dunik

@dunik_7 • 3,705 subscribers

AI-powered wallet hunter | polymarket

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> 4,200 paying subscribers. > $127,000 a month. > 5 different girls. 5 backstories. 5 personalities. > none of them exist. > Claude writes every DM. Flux generates every photo. ElevenLabs makes every voice note. > if a subscriber asks "are you real?" — the system is literally instructed to lie. > 4,200 men paying $30/message to a ghost. > this isn't a side hustle. > this is fraud at scale, dressed up as a productivity thread. > 2026 isn't the year AI took jobs. > it's the year AI took lovers — and the bill came with it.

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> 4,200 paying subscribers. > $127,000 a month. > 5 different girls. 5 backstories. 5 personalities. > none of them exist. > Claude writes every DM. Flux generates every photo. ElevenLabs makes every voice note. > if a subscriber asks "are you real?" — the system is literally instructed to lie. > 4,200 men paying $30/message to a ghost. > this isn't a side hustle. > this is fraud at scale, dressed up as a productivity thread. > 2026 isn't the year AI took jobs. > it's the year AI took lovers — and the bill came with it.

469,102 次观看

€10,000/month per AI influencer (Aitana López). $8,000–34,000 per sponsored post at the top. Lil Miquela has 2.4M followers and brand deals with Prada, Calvin Klein, Samsung, Dior. she doesn't exist. the new generation runs on a tighter stack: 1. lock a face once. one platform (Apob AI / Higgsfield) keeps it identical across every video. 2. find a viral reference clip in your niche dance, lifestyle, fitness, fashion. 3. swap your AI face onto the original creator's body. 90 seconds per clip. 4. Seedance 2.0 or Kling Motion Control handles the motion. zero animation skills required. 5. post 2–3 times a day across TikTok, Reels, Shorts, Facebook simultaneously. 6. one viral clone = 50,000–500,000 views on a brand new account in a weekend. first dollar in week 2: $9–27 digital product in bio. brand deals in month 3–6 once metrics warm up. portfolio play: same backend runs 3–5 personas in parallel. influencer economy 2024: real face, real fatigue, one bad day kills a brand deal. influencer economy 2026: a face that never ages, never cancels, never burns out. the next million-follower account on your FYP probably wasn't born. it was prompted, swapped, and shipped while the operator slept.

89,372 次观看

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NVIDIA will refund your cloud GPU bill if you let them bolt a $250,000 AI supercomputer to your desk for $2,999. NVIDIA's $2,999 DGX Spark puts a 128GB AI supercomputer on your desk runs the same 70B models you've been renting in the cloud, but nothing crosses your network and no ToS governs a machine you own. the $22K/year savings everyone's posting is the loud number. the quiet one is bigger: the contracts you stopped losing on data residency. own-compute isn't a budget optimization anymore. it's a sales motion. ↓ builder benchmark from the week the box shipped. real numbers, not marketing.

NVIDIA will refund your cloud GPU bill if you let them bolt a $250,000 AI supercomputer to your desk for $2,999. NVIDIA's $2,999 DGX Spark puts a 128GB AI supercomputer on your desk runs the same 70B models you've been renting in the cloud, but nothing crosses your network and no ToS governs a machine you own. the $22K/year savings everyone's posting is the loud number. the quiet one is bigger: the contracts you stopped losing on data residency. own-compute isn't a budget optimization anymore. it's a sales motion. ↓ builder benchmark from the week the box shipped. real numbers, not marketing.

