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AGENTS.md vs CLAUDE.md: One File to Brief Every Coding Agent
The config-file war for how you talk to a coding agent didn't end with a winner. It ended with a foundation — and that changes which file you should actually write.
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Turbopuffer vs Pinecone vs Vectorize: Serverless Vector Search in 2026
The vector database fight stopped being about speed. It's now about where your index sleeps — and whether you have one hot haystack or a million cold ones.
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Tensor Parallelism vs Pipeline Parallelism: How to Split an LLM Across GPUs
When one model won't fit on one GPU, you have two ways to cut it up — and the right cut is a description of your interconnect, not a tuning knob you guess at.
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Self-RAG vs Corrective RAG: Two Ways to Make Retrieval Check Itself
Both bolt a quality check onto RAG, but they fix different failures at different points — and the choice comes down to one question: do you control the model's weights?
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Query Rewriting vs HyDE vs Multi-Query: Fixing the RAG Question, Not the Index
Three popular RAG upgrades all transform the query before retrieval — and they're useless if your retrieval was failing for a different reason. Here's how to tell.
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Why LLM Inference Has Two Speeds: Continuous Batching and Prefill/Decode Disaggregation
A single tokens-per-second number hides two workloads pulling in opposite directions — and the whole arc of serving optimization is the field admitting they should never share a GPU.
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Serving Many Fine-Tuned Models on One GPU: LoRAX vs vLLM vs SGLang
Multi-LoRA serving turns "one GPU per model" into "one GPU per base model, amortized across hundreds of tenants." Here are the tools that do it, and the kernel trick that makes it work.
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MLX vs llama.cpp: Which Engine Should Run LLMs on Apple Silicon
Ollama just ripped out llama.cpp and bolted in Apple's MLX on the Mac. The switch is a tell about where your bottleneck actually lives — and when the older engine still wins.
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MCP Tools vs Resources vs Prompts: The Three Lanes, and Why Only One Got Paved
The Model Context Protocol defines three server primitives split by who's in control — the model, the app, the user. The ecosystem implemented one of them.
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MCP Sampling vs Elicitation: The Two Ways a Server Talks Back
Most MCP servers only answer requests. Sampling and elicitation are the two features that let a server reach back through the client — one to the model, one to the human — and almost no one implements either.
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Code Execution vs Direct Tool Calls: How Agents Actually Scale MCP
Loading every tool definition into context and round-tripping every result is how MCP agents stall. Code execution flips the model into a programmer — and moves the hard part to your sandbox.
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The Cheapest LLM Tokens Are the Patient Ones: Batch APIs vs Realtime
Every major provider sells inference at roughly half price if you can wait up to 24 hours. The discount isn't the point — the contract is, and it tells you which agent work was never realtime to begin with.
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Late Chunking vs Contextual Retrieval: Two Fixes for RAG's Context Problem
Your chunks lose the document around them before they're ever embedded. Jina and Anthropic solve it in opposite places — one in vector space for free, one in the text for a price.
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KV Cache Quantization: The Memory That Actually Caps Your LLM Throughput
You quantized the weights to 4-bit and thought memory was solved. At long context the KV cache dwarfs the weights — and it needs a different kind of quantization to shrink safely.
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How to Evaluate a RAG Pipeline: The Metrics That Predict Quality
Most RAG failures are retrieval failures wearing a generation costume — so measure the two halves separately or you'll tune the wrong one for weeks.
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How Much VRAM Do You Need to Serve an LLM? A 2026 Sizing Guide
The weights are the easy part — the math you can do on a napkin. What silently OOMs your server in production is the KV cache, and almost nobody budgets for it.
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FP8 vs INT8 vs INT4: Picking a Quantization Format for LLM Inference
The three formats aren't competing for the same job — one buys you faster math, one buys you smaller weights, and one is the fallback for hardware that can't do the first. Know which bottleneck you're paying down.
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Fine-Tuning Embedding Models for RAG: When It Beats a Bigger Model
When retrieval underperforms, everyone reaches to fine-tune the LLM. The cheaper, higher-leverage move is to fine-tune the embedding model — and almost all the gain comes from one ingredient.
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Composio vs Arcade vs Toolhouse: Tool Integration and Auth for AI Agents
MCP standardized how an agent calls a tool. It said almost nothing about how the agent logs in as you — and that gap is the whole product these three are selling.
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The Best Open-Source RAG Platforms: RAGFlow vs R2R vs Kotaemon
The real divide in open-source RAG isn't which library to import — it's whether to build with one at all, or deploy a finished engine. Three engines, three very different bets.
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Agents vs Workflows: When Your LLM App Should Not Be an Agent
The architecture decision underneath every agent framework is one most teams skip — and the math of compounding errors says the boring choice is usually right.
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Voyage vs OpenAI vs Cohere vs Gemini: Choosing a Text Embedding API in 2026
The embedding model you pick barely moves your bill. The dimensions you store and the precision you keep — that's the recurring cost, and it's the decision almost nobody makes on purpose.
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verl vs OpenRLHF vs TRL: Choosing an RL Post-Training Framework in 2026
GRPO is now a commodity all three ship. The thing that actually sorts them is who owns the distributed orchestration — and how you keep one starving inference engine fed.
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TEI vs Infinity vs vLLM: Choosing an Embedding Inference Server in 2026
Three ways to serve embeddings at scale that look like rivals but answer a different question: should embeddings be a dedicated specialist, or ride on the GPU already running your LLM?
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SWE-bench vs τ-bench vs GAIA: Which Agent Benchmark Actually Predicts Production
They look like a difficulty ladder. They're three orthogonal axes — and only one of them measures the thing that decides whether your agent survives contact with real users.
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Speech-to-Speech vs Cascaded: Two Architectures for Voice AI Agents in 2026
The new realtime models hear and speak in one step, no text in the middle. That deletes the seam where you used to read, log, and control everything. Here's the real trade.
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smolagents vs LangGraph vs CrewAI: Three Bets on How an Agent Acts
The frameworks that get the most attention disagree on something basic — what an agent's action even is. One writes code, one wires a graph, one casts a team.
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Small Language Models vs LLMs for Agents: Where the Big Model Is Just Overhead
A frontier model on every node is the default, not the optimum. Most agent calls are narrow, repetitive, and format-constrained — exactly the shape a small model was built for.
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ReAct vs Plan-and-Execute vs Reflexion: Choosing an Agent Reasoning Pattern
The listicle treats these as three flavors of the same choice. They aren't — two are ends of one axis, and the third sits on a different axis entirely. Pick by your environment, not your vibe.
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Qwen vs Llama vs DeepSeek vs Mistral vs Gemma: Choosing an Open-Weight LLM for Agents in 2026
The benchmark you compare on today expires in three weeks. The license you build on doesn't. Pick an open-weight family the way it will still matter next quarter — by what you're allowed to do with it, and what it costs to serve.