Happy

@chopeaceus great nationality mix for this model saved this playbook

A CREATOR TURNED A FICTIONAL AI MODEL INTO $1.5K MONTHLY WITH A FANVUE FUNNEL The avatar was not the product. The product was the memory loop. A creator takes one safe portrait reel: short black hair, dark turtleneck, direct camera energy, Kazakhstan plus South Korea identity cues. That becomes the public face of a fictional adult-only AI MODEL, not a real-person copy. The public feed stays clean: portraits, short clips, mood, backstory, comments, repeated until strangers can recognize the character fast. Then the money path is simple: short reels -> memorable persona -> safe public page -> Fanvue or paid private channel -> adult-only private photo content. The operational constraint is the boundary. The public page can tease the character, but it cannot become explicit bait. Same fictional identity, same visual rules, same posting rhythm, same separation between public and paid. If she looks different every week, the funnel breaks. If she feels like a copied real person, the risk jumps. So the play is not "make an AI girl." It is build a fictional persona people remember, keep the public surface safe, and repeat the same character long enough for curiosity to turn into subscriptions.

@chopeaceus yeah, great vault for notes and other stuff

@ArchitectHappy_ using notebooklm as an everything notebook is honestly a total game changer

Walter Isaacson, the biographer behind Steve Jobs and Elon Musk, is using a free Google tool to analyze Marie Curie’s journals. Meanwhile people are paying $240–$600 a year for AI memory apps that mostly remember the last conversation. NotebookLM works differently. Bookmark this before another $50 memory app convinces you to subscribe. You upload the material that shaped how you think: PDFs websites YouTube videos audio files Docs and Slides Then Gemini builds a private research brain around those sources instead of guessing from the open internet. Every answer links back to the exact passage it used. The system has three moves: Load - the free version holds up to 50 sources inside one notebook. Ask - question the entire library and get answers grounded in your own material. The surprising part comes after the first upload. A 90-minute podcast, a 40-page PDF, and years of scattered notes stop living in separate tabs. NotebookLM starts connecting the ideas across the whole pile. Steven Johnson, Google Labs’ editorial director, calls his version an “everything notebook” and uses project notebooks like another member of the team. The $50 memory app tries to remember you. If you want to see how this turns from a second brain into a full work system, start with this breakdown ↓

@Skaly__Bull your ai content assistant is a cheat code literally

Five models. 95% accurate each Stack them with nobody watching and that's a 23% failure rate Nobody catches it Nobody remembers what already broke Watch an agent forget its own plan mid-task - it doesn't get dumber, it goes blind The fix isn't a smarter model It isn't a fancier vector database either It's one controller holding the whole run in its head - every subtask, every failure, every fix - memory as plain files, not a black box Claude in that seat plans the work, routes it to Grok, GPT and Llama and lets nothing fail quietly Intelligence was never the bottleneck Amnesia was Full breakdown below

@ScottyBeamIO power of second brain is truly insane my brotha

STOP SLEEPING ON YOUR SECOND BRAIN. THIS GUY STARTED HIS IN OBSIDIAN 869 DAYS AGO, AND NOW HE HAS SOMETHING YOU CAN'T BUILD OVERNIGHT. YOU HAVEN'T STARTED AT ALL. The stack: Obsidian + Hermes. 869 days of consistent input. Every idea, every note, every piece of knowledge, fed into one connected system instead of scattered across apps and forgotten. The result isn't a notes app anymore. It's a massive, interconnected map of everything this person has learned over more than two years – all linked, all searchable, all still growing. Here's the part people underestimate: it's not impressive on day one. It's not even impressive on day 30. It's boring for months. Then one day you zoom out and realize you've built something no single note ever could be on its own. The best time to start was 869 days ago. The second best time is right now. Bookmark this, you'll want to come back to it later.

