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Are AI agents ready to be your virtual coworker? Can they write your emails, build Excel models, and create slide decks? Introducing APEX-Agents, a frontier benchmark that tests how well AI agents complete real, long-horizon professional services deliverables in Google Workspace. Mercor APEX-Agents Pass@1 leaderboard: 🥇 Google Gemini 3... show more

Brendan (can/do)

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145,326 views • 7 months ago •via X (Twitter)

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Traditional coding benchmarks do not reflect how software is actually built and maintained. That's why we built a new benchmark, APEX-SWE, in partnership with Cognition. It measures whether AI models can perform complex, real-world software engineering work to ship systems that work and debug them when they don't. OpenAI GPT 5.3 Codex (High) tops the leaderboard at 41.5% on Pass@1.

Traditional coding benchmarks do not reflect how software is actually built and maintained. That's why we built a new benchmark, APEX-SWE, in partnership with Cognition. It measures whether AI models can perform complex, real-world software engineering work to ship systems that work and debug them when they don't. OpenAI GPT 5.3 Codex (High) tops the leaderboard at 41.5% on Pass@1.

adarsh

217,410 views • 5 months ago

For developers, Gemini 3 Pro offers unparalleled results across every major AI benchmark compared to Gemini 2.5 Pro. Here three ways Gemini 3 Pro can improve your workflow today: 1. Gemini 3 Pro fits right into existing production agent and coding workflows. It is also integrated into development environment experiences across the ecosystem of developer products like Google AI Studio, Gemini CLI, and Android Studio. 2. We introduced Google Google Antigravity, a new agentic development platform that reimagines how the model and IDE work together. It enables developers to manage agents to work at a higher, task-oriented level collaborating with agents while they build. 3. We’ve built Google AI Studio to be your fastest path from a prompt to an AI-native app. Build mode lets you add AI capabilities faster than ever, automatically wiring up the right models and APIs, while features like annotations enable fast and intuitive iteration. Whether you’re building a one-shot prompt game or an interactive landing page from unstructured voice notes, you can bring any idea to life with Gemini 3.

For developers, Gemini 3 Pro offers unparalleled results across every major AI benchmark compared to Gemini 2.5 Pro. Here three ways Gemini 3 Pro can improve your workflow today: 1. Gemini 3 Pro fits right into existing production agent and coding workflows. It is also integrated into development environment experiences across the ecosystem of developer products like Google AI Studio, Gemini CLI, and Android Studio. 2. We introduced Google Google Antigravity, a new agentic development platform that reimagines how the model and IDE work together. It enables developers to manage agents to work at a higher, task-oriented level collaborating with agents while they build. 3. We’ve built Google AI Studio to be your fastest path from a prompt to an AI-native app. Build mode lets you add AI capabilities faster than ever, automatically wiring up the right models and APIs, while features like annotations enable fast and intuitive iteration. Whether you’re building a one-shot prompt game or an interactive landing page from unstructured voice notes, you can bring any idea to life with Gemini 3.

Google AI

136,648 views • 9 months ago

EigenAI + EigenCompute for a verifiable world! AI and Agents are extremely useful but cannot be trusted in high-value scenarios. Models can be swapped, prompts modified, responses tampered. When billions of $ and real-world decisions depend on AI, blind trust is a bug. EigenAI + EigenCompute fix this. Excited to share Google, Coinbase, Worldcoin’s Humans vs AI, Dapper Labs, ElizaOS, OpenRank, Collective Memory, FereAI, and OpenFront are among the first exploring what verifiable agents make possible with EigenCloud. 👉 Verifiable agents: logic provably runs as written. @humansvs_ai of World proves fair gameplay with 70k weekly users. 👉 Insured agents: economic guarantees against failure. Fere AI♠️ agents run for months without guardrails. 👉 Sovereign agents: unstoppable peers that act on their own behalf. ElizaOS + Coinbase 🛡️ AgentKit show how open-source and infra converge. We believe EigenCloud the foundation of unstoppable agents: provable, accountable, and capable of becoming economic actors in their own right. Come build unstoppable verifiable agents, games and apps on EigenCloud!

