We’re making sharing between Android devices even simpler, starting with Pixel. Use Quick Share and tap to send contact info, photos, videos, and more. This feature is rolling out to Pixel 6 and newer devices, and soon to the Samsung Galaxy Z Fold8 Ultra, Fold8, and Flip8. Coming to more Android devices by the end of the year. Learn more ⬇️

The fastest way to lose a developer is a quickstart that works and a second step that doesn't. Polish the cliff, not the on-ramp. Every ecosystem I've worked in, @zetachain included, has this bug somewhere.

Eliminate training-serving skew in ML with integrated feature and model serving. Our quickstart walks through an real-time XGBoost fraud detection pipeline with Snowflake’s Online Feature Store. The guide 👉

Secrets management doesn't have to be complicated. 👀 Join our Vault Quickstart workshop and learn how to securely store, access, and manage secrets across your infrastructure. You'll learn how Vault best practices, security lifecycle management core concepts, and how platform teams can enable workflows to protect sensitive data with a system of record and manage the full lifecycle of secrets, certificates, passwords, and keys. 📆 Tuesday, September 15 ⏰ 11 a.m. EDT 🔗

Hedera Docs MCP gives an AI coding agent live access to the official Hedera documentation: API references, SDK guides, quickstarts and code examples, searched at the moment the agent writes code. Read-only. No wallet or keys required. Set it up:

# Hermes Agent Features and Practical Usage 🚀 Five minutes from install to your first real conversation. Have the agent read files, run commands, and search the web by handing it an actual task right away. 📌 Title and Feature URL Title: Quickstart URL: 📝 Overview The Quickstart walks you from a fresh install to a working first conversation. You launch with `hermes` or `hermes --tui` and exercise core abilities: inspecting a repo, executing commands, and searching the web. Its guiding principle is to get one clean conversation working before layering on anything else. 🔧 How It Works - Launch via the classic CLI `hermes`, or the modern TUI with modal overlays `hermes --tui` (recommended). - Run the setup wizard with `hermes setup`; pick a model interactively with `hermes model`. - Resume your most recent session with `hermes --continue` (short form `hermes -c`). - Diagnose configuration issues with `hermes doctor`. - Capabilities demonstrated include file reading, terminal command execution (with approval), web search via the Tool Gateway, and multi-turn conversations. 🛠 Practical Usage - Start with specific, verifiable prompts. Example: "Summarize this repo in 5 bullets and tell me what the main entrypoint is." - Confirm file reading with something like "Check my current directory and tell me what looks like the main project file." - Try "Help me set up a clean GitHub PR workflow for this codebase" to see command execution and the approval flow together. - Type `/` mid-conversation for an autocomplete menu of slash commands: `/help` (commands), `/tools` (available tools), `/model` (switch models), `/save` (save the conversation). 🎯 Use Cases - Right after install, double as a smoke test by asking for a repo summary or file inspection. - Feel out the command-approval flow on a real task to calibrate how much you delegate. - Switch models and tools via slash commands as you settle into your workflow. ⚠️ Caveats - Hermes Agent requires a model with at least 64,000 tokens of context; avoid models that are too small. - The guide stresses getting basic chat working first, then adding gateways, skills, or automation. - Command execution involves agent approval, so watch the approval behavior closely on your first run. #HermesAgent# #AIAgents#

The hard parts of hybrid retrieval, already done. Elasticsearch Vector Database is a new serverless offering where expert-level tuning is the default: - bfloat16 storage: half the disk footprint before quantization even starts - BBQ: up to 32x vector compression, 95% less memory - Auto-calibration re-tunes quantization on every merge as your data drifts - Filtered vector search at up to 8x higher throughput than OpenSearch - Jina AI embeddings and reranking on managed GPU inference, or bring your own models You bring documents and queries. We handle the embeddings, tuning, and infrastructure. Full breakdown, including the semantic_text quickstart and what ships in vectorDB index mode: