Activity
·
15m 7s

Thinking
Evaluating video analysis options
I'm thinking of analyzing the video using Python, maybe with ffmpeg for frame and audio extraction. Since there's no built-in speech-to-text tool, I might try using whispers, if it's installed. I'll check the video metadata and sample frames, focusing on click moments and detecting visual features like cursor halos.

Analyzing video frames, audio, and metadata with Python
import os, subprocess, json, textwrap, math, statistics, re, sys, pathlib, numpy as np
video_path="/mnt/data/my-recording-cleanshot-x-compressed.mp4"
os.path.getsize(video_path), video_path
(26568135, '/mnt/data/my-recording-cleanshot-x-compressed.mp4')
Checking video metadata
I need to gather metadata from the video, so I'll probably use ffprobe for that. It’ll give me important details like video codecs, duration, and frame rates, which will help me with the next steps in video analysis. Let's start by extracting that metadata to move forward.

Extracting video metadata with ffprobe
import subprocess, json, shlex, os, textwrap, pandas as pd, numpy as np
def ffprobe(path):
cmd=["ffprobe","-v","error","-print_format","json","-show_format","-show_streams",path]
out=subprocess.check_output(cmd)
return json.loads(out)
meta=ffprobe(video_path)
meta.keys(), [(s['codec_type'], s.get('codec_name'), s.get('width'), s.get('height'), s.get('r_frame_rate')) for s in meta['streams']]
(dict_keys(['streams', 'format']),
[('video', 'hevc', 2536, 1744, '60/1'), ('audio', 'aac', None, None, '0/0')])
Analyzing video format and specs
The video is in HEVC format with a resolution of 2536x1744 and runs at 60fps. It also uses AAC audio. Now, I need to check the duration of this video so that I can properly account for the timeline and any other related details in the analysis process. Let's confirm that.

Estimating video duration
meta['format']['duration'], meta['format']['size'], meta['format']['bit_rate']
('272.967000', '26568135', '778647')
Checking for transcript tool
The video is 4.55 minutes long, so now I need to get the transcript. I’m thinking of using Whisper for this. First, though, I’ll check if Whisper is installed on the system. If it is, I can move ahead with extracting the transcript; if not, I’ll have to consider alternatives.

Checking for whisper installation
import importlib, pkgutil, sys, subprocess, textwrap, os, math, numpy as np, pandas as pd
[p.name for p in pkgutil.iter_modules() if 'whisper' in p.name.lower()][:20]
[]
Trying faster_whisper for transcription
It seems Whisper isn’t installed. Maybe I can try using faster_whisper instead? If it’s available, I can use that to generate the transcript for the video. I’ll check whether it’s installed and proceed accordingly. If not, I'll start looking for another alternative.

Listing faster_whisper and vosk packages
[p.name for p in pkgutil.iter_modules() if 'faster' in p.name.lower() or 'vosk' in p.name.lower()][:50]
[]
Adjusting to limitations and providing visual analysis
There's no ASR library available, and speech-to-text tools like pocketsphinx are likely missing. Since we can't transcribe the audio, I'll focus on visual analysis. I'll extract frames from the video, detect click highlights, and track screen changes. Scanning the video at 10 fps will give us 16,380 frames, but it might be time-consuming.

Extracting frames and detecting click highlights
import cv2, numpy as np, os, math, pandas as pd, statistics
cap=cv2.VideoCapture(video_path)
fps=cap.get(cv2.CAP_PROP_FPS)
width=int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height=int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
frame_count=int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps, width, height, frame_count
(60.0, 2536, 1744, 16378)
Detecting click ripples with HoughCircles
To detect clicks, I'll look for circular ripples with a bright white stroke. I plan to compute the difference between consecutive frames, sampled every 6 frames at 10fps. I'll use HoughCircles on high-contrast areas, downscaled by 0.33 to reduce computation. I’ll also check for possible red or yellow rings. Let’s start by inspecting a few frames from the beginning.

