How to Debug an AI Agent
Print statements debug code. But the agent's code did exactly what it was told — the bug is in the context the model saw and the decision it made there. You debug an agent by reading transcripts, not by stepping through functions.
4 min
Google Antigravity vs Cursor vs Claude Code: What 'Agent-First' Actually Moves
Google's Antigravity, Cursor, and Claude Code now all hit ~80% on SWE-bench. So the real difference isn't who writes better code — it's where each one puts the work of checking it.
6 min
Genkit vs LangChain vs Vercel AI SDK: Which GenAI Framework Should You Build On?
Google's Genkit is the framework that bundles the parts the others sell separately. The real choice isn't features — it's where your code runs and how much of your ops you want the framework to own.
5 min
Firecracker vs gVisor vs Kata: Isolating AI Agent Code Execution
Three ways to keep an agent's untrusted code off your host kernel — and why the right choice is a triangle of compatibility, cold-start speed, and operational weight, not a security ranking.
7 min
Fast-Apply Models: How Cursor, Morph, and Relace Write Edits at 4,000+ Tokens/Second
The bottleneck in a coding agent isn't the smart model deciding what to change. It's the dull mechanical work of writing that change to disk correctly — and that's a different model entirely.
5 min
Embedding Quantization: Binary vs Scalar (int8) vs float32 for Cheaper Vector Search
Storing embeddings at full precision is a tax most RAG systems don't need to pay. Binary cuts memory 32x — and the trick that buys the quality back is cheaper than the savings.
5 min
Background Coding Agents: Devin vs Codex vs Cursor vs Jules vs Copilot
The async coding agents have all converged on the same shape — a cloud VM that clones your repo, runs the tests, and opens a PR. So the thing you're actually choosing isn't the coder. It's the harness and who reviews the flood.
5 min
DeepSeek-OCR: Storing Text as Pixels to Compress Long Context
DeepSeek's October paper shows vision tokens can carry roughly 10x the text of text tokens at ~97% fidelity — which quietly reframes long context as a compression problem, not a capacity one.
5 min
Context Editing vs Compaction vs the Memory Tool: Keeping a Long-Running Agent in Its Window
A long-running agent fails when its window fills with stale tool output. Anthropic ships three levers for that — and the trap is treating them as competitors instead of a division of labor.
7 min
Code Retrieval for AI Coding Agents: Embedding Index vs Agentic Grep
The two best coding agents disagree at the architecture level on how to find the right code. One builds a vector index of your repo; the other threw the index away and runs grep. The split is about freshness, not accuracy.
4 min
Code Agents vs Tool-Calling Agents: Should Your Agent Write Code or Emit JSON?
One paradigm has an agent write a Python snippet as its action; the other has it emit a structured JSON tool call. The 20% accuracy gap everyone quotes is real — but only on the tasks where it applies.
5 min
Cloudflare Agents vs LangGraph: Where Your Stateful Agent Actually Lives
They both promise durable, resumable agents — but one is a place to run code and the other is a way to structure it. Confusing the two is how teams end up with neither.
5 min
Braintrust vs Arize vs Opik: Choosing an LLM Eval Platform in 2026
The eval-tooling field just split into three camps and lost two players to acquisition in a single month. Pick on philosophy and independence, not the feature grid.
3 min
B200 vs H200 vs H100 for LLM Inference: Pick by Memory Wall, Not Peak FLOPS
The B200's headline 5-6x throughput jump is two different upgrades wearing one number — bigger HBM and FP4 compute — and which one matters depends entirely on whether your workload is memory-bound or compute-bound.
5 min
Agentic Context Engineering: Self-Improving Agents Without Fine-Tuning
A Stanford/SambaNova method called ACE lets an agent get better by editing its own context instead of its weights — and the trick is to grow that context, not compress it.
4 min
Agent-as-a-Judge vs LLM-as-a-Judge: Grading the Trajectory, Not Just the Answer
An LLM judge scores the final answer. For a multi-step agent, that signal is sparse, late, and easy to fool — a broken trajectory can still land on a right answer, and you'd never know.
5 min
Langfuse vs LangSmith vs Braintrust: LLM Observability and Evals Compared
Three platforms that look like competitors but optimize for different primary jobs, with lock-in profiles that diverge sharply once you read the fine print.
4 min
What Are Deep Agents? The Four-Part Pattern Behind Long-Horizon AI Agents
A deep agent is not a new model or a framework breakthrough — it's four cheap, known ingredients that let a plain tool-calling loop survive a long task instead of drifting.
5 min
The Four Kinds of Agent Memory: Working, Episodic, Semantic, Procedural
Most teams buy one vector store and call it 'memory.' It solves exactly one of the four problems — which is why the agent still loses the thread and repeats yesterday's mistake.
5 min
Self-Hosting LLM Inference vs an API: The Break-Even Math
Is it cheaper to run an open model on your own GPUs than to call an API? The deciding number isn't the token price — it's how busy the GPU stays.
5 min
Self-Consistency vs Best-of-N: How to Pick the Best of Many Samples
Both spend N times the inference to make a model smarter. The difference is how they choose the winner — and that choice decides which tasks each one can help.
6 min
RL Environments for AI Agents: The Bottleneck Moved From the Algorithm to the Environment
Everyone has GRPO now — it ships in every training library. The scarce, defensible input in agent training turned out to be the environment, and it looks suspiciously like your eval.
4 min
Retrieval Metrics for RAG: Recall@k vs MRR vs NDCG (and Which One Actually Matters)
Search teams optimize NDCG. RAG teams copy them — and measure the wrong thing. For a pipeline that hands the whole top-k to a generator, recall is the floor and rank position is a second-order correction.
5 min
Parent Document vs Sentence Window vs Auto-Merging Retrieval
The chunk that matches your query best is rarely the chunk that answers it. Small-to-big retrieval fixes that — here's how the three patterns differ and which to reach for.
5 min
The OWASP Top 10 for LLM Applications, Explained for Agent Builders
The list reads like a model-safety checklist. Read it again: most of the ten are not the model misbehaving — they're your architecture trusting the model too much. Agents make exactly those entries worse.
5 min
OpenAI Apps SDK vs MCP: How to Build a ChatGPT App in 2026
The Apps SDK isn't a rival to MCP. Your ChatGPT app IS an MCP server — the only proprietary part is how ChatGPT renders and discovers it.
4 min
OpenAI AgentKit vs LangGraph: Why the Visual Builder Got Deprecated First
OpenAI shipped a drag-and-drop agent canvas in October, then posted its deprecation notice eight months later. The part that survived tells you which layer to build on.
4 min
Online vs Offline Evals for AI Agents: Why Production Traces Need a Different Scorer
Offline evals ask whether the agent matched a known answer. Online evals can't — there is no answer. Treating them as one pipeline with one metric is the mistake that lets agents pass every test and still fail in production.
4 min
MCP Goes Stateless: What Changes in the 2026 Spec Release Candidate
The July 28 release candidate rips out sessions and the initialize handshake, deprecates Sampling and Roots, and adds MCP Apps — the clean break agent developers have to plan for.
5 min
LangGraph Checkpointing vs Temporal: Why Checkpoints Aren't Durable Execution
Most teams assume LangGraph's checkpointer already makes their agents crash-proof. It doesn't — and the gap is architectural, not a missing setting. Here's exactly where it ends and where Temporal begins.
4 min