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I turned the Google Gemma Gemma 4 12B model into an enterprise RFP agent. Running locally in the Hugging Face Llama app and connected to Box, the agent: - pulls the RFP + approved source docs - drafts source-cited responses - flags gaps instead of hallucinating - pauses for... show more

Chris Kim

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150,387 görüntüleme • 14 gün önce •via X (Twitter)

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🆕SpaceXAI just launched Grok Bot, a personal AI assistant built for complex knowledge work. Connected to Box through the Box MCP server, Grok Bot can work across your governed enterprise content immediately, across any workflow and any industry. In our demo it tackles a credit committee review. Works across credit agreements, borrower financials, diligence materials, collateral documentation, and risk assessments. Reconciles key terms. Flags credit risks and inconsistencies. Produces a source-cited brief. Saves it back to Box with structured deal metadata applied. All your content. Any AI agent. Watch here. 👇

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Laptop closed. Agent still working. 103 legal contracts reviewed in 40 minutes. Most AI agents stop the moment you close your laptop. This one does not. See how DigitalOcean, @chatgptapp Codex, and Box work together to autonomously process a stack of contracts while you step away. The new DigitalOcean Codex plugin makes droplet provisioning a one-click affair. - DigitalOcean: always-on compute for long-running agents - Codex: the harness driving the workflow - Box: the governed content layer (contracts in, review packets out) Timestamps: 00:09 – Workflow setup 01:23 – Provision DigitalOcean droplet for long-running tasks 02:45 – Connect Codex to Box with Box MCP 03:01 – Start a remote session on Codex 03:26 – Implement the legal workflow 05:00 – Review the work 06:05 – Conclusion

Box

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“We need more determinism mixed with the creative capabilities of gen AI.” In our latest partner podcast, Fernando Cerenza, Senior Director of Product Management at Box, sat down with Gary Lerhaupt ❇️, VP of Product Architecture of Agentforce at Salesforce, to explore the rise of hybrid reasoning from shifting from simple chatbots to complex multi-agent workflows. Timestamps: 02:12 The transition from deterministic bots to hybrid AI agents 03:15 The shift from simple automation to multi-agent workflows 04:39 Why protocols like MCP and A2A are crucial for agent collaboration 05:36 Understanding how MCP and A2A complement each other 07:16 The importance of open-source industry governance 08:52 How open standards prevent the walled garden issue 10:24 The collaboration between Box and Salesforce in building real-world agent use cases 12:59 The future of an "app store" for agents 13:50 How Salesforce ensures the quality and security of agents in the ecosystem 15:12 Leveraging observability tools to manage and improve agent performance 16:48 Preparing IT leaders for the future of multi-agent workflows and platform readiness

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You can run a free and open-source alternative to Claude Code locally on your laptop! - Local AI model w/ Qwen3 Coder - Runs on your machine w/ LM Studio - Open-source VS Code extension w/ Cline Completely private (offline) and unlimited Setup and how it works: 00:00 Intro 01:11 LM Studio 02:10 Local AI model 03:38 Load the model 05:09 Start the server 05:33 Setup Cline 07:20 Prompt & example 09:02 Developer logs 10:12 Result reveal

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🚨 One orchestrator. 10 parallel agents. 100+ tokens a second. All local. The Google Gemma team just dropped a MASSIVE demo for Gemma 4 26B. They built a concurrent workflow that lets the 26B model coordinate an entire team of sub-agents on your machine. Out of the box, the cookbook lets you run 10 parallel agents to: → Code an entire SVG art gallery in seconds → Translate text simultaneously → Generate ASCII art → Write parallel code Spinning up multi-agent systems locally has never looked this fast or this accessible. 100% free and open-source. repo link in 🧵↓

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🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?

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8 months ago we started explaining enterprise AI on YouTube. 2M views later, here are the 5 shifts that actually defined 2025. Timestamps 00:36 Prompt Engineering → Agent Engineering 00:51 Prompting best practices 01:19 How context engineering focuses on curating information 01:51 Evolution of assistants to agents 02:12 Agents handling complex tasks 02:58 How security became foundational 03:36 The correct security architecture for organizations 04:23 The emergence of model flexibility 05:26 The importance of governance and trust

8 months ago we started explaining enterprise AI on YouTube. 2M views later, here are the 5 shifts that actually defined 2025. Timestamps 00:36 Prompt Engineering → Agent Engineering 00:51 Prompting best practices 01:19 How context engineering focuses on curating information 01:51 Evolution of assistants to agents 02:12 Agents handling complex tasks 02:58 How security became foundational 03:36 The correct security architecture for organizations 04:23 The emergence of model flexibility 05:26 The importance of governance and trust

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A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

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By 2028, the average Fortune 500 enterprise will have over 150,000 AI agents running across their organization, according to Gartner. The number one barrier to deploying them: security and privacy concerns. Not capability. Trust. Box Agent Security and Governance is built into the content layer, not bolted on top of it. Every agent request evaluated against your existing permissions. Every session logged with a content-aware audit trail. Frictionless for teams. Fully governed for security leaders.

