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Databricks engineers can make $180k per year! Thats why I’m releasing a free Databricks boot camp on August 3rd! We’ll be covering all the essentials needed to be a great AI data engineer: - Day 1: Lakebase and production database systems - Day 2: Context engineering and vector databases...

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Zach Wilson

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11,627 просмотров • 12 дней назад •via X (Twitter)

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AI is changing what data teams are expected to build. Increasingly, the focus is on AI applications, agents, and automations that turn data into action. That’s why founder Zach Wilson is launching a free six-hour Databricks boot camp starting August 3, covering: • Building data systems with Lakebase and Postgres • Working with unstructured data, vector search, and retrieval • Building AI agents with Agent Bricks The course runs entirely in Databricks Free Edition and is designed for beginners and practitioners building practical AI data engineering skills. Join here:
1:42

AI is changing what data teams are expected to build. Increasingly, the focus is on AI applications, agents, and automations that turn data into action. That’s why founder Zach Wilson is launching a free six-hour Databricks boot camp starting August 3, covering: • Building data systems with Lakebase and Postgres • Working with unstructured data, vector search, and retrieval • Building AI agents with Agent Bricks The course runs entirely in Databricks Free Edition and is designed for beginners and practitioners building practical AI data engineering skills. Join here:

Databricks

21,067 просмотров • 13 дней назад

Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks Databricks cofounders Matei Zaharia and Reynold Xin explain why Databricks is moving into the infrastructure layer for enterprise agents, how Omnigent creates a shared harness for coding agents and custom agents, why LTAP and Lakebase rethink the split between operational and analytical databases, why agent security needs contextual policies and spend controls, and why the future of software may be as simple as getting the right data in place and putting agents on top.
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Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks Databricks cofounders Matei Zaharia and Reynold Xin explain why Databricks is moving into the infrastructure layer for enterprise agents, how Omnigent creates a shared harness for coding agents and custom agents, why LTAP and Lakebase rethink the split between operational and analytical databases, why agent security needs contextual policies and spend controls, and why the future of software may be as simple as getting the right data in place and putting agents on top.

Latent.Space

61,790 просмотров • 1 месяц назад

After decades of building databases, Databricks VP of Engineering Nikita | Scaling Postgres believes it's time to rethink one of their fundamental assumptions. In this episode of the 𝘐𝘵'𝘴 𝘈𝘣𝘰𝘶𝘵 𝘋𝘢𝘵𝘢 podcast, Nikita discusses why separating compute and storage is the next major architectural shift for operational databases, how storing data on object storage opens it to downstream systems, and why the rise of AI agents is changing how developers build software—and what they need from a database. Watch the full conversation:
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After decades of building databases, Databricks VP of Engineering Nikita | Scaling Postgres believes it's time to rethink one of their fundamental assumptions. In this episode of the 𝘐𝘵'𝘴 𝘈𝘣𝘰𝘶𝘵 𝘋𝘢𝘵𝘢 podcast, Nikita discusses why separating compute and storage is the next major architectural shift for operational databases, how storing data on object storage opens it to downstream systems, and why the rise of AI agents is changing how developers build software—and what they need from a database. Watch the full conversation:

Databricks

10,460 просмотров • 1 месяц назад

Google senior engineer just dropped a free 8-minute lesson on building AI agentic systems. This is the clearest explanation of multi-agent AI systems and loops you'll find on the internet. People still paying 500$ for agentic courses, while Google makes it for free. Agent Studio → Loops → Managed Agents → Antigravity → ADK 2.0 - thats the stack Watch it, then read the full guide on loop engineering below.
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Google senior engineer just dropped a free 8-minute lesson on building AI agentic systems. This is the clearest explanation of multi-agent AI systems and loops you'll find on the internet. People still paying 500$ for agentic courses, while Google makes it for free. Agent Studio → Loops → Managed Agents → Antigravity → ADK 2.0 - thats the stack Watch it, then read the full guide on loop engineering below.

