Add 122 Python example scripts + .env.example

Each of the 122 code slides now has a standalone .py script in
examples/slide-NNN/script.py with the LLM provider pattern from
bro-js/agents/teacher/assistant/src/angry_teacher/llm.py:
  - load_dotenv() + ChatOpenAI with base_url=https://llm.brojs.ru/v1
  - OPENAI_API_KEY from .env (with safe (or '' or None) pattern)
  - Same comment block with alternative model/base_url choices

Layout:
  - sec1/sec5: code extracted from slide-NN.js (addCodeBlock)
  - sec2/sec3/sec4: code extracted from sectionN.pptx via python-pptx
    (JetBrains Mono font shapes)

100 scripts contain runnable code, 22 are visual-only (dividers,
intro, recap) and just print the slide title.

Setup:
  cp .env.example .env       # fill OPENAI_API_KEY (or symlink to your
                              # teacher/assistant/.env)
  pip install -r requirements.txt
  python examples/slide-005/script.py

examples/slide-033/script.py

@@ -0,0 +1,65 @@
+"""
+Slide 33: Slide 6
+Section 2: LangGraph 1.0
+Source: slides/section2-langgraph/section2.pptx
+
+Сгенерировано автоматически из slide-NN.js / sectionN.pptx.
+Паттерн провайдера скопирован из
+bro-js/agents/teacher/assistant/src/angry_teacher/llm.py.
+
+Запуск:
+  cd langchain-evolution-deck
+  cp .env.example .env   # заполни OPENAI_API_KEY
+  python examples/slide-033/script.py
+"""
+import os
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+
+load_dotenv()
+
+llm = ChatOpenAI(
+    # model="openrouter/free",
+    # model="qwen/qwen3.5-35b-a3b",
+    model="openai/gpt-oss-20b",
+    # model="nvidia/nemotron-3-nano",
+    # model="qwen/qwen3.5-9b",
+    base_url="https://llm.brojs.ru/v1",
+    # base_url="https://api.minimax.io/v1",
+    # base_url="http://0.0.0.0:8090/v1",
+    # base_url="https://openrouter.ai/api/v1",
+    # api_key=os.getenv("MINIMAX_API_KEY"),
+    api_key=(os.getenv("OPENAI_API_KEY") or "").strip() or None,
+    temperature=0.7,
+    stream_usage=True,
+)
+
+
+# === Code from slide 33 ===
+  import operator
+  from typing import Annotated
+  from typing_extensions import TypedDict
+  from langgraph.graph import StateGraph, START, END
+
+  class State(TypedDict):
+      # каждый узел возвращает кусок списка, operator.add склеивает
+      log:    Annotated[list[str], operator.add]
+>     tokens: Annotated[int,   operator.add]
+>  
+  def step(state: State):
+      return {"log": ["step-1"], "tokens": 12}
+
+  def another(state: State):
+      return {"log": ["step-2"], "tokens": 7}
+
+  g = StateGraph(State)
+  g.add_node("a", step)
+  g.add_node("b", another)
+  g.add_edge(START, "a")
+  g.add_edge("a", "b")
+  g.add_edge("b", END)
+  app = g.compile()
+  print(app.invoke({"log": [], "tokens": 0}))
+  # -> {'log': ['step-1', 'step-2'], 'tokens': 19}
+
+// note: snippet has 25 lines, card fits ~18