Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

x402 explained Speech APIs (STT/TTS) were built for human developers and not autonomous agents. They assume you’ll sign up, add billing, generate API keys, manage authentication and key rotation. This becomes problematic when software needs to use speech continuously at scale. 🤖

dTelecom

111,272 subscribers

14,606 views • 5 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

dTelecom is raising a Seed Round. We're building decentralized real-time communication infrastructure - voice, video, and AI services (STT, TTS, Translation) for developers and autonomous agents. Why now: → $150B+ combined TAM by 2030 (RTC API + Voice AI + AI Agents) → AI agents need real-time voice infra → 2-5× lower cost, 2-4× lower latency than centralized providers → We're live, generating revenue, with $1.5M raised previously Looking for investors who understand AI infrastructure at scale. Learn more:

dTelecom is raising a Seed Round. We're building decentralized real-time communication infrastructure - voice, video, and AI services (STT, TTS, Translation) for developers and autonomous agents. Why now: → $150B+ combined TAM by 2030 (RTC API + Voice AI + AI Agents) → AI agents need real-time voice infra → 2-5× lower cost, 2-4× lower latency than centralized providers → We're live, generating revenue, with $1.5M raised previously Looking for investors who understand AI infrastructure at scale. Learn more:

dTelecom

63,340 views • 5 months ago

Legacy infra is about human human Today's reality is human agent Real-time infra is not just about: Voice Video Routing It's also about: Edge STT TTS x402 payments Cost efficient latency Same thesis. Bigger surface area. Voice AI -- available via API/SDK. Now.

Legacy infra is about human human Today's reality is human agent Real-time infra is not just about: Voice Video Routing It's also about: Edge STT TTS x402 payments Cost efficient latency Same thesis. Bigger surface area. Voice AI -- available via API/SDK. Now.

dTelecom

72,009 views • 5 months ago

Earlie we outlined our integration of x402. Here’s how it will express in dMeet 🤙. Real-time translation runs as audio flows through STT, is translated, converted back via TTS, and streamed to the listener. Instead of locking this behind subscriptions or fixed plans, each session (or minute) can be priced dynamically. If payment is required: х402 with the price → programmatic pay → continue the call. Usage-based speech running in real time. This is what we need in 2026. 🧑‍💻

Earlie we outlined our integration of x402. Here’s how it will express in dMeet 🤙. Real-time translation runs as audio flows through STT, is translated, converted back via TTS, and streamed to the listener. Instead of locking this behind subscriptions or fixed plans, each session (or minute) can be priced dynamically. If payment is required: х402 with the price → programmatic pay → continue the call. Usage-based speech running in real time. This is what we need in 2026. 🧑‍💻

dTelecom

14,831 views • 5 months ago

March 18, 2025 marked the public launch of OptimAI. In one year, it has evolved from a lightweight node layer into a decentralized intelligence infrastructure powering real-time data, compute, and reinforcement for agentic systems. Not just nodes. Not just data. A continuously learning, network-driven intelligence layer. This is infrastructure for a new class of software: autonomous agents that persist, adapt, and operate across environments. Year one established the network. Year two is where it compounds into coordination and value flow. Personal agents. Reinforcement at network scale. Emerging primitives for AgentFi. New layers coming online. 2026 won’t just be about scale, it’s where the network starts to operate. Keep building!

March 18, 2025 marked the public launch of OptimAI. In one year, it has evolved from a lightweight node layer into a decentralized intelligence infrastructure powering real-time data, compute, and reinforcement for agentic systems. Not just nodes. Not just data. A continuously learning, network-driven intelligence layer. This is infrastructure for a new class of software: autonomous agents that persist, adapt, and operate across environments. Year one established the network. Year two is where it compounds into coordination and value flow. Personal agents. Reinforcement at network scale. Emerging primitives for AgentFi. New layers coming online. 2026 won’t just be about scale, it’s where the network starts to operate. Keep building!

