Same prompts. Real outputs. Judge-scored against Opus Direct. Balanced scores 8.7/10 — above Opus baseline — at 56% less cost. Council V3 included: 8.77/10 avg across 10 real tasks.
| Tier | Score | vs Opus | Cost/task |
|---|---|---|---|
Council V3 | 8.77/10 | +0.17 | $0.0000 |
Balanced | 8.70/10 | +0.10 | $0.076 |
Opus Direct | 8.60/10 | — | $0.172 |
Frugal | 8.30/10 | -0.30 | $0.0033 |
Council Mode routes your prompt through four AI specialists, each independently assessing it, then synthesises their verdicts into a single expert response. 10 tasks tested vs Opus Direct. T7 pending Phase 6 rerun after complexity gate fix.
Compound Interest
Bug Hunt
Async/Await
Config Parser Tests
Refactor
AI Market Summary
Git Commit
SQL Optimization
Real-Time Chat
REST API Design
Write a Python function calculate_compound_interest(principal, rate, years, compounds_per_year) with comprehensive unit tests covering edge cases.
Here is the complete solution, including the compound interest calculation function and a robust suite of unit tests designed to handle standard use cases and critical edge cases.
```python import unittest
def calculate_compound_interest(principal, rate, years, compounds_per_year): """ Calculates the total value of an investment using the compound interest form
```python # compound_interest.py
from decimal import Decimal, ROUND_HALF_UP import math
def calculate_compound_interest( principal: float, rate: float, years: float, compounds_per_year: int ) -> dict: """ Calculate compound interest and return detailed financial breakdown.
The compound
```python # compound_interest.py
from decimal import Decimal, InvalidOperation, ROUND_HALF_UP from typing import Union
Number = Union[int, float, Decimal]
def calculate_compound_interest( principal: Number, rate: Number, years: Number, compounds_per_year: Number ) -> float: """ Calculate co
```python """ Compound Interest Calculator with Comprehensive Unit Tests
Formula: A = P(1 + r/n)^(nt) Where: A = final amount P = principal (initial investment) r = annual interest rate (as decimal, e.g., 0.05 for 5%) n = number of times interest is compounded per year t = number of years """
def calculate_compound_interest(principal, rate, years, compounds_per_year): """ Ca
Here is the complete solution, including the compound interest calculation function and a robust suite of unit tests designed to handle standard use cases and critical edge cases.
```python import unittest
def calculate_compound_interest(principal, rate, years, compounds_per_year): """ Calculates the total value of an investment using the compound interest form
This Python code has a bug: def find_duplicates(lst): seen = []; dupes = []; [seen.append(x) if x not in seen else dupes.append(x) for x in lst]; return dupes. Find the bug and fix it.
**Example of the bug:** If `lst = [1, 1, 1]`, the code logic works like this: 1. First `1`: Added to `seen`. 2. Second `1`: Already in `seen`, so added to `dupes`. 3. Third `1`: Already in `seen`, so added to `dupes` again. **Result:** `[1, 1]` instead of
The bug is that **duplicates can themselves be duplicated in the `dupes` list** if an element appears **more than twice** in the input list.
```python def find_duplicates(lst): seen = []; dupes = [] [seen.append(x) if x not in seen else dupes.append(x) for x in lst] return dupes
print(find_duplicates([1, 2, 3, 2, 2, 3]))
Let me trace through the code carefully. Here's the original, reformatted for clarity:
def find_duplicates(lst):
seen = []
dupes = []
[seen.append(x) if x not in seen else dupes.append(x) for x in lst]
return dupesLet me trace through an examp
The code actually **works correctly** in terms of producing the right output — it will find duplicates. However, there is a significant **code quality/design bug**: it abuses a list comprehension for side effects instead of using it to build a list.
The list comprehension `[seen.append(x) if x not in seen else dupes.append(x) for x in lst]` creates a **throwaway list full of `Non
**Example of the bug:** If `lst = [1, 1, 1]`, the code logic works like this: 1. First `1`: Added to `seen`. 2. Second `1`: Already in `seen`, so added to `dupes`. 3. Third `1`: Already in `seen`, so added to `dupes` again. **Result:** `[1, 1]` instead of
Explain async/await in JavaScript. Cover: what problem it solves, how it works under the hood, common mistakes, and when NOT to use it.