Detecting circular ripples in video frames
import matplotlib.pyplot as plt, numpy as np, cv2, os, math, pandas as pd
def get_frame_at(t_sec):
cap=cv2.VideoCapture(video_path)
cap.set(cv2.CAP_PROP_POS_MSEC, t_sec\*1000)
ret, frame=cap.read()
cap.release()
if not ret: return None
return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame0=get_frame_at(1.0)
frame0.shape
Matplotlib is building the font cache; this may take a moment.
(1744, 2536, 3)
Implementing frame sampling for click detection

# Save a few frames to inspect

times=[1,10,20,30,40,50,60]
paths=[]
for t in times:
fr=get*frame_at(t)
p=f"/mnt/data/frame*{t}.png"
cv2.imwrite(p, cv2.cvtColor(fr, cv2.COLOR_RGB2BGR))
paths.append(p)
paths
['/mnt/data/frame_1.png',
'/mnt/data/frame_10.png',
'/mnt/data/frame_20.png',
'/mnt/data/frame_30.png',
'/mnt/data/frame_40.png',
'/mnt/data/frame_50.png',
'/mnt/data/frame_60.png']
Inspecting images with container.open_image
I can't display images directly to the user, but I can load the images using container.open_image for analysis. While doing that, I can also compute dominant colors using Python. It's more of a visual inspection tool, so I'll proceed with that and check the results.

Analyzed image
Output image
Looking for click highlights in later frames
I don't see click highlights in the current frame, so I need to check later frames like frame_10. I'll inspect those to see if the click highlights appear there. This way, I can track the clicks more accurately in subsequent frames.

Analyzed image
Output image
Checking later frame
It looks like there's still no change, so I'll move on to inspecting a later frame, frame 30. Hopefully, I can detect the click highlights there. By looking at a later frame, I might find the moment I missed earlier. Let's see!

Analyzed image
Output image
Analyzing video and preparing scene cuts
I think the user might be editing a screenshot or recording their screen, based on frame 30 showing CleanShot X's "Annotate" window. Since there’s no audio transcript, I’ll analyze the video by sampling every 2 seconds. I'll use scene change detection with resized frames at 1 fps and describe the key segments.

Detecting scene changes in video analysis
import cv2, numpy as np, math, pandas as pd, os, statistics
cap=cv2.VideoCapture(video_path)
fps=cap.get(cv2.CAP_PROP_FPS)
duration=cap.get(cv2.CAP_PROP_FRAME_COUNT)/fps
sample_rate=1.0 # fps for sampling
step=int(fps/sample_rate)
hist_prev=None
scenes=[]
times=[]
diffs=[]
t=0
frame_idx=0
while True:
ret, frame=cap.read()
if not ret:
break
if frame_idx % step==0:
small=cv2.resize(frame,(320,220))
hsv=cv2.cvtColor(small, cv2.COLOR_BGR2HSV)
hist=cv2.calcHist([hsv],[0,1],[None],[50,60],[0,180,0,256])
cv2.normalize(hist,hist)
if hist_prev is not None:
d
Fixing histogram calculation issue
Looks like the issue came from an incorrect argument in the calcHist mask. I’ll correct that and try again to make sure the histogram processing works smoothly.

Fixing mask argument in calcHist function
import cv2, numpy as np, math, pandas as pd, os, statistics
cap=cv2.VideoCapture(video_path)
fps=cap.get(cv2.CAP_PROP_FPS)
frame_count=int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
duration=frame_count/fps
sample_rate=1.0
step=max(1,int(round(fps/sample_rate)))
hist_prev=None
times=[]
diffs=[]
frame_idx=0
while True:
ret, frame=cap.read()
if not ret:
break
if frame_idx % step==0:
small=cv2.resize(frame,(320,220))
hsv=cv2.cvtColor(small, cv2.COLOR_BGR2HSV)
hist=cv2.calcHist([hsv],[0,1],None,[50,60],[0,180,0,256])
cv2.normalize(hist,hist)
if hist_prev is not None:
d=cv2.compareHist(hist_prev,hist,cv2.HISTCMP_BHATTACHARYYA)
diffs.append(float(d))
times.append(frame_idx/fps)
hist_prev=hist
frame_idx+=1
cap.release()
len(diffs), times[-1], duration

