slash1s
@slash1sol • 10,025 subscribers
smoke cigs and post about coding, ai, crypto, trading, prediction markets..
Shorts
![a Russian mathematician died in 1922. His math just made a bot $468,228 on Polymarket in 30 days. Andrey Markov never saw a prediction market. He built the exact tool to dominate them. [Bonereaper] runs BTC/ETH 1-hour windows. 1,500-2,900 shares per trade. 14,671 trades total. Two conditions. Both must fire simultaneously: Δ = p̂ − q ≥ 0.05 -> gap exists. p(j*, j*) ≥ 0.87 -> state is stable. Both true? Position entered. Every minute. 24/7. The edge: humans sleep. Markets don't. At 3AM nobody watches a 5-min BTC window. The gap widens. The bot enters. His profile: His wallet: 0xeebde7a0e019a63e6b476eb425505b7b3e6eba30 copy trades live from $10 -> Save this alpha.](https://image.24vids.com/tw-2047692500303847586/amplify_video_thumb/2047692445270392832/img/3DA82cFtKhfcwPb0.jpg)
![CHINESE ENGINEERS JUST WROTE CLAUDE SCRIPT AND TURNED $6.02 INTO $3.3 MILLION ON POLYMARKET Nobody tells you about them and you still think this is a person placing bets manually I guess. Let me disappoint you, this is a fully automated script built by Chinese engineers 100%. This is true. They called it PHANTOM X. It runs completely through Claude. Their account here: Result: $6.02 -> $3,354,000. Win rate 71%. Biggest win: $179,000 (single bet). I’m copying their trades here: (Just added their wallet to TG bot 0xee613b3fc183ee44f9da9c05f53e2da107e3debf, it's so easy) How the bot works: -> It simultaneously tracks thousands of sports markets on Polymarket and Kalshi. -> Finds discrepancies between the platforms. -> Enters positions faster than any human could imo. Just three strategies in one: -- Pairs Trading: the bot sees YES on the Rockets at $0.62 while NO is at $0.41. Total = $1.03 instead of $1.00. That’s a 3% risk-free profit. It enters automatically within milliseconds. -- Sentiment AI: scans Twitter (X) and news in real time. If something big breaks, it recalculates the probability in 2 seconds before the market reacts. -- Calendar + Volatility: 15–20 minutes before the game, volatility spikes. The bot takes positions early and closes after the first major move. Why sports is perfect? Sports O/U markets have clear paired contracts that should total exactly $1.00, but constant deviations create reliable arbitrage. This is exactly how [sovereign2013] built $3.35M. > A human physically cannot monitor 50+ markets at once, react in milliseconds, stay awake 24/7, avoid emotions after losses, and run Z-scores on 60 bars of data. > The bot does all of this in parallel without breaks. Manual trading is dying. The automation era has arrived. Start learning Claude now. If you’re interested in writing your own bot on Polymarket: Comment the word "BOT" Like and repost this post Follow me (so I can message you easly) And within 24 hours I will send you a full manual on how to build a bot that can earn $2,900+/month. Also SAVE this info and article.](https://image.24vids.com/tw-2040570406067720417/amplify_video_thumb/2040567931218886656/img/6NSDVMtNw175Niut.jpg)
![Claude just cooked the ultimate Polymarket copytrade blueprint and this sports betting monster is already printing $1,200,000+ with it. Forget guessing lines, grinding stats, or watching every game. This absolute unit joined Nov 2024 and is straight-up farming: - $1,238,417 all-time profit. - Biggest single win: $349.8K. - 108 predictions absolutely cooke. - Back-to-back 6-figure bags on Huskies, Hornets spreads, Dodgers, Arizona Wildcats - every major sports market is his playground. He’s not betting. He’s exploiting inefficient sports lines while normies chase politics. And right now this guy just dropped below the exact 24-strategy playbook: -> Standard + Sniper signals. -> Full settings matrix for every budget ($15 to $1K+) and every risk level (Conservative -> High-Octane Shark mode). -> Every advanced parameter broken down so you don’t blow up your bag. This is the 2026 meta imo. Exhibit A: [Mentallyillgambld] -> Tired of watching from the sidelines? Copy this exact whale + the rest of the top sports degens live and automatically with Just paste his wallet [0x2b3ff45c91540e46fae1e0c72f61f4b049453446] here and let it mirror every move in real time. No spreadsheets. No FOMO. Pure autopilot alpha. Stop trying to beat the spreads. Start copying the ones who already own them. Save the trader. Save the Claude blueprint. The edge just got served on a silver platter.](https://image.24vids.com/tw-2041987289547419723/amplify_video_thumb/2041986800617455616/img/8sl_Ls0-u8zBHCud.jpg)