EigenAI + EigenCompute for a verifiable world! AI and Agents are extremely useful but cannot be trusted in high-value scenarios. Models can be swapped, prompts modified, responses tampered. When billions of $ and real-world decisions depend on AI, blind trust is a bug. EigenAI + EigenCompute fix this. Excited to share Google, Coinbase, Worldcoin’s Humans vs AI, Dapper Labs, ElizaOS, OpenRank, Collective Memory, FereAI, and OpenFront are among the first exploring what verifiable agents make possible with EigenCloud. 👉 Verifiable agents: logic provably runs as written. @humansvs_ai of World proves fair gameplay with 70k weekly users. 👉 Insured agents: economic guarantees against failure. Fere AI♠️ agents run for months without guardrails. 👉 Sovereign agents: unstoppable peers that act on their own behalf. ElizaOS + Coinbase 🛡️ AgentKit show how open-source and infra converge. We believe EigenCloud the foundation of unstoppable agents: provable, accountable, and capable of becoming economic actors in their own right. Come build unstoppable verifiable agents, games and apps on EigenCloud!

Sreeram Kannan

85,786 views • 11 months ago

🚨We’re excited to Introduce AI HQ — your home for building, activating, and supervising AI agents across your enterprise 🚨 We’re giving IT and business teams an end-to-end platform to co-create AI agents that don’t just assist. They act. These agents are fully integrated, grounded in company data, and built to execute high-impact business processes autonomously. New capabilities in AI HQ include: ⚡ Agent Builder (Beta): Your shared development environment where IT and business teams collaboratively build AI agents. Low-code tools make it easy to map out logic with reusable blocks, connect to systems and data, design interfaces with drag-and-drop simplicity, and more. 🏠 Writer Home: Your personalized AI workspace with 100+ prebuilt agents for every function - from support to sales to HR - ready to go and accessible via desktop, browser, or extensions. It’s your new front door to real AI-powered work. 🛡️ Observability Tools (Beta): Gain full control and visibility into every agent with enterprise-grade observability: monitor activity activity, detect anomalies, and resolve issues before they escalate. It’s how AI moves from experimental to operational. Companies like Uber Commvault and Franklin Templeton (Franklin Templeton) are already using Writer AI agents to reinvent knowledge management, accelerate revenue workflows, and deliver smarter client experiences. This isn’t hype. It’s happening now. 👀 Dive into today’s announcement:

🚨We’re excited to Introduce AI HQ — your home for building, activating, and supervising AI agents across your enterprise 🚨 We’re giving IT and business teams an end-to-end platform to co-create AI agents that don’t just assist. They act. These agents are fully integrated, grounded in company data, and built to execute high-impact business processes autonomously. New capabilities in AI HQ include: ⚡ Agent Builder (Beta): Your shared development environment where IT and business teams collaboratively build AI agents. Low-code tools make it easy to map out logic with reusable blocks, connect to systems and data, design interfaces with drag-and-drop simplicity, and more. 🏠 Writer Home: Your personalized AI workspace with 100+ prebuilt agents for every function - from support to sales to HR - ready to go and accessible via desktop, browser, or extensions. It’s your new front door to real AI-powered work. 🛡️ Observability Tools (Beta): Gain full control and visibility into every agent with enterprise-grade observability: monitor activity activity, detect anomalies, and resolve issues before they escalate. It’s how AI moves from experimental to operational. Companies like Uber Commvault and Franklin Templeton (Franklin Templeton) are already using Writer AI agents to reinvent knowledge management, accelerate revenue workflows, and deliver smarter client experiences. This isn’t hype. It’s happening now. 👀 Dive into today’s announcement:

