I believe OP is talking about new sessions or after compaction. He’s getting at the fact that LLMs are stateless and have to rediscover your codebase on every new session.
For me, the concern about SQLite has never been if the database engine itself is “reliable for real data”, but that storing data on a single node is not “reliable for real data”. Performance aside, what you are positing is no different than dumping everything to a text file on disk. What happens if that VM dies?
There’s no better option today because it’s impossible to make it a better experience. That machine at home will need upgrades, it could fail, it costs thousands, it sucks lots of power. There is no mass market appeal.
What you’re describing is more likely to manifest as a proprietary product from someone like Samsung or Ring (likely both!) than an open standard AI server that integrates with everything in your home automatically. This is exactly like what we have today with security systems and smart appliances. You have managed services and you have Home Assistant in your homelab.
I don’t think there’s anything different between what you’re suggesting and a homelab. Most people do not have a homelab and are happy to offload services like photo storage or security to remote providers.
Seems like a great fit - kinda surprised it didn’t happen sooner. I think we are deep in the valley of local AI, but I’d be willing to bet it breaks out in the next 2-3 years. Here’s hoping!
My pessimism is mostly rooted in the VC economics of it all. The vision is great, but its a busy space and there's no actual product or business. They basically wrote the guy a check to build the spaceship in space.
Ignoring the VC economics and awful name, I won’t be as pessimistic as everyone. I see the vision.
That said, nobody knows what the AI future looks like. Entire’s entire thesis is a solution for something we don’t even know we need. It’s a massive bet and uphill battle. Traditionally, dev tool success stories come from grassroots projects of developers solving their own problems and not massive VC funded efforts that tell you what you need to do.
> The initial excitement of LLMs has significantly cooled off, the model releases show rapidly diminishing returns if not outright equilibrium and the only vibe-coded software project I've seen get any actual public use is Claude Code, which is riddled with embarrassing bugs its own developers have publicly given up on fixing. The only thing I see approaching any kind of singularity is the hype.
I am absolutely baffled by this take. I work in an objectively high stakes environment (Big 3 cloud database provider) and we are finally (post Opus 4.5) seeing the models and tools become good enough to drive the vast majority of our coding work. Devops and livesite is a harder problem, but even there we see very promising results.
I was a skeptic too. I was decently vocal about AI working for single devs but could never scale to large, critical enterprise codebases and systems. I was very wrong.
Your sentiment resonates with me a lot. I wonder what we’ll consider the inflection point 10 years from now. It seemed like the zeitgeist was screaming about scaling limits and running out of training data, then we got Claude code, sonnet 4.5, then Opus 4.5 and no ones looked back since.
That’s kind of my point. They’re not really in competition. I bet they’d have an easier time with this scale if they were on SQL Server, but obviously that migration isn’t happening and startups don’t reach for it for many reasons.
I had never written an iOS app until a couple months ago and was initially very put off when I hit the same wall. The alternative is to host on a cheap VPS and find some way to prevent other people from using your app. When you cost it out, it's close enough to the 100 bucks a year for the Apple account. However, the kicker for me is the side loading process. Way too much headache compared to a deploy script that has my changes running nearly instantly.