---

KeyboardInterrupt Traceback (most recent call last)
Cell In[10], line 13
11 frame_idx=0
12 while True:
---> 13 ret, frame=cap.read()
14 if not ret:
15 break

KeyboardInterrupt:
Optimizing frame reading speed
The current loop is slow since I’m reading all frames at 60fps. What I should do instead is seek specific frames using the CAP_PROP_POS_FRAMES property. I’ll set the position to jump every second, then read the corresponding frame. This should speed things up.

Improving frame capture efficiency with time jumps
import cv2, numpy as np, math, pandas as pd, os
cap=cv2.VideoCapture(video_path)
fps=cap.get(cv2.CAP_PROP_FPS)
frame_count=int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
duration=frame_count/fps
sample_times=np.arange(0, duration, 1.0)
hists=[]
for t in sample_times:
cap.set(cv2.CAP_PROP_POS_MSEC, t\*1000)
ret, frame=cap.read()
if not ret:
break
small=cv2.resize(frame,(320,220))
hsv=cv2.cvtColor(small, cv2.COLOR_BGR2HSV)
hist=cv2.calcHist([hsv],[0,1],None,[50,60],[0,180,0,256])
cv2.normalize(hist,hist)
hists.append(hist)
cap.release()
len(hists), sample_times[len(hists)-1]

---

KeyboardInterrupt Traceback (most recent call last)
Cell In[11], line 9
7 hists=[]
8 for t in sample_times:
----> 9 cap.set(cv2.CAP_PROP_POS_MSEC, t\*1000)
10 ret, frame=cap.read()
11 if not ret:

KeyboardInterrupt:
Switching to ffmpeg for faster frame extraction
The seeking per second method with OpenCV is too slow, so I’ll use ffmpeg to extract frames at 1fps. By using subprocess, I’ll have ffmpeg create frames in a folder quickly using ffmpeg -vf fps=1,scale=320:-1. This should speed up the process and make analysis easier.

Extracting frames from video with ffmpeg
import subprocess, os, glob, pathlib, shutil, textwrap, json, numpy as np, cv2, pandas as pd, math
out*dir="/mnt/data/frames_1fps"
shutil.rmtree(out_dir, ignore_errors=True)
os.makedirs(out_dir, exist_ok=True)
cmd=["ffmpeg","-y","-i",video_path,"-vf","fps=1,scale=320:-1","-q:v","3",f"{out_dir}/frame*%04d.jpg"]
subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True)
len(glob.glob(out_dir+"/\*.jpg"))

---

KeyboardInterrupt Traceback (most recent call last)
Cell In[12], line 6
4 os.makedirs(out*dir, exist_ok=True)
5 cmd=["ffmpeg","-y","-i",video_path,"-vf","fps=1,scale=320:-1","-q:v","3",f"{out_dir}/frame*%04d.jpg"]
----> 6 subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True)
7 len(glob.glob(out_dir+"/\*.jpg"))

File /usr/lib/python3.11/subprocess.py:550, in run(input, capture_output, timeout, check, *popenargs, \*\*kwargs)
548 with Popen(*popenargs, \*\*kwargs) as process:
549 try:
--> 550 stdout, stderr = process.communicate(input, timeout=timeout)
551 except TimeoutExpired as exc:
552 process.kill()

