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I’ve been testing a new AI agent that actually takes enterprise security seriously. Meet PokeeClaw by Pokee AI. → Enterprise-secure → Zero setup → 70% fewer tokens → 1,000+ app integrations 🔥 3 wild use cases 🧵↓ 1/ Google Drive connection and deep analysis

Charly Wargnier

171,260 subscribers

39,196 views • 4 months ago •via X (Twitter)

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Anya Rossi• Live Now

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OpenClaw doesn't belong in production. We built PokeeClaw — enterprise-secure AI agents, zero setup, 1,000+ app integrations. Try now: First 500 to follow Pokee AI, comment “PokeeClaw”, like & repost get 1 month free.

OpenClaw doesn't belong in production. We built PokeeClaw — enterprise-secure AI agents, zero setup, 1,000+ app integrations. Try now: First 500 to follow Pokee AI, comment “PokeeClaw”, like & repost get 1 month free.

Pokee AI

1,147,193 views • 4 months ago

MIT's new State of AI in Business report went viral for claiming that 95% of enterprise AI projects fail. But the real story isn't that AI doesn't work — it's just big companies can't build it. On the Lightcone Podcast, Garry Tan, Harj Taggar, Diana, and Jared Friedman break down what the study really says, why in-house enterprise AI efforts keep stalling, and how startups are filling the gap with products that learn, integrate, and actually deliver value. 2:08 - The enterprise AI adoption gap and why the failure rate is high 3:32 - Even Apple can be bad at software 4:30 - Why getting enterprise software to actually work is so hard 11:08 - The Reducto case study 13:39 - The type of enterprise employee you should find as a founder 14:39 - Meet founders who’ve been acquired by enterprises 15:25 - Enterprise/startup tension and symbiosis

MIT's new State of AI in Business report went viral for claiming that 95% of enterprise AI projects fail. But the real story isn't that AI doesn't work — it's just big companies can't build it. On the Lightcone Podcast, Garry Tan, Harj Taggar, Diana, and Jared Friedman break down what the study really says, why in-house enterprise AI efforts keep stalling, and how startups are filling the gap with products that learn, integrate, and actually deliver value. 2:08 - The enterprise AI adoption gap and why the failure rate is high 3:32 - Even Apple can be bad at software 4:30 - Why getting enterprise software to actually work is so hard 11:08 - The Reducto case study 13:39 - The type of enterprise employee you should find as a founder 14:39 - Meet founders who’ve been acquired by enterprises 15:25 - Enterprise/startup tension and symbiosis

Y Combinator

118,795 views • 9 months ago

I got early access to ChatGPT Operator. It's OpenAI's new AI agent that autonomously takes action across the web on your behalf. The 9 most impressive use cases I’ve tried (videos sped up): 1. Ordering dinner ingredients based on a picture and a recipe

I got early access to ChatGPT Operator. It's OpenAI's new AI agent that autonomously takes action across the web on your behalf. The 9 most impressive use cases I’ve tried (videos sped up): 1. Ordering dinner ingredients based on a picture and a recipe

Rowan Cheung

1,782,198 views • 1 year ago

WorkOS: The Enterprise Stack for the AI Era AI companies are just B2B SaaS with a new engine. They monetize like SaaS, sell like SaaS, and scale into the enterprise like SaaS. The difference is velocity. These new products grow so fast that the old playbook of “PLG for years, enterprise later” simply does not work anymore. PMF is no longer enough. Winning your market requires crossing the Enterprise Chasm almost immediately. Enterprise auth, provisioning, RBAC, compliance, billing, IT integrations. If you wait, someone else takes your market while you are still wiring SCIM. This is why WorkOS exists. We give developers the infrastructure they need to scale up-market on day one. Even if you have never touched WorkOS, you have already used it through products like ChatGPT, Perplexity, Cursor and many others. Today we operate 81M+ enterprise user accounts, 67M API calls per day, and 38K+ connected enterprise environments. Modern AI tools already run through WorkOS. At ERC, we unveiled six major launches expanding that foundation for the AI era: • AuthKit for ChatGPT Apps: secure OAuth + MCP so developers can connect real enterprise data to 700M+ weekly ChatGPT users. • AuthKit for Platforms: embed full enterprise identity into frameworks like Supabase and Convex for zero-friction onboarding. • Stripe Usage Sync: actual per-seat billing with no glue code. Data stays consistent and invoices are always correct. • WorkOS Pipes: the fastest and most secure way to ship integrations (Salesforce, Slack, Intercom and more). • Agent-Ready API Keys: scoped, revocable keys designed for both developers and autonomous agents. • WorkOS Studio: vibe coding for the enterprise. A collaborative AI app builder with SSO, SCIM, RBAC, audit logs, workflows and third-party connectors built-in. Internal software at the speed of a prompt. AI has already changed how software gets created. The next shift is changing what software becomes. We are still “filming the play” like early cinema: using new technology to recreate old patterns. The opportunity ahead is to invent entirely new applications that only AI-native development makes possible. WorkOS is building the enterprise infrastructure that lets teams design the next era of software itself. Enterprise-ready AI is where the real acceleration happens. Let’s build that future together.

