AI tool comparison
EvanFlow 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
EvanFlow
TDD-first workflow framework that turns Claude Code into a disciplined dev team
75%
Panel ship
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Community
Free
Entry
EvanFlow is an open-source framework that wraps Claude Code in a structured software development workflow. Built around a brainstorm → plan → execute → test → iterate loop, it adds human approval checkpoints between each stage so the AI never autonomously commits or deploys. Think of it as giving Claude Code a senior engineer's instincts: it stops before dangerous git operations, validates test assertions, detects context drift, and flags the five failure modes that routinely derail LLM-generated code. The project ships 16 integrated skills and two custom subagents for parallel development, plus a git guardrails hook that physically blocks risky operations like force-pushes or wholesale file deletions. Every iteration runs a Five Failure Modes checklist — hallucinated actions, scope creep, cascading errors, context loss, and tool misuse — before proposing the next step. Visual UI changes are verified via a headless browser before the developer signs off. EvanFlow fills a real gap: Claude Code is powerful but undisciplined by default. EvanFlow imposes structure without removing control. It's MIT-licensed, ships via npm CLI or Claude Code's plugin marketplace, and requires no backend — just Claude Code access and jq. Gained 59 upvotes on Hacker News within hours of launch.
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
“This is exactly what Claude Code needed. The git guardrails hook alone is worth installing — I've seen too many agents nuke a working branch with a confident `git reset --hard`. EvanFlow's 'conductor not autopilot' philosophy maps perfectly to how good engineers actually want to use AI: fast on the mechanical stuff, slow on the decisions that matter.”
“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.”
“Sixteen skills and two subagents sounds like a lot of complexity layered on top of a tool that's already opinionated. The approval checkpoints are nice in theory, but developers under deadline will click through them reflexively — at which point you've just added friction without safety. Also requires Claude Code, which is not cheap.”
“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.”
“The real signal here isn't EvanFlow itself — it's that the community is already building governance layers on top of AI coding agents. The 62% error rate in LLM-generated test assertions that EvanFlow cites is a sobering number. Projects like this show that safe AI-assisted development needs to be engineered, not assumed.”
“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.”
“If you're a solo builder or small team shipping fast, EvanFlow's vertical-slice TDD mode is a game-changer. It keeps the AI focused on one working slice at a time rather than hallucinating an entire architecture. The visual UI verification via headless browser is a thoughtful touch that saves embarrassing regressions.”
“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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