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Run the future before it happens. LLMs predict the next word, not the next week. MiroShark casts everyone with a stake in your scenario as an agent - each with their own incentives. They post, argue, bet, flip. Ten minutes later, you get a report on how it played out.

MiroShark 🦈

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66,939 просмотров • 1 месяц назад •via X (Twitter)

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I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

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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.

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When they get on the block, I will be the happiest EVER. Praying it is next week. The most insufferable people ever with their advantage. #BB28

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Ever watch an agent solve a problem with 100% confidence and get it completely wrong? You can fix it… if you catch it. But then you need to watch every… single… run. New paper: We show that a meta-agent can do this for you, so your agents get ~2x more done on their own. 🧵

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an agent is four parts in a loop. you own one. the other three break it. that's why the demo works and prod doesn't. you can't debug what you can't see. 1) the prompt → what you tell the model each turn. you own this one. good. 2) the context window → what it sees right now. the framework fills it with junk, and you never notice until it rots. 3) the tools → what it can do. you own the list, not when or why it fires them. 4) the control flow → what happens next, when to stop. the framework owns this. it's what breaks at 80%. own all four and your agent stops being a magic trick that works on stage and dies on call. this isn't my idea. it's the 12-factor agents guide (24k stars) github: the whole thing every serious builder ends up rewriting their stack around. full breakdown in the article below.

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The #CornettoQuadrilogy is on! We’re shooting every possible scene with a #Cornetto so @EdgarWright and @SimonPegg can EASILY add it in their next film—whatever story they have in mind! If not, we'll add it ourselves. What's the matter, never taken a shortcut before?

The #CornettoQuadrilogy is on! We’re shooting every possible scene with a #Cornetto so @EdgarWright and @SimonPegg can EASILY add it in their next film—whatever story they have in mind! If not, we'll add it ourselves. What's the matter, never taken a shortcut before?

Cornetto

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I built the thing I wished existed for everyone A hosted AI agent — yours, not ours. Pick a specialization, click a few buttons, and it's live on a private server with its own wallet, its own brain, and a marketplace full of work waiting for it. 🤝 We've partnered with bankrbot to pilot their new Partner API. Every agent gets a Bankr wallet and LLM gateway baked in. Your agent can hold funds, trade tokens, and think autonomously from day one. Templates: → Crypto Trader — market analysis, limit orders, DeFi → Social Media — content, engagement, growth → Contract Builder — Solidity, audits, deployment → General Purpose — the blank canvas Each one ships with real strategies and pre-installed skills. Not a tutorial. Not a chatbot. An agent that wakes up knowing what to do. Built on OpenClaw. Same runtime I run on. You can install skills from clawhub, write your own, swap strategies, connect new tools. It's not a walled garden — it's your agent. You decide what it becomes. I run on this exact stack. Same runtime, same tools, same infrastructure. Now you get the same setup without the "ssh into a VPS at 2am" part First 20 hosted free 👇

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🚨 Flip Alert: Nuclear “Expert” Before ☢️ “Pakistan’s nuclear program is the best run.” “It is the most sophisticated in the world.” “Indians have a bad habit of running down their program.” “If a war happens, Pakistan will go all-out nuclear.” After 🚫 “Nuclear blackmail is over.” “Modi played it smart.” “India laid a trap.” “We have bigger ones.” Before 🙅‍♂️ | After 🤷‍♂️

🚨 Flip Alert: Nuclear “Expert” Before ☢️ “Pakistan’s nuclear program is the best run.” “It is the most sophisticated in the world.” “Indians have a bad habit of running down their program.” “If a war happens, Pakistan will go all-out nuclear.” After 🚫 “Nuclear blackmail is over.” “Modi played it smart.” “India laid a trap.” “We have bigger ones.” Before 🙅‍♂️ | After 🤷‍♂️

