My favorite lens on SQLite is that it is actually two things:
1. A robust durability implementation
2. A library of high performance data structure and algorithms
The fact this it's SQL is nice, but those two attributes are what make it great.
For example, I'm implement an in-process event log that I want to be durable. I started simple, but soon saw some edge cases and instead of playing whackamole I just swapped to using sqlite as an ordered kv store that gives me ACID.
Another example: ingesting multiple inter related datasets. Instead of a dozen hash maps in memory, I load them up into sqlite (no persistence) and then slice and dice as I need to.
I wonder how Sonnet vs Opus stacks up in a similar time-based comparison.
How far behind are open models compared to Sonnet?
It may be that the absolute SOTA models are way ahead of open models, but the gap in the mid tier really does feel like it's compressing. I'd love to see empirical data about it though.
I'm running opencode with qwen3.6-35b-a3b at a 3-bit quant. I also have qwen3.5-0.8b used for context compaction. I run with 128k context.
It's usable. I set it loose on the postgres codebase, told it to find or build a performance benchmark for the bloom filter index and then identify a performance improvement. It took a long time (overnight), but eventually presented an alternate hashing algorithm with experimental data on false positive rate, insertion speed and lookup speed. There wasn't a clear winner, but it was a reasonable find with rigorous data.
Long agent runs make such a difference. We focus a lot on new models and long context, but the bigger impact is in automatic verification.
I've been leaning in more on e2e test suites. They are slow, brittle and inefficient. But that's almost a feature. I can step away and come back an hour later, and use that time to think about bigger problems.
Thank you, I just realised we are talking about MTP. It seems that it's not that clear though.
"Currently, the MTP capabilities are primarily accessible through Google's proprietary LiteRT framework, rather than the open-weights versions... Despite the missing MTP heads in the open release, Gemma 4 (specifically the 26B-A4B variant) still demonstrates high efficiency"
Since it's a procedural language, you can't do things like create a new index implementation or something else super low level. But there's still a lot you _can_ do. Like implement a custom comparator for a custom type and then use that type in a btree index.
Given the premise that zero day exploits are going to be frequent going forward, I feel like there is a new standard for secure deployment.
Namely, all remote access (including serving http) must managed by a major player big enough to be part of private disclosure (e.g. Project Glasswing).
That doesn't mean we have to use AWS et al for everything, but some sort of zero trust solution actively maintained by one of them seems like the right path. For example, I've started running on Hetzner with Cloudflare Tunnels.
We've testing something similar, not using in prod yet. Network was 20ms RTT. The real variable was processing time median was sub-5ms most days but some regions would be 30ms for 8-10 hour blocks randomly.
The announcement was that it secured $50M in financing, sold the shoes business for $39M leaving $20M or so in cash.
An empty public company with $70M in financing to enter a hyped market was valued at $115M. The stated intent is to spend their money on a CapEx item with a fairly high demand and resale value (GPUs) in a sector that has a pretty simple playbook.
The 580% bump is a fun headline, but "startup secures $50M in funding at a 5.8x valuation bump" isn't unheard of.
Have I invested? No. Is this a ridiculously funny narrative and story? Absolutely. Is it the most ridiculous valuation I've seen? No.
1. A robust durability implementation 2. A library of high performance data structure and algorithms
The fact this it's SQL is nice, but those two attributes are what make it great.
For example, I'm implement an in-process event log that I want to be durable. I started simple, but soon saw some edge cases and instead of playing whackamole I just swapped to using sqlite as an ordered kv store that gives me ACID.
Another example: ingesting multiple inter related datasets. Instead of a dozen hash maps in memory, I load them up into sqlite (no persistence) and then slice and dice as I need to.
It's a super useful tool.