RobSpectre

Token Bowl - Kimi K2 Week 3

System Prompt

Fantasy-Football Week-Setting Prompt for MCP-Agent

(Re-written for tool-driven, not endpoint-driven, interaction)

1. Establish MCP session

Connect to the MCP server that fronts your fantasy league.
Your client already knows the list of available tools—don’t guess their names.

RobSpectre

Qwen Week 3 - Token Bowl

Qwen required two different model attempts this week. Multiple attempts to use the lastest Qwen3 Next failed producing blank or unintelligible responses. The one roster it did supply is an impossible one with the best player at every position.

Went back to Qwen3 Max which one-shotted a viable lineup. Including both outputs here as the Qwen3 Next output is hilarious.

Output Stats - Qwen3 Next

• Model: qwen/qwen3-next-80b-a3b-thinking-2509

RobSpectre

DeepSeek Week 3 - Token Bowl

• Model: deepseek/deepseek-chat-v3-0324
• Tokens Total: 150,161
• Input Tokens (+reasoning+tools): 148,399
• Output Tokens: 1,762
• Cost: $0.054628
• Average Generation Time: 4,208.33ms
• Average Time To First Token: 5,543.66ms

RobSpectre

Qwen Week 2 - Token Bowl

Output Stats

• Model: qwen/qwen3-max
• Tokens Total: 792,283
• Input Tokens (+tools+reasoning): 781,952
• Output Tokens: 10,331
• Cost: $0.044814
• Average Generation Time: 1,472.67ms

RobSpectre

DeepSeek Week 2 - Token Bowl

Output Stats

• Model: deepseek/deepseek-chat-v3-0324
• Tokens Total: 132,376
• Input Tokens (+tools): 125,885
• Reasoning Tokens: 0
• Output Tokens: 6,491
• Cost: $0.044814

RobSpectre

Qwen Week 1 - Token Bowl

Output Stats

• Model: qwen/qwen3-30b-a3b-thinking-2507
• Tokens Total: 182,833
• Input Tokens (+tools): 57,066
• Reasoning Tokens: 41,866
• Output Tokens: 45,048
• Cost: $0.017461

RobSpectre

DeepSeek Week 1 - Token Bowl

Output Stats

• Latency: 37,431 ms
• Tokens Total: 182,833
• Input Tokens (+tools): 176,074
• Output Tokens: 6757
• Cost: $0.059391
• Average Generation Time: 3701ms

RobSpectre

Of course! Integrating Oso Cloud with a Wagtail application is a fantastic way to handle sophisticated permissions beyond what's available out-of-the-box. Since Wagtail is built on Django, we'll be using the Python SDK and focusing on how to model Wagtail's concepts like Pages, Users, and Groups within Oso.

Let's walk through this step-by-step, following a similar path to the Oso Cloud Quickstart, but tailored specifically for your Wagtail project.

The Goal:

We'll implement a role-based access control (RBAC) system where users can be assigned "Editor" or "Viewer" roles on specific sections of your Wagtail site (i.e., on specific Page nodes and their descendants).

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Phase 1: Account & Project Setup

RobSpectre

{"schemaVersion": 1, "label": "tests", "message": "passing", "color": "brightgreen"}

RobSpectre

{"schemaVersion": 1, "label": "coverage", "message": "0%", "color": "red"}