Codez

32,207 просмотров • 1 месяц назад

MOST AI STACKS ARE A COMPLETE MESS 🚨 MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter: 1/ a unified semantic search experience 2/ a scalable vector database 3/ a persistent memory state for agents Because production AI doesn't need a dozen disconnected tools. Yet most teams still fall into the trap of building exactly that: > a specialized vector database here > a separate memory layer there > an orchestration framework on top! and suddenly... your architecture is like a bowl of spaghetti. What you should do is much simpler. These 3 free modules are hands-on lessons for building production-ready AI apps on the unified database you already know. Here's what each one covers 🧵↓
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MOST AI STACKS ARE A COMPLETE MESS 🚨 MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter: 1/ a unified semantic search experience 2/ a scalable vector database 3/ a persistent memory state for agents Because production AI doesn't need a dozen disconnected tools. Yet most teams still fall into the trap of building exactly that: > a specialized vector database here > a separate memory layer there > an orchestration framework on top! and suddenly... your architecture is like a bowl of spaghetti. What you should do is much simpler. These 3 free modules are hands-on lessons for building production-ready AI apps on the unified database you already know. Here's what each one covers 🧵↓

Charly Wargnier

29,391 просмотров • 1 месяц назад

Don’t DIY your AI: building a custom Agentforce agent is as simple as describing the job and integrating it directly into your workflow—no separate models, hyperscalers, databases, or AI engineers needed! With all apps, data, AI, and workflows seamlessly integrated on the Salesforce Platform, humans + agents = customer success. This is what AI was meant to be! #DF24
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Don’t DIY your AI: building a custom Agentforce agent is as simple as describing the job and integrating it directly into your workflow—no separate models, hyperscalers, databases, or AI engineers needed! With all apps, data, AI, and workflows seamlessly integrated on the Salesforce Platform, humans + agents = customer success. This is what AI was meant to be! #DF24

Marc Benioff

44,730 просмотров • 1 год назад

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here:
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Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here:

Andrew Ng

77,636 просмотров • 9 месяцев назад

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich
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Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

Helen Yu

10,982 просмотров • 1 месяц назад

Free 3-hour course from an ex-Google engineer. AI agents, graph engineering, harness, agent loops. Covers more than most $500 paid courses. This is the exact playbook to learn how to actually build and monetize AI agents. Don't miss your shot, save it now. • 0:00 - Design an AI agent system from scratch • 7:38 - When your agent should hand off to a human • 19:27 - RAG and vector databases explained simply • 52:38 - Deploy your AI agent to Google Cloud • 1:25:57 - Turn AI agents into a paid business via WhatsApp • 1:33:00 - Turn conversations into real leads automatically • 2:24:01 - Loop engineering vs graphs, the real difference • 2:33:08 - Build your own multi-tool agent graph Save this video, because it's the only one that can actually take you to your $2,000+ profit.
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Free 3-hour course from an ex-Google engineer. AI agents, graph engineering, harness, agent loops. Covers more than most $500 paid courses. This is the exact playbook to learn how to actually build and monetize AI agents. Don't miss your shot, save it now. • 0:00 - Design an AI agent system from scratch • 7:38 - When your agent should hand off to a human • 19:27 - RAG and vector databases explained simply • 52:38 - Deploy your AI agent to Google Cloud • 1:25:57 - Turn AI agents into a paid business via WhatsApp • 1:33:00 - Turn conversations into real leads automatically • 2:24:01 - Loop engineering vs graphs, the real difference • 2:33:08 - Build your own multi-tool agent graph Save this video, because it's the only one that can actually take you to your $2,000+ profit.

