Brian Greene’s Post

Words like "master class" and "experts guide" get thrown around a lot, but this piece by Jeremy Morrell may be the single best thing I've ever read on instrumenting your code the modern way, using wide, traceable log events. It's also a terrific vendor-neutral overview of your options in the observability 2.0 space, including open source and hybrid options. Plus code samples. You'll want to bookmark this puppy. Pretty sure I'll be referring back to it for years to come. https://lnkd.in/gURyetg6

If you're curious to see the real power of wide-event instrumentation (i.e. lots of context through attributes), I highly recommend reading this blog post: https://lnkd.in/dm4UejTR One of the best blog posts I've read in years. It has a lot of examples of the type of questions you can ask of your data if you just give enough context. These are fundamental questions you are enabled to ask of your data very easily, spanning so many different possible disciplines (platform, quality, product, business analysis, etc).

You can build an article recommendation system with just the Jina Reranker API—no pipeline, no embeddings, no vector search, only reranking. Find out how in 20 lines of code. https://lnkd.in/eYwe_kxZ

Building Intelligent Applications with Local RAG in .NET and Phi-3: A Hands-On Guide

Building Intelligent Applications with Local RAG in .NET and Phi-3: A Hands-On Guide

Building Intelligent Applications with Local RAG in .NET and Phi-3: A Hands-On Guide

The day Decimal Arithmetic broke my code! I recently tackled a challenging bug that turned out to be quite an eye-opener! In my latest blog post, I share how a tricky issue with decimal arithmetic led me down a path of discovering JavaScript’s quirks in number handling. Curious about how floating-point precision can trip up your code? Dive into my blog to see how I solved this puzzling problem: The Day Decimal Arithmetic Broke My Code. Hope you find it as intriguing as I did! Link to the blog: https://lnkd.in/dWwTSDgh

Core Dev Call 31 dropped some big updates: 🛠️ Substreams Codegen: Easily build Substreams-powered subgraphs 🌅 Graph Horizon: Prepare for more data services 🌐 Subgraph Service 2.0: Indexing on steroids Curious? Dive into the details! https://lnkd.in/dpkWefxC

DSA Day 37 Link: https://lnkd.in/gzCVpR5M 1. Path Sum II My Approach : 💡 1. Depth-First Search (DFS): The algorithm performs a depth-first traversal of the binary tree. It recursively explores each path starting from the root node and moving towards the leaf nodes. 2. Recursive Function: The recursive function takes the current node, the current sum along the path, the target sum, and a list to track the current path. At each node, the algorithm adds the node's value to the current sum and appends the node's value to the current path. If the current node is a leaf node and the current sum equals the target sum, the algorithm adds the current path to the result. Otherwise, the algorithm recursively explores the left and right subtrees, passing the updated sum and path. 3. Base Case: If the current node is null, the algorithm returns without performing any action. If the current node is a leaf node, the algorithm checks if the current sum equals the target sum. If it does, it adds the current path to the result. 4. Backtracking: After exploring the left and right subtrees, the algorithm removes the last node from the current path. This process is known as backtracking. 5. Resultant List: The algorithm maintains a list (ans) to store the resulting paths that sum up to the target sum. 6. Result: After traversing the entire binary tree, the algorithm returns the resultant list containing all paths that sum up to the target sum. Time complexity: O(N) Space complexity: O(N + H + K) O(N) for the recursion stack. O(H) for the recursion stack space, which is the height of the binary tree. O(K) for the resultant list storing paths. #100DaysOfLeetCode

With Weaviate, you can build advanced LLM applications, next-level search systems, recommendation systems, and more. Discover features of the Weaviate vector database and learn how to install Weaviate on Docker using Docker Compose.