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SOMEONE ASKED CLAUDE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AN AI
— Om Patel (@om_patel5) April 3, 2026
and what it created is, in my opinion, terrifying and unsettling
Claude wrote python code that generated and assembled every single frame on its own with no human editing
it shows what it's like to exist as… pic.twitter.com/mATF3BTAGT
#BlackHistoryMonth https://t.co/7x30KmEJ0r
— Wendell Pierce (@WendellPierce) February 5, 2026
David Blackwell was a multifaceted scientist who excelled in various fields, including mathematics, statistics, and game theory. Born in 1919, Blackwell faced numerous challenges as a Black man in academia during the early 20th century. Despite these obstacles, he became the first Black person elected to the National Academy of Sciences and held prestigious positions at institutions like the University of California, Berkeley.
One of Blackwell’s most significant achievements was being awarded the National Medal of Science, a testament to his exceptional contributions to science and engineering. His legacy continues to inspire generations of scientists, particularly those from underrepresented backgrounds, to pursue careers in STEM fields.
David Blackwell’s innovations and contributions have had a profound impact on the development of AI and statistics. Here’s a list of some of his most notable achievements:
Blackwell-Rao Theorem: This theorem provides a way to improve the precision of estimators in statistics, which is crucial for making accurate predictions and reducing bias in data analysis.
Blackwell Spaces: These are mathematical structures that have been instrumental in the development of probability theory and statistics.
NVIDIA Blackwell Chip: In honor of his remarkable legacy, NVIDIA named its most advanced AI chip, the Blackwell chip, after David Blackwell. This chip is the engine powering the new industrial revolution in artificial intelligence, bringing the promise of AI to industries, developers, and researchers worldwide.
National Medal of Science: Blackwell's outstanding work in mathematics and statistics earned him the prestigious National Medal of Science.
Pioneer in AI: Blackwell is often referred to as one of the forefathers of artificial intelligence, with his work in game theory and probability forming the foundation for many AI algorithms.
David Blackwell’s story is one of resilience, excellence, and a passion for scientific discovery. His life and work serve as a powerful reminder of the importance of diversity and inclusion in science and the lasting impact that one individual can have on the world.
At first I did not realize that I did not have enough space to use the data from my blog so right now my chatbot only includes may resume. You can test it above. But before I realized how little source memory I actually have, I decided to try to find out how to web scrape data from my blog and then upload the data for the chatbot when I want to upgrade my bot. I used Claude to make two apps. The first is a web app that allows me to select a web page (only tested on blogger.com pages) and download the data in the posts according to year in XML files. I call it Blogger-Scraper.
The second app, is a Windows program that given a folder of XML files converts the files into PDF files into a folder where the program is installed.
If you are interested in any of the apps, please let me know. The Blogger-Scraper program is a React program.
The goal of this endeavor is to compare and document some of the popular ones: n8n, Make.com, Zapier, Pokee AI, and Pipedream.
Before we dive in, it’s important to define our approach. We are engaging in Vibe Coding: using AI to handle the syntax, boilerplate, and technical "how-to," allowing us to focus purely on the "vibe"—the flow, logic, and desired outcome of the application. The AI writes the code; we direct the symphony.
I have learned a lot about trying to solve the same problem with different tools. Each one has strengths and weaknesses. Based on my testing, here is how the landscape divides:
The Goal: Create an automation that loops through at most 30 videos in a playlist on YouTube. For each video, the workflow must:
The Logic:
I used the same logic for all tools: Get the videos from the playlist, loop through them, transcribe via code or module, summarize via LLM, compose the draft, and notify the user.
Below is the comparison of how each platform handled this specific workflow.
