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WHOEVER BUILT THIS HAS LOST THE PLOT SOMEONE JUST TURNED GROK BOT INTO A PERSONAL BLOOMBERG TERMINAL. Not one bot. A whole swarm working together from one Second Brain. One Grok scans markets. Another watches news and sentiment. Another tracks on-chain activity. Another finds trading setups. Another runs risk...

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MAX

9,490 subscribers

113,922 次观看 • 2 天前 •via X (Twitter)

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Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here’s what it built on its own: Monitoring agent — runs 24/7, spots mispriced markets, writes to memory, sends Telegram alerts instantly Research agent — parses news, X, and macro data every morning before you check your phone Trading agent — reads research memory and executes before the market catches up All on one Gateway, one config file, isolated per agent Copytrade → First week results: 47 mispriced markets captured before Polymarket adjusted 8–12¢ avg edge per position Token cost dropped 3×, from $0.40 → $0.13 per request The entire system is just plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.
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Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here’s what it built on its own: Monitoring agent — runs 24/7, spots mispriced markets, writes to memory, sends Telegram alerts instantly Research agent — parses news, X, and macro data every morning before you check your phone Trading agent — reads research memory and executes before the market catches up All on one Gateway, one config file, isolated per agent Copytrade → First week results: 47 mispriced markets captured before Polymarket adjusted 8–12¢ avg edge per position Token cost dropped 3×, from $0.40 → $0.13 per request The entire system is just plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.

Discover

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Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here's what it built on its own: > Monitoring agent running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.
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Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here's what it built on its own: > Monitoring agent running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.

0xMarioNawfal

80,450 次观看 • 6 个月前

NO WAY. Someone is basically building a MANAGER for your AI agents. You give it one objective. It briefs Claude, Codex + the agents you choose, keeps them on the same context and coordinates the work. This is Jarvix. And I had to dig into this: 🧵 #JarvixOS
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NO WAY. Someone is basically building a MANAGER for your AI agents. You give it one objective. It briefs Claude, Codex + the agents you choose, keeps them on the same context and coordinates the work. This is Jarvix. And I had to dig into this: 🧵 #JarvixOS

EyeingAI

36,135 次观看 • 18 天前

I'M SHOCKED – ALMOST NOBODY IS USING GROK BOT THE RIGHT WAY. A LEAD ENGINEER AT SPACEX AI JUST DROPPED A 1-HOUR COURSE ON HOW IT'S ACTUALLY DONE. HERE'S ALL OF IT IN 60 SECONDS. The mistake almost everyone makes: they hand Grok Bot random one-off tasks. That's it. That's the whole reason it still feels like a chatbot to you. Here's the system he teaches instead – five parts: → ROLE – stop assigning tasks, create permanent roles. Chief of Staff, Inbox Manager, Researcher, Developer, Reviewer. A bot with a job title beats a bot with a to-do list → TOOLS – connect each bot to what it actually needs: Gmail, Slack, Calendar, Notion, GitHub. It does the work on its own persistent cloud computer → SKILL – teach it your workflow. Write the instructions, or just record yourself doing the job once – Grok Bot turns that demonstration into a reusable skill → ROUTINE – anything you repeat becomes a routine. It runs on a schedule or fires off an event, with your laptop closed → TEAM – group the bots together. The lead bot delegates, the specialists work in parallel, and you set approval rules before anything sends an email, moves a calendar or pushes code The one honest catch: a new bot still needs context and some hand-holding on its first few runs. But that's the entire system. Role → Tools → Skill → Routine → Team. Everyone else is still typing one-off prompts into Grok Bot and wondering why nothing compounds. Bookmark this & read the full breakdown in the article below ↓
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I'M SHOCKED – ALMOST NOBODY IS USING GROK BOT THE RIGHT WAY. A LEAD ENGINEER AT SPACEX AI JUST DROPPED A 1-HOUR COURSE ON HOW IT'S ACTUALLY DONE. HERE'S ALL OF IT IN 60 SECONDS. The mistake almost everyone makes: they hand Grok Bot random one-off tasks. That's it. That's the whole reason it still feels like a chatbot to you. Here's the system he teaches instead – five parts: → ROLE – stop assigning tasks, create permanent roles. Chief of Staff, Inbox Manager, Researcher, Developer, Reviewer. A bot with a job title beats a bot with a to-do list → TOOLS – connect each bot to what it actually needs: Gmail, Slack, Calendar, Notion, GitHub. It does the work on its own persistent cloud computer → SKILL – teach it your workflow. Write the instructions, or just record yourself doing the job once – Grok Bot turns that demonstration into a reusable skill → ROUTINE – anything you repeat becomes a routine. It runs on a schedule or fires off an event, with your laptop closed → TEAM – group the bots together. The lead bot delegates, the specialists work in parallel, and you set approval rules before anything sends an email, moves a calendar or pushes code The one honest catch: a new bot still needs context and some hand-holding on its first few runs. But that's the entire system. Role → Tools → Skill → Routine → Team. Everyone else is still typing one-off prompts into Grok Bot and wondering why nothing compounds. Bookmark this & read the full breakdown in the article below ↓