818,662 次观看 • 18 天前

40% of the code Claude writes for you is wasted. you're paying for the rewrite. a 65-line markdown file fixes it. 120,000 developers have starred it. the author tested it on "30 codebases over 6 weeks" and reported a mistake rate drop from 41% to either 11% or 3% depending on whether you read the headline or the body. the irony is that the article is right. CLAUDE.md is the most under-leveraged file in your stack. 65 lines of behavioral rules outperform a 4,000-token preferences dump. "be careful" is useless. testable imperatives are gold. "be senior" doesn't work Claude already thinks it is. the 4 rules that ship the most leverage: / state assumptions, never guess silently / minimum code, nothing speculative / surgical changes, don't refactor adjacent code / define success, loop until verified compliance: ~80%. mistake rate: from ~40% to single digits. no human caught the contradicting numbers in the title. nobody had to.

40% of the code Claude writes for you is wasted. you're paying for the rewrite. a 65-line markdown file fixes it. 120,000 developers have starred it. the author tested it on "30 codebases over 6 weeks" and reported a mistake rate drop from 41% to either 11% or 3% depending on whether you read the headline or the body. the irony is that the article is right. CLAUDE.md is the most under-leveraged file in your stack. 65 lines of behavioral rules outperform a 4,000-token preferences dump. "be careful" is useless. testable imperatives are gold. "be senior" doesn't work Claude already thinks it is. the 4 rules that ship the most leverage: / state assumptions, never guess silently / minimum code, nothing speculative / surgical changes, don't refactor adjacent code / define success, loop until verified compliance: ~80%. mistake rate: from ~40% to single digits. no human caught the contradicting numbers in the title. nobody had to.

1,860,208 次观看 • 1 个月前

a developer posted his Claude Code bill on Reddit: $170 in 10 days. someone replied: "I bought a Mac Mini M4. Haven't paid Anthropic since." most people scrolled past that reply. the dev community didn't. Apple Stores ran out of Mac Minis the next month. not from launch hype from devs running the math. the math: / Claude Code Max + ChatGPT Pro + Cursor + Copilot = $459/month, $5,508/year / one $599 Mac Mini M4 runs the same workflows locally / one env var (ANTHROPIC_BASE_URL= redirects Claude Code to your hardware / $3/month in electricity. one box. silent. 5-inch square. the workflow stays identical. the bill stops. that's the trade. the developers who already made this swap aren't loud about it. they're billing the same clients on cheaper infrastructure and pocketing the spread.

a developer posted his Claude Code bill on Reddit: $170 in 10 days. someone replied: "I bought a Mac Mini M4. Haven't paid Anthropic since." most people scrolled past that reply. the dev community didn't. Apple Stores ran out of Mac Minis the next month. not from launch hype from devs running the math. the math: / Claude Code Max + ChatGPT Pro + Cursor + Copilot = $459/month, $5,508/year / one $599 Mac Mini M4 runs the same workflows locally / one env var (ANTHROPIC_BASE_URL= redirects Claude Code to your hardware / $3/month in electricity. one box. silent. 5-inch square. the workflow stays identical. the bill stops. that's the trade. the developers who already made this swap aren't loud about it. they're billing the same clients on cheaper infrastructure and pocketing the spread.

199,995 次观看 • 14 天前

RTX 5090 ($2,000). Ollama (free). $2,000/month from clients who can't legally use Chat GPT5.5 xhigh,. here's the trade. the 5090 has 32GB of VRAM enough to run a 30B model locally without it ever touching the cloud. Ollama is one terminal command. setup is a Saturday afternoon. what you sell: 2-4 small businesses a private AI assistant that lives on your hardware, reads their docs, answers their team's questions, and never crosses their network. $500-1,000/month per retainer. three clients = $2,000+/month, every month, on a card that paid itself back in week one. who actually pays this: / law firms whose clients' contracts can't go in ChatGPT / accountants drowning in client books they can't paste anywhere public / real estate brokerages with vendor data they signed NDAs over / clinics, e-commerce ops, anyone whose "we use ChatGPT" answer gets them a lawyer's email they don't need "smart AI." they need private AI that runs somewhere they trust. for $2,000 in hardware, that "somewhere" is now a card on your desk. the AI freelance market in 2026 isn't about who writes the best prompt. it's about who can host a 30B model on silicon no client's legal team has to ask about.

