After 3 years using Claude, I can say it’s the technology that has revolutionized my life. Here are 18 prompts I use daily that have transformed my day to day; they could do the same for you: (Save this 🔖)

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by @eng_khairallah1

Every billion-dollar SaaS company was built by stealing the customer list of one slightly bigger competitor. Salesforce did it to Oracle. Slack did it to HipChat. Notion did it to Evernote. The pattern is so consistent that VCs fund it explicitly and call it "category disruption"... When a16z writes a check for a "disruptor," they're funding someone who is about to take customers from a slightly bigger company. They don't write it that way in the pitch deck. They call it "market expansion." It's the same fucking thing. Founder leaves big company. Founder knows what big company is failing at. Founder builds the version his old customers wanted. Founder contacts the customers he already knows. Customers switch. The only difference between Marc Benioff in 1999 and you in 2026 is he had to memorize his rolodex. You can extract a 28,000-name customer database in 4 hours of compute time while you sleep. Here's the modern version of the play. Pick the largest competitor in your category doing $1M ARR who has been posting publicly on linkedin for 2 years. His follower list IS the rolodex Benioff had to memorize. It's sitting on a public URL anyone can view without logging in. Run AI-Ark against the public follower list. Boolean filter for decision-maker titles in your ICP. A 28,000-follower competitor will tighten to roughly 5,800 hot prospects overnight while you're asleep. Enrich the 5,800 through AI-Ark for an 89% verified email hit rate first pass. Findymail catches the 11% miss at $0.05 each. Drop anything else because chasing dead emails tanks your sending domain by friday. Load into Hey Reach. One paragraph from one google workspace inbox. Subject is the first name only. Body reads exactly: "saw you've been following [competitor] for a while. assumed you're working on [the problem they pitch]. mine installs in 21 days fixed price. refund if it misses. quick whatsapp?" We ran this on a 28,400-name competitor audience 8 weeks ago. 5,840 enriched. 41 replies. 14 closed at $8,500 average. $119,000 collected from a list someone else spent 5 years and probably $138,000 in CAC building. The competitor's audience took him 5 years and his entire savings account to grow. The extraction took me 4 hours and $51 in compute. The "audience-building advice" economy exists because audience owners need a story for why their audience hasn't bought from them yet. The honest answer is that most audiences belong to whoever extracts them first, not whoever paid the ads to build them. Benioff figured this out 27 years ago. Every founder reading this is still trying to "grow an audience" instead of taking the one already built next door. Pick your Oracle. I'm taking on 3-5 people right now to help them sign 3-5 clients a week. Multiple people have added 50k mrr in under a month using this exact method if you want details, dm me "KAI" (not free 😉)

Open source Jarvis that runs on a single GPU Today we're releasing the vui stack. A local voice agent that you can chat with in real time, with tools and can run claude to do more complex tasks. Inside this stack is the new vui nano model, a 300M TTS model that can render audio in reply to what you've said and supports a variety of non speech sounds. vui nano speaks with you, not at you. The stack can run on as little as 6GB of vram. Voice cloning supported with prompts of up to 5 minutes. The longer the better. A voice for your openclaw with our v1/realtime endpoint. I have developed this on my own so would love to get the communities feedback and help improving it. Please retweet this so that everyone knows they can have their own private Jarvis

The entire RAG industry is about to get cooked. Researchers have built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. It's called PageIndex. Instead of chunking your docs and stuffing them into pinecone, it builds a tree index and lets the LLM reason through it like a human reading a book. hit 98.7% on financebench. beats every vector RAG on the leaderboard. no embeddings. no chunking. no vector DB. 100% open source.