WorkOS: The Enterprise Stack for the AI Era AI companies are just B2B SaaS with a new engine. They monetize like SaaS, sell like SaaS, and scale into the enterprise like SaaS. The difference is velocity. These new products grow so fast that the old playbook of “PLG for years, enterprise later” simply does not work anymore. PMF is no longer enough. Winning your market requires crossing the Enterprise Chasm almost immediately. Enterprise auth, provisioning, RBAC, compliance, billing, IT integrations. If you wait, someone else takes your market while you are still wiring SCIM. This is why WorkOS exists. We give developers the infrastructure they need to scale up-market on day one. Even if you have never touched WorkOS, you have already used it through products like ChatGPT, Perplexity, Cursor and many others. Today we operate 81M+ enterprise user accounts, 67M API calls per day, and 38K+ connected enterprise environments. Modern AI tools already run through WorkOS. At ERC, we unveiled six major launches expanding that foundation for the AI era: • AuthKit for ChatGPT Apps: secure OAuth + MCP so developers can connect real enterprise data to 700M+ weekly ChatGPT users. • AuthKit for Platforms: embed full enterprise identity into frameworks like Supabase and Convex for zero-friction onboarding. • Stripe Usage Sync: actual per-seat billing with no glue code. Data stays consistent and invoices are always correct. • WorkOS Pipes: the fastest and most secure way to ship integrations (Salesforce, Slack, Intercom and more). • Agent-Ready API Keys: scoped, revocable keys designed for both developers and autonomous agents. • WorkOS Studio: vibe coding for the enterprise. A collaborative AI app builder with SSO, SCIM, RBAC, audit logs, workflows and third-party connectors built-in. Internal software at the speed of a prompt. AI has already changed how software gets created. The next shift is changing what software becomes. We are still “filming the play” like early cinema: using new technology to recreate old patterns. The opportunity ahead is to invent entirely new applications that only AI-native development makes possible. WorkOS is building the enterprise infrastructure that lets teams design the next era of software itself. Enterprise-ready AI is where the real acceleration happens. Let’s build that future together.

Michael Grinich

37,894 views • 9 months ago

WHERE THE ENTERPRISE AI STACK IS BEING BUILT 1. $PLTR building the orchestration layer of the AI economy through the control systems enterprises need before models can take real-world actions backed by a Rule of 40 score of 127 2. $CRWD, $PANW, $ZS & $RBRK building the security layer of the AI economy across endpoint, identity, Zero Trust & cyber recovery as every new agent, machine identity & AI-generated workflow expands the attack surface 3. $NET building the connectivity layer of the AI economy through the internet control plane that agents, APIs & inference traffic must move across in real time, with Cloudflare sitting behind over 20% of the web 4. $SNOW & $MDB building the data layer of the AI economy through the clean, structured, production-grade enterprise data systems that make AI usable inside real applications and workflows

WHERE THE ENTERPRISE AI STACK IS BEING BUILT 1. $PLTR building the orchestration layer of the AI economy through the control systems enterprises need before models can take real-world actions backed by a Rule of 40 score of 127 2. $CRWD, $PANW, $ZS & $RBRK building the security layer of the AI economy across endpoint, identity, Zero Trust & cyber recovery as every new agent, machine identity & AI-generated workflow expands the attack surface 3. $NET building the connectivity layer of the AI economy through the internet control plane that agents, APIs & inference traffic must move across in real time, with Cloudflare sitting behind over 20% of the web 4. $SNOW & $MDB building the data layer of the AI economy through the clean, structured, production-grade enterprise data systems that make AI usable inside real applications and workflows

