Evgeny Khramov built a software monitor that collected fuel-station data, but its first version still left him to make the key call: “Should I go for 95-octane now, or wait?” The useful change was to make the app answer that personal decision—using fuel availability, queues, selected stations, and data freshness—instead of merely displaying status cards. It was a software project built with an AI coding agent, an existing FastAPI template, a public API, and Telegram notifications, not a physical sensor or device. Khramov’s account was published on DEV Community on September 30, 2026.
Why a station listing was not enough
During a local fuel shortage, checking whether a station had fuel became a repeated decision. Public services exposed station availability and queue information, but Khramov still had to inspect particular locations, confirm 95-octane fuel, judge the line, and decide whether the information was fresh enough to trust.
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That gap is the difference between a listing and a decision assistant. A general status page can report what it knows; a personal monitor needs to apply the driver’s actual constraints: which stations matter, what fuel is acceptable, how long a queue is tolerable, and whether the observation is current.
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Khramov describes building the MVP from a problem description, roughly five or six curl examples of the available API, and an existing FastAPI project template. It included station search, a watchlist, periodic data collection, history, and a screen for current conditions at watched stations.
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Repository history shows a plan commit at 09:24 and an MVP commit at 11:44 on September 12. Those are commit timestamps, not a measurement of how much human labor or agent runtime the project required.
The initial screen answered, “What’s happening at my gas stations right now?” It showed data, but the driver still had to compare stations and interpret what the data meant. Reviewing the working interface exposed the real product problem: collecting and presenting information had not removed the decision burden. The redesign put the overall decision first and the station evidence underneath it.
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How the monitor turns station data into a decision
The system first normalizes the external API response into its own station state, including confirmed fuel availability, queue, observation time, and freshness. A deterministic decision engine evaluates all watched stations and returns one of four outcomes:
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- GO: at least one suitable station has confirmed 95-octane fuel and a queue within the user’s acceptable limit.
- WAIT: fuel is available, but the known choices do not meet the user’s conditions—for example, the queues are too long.
- NO_OPTIONS: no watched station has a confirmed suitable option.
- UNKNOWN: there is not enough fresh information to make an honest call.
The distinction between “no option” and “unknown” matters. Khramov’s rule is: “No fresh data does not mean no fuel.” If a station’s data is stale or missing, the monitor should not quietly treat that absence as evidence that fuel is unavailable.
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This matters especially because the source API has no formal public specification, and its fields may not share the same freshness or meaning. For instance, an overall availability status may conflict with newer queue information. The app therefore makes uncertainty visible rather than pretending that every response supports a confident verdict.
Why the decision engine is not an AI decision-maker
The AI coding agent helped build the software, but the runtime verdict comes from explicit rules, not an LLM interpreting station data. As Khramov puts it, “AI helps build the decision system, but the decision system itself doesn’t have to be an AI system.”
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That separation suits a task where a recommendation depends on a small set of stated conditions. The app can explain the verdict in terms of fuel, queues, and freshness, while avoiding an opaque prediction about whether a delivery is coming. Its history is intended to help a person notice patterns, not to forecast fuel deliveries.
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The main screen presents an aggregate recommendation and its explanation, followed by evidence for each station. This ordering makes the interface answer the driver’s question first while still letting them inspect the underlying conditions.
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Station history can be reviewed by day, week, or month. Hourly distinctions include 95-octane with no line, a short or long line, no 95-octane, and no data. These records help a user review patterns; they are not represented as a prediction system.
Notifications should mark a useful change, not every change
Telegram carries actionable notifications. Early on, reporting every status transition created noise: fuel could appear at a station even when the queue still made the trip unattractive. The app instead focuses alerts on a meaningful change in the overall decision. In Khramov’s words, “I don’t care about every transition and intermediate status. I only want to know when I can actually go.”
Suppressing noisy alerts does not mean discarding the detail. An in-app audit trail records the prior state, what changed, event and verdict changes, why a notification was sent, and the message text. That makes it possible to reconstruct a past alert without interrupting the user for every intermediate update.
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What this project demonstrates
The important product lesson is not that an agent can assemble a dashboard quickly. It is that a working MVP can reveal that the original task was framed too narrowly. A monitor that gathers and displays data may still leave the user doing the hard part. Putting personal criteria and an explicit verdict at the center makes the same underlying information more actionable.
Khramov’s account is a first-person description; it does not independently verify the application’s operation, the accuracy of station data, or the result of any particular trip. Its design discussion nevertheless offers a useful way to evaluate similar tools: check whether they handle personal criteria, queues and freshness, communicate uncertainty, explain recommendations with evidence, and reserve alerts for decisions worth interrupting someone over.
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