File /usr/lib/python3.11/subprocess.py:1207, in Popen.communicate(self, input, timeout)
1204 endtime = None
1206 try:
-> 1207 stdout, stderr = self.\_communicate(input, endtime, timeout)
1208 except KeyboardInterrupt:
1209 # https://bugs.python.org/issue25942
1210 # See the detailed comment in .wait().
1211 if timeout is not None:

File /usr/lib/python3.11/subprocess.py:2059, in Popen.\_communicate(self, input, endtime, orig_timeout)
2052 self.\_check_timeout(endtime, orig_timeout,
2053 stdout, stderr,
2054 skip_check_and_raise=True)
2055 raise RuntimeError( # Impossible :)
2056 '\_check_timeout(..., skip_check_and_raise=True) '
2057 'failed to raise TimeoutExpired.')
-> 2059 ready = selector.select(timeout)
2060 self.\_check_timeout(endtime, orig_timeout, stdout, stderr)
2062 # XXX Rewrite these to use non-blocking I/O on the file
2063 # objects; they are no longer using C stdio!

File /usr/lib/python3.11/selectors.py:415, in \_PollLikeSelector.select(self, timeout)
413 ready = []
414 try:
--> 415 fd_event_list = self.\_selector.poll(timeout)
416 except InterruptedError:
417 return ready

KeyboardInterrupt:
Optimizing frame extraction with reduced settings
The ffmpeg extraction took longer than expected, possibly due to the large resolution. I'll lower the scale further and try using a faster preset. Reducing the fps to 0.5 (every 2 seconds) and setting scale to 200 should help speed things up while maintaining quality.

Optimizing video extraction with reduced fps and scale
import subprocess, os, glob, shutil, time, textwrap
out*dir="/mnt/data/frames_0_5fps"
shutil.rmtree(out_dir, ignore_errors=True)
os.makedirs(out_dir, exist_ok=True)
cmd=["ffmpeg","-y","-i",video_path,"-vf","fps=0.5,scale=200:-1","-q:v","5",f"{out_dir}/frame*%04d.jpg"]
t0=time.time()
proc=subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
time.time()-t0, proc.returncode, proc.stderr.decode('utf-8')[-200:]

---

KeyboardInterrupt Traceback (most recent call last)
Cell In[13], line 7
5 cmd=["ffmpeg","-y","-i",video_path,"-vf","fps=0.5,scale=200:-1","-q:v","5",f"{out_dir}/frame_%04d.jpg"]
6 t0=time.time()
----> 7 proc=subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
8 time.time()-t0, proc.returncode, proc.stderr.decode('utf-8')[-200:]

File /usr/lib/python3.11/subprocess.py:550, in run(input, capture_output, timeout, check, *popenargs, \*\*kwargs)
548 with Popen(*popenargs, \*\*kwargs) as process:
549 try:
--> 550 stdout, stderr = process.communicate(input, timeout=timeout)
551 except TimeoutExpired as exc:
552 process.kill()

File /usr/lib/python3.11/subprocess.py:1207, in Popen.communicate(self, input, timeout)
1204 endtime = None
1206 try:
-> 1207 stdout, stderr = self.\_communicate(input, endtime, timeout)
1208 except KeyboardInterrupt:
1209 # https://bugs.python.org/issue25942
1210 # See the detailed comment in .wait().
1211 if timeout is not None:

File /usr/lib/python3.11/subprocess.py:2059, in Popen.\_communicate(self, input, endtime, orig_timeout)
2052 self.\_check_timeout(endtime, orig_timeout,
2053 stdout, stderr,
2054 skip_check_and_raise=True)
2055 raise RuntimeError( # Impossible :)
2056 '\_check_timeout(..., skip_check_and_raise=True) '
2057 'failed to raise TimeoutExpired.')
-> 2059 ready = selector.select(timeout)
2060 self.\_check_timeout(endtime, orig_timeout, stdout, stderr)
2062 # XXX Rewrite these to use non-blocking I/O on the file
2063 # objects; they are no longer using C stdio!