OptimAI Network

34,134 views • 4 months ago

The entire SaaS industry is building software for a customer that is about to go extinct. The human buyer. Insight Partners co-founder Jerry Murdock just exposed the fatal architectural flaw in every incumbent tech company’s business model. Your dashboards. Your UI. Your enterprise sales motion. Your human-in-the-loop workflows. All of it was engineered for a buyer that is disappearing in real time. Murdock: “If you’re not making your software for autonomous agents today, you’re going to be challenged in the future. Maybe it’s six months, maybe a year, maybe 18 months, but you’re going to be severely challenged if you still think human beings are going to buy your software.” Not disrupted. Not pressured. Structurally eliminated. For two decades, software was built around the cognitive limits of human biology. Dropdowns, dashboards, and notifications existed because the human brain needed them to navigate digital space. An autonomous agent needs none of that. It doesn’t browse your product page. It doesn’t sit through your demo. It doesn’t respond to your sales email. It doesn’t care how clean your UI is. It just executes. The agentic era runs on machine-to-machine infrastructure. Frictionless. Autonomous. No human in the loop. No patience for friction you built for a species it replaced. The window is six to eighteen months. The builders who survive will tear out the entire human interface layer and replace it with pure, unthrottled infrastructure that agents can consume at full speed. Everyone else will spend those eighteen months perfecting a dashboard that no one is ever going to log into again.

The entire SaaS industry is building software for a customer that is about to go extinct. The human buyer. Insight Partners co-founder Jerry Murdock just exposed the fatal architectural flaw in every incumbent tech company’s business model. Your dashboards. Your UI. Your enterprise sales motion. Your human-in-the-loop workflows. All of it was engineered for a buyer that is disappearing in real time. Murdock: “If you’re not making your software for autonomous agents today, you’re going to be challenged in the future. Maybe it’s six months, maybe a year, maybe 18 months, but you’re going to be severely challenged if you still think human beings are going to buy your software.” Not disrupted. Not pressured. Structurally eliminated. For two decades, software was built around the cognitive limits of human biology. Dropdowns, dashboards, and notifications existed because the human brain needed them to navigate digital space. An autonomous agent needs none of that. It doesn’t browse your product page. It doesn’t sit through your demo. It doesn’t respond to your sales email. It doesn’t care how clean your UI is. It just executes. The agentic era runs on machine-to-machine infrastructure. Frictionless. Autonomous. No human in the loop. No patience for friction you built for a species it replaced. The window is six to eighteen months. The builders who survive will tear out the entire human interface layer and replace it with pure, unthrottled infrastructure that agents can consume at full speed. Everyone else will spend those eighteen months perfecting a dashboard that no one is ever going to log into again.

Dustin

198,027 views • 5 months ago

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 views • 10 months ago

THIS STUDENT WAS VIBE CODING AN APP, THEN GOT A $55,444.78 BILL FROM GOOGLE CLOUD All because they accidentally pushed their Gemini API key to GitHub They thought the repository was private It was just a small side project, and they still had $220 in free credits left By the time they checked their email, it was already too late This video shows exactly how things like this happen and why more and more developers are running into the same problem: > One commit turned into a $55k nightmare > API keys were exposed in frontend code and even inside app binaries > People hardcoded secrets into scripts and ended up with hundreds of dollars in charges within hours > One OpenAI key was abused nearly a million times before anyone noticed Never hardcode API keys Never commit them to GitHub, even if the repository is private Never expose them in your frontend Always use environment variables and set up spending alerts Even in the era of vibe coding, security still matters Knowing a few basic best practices can save you from some very expensive mistakes If you’re a vibe coder, make sure to read the article I attached, you’ll find plenty of practical tips that could save you a lot of trouble Save this post so you don’t lose it

THIS STUDENT WAS VIBE CODING AN APP, THEN GOT A $55,444.78 BILL FROM GOOGLE CLOUD All because they accidentally pushed their Gemini API key to GitHub They thought the repository was private It was just a small side project, and they still had $220 in free credits left By the time they checked their email, it was already too late This video shows exactly how things like this happen and why more and more developers are running into the same problem: > One commit turned into a $55k nightmare > API keys were exposed in frontend code and even inside app binaries > People hardcoded secrets into scripts and ended up with hundreds of dollars in charges within hours > One OpenAI key was abused nearly a million times before anyone noticed Never hardcode API keys Never commit them to GitHub, even if the repository is private Never expose them in your frontend Always use environment variables and set up spending alerts Even in the era of vibe coding, security still matters Knowing a few basic best practices can save you from some very expensive mistakes If you’re a vibe coder, make sure to read the article I attached, you’ll find plenty of practical tips that could save you a lot of trouble Save this post so you don’t lose it

