AI tool comparison
Ferretlog vs lmscan
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
Ferretlog
git log for your Claude Code agent runs — local, zero dependencies
50%
Panel ship
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Community
Free
Entry
Ferretlog is a zero-dependency pure Python CLI that treats your Claude Code session logs like a git repository. It parses the raw JSONL logs in `~/.claude/projects/` and gives you git-style history browsing, diff between runs, per-tool-call breakdowns, and cost/token stats — entirely locally, with no network calls and no configuration required. If you've been using Claude Code heavily, you've likely experienced the frustration of losing track of what changed across sessions, what tools were called how many times, and how much each session actually cost across sub-agent calls. Ferretlog makes that history explorable and comparable the same way `git log` makes code history explorable. This is an indie solo project from Eitan Lebras, submitted as a Show HN. It's genuinely useful as a power-user tool for anyone doing serious Claude Code work, especially those managing multi-session agent pipelines where debugging "what did the agent do last time?" is a real pain. The zero-dependency, local-only design means there's no trust surface and no setup friction.
LLM Tools
lmscan
Offline AI text detector that fingerprints which LLM actually wrote it
50%
Panel ship
—
Community
Free
Entry
Most AI text detectors are cloud services with opaque models, significant false positive rates, and zero explanation for why they flagged content. lmscan is a zero-dependency Python package that runs entirely offline using 12 statistical linguistic features: perplexity scoring, burstiness analysis, vocabulary density, syntactic variety, and others. It's not just detection — it fingerprints the specific LLM family responsible, distinguishing between GPT-4, Claude, Gemini, Llama, and Mistral outputs based on their characteristic writing signatures. Every result is fully explainable, showing which features drove the classification. The design philosophy is explicitly anti-black-box: every classification comes with a feature-by-feature breakdown, making it suitable for applications where you need to explain the result to a human (academic integrity, content moderation, employment screening). The CLI interface drops into CI/CD pipelines for automated content checking, and the Python API integrates into document processing workflows. No API key, no network call, no vendor lock-in. Very early project — minimal stars and community traction as of this writing. The statistical approach trades accuracy for explainability, which means sufficiently paraphrased AI text will evade detection just as it does on competing services. But for a free, fully offline, explainable baseline for AI text analysis, it occupies a niche that no established tool does cleanly. Worth monitoring for teams that need local, auditable AI detection without vendor dependency.
Reviewer scorecard
“If you run Claude Code daily, you need this immediately. Being able to diff two sessions like git commits and see exactly which tools fired and what they cost is something that should have existed from day one. Zero-dependency Python means it just works.”
“The zero-dependency, fully offline angle makes this immediately viable for enterprise environments where you can't send content to a third-party API for compliance reasons. The LLM fingerprinting feature is genuinely novel — I haven't seen another tool that tries to attribute text to specific model families. Early days, but the CI/CD integration and explainable output make it worth piloting for document pipelines where you need auditable AI detection.”
“This is a niche tool for a niche user (heavy Claude Code power users) and the session log format Anthropic uses is undocumented and could change at any update. Tying workflows to internal log parsing is fragile infrastructure — treat it as a convenience, not a dependency.”
“Statistical AI text detection is a fundamentally broken approach — anyone who rewrites AI output a couple of times will evade it, and false positive rates on certain human writing styles (non-native English speakers, highly technical prose) can be significant. The LLM fingerprinting claim sounds exciting but needs rigorous benchmark testing before I'd trust it in a real content moderation or academic integrity context. Ship it when there's an accuracy paper.”
“Agent observability tooling built by the community, not the vendor, is how this ecosystem will mature. Ferretlog is primitive but it points at a real gap: we need git-style versioning and auditability for agent sessions, not just for code.”
“As AI-generated content saturates every channel, the tools for detecting and attributing it become infrastructure, not just features. lmscan's offline, explainable approach points toward the right architecture: detection capability should be embeddable and auditable, not locked behind API calls. The specific LLM attribution angle — figuring out which model family produced text — will become increasingly important for provenance tracking and regulatory compliance.”
“Terminal-only, Claude Code-specific, no visuals — this tool exists entirely outside my workflow. The underlying insight (session replay and cost tracking) is useful, but it needs a UI before it reaches anyone outside the developer community.”
“If you're a creator who worries about AI-generated content flooding your niche or competitors using AI to impersonate your style, this is theoretically relevant. But the accuracy question is real — statistical detection won't catch polished AI content, and false positives could flag your own work. Interesting concept that needs a lot more development before it's trustworthy for real editorial decisions.”
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