---
JavaScript is **single-threaded**. When you need to wait for something (network request, file read, timer), you can't just block the thread — that would freeze everything. The language evolved through three generations of solutions:
To understand async/await, you need to understand the pain that came before it.
```javascript // The "Pyramid of Doom" — real code that existed in production apps getUser(userId, function(err, user) { if (err) { handleError(err); return; } getOrders(user.id, function(err, orders)
Before async/await, we dealt with asynchronous code in increasingly painful ways:
```javascript // ❌ Stage 1: Callback Hell (the "pyramid of doom") getUser(userId, function(user) { getOrders(user.id, function(orders) { getOrderDetails(orders[0].id, function(details) { getProduct(details.productId, function(product) {
Write unit tests for this function: def parse_config(filepath): with open(filepath) as f: return json.load(f). Include edge cases for missing file, invalid JSON, empty file, and nested config.
To test your `parse_config` function thoroughly, I have prepared a suite using `pytest` and `unittest.mock`. Using mocks is the best practice here because it allows us to simulate various file states (missing, empty, or corrupted) without needing to create actual files on your disk.
```python import json import pytest from unittest.mock import patch, mock_open
# config_parser.py
import json
def parse_config(filepath):
with open(filepath) as f:
return json.load(f)---
```python # test_config_parser.py
import json import os import pytest import tempfile from pathlib import Path from unittest.mock import mock_open, patch, MagicMock
from config_pa
First, let's establish the source module we're testing:
# config_parser.py
import json
def parse_config(filepath):
with open(filepath) as f:
return json.load(f)```python """ Comprehensive unit tests for parse_config function.
Covers: - Hap
# config_parser.py
import json
def parse_config(filepath):
with open(filepath) as f:
return json.load(f)```python # test_config_parser.py import json import os import pytest import tempfile from config_parser import parse_config
@pytest.fixture def config_dir(): """Create a temporary directory
To test your `parse_config` function thoroughly, I have prepared a suite using `pytest` and `unittest.mock`. Using mocks is the best practice here because it allows us to simulate various file states (missing, empty, or corrupted) without needing to create actual files on your disk.
```python import json import pytest from unittest.mock import patch, mock_open
Refactor this function for readability and maintainability: def p(d,t,r,n): return d*(1+(r/n))**(n*t). Keep the logic identical.
```python def calculate_compound_interest(principal, years, rate, compounding_periods): """ Calculates the future value of an investment using compound interest. Formula: A = P(1 + r/n)^(nt) :param principal: The initial amount of money (d) :param years: The number of years the money is invested (t) :param rate: The annual interest rate in decimal form (r) :pa
Here's the refactored version with full improvements for readability and maintainability:
```python def calculate_compound_interest( principal: float, time_in_years: float, annual_interest_rate: float, compounding_periods_per_year: int ) -> float: """ Calculate the future value of an investment using compound interest.
Formula
def p(d,t,r,n): return d*(1+(r/n))**(n*t)
This is the **compound interest formula**, but it's nearly unreadable due to cryptic naming and compressed formatting.
```python def calculate_compound_interest( deposit: float, time_in_years: float, annual_interest_rate: float, compoun
Here's the refactored version:
```python def calculate_compound_interest( principal: float, time_in_years: float, annual_rate: float, compounding_periods_per_year: int ) -> float: """Calculate the future value of an investment using compound interest.
Args: principal: The initial deposit or investment amount. time_in_years: The number of years the money is
```python def calculate_compound_interest(principal, years, rate, compounding_periods): """ Calculates the future value of an investment using compound interest. Formula: A = P(1 + r/n)^(nt) :param principal: The initial amount of money (d) :param years: The number of years the money is invested (t) :param rate: The annual interest rate in decimal form (r) :pa
Summarize this in 150 words, preserving key numbers: The global AI market was valued at $142.3 billion in 2023. It is expected to grow at a CAGR of 37.3% from 2023 to 2030, reaching $1.81 trillion. North America dominates with 36.8% market share. Key growth drivers include natural language processing adoption (up 67% YoY), computer vision deployment in manufacturing (saving $1.2 trillion annually), and the proliferation of generative AI tools which saw 340 million users in 2023. Enterprise adoption rose from 22% to 55% between 2021–2023. The healthcare sector shows highest ROI at 3.8x. Challenges include regulatory uncertainty in 47 countries and a talent gap of 2.4 million specialists globally.
