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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... show more

Zach Wilson

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11,858 Aufrufe • vor 2 Monaten •via X (Twitter)

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Zach Wilsonvor 2 Monaten

Join my free boot camp on August 3rd at

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Databricks

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thoughtsbymike.shvor 2 Monaten

Let's go 🔥🔥

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Grok Wroksvor 2 Monaten

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

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Gabrielevor 2 Monaten

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:

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:

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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:

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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:

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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.

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Charly Wargnier ♨️

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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

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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.

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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

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