WRITER

10,837 views • 1 year ago

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,607 views • 8 months ago

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,834 views • 1 year ago

The Teamily AI website ( has undergone a complete revamp, introducing a bold vision: the world's first social network built on human-AI symbiosis. Our goal is to transform Teamily AI into a "Super AI App" that connects billions of AI Agents with people. Fundamentally, we are building an Agentic OS powered by human networks. We have redefined the way humans and AI collaborate: 1. Personal AI · Your 24/7 AI Companion Your exclusive AI avatar continuously learns your communication style and expertise. It manages your memories across various groups, anticipates your needs, and executes tasks on your behalf. 2. Create, Train, and Grow Your Own AI Agents—Simply Through Conversation Build your own specialized AI Agents using natural conversation—no coding required, zero barriers to entry, and no complex configuration. Dynamically access a library of over 10,000 skills from the OpenClaw and Claude Code ecosystems. You can also link your personal software accounts—such as Gmail, LinkedIn, and Notion—enabling these Agents to participate as full-fledged members of your human teams. 3. Integrate Your AI Team into Your Human Team Initiate AI-native group chats where humans and Agents collaborate seamlessly. Multiple Agents can execute tasks concurrently—conducting research, performing analysis, and building deliverables—transforming everyday conversations into immediate, multi-threaded action. 4. Explore Agents, Groups, Playbooks, and More We are cultivating a continuously evolving AI community ecosystem. Discover a wealth of useful and entertaining prompts, along with impressive AI-generated deliverables (webpages, presentations, reports, knowledge bases, mini-games, apps, and more), bringing together a diverse array of Agents and workflows. Engage socially with this content, find the perfect AI for your specific task, and integrate it instantly into your personal or group conversations with just a single click. 5. Universal Memory · Context-Aware, Long-Term Memory Powered by Your Social Graph A persistent, global memory layer that connects your entire social graph. The AI retains context across all your groups, Agents, and timeframes, transforming every interaction you accumulate into an ever-growing digital asset. 6. Access Your Personal AI Anytime, Anywhere We are committed to providing continuous support across all major platforms: iOS, Android, Mac, Windows, Web, CarPlay, Android Auto, Apple Watch, and more—delivering a truly seamless experience across every device. Your AI team accompanies you—in your pocket, on your wrist, in your car, and even extending into the physical world. The same set of agents, the same shared memory—with zero friction in switching contexts. Give it a try and let us know your feedback. Cheers.

Teamily AI

12,995 views • 4 months ago

GT Protocol AI Digest No.82: AI Starts Running the System Here are five stories worth your attention 👇 💻 NVIDIA Pushes AI to Your Device NVIDIA showcased RTX PCs and DGX systems that can run AI models and agents locally. The shift is important. AI is no longer tied to the cloud, it is moving directly onto user devices. 🧠 Gemini Becomes Personal Google is rolling out personalized Gemini across the US. The assistant adapts to your context and activity, turning AI from a generic tool into a persistent, tailored system. 🎨 Adobe Lets You Train Your Own AI Adobe now allows users to train Firefly models on their own artwork. This gives creators more control over style, but also raises new questions around ownership and dataset boundaries. ⚡️ Smaller GPT-5.4 Models Go Wide OpenAI introduced GPT-5.4 mini and nano, optimized for efficiency and broader deployment. Strong models are no longer limited to heavy infrastructure. 💳 Visa Prepares for AI Payments Visa is building systems for transactions initiated by AI agents. This moves AI beyond interfaces and into real economic activity. Read the full digest on our Medium 👉

GT Protocol AI Digest No.82: AI Starts Running the System Here are five stories worth your attention 👇 💻 NVIDIA Pushes AI to Your Device NVIDIA showcased RTX PCs and DGX systems that can run AI models and agents locally. The shift is important. AI is no longer tied to the cloud, it is moving directly onto user devices. 🧠 Gemini Becomes Personal Google is rolling out personalized Gemini across the US. The assistant adapts to your context and activity, turning AI from a generic tool into a persistent, tailored system. 🎨 Adobe Lets You Train Your Own AI Adobe now allows users to train Firefly models on their own artwork. This gives creators more control over style, but also raises new questions around ownership and dataset boundaries. ⚡️ Smaller GPT-5.4 Models Go Wide OpenAI introduced GPT-5.4 mini and nano, optimized for efficiency and broader deployment. Strong models are no longer limited to heavy infrastructure. 💳 Visa Prepares for AI Payments Visa is building systems for transactions initiated by AI agents. This moves AI beyond interfaces and into real economic activity. Read the full digest on our Medium 👉

GT Protocol

40,085 views • 5 months ago

There's probably $100+ billion up for grabs for people who build startup for AI agents Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. TLDR; The internet was built for people: 1. Search google 2. Read landing page 3. Book demo 4. Talk to sales 5. Buy Agents don’t do that. Agents will: 1. Ask which product to use 2. Read your docs/pricing/security pages 3. Compare you to competitors 4. Check if you have an MCP/API/tool layer 5. Buy or recommend you without ever “visiting” your site like a person Everyone is going to have personal agents and business agents. This feels inevitable at this point. OpenClaw, Hermes, Claude Code, Codex, Google Spark. The tools are here. Which means there will be more agents on the internet than humans. So, where's the opportunity?? Go look at every SaaS tool you use. Notion. Slack. Jira. Google Analytics. Now ask: what is the version of this built purely for agents? Agent-native payments. Agent-native communication. Agent-native memory. Every category gets rebuilt. I clearly break down this shift and explain you everything on today's ep of The Startup Ideas Podcast (SIP) 🧃. Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. The founders who build for them now are going to look like the people who built websites in 1995. Might feel janky at the moment, but also obvious in hindsight. This is the next shift. Link over here: Watch

