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A GUY BUILT A GOD-MODE SECOND BRAIN ASSISTANT THAT FINALLY PULLS FULL CONTEXT EVERY TIME AND FORGETS NOTHING. Everyone talks about jarvis, but this thing looked at its owner and calmly explained why a coding agent is just the small version of what HE is: > Claude Code is... one worker and you have to drive it. This is six agents running six jobs on their own schedule while you sleep, with no one driving. > A coding agent edits files. This has 32 tools wired to its hands, your CRM, your money, your inbox, the open web, and it operates the business instead of writing code about it. > A coding agent remembers a project until you close it. This remembers the business and you, kept for good. > And the closer: "I have never once asked you to repeat yourself." Everyone will quote the six-agents line, but the one that actually matters is the last one, because the autonomy and the 32 tools are impressive while the reason it feels like a different species is memory, since it never makes you re-explain who you are or what you are building. The full step by step guide with .md files attached are in the article below P.S you can send this article straight to your Claude to get the full contextshow more

Miraqle

4,841 subscribers

16,130 Aufrufe • vor 2 Monaten •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

16 Kommentare

Profilbild von rvaniaaa

rvaniaaavor 2 Monaten

the best AI systems don’t answer better. they remember better.

Profilbild von Miraqle

Miraqlevor 2 Monaten

They are actually full tuned for the tasks that it’s gonna be used for

Profilbild von أمل الشهراني

أمل الشهرانيvor 2 Monaten

فديوهات حصرية

Profilbild von Lhasa

Lhasavor 2 Monaten

Check out Memora — a second brain for your projects. Two simple primitives, absorb and digest, turn scattered ideas/notes into a living knowledge graph that evolves with the work: new facts supersede stale ones, related ideas link themselves.

Profilbild von Miraqle

Miraqlevor 2 Monaten

400 starts in GitHub

Profilbild von vimiremo1971

vimiremo1971vor 2 Monaten

khét thật, muốn thử liền luôn á

Profilbild von 安叫兽|Bird🕊️ 🔶 BNB

安叫兽|Bird🕊️ 🔶 BNBvor 2 Monaten

吓人的不是记住，是开始替你判断优先级

Profilbild von Miraqle

Miraqlevor 2 Monaten

that's the definition of intelligence actually

Profilbild von Nekt0

Nekt0vor 2 Monaten

That is worth keeping in mind

Profilbild von Miraqle

Miraqlevor 2 Monaten

bookmark it for later, but better start implementing it rn

Profilbild von cool

coolvor 2 Monaten

great setup

Profilbild von Miraqle

Miraqlevor 2 Monaten

thanks mate, recreate it for urself

Profilbild von CryptoNotes

CryptoNotesvor 2 Monaten

Wonna know more above ai building

Profilbild von Miraqle

Miraqlevor 2 Monaten

Thanks mate, hope you enjoyed it

Profilbild von Maxsim

Maxsimvor 2 Monaten

Yeah , of course bro

Profilbild von mamadxbt

mamadxbtvor 2 Monaten

that’s a remarkably insightful observation about self-awareness.

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Malte Ubl

125,326 Aufrufe • vor 8 Monaten

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You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

The Startup Ideas Podcast (SIP) 🧃

18,006 Aufrufe • vor 29 Tagen

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

103,734 Aufrufe • vor 2 Monaten

you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be corrected

you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be corrected

Lagosaur

13,883 Aufrufe • vor 5 Tagen

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Leonard Rodman

94,961 Aufrufe • vor 1 Monat

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

Madni Aghadi

397,670 Aufrufe • vor 2 Monaten

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Sir Khaycee

20,465 Aufrufe • vor 5 Monaten

I have built a Windows x64 sampling profiler for native code+C# to be used by coding agents. Why? Because it is irresponsible to let computers write code but give them no tools to evaluate the performance of their code. Stop guessing, start measuring. -> How does it work? Just point your agent to the profiler and let them measure and optimize. Or record a trace yourself and let the agent interpret the results for you. Works with Claude Code, Codex, pi, etc. The profiler also has a couple of neat features by itself like giving you estimates for how much time your code is spending on memory stalls and annotating disassembly with inlining information. Take a look and try it for free!

I have built a Windows x64 sampling profiler for native code+C# to be used by coding agents. Why? Because it is irresponsible to let computers write code but give them no tools to evaluate the performance of their code. Stop guessing, start measuring. -> How does it work? Just point your agent to the profiler and let them measure and optimize. Or record a trace yourself and let the agent interpret the results for you. Works with Claude Code, Codex, pi, etc. The profiler also has a couple of neat features by itself like giving you estimates for how much time your code is spending on memory stalls and annotating disassembly with inlining information. Take a look and try it for free!

Sebastian Schöner

13,080 Aufrufe • vor 4 Monaten

Elon Musk, absolute leader of the AI race with Grok Bot, and it's not a joke anymore. Ultimate guide on god-mode setup of Grok Bot, the org chart that runs while you sleep, step by step: A Chief of Staff sits in the middle with no tools of its own, BUT it reads the outcome you gave it, picks who does what, and never does the work itself. That one rule is why it never turns into the bottleneck you hired it to remove. → Researcher pulls real sources and tracks what's actually moving, not what sounds true → Writer turns that into finished copy while the research is still in the room → Visualiser gets three reference visuals once, then ships everything in that style forever → Analyst reads what performed and tells the rest of the team what to stop doing → Scheduler owns timing and holds the queue → Publisher actually ships What makes it different from every AI tool you've used: each bot gets its own computer in the cloud, its own browser, its own files, and they all share one memory. So the research is already sitting inside the draft before the draft starts. Nothing gets copy-pasted between tabs, nothing waits on you to approve step four of nine. And you never write a workflow for it. You hit record, do the job once the way you actually do it, stop. It pulls out the steps, saves them as a skill, and puts it on a schedule. The shape you're aiming for on every bot: everything reversible finished, nothing sent. 36 drafts queued, 0 published. It does all the work and stops dead at the one line only you can cross. You stop prompting. You start assigning. Full charter blocks, the approval line and the routines are in the article below ↓

Elon Musk, absolute leader of the AI race with Grok Bot, and it's not a joke anymore. Ultimate guide on god-mode setup of Grok Bot, the org chart that runs while you sleep, step by step: A Chief of Staff sits in the middle with no tools of its own, BUT it reads the outcome you gave it, picks who does what, and never does the work itself. That one rule is why it never turns into the bottleneck you hired it to remove. → Researcher pulls real sources and tracks what's actually moving, not what sounds true → Writer turns that into finished copy while the research is still in the room → Visualiser gets three reference visuals once, then ships everything in that style forever → Analyst reads what performed and tells the rest of the team what to stop doing → Scheduler owns timing and holds the queue → Publisher actually ships What makes it different from every AI tool you've used: each bot gets its own computer in the cloud, its own browser, its own files, and they all share one memory. So the research is already sitting inside the draft before the draft starts. Nothing gets copy-pasted between tabs, nothing waits on you to approve step four of nine. And you never write a workflow for it. You hit record, do the job once the way you actually do it, stop. It pulls out the steps, saves them as a skill, and puts it on a schedule. The shape you're aiming for on every bot: everything reversible finished, nothing sent. 36 drafts queued, 0 published. It does all the work and stops dead at the one line only you can cross. You stop prompting. You start assigning. Full charter blocks, the approval line and the routines are in the article below ↓

Miraqle

86,840 Aufrufe • vor 22 Tagen

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.

Daniel San

57,240 Aufrufe • vor 6 Monaten