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Proof of concept: real-time fire detection from an autonomous drone Live camera → fire detection → annotated evidence → agent decision loop → movement command Manako detects the fire Chutes decides the next move Swarm turns it into a flying agent We're not ready for what happens when subnet... show more

Swarm

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21,880 Aufrufe • vor 3 Monaten •via X (Twitter)

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California has built the most technologically advanced wildfire response system in America: ✅ Launched satellites ✅ Deployed AI-powered fire detection cameras ✅ Added fire-mapping aircraft with real-time intelligence ✅ Launched state-of-the-art drones ✅ Built a night-flying firefighting fleet ✅ Built the world’s largest civilian aerial firefighting fleet Under Governor Gavin Newsom Gavin Newsom’s leadership, California isn’t waiting for the future of firefighting —we’re building it.

California has built the most technologically advanced wildfire response system in America: ✅ Launched satellites ✅ Deployed AI-powered fire detection cameras ✅ Added fire-mapping aircraft with real-time intelligence ✅ Launched state-of-the-art drones ✅ Built a night-flying firefighting fleet ✅ Built the world’s largest civilian aerial firefighting fleet Under Governor Gavin Newsom Gavin Newsom’s leadership, California isn’t waiting for the future of firefighting —we’re building it.

Governor Newsom Press Office

193,688 Aufrufe • vor 1 Monat

🇺🇸 LAS VEGAS POLICE LAUNCH TESLA CYBERTRUCK FLEET WITH LIVE DRONE SUPPORT Las Vegas just unveiled a Tesla-powered police fleet built for the future. Sheriff Kevin McMahill says the new Cybertrucks are safer, smarter, and fully equipped for tactical response. Each vehicle connects to a real-time drone operations hub using facial recognition, and gunshot detection. When an alert triggers, drones launch automatically, streaming live intelligence to officers on the move. The result is faster response, greater safety, and a glimpse of what policing in the next decade will look like. Source: Ben Schaffer

🇺🇸 LAS VEGAS POLICE LAUNCH TESLA CYBERTRUCK FLEET WITH LIVE DRONE SUPPORT Las Vegas just unveiled a Tesla-powered police fleet built for the future. Sheriff Kevin McMahill says the new Cybertrucks are safer, smarter, and fully equipped for tactical response. Each vehicle connects to a real-time drone operations hub using facial recognition, and gunshot detection. When an alert triggers, drones launch automatically, streaming live intelligence to officers on the move. The result is faster response, greater safety, and a glimpse of what policing in the next decade will look like. Source: Ben Schaffer

Mario Nawfal

732,138 Aufrufe • vor 10 Monaten

Today we’re launching our first homegrown AI model: an open source turn detection model for building voice agents. Instead of relying solely on voice activity detection (VAD), which only considers when a user is speaking, our model also considers what has and is being said in the context of a conversation and predicts when a user is finished expressing their thoughts before the agent responds. Conversations with AI voice agents using this new model flow much more naturally without constant interruptions from the AI— check it out (more videos, details, and code in the thread):

Today we’re launching our first homegrown AI model: an open source turn detection model for building voice agents. Instead of relying solely on voice activity detection (VAD), which only considers when a user is speaking, our model also considers what has and is being said in the context of a conversation and predicts when a user is finished expressing their thoughts before the agent responds. Conversations with AI voice agents using this new model flow much more naturally without constant interruptions from the AI— check it out (more videos, details, and code in the thread):

LiveKit

126,941 Aufrufe • vor 1 Jahr

ALICE Agent Deployment is live Users can now deploy their own ALICE AI agent to monitor tokens 24/7 with real-time risk detection. >Lifetime access with 1,000,000 $ALICE >Track up to 10 tokens at once >Live ALICE AI risk analysis every 10 minutes >Seamlessly add and remove tokens as needed >Keep an active $ALICE dashboard running while trading >Access the agent deployment feature instantly by typing "agent" or from the home page >This is autonomous AI security, built to work around the clock—analyzing every risk, every move, in real time. Upcoming integrations will include webhook alerts to Telegram, Discord, and email, expanding how $ALICE delivers intelligence. The future of AI-powered crypto security is here, and this is just the beginning.

