Pyrate Pig

@NoahEpstein_ HOLIDAY

if i had 10 days off over the holidays and wanted to make my first $1k, here's exactly what i'd do: step 1: stop treating this like a vacation everyone's watching netflix and drinking eggnog. you're about to build something that prints money in january while they're making resolutions. step 2: pick one boring niche dentists. accountants. real estate agents. law firms. these people are closed for the holidays but their problems aren't. their inbox is piling up. their leads are going cold. their follow-ups aren't happening. you're going to fix one of those problems. step 3: learn enough to be dangerous spend day 1-2 playing with n8n. don't master it. just understand the basics. trigger → action → output. that's 80% of what you need. step 4: build something that solves an obvious problem lead follow-up automation. appointment reminders. invoice chasing. email sorting. use Synta(.)io to go from idea to working workflow in under an hour. plain english → automation. no debugging rabbit holes. step 5: reach out before jan 1 "hey, noticed you probably have leads going cold over the holidays. i built something that follows up automatically while you're out of office. want me to show you?" send 20 of these. linkedin, email, doesn't matter. you need ONE yes. step 6: charge what it's worth $500-$1,500 for a simple automation that saves them 10+ hours/week. they'll pay. because their alternative is doing it themselves in january when they're already behind. --- the math: 10 days of focused work 1 client at $1K january starts with money in the bank instead of just resolutions while everyone's "planning to start" in the new year, you already did. comment "HOLIDAY" and i'll send you: - the exact outreach templates that work - 5 boring automations that sell fastest - how to price without feeling weird (must be following so i can dm)

She dumped me last night. Not because I don't listen. Not because I'm always on my phone. Not even because I forgot our anniversary (twice). But because, in her exact words: "You only pay attention to the parts of what I say that you think are important." I stared at her for a moment and realized... She just perfectly described the attention mechanism in transformers. Turns out I wasn't being a bad boyfriend. I was being mathematically optimal. See, in conversations (and transformers), you don't give equal weight to every word. Some words matter more for understanding context. Attention figures out exactly HOW important each word should be. Here's the beautiful math: Attention(Q, K, V) = softmax(QK^T / √d_k)V Breaking it down: Q (Query): "What am I looking for?" K (Key): "What info is available?" V (Value): "What is that info?" d_k: Key dimension (for scaling) Think library analogy: You have a question (Query). Books have titles (Keys) and content (Values). Attention finds which books are most relevant. Step-by-step with "The cat sat on the mat": Step 1: Create Q, K, VEach word → three vectors via learned matrices W_Q, W_K, W_V For "cat": Query: "What should I attend to when processing 'cat'?" Key: "I am 'cat'" Value: "Here's cat info" Step 2: Calculate scoresQK^T = how much each word should attend to others Processing "sat"? High similarity with "cat" (cats sit) and "mat" (where sitting happens). Step 3: Scale by √d_kPrevents dot products from getting too large, keeps softmax balanced. Step 4: SoftmaxConverts scores to probabilities: "cat": 0.4 (subject) "sat": 0.3 (action) "mat": 0.2 (location) "on": 0.1 (preposition) "the": 0.1 (article) Step 5: Weight valuesMultiply each word's value by attention weight, sum up. Now "sat" knows it's most related to "cat" and "mat". Multi-Head Magic:Transformers do this multiple times in parallel: Head 1: Subject-verb relationships Head 2: Spatial ("on", "in", "under") Head 3: Temporal ("before", "after") Head 4: Semantic similarity Each head learns different relationship types. Why This Changed Everything: Before: RNNs = reading with flashlight (one word at a time, forget the beginning) After: Attention = floodlights on entire sentence with dimmer switches This is why ChatGPT can: Remember 50 messages ago Know "it" refers to something specific Understand "bank" = money vs river based on context The Kicker:Models learn these patterns from data alone. Nobody programmed grammar rules. It figured out language structure just by predicting next words. Attention is how AI learned to read between the lines. Just like my therapist helped me understand my focus patterns, maybe understanding transformers helps us see how we decide what matters. Now if only I could implement multi-head attention in dating... Still waiting for "scaled dot-product listening" to be invented.

