This is a submission for the Hermes Agent Challenge: Build With Hermes Agent
What I Built
Lodestone is an AI accountability agent that helps ambitious students stay aligned with their long-term goals.
Most people don't fail because they lack opportunities. They fail because they slowly drift away from the future they originally chose.
I'm a Computer Science student originally from Cameroon, studying at an HBCU in the United States. I founded my school's first student-led hackathon and have been part of programs ranging from a State Department exchange to ISACA mentorships. The problem I kept running into wasn't finding opportunities. It was knowing which ones actually mattered for the future I was trying to build.
Lodestone was built to solve that problem, not just for me, but for every ambitious student drowning in options with no compass.
The application helps users:
- Discover a personal North Star from natural language input
- Evaluate decisions against long-term goals with alignment scoring
- Track alignment over time with a live trend
- Detect goal drift before it becomes a habit
- Generate personalized reflections and weekly action plans powered by Hermes
Demo
Goal Discovery
Users don't need to write a perfect life mission statement. They just talk.
Hermes analyzes the input, identifies recurring themes, and generates a structured North Star with a stated motivation and long-term horizon.
The user reviews and locks it in. Now Lodestone has a committed direction to measure everything else against.
Decision Evaluation
Users log decisions as they happen in real life, no structured input is required here.
Lodestone scores each decision against the North Star and explains exactly why it helps or hurts, not a generic percentage but a reasoned breakdown of tradeoffs.
Drift Detection
Lodestone doesn't just evaluate single decisions. It detects patterns.
When alignment drops meaningfully across recent decisions, a drift warning surfaces automatically -- naming the specific decisions that contributed and how far alignment has fallen. This is the feature that makes Lodestone feel like a compass rather than a calculator.
Reflection Generation
This is the centerpiece :)
The user clicks Generate Reflection. Hermes runs a visible three-phase sequence:
- Reading memory
- Reasoning across decisions
- Building a plan
The output references the user's actual decisions by name, identifies behavioral patterns over time, and ends with a concrete weekly recommendation. It is not generic advice. It is personalized accountability.
Code
My Tech Stack
Hermes Agent (running locally via WSL/Ubuntu)
Gemini (connected to Hermes as the underlying model)
React + TypeScript
Vite
Tailwind CSS
Node.js
How I Used Hermes Agent
Hermes Agent is the reasoning engine at the center of Lodestone. Every meaningful feature runs through it.
Memory
Hermes stores and retrieves the user's North Star, motivations, and planning horizon across sessions. This means decisions logged today are evaluated against goals set months ago -- not just the last message in a chat window.
Multi-Step Reasoning
When generating reflections, Hermes doesn't evaluate a single interaction. It reasons across the full decision history, identifies trends, detects drift, and compares recent behavior to the user's intended direction. That multi-step process is what separates Lodestone from a chatbot with a scoring widget.
Planning
Hermes produces actionable next steps grounded in the user's actual history and long-term objectives. It doesn't just describe what happened. It generates a plan for what should happen next.
Agentic Workflow
The reflection flow makes the agentic behavior visible and legible to the user, with three explicit phases: memory, reasoning, and plan. This was a deliberate design choice. The goal was for users to see Hermes working, not just producing output.
Challenges I Ran Into
The hardest problem was the cold start, a compass is useless without a direction. Most productivity tools fail because they demand a perfectly articulated goal upfront. The insight that unlocked Lodestone was letting Hermes extract a North Star from natural, fragmented input. That single design decision made the whole system feel accessible instead of intimidating.
The second challenge was making the agentic behavior legible. It's not enough for Hermes to reason well internally; users need to see it happening. Designing the three-phase reflection flow around memory, reasoning, and planning solved that.
What I Learned
The most valuable part of Hermes wasn't text generation. It was the combination of memory, reasoning, and planning working together across time.
Building Lodestone changed how I think about what AI products can be. The future isn't assistants that answer questions. It's systems that help people maintain direction -- that hold the thread between who you said you wanted to be and what you're actually doing today.
What's Next
- Weekly autonomous reflections triggered without user input
- Proactive drift alerts before the drop are significant
- Voice-based goal discovery
- Calendar and task integrations
- Long-term goal forecasting across 6 and 12-month horizons
Most AI tools help people do more.
Lodestone helps people remember why they're doing it in the first place.
For students drowning in opportunities, that's not a nice-to-have. That's the whole game.
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