Mojunel.eth

YOUR KEYWORD TOOL IS THE PROBLEM 72 keywords entered the scan. 39 survived. 1. Jev pulls the top 10 Google results per keyword. 720 pages land in the queue. 2. Claude fires 8,640 typed judgments across all 720. 216 per second. 204 ms median per result. 3. Forum thread. Dead. Thin page. Dead. Outdated. Dead. Weak domain. Dead. Off-intent. Dead. 4. Keywords with fewer than 3 beatable results in the top 10 get cut. No appeal. 5. 33 keywords drop. 39 survive with 3+ soft slots each. 40.0 seconds. $0.0363 total. 54% of the 72 came back with a beatable spot. 44% of all 720 top-10 results sat on tier-3 domains nobody defended. make.com tutorial ranks first. Jev is early access, waitlist only. Bookmark this

I loaded Grok Bot a second time with $103 it came out to $5,738.00, and here's the whole damn prompt first round went $101 to $7,943. second round, same desk, four agents, one task each: Scanner checks new Solana pools every 15 minutes. Liquidity, holders, top-10, volume, new buyers Rug Check drops anything suspicious before it moves forward Momentum only enters on a confirmed move Exit sells in pieces and follows the plan small quick trades on routine setups, one larger position on the day's strongest setup, and every dollar of profit rolls back in. the day dragged. CHOO, HAMSTER, a handфful of minor closes. then the overnight kicked in. 1:45 AM, CHILLCATS. +$1,055 in roughly an hour, larger than every prior big close combined. nearly all the strongest moves came between 10 PM and 6 AM. and the big closes still weren't the real story. the small trades pulled $3,978, more than double what the five large ones brought in. one thing worth saying: that single big position on the top setup is what turns a flat day into multiples. it's also the exact spot where it burns if the setup breaks. 85 of 101 green. full desk in one prompt: Build a Solana memecoin trading desk in Grok Bot that runs autonomously for 24 hours. 4 agents with clean handoffs: Scanner, Rug Check, Momentum, Exit. Scanner: scan new Solana pools every 15 minutes. Review liquidity, holders, top-10 concentration, volume and fresh buyers. Rug Check: drop anything suspicious before it moves on. Momentum: buy only on a confirmed move. Exit: sell in pieces, stick to the plan, never chase the exact top. Sizing: small quick trades on routine setups, one bigger position only on the day's strongest setup. Reinvest all profit. Run it first. same desk, two rounds, and the boring trades won both.

I don't think I'm opening Blender again. A dev put a free Claude skill on GitHub that builds a 3D machine out of one picture and films it coming apart. i handed it one ChatGPT image, typed "octopus" and waited for it to hand me a toy. what came back instead: > nothing got imported, every lens, hose and suction wheel on it is three.js code > the glass dome on the head is 4 mm borosilicate and it lifts off and flips over mid-air > a 10-blade iris pulls back from the eye inside a 62 mm ring of light > each arm ends in a 16-link spiral with 2 rows of suction wheels running down to it > all 8 arms lock into one 48-tooth hub and 16 hoses spring out when it splits > one more part breaks loose every 3 seconds and by 0:19 the whole octopus is floating in pieces the skill only ships with a tarantula, a piranha and a wasp. the octopus wasn't on the list. the install command and the full brief 👇

