codenlearn

This is exactly what I was working on last week. You have beaten me to it. Amazing work now time for me to find a new project.

Doesn't Language itself encode multimodal experiences? Let's take this case write when we write text, we have the skill and opportunity to encode the visual, tactile, and other sensory experiences into words. and the fact is llm's trained on massive text corpora are indirectly learning from human multimodal experiences translated into language. This might be less direct than firsthand sensory experience, but potentially more efficient by leveraging human-curated information. Text can describe simulations of physical environments. Models might learn physical dynamics through textual descriptions of physics, video game logs, scientific papers, etc. A sufficiently comprehensive text corpus might contain enough information to develop reasonable physical intuition without direct sensory experience.

As I'm typing this there is one reality that I'm understanding, the quality and completeness of the data fundamentally determines how well an AI system will work. and with just text this is hard to achieve and a multi modal experience is a must.

thank you for explaining in very simple terms where I could understand

I will probably recommend csprimer, this is hands down one of the best introductions that I found and I recommended to a lot of people who reached out to me. The order I will probably recommend is the following

1. Programming: Beyond the basics

2. Computer Systems

3. Operating Systems

4. Algorithms and Data Structures

5. Computer Networks

This is going to take time but it will be worth your time.

For discrete Math I sincerely recommend you to take a look at this book "Discrete Mathematics with Applications by Susanna Epp. This is an excellent book and it has good problems.

I will also recommend you to look at https://fa24.datastructur.es/ this is a very good beginner course on data structures that is completely free by UC Berkeley

the one thing that is handy and missing here is creating and pushing tags.

I wrote my first calculator app 11 years ago using shunting yard algorithm. It’s a wonderful algorithm, hats off to Dijsktra for coming up with a simple and elegant algorithm.

The jespen analysis is amazing, thank you for sharing it.

Thank you, this is an amazing resource.

Thank you for posting the link.

This behavior is happening in the default model (GPT-3.5). I edited the question as well.

The 2 metrics I've used in the past is

1. Bounce rate combined with Session length

- Bounce rate can be defined as % of users who leave only after viewing one or few pages. The reason at least I coupled bounce rate with session length is atleast in the app we work there are few personas that prefer to stay in one page for a long period of time and do some interactions. Bounce rate combined with Session length always give us a good picture are folks using the feature(s) as intended. One thing we did was set a baseline metric where we first measured session length on the app as well as per page basis, and after that we could build the bounce rate. Amplitude works well for this case

2. Churn Rate - number of users who stop using the app over a given period. This should be easy to do by querying db if you maintain some session duration information.

I'm an engineer and talking from experience here.

1. keep the db schema simple. remember you are startup things change, so does the db schema so does your architecture.

2. One piece of advice from personal experience, Microservices usually don't work well for startups try building your solution as a modularized monolith.

3. always make decisions that are reversible (2 way door) and make sure coming back is also easy.

4. for any features you release have qualitative and quantitative metrics and more importantly guard rail metrics.

5. where ever you can use the an off the shelf solution like feature flags (launchdarkly), product analytics (amplitude) use them. These are solutions used by countless startups do not reinvent the wheel.

6. prioritize customer feedback, that is going to get you money and they are going to help make your product better. Instead of "failing fast" prioritize on "learning fast".

7. Be focussed on solving the problem don't romanticize the solution.

Thank you. This is what I was looking for.

Any API recommendations that comes to your mind?

If you can afford a monthly subscription, I would definitely recommend https://frontendmasters.com/courses/react-native-v2/. I did the Intermediate React by Brian Holt on Froentend Masters. It was very good. The quality of courses offered here is very good.

+1 to that suggestion. this is something that I experienced as well. offlate most of the webapps are hijacking the back button.

I needed this, thank you for posting this.

wim-hof is a life saver. I used during my covid battle as well. I had this video https://www.youtube.com/watch?v=tybOi4hjZFQ on loop when I was suffering with covid

https://www.youtube.com/watch?v=fvH3lGUdcNM This video is by Noah Keagan, and he talks about a similar situation that you are in. Maybe this will help you. Wishing you the best.

Absolutely no offense to the author, but IMO for an experienced programmer the five minute intro does not make any sense. They will be better served by using the documentation (or) go by example in this case.

If someone is new to programming this in no way does any justice. Programming is way too complex to be fit in 5 minutes intro. https://norvig.com/21-days.html

One resource that atleast comes close to what you are talking about is the information at go101.org, not sure if you have seen this before. Especially the section on Go Type System and Concurrent Programming is really very good and they focus on how to leverage the feature correctly for the most part. Hopefully you like it https://go101.org/article/101.html

This was one of the earliest introductions to algorithms which I could understand. Thank you Peter (and MIT OCW) for diligently cataloging the notes, you've no idea how much you helped me to get an understanding of algorithms. Thanks again.

Apart from this his awk,sed and perl oneliners are worth to act as a reference as well.

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