u/Zestyclose-Ear-6225

I get why companies need secret scanning, but I’m curious where solo devs draw the line.

If you’re building side projects alone, using private repos, and not working with a big team, do you still run secret scanning? Or do you just rely on not being careless with commits?

I’ve definitely had moments where I almost committed something dumb, so I can see the value. But some security tooling feels made for bigger teams with compliance needs, not one person shipping after work.

What’s your setup as a solo dev?

I’m choosing a managed Postgres host for a small production app, and comparing the options has been harder than I expected.

Some look cheap until you need an always-on database. Others include a full backend platform when all I need is Postgres. The larger cloud providers seem reliable, but it’s difficult to estimate the final cost once storage, backups, replicas, and networking are added.

The app isn’t huge, but it has real users, so I’d rather choose something I won’t need to move away from six months later.

For anyone running Postgres in production, which host are you using? How has it been for pricing, backups, performance, and maintenance?

I feel like the same few apps come up every time local AI gets discussed, but there are probably smaller projects people are using that don’t get much attention.

For me, GPT4All is one I don’t see mentioned as much anymore. It’s pretty easy to install and good enough for basic local chat without turning the setup into a project.

What local AI app do you think is underrated right now?

Curious what you use it for and why you think more people should know about it.

I’ve been using AnythingLLM for chatting with PDFs and project docs. It works, but I’m not fully sold on it yet.

The main issues for me are indexing, source quality, and how much the answers depend on the way the files are added. Sometimes it’s useful, sometimes I end up opening the original document anyway.

I’m looking for something local that handles document chat a bit better. Add files, ask questions, get clear sources, update the library, and not spend half the time fixing the setup.

Has anyone moved from AnythingLLM to something else? What are you using now, and is it actually better for local document chat?

I’m trying to use local AI for knowledge management, but I’m still not sure what setup is actually reliable.

Main use case is notes, PDFs, docs, bookmarks, project files, and saved research. I want to search across everything and ask questions without sending the data to a cloud tool.

I’ve tried a couple file chat setups, but the answers depend a lot on how the files are indexed and whether sources are shown clearly.

For people using local AI for knowledge management, what actually works?

What app, model, or workflow are you using, and do you trust it for important info?

I’ve been testing a couple of AI executive assistant tools recently because this category is getting super noisy.

Every tool says it can save you hours or run your day, but once you actually use them, most of them are solving very different problems.

Here’s how it felt to me.

If your calendar is the main problem, Motion, Reclaim, and Clockwise make the most sense. They help block tasks, protect focus time, and stop your week from becoming one big meeting pile.

If you want broader admin or business help, tools like Lindy and Marblism are more relevant. Lindy is powerful if you want to build custom AI agents yourself, but Marblism felt easier to actually start using because the agents are already set up for inbox, socials, SEO, leads, calls, and admin/legal work.

If your inbox is the pain, Fyxer, Shortwave, and Superhuman are probably the better lane. They help with triage, summaries, drafts, and making email less annoying to deal with.

If meetings are killing you, Granola, Fireflies, and Otter are the obvious ones. Notes, transcripts, decisions, action items, all the stuff you think you’ll remember but never do.

If the problem is repeatable busywork, Zapier, Make, and Gumloop are still hard to ignore. They’re not executive assistants in the normal sense, but they’re useful for moving data, updating records, routing leads, and triggering simple workflows.

My main takeaway is that AI executive assistant is kind of a messy label now.

• Some tools manage your time.
• Some manage your inbox.
• Some remember meetings.
• Some move work between apps.
• Some are trying to act more like a mini AI team.

The useful ones are not always the ones with the most features. They’re the ones that remove annoying small tasks from your day without becoming another dashboard you have to babysit.

Has any AI executive assistant actually saved you 6-8 hours a week?

Most local AI apps say they are easy to use, but they still assume the user already understands the space.

You are expected to know what a 7B model is, what GGUF means, why quantization matters, why one model is slow, and why the same prompt gives totally different results across models.

That is fine for technical users, but it makes local AI hard for normal people.

I think the missing layer is not another model runner. It is a product layer that explains tradeoffs in plain language.

Do you think local AI apps should hide most of this complexity, or is that complexity just part of using local AI?