Insights
Engineering perspectives on architecture, product development, AI, and the everyday decisions behind useful software.
From the engineering desk
Browse practical articles or follow the latest technology updates.
Subscribe via RSS →Curated links from external sources — not 360Softy original articles.
Version 9.3.8 of the Elastic Stack was released today. We recommend you upgrade to this latest version. We recommend 9.3.8 over the previous versions 9.3.7 For details of the issues that have been fixed and a full list of changes for each product in this version, please refer to the release notes.
Version 8.19.19 of the Elastic Stack was released today. We recommend you upgrade to this latest version. We recommend 8.19.19 over the previous version 8.19.18 For details of the issues that have been fixed and a full list of changes for each product in this version, please refer to the release notes.
Build full-text search with tsvector, GIN indexes, and pg_trgm typo tolerance on Prisma Postgres. For most apps, no Elasticsearch cluster required.
Your RAG-backed support assistant just told a customer the refund window is 30 days. It's 14. The retrieval logs look clean: chunks came back, latency was normal, nothing errored. That's what makes RAG failures slippery. The pipeline still returns an ...
The chatbot trap Open any product changelog from the last year and you'll find the same move: a chat bubble in the corner, a sparkle icon, a launch post with the word AI in the headline. The widget answers questions about the product. Sometimes it drafts an email. Everyone ships it, everyone calls it AI-native, and almost everyone is solving the wrong layer. A chat widget is a conversation about your product. It sits on top of the UI and paraphrases your docs. The model never touches your doma
Laravel Time Machine profiles every stage of the Laravel request lifecycle, from bootstrap to termination, with a timeline dashboard and SQL query capture. The post Laravel Time Machine: A Request Lifecycle Profiler appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox.
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10 Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small, top-k=5, stuff into context. It works for demos. Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks drown signal in noise Customer tickets: Conversational c
FFI binding to Google's RE2. Matching runs in linear time, so no catastrophic backtracking. Here is a validator that ships in a lot of Dart code. It checks that a string is a list of words separated by single spaces: final ok = RegExp(r'^(\w+\s?)*$'); It works. Type a name, a tag list, a short label, and it returns true or false quickly. Then someone sends 31 characters of x with no trailing separator and the isolate stops answering. I timed it. On Apple Silicon, Dart 3.11, dart:core RegExp tak
Let’s start with a conversation
An idea, a challenge, or a system that needs to work better. We’ll help you understand the next step.
Prefer email? [email protected]