RTX 5090 ($2,000). Ollama (free). $2,000/month from clients who can't legally use Chat GPT5.5 xhigh,. here's the trade. the 5090 has 32GB of VRAM enough to run a 30B model locally without it ever touching the cloud. Ollama is one terminal command. setup is a Saturday afternoon. what you sell: 2-4 small businesses a private AI assistant that lives on your hardware, reads their docs, answers their team's questions, and never crosses their network. $500-1,000/month per retainer. three clients = $2,000+/month, every month, on a card that paid itself back in week one. who actually pays this: / law firms whose clients' contracts can't go in ChatGPT / accountants drowning in client books they can't paste anywhere public / real estate brokerages with vendor data they signed NDAs over / clinics, e-commerce ops, anyone whose "we use ChatGPT" answer gets them a lawyer's email they don't need "smart AI." they need private AI that runs somewhere they trust. for $2,000 in hardware, that "somewhere" is now a card on your desk. the AI freelance market in 2026 isn't about who writes the best prompt. it's about who can host a 30B model on silicon no client's legal team has to ask about.

157,287 次观看 • 17 天前

$5,500/year subscription stack vs $599 hardware. Mac Mini M4 + Ollama + Claude Code. Apple Stores ran out of Mac Minis. not because of marketing because developers figured out the math. one env var (ANTHROPIC_BASE_URL= redirects Claude Code to your $599 box. same workflow. $3/month in electricity. forever. the smart play isn't all-local or all-cloud. it's hybrid: / Mac Mini ($599) handles 80% coding, drafts, RAG, document QA, agent loops / one $20/month subscription handles the 20% that needs frontier reasoning / total monthly: $23 instead of $459 / total annual savings: ~$5,232 Apple positioned the Mac Mini as "the most popular Mac ever." developers turned it into the cheapest AI server on earth.

$5,500/year subscription stack vs $599 hardware. Mac Mini M4 + Ollama + Claude Code. Apple Stores ran out of Mac Minis. not because of marketing because developers figured out the math. one env var (ANTHROPIC_BASE_URL= redirects Claude Code to your $599 box. same workflow. $3/month in electricity. forever. the smart play isn't all-local or all-cloud. it's hybrid: / Mac Mini ($599) handles 80% coding, drafts, RAG, document QA, agent loops / one $20/month subscription handles the 20% that needs frontier reasoning / total monthly: $23 instead of $459 / total annual savings: ~$5,232 Apple positioned the Mac Mini as "the most popular Mac ever." developers turned it into the cheapest AI server on earth.

111,699 次观看 • 14 天前

the CEO of Obsidian spent almost 3 hours on camera explaining why your "second brain" doesn't actually work yet. the core idea: file over app. your notes outlive the app you take them in. if they don't you don't own them. you're renting them. Steph Ango is handing you the deed.

the CEO of Obsidian spent almost 3 hours on camera explaining why your "second brain" doesn't actually work yet. the core idea: file over app. your notes outlive the app you take them in. if they don't you don't own them. you're renting them. Steph Ango is handing you the deed.

177,686 次观看 • 28 天前

yesterday a friend messaged me: "i have 48 hours before the lawyers find out. you need to see this" he worked as a data engineer at a hedge fund in Zurich. he got fired on friday on monday, his vpn was still working he downloaded 3 jupyter notebooks before his access got cut one of them was called polymarket_edge_model_v4_FINAL.ipynb THE CORE IDEA: the fund doesn’t predict the outcome of the event. they predict the PANIC of other traders when the price moves sharply, 80% of players close at a loss. the fund takes the other side "liquidity vacuum trade" TRIGGER — 3 conditions at the same time: / price >2% in 90 seconds / volume >3x the average / 70%+ of orders on one side the bot trades AGAINST the crowd expected_price = EMA(200) × (1 + sentiment_offset) deviation >12% = entry stop at 25% take profit at EMA ±3% win rate: 73.2% across 1,847 trades. sharpe 2.7 i rewrote it in python over the weekend. 172 lines first week: +$412 from $1,500 the formulas are above. claude will handle the rest.