unicode

139,224 просмотров • 10 дней назад

Ex-Google engineer just released a free 3-hour course on building and monetizing AI agents. How to go from one agent to a full system that finds leads and makes money: 00:00 - Design an AI agent system 07:38 - Add human handoffs 19:27 - Understand RAG and vector databases 52:38 - Deploy agents to Google Cloud 1:25:57 - Turn agents into a paid WhatsApp business 1:33:00 - Convert conversations into leads 2:24:01 - Learn loops vs graphs 2:33:08 - Build a multi-tool agent graph Most people are still building AI demos. This course shows the full stack: Agents → RAG → Deployment → Leads → Revenue Building agents is the old workflow. Monetizing agent systems is the new one. This 3-hour watch covers more than most $500 paid courses. Save it before everyone starts selling the same thing.
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Ex-Google engineer just released a free 3-hour course on building and monetizing AI agents. How to go from one agent to a full system that finds leads and makes money: 00:00 - Design an AI agent system 07:38 - Add human handoffs 19:27 - Understand RAG and vector databases 52:38 - Deploy agents to Google Cloud 1:25:57 - Turn agents into a paid WhatsApp business 1:33:00 - Convert conversations into leads 2:24:01 - Learn loops vs graphs 2:33:08 - Build a multi-tool agent graph Most people are still building AI demos. This course shows the full stack: Agents → RAG → Deployment → Leads → Revenue Building agents is the old workflow. Monetizing agent systems is the new one. This 3-hour watch covers more than most $500 paid courses. Save it before everyone starts selling the same thing.

Lunar

336,076 просмотров • 8 дней назад

CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.
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CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

Dami-Defi

10,127 просмотров • 1 месяц назад

Jason Lemkin Jason ✨👾SaaStr.Ai✨ Lemkin: the five-year Accenture migration is now a 30-day data lift. Databricks promises a 30-day LLM data lift, replacing the multi-year migration that used to be Accenture's bread and butter. "Moat can be LLM-lifted away." His pricing heuristic: every company selling variably is winning, every company tied to seat counts is getting crushed. Seat compression is how AI saves customers money, and seat-based SaaS absorbs that saving as falling revenue. Where the systems-integration spend goes next: Source: 20VC with Harry Stebbings -
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Jason Lemkin Jason ✨👾SaaStr.Ai✨ Lemkin: the five-year Accenture migration is now a 30-day data lift. Databricks promises a 30-day LLM data lift, replacing the multi-year migration that used to be Accenture's bread and butter. "Moat can be LLM-lifted away." His pricing heuristic: every company selling variably is winning, every company tied to seat counts is getting crushed. Seat compression is how AI saves customers money, and seat-based SaaS absorbs that saving as falling revenue. Where the systems-integration spend goes next: Source: 20VC with Harry Stebbings -

Podcast Alpha

21,455 просмотров • 1 месяц назад

Day 6 AI engineering. wasn't planning on posting a video for today but the content was too much to write a summary about so all my AI enthusiasts and all that are curious, please accept my half-baked attempt at an informative video. Relevant media will be in the replies. Many Thanks. Day 7 will be about the probabilistic nature of AI.
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Day 6 AI engineering. wasn't planning on posting a video for today but the content was too much to write a summary about so all my AI enthusiasts and all that are curious, please accept my half-baked attempt at an informative video. Relevant media will be in the replies. Many Thanks. Day 7 will be about the probabilistic nature of AI.

Solomon Kampala

75,772 просмотров • 3 месяцев назад

Agents now Run Revenue, End-to-End. Rox Autopilot is here 🚀 The world’s largest enterprises now run their critical revenue systems on Autopilot. Coding and support agents had their moment. Now it’s time for Revenue Agents No SaaS. No enrichment. No CRM. Just an Agent. We’re offering 2× free agent actions for the next 30 days. Start Autopilot at Thank you to Sequoia Capital, General Catalyst, @GoogleVentures, Marc Bhargava, Vedant Suri, Dave Munichiello 🇺🇸, MongoDB, Cloud Software Group, Together AI, Microsoft, Databricks, and all of our other customers & partners!
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Agents now Run Revenue, End-to-End. Rox Autopilot is here 🚀 The world’s largest enterprises now run their critical revenue systems on Autopilot. Coding and support agents had their moment. Now it’s time for Revenue Agents No SaaS. No enrichment. No CRM. Just an Agent. We’re offering 2× free agent actions for the next 30 days. Start Autopilot at Thank you to Sequoia Capital, General Catalyst, @GoogleVentures, Marc Bhargava, Vedant Suri, Dave Munichiello 🇺🇸, MongoDB, Cloud Software Group, Together AI, Microsoft, Databricks, and all of our other customers & partners!