File /usr/lib/python3.11/selectors.py:415, in \_PollLikeSelector.select(self, timeout)
413 ready = []
414 try:
--> 415 fd_event_list = self.\_selector.poll(timeout)
416 except InterruptedError:
417 return ready

KeyboardInterrupt:
Adjusting timeout for faster frame extraction
The extraction is still slow, so I’ll try using a longer timeout, maybe 120 or even 180 seconds, with the container execution tool. This should help speed up the frame extraction by allowing more time for ffmpeg to process it. Let's see if that improves the performance!

Extracting frames using ffmpeg in container
bash -lc rm -rf /mnt/data/frames*0_5fps && mkdir -p /mnt/data/frames_0_5fps && ffmpeg -y -i /mnt/data/my-recording-cleanshot-x-compressed.mp4 -vf "fps=0.5,scale=200:-1" -q:v 7 /mnt/data/frames_0_5fps/frame*%04d.jpg >/dev/null 2>&1 && ls /mnt/data/frames_0_5fps | wc -l
Checking for errors in the command
It seems like there's no output. Maybe the command didn’t process or returned an error? I should check to verify if there was an issue with how it was run or if the output simply didn’t display for some reason. Let's troubleshoot this!

Checking directory contents and previewing output
bash -lc ls -l /mnt/data/frames_0_5fps | head
total 32
-rw-r--r-- 1 root root 3939 Feb 28 08:54 frame_0001.jpg
-rw-r--r-- 1 root root 3940 Feb 28 08:54 frame_0002.jpg
-rw-r--r-- 1 root root 3928 Feb 28 08:54 frame_0003.jpg
-rw-r--r-- 1 root root 3926 Feb 28 08:54 frame_0004.jpg
-rw-r--r-- 1 root root 3943 Feb 28 08:55 frame_0005.jpg
-rw-r--r-- 1 root root 3934 Feb 28 08:55 frame_0006.jpg
-rw-r--r-- 1 root root 3920 Feb 28 08:55 frame_0007.jpg
-rw-r--r-- 1 root root 3919 Feb 28 08:55 frame_0008.jpg
Verifying frame extraction
Frames have been extracted, but I need to check the frame count now. It’s important to know how many were successfully pulled from the video so that I can ensure everything worked as expected. Let’s check the count to confirm!

Counting extracted frames
bash -lc ls /mnt/data/frames_0_5fps | wc -l
8
Checking frame extraction completion
I see only 8 frames, which suggests the extraction stopped early—maybe ffmpeg encountered an error and didn’t complete. I need to check the stderr for any error messages. Let’s rerun the extraction and make sure to capture the output this time to troubleshoot better.

Checking ffmpeg stderr for errors
bash -lc ffmpeg -i /mnt/data/my-recording-cleanshot-x-compressed.mp4 -vf "fps=0.5,scale=200:-1" -q:v 7 -f null - 2>&1 | tail -n 20
Considering command timeout issue
I didn’t get any output, which could be because the command took too long, or maybe ffmpeg is stuck. The tail isn’t showing anything, possibly because it was still running. It looks like I didn’t set the timeout properly—default is too short. I’ll set it to 180,000 ms instead.

Troubleshooting ffmpeg execution with timeout adjustment
bash -lc ffmpeg -i /mnt/data/my-recording-cleanshot-x-compressed.mp4 -vf "fps=0.5,scale=200:-1" -q:v 7 -f null - 2>&1 | tail -n 30
Investigating possible reasons for output limitation
It looks like the extraction stopped early. I suspect ffmpeg might be printing to stderr without showing in the tail, which makes this a bit tricky to debug. The earlier command might have ended due to timeout, but it only produced 8 frames, which suggests other issues like the disk or a decoder error with ffmpeg. I’ll check with a more detailed error log to learn more.

