AI tool comparison
CRAG vs Eyeball
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
CRAG
One governance file, compiled into every AI coding tool's format
50%
Panel ship
—
Community
Paid
Entry
CRAG is a governance compiler for AI-assisted codebases. The premise is simple but genuinely useful: you write one canonical `governance.md` file describing your project's coding standards, security requirements, and AI behavior rules — then CRAG compiles it into 12 target formats simultaneously: GitHub Actions workflows, pre-commit hooks, Cursor rules, GitHub Copilot instructions, Cline configs, Windsurf rules, Amazon Q Developer settings, and more. As development teams adopt multiple AI coding assistants — which is nearly universal now — maintaining separate rule sets for each tool becomes a synchronization nightmare. A security policy you update in your Cursor rules doesn't automatically propagate to your Copilot instructions or your CI checks. CRAG treats governance as a single source of truth and the tool-specific configs as build artifacts. The compiler is zero-dependency, deterministic, and SHA-verifies each output for auditability. It's early — 8 stars at the time of posting — but the problem it addresses is real and growing in proportion to how many AI coding tools a team runs simultaneously.
Developer Tools
Eyeball
Embeds source screenshots in AI analysis to kill hallucinations
75%
Panel ship
—
Community
Free
Entry
Eyeball is a GitHub Copilot CLI plugin with a deceptively simple idea: instead of trusting the AI to accurately summarize documents, it captures screenshots of the actual source material and embeds them alongside the AI's claims in the output report. If the model says "Section 10 requires mutual indemnification," the report shows that exact section highlighted in yellow directly below the claim. The underlying insight is sharp — screenshots cannot be hallucinated. Text can be subtly reworded, paraphrased incorrectly, or synthesized from nowhere. But a screenshot is a literal capture of the source. Built for legal review, compliance analysis, financial due diligence, and any domain where the stakes of an AI error are high. Built by indie developer dvelton, it handles PDFs, Word documents, and web pages. MIT licensed, free to use. Surfaced on Hacker News Show HN today, where it sparked an active discussion about AI verification and the underrated value of visual evidence in AI-assisted analysis workflows.
Reviewer scorecard
“Maintaining separate .cursorrules, copilot instructions, and CI configs is already a real headache on teams using 3+ AI tools. The single-source-of-truth approach is architecturally correct and the zero-dependency design keeps it lightweight. Early, but the concept is solid — I'd pilot this on a team project immediately.”
“This is one of those ideas that makes you think 'why isn't every AI analysis tool doing this?' The implementation is simple — capture screenshots of the source during analysis — but the trust it builds in the output is enormous. I'd use this immediately for any contract or regulatory review workflow.”
“Each AI coding tool has subtly different semantics for what rules actually do — what a Cursor rule enforces versus what a Copilot instruction suggests are meaningfully different. Compiling from a single source risks giving false confidence that all tools are behaving consistently when they're not. The abstraction may leak badly in practice.”
“Screenshots prove the source exists but don't verify the AI's interpretation of it is correct. A model can still misread highlighted text or draw wrong conclusions. Also, PDF-to-screenshot pipelines get messy with scanned documents, multi-column layouts, and complex tables — exactly the docs where hallucinations are most likely.”
“AI governance tooling is nascent but will be critical infrastructure within 2 years. The pattern of 'define once, compile everywhere' is how we handle configuration drift in infrastructure (Terraform, Ansible) — applying it to AI behavior rules makes sense. CRAG is an early prototype of what will eventually be a standard enterprise workflow.”
“Eyeball points toward a future of verifiable AI outputs — not just 'the model said this' but 'the model said this, here's the evidence, here's the reasoning chain.' Legal AI adoption hinges on explainability, and embedded source screenshots are a practical step toward outputs that hold up under professional scrutiny.”
“As a solo creator I only use one or two AI coding tools at a time, so the multi-tool synchronization problem doesn't hit me hard enough to add another tool to my workflow. This feels aimed squarely at engineering teams rather than individuals.”
“For research, journalism, and content work where you're citing sources, this is a game-changer. The ability to produce a report where every claim is visually anchored to the source makes the output publishable rather than just useful. The design of the output document matters — would love to see more control over the visual layout.”
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