content-repurposing on S Anand

Derived formats with Gemini

Sat, 18 Apr 2026 11:26:48 -0400

The natural capability of Generative AI is to generate stuff - and Gemini’s particularly good with media.

For example, we can take any document, like this MasterCard report on The State of Open Finance 2026, and generate videos, podcasts, sketchnotes, songs, and more from it.

How?

I uploaded the PDF to NotebookLM and created a 20-minute podcast by clicking on Generate Audio Overview - Deep Dive - English - Default.

Listen to the English podcast

It supports multiple languages, so I generated a Chinese and Filipino version as well.

Listen to the Chinese podcast Listen to the Filipino podcast

Clicking on Generate Video Overview - Cinematic led to this video overview:

Video

There are other formats in which we can generate videos. The Cinematic format is new, and the list is growing.

It’s not just NotebookLM that you can use to generate new formats. Gemini itself supports a variety of formats.

For example, I used my Gemini Sketchnote prompt to create a visual summary of the report:

… and, using Lyria via the “Create Music” option to generate a narrative song with this prompt:

Create a narrative summarizing this article.
Narrate it rather than sing it.
Use a voice like Bobby McFerrin's, as if he were narrating rather than singing.
Keep the music minimal, focus on the voice.

Listen to the narrative song

Next, I had Gemini create a slide deck by uploading the report and prompting:

Convert the attached report into a beautiful slide deck that conveys the most important actionable information for the audience.

STYLE:
Write it McKinsey style with action titles. Just reading the titles should give the audience the entire message of the deck.
Follow the pyramid principle. The contents of the slide should prove the title.
Make the slides content rich, i.e. clear and self-explanatory with enough detail to help the audience understand without a narrator.
Use iconography, typography, stock images, etc. as appropriate.
Write as a single page HTML application.

See the slides.

Then, a set of interactive explainers using this prompt:

Convert this report into 3 interactive explainers.
Pick the parts of the report that are best conveyed through interactive explanations. Identify the 3 most suitable ones.
Each explainer should, using animations, interactions, and simulations, explain a core point made in the report.
Render this as a single page HTML canvas.

See the explainers.

Finally, a narrative data story using Claude – which I could do with Gemini, too, but Claude is better at.

See the story.

Where this is becomes practical is in:

Proposals. No one pays attention to that company slide or RFP response. A 3-min video or 15-min podcast lets them absorb it during a walk.
Reviews. Skip copy-pasting metrics into PowerPoint. Feed the raw data and ask for a McKinsey-style deck with action titles.
Onboarding. Instead of a 100-page SOP or compliance manual, how about interactive explainers or a localized audio guide in Mandarin or Spanish?
Manuals: How about a visual sketchnotes or step-by-step interactive flows from that documentation for call center agents?
Case studies. Text-heavy fails. Maybe a 60-second narrative data story or sketchnote accompanied an upbeat narrative song?
Reports. No one reads the 10-page competitor analysis. A 5-minute podcast or a single-page visual sketchnote helps the execs.
Training. Create interactive simulations where people make actual decisions. Simsaram is my favorite example: family relationship training/simulation based on an iconic film.
Emails. Why not use illustrations, sketches, flowcharts, etc. to liven up internal / external emails?

When generative AI makes generation easy, why not generate actually interesting stuff?

Repurposing blog posts for talks

Sun, 22 Feb 2026 22:09:49 +0800

Recently, I’ve re-used my own writing / transcripts as context to LLMs. For example, I’ve used:

This repurposing can be used for so many things.

For example, before delivering a talk to journalists “Review my Feb 2026 LLM posts and generate a single-sentence, ELI15 high-impact use case for journalists.” gets me list of use cases. Now, all I have to do is show what I did and share how it’s relevant for them, like:

I found old friends with Gemini Deep Research. You can trace sources who changed names.
I transcribed the entire Dilbert archive. You can transcribe scanned court records for $20.
… and so on.

I can do this for any field.