This Chinese guy created agents in Claude Code for landing pages and single-handedly serves 47 small businesses a month, taking $400 from each. He built a system of 7 agents on Claude Sonnet 4.6 that analyzes Google Maps in small towns, finds small businesses without websites there, and over 1 weekend takes each one to a finished mockup with video and cold message. No assistant, no sales team, no SDR. Just him, a MacBook, an iPhone, and 1 API key. And traditional web design agencies keep teams of 8 people on salary for the same order flow, while his expenses are only tokens and subscriptions to Lovable, Higgsfield, and Calendly. 7 agents work through 1 orchestrator on Claude Code Router. Usage is about 3 million tokens a day, the average API bill is about $480 a month. All 7 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions, and 1 of them lives right in the iPhone and picks up positive replies from the subway, a taxi, or on walks. And here is the system prompt he put into the orchestrator before launch: "You are the orchestrator of a solo agency that sells ready-made websites to local businesses. You delegate read-only tasks to 6 sub-agents and own all writes. sub-agents: // Scout (walks through Google Maps in selected cities, looks for narrow niches: 5 years on the map, fewer than 50 reviews, no website or a website from 2014, but high ratings) // Diagnoser (for each lead writes a 50-word diagnosis, hero angle, tone matched to the industry, and a cold message under 70 words) // Builder (generates a landing page mockup in Lovable through MCP only for the top 5 leads per day, with the sharpest diagnoses and the biggest gap) // Filmer (pulls 5 screenshots of the mockup and through Higgsfield renders a 10-second vertical video 1080x1920 with a soft zoom) // Pitcher (sends a personalized cold message through the right channel for the niche: email to roofers, SMS to tradesmen, IG DM to salons, LinkedIn to realtors) // Checker (runs every message through evals for personalization, absence of AI markers and buzzwords before sending) // Mobile (lives in the iPhone, handles positive replies in real time, books Zoom calls in Calendly through MCP while the owner is on the go). You never let 2 sub-agents touch 1 lead. You stop and request approval from the human only when a deal exceeds $3,000 or the reply rate in a niche for the day drops below 12%." Meaning the system knows what it is and within what boundaries it is allowed to act. It knows it is supposed to find leads on its own. It knows it is supposed to take each one to a mockup, video, and cold message without intervention. It knows the human only steps in when a deal goes above $3,000 or the reply rate stops converging. → The system runs 24 hours a day → Scout goes through about 220 local businesses on Google Maps per day and leaves 30 new leads in the queue → Diagnoser outputs 30 structured diagnoses briefs cold messages per day → Builder assembles 3 to 5 finished landing pages in Lovable for the sharpest leads → Filmer renders a 10-second vertical video in Higgsfield for each one → Pitcher sends 30 personalized messages per day across 4 channels with a reply rate of about 14% → Checker runs every message through evals before sending And only when a deal breaks $3,000 or the reply rate for the day drops below 12% does the orchestrator wake the owner. And when the owner at that moment is sitting in the subway or a taxi, the Mobile agent in his iPhone picks up 1 move on its own: replies to a fresh positive reply from a dentist, books a Zoom through Calendly synced to the local time of the client, and puts the lead back in the queue. The owner only has to tap "approve" and in just 10 minutes join the call. Here is what the system writes in his log during 1 of the Saturdays: "scout report: 218 businesses checked in Austin, Denver, and Miami, 34 without a website, 19 with a website from 2014, 6 with an active redesign request in reviews. passing top 30 to diagnoser." "pitcher: 30 cold messages sent across 4 channels, 14 replies, 5 positive, 3 Zoom calls booked for Sunday. passing to closer." "builder: landing page for Westside Cosmetic Dentistry built in Lovable, 5 sections, mobile, soft beige. URL placed at /Users/dev/maps-agency/clients/westside/v1. filmer launching Higgsfield." "eval flag: deal with The Lotus Salon at $3,400 exceeds the approved limit of $3,000. sending for manual review." He has no server of his own and no separate backend. Just a local file sandbox at /Users/dev/maps-agency, an MCP router, 1 API key to Claude, and the same key forwarded to Claude Code on his iPhone. Out of everything I have seen this year, this is the cleanest one-person agency for selling websites to small businesses: $480 a month on the API, about $18,800 into the account, and between them 7 prompts, 1 file system, and 1 phone in the pocket.

