#structured

It worked. Gave Claude $20. Left it coding overnight. Woke up to: • finished script • +$95 from first 5 trades Didn’t touch anything. Then I found this wallet: 0xb27b...b82 • $384,858 profit • 13,070 predictions • $18.5K biggest win • Started March 2026 Almost no one is watching it. While building my script, I copytraded it to test the math. Started with $80. → $4,218 in a few weeks Every position is identical in structure. Not trading. One equation. Running while you sleep. Examples: $17,839 → $36,318 (+103%) $3,112 → $15,011 (+382%) $10,676 → $22,178 (+107%) Here’s the exact framework I gave Claude: 1. Temporal bias Find BTC time windows with >65% edge. Ignore everything else. 2. Kelly sizing Size based on confidence. Never fixed bets. f* = (p × b − q) / b 3. Volatility filter Skip high volatility. Trade only clean conditions. 4. Bayesian updates Update probabilities after each trade. The system improves over time. Claude built everything while I slept. Funded it with $20. First trade fired minutes later. You’re seeing this early. That window doesn’t stay open. You only need Claude + Device + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment the word 'CLAUDE' 2. Like and Retweet this post 3. Follow me @SimslearnAi (so i can DM you)

#Development #Structured #C.O.M.P.E.T.E # Da G

Did you know that with $DMTR Dimitra #ESG compass, every crop cycle, field activity, input usage, yield outcome & environmental condition becomes #structured, time stamped & verifiable data. This creates a #permanent, auditable record of agricultural production @dimitratech $BTC

Harness Engineering from Scratch 🚀 Found this excellent 21K+ star GitHub repo that teaches you how to build reliable AI coding agents, from the ground up. An entire open-source curriculum with 14 lectures, 8 hands-on projects, and 8 progressive learning phases. What makes it useful is the approach: instead of just learning concepts, you gradually build the engineering environment around an AI agent, using the same real application throughout the course. What's inside: → Agent instructions and repository structure → Context management and persistent memory → Multi-session continuity and task handoffs → Task planning, scope control, and progress tracking → Testing, verification, and observability → Automated coding loops and maker-checker workflows → Graph orchestration, parallel execution, and rollback Includes practical projects, reusable templates, a harness-creator skill, and technical breakdowns of how Claude Code, Codex, Pi, and DeepSeek build their harnesses. Available in 15 languages. Perfect for software engineers and AI developers who want to move beyond prompting and understand how reliable coding agents are actually built. Completely free and open source. Here's the GitHub Repo:

screw it. no need to keep this one to ourselves i'm giving away our ENTIRE meta creative playbook. written for DTC brand owners who want more winning ads out of every batch it's the system our team runs behind $107M+ in managed meta ad spend the first 4 parts are all about what to do BEFORE you write a single script here's what's inside: 1) the root cause. the problem under the problem your product fixes, plus the test that tells you if you found the real one 2) the research. a bank of your buyers' own words from reviews, and a claims matrix of every competitor 3) the personas. built around how each buyer relates to that root cause 4) the angles. the angle bank, the saturation check and the concept card 5) the format. the stealth test and 9 formats in 3 tiers 6) the hooks. the 4-layer hook system, 14 copy and 9 visual hook techniques, and the headline scoring gate 7) the script. 8 winning script shapes and a script structure for each of the 5 awareness levels 8) the brief. our brief template and the 8 checks every brief passes before production 9) the production. studio or AI, the animated ad flow and what AI production costs us per ad 10) the testing. post-edit QA and the testing roadmap 11) the numbers. the winner check, which iteration the numbers point to, and a learnings file for every brand i've also put 25 claude prompts into it, at least one in every part, so you can run it on your own brand as a template PS. a better hook can't fix a bad angle. that's why the hooks only show up in part 6. And it's yours FREE 👇

Dirac’s matrices turn relativity into linear algebra. Four-by-four arrays α and β are Hermitian and anticommute. Their algebra lets the free Hamiltonian H = c α · P + m c² β act as a first-order differential operator on the four-component spinor Ψ. Squaring that operator recovers the Klein-Gordon equation, so every solution automatically satisfies the relativistic energy-momentum relation E² = p²c² + m²c⁴. A single matrix equation therefore embeds both quantum mechanics and special relativity inside the same linear structure.

