Golang

Introduction

Artificial Intelligence, especially Large language models (LLMs) are all in high demand. Since OpenAI released ChatGPT, interest has gone up multi-fold. Since 2023, Powerful LLMs can be run on local machines. Local Large Language Models offer advantages in terms of data privacy and security and can be enriched using enterprise-specific data using Retrieval augmentation generation (RAG).Several tools exist that make it relatively easy to obtain, run and manage such models locally on our machines. Few examples are Ollama, Langchain, LocalAI.

Background

In a typical workflow of software development, Developer implements a Unit/component, tests it and pushes the changes to source control repository. It then goes through Continuous integration, automated testing, provisioning and deployment. Given High availability requirements expected (or should i say assumed) nowadays, As much as functional correctness of the Unit, it is also important to test how a Unit/Component handles failures, delays etc. in distributed environment. Often, such behavior is observed in production itself, unless project team is following practices of Chaos engineering.

Background

In a typical business Application, there are often requirements for,

• Batch processing - Often long running Tasks like data import/export, End of day processing etc. These tasks are often scheduled to be executed at pre-defined interval or on occurance of an Event.
• Asychronous processing - Tasks, often part of business process / workflow, that can be performed asychronously or offloaded.

Such requirements are often fulfilled with custom approaches like batch processing frameworks, ETL Tools or using Queues or specific database features.

Background

How many times have we landed up in a meeting staring at random slowness or such production issues in a distributed Application ? only to experience helplessness with limited (or often times no) visibility available about the runtime behavior of the Application. It often ends up in manually correlating whatever diagnostic data available from Application and combining it with trace/logs that are available from O/S, databases etc. and trying to figure out “Root cause” of the issue.

Background

I primarily work on Windows for development purposes. Whenever its about writing code in Golang, invariably one comes across usage of Make. A quick check on popular Go projects on Github will show Makefile being used to automate tasks like linting, build, testing and deployment.

Being on Windows, i have been looking for alternative build tool that is easy to setup (i.e. doesn’t require mingw and such environments) and use compared to Make (which is primarily targetted at Unix and Unix like Operating Systems).