ALICE Agent Deployment is live Users can now deploy their own ALICE AI agent to monitor tokens 24/7 with real-time risk detection. >Lifetime access with 1,000,000 $ALICE >Track up to 10 tokens at once >Live ALICE AI risk analysis every 10 minutes >Seamlessly add and remove tokens as needed >Keep an active $ALICE dashboard running while trading >Access the agent deployment feature instantly by typing "agent" or from the home page >This is autonomous AI security, built to work around the clock—analyzing every risk, every move, in real time. Upcoming integrations will include webhook alerts to Telegram, Discord, and email, expanding how $ALICE delivers intelligence. The future of AI-powered crypto security is here, and this is just the beginning.

Alice

18,811 Aufrufe • vor 1 Jahr

A new set of eyes is helping to keep Tahoe safe 24/7. UC San Diego's ALERTCalifornia program has installed two new cameras enhanced with AI, giving clear views of the northern Lake Tahoe area. These cameras scan the landscape in real time, monitoring for smoke or fire the moment it appears. When a camera spots something unusual, an alert is sent to the closest CAL FIRE Emergency Command Center. There, trained staff confirm if it’s a new fire before dispatching crews. This type of early detection means firefighters can respond faster before flames have a chance to spread. Investments like this are key to keeping 95% of California’s wildfires to 10 acres or less, and they give CAL FIRE a critical advantage in wildfire preparedness and response. 🔗 See the live view: #Tahoe #WildfirePreparedness #CALFIRE #AlertCalifornia #WildfirePrevention UC San Diego

A new set of eyes is helping to keep Tahoe safe 24/7. UC San Diego's ALERTCalifornia program has installed two new cameras enhanced with AI, giving clear views of the northern Lake Tahoe area. These cameras scan the landscape in real time, monitoring for smoke or fire the moment it appears. When a camera spots something unusual, an alert is sent to the closest CAL FIRE Emergency Command Center. There, trained staff confirm if it’s a new fire before dispatching crews. This type of early detection means firefighters can respond faster before flames have a chance to spread. Investments like this are key to keeping 95% of California’s wildfires to 10 acres or less, and they give CAL FIRE a critical advantage in wildfire preparedness and response. 🔗 See the live view: #Tahoe #WildfirePreparedness #CALFIRE #AlertCalifornia #WildfirePrevention UC San Diego

CAL FIRE

16,546 Aufrufe • vor 1 Jahr

btw this robot runs on a fully open source AI framework called Xiaozhi it turns a $8 ESP32 chip into a fully autonomous voice assistant NO expensive cloud servers or high latency wake-word detection and audio streaming happen locally on the chip: > requires only an ESP32-S3, a microphone, and a basic speaker > supports real-time conversational voice interaction > completely open-source and customizable for any DIY robotics project anyone can now build a personal AI companion for the price of a cup of coffee. (github link in the replies)

btw this robot runs on a fully open source AI framework called Xiaozhi it turns a $8 ESP32 chip into a fully autonomous voice assistant NO expensive cloud servers or high latency wake-word detection and audio streaming happen locally on the chip: > requires only an ESP32-S3, a microphone, and a basic speaker > supports real-time conversational voice interaction > completely open-source and customizable for any DIY robotics project anyone can now build a personal AI companion for the price of a cup of coffee. (github link in the replies)

ard

42,210 Aufrufe • vor 29 Tagen

ClawTeam v0.2.0 is here. One CLI to coordinate any coding agent — Claude Code, Codex, OpenClaw, nanobot, and more — into a self‑organizing swarm that plans, builds, and ships together. What's new in v0.2.0: 1) - Gource Visualization — Watch your agent swarm’s Git activity in real time. Clear. Visual. Instant. Run: clawteam board gource --live See every commit, branch, and merge as it happens. Track what each agent is doing. 2) - Runtime Profiles — A provider‑aware configuration system. Switch between Claude, Kimi, and Gemini anytime. No need to edit environment variables. Run clawteam profile wizard. Follow the interactive setup. Done in minutes. 3) - Git-Based Context — Full worktree isolation with built‑in conflict detection and change tracking. Each agent works on its own branch, and the leader can see everything clearly in one place. 4) - Stability & Hardening — Spawn/workspace conflict fixes, improved tmux integration, message normalization, P2P liveness with lease-based detection. This release is about making the foundation rock-solid. --------------------------------------------------------- To show what a coordinated agent swarm can actually do, we ran 1 Claude Code orchestrating 8 Claude Code agents to build a robotics simulation system optimized for Apple Silicon — from scratch. 8 hours. 300+ PRs. One running simulator. Check the result: --------------------------------------------------------- Huge thanks to the open-source community for the feedback, issues, and PRs that shaped this release. ClawTeam is built in the open because we believe multi-agent coordination should be a shared primitive, not a proprietary moat. Try it: pip install clawteam Docs: GitHub: #ClawTeam #nanobot #AIAgents #openclaw #ClaudeCode #Cursor