Table 1.1: Automations for Converting YouTube Videos into Blog Posts
Platform | Obtain Videos | Transcribe | Summarize | Construct Blog Post | Log to MySQL | Notify | Difficulty (1-10) | Prompting Strategy |
Zapier | Native Trigger (New Video in Playlist) | OpenAI Whisper (via Zapier integration) | ChatGPT (Conversation Action) | Blogger Action (Create Post) | MySQL Action (Insert Row) | Slack Action (Send Channel Message) | 2/10 | High. Zapier guided me through the whole process. I could edit the model through prompts, adding or removing steps easily. |
n8n | YouTube Node (Get Playlist Items) | Deepgram/OpenAI Node | LangChain / AI Agent Node | HTTP Request (Blogger API) | MySQL Node | Slack Node | 6/10 | Medium. Required setting up specific nodes. The "Loop" node is powerful but requires understanding data structure (JSON). |
Make | YouTube Watcher | Whisper Module | OpenAI Module | Blogger Module | MySQL Module | Slack Module | 4/10 | Medium. The visual "Iterator" makes looping through 30 videos very intuitive, but error handling requires extra modules. |
Pipedream | YouTube Data API (Source) | Python Code Step | OpenAI API | Blogger API (Auth required) | MySQL Resource | Slack API | 7/10 | Low. Relied heavily on code generation. Excellent for "Vibe Coding" if you can read the Python it generates. |
Pokee AI | AI Agent | AI Agent | AI Agent | AI Agent | AI Agent | AI Agent | 3/10 | High. Relies almost entirely on the prompt to configure the agents. Very "black box" but effective for standard flows. |
Python | google-api-python-client | speech_recognition | openai lib | blogger_v3 API | mysql-connector | slack_sdk | 9/10 | None. Pure coding. Requires handling OAuth2.0 tokens manually for YouTube and Blogger, which is the hardest part. |
Screenshot of the Zapier Flow showing the linear progression
Screenshot of the n8n Workflow showing the complex loop node
For the AI-driven tools (Zapier and Pokee AI), this is the prompt that successfully generated the majority of the workflow:
"Create an automation that triggers when a new video is added to my specific YouTube playlist. Loop through the videos (limit to 30). For each video, use OpenAI to transcribe the audio and then summarize the content into a blog post format. Create a new draft post on my Blogger account with the Video Title as the headline, the thumbnail as the header image, and the summary as the body. Embed the video at the end. Finally, add a row to my MySQL database with the video ID and blog URL, and send a Slack message to the '#content' channel confirming the draft is ready."
The Goal: Loop through bookmarked X.com posts, download the video (if present), log it in MySQL, and notify via Slack/WhatsApp.
Table 1.2: Automation Difficulty & Implementation
Platform | Difficulty | Key Challenge |
Zapier | 5/10 | Native "Download Video" actions are rare; often requires a third-party helper tool like "CloudConvert" or a custom webhook. |
n8n | 4/10 | Excellent HTTP Request handling makes downloading binary files (videos) and uploading them to storage easier. |
Make | 4/10 | Similar to n8n, handles binary file manipulation well. |
Python | 6/10 | yt-dlp or similar libraries make this easy in Python, but hosting the script and storage requires a server. |
The Goal: Select a previous blog post, summarize it, and cross-post to Facebook, X, Instagram, and TikTok.
Table 1.3: Automation Difficulty & Implementation
Platform | Difficulty | Key Challenge |
Zapier | 3/10 | Very strong social media integrations. Posting to TikTok/Instagram usually requires a business account connection. |
Make | 3/10 | Great for "router" logic (e.g., "If post has image -> Instagram; If text only -> X"). |
Pipedream | 5/10 | managing 4 different OAuth tokens for social platforms can be tedious in code. |
To wrap up, here are the specific tools I used for these experiments.
Table 2.0: Platform Feature Matrix
Tool | Cost | No Code | Low Code | AI Support | Custom Code | Database Int. |
n8n | Free (Self-hosted) / $20 Cloud | ✅ | ✅ | ✅ | ✅ (JS/Python) | ✅ |
Make | Free / $9/mo | ✅ | ✅ | - | - | ✅ |
Zapier | Free / $19.99/mo | ✅ | - | ✅ | ✅ (Python steps) | ✅ |
Pipedream | Free / $19/mo | - | ✅ | ✅ | ✅ (Node/Python) | ✅ |
Pokee AI | Varies | ✅ | - | ✅ | - | ✅ |
Software & APIs Used:
If you want speed and don't mind the cost, Zapier is the undisputed king of "Vibe Coding"—it just works. If you want to build a complex "software" backend without maintaining a server, n8n and Make offer the visual control you need. For those who want to own every line of logic, Python (aided by Pipedream) remains the gold standard.
(Future updates will include detailed code snippets for the Pipedream and Python implementations.)
