AI tool comparison
Eyeball vs Hopper
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
Eyeball
Inline screenshots with every AI claim — hallucination's paper trail
75%
Panel ship
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Community
Free
Entry
Eyeball is an indie tool that fights AI hallucination in document analysis by embedding inline screenshots of the actual source passages alongside each AI-generated claim. When you analyze a PDF or document with Eyeball, the output is a Word doc where every statement has a highlighted screenshot of the precise text it came from — because screenshots are harder to hallucinate than quotes. The tool emerged from a simple observation: AI systems routinely fabricate citations and misquote sources, and quote-only verification still requires humans to manually hunt down the original text. Eyeball short-circuits that by attaching the visual evidence directly to each claim in the output document. Legal, compliance, and research reviewers can audit AI outputs at a glance rather than cross-referencing. Built in Python, Apache 2.0 licensed, launched as a Show HN six days ago and gaining traction. The approach is low-tech by design — no vector embeddings, no proprietary API calls — just precise text highlighting, screenshot capture, and Word document assembly. The simplicity is the point: verifiable AI outputs shouldn't require a research budget.
Developer Tools
Hopper
The first AI agent dev environment built for COBOL and mainframes
75%
Panel ship
—
Community
Free
Entry
Hopper, from YC S24 startup Hypercubic, is the first agentic development environment purpose-built for mainframe systems. It lets AI agents navigate TN3270 terminals autonomously, write and submit JCL jobs, monitor JES output, debug failed jobs by analyzing spool data, query VSAM datasets, compile and run COBOL code, and manage CICS transactions—all via natural language prompts. Tasks that traditionally took mainframe specialists hours of manual TN3270 navigation can now be expressed as a single instruction. The technical challenge here is real: mainframes don't have nice REST APIs or modern dev tooling. They run on green-screen terminal protocols from the 1970s, and the humans who know how to operate them are retiring faster than they can be replaced. Hopper essentially wraps the entire mainframe interaction surface in an agent-friendly interface, translating intent into the arcane sequences of keystrokes and JCL that mainframes actually require. The product is free for individual developers (all core features, macOS/Windows/Linux) with Enterprise pricing for SSO, on-prem deployment, and SOC 2 reports. Hypercubic's team includes alumni from Cognition, Apple, and Windsurf. Given that mainframes still process an estimated $3 trillion in daily commerce and the COBOL developer shortage is acute, Hopper is targeting a genuinely underserved market with unusual urgency.
Reviewer scorecard
“This is the kind of clever, unglamorous tool that actually solves a real problem. The insight that screenshots are harder to hallucinate than quotes is simple but profound. Drop this into any pipeline that serves legal or compliance users immediately.”
“This solves a real crisis. I've watched financial institutions pay six-figure consultant fees for tasks that Hopper demos suggest could be automated in minutes. If it's reliable on diverse JCL and CICS environments, this is immediately commercial.”
“Screenshots of source text don't prevent the underlying problem — an AI can still misinterpret or misconstrue what the screenshot says. It adds friction to the review process without fixing the root cause. Useful for basic verification but don't mistake it for a hallucination solution.”
“Mainframe environments at major banks are extraordinarily heterogeneous—custom RACF configurations, vendor-specific CICS extensions, and decades of undocumented JCL conventions. An agent that confidently submits the wrong job in a production batch environment could be catastrophic.”
“Provenance-by-design is going to be mandatory for AI in regulated industries. Eyeball's approach — baking visual evidence into every claim — points toward a future where AI outputs are self-auditing. This is an indie tool today; it's a compliance standard in three years.”
“The $3 trillion in daily mainframe commerce has been a black box to AI modernization. Hopper is the Rosetta Stone moment—once there's an agent-friendly interface to legacy systems, every other AI tool in the stack becomes accessible to that infrastructure.”
“For editorial and research work, knowing exactly where an AI got its information is table stakes. Eyeball makes that process visual and immediate — that's a huge quality-of-life improvement for anyone who fact-checks AI-generated research.”
“There's something poetic about AI agents handling COBOL—the language written by Grace Hopper, now managed by a tool named after her. For teams modernizing legacy fintech systems, this is the missing piece.”
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