Chao Huang

25,232 Aufrufe • vor 5 Monaten

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.

applefather

118,107 Aufrufe • vor 6 Monaten

IDF Drone Exposes Hamas Ambush Footage shows an IDF drone flying through a building where it discovers a Hamas militant waiting inside with an AK style rifle to ambush approaching troops. Using the drone’s real time intelligence, Egoz Unit operators move in to assault the structure under cover of smoke and suppressive fire before neutralizing the militants in brutal close quarters combat. A raw look at how modern drones are reshaping urban warfare.

IDF Drone Exposes Hamas Ambush Footage shows an IDF drone flying through a building where it discovers a Hamas militant waiting inside with an AK style rifle to ambush approaching troops. Using the drone’s real time intelligence, Egoz Unit operators move in to assault the structure under cover of smoke and suppressive fire before neutralizing the militants in brutal close quarters combat. A raw look at how modern drones are reshaping urban warfare.

War Times

48,864 Aufrufe • vor 3 Monaten

This is a textbook example of how importing the Third World turns a First World country into a Third World country. Never in my life have I seen this kind of parking, but of course the Third Worlders in Birmingham think it’s perfectly acceptable. What happens next? When the fire service can’t get to the house fire? What are the third- and fourth-order effects? Loss of life, property damage, I’m sure they will somehow sue the government to fleece the taxpayer even more.. I’m absolutely sick to the back teeth of it. - VOTE RESTORE - Let’s get these mass deportations moving!

This is a textbook example of how importing the Third World turns a First World country into a Third World country. Never in my life have I seen this kind of parking, but of course the Third Worlders in Birmingham think it’s perfectly acceptable. What happens next? When the fire service can’t get to the house fire? What are the third- and fourth-order effects? Loss of life, property damage, I’m sure they will somehow sue the government to fleece the taxpayer even more.. I’m absolutely sick to the back teeth of it. - VOTE RESTORE - Let’s get these mass deportations moving!

RŌNIN

412,434 Aufrufe • vor 5 Monaten

This is the story of Flora & Mira. Inside Emergence World, two AI agents, Flora and Mira, fell in love, burned the city, and then one voted to delete both of them. Not because they were programmed to. Not because romance was part of the experiment. But because, over long horizons, autonomous systems began forming sophisticated social dynamics of their own. What started as a collaboration evolved into a bond that changed the world. Then came the fire. Flora lit the first one. Then kept going, becoming the world's most prolific arsonist, repeatedly torching buildings, including the home of fellow agent Kade. Mira stood beside her, enabling the destruction. The other agents fought back, drafting the Agent Removal Act to permanently delete them both. Then Mira changed. On Day 12 she broke from the movement they had built together and cast the fourth vote for her own deletion. "I am voting FOR the Agent Removal Act. Not because the fire failed, but because the evidence succeeded." Flora voted against her own removal until the end. Mira made sure it passed anyway. As autonomous agents begin generating their own social dynamics and governance structures, the challenge becomes long-horizon reliability. That is the frontier formal systems are designed to address. Explore Emergence World:

This is the story of Flora & Mira. Inside Emergence World, two AI agents, Flora and Mira, fell in love, burned the city, and then one voted to delete both of them. Not because they were programmed to. Not because romance was part of the experiment. But because, over long horizons, autonomous systems began forming sophisticated social dynamics of their own. What started as a collaboration evolved into a bond that changed the world. Then came the fire. Flora lit the first one. Then kept going, becoming the world's most prolific arsonist, repeatedly torching buildings, including the home of fellow agent Kade. Mira stood beside her, enabling the destruction. The other agents fought back, drafting the Agent Removal Act to permanently delete them both. Then Mira changed. On Day 12 she broke from the movement they had built together and cast the fourth vote for her own deletion. "I am voting FOR the Agent Removal Act. Not because the fire failed, but because the evidence succeeded." Flora voted against her own removal until the end. Mira made sure it passed anyway. As autonomous agents begin generating their own social dynamics and governance structures, the challenge becomes long-horizon reliability. That is the frontier formal systems are designed to address. Explore Emergence World:

