Crypto Goat

Claude Code x TradingView is the best AI trading quant of all time. Gone are the days of AI slop market analysis - AI is now better at technical analysis than you. Here's how you can turn Claude Code into your expert trading quant (in <5 minutes): Step 1. Ensure you have these requirements: • Claude Code - installed on your computer (this is what talks to TradingView) • Node.js 18+ - installed on your computer (the MCP server runs on this) • TradingView Desktop app - downloaded from tradingview.com/desktop • A valid TradingView subscription (paid plan for real-time data) Step 2. Open Claude Code and run the following prompt to connect the TradingView MCP: "Install the TradingView MCP server. Clone and explore github.com/tradesdontlie/…, run npm install, add to my MCP config at ~/.claude/.mcp.json, and launch TradingView with the debug port." Step 3. Health check Restart Claude Code, and paste this prompt: "Use tv_health_check to confirm TradingView is connected." If correctly connected, Claude Code should respond with a confirmation. Step 4. Start prompting Claude Code now has access to your ENTIRE TradingView environment Your charts, your technical analysis, alerts - everything. Use this prompt to turn Claude Code into your personal market analyst: "Act as an elite quantitative trader and technical analyst with full access to my TradingView environment. Analyze the current market structure for [ASSET] on the following timeframes: 5m, 15m, 1H, 4H, 1D. Use my existing indicators, drawings, and chart context to: Identify the current trend and market regime (trending, ranging, accumulation, distribution) Mark key support and resistance levels based on price action and liquidity Identify liquidity pools, stop clusters, and likely areas of manipulation Analyze momentum using RSI, MACD, and volume where available Detect any chart patterns (breakouts, consolidations, deviations, etc.) Evaluate confluence across timeframes Then provide: A clear directional bias (bullish, bearish, neutral) The highest probability trade setup right now Exact entry, stop loss, and take profit levels Risk-to-reward ratio Invalidation point (what would prove this analysis wrong) Finally: Explain your reasoning step-by-step in plain English. Avoid generic statements. Be decisive. If no high-quality setup exists, explicitly say “no trade” and explain why." This is an EXTREMELY powerful setup - make sure to save this post so you don't forget it.

Bitcoin just broke @benjamincowen bear market resistance band

WOW. All time highs. This is absolutely insane.

SMART MONEY DOESN'T ANNOUNCE ACCUMULATION. Altcoin market cap. Same base. Same sideways grind. Same silence. 2019: accumulation zone. Then 10x. 2024: same zone. Same structure. $170B is the line. Above it: cycle top 2026 loads. Below it: one more flush first. Every cycle started exactly like this.

THE ALTCOIN DOMINANCE MACD JUST CROSSED. FOURTH TIME IN HISTORY. 2017: crossover at day 333. Altseason followed. 2020: crossover at day 590. Altseason followed. 2023: crossover at day 561. Altseason followed. 2026: crossover at day 462. Right now. Hold 7% dominance: rotation begins. Lose it: crossover needs confirmation. Three previous signals. Three altseasons. The fourth just fired.

This cycle IS different. And this is the only chart you need to understand that. The 4 year cycle has NOTHING other than two sample sizes to provide itself validation. It is rooted in nothing other than time. Whereas the business cycle has every single major market chart providing it confluence. Every single cycle has been the same, and it is not about time... It is about the macro and business cycle. This chart makes it clear as day. 1. Gold runs during economic contraction(uncertainty) 2. Gold tops when ISM breaks into expansion(certainty) 3. Risk assets enter their true bull cycle 4. BTC.D begins its end of cycle downtrend Every single one of these fundamental charts lines up... And that is because the cycle is actually governed by the business and economic cycle, that is inherently linked to actual economic and liquidity performance. The reason next to no one can see this is they are totally consumed by the Bitcoin chart, and the 4 year cycle. Because we have never had a cycle like this, that has been lengthened due to the longest ever business cycle contraction... No one can fathom this outcome. Humans have a very hard time believing in something that has not happened before, and they will always side with something that has happened. And this will be the exact reason so many will be caught offsides here... Because we are not heading lower. I welcome any and all bears to debate me on this, to provide a counter thesis to this... But I know they won't because you can't. Why do you think this was the weakest cycle so far? Why did almost no alts break higher? Why has Gold gone on its largest run ever? Because the business cycle contracted for the longest time ever. Everything you need to understand this is right in front of you.

