Ask HN: Will we start seeing tools for LLM use?
TL;DR
An Ask HN post asks whether a new class of tools will emerge for LLM agents: not human-facing tools, but CLI and developer outputs shaped for model context. It points to existing projects like rtk, headroom and lean-ctx, which reduce verbosity from common Bash, Git and npm commands that agents call as tools. The tradeoff is clear: compressed output can save tokens, but if the model needs more follow-up turns, the savings can disappear.
Nauti's Take
This is a real product gap, but probably not a standalone market for lots of tiny wrappers. The winners are more likely to be libraries, CLI standards and agent runtimes that provide machine-readable compact modes by default.
Pure token-saving tools are too narrow: if the agent needs three more turns afterward, the cost was only moved around. The strong version gives the model status, failure cause, next options and relevant files in one structured response.
Briefingshow
Agents still consume many tool outputs designed for humans: logs, tables, colors, repeated lines and incidental detail. If outputs are structured for models, agents could become cheaper, faster and more reliable. The key metric is not maximum compression, but whether the output contains enough decision-relevant state to avoid extra turns.
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