yesterday a friend messaged me: "i have 48 hours before the lawyers find out. you need to see this" he worked as a data engineer at a hedge fund in Zurich. he got fired on friday on monday, his vpn was still working he downloaded 3 jupyter notebooks before his access got cut one of them was called polymarket_edge_model_v4_FINAL.ipynb THE CORE IDEA: the fund doesn’t predict the outcome of the event. they predict the PANIC of other traders when the price moves sharply, 80% of players close at a loss. the fund takes the other side "liquidity vacuum trade" TRIGGER — 3 conditions at the same time: / price >2% in 90 seconds / volume >3x the average / 70%+ of orders on one side the bot trades AGAINST the crowd expected_price = EMA(200) × (1 + sentiment_offset) deviation >12% = entry stop at 25% take profit at EMA ±3% win rate: 73.2% across 1,847 trades. sharpe 2.7 i rewrote it in python over the weekend. 172 lines first week: +$412 from $1,500 the formulas are above. claude will handle the rest.

371,482 次观看 • 3 个月前

a repository that will make your bot 1000x better affaan-m/everything-claude-code adds to your bot: / 13 agents / 32 commands / at least 2 clearly visible contexts ( / 12 hooks / 14 mcp-configs / 102+ rules / 56 skills / scripts — exact count couldn’t be reliably confirmed / 992 internal tests this can improve the performance of your bots and agents 1000x.

a repository that will make your bot 1000x better affaan-m/everything-claude-code adds to your bot: / 13 agents / 32 commands / at least 2 clearly visible contexts ( / 12 hooks / 14 mcp-configs / 102+ rules / 56 skills / scripts — exact count couldn’t be reliably confirmed / 992 internal tests this can improve the performance of your bots and agents 1000x.

313,075 次观看 • 3 个月前

the real estate agent profession is in its last decade. 2 million agents in the US take 2.5–3% of every deal. $50 billion a year for a job that boils down to controlling access to listings, scheduling viewings, and forwarding paperwork. all three layers just got automated. / listings: monitored 24/7 by n8n across Zillow, Redfin, Realtor. / viewings: replaced by 3D scans buyers walk through in a browser tab from their couch. / paperwork: AI qualifies the lead, routes the reply, schedules the follow-up. $200 to scan a property. $20/month to run the entire stack. zero employees. two deals a month = $25,000 in revenue at $100 in cost. the agent earning $90,000/year selling 6 homes is competing with the operator earning $300,000/year selling 24. the agent doesn't know the operator exists. the operator is going to be the agent's first client.

the real estate agent profession is in its last decade. 2 million agents in the US take 2.5–3% of every deal. $50 billion a year for a job that boils down to controlling access to listings, scheduling viewings, and forwarding paperwork. all three layers just got automated. / listings: monitored 24/7 by n8n across Zillow, Redfin, Realtor. / viewings: replaced by 3D scans buyers walk through in a browser tab from their couch. / paperwork: AI qualifies the lead, routes the reply, schedules the follow-up. $200 to scan a property. $20/month to run the entire stack. zero employees. two deals a month = $25,000 in revenue at $100 in cost. the agent earning $90,000/year selling 6 homes is competing with the operator earning $300,000/year selling 24. the agent doesn't know the operator exists. the operator is going to be the agent's first client.

