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your agents keep working after you quit — cmd+q → still running — sleep, memory pressure → still running — progress, context, history → intact — reopen → instant, mid-task native rust + gpui. close the app, come back to finished work.

cristi

3,434 subscribers

63,403 次观看 • 13 天前 •via X (Twitter)

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We are now live! NoelClaw The Runtime layer for agentic AI Robinhood Chain. Your AI remembers, keeps working, and survives every session. Most AI assistants disappear when the conversation ends. Noelclaw gives them persistent state - memory that accumulates, agents that keep running, vaults that version knowledge, and workflows that continue after you close the chat. Instead of starting from scratch every conversation, your AI can remember, research, automate workflows, spawn agents, and keep working long after you close the chat. Connect MCP servers, build reusable skills, deploy persistent agents, and bring your AI into the real world. CA: 0x842245B92b3932Aa8E759A1dAc1eb5ce10cC4f0e website : app : npm : docs : github :

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AGNT Hub

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Kanika

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Most AI agent setups treat every message the same. Simple question? top-tier model. complex task? top-tier model. Your token bill just keeps climbing. I tested OpenSquilla this week on a real document drafting workflow, and the routing caught me off guard. It judges each message's complexity locally, then picks the model tier that fits. Simple tasks go to cheaper models. Complex ones still get the heavy lifting done. You're not paying reasoning tokens for a "hello." I ran a longer workflow, and the context didn't collapse the way it usually does. It distills important information before compression, so you're not starting from scratch mid-session. If you run agents regularly, the bill adds up faster than you think. This is built specifically for that problem. They're running the 10M Token Bill Challenge right now. worth joining if you want to see what smart routing actually saves you in practice. #10MTokenChallenge OpenSquilla

Most AI agent setups treat every message the same. Simple question? top-tier model. complex task? top-tier model. Your token bill just keeps climbing. I tested OpenSquilla this week on a real document drafting workflow, and the routing caught me off guard. It judges each message's complexity locally, then picks the model tier that fits. Simple tasks go to cheaper models. Complex ones still get the heavy lifting done. You're not paying reasoning tokens for a "hello." I ran a longer workflow, and the context didn't collapse the way it usually does. It distills important information before compression, so you're not starting from scratch mid-session. If you run agents regularly, the bill adds up faster than you think. This is built specifically for that problem. They're running the 10M Token Bill Challenge right now. worth joining if you want to see what smart routing actually saves you in practice. #10MTokenChallenge OpenSquilla

Parul Gautam

26,685 次观看 • 3 个月前

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THIS DEVELOPER USED OPENCLAW AGENTS TO RUN HIS B2B BUSINESS VIA TELEGRAM AND MADE $15,000/MONTH he doesn't write prompts from scratch or use generic browser interfaces. he runs a multi-agent framework through a mobile chat. the agents write code, test deployments, and update sites in real-time while he just hits approve the setup is straightforward: - spin up Coolify on a free cloud instance to host your own self-hosted agent panels - link the agent loop to a Telegram gateway to approve code edits from your phone - deploy specialized skill files directly to limit token waste and context decay - containerize the terminal execution using Docker to prevent security breaches if you are still running local agents without container safety, you are leaving money on the table. read the 30-day battle between OpenClaw and Hermes Agent to see who actually wins in production Full breakdown and migration playbook ↓

marfin

26,654 次观看 • 2 个月前

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

Blaze

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This is the PDP-11/83 I've been working on... it's got a lot of pretty das blinkenlights! It's running 211BSD UNIX that I custom compiled to make it all work. I had to tweak the kernel's idle loop to get the display panel to work, as this setup uses fast PMI (private memory interface) between the RAM and CPU, so the panel can't see it happen unless you cheat a little! The dual floppy unit is wild... a single stepper motor moves the heads on BOTH drives at the same time, so they're like conjoined drives. The bus spans three chassis, which are the three grey boxes. So unlike a PC, you can just "add most slots" and keep going to your heart's content!

This is the PDP-11/83 I've been working on... it's got a lot of pretty das blinkenlights! It's running 211BSD UNIX that I custom compiled to make it all work. I had to tweak the kernel's idle loop to get the display panel to work, as this setup uses fast PMI (private memory interface) between the RAM and CPU, so the panel can't see it happen unless you cheat a little! The dual floppy unit is wild... a single stepper motor moves the heads on BOTH drives at the same time, so they're like conjoined drives. The bus spans three chassis, which are the three grey boxes. So unlike a PC, you can just "add most slots" and keep going to your heart's content!

