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Ask HN: Browser extension vs. native app for structured form filling?
I’m working on a project called Injectless — a browser extension that allows websites to explicitly declare which data they are allowed to inject into external sites, fully controlled by the user.
Note: This post was translated to English using AI. My native language is Spanish.
The Problem: Users of SaaS apps (accounting, project management, etc.) often need to repeatedly copy data into external forms (government portals, client systems, etc.). Today this is a tedious, fully manual process.
My Current Solution A browser extension where: - Websites expose an injectless.json declaring which fields they can fill and on which domains - The user explicitly installs the integration (one-click opt-in) - When visiting an allowed site, the extension offers to “paste” each field
The Doubt A friend suggested that instead of a browser extension, this should be a native app (similar to KeePassXC or Espanso) that: - Works in any browser without installing multiple extensions - Pastes sequences of fields using TAB (simpler, more universal) - Works even outside the browser - Avoids extension permissions, CSP issues, Shadow DOM, etc.
My Concerns About a Native App - Mobile: Browser extensions do work on mobile (Safari iOS, Firefox Android). Native apps would face heavy sandboxing restrictions - UX: The extension popup can show exactly which fields are available for the current page. A native app would be more “blind” - Context: The extension knows which page you’re on and can automatically validate allowed domains
The Question What seems more valuable / practical? A) Browser extension (current approach) — more context, mobile support, clearer UX B) Native app like Espanso/KeePassXC — more universal, single install, simpler C) Both — native app as a base + optional extension as a companion for better UX
Has anyone worked on something similar? What trade-offs might I be missing?
Thanks!
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Show HN: Neurop Forge: Live Demo /Real AI Action
Would like feedback...
"Freedom of Speech" Exists – But "Freedom of Reach" Is a Black Box
I posted a link to a long-form article on X (Twitter). Impressions: single digits. Nothing was deleted. No warning. No ban. It simply didn’t reach anyone.
At first glance this is easy to dismiss as “algorithm roulette.” But the same invisibility patterns show up across platforms:
- YouTube political content gets quietly demonetized / de-ranked - External links on social feeds often underperform (sometimes dramatically) - LLMs (ChatGPT/Claude/etc.) tend to sanitize or avoid politically sharp topics - Search results for some queries feel oddly thin, stale, or SEO-flooded
This makes me wonder if we’re drifting into a new mode of discourse control: not classic “state censorship,” but incentive-driven soft suppression.
Habermas called the democratic discourse space the “public sphere.” A hidden assumption in that model was simple: if you publish, people can actually see it. That assumption may be breaking.
A rough model (feel free to tear this apart):
1) Visibility layer (feeds / ranking / UI) - downranking, link suppression, shadow ranking -> speech is “allowed” but socially non-existent
2) Generation layer (LLMs) - safe-neutral framing becomes default -> controversial topics become culturally “unspeakable”
3) Discovery layer (search) - SEO + degraded results -> “can’t be found” becomes “doesn’t exist”
Stacked together:
[You post, but reach collapses] ↓ [You ask AI, but it avoids the core] ↓ [You search, but sources are buried] ↓ People learn: “speaking changes nothing” ↓ Self-censorship becomes the stable equilibrium
I’m not claiming a single actor is “censoring the internet.” It might just be: - ad-driven engagement optimization - brand safety / moderation incentives - regulatory risk management - black-box ranking artifacts
But the end result can look similar: public discourse shrinks without any explicit ban.
Questions for HN:
1) Is “freedom of reach” now a separate political variable from “freedom of speech”? 2) If you think this is real, what would be a convincing experiment / metric to measure it? (A/B tests on link posts? cross-platform comparisons? time-series reach tracking?) 3) Have you personally observed external-link downranking or “shadow ranking” behavior? 4) For LLMs: how would you measure “topic avoidance / neutralization” systematically?
I’m open to being wrong — I mostly care about what would falsify it.
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