Dante

This guy launched an AI influencer on OnlyFans and made $43,000 in the first month while sitting at home. He built a self-running content machine that generates posts, responds to subscribers, and scales audience without a real model involved. Here's how the scheme works: AI creates photorealistic images of three different characters. The system publishes content on schedule and analyzes engagement. 3 profiles run in parallel. Revenue grows while the creator snaps his fingers. Technical structure: > Midjourney + Stable Diffusion generate visuals > Claude writes captions and responds in DMs > 180+ new subscribers in 24 hours, 12 on day one, 47 by end of week, 180 after a month > Zapier syncs publications across platforms Automated sequence from concept to payout: > AI generates series of 30 photos → quality check > Claude writes captions with emojis → engagement test > publish to Instagram + TikTok → traffic collection > redirect to OnlyFans → convert to paid subscription > personal message from subscriber → Claude responds in 2 minutes > custom content request → AI generates for $25-$50 Started with 2 test characters. After funnel optimization, catalog grew to 3 unique profiles in 14 days. Sales run non-stop: athletic blonde, anime schoolgirl, girl with glasses — all convert subscribers while he drinks coffee. The system tracks active profiles. It sees conversion growth from 1.8 percent to 6.4 percent. It disables ineffective content after 100 views without likes. Revenue counter in dashboard jumped from $8.2k to $43k in 28 days. Dashboard shows top posts and redistributes promotion budget automatically. He has no manager, no team of photographers, no queue of unhappy clients. Just a laptop, subscription to AI tools, and workflow running 24/7. The system analyzes audience behavior and launches personalized offers with premium content. Question for creators: Where's the line between automation and loss of authenticity? Full autonomy gives maximum freedom. Manual control preserves brand quality. Hybrid models create bottlenecks in workflow. Is this the future of personal brands or a temporary loophole until platforms tighten the rules?

A video editor banks $23,000/month charging full agency rates for YouTube cuts that take under 4 minutes to produce using a Claude-powered Premiere Pro plugin that autopilots 87 percent of timeline work. He eliminated frame-by-frame scrubbing and replaced manual cut decisions with AI that reads speech pacing, removes bad takes, and exports polished sequences while he sits in a Thai café with his water bottle. Here is the structure: AutoEdit ingests raw talking-head footage and analyzes vocal patterns for hesitations. The plugin flags pauses, repeated lines, and filler words. Claude decides which segments survive. The editor clicks Generate Rough Cut and watches 89 segments collapse into 31 clean clips without touching the timeline. The technical setup: > AutoEdit plugin runs native inside Premiere Pro > Claude handles transcript analysis and cut logic > 89 total segments processed, 58 removed, 31 kept in one pass > 2 minutes 12 seconds of usable material extracted from 4 minutes 37 seconds raw > 312 out of 600 monthly processing minutes consumed per account The workflow from file drop to client invoice: > import raw footage → open Creator Mode dashboard > click Select Takes → review Good Take markers > hit Generate Rough Cut → timeline reconstructs itself into tight sequence > Review Edits panel shows 31 kept in green, 58 cut in red strikethrough > navigate to Captions tab → select template: Hbleed, Karrik, Retro, Satoshi > customize font weight to 800, position X and Y to 1169, toggle outline color to white or blue > apply AI Motion Graphics layer → export finished edit At baseline manual editing required 5 to 8 hours per YouTube video. Post-automation the same output takes under 4 minutes of machine work plus 12 minutes of style review. Clients receive IShowSpeed compilations, MrBeast reaction cuts, and Fortnite gameplay reels with animated captions, all processed through the same AutoEdit loop without manual timeline surgery. The system counts living clips. It tracks time removed at 2 minutes 25 seconds. It logs edit aggressiveness at maximum slider position. The dashboard lets him toggle remove weak takes, remove repeated thoughts, keep longest phrasing, preserve setup context. Caption preview cycles through font styles in real time as he taps through options. He has no render farm, no junior editor team, no offshore labor pool. Just a MacBook webcam angle pointing at a Premiere Pro window, Claude API access routed through a plugin interface, and a café table in Bangkok with BLUE CAFES merch hanging on the wall behind him. The plugin remembers cut preferences across projects and adapts pacing to match creator upload tempo. Question for service arbitrage builders: What is the correct pricing model when AI collapses production time but client perception of value stays fixed at manual labor rates? Charge per deliverable and capture time arbitrage profit. Charge per hour and lose margin as speed increases. Offer subscription tiers and risk commoditizing editorial taste into a $50 template library. Is this the infrastructure that lets solo editors capture $23k monthly revenue without hiring or does it flatten editing into a race where everyone runs identical Claude workflows and competes on turnaround speed alone?