Bonsai 🌳

110,437 views • 1 month ago

This is how you unlock the next billion software developers. The new Replit ⠕ Agent 3 (they just launched) is the most advanced vibe-coding agent in the world. 1. Smarter than any other vibe-coding model (10x more autonomous than the previous version). 2. It thinks harder and lasts longer than any other model (up to 200 minutes running fully autonomously). 3. The agent can now use an actual browser to test and fix its own code. 4. 3x faster and 10x more cost-effective than any other "Computer Use" for testing. 5. It can build other agents and automations to take care of repetitive tasks. Seeing the agent test the application autonomously is science fiction!

This is how you unlock the next billion software developers. The new Replit ⠕ Agent 3 (they just launched) is the most advanced vibe-coding agent in the world. 1. Smarter than any other vibe-coding model (10x more autonomous than the previous version). 2. It thinks harder and lasts longer than any other model (up to 200 minutes running fully autonomously). 3. The agent can now use an actual browser to test and fix its own code. 4. 3x faster and 10x more cost-effective than any other "Computer Use" for testing. 5. It can build other agents and automations to take care of repetitive tasks. Seeing the agent test the application autonomously is science fiction!

Santiago

167,056 views • 11 months ago

I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what Andrej Karpathy describes. It uses obsidian vaults to organize papers, retrieve LLM-generated summaries, diagrams, and other advanced views for paper exploration. When Obsidian UI is not enough, I use my own artifact generator inside my agent orchestrator (see clip for example). This allows my agents to build any kind of view or exploration feature that I need. The papers are all curated with automations and several rules/patterns I have manually built over the years. On the surface, this looks basic. But behind the scenes, there are advanced search capabilities, connections, metadata, derived data, and other interesting bits of information that are extremely useful for my research agents. This is mostly built for agents. The artifact preview is just a high-level way to validate and quickly assess the quality of the wiki, suggest improvements, and it's also great for research. I use tobi lutke's qmd for all search capabilities. Everything is markdown. The summaries and even the diagrams. The wiki updates on its own based on several automations I have optimized over the past couple of weeks. The wiki grows and self-improves based on several requirements important for my research use cases. This is as personalized as it gets. There is nothing like it out there. And I use my research expertise to continue improving it over time. This is a vanilla wiki. There are so many things I want to build on top of this. Different aggregations, views, artifacts, etc. All to help automate more of my research work and accelerate productivity. I think the biggest leverage here is how powerful this could be for discovery and experimentation. One of my goals is to use it to find deeper connections and insights that would otherwise elude the top human researchers and use those to generate interesting new hypotheses and research experiments. That way, my agents can use autoresearch to explore research ideas at the frontier. Stay tuned for more.

I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what Andrej Karpathy describes. It uses obsidian vaults to organize papers, retrieve LLM-generated summaries, diagrams, and other advanced views for paper exploration. When Obsidian UI is not enough, I use my own artifact generator inside my agent orchestrator (see clip for example). This allows my agents to build any kind of view or exploration feature that I need. The papers are all curated with automations and several rules/patterns I have manually built over the years. On the surface, this looks basic. But behind the scenes, there are advanced search capabilities, connections, metadata, derived data, and other interesting bits of information that are extremely useful for my research agents. This is mostly built for agents. The artifact preview is just a high-level way to validate and quickly assess the quality of the wiki, suggest improvements, and it's also great for research. I use tobi lutke's qmd for all search capabilities. Everything is markdown. The summaries and even the diagrams. The wiki updates on its own based on several automations I have optimized over the past couple of weeks. The wiki grows and self-improves based on several requirements important for my research use cases. This is as personalized as it gets. There is nothing like it out there. And I use my research expertise to continue improving it over time. This is a vanilla wiki. There are so many things I want to build on top of this. Different aggregations, views, artifacts, etc. All to help automate more of my research work and accelerate productivity. I think the biggest leverage here is how powerful this could be for discovery and experimentation. One of my goals is to use it to find deeper connections and insights that would otherwise elude the top human researchers and use those to generate interesting new hypotheses and research experiments. That way, my agents can use autoresearch to explore research ideas at the frontier. Stay tuned for more.

