scoutOS

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Inspiration

I built ScoutOS after realizing how many valuable opportunities I was missing; not because I wasn’t qualified, but because I couldn’t keep track of everything in real time. Hackathons, internships, grants, and fellowships are scattered across different platforms, and keeping up with them requires constant searching, filtering, and decision-making. I wanted to build something that removes that burden entirely.

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What it does

ScoutOS is an autonomous opportunity agent that continuously ingests new opportunities, evaluates them based on user preferences, and takes action automatically. Instead of waiting for the user to search and decide, the system monitors incoming opportunities, determines relevance and urgency, and selects actions such as ignoring, saving, prioritizing, or drafting next steps.

At a high level, the system follows a continuous loop:

$$ \text{Input} \rightarrow \text{Context} \rightarrow \text{Evaluate} \rightarrow \text{Act} \rightarrow \text{Remember} $$

This shifts the user experience from actively searching and deciding to simply reviewing what the system has already done.

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How we built it

ScoutOS is built as a modular system that separates ingestion, reasoning, and memory. Nexla is used as the ingestion layer, allowing the system to receive external opportunity signals through webhook-based pipelines. Gemini is used to convert unstructured real-world content into structured data that can be evaluated. Senso is used for memory and context, storing user preferences and enabling consistent decision-making over time.

The system is designed to support both real-world data and a fallback mode to ensure reliability during demos.

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Challenges we ran into

One of the biggest challenges was working with real-world data. Opportunity data is often unstructured and inconsistent, so I had to build a reliable way to extract and normalize it before it could be used.

Another challenge was balancing realism with reliability. Live integrations can be fragile in a hackathon environment, so I designed a hybrid system that allows real data ingestion while maintaining a stable fallback when needed.

As a solo developer, managing scope was also a challenge. I had to focus on building a strong core system rather than adding too many features.

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Accomplishments that we're proud of

I’m most proud of building a system that behaves autonomously rather than reactively. ScoutOS does not wait for user input—it continuously monitors opportunities, evaluates them, and takes action on its own.

I’m also proud of integrating real-world data into the system and making the entire decision-making process visible and understandable. The app clearly shows how each opportunity is processed, evaluated, and acted upon, making it feel like a real working system rather than a simple demo.

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What we learned

I learned that building an autonomous system is very different from building a traditional app. The challenge is not just functionality, but making the system’s behavior clear and trustworthy.

I also learned how important it is to structure messy real-world data before using it in decision-making systems. Additionally, I learned how to design for clarity, ensuring that users can understand what the system is doing at every step.

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What's next for ScoutOS

Next, I would expand ScoutOS by connecting it to more real-world data sources and enabling real-world actions, such as sending notifications or initiating application workflows automatically.

I would also improve personalization over time by refining how the system learns from past decisions and user behavior.

Ultimately, the goal is to evolve ScoutOS into a system that not only identifies opportunities, but actively helps users capture them.

Built With

• assistant-ui
• google-gemini-api
• nexla-(data-ingestion)
• next.js
• react
• senso-(memory-&-context)
• tailwind-css
• typescript