Agentic RAG vs Naive RAG: When to Let the Model Drive Retrieval
Naive RAG retrieves once and hopes. Agentic RAG turns retrieval into a decision the model makes at runtime — paying for it on every query to win the queries that silently fail.
5 min
RAG vs Long Context: When to Retrieve and When to Stuff the Window
Million-token windows were supposed to kill retrieval. The benchmarks say something stranger — the choice is really between two different failure modes, and only one of them is loud.
6 min
pgvector vs Pinecone vs Qdrant: Picking a Vector Database in 2026
All three clear the recall-and-latency bar for almost any agent you'll build. The real decision is where the operational cost lives — and there's a query volume where the answer flips.
4 min
Multi-Agent vs Single-Agent: When More Agents Actually Help
Two of the most-cited essays on agent design say opposite things. They are both right — the disagreement is really about whether your task reads or writes.
5 min
MCP vs Function Calling: When You Actually Need a Server
They are not competing ways to give a model tools. One is the engine; the other is a distribution standard wrapped around it — and you pay for the wrapper in tokens and attack surface.
5 min
MCP Transports: stdio vs SSE vs Streamable HTTP
The Model Context Protocol replaced its HTTP+SSE transport with Streamable HTTP in 2025. Choosing it does not make your server serverless-friendly — and the reason is the part nobody reads.
5 min
Hybrid Search vs Semantic Search: Why Vector RAG Misses Exact Matches
Embeddings smear error codes, SKUs, and function names into "nearby" meaning and lose the literal. Hybrid search fixes it — but the real work is in the fusion step, not the index.
5 min
How to Defend an AI Agent Against Prompt Injection in 2026
You cannot patch prompt injection out of a model. The defenses that actually hold treat it as an architecture problem — and start by taking away what a hijacked agent could do.
5 min
How to Authenticate a Remote MCP Server: OAuth 2.1, PKCE, and the 2026-07-28 Spec
The hard part of remote MCP auth was never the login. It's proving a token was minted for *your* server and no one else's — the audience claim that turns a friendly proxy back into a locked door.
5 min
HNSW vs IVF vs DiskANN: Choosing a Vector Index
Almost every vector-index comparison argues about query speed. Below ten million vectors that is the one thing that rarely decides it. The real choice is where your vectors live, and what it costs to change them.
5 min
Groq vs Together vs Fireworks: Choosing a Serverless Inference API for Open Models
Three ways to rent open-weight inference without owning a GPU — and why the fastest of them just licensed its speed to Nvidia instead of competing with it.
4 min
Fine-Tuning vs RAG: When to Actually Fine-Tune an LLM in 2026
They are not two answers to one question. RAG fixes what the model doesn't know; fine-tuning fixes what it won't do the way you need. Pick by the failure, not the fashion.
5 min
Contextual Retrieval vs Naive RAG: Fix the Chunk, Not the Model
Most RAG retrieval failures are context lost at chunk boundaries — contextual retrieval fixes them at index time, cheaper than a bigger embedding model or GraphRAG.
4 min
Context Engineering for AI Agents: Managing the Attention Budget
Prompt engineering optimized a string. Context engineering manages a finite, decaying budget — because the context window is not a bucket you fill, it is attention that rots as it fills.
5 min
Best LLM for Function Calling: Why the Leaderboard Score Lies
The model that emits a correctly-shaped tool call once is rarely the one that holds up across a multi-turn conversation and eight repeated trials. Pick by failure mode, not top-line score.
5 min
The Best Chunking Strategy for RAG in 2026: Fixed vs Semantic vs Late Chunking
The chunk-size A/B test is the most over-run experiment in RAG. The teams winning on retrieval stopped tuning how they split and started fixing what each chunk forgets.
4 min
A2A vs MCP: The Two Protocols Are Not Fighting
Stop reading "A2A vs MCP" as a fork in the road. One protocol points your agent down at tools; the other points it sideways at other agents. Here is how to use both without picking a loser.
5 min
Tavily vs Exa vs Linkup: Picking a Web Search API for AI Agents
They all give an agent the web, but they hand it back at different stages of doneness — raw links, cleaned pages, semantic matches, or a finished sourced answer. The price tracks exactly how much reading they did for you.
6 min
Semantic Caching for AI Agents: When a Cache Hit Returns the Wrong Answer
Caching LLM calls by meaning can cut your bill and your latency — or it can confidently serve last user's answer to this user's question. The whole game is the similarity threshold nobody tunes.
4 min
Prompt Caching for AI Agents: Why Your Cache Keeps Missing
Every major provider will sell you a 50–90% discount on repeated context. The catch is a single rule that quietly fights how agents are built.
4 min
LLM-as-a-Judge: How to Build an Eval That Doesn't Quietly Lie to You
Using a model to grade your model feels like measurement. Until you learn what the judge is actually rewarding — verbosity, position, and its own prose — it's closer to a focus group of one.
5 min
vLLM vs SGLang vs Ollama: How to Choose an LLM Inference Engine in 2026
The benchmark everyone argues over is the wrong one. The engine you should run is decided by how much context your requests share — not by whose tokens-per-second screenshot is biggest.
5 min
The Tell Is Amazon
Every hyperscaler stretched the assumed lifespan of its AI servers to flatter earnings. One quietly went the other way—and named AI as the reason.
5 min
The Resolution Is the Unit, and the Vendor Holds the Ruler
Outcome-based AI pricing sounds like the buyer winning. But when you pay per "resolution," the seller defines, delivers, and grades the thing you're paying for — and Fin already counts your silence as a sale.
5 min
The Price Fell. The Bill Rose. Both Numbers Are True.
The famous chart showing AI inference getting 280x cheaper measures the price of a token. Almost nobody is buying tokens. They're buying tasks, and tasks got more expensive.
5 min
The Megawatt You Cannot Rent
An agent's useful life is measured in weeks before the model is deprecated. The power to run it is measured in years before the grid will connect it. That mismatch is the real ceiling.
4 min
The Duty of Care Died Before Anyone Had to Meet It
For two years everyone braced for a patchwork of strict state AI laws. In the first half of 2026 the patchwork started unraveling from both ends — and the one substantive rule was deleted before a single company had to obey it.
5 min
The Deadline Arrives With Its Teeth Pulled
On August 2, Europe finally gets the power to fine AI companies. The same season, it quietly moved the thing it would have fined them for to the end of 2027.
4 min
The Coin-Flip Horizon
Every "AI can now do an N-hour task" headline is a 50%-reliability number — a coin flip. The reliability you'd actually deploy on sits years behind it, and the gap is the story.
4 min
The Code Was Always a Menu
On August 2 the EU's enforcement powers over general-purpose AI switch on. But the real tell is already public: xAI signed one chapter of the "voluntary" code and skipped the two that cost something.
4 min