In this article
We build tools to analyze public data. But we are not naive about how such tools can be weaponized.
When we say "misuse," we are not talking about someone pasting too many links or violating a terms of service technicality. We mean using our tools to harass, stalk, or collect information about someone for harmful purposes. This is a reality of the internet, and pretending it doesn't happen is irresponsible.
Public Data
Public does not mean harmless
There is a common defense in our industry: "We only show public data, so we aren't doing anything wrong." We reject this framing. Building tools that only access public data does not eliminate ethical responsibility. Aggregating public data and making it easily searchable changes the nature of that data. It lowers the barrier to entry for harassment.
Friction as a Feature
Because we cannot predict every bad actor, we build structural limits into Instaustad itself. These aren't just policies; they are hardcoded constraints.
- No private access: If an account is private, our tools cannot see it. Period. We do not use fake accounts or backdoors to bypass privacy settings.
- No bulk export: We do not offer CSV downloads of followers or mass scraping tools. The manual effort required to use Instaustad is intentional friction.
- No saved history: We don't keep a database of what you search for. Once you leave the page, the result is gone.
In specific design decisions, we actively choose restraint over capability. If adding a feature makes harassment significantly easier, we do not build it. Full stop.
When Things Go Wrong
Even with these limits, misuse happens. When someone reports that our tools are being used against them, we take it seriously. Our response is not automated. A real person reviews the report. Depending on the severity, we can block specific IPs or even specific targets from being queried on our platform.
The uncomfortable truth is this: we cannot prevent every instance of misuse. No platform can. But we can—and do—refuse to make misuse easier. We will never prioritize growth or feature parity over the safety of the people whose data is being analyzed.
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