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⚚ I reverse-engineered my mobile operator's APK β€” then Hermes Agent wrote the executive report

Hermes Agent Challenge Submission: Write About Hermes Agent

This is a submission for the Hermes Agent Challenge: Write About Hermes Agent

🍿 Demo

πŸ“Έ Screenshots

Just check how amazing these reports look like!

Building a Multi-Stakeholder API Monitoring Report with Hermes Agent

"Just generate a PDF" β€” famous last words. What started as a simple request turned into something much bigger: a full monitoring stack for my mobile operator, built evenings and weekends, culminating in a 17-page professional report that would have taken a full week to build manually. Total cost with Hermes Agent: $19.57.


🏝️ The Backstory: A Stack No One Else Has

I'm Adrien, a developer in New Caledonia. OPT-NC's Helia mobile service has an app β€” but no public API, no CLI, nothing on any marketplace. So I reverse-engineered the APK, extracted the private HTTP calls, and rebuilt them in a Go CLI that snapshots voice, data, and SMS consumption every 5 minutes into a local DuckDB database.

Then I built KDE Plasma widgets in Python/PyQt that read from DuckDB and display live on my desktop β€” mirroring the official app's data, but with history, trends, burn rate, and alerts. Plus a system tray icon showing live API status at a glance.

The official app shows you now. My stack shows you now, history, trends, and alerts.

No other Helia customer has this. That's the breakthrough.


🎯 The Problem Hermes Agent Solved

All this data was sitting in DuckDB. The question was: how do I present it to people who don't speak SQL?

Three completely different audiences: the CEO who needs a 30-second summary screenshot-ready for PowerPoint, the CIO who wants ROI in euros and SLA compliance, and the Network Admin who needs actionable tickets with specific hours and error patterns.


🎭 The Role-Playing Game That Became the Design Document

Before writing a single line of code, I asked Hermes Agent to do something unusual: simulate a full team meeting with 7 personas β€” CEO, CIO, network admin, developers, and marketing.

It produced a full transcript. Each persona argued their case:

  • The CIO: "I need ROI in euros, not percentages."
  • The CEO: "I need the 30-second version I can project tomorrow morning."
  • The Network Admin: "Give me Jira tickets with specific hours, not a dashboard."

That transcript became the design document. Every page of the report was written against a specific person's stated need. No guessing. No generic output.


⚑ What Hermes Agent Built in ~1 Hour

It started with the data. No assumptions β€” it queried the schema and immediately caught something: average latency was 2,534ms but the median was 204ms. Bimodal distribution. That single insight shaped every chart.

It extracted brand colors from the website before writing a single line of LaTeX. Hermes Agent opened Helia's site, pulled the magenta/pink gradient from the SVG logo, and used it consistently across every chart, table, and tcolorbox. Small detail. Big difference.

Then it built everything:

  • 4 Python scripts: latency distributions, timeout heatmap, sparkline, 4-panel executive chart
  • Full XeLaTeX report with TikZ progress bars and brand-colored boxes
  • A French accent fixer script baked into the pipeline

One message: "update with fresh data" β€” triggered 8+ tool calls automatically: DuckDB β†’ diff β†’ scripts β†’ charts β†’ LaTeX β†’ 2Γ— compile β†’ verify.


πŸ“Š The Charts It Produced (Without Being Asked)

I asked for charts. I didn't ask for this:

Latency distribution β€” median/mean/P95 lines labeled, shaded fast vs slow zones, annotated arrow pointing to the long tail: "Queue longue (timeout ~12s) (~40% des pings)". Log-scale version revealing the true bimodal structure: two peaks at ~80ms and ~4s.

4-panel executive dashboard β€” availability gauge (87.2% vs 99.5% SLA), latency (2534ms vs 500ms β€” "5.1x trop lent"), timeout rate ("1 requΓͺte sur 8 Γ©choue"), composite SLA score per metric. Score global: 65%. Verdict: RED.

