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Evgeny Khramov’s gas-station monitor is a software decision assistant, not a physical sensor. It combines public station data with personal rules—95-octane availability, queue tolerance, selected stations and data freshness—to answer a practical question: “Should I go for 95-octane now, or wait?” Its first version displayed station conditions; using it revealed that the more useful product should give a recommendation first and show the evidence behind it.
Khramov describes the project in a first-person account published September 30, 2026. The app was built around an external station-data API, an existing FastAPI project template and Telegram notifications. He does not identify the coding agent, and the account does not name a hosting provider or physical hardware. Read the original account on DEV Community.
Why a station dashboard was not enough
During a local fuel shortage, checking whether a station reported fuel was only part of the decision. Khramov still needed to verify that 95-octane was available, consider the queue, judge whether the information was fresh, and decide whether the trip was worthwhile. General-purpose availability data did not account for those personal constraints.
The first screen answered, “What’s happening at my gas stations right now?” But the user needed to know whether to make the trip. Cards containing raw conditions left the comparison and interpretation to him. Reviewing the working screen exposed that mismatch: collecting and displaying data was not the same as resolving the decision.
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What the MVP did
The initial build used a problem description, roughly five or six curl examples of the available API, and Khramov’s existing FastAPI template. The resulting app included:
- Station search and a personal watchlist.
- Periodic collection of station information and a history of observations.
- A current-conditions screen for watched stations.
The repository records a plan commit at 09:24 and an MVP commit at 11:44 on September 12. Those timestamps describe commits, not the amount of human work or how long the coding agent ran.
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How the app turns station data into a recommendation
A normalization layer maps the external API response into the app’s own station state, including confirmed fuel availability, queue information, observation time and freshness. A deterministic decision engine considers the watched stations and assigns one of four outcomes:
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches| State | Meaning |
|---|---|
| GO | At least one suitable station has confirmed 95-octane and a queue the user considers acceptable. |
| WAIT | Fuel is available, but the available choices do not meet the user’s conditions—for example, the queue is too long. |
| NO_OPTIONS | No watched station has a confirmed suitable option. |
| UNKNOWN | There is not enough fresh data to make an honest recommendation. |
The distinction between NO_OPTIONS and UNKNOWN matters: missing or stale information is not proof that fuel is unavailable. Khramov says the source API has no formal public specification, and different response fields may have different meanings or freshness. For example, an overall availability status could conflict with newer queue information. The app therefore avoids treating uncertain input as certainty.
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This is a rules-based decision system, not an LLM making the live recommendation. The stored history is meant to help a person notice patterns; the account does not present it as a model that predicts deliveries.
Why the interface puts the decision first
The main screen presents an overall recommendation and its explanation, followed by station-level evidence. This ordering responds to the original usability problem: the user should be able to see the answer to “Should I go?” before inspecting each station and doing the comparison manually. The underlying station details remain visible so the recommendation is understandable rather than a bare verdict.
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The history can be viewed by day, week or month. Hourly distinctions include 95-octane with no line, a short or long line, no 95-octane, and no data. That record can help a person review recurring conditions, but it is not evidence of a predictive fuel-delivery capability.
When a notification is worth sending
Telegram carries actionable notifications. The initial idea of alerting on every state transition proved noisy: fuel might appear at a station while a queue still made the trip unattractive. The app instead focuses alerts on useful changes in the overall decision. As Khramov puts it, “I don’t care about every transition and intermediate status. I only want to know when I can actually go.”
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Detailed event information remains in an in-app audit trail. It records the previous state, what changed, event and verdict changes, the reason for notifying, and the message text, allowing a past alert to be reconstructed without interrupting the user for every intermediate update.
What this project illustrates about AI-assisted development
The project’s most important shift was not simply building the data pipeline faster; it was recognizing that the initial framing automated the wrong task. “Show me station data” still required the user to make the decision. “Tell me whether my criteria are met, and show me why” better matched the real need.
Personal criteria make public data more actionable: the fuel grade, chosen stations, acceptable queue and minimum freshness are specific to the user. The same design also makes uncertainty visible instead of converting incomplete data into a misleading yes-or-no answer. The author summarizes the broader distinction this way: “AI helps build the decision system, but the decision system itself doesn’t have to be an AI system.”
The account is Khramov’s report; it does not independently verify the app’s operation, the accuracy of station data or the result of any trip. Its design lesson is still clear: an application is more useful when it connects information to the decision a person actually has to make, while preserving the evidence and uncertainty behind its recommendation.
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