The approach is simple. I copy recent blog posts related to LLMs via:

rg -l "^[[:space:]]*- llms" -g "*.md" | xargs rg "^date:" | sort -k2 -r | head -n 30 | cut -d: -f1 | uniq | xargs uvx files-to-prompt --cxml | xclip -selection clipboard

… and ask the LLM:

Pick articles with the best application to [FIELD]. For each, write a single-sentence, ELI15 use case DIRECTLY following from the article. Focus on high-impact, common scenarios solving a specific problem (a persona trying to do X but blocked by Y). Skip articles without a clear, practical application. Be ruthlessly concise: ~40 words per use case. Format each line as: Article Title: Use case description.

Here are a few examples (and they’re good ones):

See journalist use cases

Extracting AI Advice: A journalist who has obtained hundreds of FOI documents or deposition transcripts can use a cheap large-context model to pull one-sentence bullets from each and then ask a stronger model to rank the top patterns – turning months of reading into an afternoon.
Finding Old Friends with Gemini: An investigative journalist can use Gemini Deep Research to trace sources who have changed names, employers, or countries – surfacing government nomination lists and public records that bridge who someone used to be with who they are now.
Gemini 3 Flash OCRs Dilbert Accurately: A journalist who receives a dump of scanned physical documents – leaked government files, court records – can make the entire archive full-text searchable for roughly $20, without waiting weeks for manual transcription.
Organizing PDF Receipts: An investigative journalist who obtains a dump of expense or procurement PDFs can ask an AI coding tool to parse each one and rename it to a standard format – making it trivial to spot duplicate amounts, unusual vendors, or suspicious date gaps at a glance.
RIP, Data Engineers: A data journalist who obtains the SQL query logs of a public institution can use an AI agent to cluster what questions the database was actually built to answer – revealing whether an agency is doing tactical monitoring or genuine public-service analysis, which can itself be the story.
Transcript AI-ded Interviews: A journalist who has done twenty interviews for a long-form profile can feed all the transcripts to a large-context model and ask it to surface the best quotes on a given angle, or flag contradictions between what different sources said – without re-reading every word.
Using AI for Work News: A beat journalist without a research desk can set up a Google Workspace automation that scans their sources weekly and delivers a single Gemini-written brief – so nothing from a regulator’s filing or a council report slips through unread.
Writing Articles from My Blog Posts: A reporter with years of stories on the same beat can feed their archive to an AI, ask it to identify which past pieces form the strongest foundation for a new investigation, and get a draft synthesis written in their own voice – rather than starting from a blank page.

See civic use cases

Breaking Rules in the Age of AI: A government adult literacy programme can drop its AI ban and replace it with instant AI-graded feedback – letting learners ask questions in their own language, fail and retry freely, and delegate tedious steps – re-engaging adults who had already given up on education.
Extracting AI Advice: A city welfare department can transcribe its 300 community consultations into one-sentence bullets and rank the top 10 concerns across meetings in the residents’ words – for a few dollars and hours.
Finding Old Friends with Gemini: A social welfare officer can use Gemini Deep Research to trace former programme participants who have moved or changed names, surfacing public records that link old and new identities – closing the loop on services that were started but never finished.
Gemini 3 Flash OCRs Dilbert Accurately: A city archivist can make thousands of scanned land records, court orders, and petitions fully searchable by running them through Gemini Flash at roughly $20 for an entire archive – no in-person visit required.
Organizing PDF Receipts: An NGO finance coordinator can ask an AI coding tool to write a script that reads every vendor PDF, extracts the date, amount, and reference, and renames the file to a standard format – turning a half-day filing chore into a five-minute step before uploading to a compliance portal.
RIP, Data Engineers: A government department can expose its data culture – and fix silent metric misalignment between teams – by feeding its SQL query logs to an AI agent that clusters them and proposes a small set of shared standard tables, no data warehouse project required.
TDS Comic Generation: A district health office with no design budget can generate multilingual public health comics for low-literacy communities by defining a few recurring characters, writing their dialogue in plain language, and letting Gemini produce each strip in minutes – at near-zero cost per language.
Transcript AI-ded Interviews: A government communications officer can feed all auto-generated meeting transcripts on a topic to a large-context model and get a press-ready 150-word statement grounded in what the department actually said – in an hour instead of a week of document hunting.
Using AI for Work News: A district administrator receiving eight separate departmental reports can set up a 20-minute Google Workspace automation that delivers one weekly email surfacing cross-department clashes – like a public works delay that will knock out a scheduled health camp – that no individual report would flag.
Writing Articles from My Blog Posts: A civic think tank analyst with 40 research reports can ask AI to pick the strongest op-ed angle for a target publication and draft it from their own words – in an afternoon, not a week of rewriting from scratch.