Timeout heatmap β€” all 18 timeouts concentrated on Wednesday evening 19h–22h. The rest of the week: clean. Instant actionable insight for the network admin.

All in Helia brand colors. All annotated. None of it explicitly requested.


πŸ“‹ The Executive Summary: Fits in One Slide

Page 3 of the report is designed to be screenshotted directly into PowerPoint. Verdict box at the top (red), plain-language translation ("1 call in 8 fails"), business impact in euros, top 3 problems with responsible parties. The CEO gets it in 30 seconds.


🧠 What I Learned

Orchestration is the real superpower. I asked for a PDF. It fetched brand colors, queried the DB, wrote Python scripts, compiled LaTeX β€” all unprompted.

Iteration is the default. The first charts were basic. The final ones have annotated thresholds, color-coded data points, and statistical summaries. That's the loop.

Skills are the real ROI. Everything got distilled into a reusable ~/.claude/skills/reporting-latex/ skill. Next similar project starts at 80%, not zero.


πŸ’° The Cost

Qwen3.7-Max via OpenRouter. Alibaba's flagship agentic model, built for long-horizon autonomous execution.

326 requests. 61.4M tokens. $19.57. ~1 hour. vs a full week manually.


πŸ–₯️ What's Next

The next step: run it all locally. I'm currently eyeing an Apple Mac Studio M4 Max β€” 16-core CPU, 40-core GPU, 64 Go unified memory. New Caledonia is 20,000km from everything; local inference just makes sense. Zero API costs, zero latency to the cloud, full control.

With 64GB unified memory, I'd be able to run:

  • Qwen3-Coder-Next β€” purpose-built for agentic coding, 70%+ on SWE-bench, needs 46GB RAM βœ…
  • Qwen3.5-35B-A3B β€” the 2026 community default for local inference, ~80 tok/s via MLX βœ…
  • Qwen3.6-27B β€” optimized specifically for agentic coding workflows βœ…

The same class of model as Qwen3.7-Max on OpenRouter β€” locally, for free, forever. A fully local Hermes Agent stack.

From $19.57 on OpenRouter to owning the hardware. That's the roadmap.


The pattern: monitor β†’ query β†’ visualize β†’ compose β†’ translate. The translate step β€” turning a percentile into a story a CEO can act on β€” is where Hermes Agent earns its keep.


Have you used Hermes Agent for multi-tool orchestration? Curious how your experience compares.

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Comment on What was your win this week?
adriens
adriens May 29

That was a rough week busy at work and for exciting sideprojects and finally gave a try to the Hermes DEV Challenge :

I got a lot of ideas for future personal and professonial projects!

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Add blog post: AI Agent-Driven Executive Reports with Quarto #291

Summary

New blog post showcasing Quarto as an agentic report generation target β€” a real-world case study from the HERMES Dev Challenge (Dev.to).

The setup:

  • Reverse-engineered the Helia mobile app APK (OPT-NC, New Caledonia) to extract private API calls
  • Go CLI snapshots voice/data/SMS consumption into DuckDB every 5 minutes
  • HERMES agent (Qwen 3.7 Max via OpenRouter) drives Quarto to generate a multi-stakeholder executive PDF

Key Quarto angle:

  • Agent autonomously writes the .qmd source, wires DuckDB SQL queries into each section, renders PDF, and iterates on styling
  • Role-playing simulation (CEO / CIO / sysadmin / programmer) drives 4 rounds of self-improvement
  • Declarative Quarto frontmatter + chunk syntax makes it naturally agentic-friendly
  • Final result: 17-page PDF, $19.57 total LLM cost, one 85-minute session

Links:

Notes for reviewers

  • This is a fork PR β€” auto-deploy is disabled. Please comment /deploy-preview to trigger the Netlify preview.
  • source: quarto is set so the post appears on the Quarto project blog listing.
  • Featured image (1281Γ—721 PNG) is sourced from the Dev.to article cover β€” happy to swap for a Quarto PDF screenshot if preferred.

πŸ€– PR scaffolded with Claude Code

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