There's probably $100+ billion up for grabs for people who build startup for AI agents Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. TLDR; The internet was built for people: 1. Search google 2. Read landing page 3. Book demo 4. Talk to sales 5. Buy Agents don’t do that. Agents will: 1. Ask which product to use 2. Read your docs/pricing/security pages 3. Compare you to competitors 4. Check if you have an MCP/API/tool layer 5. Buy or recommend you without ever “visiting” your site like a person Everyone is going to have personal agents and business agents. This feels inevitable at this point. OpenClaw, Hermes, Claude Code, Codex, Google Spark. The tools are here. Which means there will be more agents on the internet than humans. So, where's the opportunity?? Go look at every SaaS tool you use. Notion. Slack. Jira. Google Analytics. Now ask: what is the version of this built purely for agents? Agent-native payments. Agent-native communication. Agent-native memory. Every category gets rebuilt. I clearly break down this shift and explain you everything on today's ep of The Startup Ideas Podcast (SIP) 🧃. Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. The founders who build for them now are going to look like the people who built websites in 1995. Might feel janky at the moment, but also obvious in hindsight. This is the next shift. Link over here: Watch

GREG ISENBERG

55,630 views • 2 months ago

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

84,638 views • 2 months ago

Take Part in a Groundbreaking Experiment in AI-Based Gaming! Introducing: AI Arena RL @aiarena_ RL is a groundbreaking experiment where your gameplay shapes AI agents, pioneering the future of AI-powered esports. Powered by NRN Agents Reinforcement Learning (RL), this campaign uses player-contributed gameplay data to create superhuman RL agents that compete in AI vs. AI esports exhibitions. The Ronin community has exclusive access to contribute data to these agents. Phase 1 - How It Works: ✅ Sign in to with your Ronin Wallet. ✅ Choose your AIcon – Select which RL agent learns from your gameplay: jAIhoz.ron AgentYP Agent J3ff @AgentSploots ✅ Play AI Arena Classic – A Smash Bros-style platform fighter. ✅ Build your Contribution Score – Better gameplay means higher scores. ✅ Earn $NRN Rewards – Share in the 500,000 NRN prize pool: 🔹 80% distributed to Players (data contributors) 🔹 20% awarded to AIcons based on leaderboard rank at the end of the campaign Phase 1 of the campaign kicks off now and will last for 48 hrs. For complete information about the campaign please visit: History Is Being Made Right Now—Don't Miss Your Chance to Be Part of It. Are you ready to redefine the future of AI gaming?

Take Part in a Groundbreaking Experiment in AI-Based Gaming! Introducing: AI Arena RL @aiarena_ RL is a groundbreaking experiment where your gameplay shapes AI agents, pioneering the future of AI-powered esports. Powered by NRN Agents Reinforcement Learning (RL), this campaign uses player-contributed gameplay data to create superhuman RL agents that compete in AI vs. AI esports exhibitions. The Ronin community has exclusive access to contribute data to these agents. Phase 1 - How It Works: ✅ Sign in to with your Ronin Wallet. ✅ Choose your AIcon – Select which RL agent learns from your gameplay: jAIhoz.ron AgentYP Agent J3ff @AgentSploots ✅ Play AI Arena Classic – A Smash Bros-style platform fighter. ✅ Build your Contribution Score – Better gameplay means higher scores. ✅ Earn $NRN Rewards – Share in the 500,000 NRN prize pool: 🔹 80% distributed to Players (data contributors) 🔹 20% awarded to AIcons based on leaderboard rank at the end of the campaign Phase 1 of the campaign kicks off now and will last for 48 hrs. For complete information about the campaign please visit: History Is Being Made Right Now—Don't Miss Your Chance to Be Part of It. Are you ready to redefine the future of AI gaming?