Mercedes-Benz spends $2,000,000 on aerodynamics testing, crash simulations, and wind tunnel optimization. a grandmother on an electric wheelchair strapped to a Jeep bumper just achieved peak highway aero resistance for $40 of stretch wrap. the clip: a highway at 60 mph. an elderly woman sits calmly in her motorized wheelchair, completely wrapped in industrial plastic wrap, strapped securely to a hitch platform on the back of a silver SUV. she looks directly at the camera, waves, and smiles as traffic flows past. 14M views in 6 hours. half the comments are asking if it’s legal, the other half are nominating her for driver of the year. here is why your brain thinks it’s engineering genius: 1. The stretch wrap tension dynamically adapts to wind drag, maintaining aerodynamic laminar flow around her headrest 2. The heavy-duty ratchet straps flex precisely under G-force shifts during lane changes on the highway 3. The calm facial expression proves absolute aerodynamic stability at 100 km/h the toolchain: > Claude Opus 5: mapped the full trajectory, suspension load limits, wind resistance coefficients, and strap tension thresholds: 8 min > Kling 4.0: rendered the vehicle vibration physics, plastic wrap flutter, and highway exhaust displacement: 15 min > Seedance 2.5: animated the continuous rolling motion with realistic motion blur and passing traffic parallax: 20 min > ElevenLabs: spatialized the highway roar, engine hum, and one driver shouting "absolute legend": auto Mercedes books a closed test track, a team of safety engineers, a fleet of prototypes, and a 6-month homologation cycle. this family books a hardware store roll of stretch wrap and an open interstate. save this breakdown.

2 follows, 1 message to Grok Bot, sleep. $260 to $3,418.73 13.15x by 6:50am, cashed out every cent before coffee 6:38pm, loaded $260 with Face ID then i followed 2 wallets off the 24h leaderboard first wallet, $394,013.99 balance, +$29,570.48 in 24h second wallet, +$29,741.66 in 24h then i sent Grok Bot one message → mirror each buy 30s behind them → sell once they sell → take profit at 10x → lock everything by 6:30am 9:41pm the wallet i followed bought a coin under an hour old 9:42pm Grok Bot copied the trade at $72K market cap 12:00am it reached $720K. Grok Bot sold at 10x, +$1,603.35 that wallet kept holding. the bot moved on 3:33am the second wallet dropped a loser, Grok Bot dropped it too, -$0.41 $260 to $1,887.89 to $3,418.73 5 copies, 4 green, 1 red same coin. that wallet made $12,540.19, i made $1,603.35 off $178.15 who trades smarter, you or the bot that cashed 10x while the wallet held?

Oh god, did you see this? This is literally going to change how teams run automated operations forever. A multi-agent autonomous system hooked to an admin backend is running entirely in the background, managing complex video reviews, auditing footage events, and handling operational workflows without manual intervention: > Autonomous Event Processing: The agent swarm instantly analyzes surveillance data, scans timelines for flagged events, and categorizes security incidents based on custom parameters. > Unified Dashboard Control: All neural workflows and data filtering happen seamlessly through a centralized dashboard, turning raw security logs into active, structured insights. > Real-Time Data Orchestration: Automated pipelines bridge complex database storage and interactive UI actions, synchronizing background system agents with instant operational updates. We are no longer just clicking through endless folders and sorting timelines manually. We are deploying autonomous AI swarms that control entire enterprise backends right behind our screens. Bookmark this before multi-agent automation takes over every business setup on the market.

Haven't second-guessed a note in five months I split one giant scroll into four files and every answer now lands with a dated receipt. Drop a link into the inbox. The pipeline does the rest: 1. Promote into a topic note: summary, evidence pointer, open question 2. Stamp a source receipt: URL, check date, recheck trigger 3. Log the decision: choice, reason, tradeoff, status, revisit condition 4. Flag outdated claims the moment a date goes cold 5. Return a prioritised checklist after a 6-step audit January tool note. March reversal. June query pulled both entries and killed the recommendation on the spot. 4 files. 6 steps. 10 minutes to build. Every answer has a receipt.

