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22 posts
Every major AI provider handles streaming differently. This guide covers SSE, WebSockets, HTTP chunked transfer, and the implementation details that…
How LLM evaluation benchmarks actually work, what they measure, what they miss, and how to build evaluation that matters for your specific application.
A technical teardown of GitHub Copilot: the models, infrastructure, and engineering decisions behind the product.
A technical teardown of Harvey AI: the models, infrastructure, and engineering decisions behind the product.
A technical teardown of Gamma: the models, infrastructure, and engineering decisions behind the product.
A technical teardown of Granola: the models, infrastructure, and engineering decisions behind the product.
A technical teardown of Klarna's AI Assistant: the models, infrastructure, and engineering decisions behind the product.
A technical teardown of Notion AI: the models, infrastructure, and engineering decisions behind the product.
A technical teardown of Perplexity AI: the models, infrastructure, and engineering decisions behind the product.
A technical teardown of Cursor: the models, infrastructure, and engineering decisions behind the product.
Taxonomy of prompt injection attacks and the layered defenses — input validation, output filtering, guardrails — that actually work at scale.
What happens between your API call and a streamed token — routing, batching, KV cache, quantization, and speculative decoding explained.
A comprehensive rundown of function calling, Model Context Protocol, agent frameworks, and the patterns that actually work in production — across every major provider.