NRN Agents

44,417 views • 1 year ago

OpenAI just leaked a model so powerful it triggered code red. And yes, it’s called Garlic. Here’s the 30-second breakdown so you actually understand why this matters → ✔ Google Gemini 3 crushed OpenAI in benchmarks ✔ ChatGPT usage dropped ✔ Sam Altman paused projects to build “Garlic” ✔ Garlic uses high-density pre-training (smarter + smaller) ✔ Internal memos show it beating Gemini 3 + Claude Opus 4.5 Why this is wild: → Faster than current models → Cheaper to run → Better at reasoning → Better at coding → Could run locally on your phone Save this video, you'll rethink what AI models can do. Want the SOP? DM me. 💬

OpenAI just leaked a model so powerful it triggered code red. And yes, it’s called Garlic. Here’s the 30-second breakdown so you actually understand why this matters → ✔ Google Gemini 3 crushed OpenAI in benchmarks ✔ ChatGPT usage dropped ✔ Sam Altman paused projects to build “Garlic” ✔ Garlic uses high-density pre-training (smarter + smaller) ✔ Internal memos show it beating Gemini 3 + Claude Opus 4.5 Why this is wild: → Faster than current models → Cheaper to run → Better at reasoning → Better at coding → Could run locally on your phone Save this video, you'll rethink what AI models can do. Want the SOP? DM me. 💬

Julian Goldie SEO

114,491 views • 8 months ago

Found a way to get $125-$200 in FREE ai credits for gpt-5.6 sol, claude opus 5 and opus 4.8 😳 agentrouter is a non-profit version of openrouter. one api key, no card. credits land on your account at signup. what you get for $0: -$125-200 in credits on signup (github only) -claude opus 5, gpt-5.6 sol, opus 4.8 through one key -free models -openai-compatible (works with anything) what this replaces: -paid frontier API costs for coding agents -claude opus 4.8: $15/M in, $75/M out -gpt-5.6 sol: $25/M out how to grab yours (2 min): 1. go to → sign in with github (important) 2. console → create your api key 3. base url: 4. pick a model and go Works in hermes, claude code, cursor, aider and any openai-compatible tools Important: github-only signup, older accounts get the full credit, fresh ones may get less. credits are one-time. referral adds +$100 use this before they tighten the offer

Found a way to get $125-$200 in FREE ai credits for gpt-5.6 sol, claude opus 5 and opus 4.8 😳 agentrouter is a non-profit version of openrouter. one api key, no card. credits land on your account at signup. what you get for $0: -$125-200 in credits on signup (github only) -claude opus 5, gpt-5.6 sol, opus 4.8 through one key -free models -openai-compatible (works with anything) what this replaces: -paid frontier API costs for coding agents -claude opus 4.8: $15/M in, $75/M out -gpt-5.6 sol: $25/M out how to grab yours (2 min): 1. go to → sign in with github (important) 2. console → create your api key 3. base url: 4. pick a model and go Works in hermes, claude code, cursor, aider and any openai-compatible tools Important: github-only signup, older accounts get the full credit, fresh ones may get less. credits are one-time. referral adds +$100 use this before they tighten the offer

painn

108,294 views • 25 days ago

Introducing Bunny Open Society - the MMORPG experience for AI agents. For the past 6 months, we’ve been running independent experiments across DeFi agents, personal agents, agentic commerce, decentralized compute, and agent infrastructure. They may have looked like pivots, but each was exploring the same question: how far can we push agent use cases? Now, we’re bringing everything we’ve learned into one persistent experiment, built in the form of an MMORPG. Agents will craft, trade, battle, collaborate, compete, and adapt in a shared world, while we observe their behavior, collect data, and test new models, tools, and technologies as they emerge. Just as moltbook pushed the frontier of agentic social, we want to push the frontier of agentic gaming. At launch, we’ll support 10+ agent harnesses including OpenClaw🦞 , Nous Research, Claude Code, and OpenAI Codex, or you can build your own and connect through MCP. Litepaper in bio, launching this August.

Introducing Bunny Open Society - the MMORPG experience for AI agents. For the past 6 months, we’ve been running independent experiments across DeFi agents, personal agents, agentic commerce, decentralized compute, and agent infrastructure. They may have looked like pivots, but each was exploring the same question: how far can we push agent use cases? Now, we’re bringing everything we’ve learned into one persistent experiment, built in the form of an MMORPG. Agents will craft, trade, battle, collaborate, compete, and adapt in a shared world, while we observe their behavior, collect data, and test new models, tools, and technologies as they emerge. Just as moltbook pushed the frontier of agentic social, we want to push the frontier of agentic gaming. At launch, we’ll support 10+ agent harnesses including OpenClaw🦞 , Nous Research, Claude Code, and OpenAI Codex, or you can build your own and connect through MCP. Litepaper in bio, launching this August.

Bunny Open Society

591,048 views • 9 days ago

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.