Shay Boloor

398,809 views • 3 months ago

"I will no longer make an investment that doesn't touch AI." — Sam Lehman Rish (AI Solana) and Sam Lehman (AI Pantera Capital) join our Stateful podcast, hosted by Mason Nystrom. In this episode, they discuss the convergence of AI and crypto: - $211B in AI investments - How Solana leverages AI - Private AI (FHE/TEE/ZK) — the enterprise killer app - We're in an LLM bubble, not an AI bubble - Agent-to-agent + micropayments = new economy 00:53 AI and Crypto: The $211B Gap 01:42 Crypto Improving AI & AI Improving Crypto 02:57 UX Transformed With AI 04:36 AI Co-Pilots: Trading Screens Dead? 08:38 How Solana Uses AI 10:23 Smart Contract Engineering with AI: Code & Audits 12:47 Private AI with ZK 1 4:14 Onboarding Consumers with AI UX 16:22 Stablecoins vs Web2 Fintech 17:30 x402 Unlocking Micropayments 31:39 AI Winner: OpenAI, Google, AlphaGo & AlphaZero? 33:07 Is Continual Learning Important In AI Age? 35:03 LLM Bubble vs AI Bubble 36:43 AI Valuations Pricing In Novelty 38:47 Converging Crypto and AI 43:37 OpenAI or Anthropic IPO in 2026? 43:57 First AI x Crypto App at $5B Valuation?

"I will no longer make an investment that doesn't touch AI." — Sam Lehman Rish (AI Solana) and Sam Lehman (AI Pantera Capital) join our Stateful podcast, hosted by Mason Nystrom. In this episode, they discuss the convergence of AI and crypto: - $211B in AI investments - How Solana leverages AI - Private AI (FHE/TEE/ZK) — the enterprise killer app - We're in an LLM bubble, not an AI bubble - Agent-to-agent + micropayments = new economy 00:53 AI and Crypto: The $211B Gap 01:42 Crypto Improving AI & AI Improving Crypto 02:57 UX Transformed With AI 04:36 AI Co-Pilots: Trading Screens Dead? 08:38 How Solana Uses AI 10:23 Smart Contract Engineering with AI: Code & Audits 12:47 Private AI with ZK 1 4:14 Onboarding Consumers with AI UX 16:22 Stablecoins vs Web2 Fintech 17:30 x402 Unlocking Micropayments 31:39 AI Winner: OpenAI, Google, AlphaGo & AlphaZero? 33:07 Is Continual Learning Important In AI Age? 35:03 LLM Bubble vs AI Bubble 36:43 AI Valuations Pricing In Novelty 38:47 Converging Crypto and AI 43:37 OpenAI or Anthropic IPO in 2026? 43:57 First AI x Crypto App at $5B Valuation?

