Turn Detection for Voice Agents: VAD vs Semantic End-of-Utterance
The reason a voice agent feels rude is almost never its voice. It's that the agent confused "the user stopped making noise" with "the user is finished" — two different questions a silence timer cannot tell apart.
4 min
tiktoken vs SentencePiece vs Hugging Face Tokenizers
Three libraries everyone compares as if you get to choose. You don't — your model already chose for you. The real question is what that choice costs, and who pays it.
5 min
The Official MCP Registry, Explained: How to Publish and Find MCP Servers
The official MCP Registry isn't an app store — it's a canonical metadata feed built to prove who owns a server name, and it leaves search and curation to everyone downstream.
5 min
Temperature vs Top-p vs Top-k: How LLM Sampling Actually Works
Three of these knobs do the same job — truncate the unreliable tail of the next-token distribution. The differences are smaller, and more contested, than the tutorials admit. And if you build agents, you probably want almost none of it.
5 min
Streaming an AI Agent's Output: Why SSE Beats WebSockets Until It Doesn't
The SSE-vs-WebSockets debate misses the real problem. An agent doesn't emit a token stream — it emits typed events. Design the envelope first; the transport falls out.
4 min
Spec-Driven Development: Spec Kit vs Kiro vs Tessl
Writing a spec before the agent writes code is the loudest idea in AI coding right now. The pitch isn't better code — it's making intent a durable artifact that survives the context window. Three tools bet on that at three different altitudes.
5 min
Intent Routing for AI Agents: When a Cosine Match Beats an LLM Call
If your agent has a fixed set of tools and intents, you probably don't need a model to pick between them. An embedding lookup is faster, cheaper, and the same input lands the same way every time.
5 min
How to Extend an LLM's Context Window: Position Interpolation vs NTK vs YaRN
Stretching a model past its trained context length isn't a memory problem — it's a positional-encoding generalization problem. The methods that work all interpolate instead of extrapolate, and the good ones interpolate unevenly.
5 min
Reasoning Effort vs. Thinking Budget: How to Control How Much Your Model Thinks
Every lab gives you a dial for how hard a model reasons before it answers — through three incompatible interfaces. The surprise is that turning it up isn't always better.
4 min
Qwen3-Embedding vs EmbeddingGemma vs BGE-M3: The Best Open-Weight Embedding Model in 2026
The open-weight embedding race stopped being one race. It split into two that don't compete — and the most interesting model isn't a single vector at all.
5 min
Process Reward Models vs Outcome Reward Models: Why Frontier RL Went Back to the Sparse Signal
Grading every reasoning step sounds strictly better than grading only the final answer. The models that actually pushed reasoning forward threw the step-grader away and rewarded the one thing they could verify by rule.
5 min
Prefix Caching vs Prompt Caching: The Three LLM Caches Everyone Confuses
They share a word and almost nothing else. One discounts your bill, one reuses GPU memory, one can hand back the wrong answer — and teams keep enabling the one they didn't mean.
4 min
NVIDIA Dynamo vs llm-d vs vLLM: How to Serve LLMs at Scale in 2026
"Dynamo vs vLLM" is a category error. One is an orchestrator across pools of GPUs; the other is the engine inside a single replica. Sort that out and the real choice gets clear.
5 min
Supervisor vs Swarm vs Handoffs: Multi-Agent Orchestration Patterns in 2026
The topology you pick for your agents is really one decision in disguise — who holds the state and the control — and that single choice sets your token bill, your latency, and whether you can ever debug the thing.
5 min
Model Merging: How TIES, DARE, and SLERP Build a New Model Without Training
Merging averages the weights of separately fine-tuned models into one — no GPUs, no gradients, just arithmetic. The methods aren't a quality ladder; they're escalating answers to a single problem: interference.
5 min
MIG vs MPS vs Time-Slicing: How to Share a GPU for LLM Inference (and When Not To)
Three ways to put more than one workload on one accelerator — and a reason most LLM serving shouldn't use any of them. Choose by failure domain, not utilization.
5 min
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