Reorganize examples into topic-based subfolders

Move 304 examples from a flat numbered directory into 14 descriptive
subfolders: getting-started, services (speech + function-calling),
transcription, vision, realtime, persistent-context,
context-summarization, update-settings (stt/tts/llm), turn-management,
thinking-and-mcp, transports, video-avatar, video-processing, and
features.

Strip numbered prefixes from filenames (e.g. 07c-interruptible-deepgram.py
becomes services/speech/deepgram.py) since the folder context makes them
redundant. Keep numbered prefixes only in getting-started/ where ordering
matters.

Update eval script paths and README to match the new structure.

examples/transcription/whisper-mlx.py

@@ -0,0 +1,119 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import time
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import Frame, TranscriptionFrame, UserStoppedSpeakingFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.audio.vad_processor import VADProcessor
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.whisper.stt import MLXModel, WhisperSTTServiceMLX
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+STOP_SECS = 2.0
+
+
+class TranscriptionLogger(FrameProcessor):
+    """Measures transcription latency.
+
+    Uses the (intentionally) long STOP_SECS parameter to give the transcription time to finish,
+    then outputs the timing between when the VAD first classified audio input as not-speech and
+    the delivery of the last transcription frame.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._last_transcription_time = time.time()
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserStoppedSpeakingFrame):
+            logger.debug(
+                f"Transcription latency: {(STOP_SECS - (time.time() - self._last_transcription_time)):.2f}"
+            )
+
+        if isinstance(frame, TranscriptionFrame):
+            self._last_transcription_time = time.time()
+
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
+
+# We use lambdas to defer transport parameter creation until the transport
+# type is selected at runtime.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = WhisperSTTServiceMLX(
+        settings=WhisperSTTServiceMLX.Settings(
+            model=MLXModel.LARGE_V3_TURBO.value,
+        ),
+    )
+
+    tl = TranscriptionLogger()
+    vad_processor = VADProcessor(
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS))
+    )
+
+    pipeline = Pipeline([transport.input(), vad_processor, stt, tl])
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()