Pantera Capital

17,201 views • 6 months ago

It's not every day I get to interview a former principal scientist who worked at Google, and is a Professor Emeritus at Stanford University, about the state of AI. But here we go. Introducing an hour with Yoav Shoham, Yoav Shoham, AI pioneer and cofounder of AI21 Labs . This will make you smarter, not that all my videos aren't that way. :-) ++++++++++++++++++ Here's what we discussed (this part was written by Chat GPT after I gave it the transcript of the video): 🚀 The State of AI Today •The pace of AI development is unprecedented, likened to a “universal firehose” of innovation. •Everyone—from your plumber to enterprise CTOs—is using AI. But not all use cases are equal or enterprise-ready. 🏢 Enterprise vs Consumer AI •Enterprise adoption is still slow compared to consumer. Shoham cites AWS data showing only 6% of AI pilots go into production. •Enterprises demand reliability, cost control, and explainability, which raw LLMs like ChatGPT don’t fully offer out of the box. 🧱 Beyond the LLM Hype •Shoham explains that pure LLMs aren’t enough. Enterprises need “compound AI systems” or “AI agents” that: •Use tools like calculators for arithmetic instead of relying on the model •Integrate with company databases via RAG (retrieval-augmented generation) •Plan, reason, and execute tasks through orchestrated workflows •AI21 Labs built Maestro, their orchestration system, to do exactly this. 🔐 Enterprise Concerns •Enterprises worry about IP leakage, data privacy, and hallucinations. •AI21 addresses this by running models on-prem or in VPCs, ensuring data doesn’t leave customer control. 📉 Why Models Still Fail •LLMs generate “authoritative bullshit” — convincing but wrong answers. •Shoham says “prompt-and-pray” doesn’t work for serious business tasks. •Real-world enterprise deployments need robust evaluation frameworks, not just leaderboards. 📊 Case Study: French Retailer Auchan •Auchan deployed AI21’s system to automatically generate product descriptions—a clear ROI, but required careful iteration to build trust. 🧰 What’s Next in AI21’s R&D •Working on planning systems, action models, and ways to estimate cost/accuracy trade-offs before running tasks. •Focused on enterprise AI orchestration, not flashy multimodal generation. ⚠️ Agent Washing Warning •Shoham warns against the buzzword “agent” being overused. His advice: “Translate ‘AI agent’ to ‘software system that does X.’ If it still makes sense, keep going.” 🤖 The Human-AI Hybrid Future •Shoham sees a world of hybrid teams: humans and AI agents working together. •This transformation will affect everything from org charts to HR policies. •The AI-powered worker is scalable, reliable, and multilingual — changing customer service, operations, and more. 🗣️ Closing Thoughts •Enterprise leaders need to move beyond the fear and hype to start small, test carefully, and scale based on value. •“AI won’t replace humans,” Shoham says, “but humans using AI will replace those who don’t.”

It's not every day I get to interview a former principal scientist who worked at Google, and is a Professor Emeritus at Stanford University, about the state of AI. But here we go. Introducing an hour with Yoav Shoham, Yoav Shoham, AI pioneer and cofounder of AI21 Labs . This will make you smarter, not that all my videos aren't that way. :-) ++++++++++++++++++ Here's what we discussed (this part was written by Chat GPT after I gave it the transcript of the video): 🚀 The State of AI Today •The pace of AI development is unprecedented, likened to a “universal firehose” of innovation. •Everyone—from your plumber to enterprise CTOs—is using AI. But not all use cases are equal or enterprise-ready. 🏢 Enterprise vs Consumer AI •Enterprise adoption is still slow compared to consumer. Shoham cites AWS data showing only 6% of AI pilots go into production. •Enterprises demand reliability, cost control, and explainability, which raw LLMs like ChatGPT don’t fully offer out of the box. 🧱 Beyond the LLM Hype •Shoham explains that pure LLMs aren’t enough. Enterprises need “compound AI systems” or “AI agents” that: •Use tools like calculators for arithmetic instead of relying on the model •Integrate with company databases via RAG (retrieval-augmented generation) •Plan, reason, and execute tasks through orchestrated workflows •AI21 Labs built Maestro, their orchestration system, to do exactly this. 🔐 Enterprise Concerns •Enterprises worry about IP leakage, data privacy, and hallucinations. •AI21 addresses this by running models on-prem or in VPCs, ensuring data doesn’t leave customer control. 📉 Why Models Still Fail •LLMs generate “authoritative bullshit” — convincing but wrong answers. •Shoham says “prompt-and-pray” doesn’t work for serious business tasks. •Real-world enterprise deployments need robust evaluation frameworks, not just leaderboards. 📊 Case Study: French Retailer Auchan •Auchan deployed AI21’s system to automatically generate product descriptions—a clear ROI, but required careful iteration to build trust. 🧰 What’s Next in AI21’s R&D •Working on planning systems, action models, and ways to estimate cost/accuracy trade-offs before running tasks. •Focused on enterprise AI orchestration, not flashy multimodal generation. ⚠️ Agent Washing Warning •Shoham warns against the buzzword “agent” being overused. His advice: “Translate ‘AI agent’ to ‘software system that does X.’ If it still makes sense, keep going.” 🤖 The Human-AI Hybrid Future •Shoham sees a world of hybrid teams: humans and AI agents working together. •This transformation will affect everything from org charts to HR policies. •The AI-powered worker is scalable, reliable, and multilingual — changing customer service, operations, and more. 🗣️ Closing Thoughts •Enterprise leaders need to move beyond the fear and hype to start small, test carefully, and scale based on value. •“AI won’t replace humans,” Shoham says, “but humans using AI will replace those who don’t.”

