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SQL injection! 💉 After finding the number of columns using the NULL trick, you can take it a step further and identify which column takes which data type. Swap 'abc' into one position at a time. The one that comes back without an error is your string column, and...

20,203 просмотров • 16 дней назад •via X (Twitter)

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Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

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65,672 просмотров • 9 месяцев назад

Yesterday, I told you about a wall I'd been hitting for years. Today I get to break through it — with two announcements. 👇 𝟭. CreativAI is out of stealth — the SQL layer for Physical and Visual AI; also enabling visual intelligence to be verifiable, reliable, and cost-effective. 𝟮. We're launching a product that works today. Not a waitlist. Not a vision deck. Something you can try right now 👇 Whether you're an individual exploring AI, a developer building the next generation of applications, or an enterprise unlocking the value of visual data, CreativAI is ready for you. Deploy in the cloud, on-premises, or integrate through our APIs—whichever fits your workflow. Grateful to our CCO Waleed, our advisors Rob Ferguson and Abdul Jarrar, and to Google for Startups, AWS Startups, Microsoft for Startups, and NVIDIA for Startups Inception for the support. I've spent my career at the foundations of vision-language AI — research at KAUST, Stanford, Meta FAIR, and Adobe. I contributed to some of the building blocks the field now takes for granted: a linear version of CLIP (ICCV13; and the first vision LLMs — VisualGPT (CVPR22), MiniGPT-4(Arxiv'23, ICLR24). Somewhere along the way, the hardest problem in visual AI moved. Every kind of data got its breakthrough. Documents got search. Tabular data got SQL. Code got GitHub. Each one gave messy data a structure — something you could query, verify, and act on. Visual data never got that SQL like accessibility; the largest data type we produce — over 80% of internet traffic, a billion cameras and climbing. And there's a deeper limit. The models got remarkably good, but the events that matter most in your operation barely exist in pretraining data. A general model may have never seen them — so it can't recognize them in your world. That's what we built at Creativ AI. Point it at anything with a lens — cameras, robots, live streams, or years of archives. Our Data Plating technology turns raw pixels into structure the instant something happens: entities, events, behaviors. Not captions. Not metadata. The video itself, as rows and columns. Live streams as they happen, archives you've had for years —all of it becomes a knowledge base you can query. In plain language for your team. Through APIs for your agents. On-device for your robots, so they can close the loop and act inside your workflows. Every row points back to the moment it came from — so every result is traceable, verifiable, and safe to build on. One structured picture. One source of truth. Everyone reading from the same record. And this is where the pretraining gap closes: you teach it your domain — your events, your entities, the rare cases that matter. CreativAI learns what a general model never could, so the long tail of your world becomes queryable data like everything else

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38,825 просмотров • 1 месяц назад