This developer turned a $150K remote interview into a live AI copilot session. Before the call, he fed the agent: 1 CV 1 job description 37 pages from the company site 24 likely interview questions 12 fake stories from his past work Then the interview started. The recruiter asked one question. Within seconds, the agent pulled the right project, matched it to the role, and surfaced the strongest answer before he finished thinking. That is the advantage most people still miss. They use AI to rewrite a CV after getting rejected. He used it as a private career team before the offer was even on the table. The setup can handle: company research tailored CV versions interview prep salary positioning follow-up drafts One strong offer can raise your income by $30K–$80K a year. A second remote role can change it by another six figures. It is removing the part where good candidates forget their best examples under pressure. The full setup takes about 30 minutes to build. I broke down every file, agent, and workflow below ↓

A founder who generated over $5M from AI businesses Just uploaded the beginner course most automation agencies would rather sell you. Liam Ottley spent two years building no-code AI systems and helping NBA teams, adopt them. He put the entire process into 100 minutes. Free. Inside: 9:21 - fastest-growing automation opportunities rn 15:48 - the exact tools beginners should learn first 29:00 - build the first sellable automation from 0 1:22:09 - the most advanced build in the course 1:30:36 - why 99% automations fails + how to fix them At 1:36:55, start Liam teaching you how to sell those automations for high ticket clients That is where most tutorials stop. They show you how to connect the tools, then leave you with a workflow nobody has agreed to pay for. This one covers the products businesses already understand: Lead qualifiers, voice agents, and proposal generators. Just the exact course Liam says he wishes existed before he built his first AI company. The full course is below ↓

@insomnia_vip sounds like a real cheat-code man

A 23-YEAR-OLD CHINESE GIRLS FOUND A WAY TO SELL DRONE FOOTAGE WITHOUT OWNING A DRONE She pulls luxury property photos from Airbnb, connects Claude to Higgsfield through MCP and turns a few static images into cinematic aerial videos that look like they were filmed with professional equipment Instead of pitching another generic marketing service, she delivers custom video assets that showcase the client's actual property, making the offer far more difficult for business owners to ignore The biggest opportunity isn't creating content anymore because AI already handles that, it's finding businesses with valuable assets and transforming them into something worth paying for Bookmark this

@claudeai nice bait anthropic :D

We're extending Claude Fable 5 access on all paid plans, as well as keeping Claude Code’s weekly rate limits 50% higher, through July 19.

Daniel Agrici just open-sourced an SEO department for Claude Code. claude-seo 25 sub-skills 18 sub-agents 11.1K stars 1.6K forks Free. Open source. Built for Claude Code and Codex. → github.com/AgriciDaniel/c… It covers the work most SEO teams split across several specialists: technical SEO E-E-A-T schema GEO / AEO backlinks local SEO semantic clustering e-commerce international SEO Google APIs PDF and Excel reporting The useful part is how the system is structured. You give Claude the goal, and the right specialist gets pulled into the workflow. Optional integrations with DataForSEO, Firecrawl, and Banana extend it with live data and deeper research. Agrici is an AI Marketing Systems Architect and co-founder of Rankenstein. Now the SEO system he was building for clients is public. Bookmark it This is how Claude stops giving generic SEO advice and starts working through the stack like a real team.

@thegreatest_sv yeah, this guy just packaged all stuff he has

@ArchitectHappy_ 192 agents omg that a lot

One developer just open-sourced an entire AI engineering department. wshobson/agents packs 192 agents 156 skills 102 commands 84 plugins into one repo. 37.8k stars | MIT -> github.com/wshobson/agents Covered: Architecture, security, testing, Kubernetes, debugging, data engineering, multi-agent orchestration The same Markdown source works across Claude Code, Codex, Cursor, Gemini CLI, OpenCode, and GitHub Copilot. No rebuilding your setup every time you switch tools. Free. Open source. Ready to install. This is how one assistant starts operating like a full engineering team ↓

@Di_Krass_ my pleasure to help you, fren

@ArchitectHappy_ Thanks, that was helpful.