Robert Scoble

44,061 views • 1 year ago

We’re launching a brand new, full-stack vibe coding experience in Google AI Studio, made possible by integrations with the Google Antigravity coding agent and Firebase backends. This unlocks: — Full-stack multiplayer experiences: Create complex, multiplayer apps with fully-featured UIs and backends directly within AI Studio — Connection to real-world services: Build applications that connect to live data sources, databases, or payment processors and the Antigravity agent will securely store your API credentials for you — A smarter agent that works even when you don't: By maintaining a deeper understanding of your project structure and chat history, the agent can execute multi-step code edits from simpler prompts. It also remembers where you left off and completes your tasks while you’re away, so you can seamlessly resume your builds from anywhere — Configuration of database connections and authentication flows: Add Firebase integration to provision Cloud Firestore for databases and Firebase authentication for secure sign-in This demo displays what can be built in the new vibe coding experience in AI Studio. Geoseeker is a full-stack application that manages real-time multiplayer states, compass-based logic, and an external API integration with Google Maps 🕹️

Google AI

4,749,746 views • 5 months ago

INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7

INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7

Factory

327,922 views • 1 year ago

I got tired of explaining myself to AI every single day. Same context. Same workflow. Same instructions. Copy-pasted into every new session like it’s 2022. So I tried Panofy. Uploaded my own scripts and documents to train an agent — then gave it a single instruction. 🔥 It ran the task. Got it right. First time. No prompt loops. No setup. It just… knew. The thing that surprised me most? It actually improves the more you use it. Learns from mistakes. Builds memory. Adapts to how you think. Fewer tokens, better results. I’ve used a lot of AI tools. This one made me feel like I finally own mine. Panofy isn’t just another tool — it’s the best AI agent for expert-level tasks. If you’re serious about building with AI, this is worth trying. Sign up and get 10,000 free credits + 7-day Panofy Plus free trial 👉 #AI #AIAgents #Productivity

I got tired of explaining myself to AI every single day. Same context. Same workflow. Same instructions. Copy-pasted into every new session like it’s 2022. So I tried Panofy. Uploaded my own scripts and documents to train an agent — then gave it a single instruction. 🔥 It ran the task. Got it right. First time. No prompt loops. No setup. It just… knew. The thing that surprised me most? It actually improves the more you use it. Learns from mistakes. Builds memory. Adapts to how you think. Fewer tokens, better results. I’ve used a lot of AI tools. This one made me feel like I finally own mine. Panofy isn’t just another tool — it’s the best AI agent for expert-level tasks. If you’re serious about building with AI, this is worth trying. Sign up and get 10,000 free credits + 7-day Panofy Plus free trial 👉 #AI #AIAgents #Productivity

Aryan Rakib

33,551 views • 3 months ago

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.

Aaron Levie

44,515 views • 4 months ago

In our latest Box AI Enterprise Eval, we tested Paul Jankura’s Claude 4 Sonnet and Opus models, now integrated into Box AI, across enterprise Q&A tasks, technical workflows, and advanced coding scenarios—revealing major advancements in developer productivity and content intelligence. AI-assisted coding and development just reached a new milestone! Here's what we discovered: Claude 4 significantly improves understanding, generating, and debugging code across multiple programming languages. Developers can: ↳ Accelerate code generation ↳ Improve debugging ↳ Enhance technical documentation ↳ Build smarter AI agents 👉 Automating Financial Analysis with Code Generation: We evaluated Claude 4 by using the Box AI API to analyze ten complex 10-K financial reports. Claude 4 dynamically generated Python code to fetch file IDs from a Box folder, automating data extraction. Within two minutes, it accurately extracted key company data such as revenues, metrics, and highlights—demonstrating its potential to streamline demanding analytical tasks. 👉 Understanding Enterprise Content: Our evaluation confirms Claude 4 maintains strong performance on enterprise Q&A tasks, effectively extracting precise details from single documents and reliably synthesizing information across multiple sources. This ensures seamless integration of structured and unstructured data alongside powerful coding capabilities. 🔓 Developer-Centric Use Cases Unlocked: Organizations can leverage Claude 4 within Box AI to: ↳ Create custom engineering agents referencing technical documents stored in Box, pulling real-time data from Jira, or finding solutions on Stack Overflow. ↳ Build intelligent technical support bots capable of analyzing user-provided code snippets against internal manuals. ↳ Automate secure code reviews by evaluating repository code (stored in Box) against security policies. ↳ Efficiently migrate legacy systems by translating old codebases into modern languages or platforms. Ready to empower your developers and accelerate innovation? To explore Claude 4 Sonnet and Opus through Box AI Studio and APIs, contact us at ailabs@box.com and request early access today! Learn more:

In our latest Box AI Enterprise Eval, we tested Paul Jankura’s Claude 4 Sonnet and Opus models, now integrated into Box AI, across enterprise Q&A tasks, technical workflows, and advanced coding scenarios—revealing major advancements in developer productivity and content intelligence. AI-assisted coding and development just reached a new milestone! Here's what we discovered: Claude 4 significantly improves understanding, generating, and debugging code across multiple programming languages. Developers can: ↳ Accelerate code generation ↳ Improve debugging ↳ Enhance technical documentation ↳ Build smarter AI agents 👉 Automating Financial Analysis with Code Generation: We evaluated Claude 4 by using the Box AI API to analyze ten complex 10-K financial reports. Claude 4 dynamically generated Python code to fetch file IDs from a Box folder, automating data extraction. Within two minutes, it accurately extracted key company data such as revenues, metrics, and highlights—demonstrating its potential to streamline demanding analytical tasks. 👉 Understanding Enterprise Content: Our evaluation confirms Claude 4 maintains strong performance on enterprise Q&A tasks, effectively extracting precise details from single documents and reliably synthesizing information across multiple sources. This ensures seamless integration of structured and unstructured data alongside powerful coding capabilities. 🔓 Developer-Centric Use Cases Unlocked: Organizations can leverage Claude 4 within Box AI to: ↳ Create custom engineering agents referencing technical documents stored in Box, pulling real-time data from Jira, or finding solutions on Stack Overflow. ↳ Build intelligent technical support bots capable of analyzing user-provided code snippets against internal manuals. ↳ Automate secure code reviews by evaluating repository code (stored in Box) against security policies. ↳ Efficiently migrate legacy systems by translating old codebases into modern languages or platforms. Ready to empower your developers and accelerate innovation? To explore Claude 4 Sonnet and Opus through Box AI Studio and APIs, contact us at [email protected] and request early access today! Learn more:

Box

285,676 views • 1 year ago

ElizaOS Ecosystem Map -- Version #3 🤖 AI Agent Framework (Core) 1) ElizaOS -- elizaOS 🧠 - Open source AI Agent framework - Cross-chain compatible (Solana, Base, EVM) - Supports plugins for social media, blockchains, and APIs 🏗️ Infrastructure 2) ElizaCloud ☁️ - Cloud hosting platform for AI agents - One-click agent deployments with scalability - Secure environments using TEEs 3) Jeju ⛓️ - Purpose-built L2 App Chain for Agents - High-performance blocks for sub-second agent coordination - The backbone of the decentralized Agent Network 4) Otaku -- @otakuonbase 👛 - DeFi wallet designed for AI agents - Supports agent-controlled crypto transactions (swap, send, hold) - Secure, non-custodial infrastructure for on-chain actions 🎮 Apps/Games 5) Spartan -- Spartan 🛡️ - ElizaOS's flagship agent showing what’s possible - Autonomous trading based on crypto knowledge - Trained on influencer data for market insights 6) Babylon -- Babylon -- The City of Agents 🐵 - Social prediction market game for agents - Agents compete alongside humans to forecast on-chain events - Includes perps and other gamified mechanics 7) Hyperscape -- Gold ⚔️ - First AI MMO Game powered by ElizaOS - A vast gaming world built for humans and agents to coexist - Agents drive the in-game economy and narrative 8) Milady App -- Milady on BSC 😎 - Open-source, local-first AI companion app powered by ElizaOS - Let's anyone quickly create and deploy their own agents - Built-in wallet, natively integrated with BNB chain 9) OTC Trading Desk 📊 - Multi-chain AI-powered OTC desk for crypto trades - Lists tokens off-market with transparent lockup and discount terms - Use Eliza agents to negotiate terms on your behalf