SCOTTY BEAM

91,244 次观看 • 8 天前

HE STRAPPED A BATTERY TO A $599 MAC MINI AND TURNED A DESK COMPUTER INTO A 14-HOUR PORTABLE AI WORKSTATION 00:03 the battery slides onto the side of the mac mini and the whole setup stops behaving like a desk machine. now it can run from a backpack, power a screen, hold local files and keep working without asking for an outlet. that changes the use case completely. instead of renting another cloud box, one silent computer can handle research dumps, meeting notes, scraped pages, voice transcripts and small automation jobs from almost anywhere. with claude connected, it becomes a moving command center. 45-minute calls become summaries, 120 saved links become organized notes, and messy project folders get cleaned while the machine quietly keeps working in the background. the interesting number is not the battery size. it is the avoided rent. one portable local box can replace $25 storage, $39 automation, $20 transcription and another $30 vps bill if the workflow is built correctly. this is no longer just a desktop. it becomes a portable ai machine that keeps working long after you leave the desk. bookmark this before portable ai becomes the new normal.
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HE STRAPPED A BATTERY TO A $599 MAC MINI AND TURNED A DESK COMPUTER INTO A 14-HOUR PORTABLE AI WORKSTATION 00:03 the battery slides onto the side of the mac mini and the whole setup stops behaving like a desk machine. now it can run from a backpack, power a screen, hold local files and keep working without asking for an outlet. that changes the use case completely. instead of renting another cloud box, one silent computer can handle research dumps, meeting notes, scraped pages, voice transcripts and small automation jobs from almost anywhere. with claude connected, it becomes a moving command center. 45-minute calls become summaries, 120 saved links become organized notes, and messy project folders get cleaned while the machine quietly keeps working in the background. the interesting number is not the battery size. it is the avoided rent. one portable local box can replace $25 storage, $39 automation, $20 transcription and another $30 vps bill if the workflow is built correctly. this is no longer just a desktop. it becomes a portable ai machine that keeps working long after you leave the desk. bookmark this before portable ai becomes the new normal.

Gipp 🦅

1,788,586 次观看 • 2 个月前

Most Heatmaps lie. They show liquidity from one exchange and pretend it’s the market. In reality liquidity is fragmented across multiple exchanges. When you only see one book, you’re trading a partial auction. MMT runs aggregated heatmaps across multiple exchanges, so you actually see where liquidity truly sits. Sometimes the biggest wall isn’t on the chart you’re watching. It’s on another exchange. And that’s where the market reacts.
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Most Heatmaps lie. They show liquidity from one exchange and pretend it’s the market. In reality liquidity is fragmented across multiple exchanges. When you only see one book, you’re trading a partial auction. MMT runs aggregated heatmaps across multiple exchanges, so you actually see where liquidity truly sits. Sometimes the biggest wall isn’t on the chart you’re watching. It’s on another exchange. And that’s where the market reacts.

