Notebooks in Gemini bring organization to complex tasks. Take the grad school application process: With notebooks, you can gather your transcripts, essay drafts and admission requirements in one place, so Gemini can help track deadlines, give feedback, and assess your progress.

PRO TIP: Gamify your notebooks Don't just read your notes— investigate them. Our new Sherlock Holmes notebook turns studying into an interactive mystery game. Deduce facts, uncover clues, & prove that even the most complex matters can be elementary. ➡️

Data teams are moving beyond isolated notebooks to embrace collaborative, AI-powered workspaces. 💻 Charles Schaefer from @_hex_tech shares how they're tackling the pain points of isolated data work, bringing technical and business teams together, and using generative AI to speed up insights. Watch the conversation 👉🏻

If you've wanted to learn vLLM but don't have a GPU sitting around, this is the path. Free Red Hat Developer Sandbox account. JupyterLab and models already deployed. You connect, run the notebooks, and learn by doing. No setup required. In ~1 hour: quantize a model, serve it via an OpenAI-compatible API, and benchmark it under real load. The same end-to-end workflow you'd run in production, without the infrastructure overhead. Start here:

# Weaviate Features and Practical Usage 🚀 Tired of standing up a vector DB server just for a test? Embedded Weaviate launches from your script in one line and disappears when you're done, making it a perfect throwaway DB for CI and notebooks. 📌 Title and Feature URL Title: Embedded Weaviate URL: 📝 Overview Embedded Weaviate is an experimental deployment model that runs a Weaviate instance from your application code rather than a standalone server. The instance lifecycle is tied to the client app, so it terminates when your app exits, though persisted data survives. Its biggest benefit is running experiments with zero infrastructure setup. 🔧 How It Works - In Python you launch it with weaviate.connect_to_embedded(version=..., headers=..., environment_variables=...). - The client checks binary_path for a cached binary; if missing, it downloads the right binary (Linux or macOS) from GitHub releases and caches it for reuse. - On first startup it creates a persistent datastore at persistence_data_path, and subsequent runs reuse it, so data survives between sessions. - The instance exits when the script ends, the app terminates, or the notebook becomes inactive. 🛠 Practical Usage - Key parameters are version (latest, a version string, or a binary URL), port (default 8079), persistence_data_path (default ~/.local/share/weaviate), and binary_path (default ~/.cache/weaviate-embedded). - For advanced setup use EmbeddedOptions and pass modules or API keys via additional_env_vars={"ENABLE_MODULES": "..."}, then call client.connect(). - If logs are noisy, quiet them with environment_variables={"LOG_LEVEL": "error"}. - TypeScript requires a separate package, weaviate-ts-embedded. 🎯 Use Cases - Running regression tests for search logic in CI with zero infrastructure setup. - Prototyping and experimentation in Jupyter notebooks. - Lightweight, single-user local validation. ⚠️ Caveats - It is experimental; APIs and parameters may change. - It is single-node only, with no clustering or distributed deployment, and is not production-grade. - Supported operating systems are Linux and macOS only. - Avoid changing XDG_DATA_HOME or XDG_CACHE_HOME, since they are widely used by other applications. #Weaviate# #VectorDatabase#

🚨 BREAKING: A HEAVILY ARMED MAN has been arrested outside Trump National Golf Club in California, after he CAMPED OUT before 47's visit ANOTHER WOULD-BE ASSASSINATION ATTEMPT. On Sunday, Jeanine John Taele had an illegally modified AR, radio devices, a .45 caliber pistol, BODY ARMOR, high capacity magazines, bulk pistol and rifle ammo, and "notebooks containing concerning statements," per NYP THANK GOD THEY GOT HIM. Keep 47 safe at all costs 🙏🏻

It’s been a busy couple of weeks! ICYMI, here’s the recap ⬇️ — Gemini Robotics 2 from @GoogleDeepmind brings whole-body intelligence to robots — Gemini 3.5 Flash-Lite is our fastest, most cost-effective model optimized for high-speed agentic workflows — Gemini 3.6 Flash delivers faster, more accurate performance across tasks while using significantly fewer tokens — Gemini 3.5 Flash Cyber is our new, cost-effective model optimized for finding and fixing software vulnerabilities at scale, available exclusively to governments and trusted partners — Nano Banana 2 in @GoogleEarth lets you reimagine places and generate custom images using satellite, aerial, and 3D imagery — Lyria 3.5 is the newest music model from Google Deepmind, now powering @googleflowmusic — @Gemini_Notebook (formerly NotebookLM) launched Collections, a new way to organize your notebooks

Three-person data team, a queue of weekly requests, and every analytical question outside predefined dashboards stuck in someone's inbox — that was LangChain's BI era. 🔍 The team decided to change. What they needed was a platform unifying dashboards, notebooks, and conversational interfaces with native AI agent capabilities. They chose Hex and designed a five-layer context architecture: dbt data model definitions, a semantic layer, workspace guides, endorsements as trust signals, and GitHub integration. The migration was complete in six weeks with 100% company adoption. The transformation's core wasn't technical — it was about explicitness. Rewriting a weak definition like "account_status: The status of the account" into a full description covering lifecycle states, default filters, and reporting conventions alone changed how reliably the agent answered questions. Key metrics — ARR, pipeline, customer health — were given single authoritative definitions in the semantic layer. Endorsements pointed agents to canonical sources wherever multiple assets addressed the same concept, preventing confusion before it could happen. Today, every function — marketing, product, sales, customer engineering — runs its own analysis without routing through the data team. Monthly agent conversations total roughly 2,200, processing 40x the volume the three-person team could handle manually. The team's role has shifted from "answer every question" to "design the system that lets others answer questions." LangChain's detailed account of this journey, "How LangChain Built an Agent-First Data Stack," makes the case — in concrete numbers and design principles — for what a data team looks like in the agent era. #DataStack# #AIAgent#

🚨 Back to School Drive is LIVE at Tesla Flower Mound! 🚨 Help us fill backpacks for local students! THIS SATURDAY’S Cars & Coffee is the perfect time to swing by and donate! Drop off new school supplies now through August 10th. 📍 1805 Justin Rd, Flower Mound, TX 🕒 Mon–Sat: 10am–7pm | Sun: 12pm–6pm Most needed: Backpacks • Pencils • Pens • Erasers • Crayons • Notebooks • Journals • Folders • Clorox wipes • Tissues Every donation makes a real difference. Thank you for supporting our community! 🙏