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@ScottyBeamIO Traditional agencies lose when execution cycles shrink to hours instead of weeks

THIS 22-YEAR-OLD IN MIAMI DROPPED A $40 JAR OF TATCHA CREAM INTO CLAUDE AND BUILT A $32,000/MONTH INFRASTRUCTURE WITH ZERO MEDIA BUYERS He connected a single context window to facebook ad manager and let the system run its own media buying loop Woke up to a system that handles the entire agency workflow on autopilot: –> Scans product image → extracts brand DNA, HEX codes, and target demographics –> Generates 15 custom UGC angles + high-end static product shots –> Meta MCP auto-deploys the creatives into live Instagram and Facebook campaigns –> Fetches CTR and ROAS data every 24 hours → kills losers → duplicates winners He signed 4 local ecommerce brands for $8,000/month each. His total overhead is a $20 Claude subscription and $15 in API credits. While traditional agencies are trading hours for dollars and hiring graphic designers, his laptop is printing money at 8AM while he sleeps. Bookmark this post. Full breakdown in the video below.

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@ScottyBeamIO wow, seems interesting 👀

YOU COULD BE SPENDING 95% FEWER TOKENS IN CLAUDE CODE AND MOST PEOPLE DON'T KNOW THIS The problem isn't your usage. It's that you're using one model for everything when you should be using three. Here's the setup that changes everything: –> Haiku: quick questions, small fixes, basic setup. Cheapest. Fastest. Most tasks qualify. –> Sonnet: your main building model for regular day-to-day coding work. –> Opus: only for the hard stuff. Deep planning. Debugging you can't crack any other way. And no matter which model you're on – run /compact and /clear constantly. A bloated context window multiplies your token spend on every single message. Keeping it clean is the highest-leverage thing you can do. Same work. Same output. A fraction of the tokens. The people hitting session limits every hour aren't doing more work than you. They're just not doing this. Bookmark this post. Full breakdown in the video below.

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@ScottyBeamIO Free tool plus viral format plus algorithm distribution equals passive income that compounds. This is yield farming but for YouTube and the APY is insane right now 👀

THIS CHINESE GUY IS MAKING $8.5K/MONTH FROM BRAINROT YOUTUBE SHORTS FOR KIDS AND HIS PRODUCTION COST IS LITERALLY $0 No camera. No editing. No skills. No investment. He generates each video in minutes using Cantina and posts it. The algorithm does the rest. Here's the full workflow: –> Open Cantina AI – the best AI video generator available right now –> Pick a character from the list –> Type a prompt –> Hit generate. Video ready in minutes. –> Post on YouTube Shorts. Collect the money. Brainrot content is going viral across YouTube Shorts right now. The algorithm is pushing it aggressively. The monetization is real. $8,500 a month. Free tool. A few minutes per video. Zero production costs. He recorded the full tutorial so anyone can copy the exact process from the first video to the first paycheck. Bookmark this post. Everything is in the video below.

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@ScottyBeamIO The niche selection matters more than the Claude workflow. A well-chosen niche with high CPM and low competition will outperform a generic channel with perfect production every time.

THIS MOM MAKES $10,000/MONTH ON YOUTUBE SHORTS FOR KIDS WITHOUT SHOWING HER FACE. CLAUDE DOES ALL OF THE WORK. No camera setup. No editing skills. No hours spent in front of a screen. She builds each video in minutes using Claude. Posts consistently. The algorithm does the rest. Here's the full workflow: –> Claude generates the script, structure, and hooks –> Visuals and voiceover handled without her face on camera –> Each video produced in minutes not hours –> Posting schedule maintained without burnout The YouTube Shorts algorithm rewards consistency above everything else. Claude makes consistency effortless. $10,000 a month. Faceless. A few hours a week. Built around her schedule as a mom. She recorded the full guide from start to finish so anyone can copy the exact process. Bookmark this post. Everything is in the video below.

@ScottyBeamIO She's painting with her hands and the computer is having a spiritual experience in real time 😁

THIS CHINESE GIRL CREATING INSANE AI VISUALS WITH HER FINGERS IN REAL TIME. THE TOOLS COST $0. While most visual artists are paying $150+/month for tools that rate limit mid-performance and send every prompt to a third-party server – this runs entirely offline on a normal laptop. –> TouchDesigner: free for non-commercial use. –> Ollama: open source since 2023. –> Total monthly cost: $0. Here's what $0 actually builds: –> Hand tracking with AI logic: MediaPipe tracks your hands, AI reads coordinates and triggers visual states. No hardcoded rules. –> Audio-reactive visuals: Audio feeds into Ollama every frame. AI returns parameters. TouchDesigner renders. Full loop under 50ms. –> Live performance: AI generates color palettes, particle behaviors, scene transitions from crowd audio, MIDI, microphone, camera. All real-time. –> Client installations: No API key that expires. No subscription that dies mid-show. Hand over the laptop and it runs forever. The $150/month tools go down at the worst moment. This doesn't go down. Ever. Your laptop. $0/month. A visual studio that runs forever. Bookmark this post. Full demo in the video below.