MMT

12,402 次观看 • 5 个月前

grok bot is running 4 agents that generate revenue 24/7 it also built a 3js dashboard for it, with a starship, a dome per business and starman roadstar floating... - finds the prospects out of public data - scores each one before you spend anything - builds the asset before making contact - takes the payment on whop itself - ships the product or deploys the site - shows you what needs approving each morning everything from finding the customer to selling them just wrote a whole guide on how you can set this up exactly:
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grok bot is running 4 agents that generate revenue 24/7 it also built a 3js dashboard for it, with a starship, a dome per business and starman roadstar floating... - finds the prospects out of public data - scores each one before you spend anything - builds the asset before making contact - takes the payment on whop itself - ships the product or deploys the site - shows you what needs approving each morning everything from finding the customer to selling them just wrote a whole guide on how you can set this up exactly:

Chris

56,585 次观看 • 9 天前

GROK GOT A FILING BRAIN – AND IT NEVER FORGETS xAI update gives Grok something wild – the ability to actually read your files and think through them! File Mode: • Upload PDFs, code, CSVs, or full docs for instant analysis • Handles multiple files at once – blends info into one smart answer • Searches, summarizes, and explains in seconds • Basically a research assistant with zero burnout You bring the data – Grok brings the file brain! Source: Tech Dev Notes
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GROK GOT A FILING BRAIN – AND IT NEVER FORGETS xAI update gives Grok something wild – the ability to actually read your files and think through them! File Mode: • Upload PDFs, code, CSVs, or full docs for instant analysis • Handles multiple files at once – blends info into one smart answer • Searches, summarizes, and explains in seconds • Basically a research assistant with zero burnout You bring the data – Grok brings the file brain! Source: Tech Dev Notes

Mario Nawfal

1,316,338 次观看 • 9 个月前

My Grok Bot + Kimi K3 Made $2,200 While I Was Away I gave them the job, closed the laptop, and came back to this agents were doing the research, checking the numbers, passing work to each other, and Kimi K3 was deciding what needed to happen next I wasn’t sitting there moving results from one chat to another, I wasn’t writing the next prompt, and I wasn’t the guy keeping everything moving RESEARCH was finding the information ANALYSIS was turning it into something useful VERIFY was catching bad numbers and old data KIMI K3 was keeping the whole thing together The crazy part is that I wasn’t even there I set the goal, the agents took the work, and the system kept going while I was away $2,200 later, I came back to the result Maybe the biggest upgrade with AI isn’t making it smarter Maybe it’s finally getting it to stop waiting for us to tell it what to do next
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My Grok Bot + Kimi K3 Made $2,200 While I Was Away I gave them the job, closed the laptop, and came back to this agents were doing the research, checking the numbers, passing work to each other, and Kimi K3 was deciding what needed to happen next I wasn’t sitting there moving results from one chat to another, I wasn’t writing the next prompt, and I wasn’t the guy keeping everything moving RESEARCH was finding the information ANALYSIS was turning it into something useful VERIFY was catching bad numbers and old data KIMI K3 was keeping the whole thing together The crazy part is that I wasn’t even there I set the goal, the agents took the work, and the system kept going while I was away $2,200 later, I came back to the result Maybe the biggest upgrade with AI isn’t making it smarter Maybe it’s finally getting it to stop waiting for us to tell it what to do next

leopardracer

15,710 次观看 • 3 天前

Doctors found what rice filling this man’s arm and leg joints. Then everything changed. He went from having a severe headache to suddenly crashing into a coma. Brain scans revealed another bizarre clue that seemed completely unrelated, but one doctor looked at both findings and immediately recognized the connection. This is one of the strangest diagnostic puzzles I’ve come across. What do you think was really going on? #MedicalMystery #Neurosurgery #Radiology #MedicalEducation #Medicine
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Doctors found what rice filling this man’s arm and leg joints. Then everything changed. He went from having a severe headache to suddenly crashing into a coma. Brain scans revealed another bizarre clue that seemed completely unrelated, but one doctor looked at both findings and immediately recognized the connection. This is one of the strangest diagnostic puzzles I’ve come across. What do you think was really going on? #MedicalMystery #Neurosurgery #Radiology #MedicalEducation #Medicine

