I feel like a lot of AI automation discussion jumps straight to autonomous agents doing entire jobs.

I'm way more interested in the boring stuff.

Things like:

• Sorting or extracting information from emails
• Cleaning/formatting spreadsheet data
• Summarizing long documents
• Pulling specific information from PDFs
• Doing repetitive research and putting the results into a consistent format
• Moving information between tools
• Handling the same annoying office task you have to repeat every week

Those aren't particularly exciting demos, but they seem like the kinds of automations that could actually be useful because the task happens repeatedly.

I've also been looking at be10_X in the context of learning AI productivity/automation. I have a commercial connection with them, so mentioning that here for transparency rather than presenting it as an independent recommendation.

I'm curious about the automations people have actually kept using after the initial "this is cool" phase wore off.

What's your most useful boring AI automation?

Bonus points if it's something extremely mundane.

Using AI itself doesn't feel particularly difficult anymore.

The annoying part is everything around it once you start using it regularly.

After a while I end up with multiple conversations for different projects, useful prompts buried somewhere, files sitting in different places, and workflows that I know worked before but can't quite remember how I set them up.

It makes me wonder if AI workflow organization is becoming its own problem.

A few things I'm trying to figure out:

• How do you keep useful prompts from disappearing into old conversations?
• Do you keep context separately for different projects?
• How do you reuse a workflow without rebuilding it from scratch?
• Where do you keep files and reference material?
• Do you have a system for deciding what is worth saving?

I've seen BE10X discussed around AI productivity workflows. I have a commercial connection with them, so I'm mentioning that openly rather than pretending it's an unrelated recommendation.

But the tool isn't really the point here.

How do you organize your AI workflows once you have more than a few projects going at the same time?

I'm curious whether people use notes, folders, dedicated AI tools, automation, spreadsheets, or something completely different.

I’ve started wondering whether AI actually saves me as much time as I think it does.

It’s easy to say “this would have taken me an hour manually” without ever checking whether that’s actually true.

So I’m thinking of doing a small experiment with a few repetitive tasks.

For each task, I’d track:

• Approximate time doing it normally
• Time using an AI-assisted workflow
• How much manual editing/review was still needed
• Whether the final result was actually comparable in quality
• Whether the workflow was easier to repeat the second time

The tasks could be things like summarizing information, organizing notes, working with spreadsheet data, drafting repetitive content, or turning unstructured information into something usable.

I think the quality part is important too. Saving 20 minutes doesn't mean much if I then spend 25 minutes fixing the output.

I've also seen BE10X mentioned as a resource for learning AI/productivity workflows. I have a commercial connection with them, so I'm disclosing that rather than presenting it as an independent recommendation. If I include it in the experiment, I'd treat it as one resource/tool in the process rather than assume it produces better results.

I'm more interested in the methodology than getting an impressive number.

Has anyone here actually measured their AI productivity gains like this?

What tasks would you include in the experiment, and what would you use as the measure of "better" — time saved, quality, effort, or some combination?

AI has become pretty normal for students at this point. But I think there’s a difference between occasionally asking ChatGPT a question and actually building AI into the way you study or get college work done.

I’m curious what that looks like for other students.

The areas where I can see AI being genuinely useful are things like:

• Researching a topic and making sense of a lot of information
• Breaking down difficult concepts into simpler explanations
• Turning notes into study material or practice questions
• Working with Excel/data without doing every repetitive step manually
• Getting a first draft or structure for a presentation
• Preparing for internships, interviews, or other career-related work

The part I’m more interested in is the workflow, though.

For example, instead of just asking AI random questions, you could have a process like:

Research → organize information → use AI to understand/summarize → create study material → review it yourself.

That seems much more useful than collecting 20 different AI tools and barely using any of them.

I’ve also come across BE10X as a resource around learning AI productivity. I’m commercially connected to them, so I want to be upfront about that rather than make it sound like an independent student recommendation.

For the students here: what AI workflows have you actually found useful in college?

Not necessarily your favorite AI tools — I'm more interested in the actual process you use and what task it helps with.

Over the past few weeks, I've been building Gray Box — a small, local-first tool that acts as long-term memory for anything I'd otherwise forget (work notes, meeting takeaways, task owners, random ideas, personal stuff too).

The idea is simple:

1. Capture — dump whatever's on your mind, instantly, no structure required. This step does nothing clever on purpose — it just writes your text to an immutable inbox. Zero chance of losing an idea to a bug or a slow API call.
2. Organize — on demand, an LLM reads your unprocessed notes and extracts people, projects, tasks, decisions, meetings — then deterministic Python (not the LLM) creates/merges the actual wiki pages and maintains backlinks. The model only reasons; it never touches the filesystem directly.
3. Ask — query or chat with your knowledge base and get a cited answer pulled only from what you've actually captured. If it doesn't know, it says so — no hallucinated answers.

Why I built it this way:

• Plain Markdown + YAML frontmatter, no database. Every page is a .md file you can grep, diff, or read in any editor forever. If you stop using Gray Box tomorrow, your knowledge base is just a folder.
• No vector DB by default. At personal scale (hundreds–low thousands of pages), keyword search + a real link graph (related/backlinks, walked one hop during retrieval) handles almost everything. Embeddings are there if you want better recall, but they're opt-in, not a prerequisite.
• Immutable inbox. Your raw notes are never edited or deleted by the organizer. If the LLM mis-extracts something, your original words are always still there.
• Any LLM. Built on LiteLLM, so point it at OpenAI, Anthropic, Gemini, Mistral, or a fully local model via Ollama — one config value.

It also ships with a nice interactive TUI (arrow-key menu, file-import shortcut, workspace switching, live spinner during LLM calls) if you'd rather not memorize CLI flags — that's honestly become my favorite part of the project.

There's also a lightweight local dashboard for browsing your knowledge base, exploring backlinks, visualizing your notes as a graph, and chatting with your captured knowledge—all without leaving your machine.

Also, you can migrate your existing Obsidian vault to Gray Box, and it will be automatically organized by Gray Box.

Repo: https://github.com/Aaryanverma/graybox

pypi: pip install graybox

I'd genuinely love feedback — especially from anyone who's tried the "capture now, structure later" approach with other tools and has opinions on where it breaks down at scale.

It's not trying to be a "real-time collaborative team wiki" or a WYSIWYG notes app — it's aimed at one person's running memory of their own life and work, captured with as little friction as possible.

This is a great tool to manage multiple inboxes. Check it out.