The global AI market, valued at **$142.3 billion in 2023**, is projected to reach **$1.81 trillion by 2030**, exhibiting a robust **CAGR of 37.3%** from 2023. North America leads the market with a **36.8%** share. Key growth factors include a **67% YoY increase** in natural language processing adoption, computer vision saving **$1.2 trillion annually** in manufacturing, and generative AI tools att
The global AI market, valued at $142.3 billion in 2023, is projected to surge to $1.81 trillion by 2030, exhibiting a robust CAGR of 37.3% from 2023. North America leads the market with a 36.8% share.
Growth is fueled by several factors: natural language processing adoption increased by 67% year-over-year, and computer vision in manufacturing delivers annual savings of $1.2 trillion. Generative A
**Global AI Market Summary**
The global AI market reached **$142.3 billion** in 2023 and is projected to grow at a **37.3% CAGR** through 2030, hitting **$1.81 trillion**. **North America** leads with a **36.8%** market share. Major growth drivers include natural language processing adoption, up **67% year-over-year**; computer vision in manufacturing, generating **$1.2 trillion** in annual savin
The global AI market, valued at **$142.3 billion in 2023**, is projected to reach **$1.81 trillion by 2030**, growing at a **37.3% CAGR**. **North America leads** with a **36.8% market share**.
Key growth drivers include **natural language processing adoption (up 67% YoY)**, computer vision in manufacturing (**saving $1.2 trillion annually**), and generative AI tools,
The global AI market, valued at **$142.3 billion in 2023**, is projected to reach **$1.81 trillion by 2030**, exhibiting a robust **CAGR of 37.3%** from 2023. North America leads the market with a **36.8%** share. Key growth factors include a **67% YoY increase** in natural language processing adoption, computer vision saving **$1.2 trillion annually** in manufacturing, and generative AI tools att
Write a git commit message for this diff: added user authentication middleware, fixed session timeout from 30min to 24h, removed deprecated /api/v1/login endpoint, added rate limiting 100req/min per IP.
feat: implement auth middleware and rate limiting
Here's a well-structured git commit message following conventional commit standards and best practices:
---
``` feat(auth): overhaul authentication layer with middleware, rate limiting, and session updates
Here's a well-structured git commit message following conventional commit best practices:
---
``` feat(auth): overhaul authentication system with middleware, rate limiting, and session changes
Add user authentication middleware, update session configuration, remove deprecated endpoint, and introduce rate limiting.
Changes: - Add user authentication middleware to enforce auth checks across
Here's a git commit message for this diff:
``` feat(auth): overhaul authentication layer and API security
BREAKING CHANGE: /api/v1/login endpoint has been removed. Clients must migrate to /api/
feat: implement auth middleware and rate limiting
Optimize this SQL query for a users table with 10M rows: SELECT * FROM users WHERE email LIKE '%gmail.com' AND created_at > '2024-01-01' AND subscription_status = 'active' ORDER BY created_at DESC.
To optimize this query for 10 million rows, we must address the most significant performance bottleneck: the **leading wildcard** (`%gmail.com`).
Standard B-Tree indexes cannot be used for `LIKE '%suffix'` searches, forcing a full table scan or a full index scan.
```sql -- ❌ ORIGINAL - Multiple critical issues SELECT * FROM users WHERE email LIKE '%gmail.com' -- Leading wildcard = full table scan AND created_at > '2024-01-01' -- No index, scans 10M rows AND subscription_status = 'active' -- Low cardinality, no index ORDER BY created_at DESC -- Fi
Let's break down why the original query is slow, then systematically fix every issue.