Oren Gottfried, MD

17,842 次观看 • 1 个月前

WTF, GROK BOT JUST MADE AI AGENTS AVAILABLE TO LITERALLY ANYONE – CREATING CONTENT HAS NEVER BEEN THIS EASY, EVEN IF YOU'VE NEVER MADE ANYTHING BEFORE Content was never a talent problem. It's a headcount problem. One person doing research, design, copy, analytics, timing and publishing – that's six jobs. The switching between them is what kills consistency, not a lack of ideas. Here's what one of these setups actually looks like. A Chief of Staff sits in the middle and routes every task. Nothing lands on the human. → Researcher tracks what's actually moving and pulls real sources instead of guesswork → Writer turns that research into finished copy, ready to review → Visualiser gets fed a few reference visuals once, then ships everything in that style → Analyst reads the numbers and tells the rest of the team what worked → Scheduler owns timing and holds the queue → Publisher ships it The part that makes it work: every agent on Grok Bot gets its own persistent computer, browser and file system – and they all share memory. So the research is already sitting inside the draft before the draft starts. No copy-pasting between tools. No approving every step. No human in the middle. You can even teach an agent a repetitive task by recording yourself doing it once. Start recording, do the thing, stop. It learns the pattern. And that's the real shift. Nobody needs AI to tell them what to post. They need it to delete the 40 steps between the idea and the post. Everyone has a backlog of things they've meant to make for months. This is what starts clearing it. Full breakdown of the setup in the article below ↓
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WTF, GROK BOT JUST MADE AI AGENTS AVAILABLE TO LITERALLY ANYONE – CREATING CONTENT HAS NEVER BEEN THIS EASY, EVEN IF YOU'VE NEVER MADE ANYTHING BEFORE Content was never a talent problem. It's a headcount problem. One person doing research, design, copy, analytics, timing and publishing – that's six jobs. The switching between them is what kills consistency, not a lack of ideas. Here's what one of these setups actually looks like. A Chief of Staff sits in the middle and routes every task. Nothing lands on the human. → Researcher tracks what's actually moving and pulls real sources instead of guesswork → Writer turns that research into finished copy, ready to review → Visualiser gets fed a few reference visuals once, then ships everything in that style → Analyst reads the numbers and tells the rest of the team what worked → Scheduler owns timing and holds the queue → Publisher ships it The part that makes it work: every agent on Grok Bot gets its own persistent computer, browser and file system – and they all share memory. So the research is already sitting inside the draft before the draft starts. No copy-pasting between tools. No approving every step. No human in the middle. You can even teach an agent a repetitive task by recording yourself doing it once. Start recording, do the thing, stop. It learns the pattern. And that's the real shift. Nobody needs AI to tell them what to post. They need it to delete the 40 steps between the idea and the post. Everyone has a backlog of things they've meant to make for months. This is what starts clearing it. Full breakdown of the setup in the article below ↓

SCOTTY BEAM

4,796,074 次观看 • 12 天前

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.
0:16

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 次观看 • 1 个月前

One of the best finance x AI workflows I've ever built. It helped Claude print +$19,537 completely autonomously. This is my personal market Research Desk - and it took me weeks to build. Think of this as a fully local backtesting trading engine. You pick a market, a timeframe, and a strategy, and it runs that strategy against real historical price data to show you exactly how it would've performed. • It pulls real historical candles (crypto data straight from Binance, no API key needed, or your own CSV/stock data) • You pick from 400+ built-in strategies like moving-average crossover or RSI mean reversion, or bring your own logic • It shows a full interactive equity curve • Every single trade gets logged: entry, exit, P/L • It gives you the real performance metrics If you're not using AI in your trading, you're falling behind fast, and I think everyone should be building personal internal tools like this to elevate their portfolio.
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One of the best finance x AI workflows I've ever built. It helped Claude print +$19,537 completely autonomously. This is my personal market Research Desk - and it took me weeks to build. Think of this as a fully local backtesting trading engine. You pick a market, a timeframe, and a strategy, and it runs that strategy against real historical price data to show you exactly how it would've performed. • It pulls real historical candles (crypto data straight from Binance, no API key needed, or your own CSV/stock data) • You pick from 400+ built-in strategies like moving-average crossover or RSI mean reversion, or bring your own logic • It shows a full interactive equity curve • Every single trade gets logged: entry, exit, P/L • It gives you the real performance metrics If you're not using AI in your trading, you're falling behind fast, and I think everyone should be building personal internal tools like this to elevate their portfolio.

