06.14.2025: Search That Actually Works

A pivotal day in the ongoing development of AutonomousMatt, the embedded AI agent designed to surface, synthesize, and speak from my creative archive. While the vision remains rooted in simplicity—ask me anything, get a deeply personalized answer from 30 years of digital work—the day was anything but simple. Here’s a full log of what happened, what broke, what got better, and what’s coming next.

✅ Major Wins

1. Keyword Mapping Got a Major Overhaul

I expanded the keywordMap with broader semantic coverage. Previously, searches were too brittle—typing "destiny" worked, but “loot grind” or “game addiction” didn’t. Now, thanks to a manual expansion of conceptual synonyms and associations, the agent routes much more intelligently.

Example:

js

CopyEdit

"queer": ["/film_blue.txt", "/film_rope.txt"], "illness": ["/film_blue.txt"], "habit": ["/talk_destiny-habituation_tactics.txt"], "rewards": ["/talk_destiny-habituation_tactics.txt"],

The challenge was balancing specificity (you want “Jarman” to go to Blue) with discoverability (someone might type “AIDS crisis” or “experimental voiceover” instead). Yesterday was all about laying that foundation, and it’s now much more robust.

2. Recommitment to Content First, Debug Second

I hit a wall debugging the "Matt is thinking..." indicator. Despite several attempts to fix it (using both JS timing tweaks and UI injection strategies), it refused to appear reliably before the AI response loaded. After several hours, I made a conscious decision: stop chasing the animation. The content engine—the archive, the semantic map, the GPT interface—is the beating heart. Everything else is chrome.

🧠 Takeaway: It's better to focus on surfacing high-quality answers from the archive than perfecting micro-UX touches right now.

3. Routing Problems Identified and Solved

The /talk_destiny-habituation_tactics.txt file was not being triggered. I realized the problem wasn’t the file—it was the keywords. Words like “grind,” “compulsion,” or “loot loop” weren’t mapped. After diagnosing and fixing this, the Destiny content now reliably appears.

🔍 Lesson: Even a single missed keyword can make it feel like a file is broken. It’s not the file—it’s the map.

4. Deduplication and Structural Cleanup

Some keywords were repeated, some pointed to multiple conflicting files, and some archive tags were redundant. I began streamlining the keywordMap into clusters that could be maintained more scalably:

• Games & Behavior

• Illness & Identity

• Migration & Memory

• Teenage Alienation & Film

• Product Work & Strategy

These clusters will eventually drive tag-based browsing and auto-suggestions in future versions.

🛠️ What Broke

1. The “Matt is Thinking” Loader Still Doesn’t Work

Despite multiple checks on DOM insertion, CSS classes, and async behavior, the thinking indicator remains stubborn. Every possible place it should show up, it just… doesn’t. I confirmed the markup is there, but it’s not rendering visibly before the async call finishes. I may need to throttle the response fetch artificially or rethink how the loader is handled entirely.

2. Semantic Collision in the Keyword Map

Some keywords (like “nostalgia” or “courage”) pointed to multiple files. This created confusion: asking about nostalgia could return either Virgin Suicides or How To Never Look Back, depending on ordering. I’ll need to either:

• Rank relevance based on query context

• Or allow multi-file response rendering

Yesterday, I opted to keep single-file resolution for simplicity, but it’s a known limitation.

3. No Surface Response from Some Files

Some correctly mapped files still didn’t return visible results due to JavaScript logic failing to match a query phrase to a root keyword. This is partly a UX issue (no feedback = user thinks it’s broken) and partly a map precision issue. A fallback strategy for unmapped queries needs to be added soon (e.g., “No match found. Try one of these topics…”).

🧭 What’s Next

Coming Priorities for AutonomousMatt:

1. Intelligent Synonym Expansion

   • Leverage conceptual clusters to auto-expand mappings (e.g., “migration” includes “emigration,” “relocation,” “identity shift”).

2. Inline Source Highlighting

   • Highlight phrases in the response that link directly to specific lines in the .txt file.

3. Multi-File Matching

   • When multiple files match a concept (like “queer”), show all hits side-by-side or paginate results.

4. Memory + Conversation Context

   • Let the model remember previous questions and refine the archive selection as a user drills down.

5. Split Long Files into Chunks

   • Improve context relevance by breaking large text files into thematic chunks. This improves both GPT performance and user discoverability.

🧠 Reflection

AutonomousMatt isn’t just a toy or a portfolio search tool—it’s a personal intelligence layer. Building it is forcing me to confront how my writing connects, how themes echo across decades, and what parts of my own digital memory are most durable.

Yesterday was frustrating. And thrilling. And deeply clarifying. I’ve got a long way to go—but the voice is starting to sound like mine.

—

Built. Broken. Fixed. Rebuilt. That’s the loop.
Onward.

To explore or contribute, visit autonomousmatt.com — or ask me anything.

---