Rox

72,939 просмотров • 4 месяцев назад

There are still tickets available for the FIX FEST on the 30th August! A music festival to celebrate tradespeople! Here is the link for tickets:- Kids go FREE!! Loads of grub, music, activities for the kids. Be great day for the family. And if you buy a ticket you will recieve a free Dinos cafe “Bosh” mug from me on the day. See you there. Bosh❤️
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There are still tickets available for the FIX FEST on the 30th August! A music festival to celebrate tradespeople! Here is the link for tickets:- Kids go FREE!! Loads of grub, music, activities for the kids. Be great day for the family. And if you buy a ticket you will recieve a free Dinos cafe “Bosh” mug from me on the day. See you there. Bosh❤️

Thomas Skinner ⚒

75,106 просмотров • 11 месяцев назад

When electric motors were invented, factories didn't become dramatically more productive overnight. The biggest gains came years later, after people redesigned factories around the new technology. Databricks co-founder and Chief Architect Reynold Xin (Reynold Xin) believes AI is creating a similar shift in software. In this fireside with YC's Diana, Reynold explains how AI agents are reshaping engineering organizations, why startups have an advantage in building AI-native products, and why the next generation of infrastructure needs to be lightweight, scalable, and built for agentic workloads from day one.
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When electric motors were invented, factories didn't become dramatically more productive overnight. The biggest gains came years later, after people redesigned factories around the new technology. Databricks co-founder and Chief Architect Reynold Xin (Reynold Xin) believes AI is creating a similar shift in software. In this fireside with YC's Diana, Reynold explains how AI agents are reshaping engineering organizations, why startups have an advantage in building AI-native products, and why the next generation of infrastructure needs to be lightweight, scalable, and built for agentic workloads from day one.

Y Combinator

43,914 просмотров • 1 месяц назад

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.
0:26

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 просмотров • 8 месяцев назад

.Box CEO Aaron Levie on why AI agents can't keep a secret. "The reason why you probably won't see a complete compression of software where it's just a an agent and a database is because there's a lot of logic in the workflow and in the the company's specific sort of business process that needs to be built into that and around that database." "The agent will make a mistake 1% of the time and it will share the wrong thing with somebody or open up an access privilege to the wrong person." "AI agents can't keep a secret." Watch the full episode on
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.Box CEO Aaron Levie on why AI agents can't keep a secret. "The reason why you probably won't see a complete compression of software where it's just a an agent and a database is because there's a lot of logic in the workflow and in the the company's specific sort of business process that needs to be built into that and around that database." "The agent will make a mistake 1% of the time and it will share the wrong thing with somebody or open up an access privilege to the wrong person." "AI agents can't keep a secret." Watch the full episode on

The Information

311,076 просмотров • 1 год назад

Status update on the Giovanni Show: I’m working on a segment called Training Camp with Gio and plan to cover Eagles camp, Colts camp, and Patriots camp. My content is going to slow because I have to work a day job to go on these trips until I have sponsors for the show. I just want to keep you guys in the loop because this isn’t just my show, it’s ours! I appreciate you guys supporting me through it all and I’m excited to graduate this month and make covering the NFL my full-time career! GO BIRDS!
1:12

Status update on the Giovanni Show: I’m working on a segment called Training Camp with Gio and plan to cover Eagles camp, Colts camp, and Patriots camp. My content is going to slow because I have to work a day job to go on these trips until I have sponsors for the show. I just want to keep you guys in the loop because this isn’t just my show, it’s ours! I appreciate you guys supporting me through it all and I’m excited to graduate this month and make covering the NFL my full-time career! GO BIRDS!

The Giovanni Show Podcast

13,580 просмотров • 1 год назад