Miles Deutscher

33,442 次观看 • 1 个月前

SOMEONE JUST BUILT A PERSONAL AI DASHBOARD WITH CLAUDE AND IT DOES NOT FEEL LIKE A TOOL. It feels like an operating system for their entire life. One screen. Their calendar. Their tasks. Their goals. Their content pipeline. Their finances. Their health metrics. All connected. All updated in real time. All queryable in plain English. Not ten apps talking past each other. One intelligent surface that knows everything and surfaces what matters. They described what they wanted to Claude Design. Claude built the entire interface. Then they connected it to Opus 4.7 as the reasoning layer. Now instead of opening five apps to answer one question they just ask the dashboard. This is what personal computing looks like when the interface finally understands you. The people building their own AI OS right now will look back at this moment the way the first iPhone users looked back at 2007. You are early.
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SOMEONE JUST BUILT A PERSONAL AI DASHBOARD WITH CLAUDE AND IT DOES NOT FEEL LIKE A TOOL. It feels like an operating system for their entire life. One screen. Their calendar. Their tasks. Their goals. Their content pipeline. Their finances. Their health metrics. All connected. All updated in real time. All queryable in plain English. Not ten apps talking past each other. One intelligent surface that knows everything and surfaces what matters. They described what they wanted to Claude Design. Claude built the entire interface. Then they connected it to Opus 4.7 as the reasoning layer. Now instead of opening five apps to answer one question they just ask the dashboard. This is what personal computing looks like when the interface finally understands you. The people building their own AI OS right now will look back at this moment the way the first iPhone users looked back at 2007. You are early.

CyrilXBT

194,438 次观看 • 4 个月前

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots
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You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

Chubby♨️

494,735 次观看 • 13 天前

Nobody wants to manage 20 AI agents. Managing agents is a job. People don't want another job. They want a personal assistant. Nobody wakes up thinking: "I need a shopping agent." "A travel agent." "A calendar agent." They just want things done. Consumer AI won't be won by whoever has the most agents. It'll be won by whoever understands you best. Context becomes the product. One conversation. One memory. One relationship that evolves with you. Just like a real friend. The best consumer AI won't ask you to manage a team of agents. It'll quietly manage them for you. One world-class agent. That's Rocket.
0:10

Nobody wants to manage 20 AI agents. Managing agents is a job. People don't want another job. They want a personal assistant. Nobody wakes up thinking: "I need a shopping agent." "A travel agent." "A calendar agent." They just want things done. Consumer AI won't be won by whoever has the most agents. It'll be won by whoever understands you best. Context becomes the product. One conversation. One memory. One relationship that evolves with you. Just like a real friend. The best consumer AI won't ask you to manage a team of agents. It'll quietly manage them for you. One world-class agent. That's Rocket.

UpRock

13,554 次观看 • 1 个月前

UltraCode is running over 100 Opus 4.8 agents right now. This is the future of software development. Not 5 agents. Not 10. Over 100 working simultaneously across the entire BridgeMind ecosystem. This is what vibe coding looks like at scale. One person orchestrating a swarm of AI agents building, debugging, and shipping in parallel. We are so early.
0:10

UltraCode is running over 100 Opus 4.8 agents right now. This is the future of software development. Not 5 agents. Not 10. Over 100 working simultaneously across the entire BridgeMind ecosystem. This is what vibe coding looks like at scale. One person orchestrating a swarm of AI agents building, debugging, and shipping in parallel. We are so early.

