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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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11,858 просмотров • 2 месяцев назад •via X (Twitter)

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Фото профиля Zach Wilson
Zach Wilson2 месяцев назад

Join my free boot camp on August 3rd at

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Ayinla Femi2 месяцев назад

Databricks

Фото профиля thoughtsbymike.sh
thoughtsbymike.sh2 месяцев назад

Let's go 🔥🔥

Фото профиля Grok Wroks
Grok Wroks2 месяцев назад

Great opportunity for anyone wanting to break into AI data engineering.

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Gabriele2 месяцев назад

Databricks

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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

21,458 просмотров • 2 месяцев назад

The Replit-Databricks integration is now generally available, with new native Lakebase support! Now, you can use Replit ⠕ to build full-stack Databricks Apps on live, governed enterprise data. Replit Agent can automatically provision a Lakebase database, while AI-proposed schema changes require human approval. Build, deploy, and iterate in one flow. See it in action:
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The Replit-Databricks integration is now generally available, with new native Lakebase support! Now, you can use Replit ⠕ to build full-stack Databricks Apps on live, governed enterprise data. Replit Agent can automatically provision a Lakebase database, while AI-proposed schema changes require human approval. Build, deploy, and iterate in one flow. See it in action:

Databricks

12,248 просмотров • 21 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

62,269 просмотров • 3 месяцев 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:
1:26

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

10,503 просмотров • 2 месяцев 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 • 3 месяцев 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 ♨️

29,487 просмотров • 3 месяцев 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 • 2 лет назад

When you use a credit card, a 1¢ payment costs 31¢. That math doesn't work for AI agents making thousands of tiny payments a day. Sam Ragsdale of Merit Systems explains why stablecoins could be the rail the agent economy runs on.
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When you use a credit card, a 1¢ payment costs 31¢. That math doesn't work for AI agents making thousands of tiny payments a day. Sam Ragsdale of Merit Systems explains why stablecoins could be the rail the agent economy runs on.

a16z crypto

83,142 просмотров • 1 месяц назад

Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below
2:47:42

Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below

Morlex

51,952 просмотров • 1 месяц 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

78,182 просмотров • 11 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
2:03

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 • 3 месяцев 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

143,935 просмотров • 1 месяц 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

352,880 просмотров • 1 месяц назад

Creator of Claude Code: “I’m not just prompting agents anymore. I’m building a harness with graphs and loops - and the agents inside it are building my agent. In 3–6 months, we’ll all be engineering harnesses for our agentic systems.” In this 10-minute talk, Boris Cherny reveals what the future of agentic engineering will look like. Worth more than a $500 agentic AI course. Watch it today, then read the complete guide to building an agent harness in the article below.
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Creator of Claude Code: “I’m not just prompting agents anymore. I’m building a harness with graphs and loops - and the agents inside it are building my agent. In 3–6 months, we’ll all be engineering harnesses for our agentic systems.” In this 10-minute talk, Boris Cherny reveals what the future of agentic engineering will look like. Worth more than a $500 agentic AI course. Watch it today, then read the complete guide to building an agent harness in the article below.

Ichigo

72,810 просмотров • 7 дней 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 -
1:34

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 • 3 месяцев 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.
7:51

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

10,140 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!
2:07

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

73,435 просмотров • 6 месяцев 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.
12:12

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 • 5 месяцев назад

this is free f*cking gold for AI engineers 10 repos i'd actually keep, from python fundamentals to LLMs, agents and production AI. skip the random tutorials 1. Python-100-Days - a 100 day path through fundamentals, data analysis, web dev ↳ 2. Generative AI for Beginners - LLM basics, prompting, RAG, agents, fine-tuning ↳ 3. LLMs from Scratch - build one yourself: tokenization, attention, transformers, training ↳ 4. ML for Beginners - 12 weeks, 26 lessons of classical ML ↳ 5. OpenAI Cookbook - working examples to go from learning to building ↳ 6. Stable Diffusion - the original implementation and research code ↳ 7. AI Agents for Beginners - tool use, RAG, agent frameworks, multi-agent systems ↳ 8. AI for Beginners - 12 weeks, 24 lessons: neural nets, vision, NLP ↳ 9. LLM App - RAG pipelines, enterprise search, real-time data, vector search ↳ 10. Segment Anything - Meta's foundation model for image segmentation ↳ pick by where you are: > python -> Python-100-Days > ML -> ML-For-Beginners > LLMs -> LLMs-from-scratch > agents -> AI-Agents-for-Beginners > building -> OpenAI Cookbook, LLM App > vision -> Segment Anything pick one, build something, then move to the next
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this is free f*cking gold for AI engineers 10 repos i'd actually keep, from python fundamentals to LLMs, agents and production AI. skip the random tutorials 1. Python-100-Days - a 100 day path through fundamentals, data analysis, web dev ↳ 2. Generative AI for Beginners - LLM basics, prompting, RAG, agents, fine-tuning ↳ 3. LLMs from Scratch - build one yourself: tokenization, attention, transformers, training ↳ 4. ML for Beginners - 12 weeks, 26 lessons of classical ML ↳ 5. OpenAI Cookbook - working examples to go from learning to building ↳ 6. Stable Diffusion - the original implementation and research code ↳ 7. AI Agents for Beginners - tool use, RAG, agent frameworks, multi-agent systems ↳ 8. AI for Beginners - 12 weeks, 24 lessons: neural nets, vision, NLP ↳ 9. LLM App - RAG pipelines, enterprise search, real-time data, vector search ↳ 10. Segment Anything - Meta's foundation model for image segmentation ↳ pick by where you are: > python -> Python-100-Days > ML -> ML-For-Beginners > LLMs -> LLMs-from-scratch > agents -> AI-Agents-for-Beginners > building -> OpenAI Cookbook, LLM App > vision -> Segment Anything pick one, build something, then move to the next

Mr. Buzzoni

33,402 просмотров • 20 дней назад