AI tool comparison
devnexus 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
devnexus
Shared persistent memory vault for AI coding agents across repos
50%
Panel ship
—
Community
Paid
Entry
devnexus creates a shared persistent memory system for AI coding agents working across multiple repositories and sessions. It spins up an Obsidian-based knowledge vault that gets synced via git every ~60 seconds, allowing multiple agents (Claude Code, Cursor, Windsurf, OpenAI Codex) to share architectural decisions, API contracts, data schemas, and cross-repo code graphs — with proper version history. The core problem it solves is "agent amnesia" on teams where multiple developers use different AI tools. Each agent starts every session fresh, unaware of decisions made by the agent next door. devnexus gives them all a common memory store that persists across sessions and codebases. Created April 14, 2026, it's early-stage but addresses a pain point that becomes more acute as teams scale up AI-assisted development. The Obsidian format is a clever choice: the vault is human-readable, searchable with standard tools, and works as a documentation layer even without the AI integration. Git sync means there's a full audit trail of what the agents "knew" at any given time — useful for debugging why an agent made a surprising architectural choice.
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
“Agent amnesia is a real tax on multi-engineer teams using AI tools. devnexus's approach of using Obsidian + git means the memory is portable, auditable, and doesn't depend on any specific AI provider's memory feature. It's rough around the edges but the concept is sound and I'd build on top of it today.”
“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.”
“This is a four-day-old project solving a genuinely hard problem in the simplest possible way — which means it'll break in interesting edge cases immediately. Obsidian vault conflicts under git are a known pain point, and 60-second sync cycles could create race conditions on busy teams. Wait for it to survive contact with a real multi-engineer setup.”
“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.”
“Shared agent memory is the missing coordination primitive for AI-assisted software teams. devnexus is a minimal implementation of an idea that will eventually be built into every enterprise AI coding platform. Getting ahead of that curve now — even with rough tooling — gives teams a learning advantage.”
“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.”
“For design systems and component libraries shared across repos, the idea is compelling — agents that remember 'we use this button component, not that one' would save a lot of correction cycles. But until this is more than a four-day-old script, I'd treat it as inspiration rather than infrastructure.”
“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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