See community builder use cases

Extracting AI Advice: A community manager who has years of recorded Q&A sessions and AMAs can map-reduce all the transcripts to find the top recurring questions members actually ask – then build a self-serve knowledge base from members’ own words rather than guessing what to put in it.
Finding Old Friends with Gemini: A community builder trying to re-engage lapsed members can use Gemini Deep Research to find where they are now – new employer, new city, new name – and reach out at the right career moment rather than to a dead email address.
Using AI for Work News: A community manager can set up a weekly automation that scans public sources for what members have been doing – new articles, talks, job changes, launches – and auto-drafts a “members in the news” section for the newsletter that would otherwise go unwritten for lack of time.
Transcript AI-ded Interviews: A community builder who runs office hours or mentorship sessions can synthesise all the session transcripts to surface the top recurring problems members raise – then use that signal to design better programming rather than guessing what the community actually needs.
TDS Comic Generation: A community builder can create a recurring comic strip with a few consistent mascots to announce events, explain community norms, or celebrate member milestones – something memorable and shareable in a way that a plain-text post is not, and producible in minutes with no design budget.

When I delivered the Society for Clinical Data Management keynote, they audience was surprised how much I knew about their field because I spoke about Informed Consent Forms and Extracting Schedule of Assessments and so on. Truth is, I know nothing about these. Claude created the slides. I asked it to explain enough so I can talk through it.

I didn’t get the implication then, but I think I do now, and the implication is stunning. I now have material to deliver a talk to any audience.

So far, I’ve been limiting myself to technical talks. Why bother? I can speak to any audience about using AI in their field.

Human Resources & Organizational Design: HR leaders are drowning in qualitative data (interviews, performance reviews, employee sentiment) and are terrified of being left behind by AI.
Marketing & Communications: CMOs are under pressure to produce more content with fewer resources. They want to see live workflows of how a single blog post or chat transcript can be repurposed into a full campaign.
Finance & Banking: This sector is heavily regulated and drowning in unstructured paperwork. Extract specific clauses from 100-page compliance documents will immediately capture the attention of risk officers.
Event Management (MICE): The industry that organizes conferences is itself looking to modernize. Matching attendees, transcribing massive archives of past events, or predict logistical needs is highly relevant.

Time to get out of my comfort zone!

Writing articles from my blog posts

Mon, 16 Feb 2026 18:03:12 +0800

You can use AI to submit not just talk proposals but entire articles from your past work.

Ranjeeta said Built In wants an article and had written one on my behalf.

If someone’s going to write for me, I’d rather pick an AI! So here’s what I did:

Research the audience

So I asked Gemini to research and suggest topics:

I received a request to write an article for Built In (https://builtin.com/), an online community and publication for startups and tech companies.

Research this publication. How large is it? How respected is it? Give me an intuition comparing with other publications.

Also help me with the type of content to write. What do its readers read? What’s popular? Among the most popular articles, what’re typical article lengths? What is the most appreciated style of writing? Are these heavily linked, lightly linked, or not at all? Are they illustrated - and if so, how, e.g. images, charts, animations, videos, etc. and in any case, is this appreciated or frowned upon? What’s the review process like?

Knowing me, what are examples of topics you’d suggest I write about that will be well received and popular with this audience?