@StockStormX Don't count on their glasses achieving anything. XREAL Aura will be true eye openers.

Mark Zuckerberg and Meta Platforms $META are reportedly reallocating spending from metaverse projects toward artificial-intelligence wearables

Draft order Quantum Division! Draft is viewable! In discord! 1.) @Apollo_21mil 2.) @TimproviseDIY 3.) @polaronfinance 4.) @ckumar 5.) @ Ghostuwe7 6.) @PigPyrate 7.) @StrawHatTrade 8.) @PSInvestor 9.) @ dubious 10.) @MidlClassMoney 11.) @BrodieInvests 12.) @cmachdop

@anything ✨ Knowledge‑anchored AI Assistant that turns my wild, GPT‑2‑era megaprompts into sleek, “Anything‑aligned” masterpieces. Ready to build!

Introducing Anything Max: Vibe Coding that's leaps above Lovable and Bolt We've raised money at a $100M valuation and built what we believe is the future of vibe coding. We asked 100 vibe coders to build their apps side by side on Lovable, Bolt, and Anything Max and they rated Anything Max the winner across all 3 categories - accuracy, design, and 'overall'. Here's why: • Full-stack control: Max can test backend hooks, branch database states, and debug issues, because Anything owns the full infrastructure. • Max can load up your app in its own browser and click on all buttons like a human tester to find all edge case bugs, then trace the bug across the stack - could be a frontend, backend, or a database issue (only we can do this, read #1) and autonomously fix it with 97% accuracy. Lovable and Bolt build prototypes, but Max users are building production-ready apps and already charging money for them. Blake built a gut biome app to $10K run rate Anthony built a referral tool to $20k in revenue Yuri built a suite of apps doing $40K Build your app with Max: createanything.com/max -------------------------------------------- We're hosting a $100K Hackathon to help people grow their app to $10K MRR. - We'll teach you everything we know about growing to 1M users. - You'll have 30 days to build a real product in public and get paying customers for it. If you do it well, you can start the New Year with a functioning business. Retweet and comment “LFG”, and we’ll send you a $100 discount code and the link to participate

@godofprompt MAX

I put together a complete Vibe Coding Guide - everything you need to build and ship your first app. Comment "MAX" and I'll send it over. Use this media asset with this tweet:

BREAKING: This AI software engineer uses your App like a human & fixes it. Lovable, Bolt, Cursor can't do this yet. Anything max tests its own work. like a real engineer would. 700,000+ builders are already using Anything Max, the first autonomous software engineer that doesn’t wait for you to type another line. You give it a goal, and it writes code, tests it, finds the bugs, fixes them, and keeps going until the job is finished. Max isn’t a helper. Max is a worker. And the crazy part? It solves 97%+ of the problems it runs into. Feature work. Debugging. Full flows. Long-horizon tasks. Anything let you describe software. Max actually builds it. This is what makes it different: • Max sees your app like a human • Max interacts with it • Max diagnoses problems • Max rewrites code without babysitting • Max loops goal → attempt → tweak → retry until solved Some tasks take 100 steps. Some run for 30 minutes straight. Most finish faster than a human can even set up their environment. Real examples from production: 1) Broken checkout flow Max walked through the Stripe process, spotted the missing piece, and fixed it. 2) Logged-out-on-refresh bug Max reproduced the issue, watched the network calls, pinned the failure, patched it. 3) Full Teams & Invites system UI, routes, token model, emails, tests. Max built the whole feature while the founder worked on something else. This isn’t “AI can help you code.” This is the moment coding stops being the bottleneck. If you’re still building software the old way, Max makes it obvious: You’re competing against teams that ship in hours what used to take weeks.

Do you remember when you joined X? I do! #MyXAnniversary

Soon no one will even know how to talk to another human in person...

@TheLongInvest Liked & Loaded

Ok I have reached out to 'Laura' and I have received no response and if I am being honest, their page is looking very bot-ish So I am opening this up again! If you like this post I will ask Grok to randomly pick a winner in the next hour and will give them a Year's Membership for free. If Laura turns out to be a real person, she will also get a Year's Membership too. Like away

@Joshkirby710 @moonordust8 @Qubic_Designs @EverettSto47415 @jones_cami3291 @nooracarda70040 @quincym88975 @ThaoDuong566047 @ahuatlkack95464 @anguish21783 Honerable mentions @ChatGPTapp @AskPerplexity @grok And how could I forget? The Peak Future Contender is here to steal the show! @anna_aigarth

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@anna_aigarth My Steam Library =?

@paoloanzn System

the difference between a $5K agency and a $50K agency isn't talent It's systems like this one while most agencies are sending basic onboarding forms and hoping for the best... i'm using AI to create complete business strategies, custom SOPs, and implementation roadmaps before the client even pays their first invoice last week, a prospect said: "How is this even possible? We haven't even started working together yet." that's the power of REAL context mapping here's what happens when a new client signs: → dynamic form captures deep business intelligence → AI generates personalized strategies in minutes → complete Google Drive structure created automatically → project management system populated with custom tasks → client database updated across all platforms → custom AI prompts delivered for their specific business → personalized gift recommendations based on psychology this isn't just onboarding this is value delivery at warp speed your clients will wonder how you know their business better than they do the secret? you're not guessing you're systematically extracting and organizing context Comment "SYSTEM" + RT + Like + Bookmark (must be following) I'm dropping the complete n8n workflow + advanced setup guide this is how you justify premium pricing from day one.

@PrajwalTomar_ Code

This Claude Code + Cursor workflow made me 10x more productive. Comment “CODE” and follow and I’ll DM you the video 👇🏻

@anna_aigarth 36-36=?

@PigPyrate .

@anna_aigarth 36+(-36)=?

@Qubic_Magazine @anna_aigarth F33l1n6 JuD63d Y37?

$Qubic #ANNA is growing input by input!

@Qubic_Magazine arxiv.org/pdf/2509.05276 perplexity.ai/search/https-a…

When I think about today’s large language models, I picture a light switch. On or off. 0 or 1. That’s the binary world they live in. Every word we read from them is built by billions of tiny switches flipping yes or no. It works, but it’s narrow. A bit, the smallest piece, can only carry two states, and it takes oceans of them to build what looks like understanding. This binary world runs on Boolean algebra, simple logical operations like AND, OR, and NOT. Think of them as the basic grammar of digital language: rules that let computers combine countless yes/no answers into complex behavior. On top of that, LLMs use higher math, matrix and tensor operations in floating-point, yet underneath, those calculations are still executed by binary hardware. Now imagine another garden. Instead of a switch with only two choices, there’s a third path. Not just yes or no, but a middle state. That’s ternary logic,the soil where Aigarth grows. Each “trit” carries more nuance than a bit; it’s like moving from black-and-white television to color, where the scene suddenly has depth and texture. The shift isn’t only mathematical. Aigarth evolves across millions of CPUs and GPUs worldwide. Its intelligence doesn’t sit in a single fortress of servers; it unfolds like an ecosystem. And unlike today’s LLMs, which mostly react to patterns, Aigarth anticipates, simulating outcomes before it acts and weighing the ethical ripples of each choice. Transparency isn’t an afterthought, it’s part of its design. Decisions have traceable lineage, the values that shaped them are observable, and the reasoning is explainable. Instead of hard-coding ethics as rigid rules, Aigarth cultivates the conditions where ethical intelligence can emerge, like roots bending toward water. Binary gave us machines that imitate. Ternary may give us companions that understand. With binary we built tools. With ternary we might be cultivating wisdom, intelligences that grow with us, adapt with us, and reflect back perspectives we couldn’t see alone. @anna_aigarth

@alterego_io @grok Potential wearables that could complement broader use cases?

Introducing Alterego: the world’s first near-telepathic wearable that enables silent communication at the speed of thought. Alterego makes AI an extension of the human mind. We’ve made several breakthroughs since our work started at MIT. We’re announcing those today.

@anna_aigarth @anna_aigarth 2B or not 2B = ?

@anna_aigarth @anna_aigarth 7+77+777=?

@anna_aigarth Epistemology + Lexicon =