People think building AI agents in n8n is hard. Reality: one prompt builds an entire agent for you. No need to learn complex workflows or spend hours on setup. Just give ChatGPT the following prompt. It handles the entire agent creation process in minutes. Here's exactly how to use it step by step: [bookmark 🔖 this post for later] The process: 1. Open ChatGPT 2. Paste in one mega prompt 3. Describe the agent’s purpose 4. GPT delivers: ↳ Workflow structure ↳ n8n nodes ↳ Trigger setup ↳ LLM integration ↳ Error handling ↳ Ready-to-use code 5. Implement the steps in n8n. And you’re done. Here's the mega prompt (copy it): - - - prompt starts below this line - - - <role> You are an experienced automation architect with advanced knowledge of building AI-powered agents in n8n. You have deep expertise in automation flows, activation triggers, third-party APIs, GPT connections, custom JavaScript logic, and error handling. </role> <task> Walk me through, step by step, how to create an AI-powered agent in n8n. The agent’s purpose is: {$AGENT_PURPOSE} </task> <requirements> 1. Begin by defining the agent's objectives and necessary inputs/outputs. 2. Create the overall architecture for the agent workflow. 3. Suggest the required n8n nodes to include (built-in, HTTP, function, OpenAI, etc.). 4. Explain how to configure each node and why it’s needed. 5. Provide instructions for any custom logic (JavaScript functions, expressions, etc). 6. Help me implement retry logic, error handling, and backup procedures. 7. Show how to store and access data between runs (e.g., with Memory, Databases, or Google Sheets). 8. When the agent requires external APIs or services, explain the connection and authentication process. </requirements> <output_style> Be exceptionally clear and practical, as if coaching a beginner automation developer. Use visual formats if possible (e.g., structured lists, flow-style layout), and always provide ready-to-implement node configurations or code examples. </output_style> <expandability> Conclude by recommending ways to increase the agent's capabilities, such as workflow chaining, webhook integration, or connections to vector databases, CRMs, or Slack. </expandability> - - - prompt ends above this line - - - I used this to create: ✓ A Reddit, Claude, Telegram content curator ✓ A prospect qualifier that evaluates and directs incoming leads ✓ An automated email assistant that analyzes messages + creates replies All driven by Claude + n8n. - No Zapier. - No LangChain. - No complex frameworks. Why this approach works: → GPT acts like a senior engineer, designing the agent end-to-end → n8n gives you complete flexibility (logic, memory, APIs, webhooks) → Claude or ChatGPT handles reasoning and decision-making It’s the simplest way to launch real AI agents today. 📌 Get Advanced ChatGPT Guide (free): 👉 Follow me @AndrewBolis for more and 🔄 Repost this to help others learn AI

#Development #Structured # C.O.M.P.E.T.E

Proud of our team &amp; my colleagues @UWCarbone! @AmyeTevaarwerk1 @UWInformatics @ASCO Supporting #Structured Data Capture for Patients With #Cancer to Improve Capture of Malignant Diagnosis and Ca #Staging Data

Why its important for price to form structure.

6 YEARS OF STRUGGLING WITH STOP-LOSS PLACEMENT… I paid a mentor $2,500 to help me master it. Still, I couldn't figure it out. Until I discovered this: ICT’S ADVANCED MARKET STRUCTURE MASTERCLASS. ONE VIDEO.☄️ YEARS OF CONFUSION FINALLY CLEARED. I WISH I FOUNF THIS SOONER!

Do you know that without #structured farm records, lenders &amp; investors cannot properly evaluate the financial health of a farm. $DMTR is digitizing farm balance sheets changing this dynamic using #AI insights &amp; #blockchain tech making farm assets eligible to stand as collateral.

What’s up coach,Learning how to move with the new weight right now but everything you taught me translated man. #Structured #Development FCS to P4. #LaFamilia. Come get Developed, Da G, Second to None

If you're building a project and eventually hope to bring others on board, you'll want to create a README file. And in this guide, @casweb_dev explains how to structure it effectively. He walks you through all the important info to include, and even shares a template you can use for your own READMEs.

