I run multiple $10K MRR companies on a $20/month tech stack

> The enterprise mindset dictates that you need an out-of-process database server. But the truth is, a local SQLite file communicating over the C-interface or memory is orders of magnitude faster than making a TCP network hop to a remote Postgres server.

I don't want to diss SQLite because it is awesome and more than adequate for many/most web apps but you can connect to Postgres (or any DB really) on localhost over a Unix domain socket and avoid nearly all of the overhead.

It's not much harder to use than SQLite, you get all of the Postgres features, it's easier to run reports or whatever on the live db from a different box, and much easier if it comes time to setup a read replica, HA, or run the DB on a different box from the app.

I don't think running Postgres on the same box as your app is the same class of optimistic over provisioning as setting up a kubernetes cluster.

For read only it can be a great option. But even then I would choose D1 which has an amazing free tier and is sqlite under da hood.

The most interesting thing in here is https://github.com/smhanov/laconic which is the author's "agentic research orchestrator for Go that is optimized to use free search & low-cost limited context window llms".

I have been doing this kind of thing with Cursor and Codex subscriptions, but they do have annoying rate limits, and Cursor on the Auto model seems to perform poorly if you ask it to do too much work, so I am keen to try out laconic on my local GPU.

EDIT:

Having tried it out, this may be a false economy.

The way it works is it has a bunch of different prompts for the LLMs (Planner, Synthesizer, Finalizer).

The "Planner" is given your input question and the "scratchpad" and has to come up with DuckDuckGo search terms.

Then the harness runs the DuckDuckGo search and gives the question, results, and scratchpad to the Synthesizer. The Synthesizer updates the scratchpad with new information that is learnt.

This continues in a loop, with the Planner coming up with new search queries and the Synthesizer updating the scratchpad, until eventually the Planner decides to give a final answer, at which point the Finalizer summarises the information in a user-friendly final answer.

That is a pretty clever design! It allows you to do relatively complex research with only a very small amount of context window. So I love that.

However I have found that the Synthesizer step is extremely slow on my RTX3060, and also I think it would cost me about £1/day extra to run the RTX3060 flat out vs idle. For the amount of work laconic can do in a day (not a lot!), I think I am better off just sending the money to OpenAI and getting the results more quickly.

But I still love the design, this is a very creative way to use a very small context window. And has the obvious privacy and freedom advantages over depending on OpenAI.

Nice tech read, but without information about which companies, doing what, just feels way too click-baity.