AI tool comparison
Apfel 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
Apfel
Your Mac's hidden on-device LLM, finally set free
75%
Panel ship
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Community
Free
Entry
Apfel is a Swift CLI that does something Apple didn't: it exposes the on-device LLM baked into every Apple Intelligence-enabled Mac as a proper OpenAI-compatible local server running at localhost:11434. Any app that speaks to Ollama's API — LM Studio, Continue, OpenWebUI, your own scripts — can now route requests to Apple's FoundationModels framework without modification. The feature set is more complete than most indie wrappers: streaming responses, tool calling with MCP support, file attachments, an interactive chat mode, and a debug SwiftUI GUI for inspecting token flow. Inference is fully on-device with no API keys, no telemetry, and no cost beyond electricity. On an M-series Mac, it runs at native Apple Neural Engine speeds — typically 40-80 tokens/second depending on the model variant active. The catch is real: you need macOS 26 Tahoe (currently in beta) and Apple Intelligence enabled. But for the tens of millions of Apple Silicon Mac users who already qualify or will soon, this is the quiet unlock of a model they already own. The "your Mac already has a free LLM" framing is resonating — the repo hit 3,500 stars in days.
Developer Tools
Eyeball
Embeds source screenshots in AI analysis to kill hallucinations
75%
Panel ship
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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
“If you're already on the Tahoe beta, this is an instant install. Drop-in Ollama compatibility means every tool I already use just works — no friction, no cost. The MCP + tool calling support is unexpectedly polished for a one-dev project.”
“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.”
“The 'free LLM on your Mac' pitch is compelling but the reality is gated behind a beta OS most professionals won't run for months. Apple's FoundationModels API can also change or restrict access at any time — this kind of undocumented wrapper has a short shelf life if Apple decides to lock it down.”
“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.”
“Apple quietly shipped a capable on-device model and Apfel is the key that unlocks it for the developer ecosystem. This is a preview of a future where every device has sovereign AI — no network, no subscription, no permission slip from a cloud provider.”
“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.”
“Running AI locally for writing assistance without sending my drafts to a cloud feels like a material privacy win. Once macOS Tahoe ships properly, this is going to be the default starting point for privacy-conscious creators who already own a Mac.”
“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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