Context Rot: Why a Bigger Context Window Doesn't Mean Better Recall
A million-token window is not a million usable tokens. Models degrade non-uniformly as input grows — sometimes performing worse than with no documents at all. The lever for agents isn't a bigger window; it's a cleaner one.
4 min
How AI Coding Agents Edit Code: Diff vs Whole-File vs Search-Replace
Everyone argues about which model to use. The under-discussed variable is how the agent writes its changes to disk — and that edit format is often the real bottleneck.
5 min
CodeRabbit vs Greptile vs Qodo: Choosing an AI Code Review Tool in 2026
Every vendor leads with its bug-catch rate. But code review is the one place in the AI stack where precision beats recall — a reviewer you learn to ignore catches nothing.
4 min
AG-UI vs MCP vs A2A: The Protocol That Connects Agents to Users
MCP wired agents to tools and A2A wired them to each other. The last hop — the agent talking to a human's screen — was still hand-rolled in every app. AG-UI is the standard for it.
5 min
Cloudflare Agents vs Bedrock AgentCore vs Vercel: Where to Run a Long-Running AI Agent
The three managed agent runtimes don't really compete on price or region. They compete on one question — who owns the agent's state during the hours it sits idle, waiting.
5 min
Pydantic AI vs OpenAI Agents SDK vs Agno: Choosing a Lightweight Python Agent Framework in 2026
The lightweight, type-first agent frameworks have arrived — and they quietly disagree about how much of your stack a framework should own. Pick on that, not on syntax.
5 min
Parallel vs Sequential Tool Calling: Why Turning It On Often Does Nothing
Parallel tool calling is two decisions people treat as one — the model emitting several calls, and your runtime actually running them at once. The API gives you the first for free and does nothing about the second.
5 min
BM25 vs Dense vs Hybrid Search: How to Actually Combine Them for RAG
Vector search quietly fails on product codes and function names. Here's why, what BM25 fixes, and why rank-based fusion beats score-mixing.
6 min
How to Evaluate an AI Agent's Tool Use, Not Just Its Answer
There is rarely one correct path through a task, so grading an agent against a golden trajectory fails. Grade invariants over the path, and the final state, instead.
5 min
How to Add Human-in-the-Loop to an AI Agent (It's a State Problem, Not a UI Problem)
Pausing an agent for a human approval is the same engineering problem as surviving a crash — both require serializing the run and resuming it later. Here's why, and what each framework gives you.
5 min
Few-Shot vs Zero-Shot vs Chain-of-Thought: When Each Prompting Style Wins in 2026
They were taught as a quality ladder. They're not — and on reasoning models the ladder is upside down. A field guide to which prompting style actually helps which model.
5 min
Sleep-Time Compute vs Test-Time Compute: Where Agents Should Spend Their Thinking
Test-time compute makes the model think harder while the user waits. Sleep-time compute moves that thinking off the critical path — but only pays off when the context is known early and reused across queries.
4 min
Python vs TypeScript for AI Agents in 2026: Which Stack to Build On
The library-count argument is over — vendors ship both languages now. The real choice is where your agent runs and what it sits next to.
4 min
Pre-Filtering vs Post-Filtering: Metadata Filters in Vector Search
Bolting a WHERE clause onto a vector search sounds trivial. It quietly breaks the index — and the fix is different in Qdrant, Weaviate, pgvector, and Pinecone.
5 min
Responses vs Assistants vs Chat Completions: Which OpenAI API to Build Agents On
OpenAI now ships three ways to call its models — but one of them has a death date. Here is how to choose, and the one reason reasoning models behave better on the newest surface.
4 min
Model2Vec vs Sentence Transformers: Static Embeddings and the 500x CPU Speedup
You can distill a sentence transformer into a token lookup table that needs no forward pass at inference — up to 500x faster on CPU, ~50x smaller, and it keeps more quality than the speedup suggests it should.
4 min
Matryoshka Embeddings: How to Shrink Vectors Without Wrecking Recall
A Matryoshka-trained embedding lets you chop off the tail of every vector and still search well — and a two-pass trick gets you the storage savings and the accuracy at the same time.
4 min
GRPO vs PPO: Why DeepSeek's RL Algorithm Deleted the Critic
GRPO didn't win on optimization theory. It won by removing a policy-sized value network from the training loop — and the memory it saved is what put RL post-training within reach of a single node.
4 min
Claude vs GPT vs Gemini for AI Agents in 2026: Choosing a Model for Tool Use
Agents don't run on chatbot leaderboards. The model that wins your tool loop is decided by function-calling reliability, agentic benchmarks, and an "agent tax" the headline price hides.
5 min
Claude Code vs Codex CLI vs Gemini CLI: Picking a Terminal Coding Agent in 2026
Three bets on the same idea — that the command line, not the IDE, is where coding agents live. And as of this month one of the three just changed its name and its terms.
4 min
CAG vs RAG: When Cache-Augmented Generation Beats Retrieval
Cache-augmented generation deletes the retriever and preloads your whole knowledge base into the KV cache. The real question isn't speed — it's whether your corpus fits and how often it changes.
5 min
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.
4 min
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.
4 min
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?
4 min
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.
5 min
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.
5 min
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.
4 min
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.
5 min
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.
4 min
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.
5 min