MHA vs MQA vs GQA vs MLA: How Attention Stopped Eating Your KV Cache
Every attention variant since 2019 has been one argument about the same scarce resource — the key-value cache — and the newest answer changes the terms of the deal.
5 min
MCP Security: Tool Poisoning, Rug Pulls, and Why the Dangerous Server Is Never the One You Call
The worst MCP attacks aren't bugs in a server's code — they're features of a trust model that drops every tool's description into one undifferentiated context. Here's the threat map, and the defenses that actually hold.
5 min
Mamba vs Transformer: Do State-Space Models Matter for Agents Yet?
Pure Mamba never beat the Transformer outright — but a wave of hybrids that keep ~8% of layers as attention now cut long-context memory 70%+ and triple decode throughput.
5 min
Lovable vs Bolt vs v0 vs Replit: Choosing an AI App Builder in 2026
They all promise an app from a prompt. They differ on the question none of them advertises: when you outgrow the tool, do you get to take the code with you?
4 min
LLM Inference Latency: TTFT vs TPOT vs Throughput, and Why 'Tokens Per Second' Is Two Numbers
The three numbers everyone quotes measure three different bottlenecks — and per-user speed and system throughput move in opposite directions, so a vendor's headline tok/s can mean whatever flatters it.
5 min
Knowledge Distillation for LLMs: Copying Behavior, Not Weights
Distillation is the only model-compression method that moves a capability across a size class. The decade-long arc: the supervision signal went from "match the teacher's answer" to "let the student practice and have the teacher grade it."
4 min
How to Manage Context in a Long-Running Agent: Clearing vs Compaction vs Memory
An agent that runs for a hundred turns will blow past any context window. The fix is three different mechanisms — and the order you reach for them is the opposite of most people's instinct.
4 min
How to Detect LLM Hallucinations: Faithfulness Is Not Factuality
Almost every hallucination detector measures one thing — whether the answer is grounded in the context it was given. That is not the same as whether the answer is true.
4 min
How to Authenticate an AI Agent: Workload Identity vs Delegated Identity
An agent needs two identities at once — proof it is itself, and proof of whose authority it's borrowing right now — and the dangerous failures all live at the seam between them.
6 min
GSPO vs GRPO: Why Qwen Threw Out Token-Level Importance Sampling
GRPO scores a whole response, then corrects the policy one token at a time — and on long outputs and MoE models that mismatch quietly destroys training. GSPO's fix is almost embarrassingly simple: optimize at the same unit you reward at.
5 min
GEPA vs MIPROv2: Why Reflective Prompt Optimization Beats More Samples
GEPA optimizes prompts by reading the agent's own failure traces in plain language instead of chasing a scalar score — and reports beating an RL baseline with up to 35x fewer rollouts.
5 min
FlashAttention vs PagedAttention vs FlashInfer: Three Different Problems, One Word
Stop choosing between them. FlashAttention is the compute kernel, PagedAttention is the memory layout, FlashInfer is the engine — a modern stack runs all three at once.
5 min
Diffusion LLMs vs Autoregressive: Why 'Parallel Generation' Wasn't Actually Faster
Diffusion language models generate every token at once instead of left-to-right, which sounds like a guaranteed speedup. The early open models were slower than the autoregressive baseline anyway — and the reason they finally got fast is the opposite of what the pitch implied.
6 min
Continuous Batching vs Static Batching: Why LLM Serving Throughput Jumps an Order of Magnitude
Static batching wastes the GPU because LLM outputs are variable-length — short replies idle while the batch waits for the longest. Continuous batching schedules at every token step instead. The catch is that the same trick that wins throughput can spike latency.
4 min
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