Box

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I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

Matt Dancho (Business Science)

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Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History

Raoul Pal

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I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

Matt Dancho (Business Science)

242,084 görüntüleme • 1 yıl önce

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 görüntüleme • 5 ay önce

A personal AI knowledge base works great for one person. The moment it becomes a team workflow, the personal vault starts to break. Our demo shows what the fix looks like. A shared company brain in Box where a founder updates a product decision, an engineer applies it via Claude Code, and a customer success lead generates an onboarding guide via MCP. All from the same governed source of truth. Agents become the reasoning layer. Box becomes the governed memory layer. Watch here. 👇

A personal AI knowledge base works great for one person. The moment it becomes a team workflow, the personal vault starts to break. Our demo shows what the fix looks like. A shared company brain in Box where a founder updates a product decision, an engineer applies it via Claude Code, and a customer success lead generates an onboarding guide via MCP. All from the same governed source of truth. Agents become the reasoning layer. Box becomes the governed memory layer. Watch here. 👇

Box

4,994,885 görüntüleme • 2 ay önce

She literally explained how her $100M AI startup runs completely out of an operating system in Claude Code: 2:04 - The Company OS GitHub structure 5:40 - The 1% vs 99% problem 9:00 - 3 steps to build your own Company OS 12:30 - Slack automation demo: feature request triage 14:31 - Playbook to agent pipeline 22:51 - Company culture needed 29:02 - PMs shipping front-end + back-end 29:44 - The captain model explained 32:37 - Continuation to captain model 37:38 - Two-track product reviews 50:08 - The AI Ops team and the Sasha model 57:59 - The screen-share interview 59:01 - The 4 levels of AI maturity

She literally explained how her $100M AI startup runs completely out of an operating system in Claude Code: 2:04 - The Company OS GitHub structure 5:40 - The 1% vs 99% problem 9:00 - 3 steps to build your own Company OS 12:30 - Slack automation demo: feature request triage 14:31 - Playbook to agent pipeline 22:51 - Company culture needed 29:02 - PMs shipping front-end + back-end 29:44 - The captain model explained 32:37 - Continuation to captain model 37:38 - Two-track product reviews 50:08 - The AI Ops team and the Sasha model 57:59 - The screen-share interview 59:01 - The 4 levels of AI maturity

Aakash Gupta

387,124 görüntüleme • 2 ay önce

A $100K/year customer gets white-glove treatment while a $10/year customer gets a help center. Decagon's bet is that AI closes that gap, and enterprise is buying it. Co-founders Jesse Zhang and Ashwin Sreenivas sit down with a16z's Kimberly Tan and Sarah Wang to discuss what they've learned running agents inside the biggest banks, airlines, and telcos: - Smart-vs-cheap models are a false trade-off - Start with frontier models on new use cases, then migrate to open-source after they mature - Fine-tuning per use case works at the application layer - Support demand always outran supply: make it cheaper and companies buy more of it 00:00 Intro 01:07 90% of Decagon runs on open-source 05:00 The smart vs. cheap trade-off is false 09:26 Building a model factory in-house 15:07 Are the labs the last startups? 21:21 Are forward deployed engineers a trap? 28:36 The agent that builds the agent 37:02 Glass box beats black box 47:55 From help desk to AI concierge 1:14:45 Jevons paradox in customer support Jesse Zhang Ashwin Sreenivas Kimberly Tan Sarah Wang

A $100K/year customer gets white-glove treatment while a $10/year customer gets a help center. Decagon's bet is that AI closes that gap, and enterprise is buying it. Co-founders Jesse Zhang and Ashwin Sreenivas sit down with a16z's Kimberly Tan and Sarah Wang to discuss what they've learned running agents inside the biggest banks, airlines, and telcos: - Smart-vs-cheap models are a false trade-off - Start with frontier models on new use cases, then migrate to open-source after they mature - Fine-tuning per use case works at the application layer - Support demand always outran supply: make it cheaper and companies buy more of it 00:00 Intro 01:07 90% of Decagon runs on open-source 05:00 The smart vs. cheap trade-off is false 09:26 Building a model factory in-house 15:07 Are the labs the last startups? 21:21 Are forward deployed engineers a trap? 28:36 The agent that builds the agent 37:02 Glass box beats black box 47:55 From help desk to AI concierge 1:14:45 Jevons paradox in customer support Jesse Zhang Ashwin Sreenivas Kimberly Tan Sarah Wang

a16z

131,728 görüntüleme • 1 ay önce