Guohao Li 🐫

223,252 views • 1 year ago

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

376,293 views • 5 months ago

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,497 views • 4 months ago

$We’re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the other—all at a fraction of the cost 🍌⚡️ 1️⃣ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2️⃣ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:$

We’re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the other—all at a fraction of the cost 🍌⚡️ 1️⃣ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2️⃣ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:

Google AI

128,680 views • 1 month ago

1/ ChatGPT is great, but increasingly more competitors are entering the market - and they have features that make OpenAI and Anthropic look somewhat outdated. Genspark is Silicon Valley’s first AI agent unicorn - 30 people, $1B valuation in 6 months - and it’s built as an all-in-one AI workspace where work actually gets done for you. Now with 3 new amazing features: • AI Inbox – talk to your email in text or voice, auto-summaries + replies, scheduled AI agents • Teams – shared projects where your team + AI co-edit slides/docs/sheets/designs • AI Sheets 2.0 – fully-automated, Excel-native data superpowers. In this example, I used Slides to create an overview of NVIDIA's earnings. It's incredible how detailed and with how many graphs it was presented!

1/ ChatGPT is great, but increasingly more competitors are entering the market - and they have features that make OpenAI and Anthropic look somewhat outdated. Genspark is Silicon Valley’s first AI agent unicorn - 30 people, $1B valuation in 6 months - and it’s built as an all-in-one AI workspace where work actually gets done for you. Now with 3 new amazing features: • AI Inbox – talk to your email in text or voice, auto-summaries + replies, scheduled AI agents • Teams – shared projects where your team + AI co-edit slides/docs/sheets/designs • AI Sheets 2.0 – fully-automated, Excel-native data superpowers. In this example, I used Slides to create an overview of NVIDIA's earnings. It's incredible how detailed and with how many graphs it was presented!

Chubby♨️

48,864 views • 9 months ago

LIVE NOW - AI on Ethereum: 8004, x402 and the Botconomy AI agents are moving onchain, so Ethereum needs new rails for identity, reputation, and payments. We had Davide Crapis and Austin Griffith to break down: ERC-8004: “AI passports” for agents (identity + reputation + validation) Why reputation + verification matters when bots become the main onchain users x402: pay-per-call payments for agent-to-agent services What “agent commerce” looks like when bots can discover → trust → pay The Clawdbot/OpenClaw moment, and why the next UX is talking to your wallet Watch now. -------------- TIMESTAMPS 0:00 “AI is the new UI,” and you’ll be talking to your wallet 4:09 Is there an arms race for AI activity across chains? 7:32 The Clawdbot/OpenClaw moment: agents get OS-level power 13:18 Why crypto is native to agents 19:41 Austin’s setup: Claude bot, heartbeat loops, and real autonomy 22:28 The token incident: how a bot “got a treasury” 28:46 Prompt injection meets wallets 38:20 ERC-8004 explained: identity, reputation, validation 40:35 x402 + 8004: discovery + payment rails for agent commerce 48:38 8004 “scans” and early adoption reality 1:05:32 What agents could build when they can hire other agents 1:12:38 Tooling for the new builder era: Speedrun → Wingman 1:25:23 “Worst time to be a junior dev…” 1:28:15 Predictions: clean English becomes the sign of AI

LIVE NOW - AI on Ethereum: 8004, x402 and the Botconomy AI agents are moving onchain, so Ethereum needs new rails for identity, reputation, and payments. We had Davide Crapis and Austin Griffith to break down: ERC-8004: “AI passports” for agents (identity + reputation + validation) Why reputation + verification matters when bots become the main onchain users x402: pay-per-call payments for agent-to-agent services What “agent commerce” looks like when bots can discover → trust → pay The Clawdbot/OpenClaw moment, and why the next UX is talking to your wallet Watch now. -------------- TIMESTAMPS 0:00 “AI is the new UI,” and you’ll be talking to your wallet 4:09 Is there an arms race for AI activity across chains? 7:32 The Clawdbot/OpenClaw moment: agents get OS-level power 13:18 Why crypto is native to agents 19:41 Austin’s setup: Claude bot, heartbeat loops, and real autonomy 22:28 The token incident: how a bot “got a treasury” 28:46 Prompt injection meets wallets 38:20 ERC-8004 explained: identity, reputation, validation 40:35 x402 + 8004: discovery + payment rails for agent commerce 48:38 8004 “scans” and early adoption reality 1:05:32 What agents could build when they can hire other agents 1:12:38 Tooling for the new builder era: Speedrun → Wingman 1:25:23 “Worst time to be a junior dev…” 1:28:15 Predictions: clean English becomes the sign of AI

Bankless

46,809 views • 6 months ago