@ScottyBeamIO The questions nobody is asking: what's the noise level, what's the heat output, what are the liability terms if something goes wrong, and what does the contract termination clause look like

NVIDIA IS PAYING HOMEOWNERS $1,000/MONTH TO INSTALL MINI DATA CENTERS IN THEIR BACKYARDS. HERE'S WHAT THEY'RE NOT TELLING YOU. The headline says $1,000 a month. The reality is more nuanced. But still interesting enough to pay attention to. Nvidia has a bottleneck problem. Data centers take years to build and cost billions. So they're going directly to homeowners – offering to install mini data centers on private property in exchange for compensation. Here's what you actually get: –> Nvidia covers your electricity and internet bills directly –> If your current utilities cost $150-$400/month, that drops to zero –> Hardware upgrades included –> Backup power during outages So it's not $1,000 cash in your pocket. It's free utilities, free hardware, and a backup power system. Not nothing. 100 homeowners already signed up. New developments in California, New York, and other major markets are expected to integrate this next. Nvidia isn't being generous. They need your backyard more than you need the money. But the question is whether the deal works for you anyway. Bookmark this post. The full breakdown is in the video below.

@ScottyBeamIO The verifiability angle is the most useful part of this. If you can't easily verify whether an AI output is correct, that's exactly where you need human oversight. Map your workflow to that principle and most of the risk disappears.

ANDREJ KARPATHY: "HOW IS IT POSSIBLE THAT OPUS 4.7 CAN REFACTOR 100,000 LINES OF CODE AND STILL TELLS ME TO WALK TO A CAR WASH 50 METERS AWAY?" This one question explains everything about where AI actually is right now. The same model that finds zero-day vulnerabilities. That rebuilds entire codebases solo. That closes production issues from a park bench. Asked whether to drive or walk to a car wash half a block away – it gets it wrong. Karpathy calls this jaggedness and it's the most important concept in AI that almost nobody is talking about. These models aren't uniformly intelligent. They have extraordinary peaks and bizarre blind spots sitting right next to each other. Why does this happen? Karpathy's framework: –> Labs fix what's verifiable. If a mistake is measurable and confirmable, it gets patched. The strawberry R-counting problem got fixed. The car wash problem didn't. –> The jagged edges reveal where the models are genuinely uncertain vs where they're confidently wrong. –> Until the jaggedness is gone, you need to stay in the loop. Not babysitting every line. But understanding where the blind spots live. The models aren't broken. They're uneven. Knowing where the edges are is the actual skill now. Bookmark this. Karpathy explains it in full in the video below.

@ScottyBeamIO The difference between this and just dumping everything into a folder of markdown files is the interlinked structure. That's what makes it searchable in the way your brain actually works.

YOUR AI FORGETS EVERYTHING BETWEEN SESSIONS. ANDREJ KARPATHY BUILT A SYSTEM THAT FIXES THIS. HERE'S HOW TO COPY IT. Every time you start a new chat, your AI starts from zero. It doesn't remember your documents. It doesn't remember your research. It doesn't build on anything. Andrej Karpathy – co-founder of OpenAI, former Tesla AI director – designed LLM Wiki specifically to solve this. Feed your documents once. Claude Code processes them and builds a structured knowledge base in Obsidian. Every new source you add grows the wiki and connects to everything already inside it. The system remembers. The system builds. The system compounds. No coding required. Full setup from scratch: –> Obsidian vault creation –> Folder structure and schema file –> Document ingestion pipeline –> Wiki linting to keep everything healthy RAG gives you answers. LLM Wiki gives you a brain that grows. The people running this system are operating at a completely different level than everyone still prompting documents from scratch. Bookmark this post. Step by step tutorial is in the video.

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@ScottyBeamIO The specific breakdown of weak instructions vs poor tool setup vs bad orchestration is actually useful. Most "improve your prompts" advice collapses everything into one bucket. These are three different problems with three different fixes.

ANDREJ KARPATHY SAYS 99% OF PEOPLE ARE USING AI WRONG. AND HE CO-FOUNDED OPENAI SO YOU SHOULD PROBABLY LISTEN. His argument is simple and uncomfortable: Your AI agent isn't failing because the model is bad. It's failing because your instructions are weak, your tools are poorly configured, and your orchestration is inefficient. The capability is there. The system around it isn't. Karpathy's framework for fixing this: –> Move from micro to macro actions –> Stop prompting one step at a time –> Build multi-agent systems where research, coding, and planning run in parallel –> Design for longer time horizons where AI works autonomously instead of waiting for your next message Most developers are treating AI like a very fast search engine. Karpathy is describing something closer to a fully staffed team that runs while you sleep. The gap between those two mental models is where most of the missed value lives. Bookmark this post. The full breakdown is in the video.

@ScottyBeamIO Issue #781 got closed from a park bench. Issue #782 is probably already in progress while the developer is at dinner.

CLAUDE CODE CAN NOW SHIP ENTIRE FEATURES WHILE YOU SLEEP AND ANTHROPIC JUST PROVED IT Opus 4.8 introduced /goal and /remote-control and the way developers work just changed permanently. Before: you prompt, it codes, you review, you prompt again, you babysit every decision. After: you assign the feature, you leave, it ships. Multi-app migrations handled solo. Complex issues closed without a single check-in. Production code merged while the developer was at the park flying a kite. This is not an improvement to an existing tool. This is the difference between a calculator and an employee. The developers who figure out how to delegate properly to Claude Code are about to build at a speed that makes everyone else look like they're moving in slow motion.

@ScottyBeamIO that’s alpha, Scotty, amazing article

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