Diagnosing ffmpeg frame extraction issues
bash -lc ffmpeg -v error -i /mnt/data/my-recording-cleanshot-x-compressed.mp4 -f null - 2>&1 | head
Adjusting frame extraction strategy
It seems like the earlier extraction might have been killed due to a timeout. Since the video is only 273 seconds long, we'll try something more efficient. Instead of full decoding, I’ll extract keyframes at specific timestamps using seek commands for every 30 seconds. This should be faster and still grab the necessary frames.

Extracting keyframes from video at specific timestamps
bash -lc rm -rf /mnt/data/sample*frames && mkdir -p /mnt/data/sample_frames; for t in 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270; do ffmpeg -y -ss $t -i /mnt/data/my-recording-cleanshot-x-compressed.mp4 -vframes 1 -vf scale=1200:-1 -q:v 4 /mnt/data/sample_frames/t*${t}.jpg >/dev/null 2>&1; done; ls /mnt/data/sample_frames | wc -l
Investigating missing output
The command seems successful, but I'm not getting any output, even though wc -l should show something. The fact that it's blank suggests the tool might have returned nothing for stdout. I'll check the directory to see if the output files are there or if something went wrong.

Listing directory contents with limit
bash -lc ls -1 /mnt/data/sample_frames | head
t_0.jpg
t_15.jpg
t_30.jpg
t_45.jpg
Counting files in directory
bash -lc ls /mnt/data/sample_frames | wc -l
4
Adjusting frame extraction parameters
I got only 4 frames, which might be because ffmpeg was too slow and hit the 180s timeout. To improve things, I'll reduce the number of timestamps, use a faster scale of 800, and limit the frames to 9. I'll also extend the timeout to 240 seconds and rerun the command.

Extracting video frames at timestamps
bash -lc rm -rf /mnt/data/sample*frames && mkdir -p /mnt/data/sample_frames; for t in 0 30 60 90 120 150 180 210 240 270; do echo $t; ffmpeg -y -ss $t -i /mnt/data/my-recording-cleanshot-x-compressed.mp4 -vframes 1 -vf scale=900:-1 -q:v 6 /mnt/data/sample_frames/t*${t}.jpg >/dev/null 2>&1; done; ls /mnt/data/sample_frames | wc -l
0
30
60
90
120
Optimizing method for frame extraction
It seems the timeout might've been caused by slow extraction at later timestamps. I used -ss before -i for speed, but it still needs decoding from keyframes, which might be large. I’ll switch to using OpenCV’s get_frame_at function for faster extraction. This should complete within 60 seconds, using 10 calls.

Extracting frames from video using OpenCV
import cv2, os, numpy as np, time, math
def save_frame(t, out_path, max_w=1600):
cap=cv2.VideoCapture(video_path)
cap.set(cv2.CAP_PROP_POS_MSEC, t*1000)
ret, frame=cap.read()
cap.release()
if not ret:
return False
h,w=frame.shape[:2]
if w>max_w:
scale=max_w/w
frame=cv2.resize(frame,(int(w*scale), int(h\*scale)))
cv2.imwrite(out_path, frame)
return True

out_dir="/mnt/data/py_sample_frames"
import shutil, glob
shutil.rmtree(out_dir, ignore_errors=True)
os.makedirs(out_dir, exist_ok=True)
timestamps=[0,15,30,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270]
t0=time.time()
ok=[]
for

---

KeyboardInterrupt Traceback (most recent call last)
Cell In[14], line 25
23 for t in timestamps:
24 p=os.path.join(out*dir,f"t*{t}.jpg")
---> 25 ok.append((t, save*frame(t,p, max_w=1600)))
26 time.time()-t0, sum(o for *,o in ok), ok[:5], ok[-3:]

Cell In[14], line 6, in save_frame(t, out_path, max_w)
4 cap.set(cv2.CAP_PROP_POS_MSEC, t\*1000)
5 ret, frame=cap.read()
----> 6 cap.release()
7 if not ret:
8 return False

KeyboardInterrupt:
Reducing timestamps for faster extraction
It looks like the process timed out with 19 timestamps. To speed things up, I’ll try extracting fewer frames, maybe one every 30 seconds, so roughly 10 points in total. This should help avoid the timeout while still getting a representative sample.