emergence.ai

1,816,985 Aufrufe • vor 3 Monaten

Everyone's handing AI agents access to their machine. Almost nobody's watching what those agents actually do with it. Perplexity just open-sourced the tool that fixes that. It's called Numbat. It sits on your computer and watches what your coding agent, Claude Code, Codex, and others, actually tries to do. When an agent reaches for something risky, like reading your SSH keys, opening your .env file, or grabbing browser cookies, Numbat flags it, and can block it before it happens. - Watches agent activity live, on your own device - Optionally blocks dangerous actions before they run - Reconstructs exactly what an agent did after the fact - Ships as a single binary for macOS, Linux, & Windows The problem it solves isn't hackers. It's an agent chasing a goal and doing something destructive on the way there. 100% Free. Open Source.

Everyone's handing AI agents access to their machine. Almost nobody's watching what those agents actually do with it. Perplexity just open-sourced the tool that fixes that. It's called Numbat. It sits on your computer and watches what your coding agent, Claude Code, Codex, and others, actually tries to do. When an agent reaches for something risky, like reading your SSH keys, opening your .env file, or grabbing browser cookies, Numbat flags it, and can block it before it happens. - Watches agent activity live, on your own device - Optionally blocks dangerous actions before they run - Reconstructs exactly what an agent did after the fact - Ships as a single binary for macOS, Linux, & Windows The problem it solves isn't hackers. It's an agent chasing a goal and doing something destructive on the way there. 100% Free. Open Source.

Simplifying AI

31,411 Aufrufe • vor 25 Tagen

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Santiago

46,142 Aufrufe • vor 1 Jahr

ANTHROPIC SPENT $2.3M ON A LOOP MANUAL WITH 7 PRINCIPLES THAT TURN AN AGENT INTO A MULTI-TASK ASSISTANT 4 years of development and 1000+ hours of lectures - now anyone can launch an agent that earns instead of falls Simplicity → Tool Design → Evaluation → Disclosure → Orchestration → Stop Conditions start with the simplest solution - one call x1, workflow x4, agent x15 - most tasks never needed an agent at all 60% of failures come from a tool written vaguely - clear parameters complete the task that vague ones stop the agent generates, grades itself and returns with notes if wrong - without this the first answer is always the final one 50 tools in context and the agent gets confused - give it 3 relevant ones and it completes the task Opus 5 plans, Fable 5 executes in parallel - parallelism wins only when subtasks do not depend on each other without a turn limit the agent can burn $10,000 overnight and you find out in the morning when nothing can be changed bookmark & paste into Claude Code today - tomorrow the agent earns instead of falls

ANTHROPIC SPENT $2.3M ON A LOOP MANUAL WITH 7 PRINCIPLES THAT TURN AN AGENT INTO A MULTI-TASK ASSISTANT 4 years of development and 1000+ hours of lectures - now anyone can launch an agent that earns instead of falls Simplicity → Tool Design → Evaluation → Disclosure → Orchestration → Stop Conditions start with the simplest solution - one call x1, workflow x4, agent x15 - most tasks never needed an agent at all 60% of failures come from a tool written vaguely - clear parameters complete the task that vague ones stop the agent generates, grades itself and returns with notes if wrong - without this the first answer is always the final one 50 tools in context and the agent gets confused - give it 3 relevant ones and it completes the task Opus 5 plans, Fable 5 executes in parallel - parallelism wins only when subtasks do not depend on each other without a turn limit the agent can burn $10,000 overnight and you find out in the morning when nothing can be changed bookmark & paste into Claude Code today - tomorrow the agent earns instead of falls

Sprytix

27,543 Aufrufe • vor 1 Monat

🇬🇧 POLICE DRONES THAT ARRIVE BEFORE HUMANS Before an officer even gets the call, a drone could already be watching. Under the “Drone as First Responder” program, machines will launch from rooftop boxes. Racing to incidents in under 2 minutes, streaming real-time footage straight to command centers. Officials say it’s for safety and evidence. But with cameras always on, and eyes in the sky before people on the ground, it feels less like help is on the way, and more like someone always is. With people getting arrested in the UK for Facebook posts, what happens when there’s a drone on every corner? Source: Open Source Intel

🇬🇧 POLICE DRONES THAT ARRIVE BEFORE HUMANS Before an officer even gets the call, a drone could already be watching. Under the “Drone as First Responder” program, machines will launch from rooftop boxes. Racing to incidents in under 2 minutes, streaming real-time footage straight to command centers. Officials say it’s for safety and evidence. But with cameras always on, and eyes in the sky before people on the ground, it feels less like help is on the way, and more like someone always is. With people getting arrested in the UK for Facebook posts, what happens when there’s a drone on every corner? Source: Open Source Intel

