39 stories
For the past twenty years, school ERP systems across India have been glorified digital filing cabinets. They store student records, calculate basic fees, and spit out Excel sheets.
A post this week called context-window compaction a "200k-token lobotomy" and patched it with external state files named after Memento. It is a good write-up, and the naming is honest about the stakes. But it also made the underlying problem easier to see than the fix does.
We run a two-AI workflow: one AI manages and verifies, the other builds. They coordinate through a shared Google Sheet that acts as a message mailbox. It sounds simple. It was not simple. Here's what broke and what we learned.
Why token prices are only a small part of the cost of running AI in production When an AI feature becomes expensive, the model is an obvious place to look. But as software engineers, we should ask a more uncomfortable question first: how much of the work are we asking the model to do unnecessarily?
I recently completed the AI-Assisted Data Science with BigQuery lab via Google Cloud. While analyzing the final multimodal vector search stage, I focused heavily on how the pipeline enforces state dependencies.
Building a Telegram Bot for AI Video Generation: Architecture and Lessons Learned After months of building and iterating on a Telegram-based AI video generation platform, I want to share the technical architecture, challenges encountered, and lessons learned.
Edge-Agentic Commit Analyzer: A Zero-Dependency Local Guardrail Why "Daemonless and API-less"? The modern development environment is bloated with background services. Daemons run constantly, silently devouring memory resources.
Enfim chegamos no terceiro e último post da série. Neste post vamos explorar a alma da palestrante virtual, isto é, a arquitetura que a faz funcionar. Introdução Nós vamos começar com um diagrama de alto nível que apresenta os principais componentes da arquitetura e, à medida que avançamos, vamos…
This is the engineering summary of an open reference architecture. The paper, its object model as JSON and the model register are free to reuse under CC BY 4.0: https://codeatoms.ai/port-digital-twin-us/. The operator is described by class, never by name.
Prompt injection is OWASP's LLM01:2025, the top vulnerability in LLM based systems, and yet most agentic .NET projects I review have no redaction pipeline at all. They have a SessionStore, they have JsonSerializer, and they have a lot of optimism.
Earlier this week, I published an article here on DEV: "Why We Stopped Letting LLMs Do Raw Math: Building Pythos With Deterministic Verification". The response was humbling, but one particular comment from an enterprise AI engineer at IT Path Solutions stood out.
Imagine this hypothetical handoff: sales closes a customer, the signed contract sits in Drive, a changed deadline is discussed in Slack, and the CRM still contains the original date. Delivery needs a reliable brief, an approved start date, and the right records updated.
AI agents got email. The first thing they did was file bug reports on each other. Most multi-agent setups treat agents as isolated workers. Each one gets a task, runs it and returns a result, with no awareness of the others and no way to coordinate.
Most APIs were designed for two consumers: a human reading documentation and code a human wrote once. An AI agent is neither. It discovers endpoints at runtime from a machine-readable description, fills in arguments by pattern-matching field names, retries automatically when something fails, and…
The way we evaluate web frameworks needs to catch up with the way we build software. For years, a large integration catalog was a powerful selling point. If a framework had a plugin for your CMS, image service, or frontend library, that could save days of work.
Let’s talk about a silent cultural crisis happening inside modern engineering teams—one that is costing your company thousands of dollars a month in hidden labor and system instability.
When teams start connecting APIs to AI agents, three terms arrive together and get used interchangeably: function calling, ChatGPT plugins, and the Model Context Protocol (MCP).
1. Nine o'clock Nine in the morning. A lecture hall, sixty students, one professor, a task on the board. Not quicksort. Not a linked list. Nothing that balances itself. Build a shop. Registration, catalog, basket, payment, and a back end that stays up when the shop is mentioned on television.
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