45,480 次观看 • 1 个月前

after seeing this post, I decided to build a small tool that shows the price on Polymarket and Chainlink side by side on 5-minute BTC Up or Down markets, Polymarket lags behind by around 6–10 seconds on average, which creates an inefficiency you can take advantage of in the future, I want to improve this tool and add the ability to place trades directly from it

after seeing this post, I decided to build a small tool that shows the price on Polymarket and Chainlink side by side on 5-minute BTC Up or Down markets, Polymarket lags behind by around 6–10 seconds on average, which creates an inefficiency you can take advantage of in the future, I want to improve this tool and add the ability to place trades directly from it

29,091 次观看 • 3 个月前

i found a bot that basically hacked Polymarket and is making around $30,000 per day a user called 0x8dxd has been making a crazy amount of money on polymarket for the past few months he built a bitcoin/ethereum/solana/xrp up or down bot that trades instead of him he enters aggressively on short up or down 15-minute markets here’s how his algorithm works, super simple: 1/ takes the last 10 trades and calculates the asset’s “fair value” (fv); 2/ if the current price > fv -> he accumulates (up sponge mode); 3/ if the current price ≤ fv -> he doesn’t accumulate / likely dumps (dump mode) nothing complicated just math and calculations

i found a bot that basically hacked Polymarket and is making around $30,000 per day a user called 0x8dxd has been making a crazy amount of money on polymarket for the past few months he built a bitcoin/ethereum/solana/xrp up or down bot that trades instead of him he enters aggressively on short up or down 15-minute markets here’s how his algorithm works, super simple: 1/ takes the last 10 trades and calculates the asset’s “fair value” (fv); 2/ if the current price > fv -> he accumulates (up sponge mode); 3/ if the current price ≤ fv -> he doesn’t accumulate / likely dumps (dump mode) nothing complicated just math and calculations

28,095 次观看 • 4 个月前

two of the best repos for building your own product on Polymarket recently found some interesting projects by Daniel Sapkota that’ll make your life easier if you’re a builder market making: data analytics: poly-maker is an automated market making bot for Polymarket that keeps orders on both sides of the book and is driven by parameters from Google Sheets key features: / real-time order book monitoring via WebSocket; / positions + risk controls (position management); / parameters/hyperparameters managed in Google Spreadsheets; / poly_merger (Node.js) a "merge positions" utility to consolidate positions, use less gas, and improve capital efficiency; / companion modules: poly_stats, poly_utils, data_updater, poly_data. poly_data is a "data combine" for collecting, processing, and structuring Polymarket trading data: markets > order-filled events > trades dataset 3 pipeline stages: 1/ market data collection - pulls all markets from the Polymarket API; 2/ order event scraping - fetches order-filled events from the Goldsky subgraph; 3/ trade processing - converts raw events into structured trades: price, direction, size, maker/taker, etc. you can use these two great repos in different ways if your goal is content/osint/spreadsheets/wallet analysis > poly_data is the most valuable. if your goal is lp/market making > poly-maker is a great infrastructure reference implementation.

two of the best repos for building your own product on Polymarket recently found some interesting projects by Daniel Sapkota that’ll make your life easier if you’re a builder market making: data analytics: poly-maker is an automated market making bot for Polymarket that keeps orders on both sides of the book and is driven by parameters from Google Sheets key features: / real-time order book monitoring via WebSocket; / positions + risk controls (position management); / parameters/hyperparameters managed in Google Spreadsheets; / poly_merger (Node.js) a "merge positions" utility to consolidate positions, use less gas, and improve capital efficiency; / companion modules: poly_stats, poly_utils, data_updater, poly_data. poly_data is a "data combine" for collecting, processing, and structuring Polymarket trading data: markets > order-filled events > trades dataset 3 pipeline stages: 1/ market data collection - pulls all markets from the Polymarket API; 2/ order event scraping - fetches order-filled events from the Goldsky subgraph; 3/ trade processing - converts raw events into structured trades: price, direction, size, maker/taker, etc. you can use these two great repos in different ways if your goal is content/osint/spreadsheets/wallet analysis > poly_data is the most valuable. if your goal is lp/market making > poly-maker is a great infrastructure reference implementation.

11,319 次观看 • 4 个月前

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