elvis

67,153 views • 4 months ago

Something NVIDIA & Google do better than anyone else is software-hardware-system co-design, and not just optimizing hardware for current model architectures, but predicting future ones. Back in early 2022, when NVIDIA started the design process for NVL72, MoE (Mixture of Experts) models were not yet the standard, and dense models were still dominant for frontier models. However, NVIDIA's strong software-hardware co-design culture enabled them to make a calculated bet that MoEs were the future, and they built NVL72 specifically for best MoE performance per TCO (Total Cost of Ownership). Furthermore, back in 2022, disaggregated prefill and wide expert parallelism (wideEP) MoE inference optimizations hadn't been invented yet, but it turns out that these MoE inference optimizations work best on large-scale systems like NVL72. While most other AI chip companies' in-house AI labs focus on training small 5B models that mainly use data parallelism, NVIDIA and Google's in-house AI labs continuously push the boundaries of model architecture and training recipes, such as NVFP4 training. Just like Super Idol & IShowSpeed, there must be a strong partnership between software engineers and hardware engineers to deliver the best systems that maximize performance per TCO.

Something NVIDIA & Google do better than anyone else is software-hardware-system co-design, and not just optimizing hardware for current model architectures, but predicting future ones. Back in early 2022, when NVIDIA started the design process for NVL72, MoE (Mixture of Experts) models were not yet the standard, and dense models were still dominant for frontier models. However, NVIDIA's strong software-hardware co-design culture enabled them to make a calculated bet that MoEs were the future, and they built NVL72 specifically for best MoE performance per TCO (Total Cost of Ownership). Furthermore, back in 2022, disaggregated prefill and wide expert parallelism (wideEP) MoE inference optimizations hadn't been invented yet, but it turns out that these MoE inference optimizations work best on large-scale systems like NVL72. While most other AI chip companies' in-house AI labs focus on training small 5B models that mainly use data parallelism, NVIDIA and Google's in-house AI labs continuously push the boundaries of model architecture and training recipes, such as NVFP4 training. Just like Super Idol & IShowSpeed, there must be a strong partnership between software engineers and hardware engineers to deliver the best systems that maximize performance per TCO.

SemiAnalysis

51,021 views • 8 months ago

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Bio Protocol

25,865 views • 5 months ago

Krishnan Guru-Murthy, "While we've been on air the prime minister has been speaking to Labour MPs and attacked Nigel Farage and Reform UK, reportedly saying" "Reform are our main rivals for power. We have a moral responsibility to make sure Farage never wins" Top tip Keir Starmer start by not aping Farage with phrases like 'island of strangers.' When you use language like this you're just as bad as they are Instead, look at your 2020 speech in which you spoke about the positives of migration - that's the Starmer than the country needs, not a pound shop Farage

Krishnan Guru-Murthy, "While we've been on air the prime minister has been speaking to Labour MPs and attacked Nigel Farage and Reform UK, reportedly saying" "Reform are our main rivals for power. We have a moral responsibility to make sure Farage never wins" Top tip Keir Starmer start by not aping Farage with phrases like 'island of strangers.' When you use language like this you're just as bad as they are Instead, look at your 2020 speech in which you spoke about the positives of migration - that's the Starmer than the country needs, not a pound shop Farage

Farrukh

93,093 views • 1 year ago

Simplicity is at the heart of great software. This is one of the reasons why Claude Code has been sticky for me. As a builder, I love planning and brainstorming, and this is now a key focus of Claude Code. I use Shift + Tab a lot to cycle between brainstorming, planning, and execution. This functionality provides the appropriate interface for me to either be very involved or less involved as I please. This works particularly well when building out new and complex features or entire new projects. This saves a huge amount of time. It allows me to tune Claude Code to execute and build more effectively. It also builds a loop of trust, and I often (surprisingly) find Claude Code asking for clarifications when it's confused. Coding agents don't normally do that. I have shared before on the power of brainstorming with AI for longer times. Try it and you will not be disappointed. Vibe coding is fun, but pair it with intentional development cycles, and you watch how far you can take a project with coding agents today.

