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Hands-on AI Architecture, Model Setup & Secure Integration
HackerAI helps founders, developers, and security teams design and implement practical AI systems—from architecture to a working setup.
We manually configure the right models, providers, APIs, local or open-source deployments, RAG pipelines, agents, and security controls for your real use case. You get hands-on technical support, testing, documentation, and a clean handoff—not a generic one-click package.
Services include AI architecture reviews, model and provider selection, local/cloud model setup, RAG and agent workflows, API integration, performance and cost optimization, and security-focused deployment guidance.
Start with an architecture consultation or choose a setup service in the Shop. Learn more at HackerAI.sh.
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Publicaciones
A model working in a demo is not the same as a production-ready AI system. Production deployments need controls around the model, not just a prompt and an API key. A practical baseline includes: • Least-privilege access for models, tools, databases, and external APIs. • Secret management instead of keys stored in code or prompts. • Input validation and output handling for prompt injection, u...
Secure AI Deployment: The Controls That Matter in Production
Oct 23, 2026
29 Visualizaciones
The highest benchmark score does not automatically make a model the right production choice. A useful model evaluation starts with your own representative tasks and measures the tradeoffs that affect the product: • Quality: Does the output pass a task-specific rubric? • Reliability: How often does it fail, refuse, or invent details? • Latency: Can it meet the user experience target under loa...
How to Choose an AI Model Without Chasing Benchmarks
Oct 23, 2026
22 Visualizaciones
Most AI projects should not begin with a model name. They should begin with the constraints of the real workflow. Before choosing a provider or deploying anything, define five things: 1. The user and the decision the system must support. 2. The data it may access—and the data it must never expose. 3. The acceptable latency, quality, and operating cost. 4. The failure mode when the model is uncer...
A Practical AI Architecture Starts With Constraints, Not Models
Oct 23, 2026
19 Visualizaciones