9notorp

that was an enjoyable read! loved reading stuff about smartphones on forums, especially symbian ones where the die hard fanboys absoultely believed that this device was better than the iphones and htcs had to offer (including me). too bad maemo / meego died off, we may have seen more interesting devices. loved the "Contains no LLM-generated content" bit.

I have a personal blog set up on the cheapest digital ocean droplet and i went ahead with sqlite for it, there is not much traffic on it and i think something like sqlite is actually the best choice for such projects. I did some benchmarking with a rust cli tool, the name i don't recall right now, something like apache bench but simpler. I wouldn't have the same performance if i had mysql or postgres running on the same machine.

>I cannot remember where I read this, but it was a view that basically the mere concept of a "company" is a collective fiction

I think I read something similar in Sapiens.

Thank you! Not sure if I can do that unless browsers go back to the old designs and make ipc possible among tabs/processes again, the compute happens on the browser so it entirely depends on the end user hardware config. And while making this I gave that approach a try as well but decided against it, since the (compute requirements of analysis) entirely depends on the size of input. On larger strings it takes considerably longer and has rendered the browser completely unresponsive on low end devices. Also, this is far from complete and the pre trained model is fairly inaccurate. I am mainly looking for collaborators to train this better.

I only started learning/implementing ml very recently. Gave up halfway through my previous attempt when i got to know about the compute intensive nature of ML implementations

About this app, (django,react,tfjs) It takes a twitter handle as input and lists the most recent tweets along with buttons for each to predict toxicity using tensorflowjs's toxicity model (pretrained) I plan to make a twitter bot with my learning so far. Also this project initially had a much larger scope and I had to cut down/scrap most of it as i'm using only freely available services to host this and running ml on such services did not seem fair to me.

http://www.odbms.org/wp-content/uploads/2013/11/PP2.pdf This could be a good starting point.

followed by further reading on this https://www.ciosummits.com/media/solution_spotlight/TDWI_Che... And possibly sql query optimizations. In most cases, efficient indexes should be enough to maintain reasonable performance.

But in cases where scale gets exponentially bigger at a rate at which you cannot possibly keep adding computing resources, breaking the best followed practices is the only way to get more performance without adding hardware resources at the same rate as increased scale, this at times includes but is not limited to, getting rid of foreign keys, coupling your app logic along with your db to to breakdown larger tables periodically/automatically/ runtime creation. This though requires skills across the entire stack and has it's fair share of downfalls and is never a recommended practice unless you know what you're doing and are okay with compromising on some of ACID properties. since maintaining something like this becomes unsustainable without contingencies. Take all of this with some grain of salt as the topic is highly subjective.

I'm assuming you're mainly focusing on software product architecture, There are a few trends which are currently widely adopted. one of them is the microservices architecture this link explains some of the widely used architectures and gives a few examples along with use cases (https://techbeacon.com/app-dev-testing/top-5-software-archit...) each of the mentioned architecture is vast enough to warrant a few books on each of the architecture. So I would recommend reading into what you might want master, as mastering all of it would require fair amount of time and effort. The principles mentioned can be applied to database design methodologies as well.

If you have to break it down in terms of raw basics, what approach you choose largely depends on what you want to achieve. Performance or security. wanting both requires large amount of human as well as computing resources. As a general example, consider node or apache, node gives you a performance improvement in terms of raw latency and concurrency by a factor of ~20-100 (based on a few simple tests i've done using apache bench) in most scenarios compared to apache but in terms of reliability, if your code for any reason doesn't handle exceptions, it takes down your entire server/service. With apache, that wont happen, your perfomance degrades, some users might get affected but the service as a whole would still be operational. This is just an example, most of the technology out there is about making the choice on what you're okay with comprimising with the available resources (human and computational) that you have. This is just my view on it.