Videos
THE ENGINE DIRECTOR BEHIND AAA CONSOLE GAMES WALKED ON STAGE AT A C++ CONFERENCE AND TOLD A ROOM OF EXPERTS THAT THE CLEAN, OBJECT ORIENTED CODE THEY ARE PROUD OF IS A LIE AND IT QUIETLY THROWS AWAY 90% OF THE MACHINE THEY PAID FOR 85 minutes from Mike Acton -- engine director at Insomniac Games, where wasting cycles is not an abstract debate, it is the difference between a game that ships and one that dies. -> His first principle is blunt: the only purpose of any program is to transform data. Not to model the world in pretty classes. Move data, fast. 06:33 -- The lie he calls out: "Software is the platform". No. The hardware is the platform. Ignore it and you cannot even reason about the cost of your own code. Most devs never look at what the CPU actually does. They stack abstractions until the machine spends its life waiting on memory it should never have touched. 49:38 -- He prints a value, zips it, and measures the waste directly. A brutal little trick that exposes how much of your data is pure noise. And this is the AI era's blind spot too. Agents generate more abstract code faster, on hardware nobody profiles, while the GPU bill quietly explodes. You thought clean code was the goal. This is the man shipping real engines saying the machine is the goal, and clean code is often how you lose it. Bookmark it & you'll never look at your own abstractions the same ↓
slash1s168,917 просмотров • 4 дней назад
A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓
slash1s680,508 просмотров • 17 дней назад
A NOBEL WINNING PHYSICIST ARGUED THAT NO AI, NO MATTER HOW POWERFUL, WILL EVER TRULY UNDERSTAND A SINGLE THING IT SAYS. HIS REASON IS NOT COMPUTE OR DATA -- IT IS A MATH THEOREM FROM THE 1930s THAT SAYS SOME TRUTHS CAN BE SEEN BUT NEVER COMPUTED 81 minutes with Roger Penrose -- the Oxford physicist who won a Nobel for his work on black holes and general relativity. -> His claim: whatever consciousness is, it is not a computation. A machine following rules can imitate understanding, but never actually have it. He builds it on Gödel: a human can just "see" that certain statements are true, even though no algorithm can ever prove them. That seeing, he argues, is non-computational. If he is right, understanding is not something you scale into. You can stack a trillion parameters and still have zero awareness underneath. Which cuts straight through the AI moment. Everyone assumes bigger models will eventually "wake up". Penrose says that is a category error -- more computation is still just computation. You thought intelligence and understanding were the same thing. This is the conversation that pulls them apart. Save this. It is the sharpest case against the hype ↓
slash1s274,193 просмотров • 12 дней назад
A DEVELOPER STOOD UP AT A CONFERENCE AND APOLOGIZED TO THE ENTIRE INDUSTRY FOR ONE LINE OF CODE HE WROTE IN 1965. THAT SINGLE DECISION HAS SINCE CAUSED CRASHES, BREACHES AND ROUGHLY A BILLION DOLLARS OF DAMAGE AND YOU HIT IT EVERY SINGLE DAY 61 minutes from Tony Hoare -- the man who invented quicksort, won the Turing Award, and passed away in early 2026, leaving this as one of his most honest talks. -> His confession: he added the null reference, letting any variable secretly mean "nothing", simply because it was easy to implement. That one shortcut became the null pointer, the undefined, the None -- the crash waiting inside almost every program ever written. He calls it his billion-dollar mistake, and he is not exaggerating. Whole classes of bugs and security holes trace straight back to it. And this is the deeper lesson under the AI rush: the easy shortcut you ship today becomes the landmine everyone steps on for the next 40 years. You thought null was just part of how code works. This is the talk where the man who made it tells you it never had to be. Bookmark & Watch this one. You'll never type null the same way ↓