Saving frames at specific timestamps
import cv2, os, numpy as np, time, math, shutil
out_dir="/mnt/data/py_sample_frames"
shutil.rmtree(out_dir, ignore_errors=True)
os.makedirs(out_dir, exist_ok=True)
def save_frame_one(cap,t,out_path,max_w=1400):
cap.set(cv2.CAP_PROP_POS_MSEC, t*1000)
ret, frame=cap.read()
if not ret:
return False
h,w=frame.shape[:2]
if w>max_w:
scale=max_w/w
frame=cv2.resize(frame,(int(w*scale), int(h\*scale)))
cv2.imwrite(out_path, frame)
return True

cap=cv2.VideoCapture(video_path)
timestamps=[0,30,60,90,120,150,180,210,240,270]
t0=time.time()
oks=[]
for t in timestamps:
p=os.path.join
(40.418460845947266,
[(0, True),
(30, True),
(60, True),
(90, True),
(120, True),
(150, True),
(180, True),
(210, True),
(240, True),
(270, True)])
Inspecting frames for major segments
I'll now check the extracted frames to understand the key segments of the video. I'll open frames at timestamps like t_0, t_30, t_60, t_90, t_120, t_150, t_180, t_210, t_240, and t_270 to get a good overview of the video’s content across its major sections.

Analyzed image
Output image
Checking if file is being opened correctly
It seems the file is being opened in the "QuickTime Player — iCloud" dialog, which might suggest it's trying to open in the browser. I wonder if this is affecting my workflow. Also, let's check the frame at t_30 to see how this impacts the video’s progression.

Analyzed image
Output image
Inspecting screen capture progress
It looks like the CleanShot annotate window is still open with the Layerpath dashboard visible, and the toolbar with a “Done” button is also showing. I’ll check the frame at t_60 to get a clearer idea of the visual changes or data that might have been captured.

Analyzed image
Output image
Checking frame at t_90
It looks like the situation hasn't changed much yet. I’ll check the frame at t_90 to see if there are any further visual updates that stand out or any distinct differences in the content compared to earlier frames. Need to stay sure!

Analyzed image
Output image
Reviewing changes at t_120
Hmm, something might have shifted with the zoom or view at this timestamp. I’ll keep an eye out for any noticeable changes or updates in the visuals. It could be subtle, but I want to make sure I’m not missing anything important. Let's see.

Analyzed image
Output image
Analyzing screenshot annotations at t_150
It looks like the user is annotating the screenshot with red numbered markers: "1" near the workspace dropdown and "2" near the dashboard item. This is helpful! The annotations are clearly identifying key areas of interest. Let's continue to observe for any other details or changes.

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Updating annotations at t_180
Now I see that the user has placed markers 1 to 5. Marker 4 is near "My demos," marker 3 is near the top of the first demo card, and marker 5 seems to be near the bottom row. It looks like they're continuing to number key areas for clarification. I'll keep monitoring for more updates!

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Observing CleanShot X edits at t_240
The background panel is now open with gradients and blurred wallpapers, featuring plain color settings. The user has added a wallpaper behind the screenshot, along with padding, shadow, and rounded corners. Additionally, a big red arrow has been placed from the top right to marker 5, and the zoom is set to 68%. Looking good!

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Noticing CleanShot cursor highlight at t_270
The zoom is now at 72%. I see a white ring around the cursor at the corner's slider. That could be a click highlight from CleanShot's screen recording overlay. It looks like it’s showing where the user clicked, confirming that it’s indeed a click highlight – nice touch!

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Thought for 15m 7s
Done
