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The blog introduces ClawBytes, a cookbook of ready‑to‑use automation recipes built for KiloClaw and OpenClaw. It positions the offering between basic setup guides and elaborate multi‑agent projects, delivering practical workflows such as GitHub triage, Todoist management, and research sourcing. Currently eight recipes span work, personal, creative, home, and health categories, each delivered as a copy‑paste prompt with tool requirements and tweak tips. The project invites community contributions, promising regular additions and a growing library of low‑code automations.
KiloClaw’s one‑click, 60‑second deployment removed the infrastructure hurdle for AI agents. However, users quickly hit a second wall: configuring external integrations and defining workflow logic. The company discovered that documentation alone didn’t move users past this point. To solve it,...
KiloClaw released a suite of March updates that make agents more durable and connected. Users can now link Google and GitHub accounts directly, while package installations via pip, uv, and npm persist across restarts. The default image now includes a...
Kilo’s latest post argues that the next generation of user interfaces is a single natural‑language sentence, not a dashboard or button. By decoupling the UI layer from individual applications, KiloClaw lets users command Todoist, Linear, calendars and email through one...
KiloClaw is a managed compute platform for OpenClaw AI agents that places security at its core. Each customer runs on a dedicated Firecracker microVM, providing hardware‑level isolation, while five independent layers—identity routing, dedicated app environments, network isolation, VM boundaries, and...
NVIDIA has launched the 120‑billion‑parameter Nemotron 3 Super, a hybrid mixture‑of‑experts model optimized for Blackwell GPUs, and made it freely available on the Kilo platform. Early benchmarks show strong results – 86.01 on MMLU, 79.40 on HumanEval, and 60.5 on SWE‑Bench –...
Current AI coding assistants struggle with large repositories because they rely on simple prompt stuffing rather than true code understanding. Even frontier models and massive context windows cannot compensate for missing dependency graphs, stale indexes, and stateless interactions, leading to...
