HN user
It is surprising that it works (I haven't tried it). `Content-Length` had one goal - to ensure data integrity by comparing the response size with this header value. I expect http client to deal with this out of the box, whether gzip or not. Is it not the case? If yes, that changes everything, a lot of servers need priority updates.
I read the complete article. It does feel like written by human but only if you don't read it all. So overall, I would have saved time if it was written by AI completely (by choosing to not read after a glance)
Only 20 training samples improved llm performance, that sounds unrealistic! My experience with RLHF for LLM perf differs. Can you be more specific about the case where you achieved this and share technical details about how do you do that?
why everyone ends up using yjs as their choice of CRDT framework. aren't there better alternative or it is just following the popular choice?
the author posted it on HN, marked their task done, and went to sleep. and we are wondering why do we need another project management tool.
What is the architecture/tech-stack used in building this? I didn't find this info neither on github readme, nor on website.
I like the fact that it is written in Go and small enough to skim over the weekend, but after repeatedly burning my time on dozens of llm ecosystem tools, I'm careful in choosing to even explore the code myself without seeing these basic disclosures upfront. I'm sure you'd see more people adopting your tool if you can provide a high-level overview of the project's architecture (ideally in a visual manner)
Love the creativity in the branding but it did not work in my case either. Gibberish raw content and error in answering any question.
I got most of my answers from the README. Well written. I read most of it. Can you share what kind of resources (and how much of them) were required to fine tune Wav2Vec2-BERT?
I taught my parents how to use LLM chat apps. I was pleasantly surprised to see them use it all the time. And even more shocked to see them pasting entire whatsap messages containing passwords, upload income tax files, and a lot more private details with LLMs. They rarely pause to think about privacy/security before sharing info with LLM services. So I'm working on an interface that works as a privacy filter, making sure the private info does not leave the device. It redacts /anonymizes/obfuscates private information from what we share with LLMs via on-device model, and plugs back the output with the private info to make it appear almost similar to the output as before.
SaaS starter kit, MIT license - https://github.com/makeitcount/saas-express-starter-kit
RudderStack entered a crowded Customer Data Platform market in 2019. It clearly differentiated from other platforms by choosing to not host the customer data on its platform rather choosing warehouse-native and Open Source approach where you store data on your own central warehouse only. It made the solution best-in-class solving obvious data quality and governance issues. The early success in such market is seen clearly in the fact that customers replace their existing product (Customer Data Platform providers, the leading ones) with your product (RudderStack).
Is this correct categorization (I feel lines are quite blurred between the categories but nevertheless useful)?
I have had challenges with readability. The output is good for blogs but when we try it for other type of content, it misses on important details even when the page is quite text-heavy just like blog.
how is it compared to mozilla/readability?