Most developers start by asking the wrong question.
“Which APIs can I build on?”
That question quietly limits what you can build before you write a single line of code.
APIs exist where competition is already dense. Twitter, Reddit, Stripe, Maps. Same data, same constraints, same rate limits, same policy risk. You are not discovering leverage. You are sharing it.
The data that actually matters rarely ships with an API.
Local clinics. Shelters. Small suppliers. Government portals. Booking systems. Legacy dashboards. Public data, real demand, zero developer surface. These sites will never invest in APIs. They barely maintain the website.
Yet this is where the most underserved markets live.
For years, scraping was the workaround. It worked until the web changed.
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Most developers start by asking the wrong question.
“Which APIs can I build on?”
That question quietly limits what you can build before you write a single line of code.
APIs exist where competition is already dense. Twitter, Reddit, Stripe, Maps. Same data, same constraints, same rate limits, same policy risk. You are not discovering leverage. You are sharing it.
The data that actually matters rarely ships with an API.
Local clinics. Shelters. Small suppliers. Government portals. Booking systems. Legacy dashboards. Public data, real demand, zero developer surface. These sites will never invest in APIs. They barely maintain the website.
Yet this is where the most underserved markets live.
For years, scraping was the workaround. It worked until the web changed.
Why Scraping Was the Wrong Primitive
Scraping treated the web as a collection of static documents.
The model was simple:
- Fetch HTML
- Locate selectors
- Extract text
- Repeat
This worked when websites were content delivery systems. It collapsed when websites became state machines.
Modern sites defer data behind interaction:
- Availability appears after selecting dates
- Pricing appears after choosing a service tier
- Inventory appears after authentication
- Eligibility appears after form submission
The data does not exist in the DOM at rest. It is computed conditionally during a workflow.
Scrapers observe structure, not behavior. Once data moved behind behavior, scraping became an endless maintenance exercise. Selectors drifted. JavaScript delayed rendering. Edge cases multiplied. Every site redesign reset the system.
Scraping did not fail because it was brittle. It failed because it tried to read a system that only reveals state through execution.
Interaction Is the Actual Interface
If you model modern websites honestly, they are not documents. They are distributed state machines with UI-driven transitions.
The real interface is not HTML. It is the sequence:
- Click
- Select
- Submit
- Wait
- Branch
If your system cannot execute that sequence, it cannot observe the state you care about.
This is the key mistake most data extraction systems made. They optimized extraction before modeling interaction.
Why Browser Agents Are Not a Production Solution
Browser agents appear to solve interaction by mimicking humans.
They perceive the page visually. They reason about what they see. They decide what to click next.
This approach optimizes for generality. It fails on repeatability.
Every step requires perception and inference. Vision models re-interpret pixels on each scroll and click. Latency compounds. Costs scale with depth, not volume. Results vary run to run.
This architecture has several structural problems:
- Non-determinism: The same task can take different paths.
- High marginal cost: Each interaction incurs model inference.
- Poor parallelism: Long-running sessions block throughput.
- Low debuggability: Failures are hard to localize or reproduce.
This is acceptable for one-off tasks. It breaks when you need to run the same workflow thousands of times per day.
A production system cannot afford to rediscover how to click a calendar on every execution.
Navigation as an API Layer
An API is not defined by HTTP. It is defined by a contract.
- Inputs.
- Behavior.
- Outputs.
Most navigation systems fail when they move from demo to production. They can execute a workflow once, but they cannot do it reliably, repeatedly, and cheaply.
Mino solves that specific gap.
Mino by TinyFish is a web agents infrastructure that treats navigation as a first-class API, not as a side effect of browser automation. Instead of re-reasoning over screenshots on every run, Mino separates workflow understanding from execution and optimizes for repeatability at scale.
Their launch video says a lot about it:
Mino follows a simple rule:
Use AI to learn workflows. Use code to run them.
On the first execution, models reason about the site structure, UI semantics, and interaction flow. That reasoning is then compiled into a deterministic navigation plan. Subsequent runs execute that plan directly.
This avoids the two biggest failure modes of browser agents:
- repeated vision inference
- probabilistic decision-making on every step
The result is lower latency, lower cost, and higher consistency as usage increases.
Handling Real-World Website Complexity
Navigation-based systems must handle conditions that scrapers cannot.
Dynamic Client-Side State
Modern sites rely heavily on JavaScript frameworks. Data loads asynchronously and mutates DOM state over time. Navigation systems operate at the interaction level, not the markup level, making them resilient to client-side changes.
Multi-Step and Conditional Flows
Booking systems, eligibility checks, and gated content often branch based on input. Navigation workflows encode these branches explicitly rather than relying on heuristics.
Layout Drift
Selectors break when layouts change. Interaction semantics rarely do. Buttons still represent actions. Calendars still represent date selection. Navigation logic binds to behavior, not pixel position.
Parallel Execution
Because workflows are deterministic, they can be executed concurrently across many URLs. This enables aggregation use cases that were previously manual.
Anti-Bot Measures
Stealth browser profiles and residential routing reduce friction without resorting to brittle scraping hacks. The system behaves like a legitimate interactive client, not a crawler.
System Constraints and Tradeoffs
Navigation is not a silver bullet.
Human-in-the-loop steps such as 2FA, email verification, or SMS callbacks still block full automation. Aggressive bot defenses can introduce latency or failure. Success rates are probabilistic, not absolute.
These constraints are structural, not incidental.
The correct comparison is not with perfection. It is with alternatives:
- Scraping fails silently and requires constant maintenance.
- Browser agents are slow, expensive, and inconsistent.
- Manual work does not scale.
Navigation-based systems occupy a pragmatic middle ground with viable economics.
New Classes of Applications
Once navigation is reliable, several application categories become tractable:
- Real-time availability aggregators across heterogeneous providers
- Cross-site marketplaces built on long-tail vendors
- Competitive intelligence from interactive dashboards
- Local data platforms where APIs will never exist
- Workflow automation across vendor portals
These applications were previously constrained by human effort or fragile tooling. Navigation removes that constraint.
Conclusion
Scraping assumed the web was static. APIs assumed platforms would cooperate. Browser agents assumed reasoning could replace systems.
Navigation assumes none of that.
Tools like Mino by TinyFish treat the web as it actually is:
- interactive,
- fragmented, and
- unwilling to help you.
That is why they scale.
Huge Thanks to TinyFish for supporting this Blog post!
If you are building products that depend on real-world web data, this is the new baseline.
Thanks a lot for reading till the end!