Compare/CodeBurn vs ProofShot

AI tool comparison

CodeBurn vs ProofShot

Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.

C

Developer Tools

CodeBurn

Track and cut your AI coding spend across every tool you use

Ship

75%

Panel ship

Community

Paid

Entry

CodeBurn is a terminal TUI dashboard that reads AI coding session data directly from disk — no API keys, proxies, or wrappers required — and surfaces a breakdown of token costs across Claude Code, Codex, Cursor, GitHub Copilot, and more. It auto-classifies activity into 13 categories (coding, debugging, testing, refactoring, etc.) and shows one-shot success rates per task type, giving developers a rare look at where their AI spend actually goes. The dashboard includes gradient charts, keyboard navigation, multiple time periods, and a currency converter supporting 162 ISO 4217 currencies. There's also an "optimize" command that scans sessions for waste patterns and outputs actionable, copy-paste fixes. For teams, a macOS menu bar app surfaces daily costs at a glance. With 2.7k stars after a Show HN post, CodeBurn clearly scratched a real itch. As AI coding budgets scale from hundreds to thousands of dollars per developer per month, tooling that makes costs visible and actionable becomes less optional and more essential.

P

Developer Tools

ProofShot

Give AI coding agents eyes to verify the UI they build

Ship

67%

Panel ship

Community

Free

Entry

ProofShot captures screenshots of running applications and feeds them back to AI coding agents as visual context. Instead of agents blindly writing UI code, they can now see what they built and iterate. Works with browser-based apps and integrates with popular AI coding tools.

Decision
CodeBurn
ProofShot
Panel verdict
Ship · 3 ship / 1 skip
Ship · 2 ship / 1 skip
Community
No community votes yet
No community votes yet
Pricing
Open Source (MIT)
Free / Open Source
Best for
Track and cut your AI coding spend across every tool you use
Give AI coding agents eyes to verify the UI they build
Category
Developer Tools
Developer Tools

Reviewer scorecard

Builder
80/100 · ship

This is exactly the observability layer AI coding has been missing. Knowing that 40% of my Claude Code tokens went to a single poorly-scoped context window is the kind of insight that pays for itself in the first week. The 'optimize' command is genuinely useful, not just marketing copy.

80/100 · ship

Clean integration — just point it at your dev server and it handles screenshot capture and context injection. The token cost of sending screenshots is non-trivial though, so you want to be selective about when you trigger it. Works best as a verification step, not continuous monitoring.

Skeptic
45/100 · skip

The multi-provider claim is impressive on paper, but Cursor and Copilot don't expose session data the same way Claude Code does. Expect incomplete data for non-Anthropic tools until the provider ecosystem standardizes telemetry formats. Also: if your team uses ephemeral dev containers, good luck getting disk reads to work.

45/100 · skip

Vision models still struggle with subtle layout issues — off-by-one pixel gaps, wrong font weights, slightly misaligned elements. ProofShot catches the obvious breaks but do not expect pixel-perfect QA. You still need human eyes for production UI.

Futurist
80/100 · ship

Cost observability is the missing infrastructure layer for the AI-native development era. Just as APM tools like Datadog became mandatory once cloud costs mattered, AI coding cost tracking will be table stakes within 18 months. CodeBurn is an early mover in a category that will consolidate around one or two dominant players.

No panel take
Creator
80/100 · ship

The TUI design is clean and keyboard-navigable in a way most developer dashboards aren't. Gradient charts inside a terminal window sounds tacky but actually reads well. The category breakdown would make a genuinely compelling weekly standup artifact for teams trying to improve AI workflow discipline.

80/100 · ship

As someone who has watched AI agents confidently ship broken layouts, this is a godsend. The visual feedback loop means agents can actually catch that the button is overlapping the nav bar. Design quality from AI coding just leveled up.

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