Dave W Plummer

43,343 次观看 • 1 年前

🚨The Clarity Act just made it out of committee. THAT IS A WIN, no question. And yes, I believe American-made digital assets like $XRP $HBAR $XLM $ONDO $TEL $ZBCN $ADA $LINK $ALGO and others will benefit over the long term. BUT SLOW DOWN! This does not mean you sell your house and lever yourself to the moon. This is not the finish line. This is the warm-up lap. There is still more work ahead. The Senate still has to debate it, vote on it, and work through the rest of the process. Then it goes to the President’s desk. And even after that, the real regulatory work begins. So yes, enjoy the progress. Raise a glass tonight if you want. But do not bet the farm. The rails are being built. This is a long-term shift. Do not expect straight-up price action from here. There can still be shakeouts. There can still be pullbacks. There can still be downtrends that test your conviction. But if you zoom out and look at the bigger picture, the direction still looks up to me. That is why I keep saying patience matters. Clarity will not reward the most emotional people. It will reward the people who stay focused long enough to let the thesis play out. Do you have the guts to think long term and hold for generational gains? 👇

🚨The Clarity Act just made it out of committee. THAT IS A WIN, no question. And yes, I believe American-made digital assets like $XRP $HBAR $XLM $ONDO $TEL $ZBCN $ADA $LINK $ALGO and others will benefit over the long term. BUT SLOW DOWN! This does not mean you sell your house and lever yourself to the moon. This is not the finish line. This is the warm-up lap. There is still more work ahead. The Senate still has to debate it, vote on it, and work through the rest of the process. Then it goes to the President’s desk. And even after that, the real regulatory work begins. So yes, enjoy the progress. Raise a glass tonight if you want. But do not bet the farm. The rails are being built. This is a long-term shift. Do not expect straight-up price action from here. There can still be shakeouts. There can still be pullbacks. There can still be downtrends that test your conviction. But if you zoom out and look at the bigger picture, the direction still looks up to me. That is why I keep saying patience matters. Clarity will not reward the most emotional people. It will reward the people who stay focused long enough to let the thesis play out. Do you have the guts to think long term and hold for generational gains? 👇

X Finance Bull

139,024 次观看 • 3 个月前

You may not have realized it yet. But the market for routine business operations is about to collapse. Because this is what their replacement looks like. A telegram chat. A team of AI agents. He types what he needs. They execute. He approves in ten minutes and moves on. Code written. Sites built. Leads qualified. Support handled. Invoices sent. Competitors monitored. All of it. Ten minutes a day. A 6-person ops team costs $28,000/month. His agents do the same work for $200/month in tools. They didn't hire the team back. The person who built those agents charges $2,500/month to maintain them. The gap between someone running this system and someone still doing it manually is not closing. It widens every week.

You may not have realized it yet. But the market for routine business operations is about to collapse. Because this is what their replacement looks like. A telegram chat. A team of AI agents. He types what he needs. They execute. He approves in ten minutes and moves on. Code written. Sites built. Leads qualified. Support handled. Invoices sent. Competitors monitored. All of it. Ten minutes a day. A 6-person ops team costs $28,000/month. His agents do the same work for $200/month in tools. They didn't hire the team back. The person who built those agents charges $2,500/month to maintain them. The gap between someone running this system and someone still doing it manually is not closing. It widens every week.

Superior

34,352 次观看 • 1 个月前

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.

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 • 28 天前

Introducing: Control Tower. After running agencies for over a decade, three problems were clear: 1. Task management tools aren't built for client work—the constant back-and-forth, the private and public feedback loops. 2. Financial tools don't understand service businesses. We need to know what's coming in, what clients owe, when they're paying, and how that stacks up against what we're paying our team. 3. We need easy tools to track and manage our team's capacity in real-time. But here's the thing—these tools need to work together. When you work with clients, your team's capacity dictates the projects you can take on. The projects you take on dictate your revenue. And the pace of those projects restarts the entire cycle. That's what Control Tower does. Today we're announcing the foundations: Tasks, financials, and capacity. Very soon we'll be introducing Chief—the AI layer that connects everything and works proactively to keep your team moving. We built this from the ground up to be agent-native. What we have today is good. It's not great yet. There's a lot of work to do, and a lot we haven't even shipped to our beta users. I'd love for you to check it out. Sign up for the beta—we're inviting people as quickly as we can and doing personal walkthroughs.