@shedntcare_ 10 minutes is wild what sites even had this

$100/hr remote job. I found it in 10 minutes. Use these 7 remote job websites before applying for your next job:

@GaryMarcus the restraint is refreshing tbh. everybody wants a take before the data drops.

nor do we know how the (new) model works nor how it does on anything else nor how it was trained. scientists wait for facts; cheerleaders (over and over) rush to judgments that have often been wrong. let’s see what we actually have here.

@sharyph_ nd of Claude

There's a skill that builds your skills for you. Anthropic shipped it quietly. It drafts the file, writes the tests, runs evaluations, fixes failures, and iterates automatically. No coding. No prompt engineering. Just describe what you want. Get a working skill. Full breakdown 👇 newsletter.thedigitalcreator.co/p/claude-code-…

@Teknium @yoniebans a 40% disk cut while making the code cleaner thats the kind of upgrade nobody claps for but everyone silently benefits from

Our database and data engineering expert @yoniebans made some major improvements to the way sessions are stored and accessed. This will save something like 20-40% of the disk space used by Hermes Agent to operate, speed up session loading, and overall makes the codebase cleaner, simpler, and better architected! `hermes update` to access early or wait for the next major release :)

@aakashgupta so the one thing radar couldnt solve now has a physical spoofing layer. billion dollar problem just got more expensive

The single biggest problem in counter-drone defense for the last decade has been distinguishing drones from birds on radar. Billions spent on it. Entire companies built around it. The U.S. Army's LIDS program, Raytheon's KuRFS radar, DeTect's HARRIER system, all of them exist because conventional radar sees a small slow-moving object and can't tell if it's a $200 quadcopter or a seagull. The technical term is "clutter filtration." Radar returns from drones match bird signatures so closely that every counter-drone system on the market has a false positive problem. Birds trigger intercept alerts. Drones get classified as wildlife. The entire defense industry has been racing to solve one question: how do you teach a radar to tell a drone from a bird? China's answer: stop trying. The "Pigeon Program" has been running since 2018. The magpie variant weighs 90 grams, carries a live-feed micro camera, and can be hand-launched. The sparrow variant mimics a Eurasian tree sparrow so precisely that PLA Sea Commandos launched one from water during a live exercise and it was visually indistinguishable within seconds. The larger variants match ravens and hawks. Each model can be customized to replicate local bird species in whatever theater they're deployed. They flap. Real wing-flapping flight dynamics, not fixed-wing glide. Which means they don't just look like birds to human observers. They generate radar returns that are functionally identical to actual birds. Every dollar the West spent teaching radar to filter out birds just became the exact capability that lets these through. The defense industry spent a decade building a door. China walked through the window.

@rileybrown the barrier to entry is the whole point though wouldnt that kill the very thing making them useful

@0rdlibrary @PhoenixTrade @ImperialPerps @perps @solana @Pumpfun browser based lm studio is a wild but accurate description been wanting something that unifies dex feeds like this

The new terminal is getting dope! Essentially a browser based lm studio for trading. Now with complete @PhoenixTrade and @ImperialPerps @perps on @solana Paired with my compete @Pumpfun data stream unified with real time DEX screening by my “clawd crawl.” Interaction by @firecrawl Rip api. X402.wtf/terminal/free

@fofrAI gotta respect when a creature is equally dedicated to two opposing lifestyles

The way it gets the dog movement and the chicken movement at the same time.

@tom_doerr actually curious how these compare to just raw claude chatting do the prompts really make that much difference?

Claude Code-inspired prompts for AI coding agents github.com/repowise-dev/c…

@theo the trick is knowing when slop actually just needed shipping and when u were right to gatekeep

If Codex maintains a high quality bar and refuses to ship slop, they will win.

