ZaibatsuBTC @menessential1

🚨BREAKING: GEN Z ARE NOW THE POOREST AMERICANS IN THE HISTORY OF THE US From the time they were born, inflation has eaten HALF of the dollar's purchasing power, on paper In reality, house, gas, and food prices today are 2-3 times as expensive as 25 years ago While median income merely covered inflation New generation is in big trouble

The $260M Axe Compute contract is not just signed. It is in active deployment. AGPU confirms payments are flowing and Aethir is provisioning the cluster. $43 million confirmed payment signaling deployment is in motion.

@AxeCompute Nice validation for the build program

Axe Compute (NASDAQ: $AGPU) has received $43M in prepayment against its $260M enterprise GPU contract. This is the first contracted cash milestone under the agreement and confirms the build is underway and on track for Q3 2026 deployment. CEO Christopher Miglino - We are committed to continued execution and delivering on schedule, and proving that Axe Compute’s build program does what we said it would do. There are many other prospects for large-scale deployments and we are doing our job to get them launched - Full release: investors.axecompute.com/news-releases/…

$LUNC Made a 80% Surge in the Past Month, but is That the End? $LUNC has surged 80% over the past month, delivering strong gains for Terra Luna Classic holders. However, the token has fallen by almost 30% over the past two weeks. Per current metrics obtained from Coingecko: • Price: $0.00007716 • Market Cap: $429.9 million • 24h Trading Volume: $33.1 million With the current bearish sentiment across the crypto market, is this the end of $LUNC surge?

@BaronAxelrod_ Commodities gonna pump on this.

China cutting tariffs after the Trump-Xi summit? This reeks of desperation masked as diplomacy. Those minor farm market tweaks won't stabilize anything meaningful in global supply chains. Commodity prices might flicker, but the real fractures run deeper than any handshake can mend. Skeptical minds see through this theater.

The beauty of Ruyi Bridge in Taizhou, Zhejiang China. It's made up of three bridges across the Shenxianju Valley and it features a glass-bottomed walkway.

@KashiCalls What exact levels Sensei?

Bitcoin's flashing that liquidity hunt pattern: consolidating below key levels with pools stacking above. Per recent analysis, this screams volatility after grabbing those stops first. Lived it in 2022 bear – same setup, held through fakeouts, shorted the hunt down, flipped on reversal for 5x PnL in days. Discipline turned pain into gains. Watch highs tight, degens. Directional blast next. Risk smart.

$AGPU just closed a $260M enterprise contract — 2,304 NVIDIA B300 GPUs, dedicated, U.S.-based, deploying Q3 2026. Enterprise AI buyers are done waiting. They want dedicated infrastructure, their hardware, their location, their terms. That is exactly what Axe Compute delivers. $AGPU #AI #neocloud #EnterpriseAI microcaps.com/axe-compute-se…

Majestic peacock 🦚✨

Wall repair roller

Anthropic locked down Claude Mythos, saying it's too dangerous for public use. But what makes an AI "dangerous"? It boils down to a few key capabilities: 1. Autonomous Web Access: Mythos uses tools like Browserbase to access the entire web without human intervention. This means it can gather info, make decisions, and execute tasks independently. 2. Complex Reasoning: It can analyze vast amounts of data and identify patterns or vulnerabilities that humans might miss. Think identifying security loopholes or manipulating markets. 3. Advanced Social Engineering: Mythos can craft highly convincing and personalized scams or propaganda, making it difficult to distinguish from genuine communication. 4. Code Generation & Execution: It can write and execute code, allowing it to automate malicious tasks or create new exploits. How do you guard against this? Implement strict access controls, monitor AI activity, and develop robust defenses against social engineering. Is AI safety just a game of cat and mouse?

BITCOIN RAINBOW CHART. TWO ZONES THAT MATTER. Blue: DCA. Purple: all in. Red: sell. 2015, 2019, 2022: blue zone. Biggest returns followed. 2018, 2021: red zone. Tops followed. We're approaching the ALL IN ZONE. The extremes are where the money is made.

@MRRydon @AethirCloud ngl this is the best use case I've seen

I built a CMO agent on AethirClaw (thanks @AethirCloud!) - and walk you through the entire process below Few tips from the build: → Found a great Twitter API provider for non-technical users. $10 gets you more than enough credits to last a long time. → Used RSS feeds to pull live data from Hacker News and Reddit - completely free. → Youtube transcripts are a goldmine. → Everything you see in the video was generated in a single shot by the AI. One thing I'll be honest about: don't just ship what the agent gives you raw. The real value comes when you treat the output as a first draft and put in the work to refine. The better your inputs and the more you iterate on the reports, the sharper the results get. Full walkthrough in under 8 mins 👇

Grok is officially taking over the 𝕏 algorithm next week 𝕏's Head of Product (Nikita Bier) just announced they’re are unleashing the "full power of Grok," calling it the most important change they've ever made to the platform This could be the biggest leap in social media AI we've seen yet - shifting from standard engagement farming to a truly AI-curated, multimodal timeline Prepare for a completely different feed… Powered by Grok...

@learn_ai_daily What does better ean is literally the whole research question in one sentence

You can run self-improving AI experiments on a single GPU while you sleep. Here's the exact setup to turn your home hardware into an automated research lab: Step 1: Hardware check You need minimum 24GB VRAM. RTX 4090, RTX 5090, or any workstation GPU. Consumer cards work fine — you don't need a DGX. Step 2: Install the autoresearch framework Clone Karpathy's autoresearch repo. It orchestrates the entire loop: propose experiment → run training → evaluate results → keep or discard → repeat. Step 3: Pick your base model Start with a small open-weights model. Llama 3.3 8B or Qwen 2.5 7B. Smaller = faster iteration cycles = more experiments per night. Step 4: Define your search space The framework needs boundaries. Tell it what to mutate: learning rate ranges, architecture tweaks, data mixture ratios. Tighter bounds = faster convergence. Step 5: Set your eval function This is critical. The system needs to know what "better" means. Perplexity on a held-out set is default. But you can add custom evals — code execution, reasoning benchmarks, whatever matters to you. Step 6: Launch and sleep One command starts the loop. Your GPU runs 50-100 micro-experiments overnight. Morning: check logs, see what worked. The real insight: you're not training one model. You're searching model-space automatically. Each night compounds. Corporate labs have done this for years with thousand-GPU clusters. Now the same methodology fits on your desk. What would you optimize for first?