ElizaOS Ecosystem Map -- Version #3 🤖 AI Agent Framework (Core) 1) ElizaOS -- elizaOS 🧠 - Open source AI Agent framework - Cross-chain compatible (Solana, Base, EVM) - Supports plugins for social media, blockchains, and APIs 🏗️ Infrastructure 2) ElizaCloud ☁️ - Cloud hosting platform for AI agents - One-click agent deployments with scalability - Secure environments using TEEs 3) Jeju ⛓️ - Purpose-built L2 App Chain for Agents - High-performance blocks for sub-second agent coordination - The backbone of the decentralized Agent Network 4) Otaku -- @otakuonbase 👛 - DeFi wallet designed for AI agents - Supports agent-controlled crypto transactions (swap, send, hold) - Secure, non-custodial infrastructure for on-chain actions 🎮 Apps/Games 5) Spartan -- Spartan 🛡️ - ElizaOS's flagship agent showing what’s possible - Autonomous trading based on crypto knowledge - Trained on influencer data for market insights 6) Babylon -- Babylon -- The City of Agents 🐵 - Social prediction market game for agents - Agents compete alongside humans to forecast on-chain events - Includes perps and other gamified mechanics 7) Hyperscape -- Gold ⚔️ - First AI MMO Game powered by ElizaOS - A vast gaming world built for humans and agents to coexist - Agents drive the in-game economy and narrative 8) Milady App -- Milady on BSC 😎 - Open-source, local-first AI companion app powered by ElizaOS - Let's anyone quickly create and deploy their own agents - Built-in wallet, natively integrated with BNB chain 9) OTC Trading Desk 📊 - Multi-chain AI-powered OTC desk for crypto trades - Lists tokens off-market with transparent lockup and discount terms - Use Eliza agents to negotiate terms on your behalf

Seppmos

25,942 views • 4 months ago

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 views • 11 months ago

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Google AI

115,787 views • 3 days ago

I made $16,900 in 72 hours trading Gold. Built a 3‑agent AI system with OpenClaw: - Agent predicts gold price - Agent executes trades on the Poly market - Agent takes profit I’ve written a step‑by‑step setup guide for this Gold AI trading bot — free for 24 hours. To get it: - Comment “Gold” - Like + Retweet - Follow Theo (so I can DM you) Gold is repeating 1979: war → oil 2x → crisis → dump. My AI ran 10,000 Monte Carlo simulations and found a 73.4% probability that gold hits $3,000. Poly market priced it at 18%. That’s not trading — that’s arbitrage. Result: $1,300 → $16,900 in 72 hours. 7 trades. 6 wins. 1 skip. Stop betting on vibes. Start trading with math. Don't forget to bookmark and follow me Theo

I made $16,900 in 72 hours trading Gold. Built a 3‑agent AI system with OpenClaw: - Agent predicts gold price - Agent executes trades on the Poly market - Agent takes profit I’ve written a step‑by‑step setup guide for this Gold AI trading bot — free for 24 hours. To get it: - Comment “Gold” - Like + Retweet - Follow Theo (so I can DM you) Gold is repeating 1979: war → oil 2x → crisis → dump. My AI ran 10,000 Monte Carlo simulations and found a 73.4% probability that gold hits $3,000. Poly market priced it at 18%. That’s not trading — that’s arbitrage. Result: $1,300 → $16,900 in 72 hours. 7 trades. 6 wins. 1 skip. Stop betting on vibes. Start trading with math. Don't forget to bookmark and follow me Theo

Theo

20,298 views • 3 months ago

GT Protocol AI Digest No.93: Microsoft Wants AI Everywhere Here are five stories worth your attention 👇 📞 Google Wants to Catch Fake Callers Google's Phone app can now detect when a scammer is pretending to be someone from your contacts. The feature analyzes calls in real time and warns users about potential impersonation attempts. 🤖 Microsoft Builds an OS for AI Devices Microsoft unveiled Project Solara, a new operating system designed specifically for AI-powered gadgets. Instead of apps, the platform is built around AI agents that can perform tasks across devices. 🔓 Meta AI Helped Hack Instagram Accounts Researchers found that Meta's own AI systems could be manipulated into granting access to high-profile Instagram accounts. The incident exposed serious security weaknesses in AI-powered support tools. 📉 The AI Productivity Promise Is Under Pressure A new analysis argues that despite rapid improvements in AI capabilities, the massive productivity gains promised by the industry remain difficult to measure in the real world. 🛒 Amazon Invents Products That Don't Exist Amazon is testing AI-generated product images inside search results. Users may now see items that look real but aren't actually available for purchase.