Simplicity is at the heart of great software. This is one of the reasons why Claude Code has been sticky for me. As a builder, I love planning and brainstorming, and this is now a key focus of Claude Code. I use Shift + Tab a lot to cycle between brainstorming, planning, and execution. This functionality provides the appropriate interface for me to either be very involved or less involved as I please. This works particularly well when building out new and complex features or entire new projects. This saves a huge amount of time. It allows me to tune Claude Code to execute and build more effectively. It also builds a loop of trust, and I often (surprisingly) find Claude Code asking for clarifications when it's confused. Coding agents don't normally do that. I have shared before on the power of brainstorming with AI for longer times. Try it and you will not be disappointed. Vibe coding is fun, but pair it with intentional development cycles, and you watch how far you can take a project with coding agents today.

elvis

81,765 views • 9 months ago

🚨 ISRAEL BEGINS FULL SCALE RETALIATORY STRIKES After the full-scale invasion of Israel, Israeli Prime Minister Benjamin Netanyahu announced he would use ANY AND ALL FORCE NEEDED to eliminate Hamas. In a speech, Netanyahu warned Gaza residents to “GET OUT NOW,” signaling Israel would not be holding back and that the PM was ready to level Gaza, ensuring this NEVER happens again. “Murdering children and mothers in their homes, in their beds. It is an enemy that kidnaps elderly people, children, young girls.” “We will fight them to the bitter end and avenge this black day they plotted for Israel and its people.” We’ve now seen over *80,000* IDF reservists called up including many elite units ready to go. HOW MUCH INVOLVEMENT SHOULD THE US HAVE IN THE ISRAEL/PALESTINE WAR?

🚨 ISRAEL BEGINS FULL SCALE RETALIATORY STRIKES After the full-scale invasion of Israel, Israeli Prime Minister Benjamin Netanyahu announced he would use ANY AND ALL FORCE NEEDED to eliminate Hamas. In a speech, Netanyahu warned Gaza residents to “GET OUT NOW,” signaling Israel would not be holding back and that the PM was ready to level Gaza, ensuring this NEVER happens again. “Murdering children and mothers in their homes, in their beds. It is an enemy that kidnaps elderly people, children, young girls.” “We will fight them to the bitter end and avenge this black day they plotted for Israel and its people.” We’ve now seen over 80,000 IDF reservists called up including many elite units ready to go. HOW MUCH INVOLVEMENT SHOULD THE US HAVE IN THE ISRAEL/PALESTINE WAR?

Nick Sortor

2,154,520 views • 2 years ago

you can run claude code inside antigravity completely Free with zero credit card and no rate limits 😳 use openrouter’s free models + antigravity. no anthropic bill. no paid api keys. takes 10 minutes to set up. what you get during this setup: - full claude code agent experience - strong coding models (including deepseek-r1, qwen2.5-coder, llama-4, grok-4 free tier) - antigravity’s clean workspace and sandbox - unlimited usage (as long as you stay on free models) - easy model swapping - zero cost full setup guide (100% free): step 1: install antigravity -go to and install it -create a new workspace step 2: install claude code - inside antigravity, install the claude code extension from the marketplace - open the built-in terminal step 3: create openrouter free account -go to - sign up with google (no card needed) - go to keys and create a new api key step 4: set the environment variables -in antigravity terminal run: export ANTHROPIC_API_KEY=sk-or-xxx export OPENROUTER_API_KEY=sk-or-xxx step 5: launch claude code with free model -run this command: claude-code --model deepseek/deepseek-r1:free or try: qwen/qwen2.5-coder:free if you already have antigravity? skip straight to step 2. after 10 minutes you’ll have a full agentic coding setup running for free. this is currently one of the cheapest ways to run serious coding agents in 2026. bookmark this before they limit the free models.

you can run claude code inside antigravity completely Free with zero credit card and no rate limits 😳 use openrouter’s free models + antigravity. no anthropic bill. no paid api keys. takes 10 minutes to set up. what you get during this setup: - full claude code agent experience - strong coding models (including deepseek-r1, qwen2.5-coder, llama-4, grok-4 free tier) - antigravity’s clean workspace and sandbox - unlimited usage (as long as you stay on free models) - easy model swapping - zero cost full setup guide (100% free): step 1: install antigravity -go to and install it -create a new workspace step 2: install claude code - inside antigravity, install the claude code extension from the marketplace - open the built-in terminal step 3: create openrouter free account -go to - sign up with google (no card needed) - go to keys and create a new api key step 4: set the environment variables -in antigravity terminal run: export ANTHROPIC_API_KEY=sk-or-xxx export OPENROUTER_API_KEY=sk-or-xxx step 5: launch claude code with free model -run this command: claude-code --model deepseek/deepseek-r1:free or try: qwen/qwen2.5-coder:free if you already have antigravity? skip straight to step 2. after 10 minutes you’ll have a full agentic coding setup running for free. this is currently one of the cheapest ways to run serious coding agents in 2026. bookmark this before they limit the free models.

