29 November 2023
Use PromptingTools.jl to quickly analyze YouTube video transcripts with Generative AI, allowing you to determine in just 30 seconds whether a video is worth watching, saving time and effort.
In today's digital age, keeping up with the constant stream of online content can feel like trying to drink from a firehose. So, what if I told you that you could scan a YouTube video for its key takeaways in just 30 seconds? Let's dive into how you can use Generative AI, specifically with Julia Language's PromptingTools.jl package, to achieve this.
Let's take a "not-so-random" video from this year's JuliaCon: Julia-fying Your Data Team: the Ultimate Upgrade. Perhaps, you are curious about the content but unsure if it's worth your time. Here's how you can quickly find out:
Step 1: Download the Transcript
Go to the YouTube video
Below the video, click on the 'more' option to expand extra information
Scroll to the Transcript section and click 'Show Transcript'
Copy the transcript (you can save it to a file or a variable, depending on its size)
Step 2: Analyze with GenAI
In Julia, use PromptingTools.jl and run the template :AnalystChaptersInTranscript on your transcript
In moments, you'll get a detailed chapter analysis
txt = read(open("youtube-juliafying-your-data-team.txt", "r")) |> String
# See the details: aitemplates("AnalystChaptersInTranscript")
msg = aigenerate(:AnalystChaptersInTranscript; transcript=txt, instructions="None.", model="gpt4t")[ Info: Tokens: 4082 @ Cost: \$0.0543 in 46.9 seconds
AIMessage("# Chapter 1: Introduction to Julia Adoption in Business [00:00:00]
- The speaker discusses the adoption of the Julia programming language in business settings, particularly for decision intelligence.
- A quick audience interaction reveals that many use Julia within their teams.
- The speaker introduces himself as Ian Shimo, a data head working on decision intelligence at LexisNexis and presents his wide experience across different roles where Julia could be impactful.
# Chapter 2: What is Decision Intelligence and Why Use Julia? [00:01:29]
## Section 2.1: Defining Decision Intelligence [00:01:29]
- Decision intelligence involves taking on complex business problems that require coordination across multiple disciplines and data sources, aligning closely with stakeholders, and delivering business value.
- The speaker posits that Julia is the ideal tool for decision intelligence due to its capabilities in helping users learn faster, build faster, and create better solutions for business advancement.
## Section 2.2: Learning and Building Faster with Julia [00:02:27]
- Julia enables quick learning because knowledge from one application easily transfers to others.
- The speaker highlights Julia's excellent readability, reusability, and easy look-under-the-hood features, leading to a quicker time to learn.
- Julia's power lies in its composability and its tool ecosystem, which allows seamless work across various environments.
## Section 2.3: Building Better with Julia [00:04:57]
- Julia doesn't confine the user to the limitations of available packages and allows for solving unique business problems efficiently.
- The speaker presents an example of building an insights recommender system in just 200 lines of code.
- Emphasizes that Julia helps write code that is easier to understand, less error-prone, and tailored to one's unique business needs.
# Chapter 3: Return on Investment and How to Implement Julia [00:05:53]
## Section 3.1: Assessing the Business Case for Julia [00:05:53]
- With an initial learning time investment of one to two months, Julia can potentially halve project completion times.
- Investing in Julia can thus result in significant business returns.
## Section 3.2: Strategy for Adopting Julia [00:06:19]
- For success with Julia, businesses should focus on decision intelligence work, foster a team that is curious and eager to learn, and embrace necessary shifts in mindset.
- Key mindset shifts include proper use of Julia environments, embracing Julia's methods and types, and defining problems as unitary as possible.
# Chapter 4: Lessons Learned and Conclusion [00:08:28]
- Avoid trying to convert everyone to Julia immediately and refrain from premature optimizations—focus on building first.
- The speaker encourages trying out Julia, suggesting that with its powers, businesses can blaze their own trail rather than just keeping up with the times.
# Chapter 5: Q&A and Advice for Promoting Julia Adoption [00:09:16]
- In response to a question on promoting Julia at lower positions within a company, the speaker advises showcasing Julia's strengths through practical demonstrations.
- Examples such as fast-loading Pluto notebooks can visually demonstrate Julia's advantages over competitors like Tableau.
- The approach is to showcase focused powerful applications of Julia to gain buy-in from higher-ups.")For a nicer display, I would recommend using Julia Markdown Markdown.parse(msg.content) or just copying it to a separate window in VSCode clipboard(msg.content)
All this in just a few seconds! Yeah, this video sounds interesting, I should probably watch it! 😃
GenAI isn't just about quick analysis; it's about customization.
One of the best things is that it allows you to effortlessly increase or decrease the level of detail of a summary, similar to having zoom-in and zoom-out buttons but for the amount of information!
My favorite instruction when I want to quickly scan something is to say “Make it 5 words at maximum” or "Maximum 5 words per bullet point" and run it again:
msg = aigenerate(:AnalystChaptersInTranscript; transcript=txt, instructions="Maximum 2 bullet points per section or chapter. Maximum 5 words per bullet point.", model="gpt4t")[ Info: Tokens: 3729 @ Cost: \$0.0434 in 21.5 seconds
AIMessage("# Chapter 1: Julia Adoption in Business [00:00:00 - 00:09:18]
- Discussing Julia's advantages.
- Focused on decision intelligence.
## Section 1.1: Benefits of Julia [00:17:00 - 00:59:00]
- Enhances learning and building.
- Encourages coding flexibility.
## Section 1.2: Decision Intelligence Defined [01:29:00 - 02:05:00]
- Involves complex end-to-end problems.
- Julia suits this niche.
## Section 1.3: Learning and Composability [02:07:00 - 04:49:00]
- Julia's learning curve short.
- Composable tools increase flexibility.
## Section 1.4: Building Better with Julia [04:57:00 - 05:49:00]
- Focus on unique problems.
- Write less, achieve more.
## Section 1.5: Implementing Julia [05:56:00 - 08:25:00]
- Start with right projects.
- Team needs curiosity and bravery.
## Section 1.6: Challenges and Mindset Shifts [08:01:00 - 08:45:00]
- Utilize environments, methods, types.
- Solve smallest unit problems.
## Section 1.7: Adopting Julia at Lower-Company Levels [09:18:00]
- Present Julia's strong features.
- Showcase through practical demonstrations.")Talk about a lifesaver! Now, I can jump to the specific section I need without having to watch the entire video.
Of course, use it whenever you can! The benefits of using PromptingTools.jl are:
The full power of Julia REPL at your disposal (eg, chunk long documents, merge answers, scrub sensitive information)
Automate tasks, eg, "Summarize these 20 Youtube videos in my Watch Later list and save it as a nicely formatted Markdown file or a Quarto document"
Leverage intricate templates in PromptingTools.jl and placeholders in them (eg, just provide the transcript and we take care of the rest)
In less than a minute, you've analyzed the key messages and extracted their timestamps - that's ~10x timesaving compared to watching the entire video!
Stay tuned for more GenAI mini-task magic in upcoming posts, where we'll continue to explore practical, time-saving uses of Generative AI for everyday tasks. Happy coding and watching! 🚀📺