250
Most of us will leave behind a large ‘digital legacy’ when we die. Here’s how to plan what happens to it
(theconversation.com)
this post was submitted on 25 May 2025
250 points (97.3% liked)
Technology
72049 readers
2734 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Bear in mind, though, that the technology for dealing with these things are rapidly advancing.
I have an enormous amount of digital archives I've collected both from myself and from my now-deceased father. For years I just kept them stashed away. But about a year ago I downloaded the Whisper speech-to-text model from OpenAI and transcribed everything with audio into text form. I now have a Qwen3 LLM in the process of churning through all of those transcripts writing summaries of their contents and tagging them based on subject matter. I expect pretty soon I'll have something with good enough image recognition that I can turn loose on the piles of photographs to get those sorted out by subject matter too. Eventually I'll be able to tell my computer "give me a brief biography of Uncle Pete" and get something pretty good out of all that.
Yeah, boo AI, hallucinations, and so forth. This project has given me first-hand experience with what they're currently capable of and it's quite a lot. I'd be able to do a ton more if I wasn't restricting myself to what can run on my local GPU. Give it a few more years.
You said you released it on your writing. How did you go about doing that? It's a cool use case, and I'm intrigued.
It's a bit technical, I haven't found any pre-packaged software to do what I'm doing yet.
First I installed https://github.com/openai/whisper , the speech-to-text model that OpenAI released back when they were less blinded by dollar signs. I wrote a Python script that used it to go through all of the audio files in the directory tree where I'm storing this stuff and produced a transcript that I stored in a .json file alongside it.
For the LLM, I installed https://github.com/LostRuins/koboldcpp/releases/ and used the https://huggingface.co/unsloth/Qwen3-30B-A3B-128K-GGUF model, which is just barely small enough to run smoothly on my RTX 4090. I wrote another Python script that methodically goes through those .json files that Whisper produced, takes the raw text of the transcript, and feeds it to the LLM with a couple of prompts explaining what the transcript is and what I'd like the LLM to do with it (write a summary, or write a bullet-point list of subject tags). Those get saved in the .json file too.
Most recently I've been experimenting with creating an index of the transcripts using those LLM results and the Whoosh library in Python, so that I can do local searches of the transcripts based on topics. I'm building towards writing up something where I can literally tell it "Tell me about Uncle Pete" and it'll first search for the relevant transcripts and then feed those into the LLM with a prompt to extract the relevant information from them.
If you don't find the idea of writing scripts for that sort of thing literally fun (like me) then you may need to wait a bit for someone more capable and more focused than I am to create a user-friendly application to do all this. In the meantime, though, hoard that data. Storage is cheap.
It sounds like something similar to RAG (retrieval augmented generation) or a database lookup. Are you storing the transcripts in a SQL like database or noSQL db or doing semantic similarity on any of it?
I was thinking of a similar project and building a knowledge graph for each person.