It said basically told me:

Built In is basically “HBR for coders” - huge reach (3M+), totally legit, and targets mid-to-senior tech pros who want the real scoop, not just fluff.
They want “in the trenches” war stories and “how-to” career/tech guides (1k-1.5k words). Less “breaking news,” more “here’s how I actually built this.”
Keep it conversational and punchy (no jargon); no selling, but linking your blog/open-source stuff is cool.
Pitch “I replaced my workflow with AI” (hot topic), “Chat is the new Dashboard” (your wheelhouse), or “Prototyping your Career” (agile life advice). The AI angle works.

Suggest topics from existing content

This is the cool part. I told it:

I’d like to re-purpose one or some of my blog posts. Here are my LLM-related blog posts in 2025-26. I’d like you to recommend the top 3 articles I could write. Each article could cover 1 or more posts about a single topic. Think about what will be most impactful for this audience as well as unique, i.e. a lot less covered elsewhere.

… and then passed it all of my 2025-2026 blog posts related to LLMs using:

ug -l ' - llms' \                 # Find all LLM-related posts
  | ug '2025|2026' \              # From 2025-206
  | uvx files-to-prompt --cxml \  # Convert to XML for better LLM ingestion
  | xclip -selection clipboard    # Copy to clipboard and paste into Gemini

Gemini churned and suggested three topics.

Vibe Coding: It’s not magic, it’s a gamble-awesome for rapid prototypes but risky for production.
RIP Data Engineering: Stop wasting 15 years fixing fragile web scrapers-just tell the AI.
The AI Mirror: Flip the script and use AI to roast you-feed it your meeting transcripts.

Find best content for topic

I like #3 - AI Mirror: Using LLMs to Decode Your Leadership Style.

Look at my blog posts and see which ones are most apt for this topic. Then synthesize from these, writing as much as possible with the same words I used in my blog posts, and combine them into an article for Built In.

Plan first. Think hard about the approach, then execute carefully.

It wrote a nice, long rubbish article that had little of my style. But, why be rude?

Nice structure. But rewrite it using exactly my words in every place unless unavoidable.

It wrote another nice, long, rubbish article with little of my style. I don’t give third chances.

List all the files you used as reference. Then list other files that would align with this topic. In other words, I would like all the file names directly or indirectly related to this topic that can serve as source material for this.

… and it gave me the most relevant articles.

Generate the article using my words

I started a new chat to avoid confusion and told it to:

Write an article for Built In. Read the section below for context on Built In’s audience, style, and content preferences. Then read my blog posts further down. Based on that, write an article titled: “AI Mirror: Using LLMs to Decode Yourself”.

This should be a synthesis of my words, i.e. always use my blog post content, except for transitions, etc. Select content and include images, data visualizations, links, etc. aligned with Built In’s style.

Then I fed it all the context about Built In as well as the relevant blog posts.

The result was pretty good. It was mostly in my voice - literally copy-pasted from my articles. I just made some edits where it had used its own words.

The best part is that when AI is assembling your own words, it’s acting more like an editor, not an author. That probably isn’t slop, right?

Mon, 01 Dec 2025 05:36:05 +0000

I joined Madhu Sathiaseelan’s podcast to talk about LLM Psychology.

But it’s also fascinating to see how much SECONDARY content you can generate from a video.

Do you prefer sketch-notes? See Nano Banana Pro’s version below.

Or are you a slides person? https://sanand0.github.io/talks/2025-11-06-llm-psychology/

How about a Malcolm Gladwell article? https://github.com/sanand0/talks/raw/refs/heads/main/2025-11-06-llm-psychology/mind-readers.docx

Or reading the raw transcript? https://github.com/sanand0/talks/tree/main/2025-11-06-llm-psychology

The way in which we consume information is entirely up to us. This is making a lot more content (e.g. research papers, government regulations, medical reports, policy documents, product manuals, …) accessible to me - just by asking it to rewrite it as a sketch-note, slides, article, or anything I prefer.