
MT - Mosh Terminal
The mobile companion for Claude Code, Codex, and AI coding.
What it does
MT is a $14.99 one-time iOS terminal with a 2-week free trial, built for developers who live with AI coding agents. From-scratch Mosh in Rust, 15 one-tap tmux actions, and system push notifications via Claude Code Hooks — tap the notification and MT auto-resumes the exact tmux session over Mosh. Not a VPN, not a subscription.
Does a similar job
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- WAWarp, a Rust-based terminal2022 · warp.dev · ▲946
Hi HN community, I’m Zach, founder and CEO of Warp, and am excited to show you Warp, a fast Rust-based terminal that’s easy to use and built for teams. As of today, Warp is in public beta and any Mac user can download it. It works with bash, zsh, and fish. The terminal’s teletype-like interface has made it hard for the CLI to thrive. After 20 years of programming, I still find it hard to copy a command’s output; I always forget how to use `tar`; and I always have to relearn how to move my cursor. To fix fundamental accessibility issues, I believe we need to start innovating on the terminal,…
MOTHER Aug 2026 · coastcreative.studio · ▲73A terminal built for Claude Code w/ one-click session resume


- CACodeBurn – Analyze Claude Code token usage by taskApr 2026 · github.com · ▲112
Built this after realizing I was spending ~$1400/week on Claude Code with almost no visibility into what was actually consuming tokens. Tools like ccusage give a cost breakdown per model and per day, but I wanted to understand usage at the task level. CodeBurn reads the JSONL session transcripts that Claude Code stores locally (~/.claude/projects/) and classifies each turn into 13 categories based on tool usage patterns (no LLM calls involved). One surprising result: about 56% of my spend was on conversation turns with no tool usage. Actual coding (edits/writes) was…
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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