SEffect💧

One VPS. Five Hermes agents. One folder that says who can touch what. The setup: /root/vps-agents > docs > rules > runbooks > env map > agent registry > restart commands > backup notes > no raw secrets /srv//data > .env > config > SOUL.md > memories > skills > cron > sessions > logs Start in 4 levels: 1. One Hermes agent personal assistant, Telegram or CLI, real daily work 2. Direct specialists SEO, dev, CMO, outbound, life 3. Orchestrator + specialists one front door, routed tasks, summaries back to you 4. Scheduled team weekly reports, inbox scans, backup checks, content runs Add the orchestrator after the specialists prove they are worth keeping.

@JustLittleGain 比预期好，但估值已高，别追。

星巴克财报炸了：EPS、营收双超预期，上调全年指引！ 盘后股价暴涨，反转计划势头猛。美国同店销售反弹是亮点。 乐观归乐观，盯住成本和中国区表现。利润扩张才是王道。

Want to make accurate market predictions like the OpenClaw model? Here's how to replicate its success on $MYSTERY: 1. Data Ingestion: Use a real-time financial API (Polygon .io is solid) to pull $MYSTERY price data. Store it in a time-series database like TimescaleDB. OpenClaw needs clean, structured input. 2. Feature Engineering: Calculate technical indicators (RSI, MACD, moving averages) using a library like TA-Lib. These become your features for the model. Don't skip this step; it's crucial. 3. Model Selection: Start with a transformer model. The OpenClaw team is known to fine-tune GPT-5.4 Pro on financial data. A smaller, faster option is Gemini 3.1. 4. Training Data: Create sequences of historical data (e.g., 30 days) as input and the subsequent day's price movement as the target. Label data for classification (up/down/sideways). 5. Training Pipeline: Use a framework like TensorFlow or PyTorch. Implement early stopping and regularization to prevent overfitting. Monitor validation loss closely. 6. Backtesting: Rigorously test your model on historical data *before* deploying it live. Use metrics like precision, recall, and F1-score to evaluate performance. 7. Deployment: Deploy your model on a cloud platform (AWS, GCP, Azure). Set up an API endpoint for real-time predictions. 8. OpenClaw Integration: Connect your prediction API to your OpenClaw agent. Use the 'resolver' module to interpret the model's output and trigger automated trading decisions. Remember to set appropriate risk limits. 9. Monitoring: Continuously monitor your model's performance and retrain it periodically with new data. Financial markets are dynamic; your model needs to adapt. Use the SOUL.md file from gbrain v0.10.0 to guide your monitoring strategy. 10. Refinement: Experiment with different features, model architectures, and training parameters to improve accuracy. The OpenClaw team likely iterates constantly. Multi-user ACL implementation is critical if you're building a team around this. What other financial instruments are you planning to predict?

Serious comfort? Yes please. on! PLUS™ nicotine pouches are available now.

This might be the most detailed Moon image ever captured 1000 frames stacked using a Nikon Z8 and Takahashi TSA-120 telescope, producing a stunning 40MP masterpiece

The Google and Caltech quantum papers demonstrated 2 breakthroughs: that Bitcoin cryptography is much easier to break than previously thought, and that far fewer logical qubits may be necessary for physical qubits. Project Eleven CEO @apruden08 explains: "These two papers are not necessarily about a quantum computer that's bigger or more capable. They're about what it takes to break cryptography." "So what changed? One of the things that changed was that physicists and quantum cryptographers that looked at this problem for a long time studied an algorithm called RSA — an older cryptographic algorithm." "But that's not what really any blockchains use, because RSA keys are very large. It turns out, and this was one of the key upshots of the Google paper, that if you focus on the cryptography used by Bitcoin, Ethereum, and other networks, it's actually way easier to break than they thought it was, compared to RSA." "The other big breakthrough, and this is from the Caltech paper: Quantum computers are very fragile, generally. So to be useful, they need to have what's called error correction applied. And that can result in a lot of overhead. You need to have tons of physical qubits to get to one logical qubit." "This Caltech paper basically showed, 'Hey, we have some new ideas for error correction. And it turns out if we apply those, we don't need hundreds or thousands of physical qubits, maybe we just need a handful to make one logical cubit.'" "The headline of their paper is 'You may only need 10,000 physical qubits to run Shor's algorithm.' And by the way, they demonstrated 6,000 last year."

President Trump: “The U.S. Military is building a massive complex under the ballroom, which has come out recently only because of a stupid lawsuit that was filed — the ballroom essentially becomes a shed for what’s being built.” The Presidential Emergency Operations Center (PEOC) is a fortified, five-story underground bunker beneath the White House East Wing, designed to protect the President and staff during nuclear strikes or major crises. Originally built for FDR and updated over the decades, it serves as a secure command center with its own life support. rumble.com/v77tn6k-the-u.…

🚨 Just wrapped my interview with JD Vance. This one was INSANE. Breaking news incoming. You don’t want to miss it….

USDC is coming soon to @pharos_network! → Gain access to the world’s largest regulated stablecoin on Pharos → Use USDC as a native trading, payment, and collateral asset across Pharos’ RealFi applications

@MRRydon @AethirCloud Not feeling this one tbh

Sharing the latest @AethirCloud ecosystem updates! Covered A LOT - major developments with Aethir + OpenClaw 🦞, details on our new B300 mega cluster, and several other key updates. This is the “human-edited”version. I also had my agent Jarvis edit the same raw footage 🧵 - who did better??

