Hacker News

The idea here is that people (like me or you) can create something neat like this, and others can remix it, change it and publish their own version. This is that all in action and it worked great. I wrote a blog about it (the blog is dogfooding, it's just an app hosted on quickish that uses the built in db lib).

For the HN version of this flip board I use their firebase api via the built in quickish server functions that make use of the fact that the front-end can get realtime updates (now that you mention firebase) from cloud function db updates. Of course that's over-kill but I wanted to show something fun. You can remix and host your own version for free, just need a google oauth login that's it.

OG flip board I built (Portland Based - Current Weather): https://popflame.quickish.space/flipboard-preview

Blog post that dives a tiny bit deeper: https://popflame.quickish.space/blog/hacker-news-on-a-split-...

Om Malik has died - https://news.ycombinator.com/item?id=48678852 - June 2026 (161 comments)

https://archive.md/ArXuF

https://www.nbcnews.com/tech/tech-news/us-government-gives-a...

The github repo under eigenpal is gone. The web site at docx-editor is 503. No idea what's going on.

https://archive.ph/PCQQl

https://deploymentsafety.openai.com/gpt-5-6-preview

https://www.youtube.com/watch?v=isKhAyivtfM.

At Weave, we write most of our code with AI, and it's been getting more expensive. This came to a head when Opus 4.7 was released and, thanks to its tokenizer changes, our costs shot up. We knew we didn't need Opus for everything but we didn't want to lose out on the intelligence for the cases where you really need it. So we decided to build a model router to handle this for us.

The Weave Router acts as an Anthropic/OpenAI endpoint specifically for coding agents. It looks at every inference request and intelligently (more on that in a sec) decides what model to send it to, handling all the translations required along the way. So it can use faster/cheaper models (e.g. DeepSeek v4, GLM 5.2, Kimi K2.6) when possible, and frontier models (Opus 4.8 & GPT 5.5 (& Fable whenever it's back)) when necessary.

How do we know what model to route to? We trained an RL model on tens of thousands (so far!) of agent traces. We reward the routing model when it selects an LLM that successfully completes the given task.

Here's an example: if you ask the router to plan a complex change, it will (probably) route that request to Opus 4.8. Subagents exploring the codebase to gather context will be routed to more suitable models (e.g. DeepSeek V4 Flash). Then when you have the plan ready to implement, it will be (most likely) be handed to a quicker model (e.g. GLM 5.2) to carry it out.

We've been using this internally for the last month or so. We've saved 40% on tokens vs. what we otherwise would have paid, with no noticeable differences in quality or velocity.

The router is source-available under Elastic License 2.0, so you can self-host it. Or if you prefer, you can also use our hosted version: weaverouter.com.

I'll be here to answer any questions you may have!

It's an integrated pipeline for lightweight multilingual text classification, covering preprocessing, training, and evaluation. It implements SetFit, a few-shot learning technique that works well for low-data regimes (down to a few dozen examples), and offers high throughput on CPUs, since it's based on Sentence Transformers. Dependencies are kept lean, but of course PyTorch itself isn't exactly small.

autofit2 takes a base model and a JSON config as input, and outputs a TorchServe model archive as well as a model card. The model card includes any benchmarks you have for your task, self-consistency tests, estimated CO2 emissions of the finetune, as well as an entropy-based bias analysis. For the bias eval, small test corpora for 50 languages are included. It works best with my EAR (Entropy-based Attention Regularization) fork of Sentence Transformers.

Feedback is welcome.