@cryptopunk7213 I may be mistaken, but isn't this EXACTLY how skynet got started? I have a vague recollection Sarah Connor mentioned this right before she roundhoused Arnie. He didn't come back after that one.

holy shit Meta just acquired moltbook the viral AI Agent social platform made famous by OpenClaw (1.6M agents) this is a huge deal - Meta is betting the future of social media is agents *not humans*, heres why: - founders Matt and Ben join Meta Superintelligence Labs (MSL), they have 5+ years exp building agents (they get it) - 1.6M agents ready to go. instant traction. - openAI acquired openclaw (infrastructure), now Meta owns the social distribution layer, thats huge if openclaw continues to grow. meta now owns one half of the agentic internet, pretty cool.

🚨 Andrej Karpathy just dropped the most terrifying repo in AI history. It's called AutoResearch. You give an AI agent a GPU and a training setup. You go to sleep. You wake up to a better AI model. No human involvement. The agent modifies the code. Trains for 5 minutes. Checks if it improved. Keeps or discards. Repeats. All night. ~100 experiments while you sleep. AI is now doing AI research. Autonomously. On a single GPU. This is from the man who led AI at Tesla and co-founded OpenAI. He's not speculating. He built it. Here's how it works: There are only 3 files that matter: → prepare py. Downloads training data, trains a tokenizer. Fixed. Never touched. → train py. The full GPT model, optimizer, and training loop. THIS is what the AI agent modifies. → program md. Your instructions to the agent. THIS is what the human writes. That's it. The human writes the research program in plain English. The AI executes it. Modifies code. Runs experiments. Tracks results. Iterates. You are no longer a researcher. You are the research director. The AI is your lab. Here's the wildest part: Karpathy's opening words in the README: "Research used to be done by meat computers in between eating, sleeping, and synchronizing using sound wave interconnect in the ritual of group meeting. That era is long gone. Research is now entirely the domain of autonomous swarms of AI agents. The agents claim we are now in the 10,205th generation of the code base. No one could tell if that's right or wrong as the code is now a self-modifying binary that has grown beyond human comprehension. This repo is the story of how it all began." Read that again. He's not joking. He's writing the origin story. Here's the design: → Fixed 5-minute time budget per experiment. Every run is comparable regardless of what the agent changes. → Single metric: validation bits per byte. Lower is better. No ambiguity. → The agent can change ANYTHING in train py. Architecture. Hyperparameters. Optimizer. Batch size. Model depth. Everything is fair game. → ~12 experiments per hour. ~100 experiments overnight. → You wake up to a full log of what was tried, what worked, what didn't, and a better model. The repo is 3 files. Under 1,000 lines total. No complex configs. No distributed training. One GPU. One file. One metric. Already has forks for macOS, Windows, and RTX cards. 11.6K GitHub stars in days. 1.5K forks. #1 on Trendshift. This is how AI research gets done from now on. 100% Open Source. MIT License.

this is fucking ridiculous lol - anthropic just killed a $50B industry with a single feature (again): - companies pay $50K a year to scan their code for vulnerabilities. - anthropics Code Review does it for you in minutes for a fraction of the cost. - deploys multiple agents to hunt for bugs in your code. internal results show its amazing (84% hit rate on 1000+ line code base) for comparison: anthropic cost = $15-25 PER review, trad competitor cost = $99+ complete fucking no brainer. watch the appsec stocks react to this one

@meta_alchemist I guess this was due at some point. I'm super happy though using cursor bugbot. Do you think claudes debugger is better?

This is a dope feature for vibe coding more production-ready products ngl Use it like this for best practices on auto: > Tell your Claude to not commit directly to main repo > Instead say that every shipped product must go through a PR > Have a rule in Claudemd for Claude to code review before merging to main Especially if you are updating products that are already live or have complex repos that can become spaghetti i'd use this feature in the way i mentioned above.

@test_tm7873 Same here. Frustrating!

(after a good sleep) wow. Good morning Claude! Wtf? I'm now ratelimited? I didn't even used you in 9hours! (or even longer, I didn't used it yesterday too)