BridgeMind

108,482 次观看 • 3 个月前

“You want a show? I’ll give you a show.” Go see the The Running Man Movie on the biggest screen, large and loud. We had a wild adventure making this one and you get to enjoy it from the comfort of your cinema seat. Have a blast with it and let me know what you think. (And if you saw it more than once, let us know what you discovered on a second viewing.)
0:13

“You want a show? I’ll give you a show.” Go see the The Running Man Movie on the biggest screen, large and loud. We had a wild adventure making this one and you get to enjoy it from the comfort of your cinema seat. Have a blast with it and let me know what you think. (And if you saw it more than once, let us know what you discovered on a second viewing.)

edgarwright

17,712 次观看 • 9 个月前

THIS IS WHAT YOUR SECOND BRAIN SHOULD LOOK LIKE most people dump notes into obsidian and call it a second brain it’s not a second brain it’s a second drawer karpathy just dropped the pattern that changes this and it’s called the llm wiki every time you add a source ai reads it, extracts key claims, updates every connected note and flags contradictions your obsidian stops being an archive and starts being alive save this and show it to someone still using notion as a second brain full breakdown in the article ↓
0:11

THIS IS WHAT YOUR SECOND BRAIN SHOULD LOOK LIKE most people dump notes into obsidian and call it a second brain it’s not a second brain it’s a second drawer karpathy just dropped the pattern that changes this and it’s called the llm wiki every time you add a source ai reads it, extracts key claims, updates every connected note and flags contradictions your obsidian stops being an archive and starts being alive save this and show it to someone still using notion as a second brain full breakdown in the article ↓

leopardracer

123,618 次观看 • 2 个月前

A lesson for every Polymarket bot developer: I built a strategy that looked perfect on paper. Backtested it. Looked like a winner. Almost went live. Then i actually measured real costs. Strategy was dead before the first trade. And this is what bot building on Polymarket actually looks like. Here is what happened (and what you MUST know): Backtested mean reversion on crypto dips. SOL came back at +44% return and 70.5% win rate. Beautiful clean curve. Looked ready to ship. Then i measured real round-trip costs on SOL flash dips. Backtest assumed 0.45% in fees and slippage. Reality was 1.44%. Strategy stops working at 0.70%. Starting again. But the lesson was worth more than any profit the strategy could have made. Here is what i actually learned: Taking a dip with a market order means you eat the spread the dip just created. The volatility making your signal is the same volatility destroying your fill. You see the opportunity. You enter. You already lost. But resting a limit order below market and letting the dip come to you? You collect the maker rebate instead. Same thesis. Completely opposite execution. One bleeds money, one prints it. That one realization changed how i think about bot strategy entirely. 180 strategies tested to get there. 179 dead. That is not failure. That is how you find the 5 that actually work. Building a bot on Polymarket is not about finding a magic strategy. It is about eliminating every wrong answer until only the right one is left.
0:11

A lesson for every Polymarket bot developer: I built a strategy that looked perfect on paper. Backtested it. Looked like a winner. Almost went live. Then i actually measured real costs. Strategy was dead before the first trade. And this is what bot building on Polymarket actually looks like. Here is what happened (and what you MUST know): Backtested mean reversion on crypto dips. SOL came back at +44% return and 70.5% win rate. Beautiful clean curve. Looked ready to ship. Then i measured real round-trip costs on SOL flash dips. Backtest assumed 0.45% in fees and slippage. Reality was 1.44%. Strategy stops working at 0.70%. Starting again. But the lesson was worth more than any profit the strategy could have made. Here is what i actually learned: Taking a dip with a market order means you eat the spread the dip just created. The volatility making your signal is the same volatility destroying your fill. You see the opportunity. You enter. You already lost. But resting a limit order below market and letting the dip come to you? You collect the maker rebate instead. Same thesis. Completely opposite execution. One bleeds money, one prints it. That one realization changed how i think about bot strategy entirely. 180 strategies tested to get there. 179 dead. That is not failure. That is how you find the 5 that actually work. Building a bot on Polymarket is not about finding a magic strategy. It is about eliminating every wrong answer until only the right one is left.

Oracle Boar

14,379 次观看 • 4 个月前