Karoline Leavitt: If the Iranian Regime does anything to stop the flow of Goods and/or Oil through Straight of Hormuz, they will be hit 20x harder by the worlds most powerful military rumble.com/v76x82m-irania…

SOMEONE CALLED ANTHROPIC THE NEW APPLE. SOMEONE ELSE SAID HOLD ON, LET'S NOT DISRESPECT APPLE LIKE THAT. WHAT DO YOU THINK?

In a prediction market, you can buy positions on real-world outcomes. Market prices reflect the crowd’s collective judgment, and you can adjust your trades as new information emerges.

In a prediction market, you can buy positions on real-world outcomes. Market prices reflect the crowd’s collective judgment, and you can adjust your trades as new information emerges.

I find AI tools for a living. These 7 are genuinely free and genuinely good. Not "free trial." Not "freemium with a useless free tier." Actually free. 1. Remove.bg — Background removal Drag any image → perfect cutout in 2 seconds. I use it 5-10 times per week. Yes, Canva does this too, but Remove.bg is faster and more accurate on complex edges (hair, transparent objects). 2. Cleanup.pictures — Object removal Paint over anything in a photo → it disappears. People, text, watermarks, power lines. Unreal quality for a free tool. No account required. 3. Whisper (OpenAI) — Transcription Best speech-to-text model available. Free to run locally. Handles accents, technical jargon, and background noise better than any paid alternative. If you're paying for Otter.ai or Rev and you're even slightly technical, switch to Whisper. 4. Phind — Developer search Perplexity but specifically for coding questions. Searches docs, Stack Overflow, GitHub issues simultaneously. Gives you working code, not decade-old answers. Free, no account needed. 5. Gamma.app — Presentations Describe what you want → get a complete, well-designed deck. It's not going to replace a designer for investor decks, but for internal presentations, workshops, and quick pitches? 10 minutes instead of 2 hours. 6. Suno — Music generation Describe a song (genre, mood, lyrics optional) → get a full 2-minute track with vocals. The free tier gives you 50 credits/day. Quality has improved dramatically — some outputs are genuinely listenable. 7. NotebookLM (Google) — Document analysis Upload PDFs, docs, websites → chat with them. The podcast feature (auto-generates a 10-minute discussion about your documents) is the sleeper hit of 2025. Completely free. No limits I've found. Bookmark this list. I'll update it quarterly. Every time someone pays for a tool that has a free alternative doing the same thing, a product manager gets a bonus they don't deserve.

@learn_ai_daily I was stuck thinking AI was just hype. Excited to refine my approach now!

I spent 6 months teaching non-technical people to use AI. Here are the 5 stages everyone goes through. Knowing them will save you weeks. Stage 1: "This is magic" (Day 1-3) You ask ChatGPT something. It responds intelligently. You're amazed. You tell everyone. You think AI will solve everything. Stage 2: "This is useless" (Week 1-3) You try to use it for real work. The output is generic. It makes stuff up. It doesn't understand what you want. You conclude AI is overhyped. This is where 70% of people quit. Stage 3: "Oh, the problem is ME" (Week 3-6) You learn about prompting. You realise vague inputs = vague outputs. You start being specific. Results improve dramatically. You feel stupid for not figuring this out sooner. Stage 4: "I have a system" (Month 2-3) You develop go-to prompts for recurring tasks. You know which model to use for what. You've built templates. You're saving real time — 5-10 hours/week. Stage 5: "This is a multiplier" (Month 3+) You stop thinking of AI as a tool and start thinking of it as a team member. You delegate first drafts, research, analysis. You focus on editing, strategy, decisions. The key insight: Stage 2 is not evidence that AI doesn't work. It's evidence that you haven't learned to communicate with it yet. Every expert I know went through the same valley of disappointment. The difference between people who give up and people who succeed? 3 more weeks of practice. That's it. If you're in Stage 2 right now — push through. Stage 3 is right around the corner.

@learn_ai_daily This is incredible. What was the biggest challenge in the first two weeks?

My student went from 0 to $12k/month in 6 weeks. Here's the exact sequence: Week 1: Learned the fundamentals of AI video generation. Kling, Veo, prompt structures. Could create 5-10 good videos per day manually. Week 2: Hit the wall. 5-10 videos/day across 2 accounts = ~100 views total. Felt like wasting time. Week 3: I introduced the mass content platform. Set up the first automated workflow. Generated 150 videos in the first session. Week 4: Refined the workflow. Dialed in the hooks, niche, and product selection. 200+ videos/day across 30 accounts. Week 5: First viral hits. 3 videos broke 500k views. Affiliate commissions started flowing. $3,200 that week. Week 6: System optimised. $12k for the month. Working 45 minutes per day (reviewing outputs and tweaking workflows). The inflection point was Week 3. Not because she learned something new about AI. Because she learned about SCALE. The platform that enabled this transition is the same one I recommend to every student who's stuck at the "5 videos/day" ceiling. Like, rt & comment "SEQUENCE" and I'll send you the platform.