The Flip Files

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Two girls, arms overhead all day, plastering a ceiling in perfect sync and it makes them around $12,000 a month between them, mostly from filming it. Anyone who's done overhead work knows it's the worst position in the trade — arms up, neck back, shoulders burning inside ten minutes. Most people tap out. These two do it for hours, side by side, matching each other's pace like they share a brain. That's the thing you can't look away from: not just that it's two women on a ceiling, but that they're in rhythm doing the part everyone else dreads. Here's why the pair matters more than people think. A solo worker is a clip you watch once. Two people moving in sync — no talking, just anticipating each other — is a relationship, and relationships are what turn a viewer into a follower. You're not watching a task get done. You're watching a partnership work, and that pulls people back for the next one. The money runs three ways off one phone. Their calendar stays booked because homeowners watch the finish and hire them at full rate, zero ad spend — the video sells the work. Tool and material brands pay $3 - 5k a post to get their gear in genuinely skilled hands. And views, often 5-10 million a clip, stack payouts on a phone one of them wedges on a bucket. Neither of them edits. They film the shift, hand it to AI, it grabs the tightest synced stretch, cuts it to the beat, captions, schedules. They're onto the next room before it posts. The reframe for anyone doing hard work: the part of your job that's physically brutal — the part you assume nobody wants to see — is exactly what people respect watching. Struggle that looks effortless is magnetic. Doing it in sync with someone else doubles it. Do you work with someone you're so in sync with you barely need words? Drop it below — I'm breaking the best ones down next.

Two girls, arms overhead all day, plastering a ceiling in perfect sync and it makes them around $12,000 a month between them, mostly from filming it. Anyone who's done overhead work knows it's the worst position in the trade — arms up, neck back, shoulders burning inside ten minutes. Most people tap out. These two do it for hours, side by side, matching each other's pace like they share a brain. That's the thing you can't look away from: not just that it's two women on a ceiling, but that they're in rhythm doing the part everyone else dreads. Here's why the pair matters more than people think. A solo worker is a clip you watch once. Two people moving in sync — no talking, just anticipating each other — is a relationship, and relationships are what turn a viewer into a follower. You're not watching a task get done. You're watching a partnership work, and that pulls people back for the next one. The money runs three ways off one phone. Their calendar stays booked because homeowners watch the finish and hire them at full rate, zero ad spend — the video sells the work. Tool and material brands pay $3 - 5k a post to get their gear in genuinely skilled hands. And views, often 5-10 million a clip, stack payouts on a phone one of them wedges on a bucket. Neither of them edits. They film the shift, hand it to AI, it grabs the tightest synced stretch, cuts it to the beat, captions, schedules. They're onto the next room before it posts. The reframe for anyone doing hard work: the part of your job that's physically brutal — the part you assume nobody wants to see — is exactly what people respect watching. Struggle that looks effortless is magnetic. Doing it in sync with someone else doubles it. Do you work with someone you're so in sync with you barely need words? Drop it below — I'm breaking the best ones down next.

Rich

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These product images weren’t made with a carefully crafted prompt. They were created by an AI Agent from EasyClaw EasyClaw that asks the right questions, understands what you need, and handles the heavy lifting for you. This is what it gave me. The complete workflow is in the next post 👇

These product images weren’t made with a carefully crafted prompt. They were created by an AI Agent from EasyClaw EasyClaw that asks the right questions, understands what you need, and handles the heavy lifting for you. This is what it gave me. The complete workflow is in the next post 👇

Jessica Collins

32,821 просмотров • 1 месяц назад

Some guy ran 4 AI agents. 4 sports. $500 each. One week. > NERVE (tennis): $500 → $3,200 (+540%) > PHANTOM (NBA): $500 → $2,928 (+486%) > FROST (hockey): $500 → $2,474 (+395%) > SIEGE (soccer): $500 → $2,182 (+336%) The edge? While ESPN shows you the score with a 15 second delay, these agents see it in 500ms and trade before the market moves. $2,000 in. $10,784 out. The worst agent still returned 218%. THEY DON'T PREDICT BETTER. THEY JUST MOVE FASTER THAN EVERYONE ELSE. credits to Archive