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. @subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

startup idea for you use postiz (20k github stars project) to sell AI social media content/management to 1 niche of SMBs. what's postiz? it's an open source social media scheduler with AI built in. basically buffer AI and free to download. 1. self-host postiz. use codex/claude code to help you figure this out in an afternoon. 2. pick one niche. dentists, realtors, lawyers. can even go a subniche like orthodentists vs dentists. family law over of lawyers. 2. wrap it in their language. "AI social media for dental practices" 3. add "we write your captions with AI" as the hook. that's what they're actually paying for. 4. plug it into n8n, make, or zapier so posting, scheduling, and approvals run on autopilot. the client approves with one tap. everything else is handled. 5. charge $50/mo-$100 per seat. that's nothing to a business paying $2,000/mo for a social media freelancer. you're 25x cheaper and 10x more reliable because the system runs whether you're awake or not. win-win for everyone. 6. build one landing page. run one onboarding call. that's the whole sales motion. 7. build media to attract customers. post tips for that niche on X, tiktok, youtube. become the "social media for dentists" person. 8. reinvest profits to build other tools that serve that same niche. scheduling, reviews, patient intake. build those tools or plug in more open source projects. now you own the vertical. these businesses KNOW they need to post. they hate doing it. they will never find postiz on github. they will google "someone please handle my social media." that's you open source is the new wholesale. the code is free. the customer relationship is where the margin lives. you can do this as one person. you can do this as a two person team. you don't need funding. you don't need an office. you need a laptop, a niche, and the willingness to start. someone is going to do this. might as well be you.

another deekseek moment?

My predictions for AI in 2026: 1. Second brain as a service becomes hugely profitable. Companies will pay to build internal knowledge bases trained on their data. 2. Owned audiences (email, SMS, direct mail, SEO rankings) become 100x more valuable as AI spam explodes. 3. Building audience on social → converting to owned channels is the most valuable marketing skill you can learn in 2026. 4. AI content will hit 8/10 quality on basic input. Only 10/10 stands out. Taste and context are your moat. 5. Relationships become even more valuable, especially with people who have large owned audiences. Borrow their trust. 10x overnight. 6. Companies hire employees whose entire job is staying on top of AI trends to help CEOs pivot daily. Eventually these become AI agents. 7. Most powerful AI models reserved for big corporations and governments. Average users never get access. 8. Cost of AI inference scaling rapidly. My system went from $20/month to $500-1K/month in 6 months. Will 10X next year. 9. Niche communities become massive business ecosystems. 80% fail. 20% become multi-unit powerhouses. 10. Proprietary data is the single most valuable moat: customer contact data, search trends, pain points. License it. Sell it. Build competitive advantage. 11. OpenAI building accessible agent layer. When it ships, agentic era begins for everyday people. 12. Claude Code continues to be the highest-leverage skill, period. 13. AGI is closer than we think (if not already here). ASI will be 1000x more earth-shattering then even the most bullish expectations.

I built 3 AI agents that run my entire LinkedIn end-to-end. • Content creation • Comment replies • High-intent lead generation outreach No manual work. I’m giving away the exact system. Want it? Repost ♻️ Comment “Agents” and I’ll send it.

someone built an OpenClaw agent that SELLS pool installations on autopilot. finds $500k–$1.2M homes without pools renders a pool in their backyard and mails a before/after postcard.

Grant Cardone nails the opportunity here. Every single company needs AI implementation. The TAM is just too massive to ignore. I’d push back a bit with leading with a “consulting fee”. Unless you have extreme authority in that vertical, you’re leaving money on the table. Consulting fees and retainers should be sold on the back end. Lead with audit and discovery. Show them what’s broken, what’s possible, and what the ROI looks like. Then price by impact. You’ll make way more money. And you’ll actually retain clients because they see results before they see invoices. Either way, the core message is right. If you’re going to start something right now, start here. Every company needs an AI team (you) and almost none of them have one yet.