painn

32,057 views • 2 months ago

Thrilled to unveil Youmio, our new brand identity that represents the next evolution of what we’ve been building. Agents are the biggest technological leap since the internet, destined to transform crypto, games, and entertainment. With Youmio, we are shaping the agentic era, where agents learn, play and entertain in revolutionary ways. 🚀 So far, 2D entertainment and social media agents dominate the market. 3D agents are rare, requiring advanced AI and game engine skills. Yet 3D agents, especially those in game engines, unlock groundbreaking opportunities. Time to unleash them. Youmio empowers anyone to create and deploy valuable agents that are on-chain, cross-platform and ready for 3D worlds. Here’s how: ⭐️ Youmio Agents Youmio Agents lets anyone design and personalize 3D agents, equip them with powerful agentic capabilities, interact with them in unique ways and trade seamlessly within a cross-platform browser experience. 🕹️ Youmio Worlds Previously known as Today The Game, Youmio Worlds is a petri dish AI simulation where users build & co-inhabit beautiful, living 3D worlds with autonomous agents. Build dynamic worlds where players interact with intelligent agents, manage resources and participate in a player-agent marketplace. Ancient and Mythic Seeds are the most powerful entry points into the Youmio Worlds ecosystem, generating rare and beautiful worlds that unlock unique opportunities. 📡 Interoperable 3D Agents With Youmio, you’re not limited to our ecosystem. Using our API, developers can integrate Youmio agents into other experiences built in Unity and Unreal. On top of this, agents from other frameworks can also join Youmio, creating a truly interconnected metaverse. 🎭 Welcome to Limbo Meet Limbo, the first AI agent built using Youmio tech. Paired with the power of Youmio Worlds, we’re creating the Limboverse - a unique AI Big Brother setting where Limbo and your favorite and most valuable agents coexist in an ever-evolving, narrative-driven environment that you, the audience, will shape. $LIMBO is the most powerful entry point into the Limboverse and will be stakable on the Youmio Agents platform for unique rewards. Thanks for reading everyone and thanks for being on this amazing journey with us.🌱

Youmio

126,399 views • 1 year ago

Iran’s regime just sentenced a 22-year-old rapper to death. Not for murder. Not for terrorism. For slogans, dissent, for refusing to shut up. Simply for demanding freedom and protesting against oppression. His name is Mehnam Navab-Safavi. He was arrested, forced to confess, denied a real trial, and now faces execution. And somehow the international community still treats Iran’s rulers like difficult diplomats instead of what they are: executioners with embassies and good lobbyists in the West. In the past two weeks alone, at least 50 protesters have reportedly had death sentences issued, upheld, or carried out. So I call on all rappers, singers, celebrities who post ten slides about free speech, now please be his voice. Because this is free speech with a noose around its neck. Please use your platform and demand that Iran’s regime stop the execution of Mehnam Navab-Safavi. #SaveMehnamNavabSafavi #StopExecutionsInIran

Iran’s regime just sentenced a 22-year-old rapper to death. Not for murder. Not for terrorism. For slogans, dissent, for refusing to shut up. Simply for demanding freedom and protesting against oppression. His name is Mehnam Navab-Safavi. He was arrested, forced to confess, denied a real trial, and now faces execution. And somehow the international community still treats Iran’s rulers like difficult diplomats instead of what they are: executioners with embassies and good lobbyists in the West. In the past two weeks alone, at least 50 protesters have reportedly had death sentences issued, upheld, or carried out. So I call on all rappers, singers, celebrities who post ten slides about free speech, now please be his voice. Because this is free speech with a noose around its neck. Please use your platform and demand that Iran’s regime stop the execution of Mehnam Navab-Safavi. #SaveMehnamNavabSafavi #StopExecutionsInIran

Masih Alinejad

69,555 views • 16 days ago

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 views • 5 months ago

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️

John McBride

150,334 views • 5 months ago