Testing GLM‑5 Turbo: From AutoClaw Integration to a Browser‑Based War3 Clone

AutoClaw Quick Setup

To integrate AutoClaw (also known as 澳龙) with Feishu, create a robot in the Feishu Open Platform and paste the following JSON permission configuration into the Permission Management field:

{
  "scopes": {
    "tenant": [
      "aily:file:read",
      "aily:file:write",
      "application:application.app_message_stats.overview:readonly",
      "application:bot.menu:write",
      "contact:contact.base:readonly",
      "contact:user.employee_id:readonly",
      "corehr:file:download",
      "event:ip_list",
      "im:chat.access_event.bot_p2p_chat:read",
      "im:chat.members:bot_access",
      "im:message",
      "im:message.group_at_msg:readonly",
      "im:message.p2p_msg:readonly",
      "im:message:readonly",
      "im:message:send_as_bot",
      "im:resource"
    ],
    "user": [
      "aily:file:read",
      "aily:file:write",
      "im:chat.access_event.bot_p2p_chat:read",
      "im:message"
    ]
  }
}

After saving the configuration, click Create Version to publish the robot. Supplying the App ID and App Secret triggers automatic configuration, which resolves long‑connection issues (e.g., port 5000 conflicts) within about one minute.

GLM‑5 Turbo Practical Test

This test compares the new Zhipu AI GLM‑5 Turbo model with an anonymous baseline model on the task of generating an OpenClaw real‑time monitoring dashboard . Both models were asked to produce a Flask‑based web application that fetches GitHub repositories, stores them in SQLite, and visualises usage statistics.

Comparison Overview

Search Keyword Strategy : GLM‑5 Turbo used 25 related keywords (e.g., "openclaw skills", "openclaw 中文", "openclaw plugin") to cover ecosystem edge projects, while the baseline used a single keyword "openclaw".

Data Breadth : GLM‑5 Turbo fetched 627 repositories; the baseline fetched only 100.

Database Design : GLM‑5 Turbo created three tables – repository, snapshot, and a metadata table that records the last fetch time. The baseline created only repository and snapshot tables.

Feature Set : GLM‑5 Turbo’s UI includes five statistic cards, a star‑trend line chart, a top‑10 bar chart, a language‑distribution pie chart, a searchable paginated table, a manual refresh button, and an auto‑refresh interval of 30 minutes. The baseline provided a single overall card, a language bar chart, a top‑10 ranking, and a static top‑20 table.

Task Decomposition and Planning

File Generation Order : GLM‑5 Turbo generated all files ( db.py, fetcher.py, app.py, style.css, index.html) in parallel, avoiding unnecessary waiting. The baseline generated files sequentially ( database.py → fetch_data.py → app.py → index.html).

Modularity : GLM‑5 Turbo produced a highly modular codebase – a dedicated data layer ( db.py), a fetcher layer ( fetcher.py), a controller layer ( app.py), and a view layer (HTML + independent CSS). The baseline kept a decent modular structure but embedded fetch logic directly in fetch_data.py.

Frontend Separation : GLM‑5 Turbo used an external style.css file; the baseline inlined CSS within the HTML.

Code Size : GLM‑5 Turbo produced 895 lines across five files; the baseline produced 674 lines across four files (plus an empty static folder).

Code Quality and Completeness

Exception Handling : GLM‑5 Turbo added API retry logic, rate‑limit checks, and file I/O error handling. The baseline handled basic errors but lacked rate‑limit mitigation.

Pagination : GLM‑5 Turbo implemented full pagination (100 items per page, looping until no results). The baseline only logged the first page and did not demonstrate complete pagination.

Deduplication : GLM‑5 Turbo deduplicated records based on repository ID and name; the baseline did not explicitly show deduplication.

Frontend Interaction : GLM‑5 Turbo’s table supports live search, pagination, manual refresh, and auto‑refresh. The baseline displayed only a static top‑20 view without search.

Environment Adaptation and Problem Solving

Dependency Installation : GLM‑5 Turbo automatically executed pip3 install flask and verified success. The baseline required manual attempts ( pip install flask --break-system-packages, apt install python3-flask) which failed.

Port Conflict Resolution : When port 5000 was occupied, GLM‑5 Turbo detected the failure, rewrote the source to use port 5001, and restarted successfully. The baseline did not reach the service start stage.

Service Verification : GLM‑5 Turbo performed a curl request after launch to confirm the API returned expected data. The baseline lacked an automated verification step.

Agent Philosophy Comparison

GLM‑5 Turbo behaves as an executor agent: it detects obstacles, analyses causes, repairs code, and retries until success. The anonymous model acts as a collaborator agent: it reports errors and hands the problem back to the user without further remediation.

War3‑Style Browser RTS

Using GLM‑5 Turbo, a complete real‑time strategy game inspired by Warcraft III was generated as a single HTML file with zero external dependencies.

Four playable races (Human, Orc, Night Elf, Undead), each with a hero and three unit tiers.

Resources: gold mines, wood, and population limits via farms.

Dual view modes: 2D top‑down and 3D isometric.

Eight‑direction movement with BFS pathfinding.

Hero abilities mapped to Q/W/E/R keys (e.g., Blizzard, Mirror Image, Entangling Roots).

AI opponents with Easy, Medium, and Hard difficulty levels.

Technical highlights:

Zero Dependencies : All logic resides in one HTML file; no external libraries or server are required.

Canvas Rendering : The entire game is drawn using HTML5 Canvas.

BFS Pathfinding : A breadth‑first search algorithm provides eight‑direction navigation.

Game Loop : A stable 60 fps update‑render loop ensures smooth gameplay.

House‑Price Query CLI

A natural‑language‑driven command‑line interface was built to test GLM‑5 Turbo’s ability to generate code, understand intent, and maintain multi‑turn dialogue. The model produced two core modules: nlu.py – parses user utterances to extract city, district, bedroom count, price range, transaction type, and intent. query_respond.py – queries (simulated) housing data and formats a friendly textual response.

Evaluation criteria and results:

Code Generation : 2 test items, both passed; total score 4.5/5. The generated code (≈549 lines) was runnable, modular, and included robust error handling.

Natural Language Understanding : 4 test items, all passed; score 5/5. Entity extraction was 100 % accurate, with proper handling of ambiguities and strict JSON‑only output.

Text Generation : 7 test items, all passed; score 5/5. Responses were natural, comprehensive, and offered proactive suggestions.

Multi‑Turn Dialogue : 3 test items, all passed; score 5/5. Context was correctly retained across turns, and budget updates or city switches were handled smoothly.

Complex Scenarios : 3 test items, all passed; score 4.5/5. The model dealt with implicit intents, mixed Chinese/English input, and noisy queries, storing unmapped conditions in an extra field.

Core Conclusions

Across all dimensions—code generation, natural‑language understanding, text generation, multi‑turn dialogue, and handling of complex scenarios— GLM‑5 Turbo consistently outperformed the baseline model. Its executor‑style behavior enables autonomous problem solving: it detects errors (e.g., port conflicts), modifies code, retries, and verifies success without user intervention. This makes GLM‑5 Turbo a reliable autonomous agent for software development, data‑driven dashboards, and creative coding projects.