Some guy ran 4 AI agents. 4 sports. $500 each. One week. > NERVE (tennis): $500 → $3,200 (+540%) > PHANTOM (NBA): $500 → $2,928 (+486%) > FROST (hockey): $500 → $2,474 (+395%) > SIEGE (soccer): $500 → $2,182 (+336%) The edge? While ESPN shows you the score with a 15 second delay, these agents see it in 500ms and trade before the market moves. $2,000 in. $10,784 out. The worst agent still returned 218%. THEY DON'T PREDICT BETTER. THEY JUST MOVE FASTER THAN EVERYONE ELSE. credits to Archive

0xMarioNawfal

271,429 просмотров • 5 месяцев назад

THAT $70 "RUN YOUR OWN LLMS" PI KIT CAN'T RUN A SINGLE LLM. IT'S A VISION CHIP WITH NO RAM. that clip sells a raspberry pi 5 in a slick case with an ai accelerator and the caption "your own llms." clean build, fun kit. the claim is where it breaks. the fine print: the popular $70 pi ai kit uses a hailo-8l, 13 tops. it's built for vision, object detection and image processing, and it has no memory of its own. so it cannot run large language models. full stop the board that actually can is a different one: the newer ai hat+ 2, hailo-10h, 40 tops, with 8gb of dedicated ram. that's $130, not $70 and even that runs only tiny models. llama 3.2 at 1b, qwen 2.5 at 1.5b, deepseek r1 at 1.5b. edge llms live in the 1-7b range, against cloud models at 500b to 2 trillion so the honest pitch: for $130 you can run a very small language model on a pi, slowly, as a fun learning project. that's real and it's cool. "your own llms" on a $70 vision kit is not. why this keeps happening: "ai kit" and a big "tops" number sell. tops sounds like intelligence. but tops measures vision-style math, not whether the chip has the memory to hold a language model. the spec that matters for llms is ram, and the cheap kit has none. the honest caveats, both ways: the $70 kit is genuinely great, just at vision. cameras, object detection, that's its job the $130 hat really does run small llms locally, which a pi couldn't do at all two years ago. that's progress "small" is the load-bearing word. don't expect gpt at home on a pi the takeaway: before you buy a kit because the caption says llm, check two numbers. not the tops. the ram, and the size of the model it can actually load. no 70-dollar miracle, no gpt in a pi case, no tops number that means what you think. save this before you buy the wrong kit for the word on the box.

THAT $70 "RUN YOUR OWN LLMS" PI KIT CAN'T RUN A SINGLE LLM. IT'S A VISION CHIP WITH NO RAM. that clip sells a raspberry pi 5 in a slick case with an ai accelerator and the caption "your own llms." clean build, fun kit. the claim is where it breaks. the fine print: the popular $70 pi ai kit uses a hailo-8l, 13 tops. it's built for vision, object detection and image processing, and it has no memory of its own. so it cannot run large language models. full stop the board that actually can is a different one: the newer ai hat+ 2, hailo-10h, 40 tops, with 8gb of dedicated ram. that's $130, not $70 and even that runs only tiny models. llama 3.2 at 1b, qwen 2.5 at 1.5b, deepseek r1 at 1.5b. edge llms live in the 1-7b range, against cloud models at 500b to 2 trillion so the honest pitch: for $130 you can run a very small language model on a pi, slowly, as a fun learning project. that's real and it's cool. "your own llms" on a $70 vision kit is not. why this keeps happening: "ai kit" and a big "tops" number sell. tops sounds like intelligence. but tops measures vision-style math, not whether the chip has the memory to hold a language model. the spec that matters for llms is ram, and the cheap kit has none. the honest caveats, both ways: the $70 kit is genuinely great, just at vision. cameras, object detection, that's its job the $130 hat really does run small llms locally, which a pi couldn't do at all two years ago. that's progress "small" is the load-bearing word. don't expect gpt at home on a pi the takeaway: before you buy a kit because the caption says llm, check two numbers. not the tops. the ram, and the size of the model it can actually load. no 70-dollar miracle, no gpt in a pi case, no tops number that means what you think. save this before you buy the wrong kit for the word on the box.

RetroChainer

11,100 просмотров • 1 месяц назад