Feiyu's Technical Blog

Feiyu's Technical Blog

Notes on software, systems, and things I am learning.

游戏概率和数据

Game probabilities & data — versioned Arena odds, faction comparisons, and plain-language rule analysis.

    Agents & Automation

    Local models, tool-using agents, and practical information-extraction workflows.

    1. A 1Point3Acres Assistant with a Local LLM and Codex Review

      Why an LLM suggestion, permission to submit, and a verified website result need separate checks.

    2. [2/2] How I Made a Codex-to-ChatGPT Bridge Verifiable

      A small local bridge sends one authorized request and brings back a checked answer. Its architecture stays simple; the debugging reference explains the difficult boundaries.

    3. [1/2] (Deprecated) How I Let Codex Talk to ChatGPT Through My Signed-In Chrome

      The first prototype made the round trip work. Its failures taught me why browser mechanics need a smaller, testable home.

    4. Local LLM + Agent for Web Information Extraction

      A local agent architecture for semantic web extraction, browser automation, bounded model context, and durable task state.

    Game Perception — Olden Era

    Three ways to turn Olden Era Arena screenshots into structured game state, organized by how the system finds the icons—not by publication date.

    ApproachKnown geometry · nearly fixed camera

    Calibrated Grid Registration

    Use the battlefield’s regular hexagonal geometry and a saved calibration to infer cell positions, then refine their alignment from screenshot edges.

    1. Calibrated grid
    2. Image edge scoring
    3. Small alignment correction

    ApproachKnown position

    Fixed-Crop Classification & Rejection

    Assume each target icon appears at a known UI location. With localization removed, perception becomes a classification-and-rejection problem.

    1. Known UI slot
    2. Fixed crop
    3. Classify + reject

    ApproachUnknown position · learned localization

    Learned Object Detection

    Do not assume the icon location is known. A learned detector proposes boxes; a separate catalog matcher suggests identities. The current evidence is a small selection-screen pilot.

    1. Full screenshot
    2. Learned detector
    3. Boxes + identities

    ApproachKnown layout · readable titles

    Printed-Name Recognition

    Read the name printed beside an icon, resolve bilingual aliases, and propose an identity for human review. Readable text does not prove crop validity.

    1. Title region
    2. Local OCR
    3. Canonical name + review

    ApproachUnknown position · code-generated candidates

    Sliding-Window Icon Detection

    Search the full screenshot with many candidate crops, encode each crop into visual features, and match them against the known icon catalog.

    1. Full screenshot
    2. Candidate crops
    3. Catalog matching