slash1s278,746 просмотров • 13 дней назад
IN A 1997 KEYNOTE A DEVELOPER TOLD A ROOM FULL OF PROGRAMMERS THAT THE COMPUTER REVOLUTION HAD NOT ACTUALLY HAPPENED YET. THEN HE PLAYED A CLIP OF WINDOWS, ICONS AND LIVE EDITING RUNNING ON A MACHINE FROM 1973 AND THE ROOM WENT QUIET. 62 minutes from Alan Kay -- the man who invented the word "object-oriented" and helped build the first modern personal computer at Xerox PARC. -> The idea that lands: almost everything you call "computing" is just paper, digitized. Documents, mail, folders. We took the most powerful medium ever made and used it to imitate the office. The real machine -- the one that thinks with you, that you shape live instead of typing at -- was sketched in the 60s and 70s, then quietly abandoned. He calls modern software an Egyptian pyramid: millions of bricks stacked by brute force, no structure underneath. And now AI is quietly hauling us back toward what he wanted -- you describe intent, the machine builds the how. Not a revolution from nowhere. A return to a dream we walked away from. You thought this was the future. This is the talk that shows you it is a detour we have been on for 40 years. Save this. It reframes the whole industry ↓
slash1s292,619 просмотров • 16 дней назад
IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓
slash1s431,502 просмотров • 28 дней назад
IN 1986 MIT FILMED A LECTURE THAT OPENS BY TELLING YOU COMPUTER SCIENCE IS NOT A SCIENCE AND HAS ALMOST NOTHING TO DO WITH COMPUTERS 72 minutes from Hal Abelson and Gerald Sussman, the lecture an entire generation of engineers calls the one that rewired how they think. -> The line that lands: computer science is about computers the way astronomy is about telescopes. The tool was never the point. The real subject was always one thing -- controlling complexity. Everything else is detail. Forty years later it reads like a prophecy. AI writes the syntax now. What's left is exactly what they taught: taming complexity nobody can hold in their head. The language was never the skill -> the thinking was. This is where you learn it. Most people chase the newest framework. The ones who watched this think on a level frameworks can't touch. Bookmark & Watch today it, this one's a legend ↓
slash1s359,956 просмотров • 29 дней назад
MIT DEDICATED A FULL LECTURE TO GIT'S INTERNALS -- BECAUSE THEY FOUND MOST DEVS MEMORIZE THE COMMANDS AND HAVE NO IDEA WHAT THE TOOL ACTUALLY DOES A whole 85 minutes MIT session that refuses to teach git as a list of commands to copy, and instead shows you the data model underneath -- the thing that makes every command finally make sense. -> The moment it clicks, git stops being scary magic. You stop memorizing "The incantation that fixed it last time" and start actually knowing what's happening. Most people learn just enough git to not get fired. Four commands, blind faith, and a prayer before every merge. In 2026 that's not enough anymore -> git is the literacy test for being in the room, and "I'll just reclone it" is the fastest way to look junior. An AI agent will branch, commit and rebase faster than you can read. When it tangles the history, untangling it runs on understanding the model MIT teaches in this one hour. Anyone can run git push. The person who understands the graph underneath is the one who saves the repo when it breaks. Bookmark & Watch it ↓
slash1s457,083 просмотров • 1 месяц назад
