The best Hacker News stories from All from the past week
Latest posts:
OpenTF announces fork of Terraform
OpenTF announces fork of Terraform
Code Llama, a state-of-the-art large language model for coding
Code Llama, a state-of-the-art large language model for coding
AI real-time human full-body photo generator
Cash payments above €3000 to be outlawed in Netherlands
Chandrayaan-3 Soft-landing [video]
Microsoft is bringing Python to Excel
The first conformant M1 GPU driver
We’re all just temporarily abled
Short session expiration does not help security
Ask vs. Guess Culture
Htmx is part of the GitHub Accelerator
LK-99 isn’t a superconductor
Things you forgot (or never knew) because of React
Tell HN: t.co is adding a five-second delay to some domains
Go to Twitter and click on a link going to any url on "NYTimes.com" or "threads.net" and you'll see about a ~5 second delay before t.co forwards you to the right address.<p>Twitter won't ban domains they don't like but will waste your time if you visit them.<p>I've been tracking the NYT delay ever since it was added (8/4, roughly noon Pacific time), and the delay is so consistent it's obviously deliberate.
Firefox finally outperforming Google Chrome in SunSpider
We reduced the cost of building Mastodon at Twitter-scale by 100x
Show HN: LLMs can generate valid JSON 100% of the time
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming.<p>Recently we came up with a fast way to generate text that matches a regex (<a href="https://blog.normalcomputing.ai/posts/2023-07-27-regex-guided-generation/regex-guided-generation.html" rel="nofollow noreferrer">https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...</a>). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We can transform this DFA into a generative model: in each state we get a list of symbols which correspond to completions that partially match the regular expression. We mask the other symbols in the logits returned by a large language model, sample a new symbol and move to the next state. The subtelty is that language models work with tokens, not symbols, so we derive a new FSM whose alphabet is the model's vocabulary. We can do this in only one pass over the vocabulary.<p>Generating the token masks thus only requires a dictionary lookup at each state. Our method blows other libraries like Microsoft's guidance out of the water.<p>From there it was only a small leap to be able to generate text that follows a JSON schema (<a href="https://json-schema.org/" rel="nofollow noreferrer">https://json-schema.org/</a>), or is parseable into a Pydantic model (<a href="https://docs.pydantic.dev/latest/usage/models/" rel="nofollow noreferrer">https://docs.pydantic.dev/latest/usage/models/</a>). The method works with union types, optional types, nested schemas, arrays, everything. It is guaranteed that the output is parseable.<p>I think it's cool, and I've spent a lot of time watching even tiny models output valid JSON over the weekend. Hope you will too.<p>I look forward to feedback, bug reports, feature requests and discussions!<p>Edit: Link to our pre-print explaining the method and how this can be extended to generate text that follows a Context-Free Grammar <a href="https://arxiv.org/abs/2307.09702" rel="nofollow noreferrer">https://arxiv.org/abs/2307.09702</a>
Azure ChatGPT: Private and secure ChatGPT for internal enterprise use