Corporations are people under the current SCOTUS interpretation. Which means they have the same rights under the US Constitution, so this should be struck down in no time in the courts. Another nothing burger likely to end up just manipulating the equity market and the derivative markets in the short term for large investors to capitalize.
For anyone looking for basic information of financial statements in business, the assumptions and estimates that go into it, I recommend Financial Intelligence by Joe Knight and Karen Berman. It helped me understand how much fuzziness happens in financial statements and how they can affect a business operation
The fundamental change we need is to make corporations pay taxes on revenue (top line) and not on profits (bottom line). It's simple, easy to track and in par with income tax of regular people.
Can someone explain what's technically better in the recent embedding models. Has there been a big change in their architecture or is it lighter on memory or can handle longer context because of improved training?
Care to explain more? I understand the prompt might not be used for training, but how about sanitizing the PII from tracking or logging or memory bugs in these serverless functions
The more I look at it, the more I realise the reason for cognitive overload I feel when using LLMs for coding. Same prompt to same model for a pretty straight forward task produces such wildly different outputs. Now, imagine how wildly different the code outputs when trying to generate two different logical functions. The casings are different, commenting is different, no semantic continuity. Now maybe if I give detailed prompts and ask it to follow, it might follow, but from my experience prompt adherence is not so great as well. I am at the stage where I just use LLMs as auto correct, rather than using it for any generation.
Teams is a must if you are a professional in Australia. However, I love my Debian+ i3 too much to give it up. Using it through chrome is not easy especially to chat. So, I install it as appomage through appman and it works beautifully well that I can automatically update as well.
There was a paper that tried to integrate NER (Named Entity Recognition) with whisper to one shot for similar situation, not sure what is the current status
Dumb question from someone who uses old cars without OTA, how can cars get OTA if you don't pay for any internet subscription? Can't you say I don't want to pay for any internet costs when buying the car? Also ensure you don't connect the car to wifi. Isn't that suffice?
That looks like a great usecase. Would you be able to write about the architecture. A lot of us would love to be able to do things like this in Sheets, I'm personally trying to integrate a forecast estimate into Sheets
Dumb question maybe. Can the users in Europe raise a GDPR request to extract all their data from Slack? I realise it's not easy to port the data to other platforms yet, but atleast you have a copy of the data
This consistent with Gall's law, that says complex systems that work can only be achieved by building complexity over simple systems. Complex systems built from scratch do not work. So build the simplest system that works and then keep adding complexity to it based on requirements
Why should we start fine tuning gemma when it is so bad. Why not instead focus the fine-tuning efforts on Qwen, when it starts off with much, much better outputs?
Thank you for taking the time to build something and share it. However what is the advantage of using this over whisper.cpp stream that can also do real time conversion?
One thing I wished YouTube had like Twitter was to see what other channels the channels you like to subscribe. This way you are not held hostage by YouTube recommendation, which is definitely not in favor of the viewers
As a tangent, one issue I face is how to force cursor to use uv over pip. I have placed it in rules and also explicitly add a rules.yml file in every agent conversation. It still tries to push through pip 4 out of 10 times. Any recommendations on best practice to force cursor to use uv will make a significant dent in productivity
One thing that I feel that is discussed less is, what are the high level constraints and levers that can be set to enable a group of programmers to become a better programmers. For example, how much impact does architecture have? bug tracing, choice of language, tools. People handwave and say that one is better than another because of <reason 1>, <reason 2>, but to be able to explain how each of this choice impacts the code in a semi-rigorous way by explaining all the way of how it impacts individual code components and their interaction (as a graph or a tree) would be immensely helpful. If anyone knows of resources like that, please let us know
I understand this is targetted towarda devwlopers, bur Can someone explain why should I go through this complex install process instead of just using ChatterUI? It can handle same GGUF format and works great with Gemma and Qwen. What kind of usecases am I missing?
My usecase is to extract CSV or other data files from websites pertaining to water. For example here is the historical reservoir level data in South Australia[1]. Extracting the data was a big pain (at least for me with minimal front end experience). If downloading this can be automated with agents, I'm definitely giving it a shot.
Awesome initiative. For people not physically in Bangalore, maybe start a discord and we can asynchronously try to work through problems and get support