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Curated links from external sources — not 360Softy original articles.

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Modernising a legacy AS/400 (IBM i) system without rewriting it

Many companies still run production, warehousing, orders and invoicing on a business system that has been going for twenty years or more on an AS/400. Today that platform is called IBM i (it went from AS/400 to iSeries, System i and finally IBM i), but the name almost everyone still uses is the first one. Faced with a system this old, the instinctive reaction is to think it should be scrapped and rebuilt. Almost always that is the wrong conclusion. The problem is rarely the machine, which remain

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ExternalSoftware Engineering
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I Built a $0 Local AI Automation Stack: Agentic Coding + Market Data Over MCP

Serious AI-assisted development has a habit of turning into a stack of subscriptions: a coding-assistant plan, a market-data API, a backtesting service, model credits. Each one is reasonable on its own, and together they quietly become a monthly bill that also locks you into someone else's rate limits. I wanted to know how far you can get without any of that. The answer turned out to be: surprisingly far. Here's the stack I ended up with, running at $0 recurring cost and scripted so it rebuilds

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ExternalSoftware Engineering
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La capa sobre el asistente: por qué tu contexto muere entre IDEs y cómo construir el puente

El problema de los workflows multi-IDE no es la calidad del modelo, es que cada sesión empieza desde cero. Tienes tres asistentes de IA en tu flujo de trabajo. Kiro para triage y razonamiento sobre el codebase, Cursor para implementación rápida, y Claude Code para sesiones largas de refactor donde necesitas un modelo con ventana de contexto grande. Cada uno hace bien su parte. El problema está en el medio. Kiro encuentra el bug, analiza el stack trace, entiende el contexto. Cambias a Cursor o a

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ExternalSoftware Engineering
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Common Problems in AI-Generated Frontend Code and How to Avoid Them

Over the last year, I’ve reviewed about a dozen projects where AI was used heavily in development. These were real products with real users, built fast and shipped to production. Most worked fine. Some had interesting patterns that kept showing up across different codebases. These aren’t unique to any one team or project, they’re just common things that happen when AI writes code and humans skip the review step. Here’s what I noticed: AI is exceptional at writing code quickly. It’s less aware of

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ExternalSoftware Engineering
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NIS2 e le PMI: chi è obbligato davvero, e la parte software che ti chiederà il cliente

«NIS2» è diventata una di quelle sigle che generano più ansia che chiarezza. La prima cosa utile da sapere è anche la più rassicurante: la maggior parte delle PMI non è direttamente obbligata a NIS2. Il motivo per cui potrebbe riguardarti comunque è un altro, meno noto, e sta nella catena di fornitura. Questo articolo separa gli obblighi reali dal panico, e si concentra sulla parte che chi sviluppa software può affrontare davvero: il codice, gli accessi, i dati. NIS2 è la direttiva europea sulla

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ExternalSoftware Engineering
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A Backend Engineer's Field Notes on Cheap AI APIs in 2026

A Backend Engineer's Field Notes on Cheap AI APIs in 2026 Last quarter, my team's LLM bill crossed five figures and someone — not naming names — got an email from finance. So I went down the rabbit hole of comparing every API I could find, ranked them by output price, and figured out which models actually earn their place in a production stack. These are my notes, and yes, the numbers are real. The short version: the gap between the cheapest and most expensive API on the same platform is genuine

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