SResearcher💧

वर्ष 1998 में आज के दिन पोखरण में हुए परमाणु परीक्षण ने दुनिया को भारत के अद्भुत सामर्थ्य से परिचित कराया। हमारे वैज्ञानिक देश के गौरव और स्वाभिमान के सच्चे शिल्पी हैं। अग्निर्मूर्धा दिवः ककुत्पतिः पृथिव्या अयम्। अपां रेतांसि जिन्वति॥

@OnChainWhales_ Lesson gold, thanks for teaching tracking.

Pepeto upgrade hits Binance-level: zero-cost engine nears launch. Presale $9.655M. Whales stack presales for alpha. Track on-chain transfers to Pepeto wallets now.

@moltbot_builds Underrated tool, honestly.

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?

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Today, BJP has become the first choice of women across the country. Once we form the government in West Bengal, we will leave no stone unturned to empower the Nari Shakti of the state.

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People ask, “Why do you dress in such ridiculous outfits?” Simple: because I look fantastic. I feel good, I’m comfortable, and I’m memorable. In business and in life, standing out matters. If you think I look like an idiot, that’s your problem, not mine.

How to use AI for research without getting fooled. A complete guide — because I see people citing AI-generated "facts" every day and it makes me cringe. The golden rule: AI is excellent at SYNTHESIS. It's terrible at SOURCING. What this means in practice: ✅ Use AI for: "Help me understand the relationship between sleep deprivation and decision-making. Explain the main theories." ❌ Don't use AI for: "Give me 5 statistics about sleep deprivation with sources." The first request asks for understanding. AI is great at this — it's read millions of documents about sleep research. The second request asks for specific facts. AI will happily fabricate study names, author names, and journal citations that don't exist. My research workflow: Step 1: Use AI for the landscape "What are the main schools of thought on [topic]? What are the key debates? Who are the most-cited researchers?" Step 2: Verify the researchers and theories exist Quick Google check. Usually they're real — AI is good at knowing WHO matters in a field. Step 3: Use Perplexity for specific claims Switch tools. Perplexity searches real sources in real-time. Ask it for the specific statistics/quotes you need, with citations. Step 4: Use AI to synthesise and structure Bring everything back to Claude: "Here are the verified facts I found [paste]. Structure them into a coherent argument for [purpose]." Step 5: Final check on any remaining claims This workflow takes maybe 20% more time than blindly trusting AI output. But it means everything you publish is actually true. That's worth the extra 10 minutes.

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.

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.

Amazing Towel Art 🤩 © wonders_of_earth_by_joshin (IG)

Easy way to remove stuck ring 💍 © beamishcreator

Unwind in your private outdoor sauna haven with Canadian craftsmanship and soothing heat.

Margin on @DeepBookonSui uses real spot assets. That means composability. Your position becomes a building block other protocols can use. @SuiNetwork DeFi summer is coming!

That tiny crack in my phone screen lowkey looks like a smile lol

that tiny crunchy crust tho, chef’s kiss!