@tengyanAI wait so NVIDIA was basically just the GPU company until now? $200B TAM with zero competition feeling that

IMO the most underappreciated part of NVIDIA’s call is not the GPUs or the blowout rev + Q2 guidance It's Vera. Jensen framed Vera CPU as a "brand new $200B TAM” for NVIDIA, in a market it has never officially addressed before. That's quite a change because NVIDIA has historically treated CPUs as complements to GPUs.. the underappreciated older sibling Grace made the CPU strategic because it helped feed the GPU. “buy our CPU because it makes the GPU system better.” Vera is different. The pitch is that NVIDIA now has a standalone CPU business. that is a major strategic expansion. during Q&A, management also referenced a $20B standalone CPU opportunity, which makes this feel less like a vague long-term TAM slide and more like a near-term revenue vector. TL;dr: NVIDIA is moving laterally across the AI data center stack. GPU. CPU. Networking. Rack-scale systems. Software. Robotics. Every cycle, NVIDIA turns an adjacent component into part of its platform surface area. Vera is the CPU wedge.

@tom_doerr now i can track exactly how late my bus is in real time instead of just feeling it

Home Assistant integration for global transit departures github.com/alex-jung/ha-d…

@TheRealAdamG the real flex is transformers brute-forcing arithmetic without symbolic logic

Next token predictors are good at math. Who woulda thunk?

@cloneofsimo people really underestimated how fast the ceiling moves three years is nothing in hindsight but feels like a lifetime in this space

Just three years ago some people were certain these models will not have genuine, out of distribution capability. What an incredible achievement. It almost feels like end game.

@ctatedev nice progress on the type inference. the place-level conflict detection is the real improvement

zerolang 5 days later General - 3,978 stars - 223 commits Compiler - Full borrow provenance system with conflict detection by place - New type_core module for all generic inference - New unify module for binder-aware type unification - New call_resolve module for unified call resolution - New specialize module for direct generic specialization - Threaded checker through semantic context - Rewrote import system - use parses into graph facts - Rejects generic type-name shadowing (built-ins, Self, static annotations) - Fallibility propagation through wrappers - Checked allocation with safe geometric growth (z_grow_capacity) - Semicolons now tokenized Runtime + Backends - Embedded C runtime library (zero_runtime.c, zero_http_curl.c) - Hosted HTTP client with curl lifecycle and JSON helpers - HTTP runtime patching in both Mach-O and ELF64 backends - Target-neutral backend diagnostics - Callee-saved register seeding (x20/x21) on aarch64 - Fixed Mach-O codegen (stack metadata, scratch spills, literal widths, UUID) - Fixed Windows release build Diagnostics + Tooling - `check --target-readiness` with structured backend blockers - `check --trace` flag for borrow trace output - BOR001 borrow traces show full provenance chains - TYP027 diagnostic for recursive generic stability - New `zero explain` entries (import cycles, target capabilities, recursive generics) - `zero doctor` cleaned up (removed emscripten checks) CLI - Added zero run, zero copy, zero skills - bin/zero wrapper now execs native compiler directly - 7 embedded skill-data files for agent-first tooling Tests + Conformance - 224 new conformance tests - 21 new real-program tests (fib, GCD, sieve, sort, JSON roundtrip, etc.) - Agent-surface and provenance-surface conformance categories - 4 new examples: HTTP headers, HTTP JSON, HTTP requests, JSON bytes - New smoke tests: provenance guardrails, type-core, HTTP runtime, compiler metrics - Claude eval harness

@nickcammarata missed the part where being fast makes the ride more fun but good luck living 208 years by june

you're planning 12 months until true recursive self improvement. i've fractionalized my months and live ten months per month now, i'm almost 7 mays into this may. i dont have a year until takeoff i have a decade. enjoy being baseline

@emollick three almonds is wild, we live in a timeline where we compare ai to snack food

If this is true, using the best public estimates we have of LLM resource use, solving this Erdos problem took 0.6–6.3 kWh of electricity and about 3–31 liters of water. So that is less than three almonds worth of water and the electricity equivalent of 2-20 miles of EV driving.

@tom_doerr 110 questions is excessive but honestly the production design guide might actually be worth bookmarking

110-question AI interview bank plus production system design guide github.com/ombharatiya/ai…