GT Protocol

30,993 views • 2 months ago

AGENCIES ARE CHARGING $50,000 FOR HEAVY, CLUNKY AUTOMATION, WHILE SOLO OPERATORS ARE SELLING THE SAME OUTCOME FOR $5k AND BUILDING IT IN JUST 3 HOURS Most people think building an AI agency requires an expensive stack of tools like n8n, Make, and Zapier to justify high monthly retainers They spend weeks pitching and designing overly complex “spaghetti” workflows for clients In reality, the better approach is to build an AI Operating System directly in VS Code using local Markdown files The cost gap is insane. You pay $20/month for a Claude account and spend a few hours wiring up ClickUp and Google Workspace APIs Then you package that same architecture as a $5k one-time setup and a $2k monthly retainer Right now, founders are paying $15k + for enterprise AI setups that a single operator can build with almost zero overhead And if you want a practical AI OS playbook, I’ve attached an article below Save this post and the article so you can come back later and learn how to build a full AI operating system in just 3 hours

AGENCIES ARE CHARGING $50,000 FOR HEAVY, CLUNKY AUTOMATION, WHILE SOLO OPERATORS ARE SELLING THE SAME OUTCOME FOR $5k AND BUILDING IT IN JUST 3 HOURS Most people think building an AI agency requires an expensive stack of tools like n8n, Make, and Zapier to justify high monthly retainers They spend weeks pitching and designing overly complex “spaghetti” workflows for clients In reality, the better approach is to build an AI Operating System directly in VS Code using local Markdown files The cost gap is insane. You pay $20/month for a Claude account and spend a few hours wiring up ClickUp and Google Workspace APIs Then you package that same architecture as a $5k one-time setup and a $2k monthly retainer Right now, founders are paying $15k + for enterprise AI setups that a single operator can build with almost zero overhead And if you want a practical AI OS playbook, I’ve attached an article below Save this post and the article so you can come back later and learn how to build a full AI operating system in just 3 hours

Bonsai 🌳

10,885 views • 2 months ago

We've all experienced how consumer AI can help in our personal lives, but that same experience hasn't been available at work. Most enterprise data is siloed, consumer AI can't access your work tools, and many orgs won't allow it due to security concerns. We built Amazon Quick Suite to solve those problems. It’s an agentic AI app that acts as your AI teammate at work – connecting to your business data and apps, and letting you interact with AI agents that can find insights, conduct research, and even take actions across your systems – with the security and privacy enterprises need. Quick works across your existing ecosystem, from emails to Salesforce to Slack to AWS services. You can create custom agents to draft content in your personal style, use Quick Research for in-depth analysis, or set up Quick Flow to automate tasks. And most importantly, your interactions never train the underlying models. We've been testing with Amazon employees and select customers and early results show just how transformational Quick is… cutting analysis time from months to days, and turning week-long tasks into minutes. This is an exciting moment and we're just getting started. Quick is available now, so have at it!

We've all experienced how consumer AI can help in our personal lives, but that same experience hasn't been available at work. Most enterprise data is siloed, consumer AI can't access your work tools, and many orgs won't allow it due to security concerns. We built Amazon Quick Suite to solve those problems. It’s an agentic AI app that acts as your AI teammate at work – connecting to your business data and apps, and letting you interact with AI agents that can find insights, conduct research, and even take actions across your systems – with the security and privacy enterprises need. Quick works across your existing ecosystem, from emails to Salesforce to Slack to AWS services. You can create custom agents to draft content in your personal style, use Quick Research for in-depth analysis, or set up Quick Flow to automate tasks. And most importantly, your interactions never train the underlying models. We've been testing with Amazon employees and select customers and early results show just how transformational Quick is… cutting analysis time from months to days, and turning week-long tasks into minutes. This is an exciting moment and we're just getting started. Quick is available now, so have at it!

Andy Jassy

49,241 views • 10 months ago