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Building Talon MCP: A Secure Bridge Between AI Agents and Hiring Workflows
How we designed and built a production-capable Model Context Protocol service for public job discovery and permission-aware Talon workflows.
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显示中 8 文章s 在 软件开发. Architecture, delivery workflows, and product engineering decisions that compound.

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How we designed and built a production-capable Model Context Protocol service for public job discovery and permission-aware Talon workflows.
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We had good tools, each doing its job well. The problem was that nothing truly tied them together. Sales lived in one place, projects in another, finance somewhere else, and client communication was scattered across email, chat and meetings. So we asked a simple question: what if the systems running the business actually worked as one? That question became Relay.
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The economics of artificial intelligence present a stark paradox: Africa is poised to become one of the world’s largest consumers of AI, yet it risks owning virtually none of the underlying infrastructure, models, or intellectual property. From data to energy, the continent is supplying the raw inputs for the global tech economy while renting the finished intelligence. Without regional compute strategies and sovereign data governance, Africa will transition from a landscape of technological opportunity to one of permanent digital tenancy.
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A few years ago, watching a large language model generate a working API endpoint felt magical. Today, it is baseline reality. Provide a well-structured prompt, some repository context, and a few reference files, and a model will reliably output a controller, a database migration, an integration test, a Dockerfile, or a polished React component. It can even spin up the initial architecture of a lightweight application.
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What actually happened is that the AI showed up on Monday asking where the repository lives, spent Tuesday reading the entire codebase, and by Wednesday was submitting pull requests while openly questioning architectural decisions made by senior engineers. The latest models don't just "write a function." They operate in "refactor the entire billing system while I grab a coffee" cycles. Somewhere, a developer who spent a decade mastering design patterns is watching an AI finish a two-week sprint before the morning standup even starts
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I spent a week running Gemma locally, comparing it against Llama, Qwen and GPT-4o, trying to answer one question: Is Google's open model strategy finally good enough for real production workloads?
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In many parts of Africa, connectivity is intermittent, devices are heterogeneous, and bandwidth is not cheap. This changes how software must be built. Bloated applications, excessive API calls, or inefficient data transfers are not just minor annoyances—they are product failures.
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