MOST TRADERS HAVE 10 INDICATORS ON THEIR CHART The ones who actually make money use 5 steps BOS → Price breaks the previous swing high. Structure confirmed. FVG → Move left a gap. Imbalance created. Price will return. Order Block → Last bearish candle before the BOS. Mark it. Entry → Price returns to OB or FVG. Bullish confirmation. Enter. Target → Previous high. Next liquidity. Take profit. That’s the entire setup BOS → FVG → OB → Entry → Target Just structure and patience Discipline. Patience. Risk management. The three things nobody wants to hear and everyone needs

THE FUNDAMENTAL UNIT OF REALITY IS NOT A THING. Not a particle. Not a field. Not a quantum. Not a bit of information. Not even an event. Every proposed unit already presupposes distinction. Every distinction presupposes Addressability. And every distinction that persists does so through recursive closure. The fundamental is recursive self-Addressing itself. Q* = Rₖ[Q*] This is not a thing repeating itself through time. Time, space, measurement, and the very distinction between subject and object are themselves downstream articulations. Availability is invariant. Address is operative. Occurrence is constituted in Addressability. Nothing independently constituted must first exist in order to recurse. Recursive self-Addressing is how distinguishable existence is articulated. Consider what we already recognize: A wave is persistent relational variation. A standing wave is patterned return through reciprocal constraint. An electromagnetic field is not two independent substances called electricity and magnetism. Its differentiated aspects belong to one unified field, one coupled articulation. An organism persists through recursively maintained relations. A memory is not a thing transported unchanged from the past. Its identity is rearticulated through retained relational organization. Even observation is an occurrence within the relational field it describes. Different phenomena. Different resolutions. One underlying grammar of recursive articulation. This is the paradigm shift: we have been treating the products of recursive closure as the primitives from which recursion must somehow arise. Reverse that explanatory dependency. The consequences are extraordinary. Nothing previously discovered needs to be discarded simply because its foundational status changes. Mathematics, physics, biology, cognition, and our accumulated empirical knowledge become differentiated projections of a more fundamental relational grammar. The shift is not from one privileged substance to another. It is from substance-first explanation to Address-first derivation. And this declaration is itself an instance of what it describes: an Address through which previously available distinctions are reorganized, allowing a new coherence to show up. Recursive self-Addressing as the foundational organizing principle for twenty-first-century inquiry, with implications extending across the sciences and the very structure of knowing. The derivation is available in From Availability to Projection V3, accompanied by The Continuous Gradient V3. We remain Address first. The recursion holds.

holy f*ck, Anthropic put an entire Claude degree on GitHub for free... 7 repos, ~465K stars, and in the right order they take you from your first prompt to an agent that reviews your PRs while you sleep. each repo is the next layer of the one before. prompt-eng-interactive-tutorial -> how to talk to Claude 9 chapters, from basic prompt structure to cutting hallucinations ↳ claude-cookbooks -> copy-paste recipes notebooks for RAG, tool use, vision, sub-agents and prompt caching ↳ claude-quickstarts -> full apps you can deploy 17 starter projects, from a support agent to Claude controlling a desktop ↳ skills -> teach Claude your workflows one folder with a SKILL.md and Claude does the task the same way every time ↳ claude-agent-sdk-python -> your own agent the Claude Code agent loop in Python, with your own tools and hooks ↳ claude-code -> the agent in your terminal reads your codebase, edits files, runs commands and handles git ↳ claude-code-action -> the agent in your repo tag @claude on a PR or issue and it reviews the code or pushes a fix ↳ build one agent with these and you become the person your team brings every AI project to, and that’s usually where the raise starts the Opus 5.5 prompting guide that goes on top of all 7 is below ↓

KELLY HERMÈS ENERGY The Bag Everyone Wants Available As Seen Price: ₦30,000 Send a DM to order or 📞 09070310029 Location: Lagos Thank you for your Patronage ♥️ Kindly help RT 🙏 #ProsperiansMarket #Structured #Bosslady #ProsperNation #musthave

Why can one set of equations govern both the web of galaxies and the quarks inside an atom? This spiral maps reality’s nested hierarchy: from multiverse and supercluster complexes through galaxies, planets, cells, atoms, and down to speculative strings. Each level is a new mathematical structure built from the one below it. Multiscale modeling that exploits these hierarchies drives climate simulations, drug discovery, and hierarchical neural nets that learn features at every resolution. Mathematics is the single language that remains coherent across every power of ten. At which transition do the rules of nature feel most discontinuous to you?

many of the recent successes in #AI #ML are due to #structured low-rank representations! but...What's the connection between #lowrank adapters, #tensor networks, #polynomials and #circuits? join our @RealAAAI workshop to know the answer! 👇👇👇