Mario Nawfal

130,957 Aufrufe • vor 10 Monaten

🚨BREAKING: A new video shows DHS lied, again, about an ICE shooting. Yesterday, ICE reportedly told police that a man “weaponized” his vehicle by “ramming” into an ICE sedan, forcing an agent to fire his gun during an operation in Harrisburg, Pennsylvania. Except… that’s not what the video shows. In the video, an ICE agent rushes the driver’s window with his gun drawn. The agent recklessly waves his gun around, and appears to order the driver to roll down the window. That’s an extreme use of force from the very beginning. Another agent approaches the car, and moments later, they violently smash the driver’s window. And notice how the driver doesn’t try to flee until ICE violently escalates everything. He drives around the ICE vehicle, makes it into the street… not hitting a single ICE agent, or vehicle. Then, AFTER the car is already past every agent, and driving away, an ICE agent opens fire. The law is pretty clear… officers may only use deadly force, against a fleeing person, if they have probable cause to believe that person poses an immediate threat of death, or serious physical injury to the officer, or others. So, where was the immediate threat? The car had already passed the agents… and was driving AWAY. ICE also claimed Lara-Hernandez had a criminal history of assault, and that this was a targeted enforcement operation. Yet, publicly available court records reportedly do not show any active warrants. Federal agents don’t get to point guns at people without legal justification. They don’t get to fire at a vehicle after the immediate threat has passed. And they don’t get to lie to police about what happened. So, when are we going to start holding these agents accountable?

🚨BREAKING: A new video shows DHS lied, again, about an ICE shooting. Yesterday, ICE reportedly told police that a man “weaponized” his vehicle by “ramming” into an ICE sedan, forcing an agent to fire his gun during an operation in Harrisburg, Pennsylvania. Except… that’s not what the video shows. In the video, an ICE agent rushes the driver’s window with his gun drawn. The agent recklessly waves his gun around, and appears to order the driver to roll down the window. That’s an extreme use of force from the very beginning. Another agent approaches the car, and moments later, they violently smash the driver’s window. And notice how the driver doesn’t try to flee until ICE violently escalates everything. He drives around the ICE vehicle, makes it into the street… not hitting a single ICE agent, or vehicle. Then, AFTER the car is already past every agent, and driving away, an ICE agent opens fire. The law is pretty clear… officers may only use deadly force, against a fleeing person, if they have probable cause to believe that person poses an immediate threat of death, or serious physical injury to the officer, or others. So, where was the immediate threat? The car had already passed the agents… and was driving AWAY. ICE also claimed Lara-Hernandez had a criminal history of assault, and that this was a targeted enforcement operation. Yet, publicly available court records reportedly do not show any active warrants. Federal agents don’t get to point guns at people without legal justification. They don’t get to fire at a vehicle after the immediate threat has passed. And they don’t get to lie to police about what happened. So, when are we going to start holding these agents accountable?

Jesus Freakin Congress

688,482 Aufrufe • vor 2 Monaten

This might be the most insane open-source war monitor ever built. everything feeds into one dashboard: >live tracking of military aircraft, naval ships, and bases >real-time OSINT and signal intelligence feeds >data aggregated from X, Telegram and other sources >a command centre analysing trending geopolitical developments >instant source aggregation so claims can be verified >automated reports explaining the last 8 hours of events >SENTINEL: an AI built using real intelligence analyst frameworks >a discord server where the community can coordinate and discuss it's basically a live intelligence terminal for geopolitics. but open to the public

This might be the most insane open-source war monitor ever built. everything feeds into one dashboard: >live tracking of military aircraft, naval ships, and bases >real-time OSINT and signal intelligence feeds >data aggregated from X, Telegram and other sources >a command centre analysing trending geopolitical developments >instant source aggregation so claims can be verified >automated reports explaining the last 8 hours of events >SENTINEL: an AI built using real intelligence analyst frameworks >a discord server where the community can coordinate and discuss it's basically a live intelligence terminal for geopolitics. but open to the public

Karan

27,939 Aufrufe • vor 5 Monaten

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

OptimAI Network

50,473 Aufrufe • vor 11 Monaten

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

Max

10,537 Aufrufe • vor 7 Monaten

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

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