If it helps in the short-term, but hurts Duolingo in the long-term, it’s not right

When we disagree, we test ideas and let the metrics decide.

Learners need to get utility out of whatever we’ve built. Otherwise, we’ve made something that adds more complexity to the app and distracts learners from what they’re here to do.

Every feature we ship must be intuitive, delightful, useful, and polished.

If a feature or screen requires explanation or additional context, it’s not right.

Some of our best features and campaigns have come from asking ridiculous, unlikely questions

Talking about ideas is rarely as effective as building them

We don’t chase blame. We dig in and figure out what happened. This is how we continuously raise the bar—not by being perfect, but by being a little bit better each day.

from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression
import pandas as pd

X, y = make_regression(n_samples=1000, n_features=10, noise=10, random_state=42)
feature_names = [f"feature_{i}" for i in range(X.shape[1])]

model = LinearRegression()
model.fit(X, y)

importance = pd.Series(model.coef_, index=feature_names).sort_values(ascending=False)

from catboost import CatBoostRegressor

model = CatBoostRegressor(iterations=200, verbose=False)
model.fit(X, y)

feature_importance = pd.Series(model.get_feature_importance(), index=feature_names)

from sklearn.ensemble import RandomForestRegressor
from sklearn.inspection import permutation_importance
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)

perm_importance = permutation_importance(model, X_test, y_test, scoring="r2")
importance = pd.Series(perm_importance.importances_mean, index=feature_names)

import shap

explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

shap.summary_plot(shap_values, X_test, feature_names=feature_names)

Линейная регрессия подходит для простых зависимостей, деревья и бустинг – для сложных, Permutation Importance – когда нужна честная оценка вклада признаков, а SHAP – для объяснения модели

WITH old_product AS (
    SELECT 
        user_id, 
        COUNT(order_id) AS old_orders, 
        SUM(revenue) AS old_revenue
    FROM orders
    WHERE product_id = 'old_product'
      AND order_date < '2024-01-01'
    GROUP BY user_id
),

new_product AS (
    SELECT 
        user_id, 
        COUNT(order_id) AS new_orders, 
        SUM(revenue) AS new_revenue
    FROM orders
    WHERE product_id = 'new_product'
      AND order_date >= '2024-01-01'
    GROUP BY user_id
)

SELECT 
    o.user_id,
    o.old_orders,
    n.new_orders,
    o.old_revenue,
    n.new_revenue,
    CASE 
        WHEN o.old_orders > 0 AND n.new_orders > 0 AND n.new_revenue < o.old_revenue 
            THEN 'Перешли, но платят меньше'
        WHEN o.old_orders > 0 AND n.new_orders > 0 AND n.new_revenue >= o.old_revenue 
            THEN 'Перешли, но выручка сохранилась'
        WHEN o.old_orders > 0 AND n.new_orders = 0 
            THEN 'Остались на старом'
        WHEN o.old_orders = 0 AND n.new_orders > 0 
            THEN 'Новый клиент'
        ELSE 'Нет активности'
    END AS user_behavior
FROM old_product o
LEFT JOIN new_product n ON o.user_id = n.user_id;

user_id | order_id | gmv | date 

user_id - id пользователя 
order_id - id заказа 
gmv - стоимость заказа (для пользователя)
date - дата оплаты заказа

user_id | action_name | date

user_id - id пользователя
action_name - обычно таблица с событиями (сюда льются любые заходы пользователя в приложении)
date - дата экшна

-- Считаем ARPU и ARPPU за март 2025 года

WITH
-- Количество уникальных активных пользователей (по действиям)
active_users AS (
  SELECT COUNT(DISTINCT user_id) AS active_user_count
  FROM actions
  WHERE date BETWEEN '2025-03-01' AND '2025-03-31'
),

-- Количество уникальных платящих пользователей (по заказам)
paying_users AS (
  SELECT COUNT(DISTINCT user_id) AS paying_user_count
  FROM orders
  WHERE date BETWEEN '2025-03-01' AND '2025-03-31'
),

-- Общая выручка за период
total_revenue AS (
  SELECT SUM(gmv) AS gmv_total
  FROM orders
  WHERE date BETWEEN '2025-03-01' AND '2025-03-31'
)

-- Финальный расчёт метрик ARPU и ARPPU
SELECT
  ROUND(gmv_total * 1.0 / active_user_count, 2) AS arpu,   -- Средняя выручка на активного пользователя
  ROUND(gmv_total * 1.0 / paying_user_count, 2) AS arppu   -- Средняя выручка на платящего пользователя
FROM total_revenue
CROSS JOIN active_users
CROSS JOIN paying_users;

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain.chains import RetrievalQA, ConversationalRetrievalChain

loader = TextLoader("text_for_gpt.txt")
documents = loader.load()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(texts, embeddings)
retriever = vectorstore.as_retriever()

llm = ChatOpenAI(model="gpt-3.5-turbo")
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
response = qa_chain.invoke({"query": 'О чем статья? Расскажи вкратце'})

print(response["result"])