IN 1985 ONE OF THE GREATEST PHYSICISTS WHO EVER LIVED SAT DOWN TO EXPLAIN HOW COMPUTERS ACTUALLY WORK AND TOLD A ROOM FULL OF ENGINEERS THE MACHINE IS COMPLETELY DUMB 76 minutes from Richard Feynman, still called the clearest explanation of what a computer really is ever given. -> The idea that lands: a computer is just a very, very fast, very, very dumb file clerk. It doesn't think. It follows tiny simple rules, billions of times a second. All the complexity you're in awe of comes from stacking simple things. There's no magic underneath. There never was. Forty years later everyone calls the model "Intelligent". Feynman already told you what it really is: speed, not thought. Being amazed by the machine was never the point -> understanding what it's actually doing is. Most people are dazzled by what AI says. The ones who watched this know exactly what's happening underneath. Bookmark & Watch it today. This one's a legend ↓
slash1s256,728 просмотров • 27 дней назад
A DEVELOPER PROVED THAT MOST OF THE CLASSES YOU WRITE SHOULD NEVER HAVE BEEN CLASSES AT ALL 27 minutes from Jack Diederich, a Python core developer, on how much of your code is just a function wearing a costume. -> The moment it lands, the rule is brutal: if a class has two methods and one is __init__, you meant to write a function. Most people add structure to feel like real engineers. It just adds weight no one ever needed. Writing more code was never the skill -> writing less of it is. And when an AI agent happily generates ten classes for a job that needed three lines, the person who knows what to delete is the one who ships something clean. Anyone can add abstraction. Knowing when not to is the whole job. Bookmark it and Watch ↓
slash1s268,436 просмотров • 1 месяц назад
A LINUX KERNEL DEVELOPER PROVED THE THING YOU PUSH CODE TO IS SECRETLY A DATABASE THAT CAN VERSION ALMOST ANYTHING AND THAT MOST DEVS HAVE ONLY EVER TOUCHED A TENTH OF IT 42 minutes from Josh Triplett -- a longtime Linux kernel and Debian developer -- showing that Git is a general-purpose, tamper-evident versioning engine that just happens to be famous for code. -> The moment it clicks, Git stops being "Where my code lives" and becomes what it really is underneath: a content-addressable store that can version almost anything -- your configs, your notes, your servers' state, entire datasets. People run whole wikis on it. They version their entire machine's configuration with it. They ship websites by pushing to it. They track data too big to email. None of it is a hack -- it's the same handful of objects you already use for code, pointed somewhere new. Treating Git as a code-only tool was never the ceiling -> it's a versioning engine for anything, and the people who see that automate what the rest of the team still does by hand. And as AI agents start spitting out not just code but configs, docs and data, the one system that can version and audit all of it at once is already sitting on your machine. You learned five commands to survive. This is the talk that shows you were standing on top of a database the whole time. It changes what you think the tool is even for. Bookmark & Watch it today ↓
slash1s383,642 просмотров • 1 месяц назад
![Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:](https://image.24vids.com/tw-2032564109791703167/amplify_video_thumb/2032562579898310658/img/VqnTgfL8TXHxtSq-.jpg)
Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:
slash1s1,135,450 просмотров • 4 месяцев назад
ONE OF THE MINDS BEHIND JAVA GAVE A LECTURE WHERE HE BANNED HIMSELF FROM USING ANY BIG WORD UNTIL HE DEFINED IT FIRST AND IN DOING SO QUIETLY EXPLAINED HOW EVERY GREAT SYSTEM GETS BUILT A talk from Guy Steele -- co-author of the Java spec and a designer of Scheme -- where the form of the talk is the lesson. -> The moment you catch what he's doing, it rewires you. He starts with only the smallest words, then builds every larger idea live, in front of you, from the pieces he already gave you. The talk grows its own vocabulary as it goes. That's the whole secret of language and system design he's smuggling in: you don't ship something huge and finished. You ship a small core and the means to grow it. Miss that and you spend your career fighting your own tools. Memorizing a language was never the skill -> understanding how a good one is meant to grow is. And as AI starts generating the primitives you build on, knowing what makes a foundation extensible instead of brittle is the entire game. Twenty-five years on, language designers still point to this talk as the cleanest demonstration of the idea ever performed. Bookmark it & Watch how he builds the whole thing from nothing ↓
