Deno KV

Deno is a JavaScript and TypeScript runtime created by Ryan Dahl as a reimagining of Node.js. It includes a formatter, linter, test runner, TypeScript support, security by default, and package management. The video also reports that Deno's company was acquired by Cloudflare and that development will end after a year of bug fixes.

Deno Deploy is Deno's serverless deployment platform, launched in 2021 for running applications and edge functions. The videos describe it as the platform on which Netlify and Supabase built edge-function capabilities and Slack used its app platform. They also report that its deployment footprint contracted from 35 regions to six and that users have about six months to migrate.

Databench by Alkera is a collaborative agentic data platform for data engineering, analysis, and science. It provides multiplayer workspaces where people and AI agents can work with shared chats and reactive Python and SQL notebooks in a sandbox on selected machines, with a model gateway that uses users' own keys. Alkera's site presents the platform as a workspace for humans and agents and lists connections to databases, warehouses, lakehouses, orchestration and transformation tools, business-intelligence systems, knowledge sources, code repositories, and communication tools. The video describes it as self-hostable and open source under the Apache 2.0 license.

KurrentDB is an event-native database for event-sourced and event-driven systems. It appends typed events to named streams, preserves the complete event history, streams changes in real time, and supports replaying, rewinding, or rebuilding state from past events. The service provides client libraries for Java, .NET, Go, Python, Node.js, Rust, and TypeScript, with Cloud, Enterprise, and Community deployment options.

NVIDIA Dynamo is a developer toolkit from NVIDIA for managing distributed key–value (KV) context caches used in large‑language‑model inference. It provides KV‑aware routing, cache offloading, and prefill/decode disaggregation to move KV cache and related activation/state across GPU clusters, reducing memory pressure and supporting scalable LLM serving. It is intended for integration into GPU‑based inference and serving stacks.