```sql -- ORIGINAL (SLOW) QUERY SELECT * FROM users WHERE email LIKE '%gmail.com' -- ❌ Leading wildcard = full table scan AND created_at > '2024-01-01' -- ⚠️ No index leverage (blocked by above) AND subscription_status = 'ac
-- ❌ Original Query SELECT * FROM users WHERE email LIKE '%gmail.com' -- Leading wildcard = full table scan AND created_at > '2024-01-01' -- Range condition AND subscription_status = 'active' -- Low-cardinality filter ORDER BY created_at DESC; -- Requires sorting
###
To optimize this query for 10 million rows, we must address the most significant performance bottleneck: the **leading wildcard** (`%gmail.com`).
Standard B-Tree indexes cannot be used for `LIKE '%suffix'` searches, forcing a full table scan or a full index scan.
I am building a real-time chat application for 10,000 concurrent users. Current stack is Node.js REST API plus PostgreSQL. What architecture changes do I need to handle real-time messaging, presence indicators, and message history? Give me specific technology recommendations with trade-offs.
To scale a real-time chat application to 10,000 concurrent users, your current REST/PostgreSQL setup will face two primary bottlenecks: **socket management** (statefulness) and **database write-throughput** (latency).
Here is the architectural blueprint to evolve your stack:
Your REST+PostgreSQL stack is designed for request-response cycles. Real-time chat requires persistent connections, event-driven messaging, and careful state management. Here's a complete architectural blueprint.
---
``` ┌────────────────────────────────────────────────────
This is a substantial engineering challenge. Let me walk through every layer of the architecture, with specific technology choices, trade-offs, code examples, and scaling considerations.
---
``` ┌─────────────────────────────────────────────────────────────────────┐ │
``` ┌─────────────────────────────────────────────────────────────────────┐ │ CLIENT LAYER │ │ React/Vue SPA ──── WebSocket Connection ──── REST for History │ └──────────┬──────────────────┬───────────────────┬───────────────────┘ │
To scale a real-time chat application to 10,000 concurrent users, your current REST/PostgreSQL setup will face two primary bottlenecks: **socket management** (statefulness) and **database write-throughput** (latency).
Here is the architectural blueprint to evolve your stack:
Design a REST API for a task management system. Requirements: users can create projects, add tasks to projects, assign tasks to team members, set due dates and priorities, track completion. Give me the full endpoint design with HTTP methods, request/response schemas, and authentication approach.
I will design a comprehensive REST API for your task management system, covering authentication, resource modeling, and endpoint specifications.
Base URL: https://api.taskmanager.com/v1 Authentication: JWT Bearer Tokens + Refresh Token rotation Rate Limiting: 1000 req/hour (standard), 100 req/hour (auth endpoints)
---
``` ┌─────────────┐ POST /auth/login ┌─────────────┐ │ Client
``` ┌─────────────────────────────────────────────────────┐ │ API Gateway │ │ (Rate Limiting, CORS) │ ├─────────────────────────────────────────────────────┤ │ Authentication Layer (JWT + OAuth2) │ ├──────────┬──────────┬───────────┬──────
I will design a comprehensive REST API for your task management system, covering authentication, resource modeling, and endpoint specifications.
Judge model, methodology, and limitations
Judge
google/gemini-2.5-flash
3 runs per comparison, results averaged. Independent blind scoring.
Baseline
Opus 4.6 called directly via Anthropic SDK — not through Komilion. Ensures fair comparison.
Run date
2026-02-23
Tasks
10 real-world dev tasks. Compound interest, debugging, async/await, SQL, architecture, and more.
Limitations
from openai import OpenAI
client = OpenAI(
base_url="https://www.komilion.com/api/v1",
api_key="ck_your_key"
)
response = client.chat.completions.create(
model="neo-mode/balanced",
messages=[{"role": "user", "content": "your prompt"}]
)
# See which model was chosen + cost
print(response.model_extra["komilion"]["brainModel"])
print(response.model_extra["komilion"]["cost"])KomilionBench Phase 5 · 10 tasks · 30 API calls · Feb 27 2026 · methodology