slash1s358,660 просмотров • 1 месяц назад
MICROSOFT PUT THE ENTIRE SOURCE CODE OF WINDOWS INTO A SINGLE GIT REPO -- 300 GIGABYTES, 3.5 MILLION FILES AND MADE IT CLONE IN SECONDS 31 minutes from the Microsoft engineer who actually did it, bending the tool you use for a 50-file side project to hold the biggest codebase on the planet. -> The moment it lands, every excuse dies. You've heard "Git doesn't scale, use something else" your whole career. They ran git status on the largest repo alive and it came back instantly. The secret wasn't a different tool. It was understanding what git actually stores and realizing you never need the whole thing on disk. Pull only the files you touch. Ignore the 3.4 million you never will. Knowing the commands was never the ceiling -> understanding how git stores and moves things is what lets you do what everyone swears is impossible. And as AI agents spin up repos that balloon overnight, knowing how git scales is what keeps you from drowning in your own history. Everyone treats git like it maxes out at a hobby project. One team quietly proved it can hold an operating system. Bookmark & Watch it today ↓
slash1s255,565 просмотров • 1 месяц назад
A WRITER WENT ON STAGE AND CALLED IT YEARS EARLY: THE MACHINE ON YOUR DESK WOULD SLOWLY STOP BEING YOURS AND ALMOST NO ONE WOULD NOTICE THE MOMENT IT FLIPPED 54 minutes from Cory Doctorow, arguing the copyright fights were just the warm-up -- that the real battle is whether general-purpose machines, the kind that run whatever you tell them, are even allowed to keep existing. -> The moment it lands, you see the move. Nobody takes your computer away. They sell you one that looks like yours but answers to them -- locked down, logging, checking with a server you'll never see before it obeys you. For years that was DRM and locked phones. Now it's intelligence itself. Rent your AI from the cloud and the most powerful tool on your desk was never yours: it reads your prompts, sets your limits, keeps your data, and can cut you off tomorrow. You don't own the computation. You're licensed to borrow it. Using the smartest model was never the whole game -> owning the machine that runs it is. And the post below is what winning that war looks like now: one box, private AI running on it, every byte of client data kept off the cloud, and the line item that drains everyone else's account quietly turned into income. A rented computer obeys whoever signs the check. The one you own obeys you. In the AI era, that's the entire difference. The full breakdown below. Bookmark & Watch it today ↓
slash1s240,684 просмотров • 1 месяц назад
A DEVELOPER MADE A REAL COMMIT WITHOUT EVER TYPING GIT ADD OR GIT COMMIT -- JUST TO PROVE THE COMMANDS YOU LIVE BY ARE A THIN SHELL OVER A DATABASE YOU'VE NEVER ONCE OPENED 55 minutes from Tim Berglund, a longtime Git teacher and GitHub evangelist, taking the tool apart down to the raw objects almost nobody who uses it every day has ever touched. -> The moment it clicks, Git stops being a pile of memorized commands and becomes what it actually is underneath: a tiny content-addressed database of blobs, trees and commits. git add and git commit are just polite wrappers around writing objects into it by hand. Every commit you've ever made was Git hashing a snapshot and filing it by fingerprint. Branches are just labels pointing at one of those objects. The work you thought you destroyed with a bad reset is still sitting in the reflog. Once you can see that graph, the commands that used to terrify you stop being scary at all. Memorizing commands was never the skill -> reading the object graph in your head is. And with an AI agent now committing and rebasing on your machine faster than you can follow, the one person who can untangle the mess it leaves is the one who knows what's really stored down there. There's a person on every team everyone runs to when Git breaks. This is the talk that quietly turns you into them. You'll reach for it the next time a rebase goes sideways. Bookmark & Watch it today ↓