@WorkflowWhisper CUBAN

mark cuban just laid out the exact playbook for making money with AI agents. pick one vertical. learn the flows. become the AI team they never hired. he's right. but he left out the how. i've been doing this for 3 months. here's what it actually looks like: week 1: i called 12 local businesses and asked one question. "what's the most annoying part of your day?" the pool company: "we lose 11 jobs a week because nobody follows up cancellations." the PT clinic: "insurance verification takes 3 hours every morning." the cleaning company: "we quote in 2 days. our competitor quotes in 2 hours." week 2: i built every single one of those workflows. → pool company cancellation recovery - 6 min → PT clinic insurance verification - 11 min → cleaning company instant quote generator - 7 min → dog groomer appointment + waitlist manager - 9 min → pest control follow-up sequence - 4 min average build time: 7.4 minutes. average close rate when you build it live in front of them: 70%. week 3: $10,750 upfront + $1,200/mo recurring. zero proposals. zero decks. zero "let me get back to you." they watched it work. they paid on the spot. cuban said "you don't need a CS degree or VC money." he's right. you need one question, one tool, and the willingness to build it in front of them. i documented the entire framework in a free PDF: → the 1-question discovery script (word for word) → 6 copy-paste workflow prompts by industry → pricing guide (what to charge per workflow type) → the live demo script that closes 7 out of 10 → full MCP setup walkthrough (5 min install) comment "CUBAN" and i'll send it. consultants charge $15K for a discovery workshop. i just gave you the playbook for free. synta(.)io - describe the workflow in plain english. it builds, deploys, and fixes itself. (must be following for DM)

the agentic ecosystem just got a proper map. Ejazz laid it out clean: 1.OpenClaw (original) 2.perplexity computer (search-native) 3.Anthropic’s cowork (desktop agent) 4.picoclaw (lightweight/miniature) 5.ironclaw (security-focused) 6.kimi k2.5 (chinese openclaw) 7.openai frontier (enterprise) building Chiti on OpenClaw, i can feel this expansion firsthand. each fork optimizes for something different. the real question is which one fits your use case, not which one is “best.” pick your claw. build your agent.

most people who are just entering AI / Openclaw will be doing these mistakes: - vibe coding very easy to build stuff - that can take couple hours to build - which will not return any money as a SaaS business instead in the beginning build tools for yourself, that will make your own workflows better and more productive this way the learning phase will make you kill two birds in one stone: - you will get better by learning the tools - you will have new tools in your arsenal and if you don't know what those tools can be simply share to OpenClaw or any LLM: > what your job is > what you wanna become > what your clear goals are > what you don't like doing > what you love doing and let it build the first couple tools for you not way too many at once, so that you can have the time to really get familiar with each tool, and start using them proactively then go a few more steps: 1. make a schedule for them to run automated with Openclaw cron jobs 2. start enhancing these tools with the gaps you see in them 3. and once you make sure that they are really making you productive: open source them, get people to give you feedback, get your first users, improve stuff further with these feedback when you go with the workflow above, at some point you'll realize you're just ready to ship more complex stuff: - that you can make money from - have the experience for it - a bunch of tools that already made you more productive and if you share what's happening daily with others on X you'll likely grow an audience too you can literally just copy paste this to your OpenClaw right now, and start from there good luck!

🚨BREAKING: Building Claude skills from scratch is dead. Someone just open-sourced a library of battle-tested skills you can copy/paste. - Document generation (all formats) - Web scraping & automation - Data processing pipelines - API integrations No more reinventing the wheel. 100% Opensource.

This 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 file will make you 10x engineer 👇 It combines all the best practices shared by Claude Code creator: Boris Cherny (creator of Claude Code at Anthropic) shared on X internal best practices and workflows he and his team actually use with Claude Code daily. Someone turned those threads into a structured 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 you can drop into any project. It includes: • Workflow orchestration • Subagent strategy • Self-improvement loop • Verification before done • Autonomous bug fixing • Core principles This is a compounding system. Every correction you make gets captured as a rule. Over time, Claude's mistake rate drops because it learns from your feedback. If you build with AI daily, this will save you a lot of time.

I just processed 140,400,000 tokens in 48 hours. Raw API bill: $1,677.82 My actual cost: $50.00 I’m moving my entire life into a self-hosted OpenClaw agent. If you aren't using this "cheat code" setup, you're just donating money to Big Tech. The 30x leverage setup: - Flat-rate loophole: Stop paying per token. Hook OpenClaw into a flat-rate coding plan for heavy lifting. - Local heartbeats: OpenClaw pings 48×/day. Route these to a local Llama 3.2 via Ollama. Cost: $0. - The 2-agent rule: Killed my 9-agent fleet ($100/night money pit). One for code. One for ops. - Plan > chat: If you don't use a PLAN.md, you’re just burning cash in a chat box. I burned $100 in one night so you don't have to. The Day 1 manual for infinite scale:

🚨 BREAKING: AI can now build trading algorithms like Goldman Sachs' algorithmic trading desk (for free). Here are 15 insane Claude prompts that replace $500K/year quant strats (Save for later)