Introducing: Control Tower. After running agencies for over a decade, three problems were clear: 1. Task management tools aren't built for client work—the constant back-and-forth, the private and public feedback loops. 2. Financial tools don't understand service businesses. We need to know what's coming in, what clients owe, when they're paying, and how that stacks up against what we're paying our team. 3. We need easy tools to track and manage our team's capacity in real-time. But here's the thing—these tools need to work together. When you work with clients, your team's capacity dictates the projects you can take on. The projects you take on dictate your revenue. And the pace of those projects restarts the entire cycle. That's what Control Tower does. Today we're announcing the foundations: Tasks, financials, and capacity. Very soon we'll be introducing Chief—the AI layer that connects everything and works proactively to keep your team moving. We built this from the ground up to be agent-native. What we have today is good. It's not great yet. There's a lot of work to do, and a lot we haven't even shipped to our beta users. I'd love for you to check it out. Sign up for the beta—we're inviting people as quickly as we can and doing personal walkthroughs.

Hunter Hammonds

53,302 次观看 • 7 个月前

ICE agents: you’re being played. That “$50,000 bonus” you were sold? It’s not a bonus. It’s a leash. It’s paid out in pieces over five years—and the fine print is the punchline: if you quit or don’t hit crazy performance targets, you don’t just lose what’s left… you are forced to pay it back. That’s not an incentive. That’s a trap. And here’s what you can’t pay back or outrun: your family, your friends, and your neighbors will remember what you did. The fear. The humiliation. The harm you inflicted. You’re being asked to do things that will follow you long after the politicians move on to the next lie. So hear this plainly: walk away now. Don’t trade your conscience for money you’ll never see. Don’t let a “bonus” turn into a debt—and don’t let this become the story you spend the rest of your life trying to justify to the people who know you best. If you still have a choice, make it. Choose decency. Choose law. Choose people. Help fight the ones that don’t quite:

ICE agents: you’re being played. That “$50,000 bonus” you were sold? It’s not a bonus. It’s a leash. It’s paid out in pieces over five years—and the fine print is the punchline: if you quit or don’t hit crazy performance targets, you don’t just lose what’s left… you are forced to pay it back. That’s not an incentive. That’s a trap. And here’s what you can’t pay back or outrun: your family, your friends, and your neighbors will remember what you did. The fear. The humiliation. The harm you inflicted. You’re being asked to do things that will follow you long after the politicians move on to the next lie. So hear this plainly: walk away now. Don’t trade your conscience for money you’ll never see. Don’t let a “bonus” turn into a debt—and don’t let this become the story you spend the rest of your life trying to justify to the people who know you best. If you still have a choice, make it. Choose decency. Choose law. Choose people. Help fight the ones that don’t quite:

Joe Walsh

203,164 次观看 • 6 个月前

Danica Patrick Claps Back at Body Shamers With Zero Apologies Danica Patrick is not here for the noise. After posting bikini pics from her Ibiza vacation, the critics rolled in with the usual “eat something” comments. Her response? Pure defiance and receipts. She posted her actual meals and fired back: “Since many have told me to eat after the last post, I shall show you more meals.” Then she went further, flexing after paddleboarding: “If you don’t lift weights (like for real) and eat your damn protein (not 2 eggs), you don’t have to get muscles… And the more muscle you have, the more you get to eat!!!!! The end.” Love this energy. At 44, Danica is still putting in the work, prioritizing strength and real nutrition, and refusing to shrink herself for strangers online. Respect for sticking up for herself and staying locked in on fitness. Keep lifting. Keep eating. Keep winning.

Danica Patrick Claps Back at Body Shamers With Zero Apologies Danica Patrick is not here for the noise. After posting bikini pics from her Ibiza vacation, the critics rolled in with the usual “eat something” comments. Her response? Pure defiance and receipts. She posted her actual meals and fired back: “Since many have told me to eat after the last post, I shall show you more meals.” Then she went further, flexing after paddleboarding: “If you don’t lift weights (like for real) and eat your damn protein (not 2 eggs), you don’t have to get muscles… And the more muscle you have, the more you get to eat!!!!! The end.” Love this energy. At 44, Danica is still putting in the work, prioritizing strength and real nutrition, and refusing to shrink herself for strangers online. Respect for sticking up for herself and staying locked in on fitness. Keep lifting. Keep eating. Keep winning.

Paul A. Szypula 🇺🇸

354,274 次观看 • 7 天前