slash1s236,545 просмотров • 1 месяц назад
Game Changer: Chinese college student Guo Hangjiang (GitHub: 666ghj) codes MiroFish AI swarm engine solo in 10 days with AI assistants, explodes GitHub to 23k+ stars, bags $4.1M from Shanda Group in 24 hours, ditches dorm life to become Shanghai CEO. Tech Highlights: .GraphRAG builds detailed knowledge graphs (e.g., 905 entities, 3,822 relations in demos). .Agents with Zep memory, unique traits, sims capped at ~40 rounds for efficiency. .Hybrid Node.js/Python backend, Docker deploy, OpenAI-compatible LLMs. .Low-resource scaling: 8B param dragon boat festival sim runs smooth (vid demo). .AGPL license, credits CAMEL-AI, recent fixes for UI/errors. .Online demo: - Test predictions interactively. Creator Quick Look: >> BaiFu (Guo Hangjiang), BUPT senior turned Shanghai CEO at Shanda. >> Built on 38.6k-star BettaFish & MindSpider for full data-prediction pipelines. >> 790 contribs last year. Real-World Plays -> -> Sentiment: Wuhan Uni backlash sim predicts trends (repo vid). -> Literary: Agents finish Dream of the Red Chamber's lost ending. -> Finance: Historical data for market forecasts; like bags on Polymarket SPX bets. -> Policy: "What-if" for bills/geopolitics. -> Cultural: Dragon boat fest agent dynamics. -> Ecosystems: Integrates crawlers for PR/decision tools. Repo:
slash1s684,221 просмотров • 4 месяцев назад
A PHYSICS PROFESSOR GAVE THE SAME LECTURE 1,742 TIMES TO WARN ABOUT ONE IDEA QUIETLY BREAKING THE HUMAN RACE. IT IS THE WORD EVERY AI LAB THROWS AROUND TODAY AND ALMOST NONE OF THEM ACTUALLY UNDERSTAND IT. 72 minutes from Albert Bartlett, a University of Colorado physicist, on the one piece of math he called humanity's greatest blind spot. -> The idea that lands: a small steady percentage is never small. 7% a year sounds calm, but it doubles in a decade, doubles again in the next, and quietly runs away from you. He shows it with bacteria in a bottle, with money, with whole cities -- the same curve that looks flat for ages and then explodes almost overnight. Everyone calls that curve "Exponential". He proves most people hear it as "Fast" when it actually means something far more violent. Right now every lab and every headline calls AI exponential. This is the talk that shows you what the word truly does -- so you see both the hype and the real danger the way the math demands. You thought it was a boring math word. It was the whole story the entire time. Save this one. You'll never hear "Exponential" the same way ↓
slash1s99,512 просмотров • 20 дней назад
A DEVELOPER TAUGHT GIT WITH A BOX OF CHILDREN'S TOYS AND ENGINEERS WITH TEN YEARS IN SAY IT'S THE FIRST TIME THE THING EVER ACTUALLY MADE SENSE 90 minutes, one table, a pile of Tinkertoys. No wall of jargon -- he builds a real Git repo out of plastic rods right in front of you. -> The moment he snaps the first pieces together, Git stops being scary command-line magic and becomes what it really is: a chain of tiny objects pointing at each other. Branches, merges, rebase, the staging area -- every concept that's ever burned you at 2am -- he rebuilds with toys until a four year old could follow. He calls Git a two-trick pony. After this you'll see exactly why. Memorizing commands was never the skill -> holding the graph in your head is. And with an AI agent now committing and rebasing on your machine all day, that mental model is the only thing between you and a history you can't read. Scroll the comments and you'll see the same thing over and over: this is the talk that finally made Git click and made people the one their whole team comes to when it breaks. Bookmark & watch it today. It's the 1.5 hours that pays you back for the rest of your career ↓
slash1s226,935 просмотров • 1 месяц назад
MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓
slash1s236,758 просмотров • 1 месяц назад