I built a tool to un-dumb Claude Code's CLI output (Local Log Viewer)

In plain words

Local Log Viewer is an Electron app that displays detailed execution traces from Claude Code CLI sessions in real time. It reads logs stored locally and presents file changes with inline diffs, token usage breakdowns, and agent execution trees in a readable format. Built for developers who use Claude Code and need visibility into what the CLI is actually doing without verbose JSON output cluttering their terminal.

written from the facts on this page · September 2026

From the sources

The missing DevTools for Claude Code — inspect session logs, tool calls, token usage, subagents, and context window in a visual UI. Free, open source. - matt1398/claude-devtools

In the maker’s words, at launch

Hi HN, I built this because I got tired of the Claude Code CLI hiding details from me. Recent updates have replaced critical output with summaries like "Read 3 files" or "Edited 2 files". To see what actually happened, I was forced to use `--verbose`, which floods the terminal with unreadable JSON and system prompts.…

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  Hi HN, We use Claude Code a lot, and after a long session there isn't a simple way to share exactly what happened with someone else. Prompts, responses, file edits, and tool calls all stay inside the terminal. We built Claudebin to make that easier. It's a plugin that lets you export the current session as a URL containing: - the full message thread - file reads and writes - bash commands - web and MCP calls You can send the link to someone, attach it to a PR, embed part of it, or resume the session locally. It's open source. GitHub:…

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  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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