@ScottyBeamIO thanks, this repo is definitely worth checking

@ArchitectHappy_ I'll check this repo today bro, like your content

@ScottyBeamIO what this Chinese bro did..

WTF, THIS CHINESE ENGINEER TURNED HIS OBSIDIAN + HERMES AGENT SETUP INTO A 3D SECOND BRAIN HE CONTROLS WITH HAND GESTURES He's not scrolling through notes anymore. He's physically reaching into them. The stack: Obsidian + Hermes Agent. Using gesture interaction, he projected the semantic meaning of his Obsidian notes into 3D space – with Hermes Agent handling the reasoning underneath – and built three different ways to see how his own thoughts actually connect: → Radial structure – one core idea sits at the center, everything else radiates out from it. A cognitive star map built around a single hub. → Clustered topology – no single center at all. Notes group themselves naturally based on shared themes, forming their own independent clusters of meaning. → Semantic connection network – the deepest layer. Hermes Agent analyzes the actual relationships between ideas and builds a distributed graph where every connection is labeled and shaped by the algorithm itself, live, as the thinking happens. This isn't a note-taking app anymore. It's watching your own thought patterns take physical shape in front of you, and grabbing them with your hands. Most people are still using AI to summarize notes. This guy's using it to visualize how his mind actually connects them – in 3D. Bookmark this post. Full experiment in the video below.

@insomnia_vip these gamers are crazy

BILIBILI GAMING ISNT THE FAVORITE BY ACCIDENT Knight has looked like one of the most dominant players throughout MSI And BLG keep finding another level when the pressure gets higher They already beat Hanwha once in these playoffs Polymarket is still backing them to lift the trophy Around 59% of the money is on BLG despite Hanwha being one of the strongest teams in the tournament Finals can always produce surprises But right now BLG still look like the team everyone else has to beat

@oliviscusAI useful skill for these days, added to my workflow

someone built a free hermes agent skill that removes your digital footprint automatically. it's called unbroker. think incogni, but self-hosted, free, and running on your own agent. → scans every broker and finds your exposed data → sends opt-out and gdpr/ccpa removal requests automatically → handles verification emails and re-checks on a schedule → only escalates when a site requires a government id or phone call hermes skills install official/security/unbroker 100% free.

@lagerskoy like having a whole room of dropshipping experts, wonderful

HE BUILT AN AI COMPANY WHERE FIVE AGENTS RUN THE STORE This looks like a cyberpunk dashboard, but every glowing profile represents a separate AI role. JARVIS handles sales and customers, BOSS watches the content pipeline, AURA runs growth, CASH tracks finances and FORGE searches for products while the owner watches the entire operation from one screen The video then shows why this is more than a pretty interface. The system moves from detecting a viral jumbo duck across social media to researching the product, preparing its images and creating a $29.99 store listing, connecting steps that normally live across five different tools and several people The important part is the architecture behind the agents. Each one needs its own objective, memory, tools, permissions and measurable KPI, while the shared dashboard acts like a war room that exposes failed tasks, unusual spending and decisions that still require human approval This is where AI agents become useful instead of theatrical. One model doing everything quickly loses focus, but a team of specialized agents can divide the workflow, pass context between departments and continue operating in the background without turning every small task into another chat conversation The founder is not removed from the business, the founder moves one level higher. Instead of manually researching products, writing listings and checking every metric, one person supervises a digital organization and intervenes only when the system flags something important

@mikenevermiss sharing this repo is crime broo, saved

this is f**king gold. a free GitHub repo with 13.8k stars just dropped the complete guide to context engineering for AI agents. for free. it’s better than most $1,000 AI courses. inside are the 7 layers Anthropic engineers use to give AI agents the context they need to perform at a much higher level: global context → project context → memory → task files → current state → success criteria → Claude acts if you’re building AI agents, this is one of the most valuable resources you’ll read all year. save this. bookmark it. this will be the most productive thing you do this weekend.