NodeSource’s Post

❓ When to Use Node.js? 🚥 Ideal for real-time applications like chat applications, online gaming, and collaborative tools due to its event-driven architecture. Excellent for building lightweight and scalable RESTful APIs that handle a large number of concurrent connections. Well-suited for building microservices-based architectures, enabling modular and scalable systems. ❓ When Not to Use Node.js? (Disadvantages): 🚥 CPU-Intensive Tasks: Avoid for applications that involve heavy CPU processing (e.g., Image/Video Processing, Data Encryption/Decryption) as Node.js may not provide optimal performance in such scenarios because it is single-threaded. For heavy computation, a multi-threaded approach is better

Chapter 3 of our Co-Processor deep-dive is out. This particular edition zooms in on Trusted Execution Environments (TEEs) --> tamper-proof hardware setups which bring cheap, verifiable compute to the masses and unlock a host of new use cases in Web3 (but also in Web2!) Hope you enjoy the read, we tried our best to keep the technical jargon overkill to a minimum! https://lnkd.in/ez7-umWK

🔥🔥🔥 Infinite #LLM context length a mind blowing news just get announced by #google. 🔥🔥🔥 "Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention" is probably the paper of 2024. paper link : https://lnkd.in/eppdVMU7 Just a quick reminder about why the #Transformer-based models did not scale and could not deal with long context. Transformers suffered from the Quadratic complexity O(n^2) caused by the attention mechanism. It simply means that it consumes n^2  of computing power and memory as the context length becomes bigger and bigger… That is why you see that context length was restricted, for instance the #BERT model was about 512 tokens, #GPT-3.5 has 4096 tokens and #GPT-4 Turbo has a context window of 128,000 tokens … But This new research everything will change with the infinite context length if it is true.

Concurrency VS Parallelism my thought was concurrency is parallel execution of processes in a system, i am wrong. https://lnkd.in/gftJaQwR a beautiful article suggested by my friend ->Concurrency means executing multiple tasks at the same time but not necessarily simultaneously. ->Concurrency is about dealing with lots of things at once. Parallelism is about doing lots of things at once ->An application can be concurrent — but not parallel, which means that it processes more than one task at the same time, but no two tasks are executing at the same time instant. ->An application can be parallel — but not concurrent, which means that it processes multiple sub-tasks of a task in multi-core CPU at the same time. for more detailed explaination checkout this article with very nice example.

BitNet is Microsoft's new 1 bit Inference framework. Something worth testing if you need to run LLMs on devices with limited resources. https://lnkd.in/d5TaG9qg #bitnet #genai #slm

Mark my words: on-device AI is future, not far from where it won’t be a big deal running large scale local LLM/GenAI or any any AI models on your mobile phone or preferred mobile device but differentiating factor would be what small Device backed by right hardware/chip your are on. That’s seems to be the race starting now among real player in this game. In other words, achieving something like that might be real distributed computing @scale in real world, servers won’t scale to that extent if hand held devices can do what server expected to do today, in addition if servers doing a millions times more and same time being energy efficient is cherry on top of the cake. Note/disclaimer: my own opinio or viewes.

Performance cores = powerful #AI performance 🦾 The moment we’ve all been waiting for…#IntelXeon 6 processors with Performance-cores are built for a wide range of workloads for your most data-intensive high bandwidth apps. https://intel.ly/3MUtFZt #GenAI #HPC

In this tech blog, read about the major new capabilities and updates in the latest release of NVIDIA AI Workbench. ✅ Expanded Git support ✅ Multicontainer support with Docker Compose ✅ Web application sharing through secure URLs Learn more now ➡️ https://nvda.ws/48ZkRvt

I think this is going to be incredibly useful. Yes, this is ChatGPT4, but according to the numbers, there are opensource models that do nearly as well. My testing shows that I can get similar results using https://lnkd.in/gnQFt_Xs, and now I'm testing https://lnkd.in/g-ZxdTDx. That would mean I can control my costs by running this on my own hardware. That would be a big win since I already own two powerful GPUs.