The prompt isn't relevant to this question though. The quality of output can be improved with better input but in this case, I am curious about the underlying mechanics in the model that leads to such behavior.
I recently developed an interest in UX and have been on the lookout for resources. This helps, thanks!
Do you have recommendations for forums too? Basically places where UX experts and practitioners hang out. I have discovered some Slack channels but haven't had much success otherwise.
As an early-stage startup employee and now, as a startup co-founder, I often end up deep in conversation with customers, trying to understand their pain points and preferences. My co-founder and I discovered that it’s really hard to
(1) be objective about our findings (do people really care about X?),
(2) keep each other updated about learnings from our latest calls. This ends up being a huge time sink (for time we don’t have!)
...
We got so frustrated that we built Loop which lets you:
(1) Automatically detect interesting parts of your call notes / transcript by highlighting emotional parts of the conversation, keywords
(2) Compile all your findings in Google Sheets or Miro so you can have a running list of all your key learnings, group, filter, sort them to make sense of what your customers really care about
(3) Share powerful quotes with your team with the click of a button so that you’re always on the same page
Happy to answer any questions if this is of interest!
Over the last couple of months, I have taken a rather freewheeling approach to understand the recent advances in NLP. This post documents my approach, relevant resources and some learnings. Hope it comes in handy for someone trying to understand the field better!
I do find some value in quantification but I resonate with your thoughts.
I am curious about ways in which people summarize and take notes when reading books. Do you have any specific quirks or methods for doing so?
Hi HN!
The world of data is unique, complex and ever-changing. And full of jargon. That makes it challenging for someone who’s starting out in the data universe.
Our team started an internal initiative to serve as a knowledge base for data teams and professionals (aka the humans of data) everywhere—explaining topics in the data universe in the simplest possible manner. The goal was to make learning about the data universe fun and easy.
And that’s how The Data Wiki was born! (Shout out to the folks at Gitbook for building the incredible tool that hosts the wiki.)
I'm interested in the possibilities that graph data structure provides and strongly believe that it has a bigger role to play in the future of software engineering and data analytics. Knowledge graphs are a great example of graph technology in practice.
I wrote this article with the intention to provide an introduction to this fascinating topic and hope that you find it useful.
Graph theory had fascinated me as a student. On my first job, I'd briefly worked with Neo4j, a graph database, as part of a proof-of-concept project. At my current gig, I've had the opportunity to delve deep into the world of graph tech, especially databases, over the last one year.
Graph-like data models have been around since forever but their mainstream promise is relatively new. A resource which helped me understand the historical as well as fundamental aspects when starting out was the amazing book, "Designing Data Intensive Applications" by Martin Kleppmann. There also exist various resources academic and industry resources around graph tech. But piecing them together to get a holistic picture to evaluate potential use-cases has been an arduous process, to say the least.
Hence, I wrote this introductory piece to help anyone interested get started. I'd given a talk on the same topic at PyCon Italy (https://www.youtube.com/watch?v=t0Ra8G8gD-w). I plan to write more on related topics.
Look for folks who've partnerships, community or evangelists in their title. If not, aim for someone from marketing. If nothing else, make an informed guess.
1. Tap on existing networks. It could range from your first or second-degree contacts to sponsorship programs. Many companies, non-profits and open source organizations have programs which encourage community events. Look for one.
Also, social media would be key to generate interest. Register on Meetup and create a Twitter page.
If there are existing meetups in your vicinity, collaborate with them.
2. and 3.
Interactivity and participation is key to sustainable community model and isn't just limited to event day. Try to generate some interest on social media (Meetup, Twitter) through a poster, questionnaires and announcements.
During the event, allocate time for networking. Organizing an AMA can also help make things interactive.
Most importantly, involve folks who're interested enough to contribute to meetup organization. As you can guess, it can be a lot of work for a single person but will be a breeze when responsibilities are divided.
I live in New Delhi, India and it can get unbearably hot here during the summer. Temperatures as high as 40 degree Celsius and above are taken for granted.
Air conditioning is of immense value in a region like this. But for majority of the population, it is a luxury. Even those with air conditioning use it in moderation to keep electricity bills in check.
Having moved across three different apartments in the same locality over the past 2 years, I've experienced how even slight improvements in architecture can significantly decrease the effects of heat-wave. The author's point does hit home.
Owing to my inexperience with the topic, I am not sure if the term 'differential programming' belongs here. However, this book looks promising. Thanks.
I do agree that for applying technologies in real world problems, a much deeper understanding is required than what majority of MOOCs provide.
That being said, different learning methods suit different type of learners. For some, starting out with a hands-on overview of the topic at hand works best. This is where tutorials such as this one shine through.
The point about it being almost exclusively a consumption device might be, in a way, extreme but not at all silly.
" It's a messaging device, a photo and video taking device, it's great for email, I record audio for my college classes on it etc."
Majority of what you are referring to as productive tasks are merely the users consuming various services on offer. Although they are useful devices, I agree with the opinion that any significant production work is rarely performed on them. If anything the we subject ourselves to unnecessary consumption more than increasing our productivity.
I am pretty sure that a lot of people must have took an interest in the passage. For anyone who has serious issues with the BS that the academic life has to offer, it offers a rather interesting take on the matter.
One was a billionaire business honcho who created one of the most identifiable brands in the world. Another was one of the greatest Computer Scientists who ever lived.
As great as Ritchie was, he never stood a chance. There are a number of relevant reasons to bash Jobs but to say that he overshadowed Ritchie's death is one of the more stupid ones. Dennis Ritchie was mourned by those who respected and adored him regardless. His death would have only received a minuscule amount of increased attention than it did probably had it not coincided with Jobs' death.
Moreover, his death probably received even greater attention due to the increased effort people have put in since to highlight him.
Those well, alive and kicking should and will continue to get the spotlight whenever its due. Some less than others but that's just how media works.
That being said, media (and its consumers) will continue to display a heightened interest in the dead because it gives them a sense of certainty to work with. That and our own mix of sad and voyeuristic attitude towards the dead.
Aaron's case in particular captivates the internet because of what he stood for, and what a lot of us hope to stand for. This article is highly relevant because as great as Aaron was, he had his shortcomings as well. And for those who really look up to him (including myself), it's insightful to look at them.