Skip to content
360SOFTY
HomeInsights

Insights

Ideas for building
better software.

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.

ExternalCloud
Google Cloud Blog

Open models, global networks: How AT&T and GSMA are accelerating telecom innovation with Gemma

Telecommunications is an incredibly complex, highly specialized domain. Modern mobile networks are inherently multi-vendor, featuring diverse and often proprietary data structures. While AI has made massive leaps in general language and coding, telecom domain knowledge is rarely accessible on the open internet — there is simply no "Wikipedia" for telecoms. This data scarcity creates a major hurdle for AI models trying to deeply understand network operations. When operating at an immense global s

Telecommunications
Google Cloud BlogRead original
ExternalCloud
Cloudflare Changelog

Workflows - Workflows rollback handlers now include step context

Workflows makes it easier to build reliable multi-step applications that can recover when downstream systems fail. Rollback handlers now receive the original step context via a ctx object for the step being rolled back. This includes ctx.step.name, ctx.step.count, ctx.attempt, and the step config with defaults applied. The step configuration includes the retry and timeout settings used for that step, so you can customize your step recovery logic according to those fields. await step.do( "cr

Workflows
Cloudflare ChangelogRead original
ExternalSoftware Engineering
DZone

Data Governance Checklist for AI-Driven Systems

Editor’s Note: The following is an article written for and published in DZone’s 2026 Trend Report, Cognitive Databases, Intelligent Data: Unified Infrastructure for Vector Search, AI-Optimized Queries, and Hybrid Workloads. Many teams find governance gaps only after a retrieval system surfaces stale or unauthorized content in production. Models, agents, and retrieval workflows all depend on enterprise data. Before any of that data reaches an AI system, teams need to know where it originates, how

DZoneRead original

Let’s start with a conversation

Tell us what you’re working on.

An idea, a challenge, or a system that needs to work better. We’ll help you understand the next step.

Discuss your project

Prefer email? [email protected]