaStEnRaY1 (𝐅𝐨𝐫 𝐢𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲 𝐚𝐮𝐭𝐨 𝐃𝐌)] Here's what makes it different: 🧠 6 AI agents monitor newly launched Solana coins. ⚡ The system analyzes buyer activity, connected wallets, selling pressure, and holder concentration. 🎯 Every launch is evaluated across 3 probability scenarios: - +60% reached first - -30% reached first - Neither happens within 60 minutes QUANT evaluates profitability after execution costs. GUARD filters out trades with unacceptable risk. And here's the crazy part: Even the trades it SKIPS become learning data. Every new signal helps the system improve its next decision. No panic. No FOMO. No emotional trading. Just AI-powered market analysis and execution. 🔥 NOW I'M GIVING AWAY MY EXACT CLAUDE CODE SETUP FOR FREE! Want to build your own AI trading system? I'm sharing the setup for the next 24 HOURS ONLY. 👇 HOW TO GET IT: 1️⃣ Like this post 2️⃣ Repost 3️⃣ Comment "CLAUDE" 4️⃣ Follow @EaStEnRaY1 so I can DM you the setup. Don't just watch AI change trading. Learn how to build with it. 🚀

Todo el mundo habla del "harness" de los agentes de IA y casi nadie explica qué es Una ingeniera de Google lo explica en 9 minutos y después arma en vivo un agente que se mejora solo. 1h35 gratis 0:00 Qué es un harness 1:40 Contexto y memoria 3:37 Herramientas y MCP 5:18 Sandbox y prompt injection 7:25 Cómo evaluar un agente 9:23 Agente que se mejora solo 1:00:04 ADK Optimize 1:10:41 Workflows con grafos Compártelo para que a todos les llegue este conocimiento

Resignation of Nvidia CEO has accelerated a cross party push in Washington to tighten US export controls on advanced AI chips bound for China. 🇨🇳 🇺🇸 Previously, every proposed tightening of chip export controls to China ran into some obstacle. Jensen Huang personally argued that excessive restrictions would simply hand the Chinese market to Huawei without achieving a security benefit. That argument rested on Huang’s credibility as the person Washington trusted to make honest representations about what Nvidia was actually selling, to whom, and through which intermediaries. Megaspeed episode shows existing controls are being systematically evaded through Southeast Asian shell companies.

SpaceX has just unveiled a massive new $50 billion Supercomputing Campus that they want to build in Cochise County, Arizona. All info: • Up to 6 GW across the 9 supercomputing buildings • Construction starts 2027 • Size of each supercomputing building: ~493,000 square feet. • Total supercomputing building space: 4.44 million square feet, excluding support facilities. • SSVEC residential customers are projected to see electricity bill reductions of 10–40%. • Up to 24 GWh of grid-connected battery storage to provide backup power, stabilize the regional electric grid, support local voltage, and absorb fluctuations in electricity demand. Among the largest planned battery installations in the United States. • Additional solar generation in the region is planned to supply renewable electricity during the day and reduce demand on the local grid. • No dedicated on-site power plant or fuel storage yard is planned. • Each supercomputing building will have 10 MW of natural-gas-powered standby generation for critical equipment, operating only during utility outages and periodic testing. • No gas turbines will be installed at the site. • SpaceX plans to fund $3.6 billion in grid infrastructure upgrades, transferred to AEPCO at no cost to its members. • Up to $260 million annually in additional benefits for cooperative members at full buildout, on top of residential bill reductions. • Up to $200 million annually for cooperative members statewide, in addition to SSVEC benefits. • The supercomputers will use dry cooling with a closed-loop water circulation system rather than evaporative cooling. • SpaceX projects zero ongoing industrial water consumption. • The cooling system will be filled initially, with water reused for up to 10 years. • SpaceX estimates annual water usage equivalent to approximately 20–30 Arizona residential homes. • Projected annual water use would be less than 2.5% of a single center-pivot irrigation system's annual consumption. • Permanent jobs: Approximately 1,000. • Construction jobs: Approximately 5,500. • Annual property tax revenue: Up to $103 million in estimated new annual revenue. • Regional economic benefits: SpaceX says its investment will support public education, infrastructure, essential local services, and economic development. • Local suppliers: SpaceX is encouraging suppliers to participate in construction and development of the campus. • Noise mitigation: Equipment will be positioned away from neighboring properties where possible, with setbacks and additional screening to reduce noise. • Nighttime lighting: Outdoor lighting will remain off unless work is underway, with active lights shielded and directed downward. • Dark-sky protection: The campus will comply with Cochise County's dark-sky requirements to limit light pollution. • Nighttime construction: SpaceX says it will seek waivers when nighttime construction lighting is necessary. • Wildlife protection: The campus is being designed with consideration for the region's sandhill crane wintering habitat. This campus's purpose is to build one of the world's most advanced AI supercomputing campuses to train SpaceX's superintelligence models and support scientific and engineering breakthroughs. Learn more:

Jensen Huang and the Nvidia board just made a massive mistake. He should not have resigned. The board should have taken the time to conduct a thorough investigation, consult with shareholders, and carefully evaluate the long-term implications of this decision. There is no clear replacement for Jensen Huang, and we believe his departure will ultimately be a net negative for Nvidia, $NVDA, and its shareholders. This will go down as the most costly mistake of the AI Revolution.