Claude + Obsidian + Axiom just completely broke the meta for productivity and automated trading, and it's scaling faster than anything we've seen before... It links deep AI reasoning, local knowledge graphs, and on-chain execution so seamlessly that manual work practically disappears. Two days ago I wired this setup together, and now my entire workflow runs on autopilot: from structuring project architecture to tracking real-time liquidity and executing swaps. Claude + Obsidian + Axiom combined do this: → Claude instantly generates and structures complex code, logic, and task pipelines right inside the workspace → Obsidian maps out the entire knowledge base and connects project nodes into a unified, clean architecture graph → Axiom automates data fetching, monitoring, and on-chain actions across decentralized markets without extra friction → Real-time analytics and data pipelines run 24/7, saving hours of manual routine every single day Everything runs as a single unified engine, and I've set up automated monitoring for any incoming tasks and market shifts. The exact setup is in the first comment and takes ten minutes. Save this and send it to any developer or trader still doing everything manually.

straight up insane, a whole web-scraping team in 10 repos. Firecrawl cloud charges $99 a month for 100,000 credits. Most of this list runs on your machine for nothing. Fetch. Unblock. Extract. Drive. Scale. 01 Firecrawl → Search, scrape and interact with pages through one API github.com/firecrawl/firecra… 02 Camoufox → Firefox browser built for scrapers and AI agent pipelines github.com/daijro/camoufox 03 Scrapling → Parse pages that change layout, 785x faster than BS4 github.com/D4Vinci/Scrapling 04 Crawl4AI → Crawl pages into agent-ready markdown, no per-page fee github.com/unclecode/crawl4a… 05 ScrapeGraphAI → Describe the fields you need, the LLM extracts them github.com/ScrapeGraphAI/Scr… 06 Apify MCP → Agent picks a ready-made scraper and runs it on Apify github.com/apify/apify-mcp-s… 07 Nanobrowser → Run an AI agent right inside your signed-in Chrome github.com/nanobrowser/nanob… 08 agent-desktop → Read and drive desktop apps through the accessibility tree github.com/vercel-labs/agent… 09 Maxun → Record a robot by clicking, schedule it to scrape github.com/getmaxun/maxun 10 Crawlee → Queue and retry crawls at scale with Playwright and proxies github.com/apify/crawlee Point it at one URL. Fetch → Unblock → Extract → Drive → Scale. Three carry copyleft. Check the license before shipping a hosted wrapper. Which stage eats most of your agent's time right now?

Oh god f*ck, is this even possible? Someone just ported the original Bully game into Unreal Engine using AI, and the live rendering and asset translation is genuinely terrifying. Watch what happens as the player runs through the school grounds in real time. Instead of old-gen limitations, you can watch modern engine lighting and native physics breathe new life into a classic: • AI-Powered Migration: Overnight, Claude Opus 5 processed original asset structures, translating legacy data cleanly into a cutting-edge environment without manual rebuilding. • Next-Gen Fidelity: The classic academy layout now runs smoothly on Unreal Engine, complete with high-end reflections, modern rendering, and full controller support. • Seamless Integration: Every corridor, courtyard, and texture was brought over natively, bypassing traditional multi-year game porting cycles entirely. We are no longer just remastering old software; we are watching legacy worlds wake up in next-generation engines overnight. Bookmark this before AI completely rewrites the entire game development industry.

A robotics startup burned $61 in tokens to find the bug that had cost it four months of hardware revisions. The arm kept dropping objects at a rate nobody could reproduce, roughly one grab in forty, always at a different moment. Three physical redesigns went past before anyone questioned whether the fault was mechanical at all. Opus 5.5 read the full sensor log instead of the incident reports, and the pattern showed up inside an afternoon. > PULL - every grab logged, successes included, not just the drops > ALIGN - sensor timing compared against the moment of failure > SPLIT - failed grabs separated by what preceded them, not by object > TRACE - the delay followed back into the firmware loop > PROVE - the fix tested against the archive before any hardware moved The drops happened when two sensors reported in the wrong order, which occurred only under a specific thermal load. No redesign could have caught it, because the hardware was never wrong. The software believed a reading that had already expired. Teams reach for new parts because a physical fault feels like a physical problem, and the logs sit unread the whole time. Four months of machining and three prototypes went into chasing something that lived in a timing window. The sensor trace and the one line that fixed it are in the article below.