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NEW

Mastering LLM Fine-Tuning and Inference Techniques

The short version: Building AI agents that work in production means mapping your fine-tuning approach to the data and infrastructure you actually have. Teams stall out when they pick a technique because it sounds advanced rather than because it fits their constraints. Parameter-efficient…
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NEW

Reactive, Deliberative, and Multi-Agent: Choosing the Right Agent Type Before Writing Code

The choice between Reactive, Deliberative, and Multi-Agent patterns isn't a progression. These are different decision models. Pick the wrong one, and you pay in latency, tokens, and debugging time. Reactive agents respond to current input without memory or planning. Think security alerts that…
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NEW

dotenv in Node for AI Coding Tools: Safe API Key Setup for OpenAI, Anthropic, and MCP Apps

Every AI key you paste into a source file is one git push away from going public. Setting up dotenv in Node takes a few minutes. dotenv is a small package that moves those keys into a local .env file, and your code reads them at runtime through process.env (Node's built-in object for environment…
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    NEW

    Build Your First AI Agent: Define the Tool Contract Before the Loop

    {"article":" \n\n## Why the tool contract comes before the loop\n\nThe tool contract stopped being optional the moment you tried to ship an assistant that does real work. Skip it and you get loops that never terminate, tools called with malformed arguments, and a token bill that grows every turn.…
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    Examples of Reinforcement Learning in LLM Apps: From RLHF to Safer Business Outputs

    Watch: Reinforcement Learning from Human Feedback (RLHF) Explained by IBM Technology In our experience, preference-tuned models can outperform much larger unaligned counterparts on human preference evaluations — alignment work often matters as much as raw scale when the target is human preference.…
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