dunik
@dunik_7 • 4,372 subscribers
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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.
dunik1,867,251 görüntüleme • 2 ay önce
a Tsinghua University lab just put a project on GitHub that replaces a $400,000 H100 rack with a single 24GB graphics card. it's called ktransformers, and the trick is almost stupidly simple: the experts you actually use stay on the gpu, the ones you don't sit on the cpu until they're called / deepseek-v3 and r1 with 139K context in 24gb of vram / up to 28x speedup over the standard setup / fine-tune deepseek-v3 across four rtx 4090s instead of a datacenter / built by tsinghua university's madsys lab, not a startup with a landing page apache 2.0, and already past 17,000 stars. -> bookmark it.
dunik311,857 görüntüleme • 19 gün önce
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.
dunik820,065 görüntüleme • 2 ay önce
running a 405-billion-parameter model used to mean a $30,000 GPU rack. one developer made it run on a single 8GB card the kind that's been sitting in gaming laptops for years. it's called AirLLM, and the trick is almost stupidly simple: instead of loading the whole model, it keeps one layer on the GPU at a time and streams the rest through. / Llama 3.1 405B on 8GB / DeepSeek-V3, all 671B of it, on about 12GB / no quantization, no distillation, no pruning the full-precision model / works with Llama, Qwen, DeepSeek, Mistral, Gemma, and more it's Apache 2.0 and already past 22,600 stars. → bookmark it.
dunik148,663 görüntüleme • 21 gün önce
he made 405-billion-parameter models run on a single 8GB gaming GPU. instead of turning it into a paid service, he open-sourced the whole thing. Gavin Li Founder and CEO of Anima AI in the Bay Area. 'airllm' - it keeps one layer on the GPU at a time and streams the rest, so a 70B model fits in 4GB and DeepSeek's full 671B fits in about 12GB 22,700 stars. Apache 2.0. → / he has 808 followers. / his repo has 22,700 stars. bookmark it. that's what shipping something impossible looks like.
dunik109,987 görüntüleme • 20 gün önce
The guy who kicked off the entire "loop engineering" wave Peter Steinberger: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." One post. 6.5M views in a week. In this talk he walks the real stack: the agent loop, a verifier that fails its own work and retries, and a loop that rewrites the agent while he sleeps. Worth more than any $500 vibe-coding course. Watch it, then read the full breakdown of the 4 loops below.
dunik219,808 görüntüleme • 1 ay önce
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.
dunik199,995 görüntüleme • 2 ay önce
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.
dunik371,724 görüntüleme • 5 ay önce
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.
dunik157,551 görüntüleme • 2 ay önce
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.
dunik179,439 görüntüleme • 2 ay önce
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.
dunik313,259 görüntüleme • 5 ay önce
Met someone whose agents ship more code in a week than my whole team does in a month. I asked him what actually made him this good - which loop framework, which prompt pack. He laughed and sent me a Stanford lecture. Andrew Ng - CS230, Intro to Deep Learning. Free, an hour long. "Everyone memorizes loop tricks," he said. "Almost nobody understands the thing the loop is wrapped around." I watched it last night. Halfway through, I realized I'd spent a year duct-taping agents together without ever understanding what was happening underneath any of it. Bookmark it. Watch it today - then the loop guide below finally clicks.
dunik35,917 görüntüleme • 1 ay önce
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.
dunik45,480 görüntüleme • 2 ay önce
Anthropic pays its ML engineers $500K+/year to deploy what Percy Liang just taught for free in Lecture 3 of CS221. 80 minutes. one whiteboard. everything between "a neuron" and the model running in your IDE right now. three things click after this one: / why two Claude prompts that "mean the same thing" land in different regions of weight space / why fine-tuning sometimes makes a model dumber, not smarter / what RLHF actually changes when Anthropic aligns Claude same $90K Stanford sequence. lecture 3 of ~20. free. the gap between "using Claude" and "understanding it" is exactly one lecture wide.
dunik24,381 görüntüleme • 2 ay önce
Percy Liang teaches Stanford students how AI actually thinks now we get to learn it too. same lectures Stanford undergrads pay nearly $90,000 a year for. it teaches the math underneath every AI tool you use: / search and planning -> why agents loop the way they do / MDPs and RL -> why models reward-hack and how to spot it / probability and Bayes nets -> why outputs are calibrated (or aren't) / machine learning fundamentals -> what fine-tuning actually changes
dunik18,446 görüntüleme • 2 ay önce
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
dunik29,091 görüntüleme • 5 ay önce
the dirty secret of every DTC brand running TikTok ads in 2026: / they don't care if the creator is real anymore. / they used to care a lot. they paid $800 per video for "authenticity," waited 2–3 weeks, accepted whatever 3 angles the human delivered, and prayed one converted. now they pay $400 per video, get 10 angles in 48 hours, A/B test all of them, kill the 8 that don't work, and run the winners until the format saturates. the creator brief used to read "find a 23-year-old who tried our product." in 2026 it reads "deliver 10 variations by Tuesday." they stopped specifying species. three retainers at $4,000/month = $12,000 MRR for one operator. all from a workflow nobody on X is talking about, and most agencies don't know they lost yet.
dunik17,417 görüntüleme • 2 ay önce
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
dunik28,095 görüntüleme • 6 ay önce
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.
dunik11,319 görüntüleme • 6 ay önce