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-051/script.py

@@ -0,0 +1,54 @@
+"""
+Slide 51: Slide 24
+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-051/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 51 ===
+  cfg = {"configurable": {"thread_id": "u1"}}
+  # values    -- весь state после каждого super-step
+> for snap in app.stream({"messages": []}, cfg, stream_mode="values"):
+      print(snap["messages"][-1].content)
+  # updates   -- delta: только что вернул каждый узел
+> for upd in app.stream({"messages": []}, cfg, stream_mode="updates"):
+      print(upd)
+  # events    -- низкоуровневые события (start, end, error, interrupt)
+> for ev in app.stream({"messages": []}, cfg, stream_mode="events"):
+      print(ev["event"], ev["name"])
+  # messages  -- токены LLM по мере генерации
+> for tok, meta in app.stream({"messages": []}, cfg, stream_mode="messages"):
+      print(tok.content, end="|")
+  # custom    -- только то, что узлы пишут через get_stream_writer()
+> for chunk in app.stream({"messages": []}, cfg, stream_mode="custom"):
+      print(chunk)