Chandan Kumar

@intuitiveml Great post ! We’re on a similar journey rebuilding SDLC with an AI first approach. Monorepo as shared context is a strong move vs maintaining external agent docs. At enterprise scale, agents need to be embedded into the delivery process, not treated as optional tools.

View my verified achievement from @machalliance. credly.com/badges/7cfe177… via @credly

The extent to which a team balances psychological safety and intellectual honesty can be mapped to four innovation cultures, along with a neutral culture that is subject to neither the dangers nor the benefits of the others. ▶️ mitsmr.com/3XyG09v

Replacing old batch jobs with new ones isn't modernization—it's repetition.

SQS vs FIFO SQS vs Kafka A Practical Guide to Choosing a Message Broker infoq.com/articles/choos…

@lucamezzalira Composability is little broader than Micro-frontend . Micro frontend with right composable backend will be right fit .

Lately, I've been diving into composable architectures, and it got me thinking: Where do micro-frontends fit into all of this? As more and more systems are being built with composability in mind, where backend services are modular, flexible, and can be swapped out easily, I started to realize that micro-frontends are the frontend’s answer to the same problem. We are going to talk more about it in the next issues, in the meantime... How micro is a micro-frontend? In the link to the new version of building micro-frontends you will find a clear heuristic to test your micro-frontends boundaries. After that, you can find a podcast episode where I answer all the questions from Ido Evergreen. For once I was a guest and not the host of the podcast 😂. Finally, a great case study on how to apply #microfrontends on mobile by Chase! I appreciated the insights and reasoning behind building their app, I warmly recommend it. READ & SUBSCRIBE: preview.mailerlite.io/preview/893059… #frontend #web #javascript #composable #architecture @bitdev_

Architecture Diagramming Tools List generativeprogrammer.com/p/architecture… [bookmark for later]

A Cheatsheet on Database Performance

How Digital Signatures Work?

One more year of likes, retweets, and confusion! ❤️🔁15 years? #MyXAnniversary

I am so inspired by @RudiKhoury demonstrating the unmatched power of #Agentforce! Discover how Agentforce integrates seamlessly with #Salesforce, delivering superior accuracy, reducing costs, and automating the entire customer experience. Don’t miss this game-changer in action! This is what AI was meant to be. 💥 #AI #Customer360 #Innovation #DF24

waiting for the next surprise.

⚠️ Node.js 16 is now end of life, please upgrade to Node.js 18. Details: hubs.la/Q021R7wm0

QR Code generation in Python

According to our latest MACH Annual survey, 20% of decision-makers admit their infrastructure is failing to meet end-user demands. The urgency to innovate is high, but legacy infrastructure is hindering progress. Full report here: machalliance.org/newsroom/mach-… #MACHAlliance #MACH

What is a 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲? With the rise of Foundational Models, Vector Databases skyrocketed in popularity. The truth is that a Vector Database is also useful outside of a Large Language Model context. When it comes to Machine Learning, we often deal with Vector Embeddings. Vector Databases were created to perform specifically well when working with them: ➡️ Storing. ➡️ Updating. ➡️ Retrieving. When we talk about retrieval, we refer to retrieving set of vectors that are most similar to a query in a form of a vector that is embedded in the same Latent space. This retrieval procedure is called Approximate Nearest Neighbour (ANN) search. A query here could be in a form of an object like an image for which we would like to find similar images. Or it could be a question for which we want to retrieve relevant context that could later be transformed into an answer via a LLM. Let’s look into how one would interact with a Vector Database: 𝗪𝗿𝗶𝘁𝗶𝗻𝗴/𝗨𝗽𝗱𝗮𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮. 1. Choose a ML model to be used to generate Vector Embeddings. 2. Embed any type of information: text, images, audio, tabular. Choice of ML model used for embedding will depend on the type of data. 3. Get a Vector representation of your data by running it through the Embedding Model. 4. Store additional metadata together with the Vector Embedding. This data would later be used to pre-filter or post-filter ANN search results. 5. Vector DB indexes Vector Embedding and metadata separately. There are multiple methods that can be used for creating vector indexes, some of them: Random Projection, Product Quantization, Locality-sensitive Hashing. 6. Vector data is stored together with indexes for Vector Embeddings and metadata connected to the Embedded objects. 𝗥𝗲𝗮𝗱𝗶𝗻𝗴 𝗗𝗮𝘁𝗮. 7. A query to be executed against a Vector Database will usually consist of two parts: ➡️ Data that will be used for ANN search. e.g. an image for which you want to find similar ones. ➡️ Metadata query to exclude Vectors that hold specific qualities known beforehand. E.g. given that you are looking for similar images of apartments - exclude apartments in a specific location. 8. You execute Metadata Query against the metadata index. It could be done before or after the ANN search procedure. 9. You embed the data into the Latent space with the same model that was used for writing the data to the Vector DB. 10. ANN search procedure is applied and a set of Vector embeddings are retrieved. Popular similarity measures for ANN search include: Cosine Similarity, Euclidean Distance, Dot Product. Some popular Vector Databases: Qdrant, Pinecone, Weviate, Milvus, Faiss, Vespa. -------- Follow me to upskill in #MLOps, #MachineLearning, #DataEngineering, #DataScience and overall #Data space. Also hit 🔔to stay notified about new content. 𝗗𝗼𝗻’𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗹𝗶𝗸𝗲 💙, 𝘀𝗵𝗮𝗿𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝗲𝗻𝘁! Join a growing community of Data Professionals by subscribing to my 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: newsletter.swirlai.com

Use Kubernetes, they said. It is fun, they said.

This article explores how to take a GitOps approach with Crossplane, Anthos and ArgoCD to simplify the deployment and maintenance of modern cloud platforms ➜ @vincn.ledan/build-a-modern-platform-with-crossplane-anthos-and-argocd-the-future-of-infrastructure-management-9211ef107cb9" target="_blank" rel="nofollow noopener">medium.com/@vincn.ledan/b…