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AI-assisted software engineering is becoming part of software development on a daily basis. AI tools are used now for code creation, writing tests, reviewing changes, troubleshooting, documentation creation, and architecture and design decisions. But most teams still use AI largely in an ad hoc fashion. Every developer
A proof of concept is often the easiest part of an AI project. The scope is narrow, the users are friendly, the data sample is controlled, and the success criteria are usually simple enough to prove that something can work. A chatbot answers support questions. A model predicts churn with acceptable accuracy. A document
Traditional identity mechanisms weren’t built to account for the autonomous and unpredictable nature of AI agents. So enterprises shouldn’t be surprised when they make unpredictable – or even destructive – decisions, like deleting a company’s entire production base. Without the right guardrails in place, these new acto
Any input/output operation, be it accessing a file, handling an HTTP request, or a database connection, is based on 3 fundamental system concepts — file descriptors, kernel memory, and heap size. This article discusses how modern languages help developers handle behind-the-scenes file descriptor, kernel memory, and hea
Today, stream processing platforms facilitate the real-time analysis of data flowing continuously from Internet of Things (IOT) devices, financial transactions, web applications and servers at banks, manufacturing equipment, logistical systems in warehouses and ships, as well as customer activities with conversational
An AI agent is not defined by how intelligently it talks. It's defined by what it's trusted to do. Give a language model a chat window, and you have an interface. Give it access to production APIs , identity, business logic, memory, and the authority to execute actions on your behalf, and you have something categorical
I needed a job to run once a day, remember what it did yesterday, and cost nothing to operate. The obvious answer is a small VM with cron, or a Lambda plus DynamoDB. I did not want to pay for either, and I did not want a server to patch. So I pushed the whole thing onto GitHub Actions and used a JSON file committed bac
When someone uploads an image to your application, it might look perfectly fine at first glance. It might open correctly, pass file validation, and avoid triggering any obvious red flags. But that still doesn’t necessarily mean the file is trustworthy. In modern systems, especially marketplaces, identity verification f
Before getting into the architecture, I want to address the first objection I hear from every platform team: "We already have Kong / NGINX / AWS API Gateway — can't we just plug AI calls through that?" Short answer: no. Longer answer: it depends on what you mean by "plug in," but also still no.
Hi everyone! This is Mikhail Polivakha, tech lead of the Axelix project (btw, give us a star!). In my experience consulting teams that build enterprise applications, I keep getting asked: What about natural keys in the database? Say I have a column that lets me explicitly identify a record, should I use it as the Prima
The rise of autonomous AI agents within business software demands a fresh approach to security. Unlike earlier chatbot tools, modern agents act with real privileges, such as updating databases, calling microservices, composing and even executing code, or triggering workflows on their own. This shift expands the blast r
When putting their model into production, every team or organization encounters the same issue. Failures go unnoticed for days at first because there is no monitoring. As teams begin to fix the issues, they identify areas where production results deviate from the training data, create dashboards for every metric, and s
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