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Naive RAG trusts whatever it retrieved, which is how it produces confident answers grounded in the wrong documents; self-correcting retrieval adds the step it was missing — checking the results before using them.
Naive RAG trusts whatever it retrieved, which is how it produces confident answers grounded in the wrong documents. Self-correcting retrieval adds the missing step: check the results before using them.
Not all AI work needs an answer this second, and for the work that can wait, batching and asynchronous processing buy meaningfully cheaper tokens in exchange for latency you were not using anyway.
Not all AI work needs an answer this second, and for the work that can wait, batching and asynchronous processing buy meaningfully cheaper tokens in exchange for latency you were not using anyway.
Tool definitions and structured data quietly consume a large share of the context budget, and how you select, describe, and format them shapes both what fits and how well the model uses it.
Tool definitions and structured data quietly consume a large share of the context budget, and how you select, describe, and format them shapes both what fits and how well the model uses it.
Long-running agent work needs a way to report progress without the client holding its breath, and A2A offers two: stream the updates live, or register a webhook and get called back.
Long-running agent work needs a way to report progress without the client holding its breath. A2A offers two: stream the updates live over SSE, or register a webhook and get called back.
A server is only half the story; the client is what connects to it, discovers its capabilities, and turns a model's intent into real tool calls.
Build an MCP client that launches a server, discovers its tools, and drives them from a language model — the core of what every MCP host does internally.
A system that changes itself can improve itself right off a cliff, so the evaluation and guardrails are not an afterthought to self-evolving agents — they are the thing that makes them safe to run at all.
A system that changes itself can improve right off a cliff. Measuring evolution honestly, reward hacking, drift and collapse, and the guardrails that keep it safe.
Keyword search has one incurable weakness: it matches words, not meaning, so it misses relevant results that use different vocabulary than the query. Semantic search fixes this by representing queries and documents as vectors of meaning and matching by proximity in that space — the technology behind modern RAG. But the surprising lesson of production search is that semantic didn't replace keyword; the best systems combine them. This post explains vector search and why hybrid retrieval wins.
Keyword search matches words, not meaning. Semantic search embeds queries and documents into a vector space and matches by proximity — the engine of RAG — but it's weak at exact matches. Why keyword and semantic have opposite strengths, and why hybrid retrieval (not replacement) is the modern default.
Metrics and judges are ingredients; a harness is the kitchen. An evaluation harness is the system that takes a dataset of test cases, runs your LLM system over them, scores the outputs, and reports the results — reproducibly, every time. Building one well is what turns evaluation from a one-off spreadsheet into an engineering asset you run on every change, like a test suite.
Metrics and judges are ingredients; a harness is the kitchen. An eval harness takes a dataset of test cases, runs your system over them, scores the outputs, and reports results — reproducibly, every time. Building one well turns evaluation from a one-off spreadsheet into an engineering asset you run on every change.
TLS is the protocol securing nearly every connection you make, and it's not a single cryptographic trick — it's the whole toolkit orchestrated into one handshake. Key exchange, certificates, signatures, and authenticated encryption each solve one sub-problem, and TLS composes them so that two parties who've never met can establish a private, tamper-proof, authenticated channel over a hostile network. Understanding the handshake is understanding how every earlier piece fits.
TLS secures nearly every connection you make, and it's not a single trick — it's the whole toolkit orchestrated into one handshake. Key exchange, certificates, signatures, and AEAD each solve one sub-problem, and TLS composes them into a private, authenticated channel over a hostile network.
Rust makes a promise that sounds impossible: memory safety without a garbage collector, and fearless concurrency without data races — all checked at compile time, with no runtime cost. The price is a compiler that argues with you until your program is correct. Understanding that bargain is the key to understanding why Rust exists and why people love it.
Rust makes a promise that sounds impossible: memory safety without a garbage collector, and fearless concurrency without data races — all checked at compile time, with no runtime cost. The price is a compiler that argues with you until your program is correct.
Most agent frameworks have the model call tools by emitting JSON. smolagents, Hugging Face's deliberately tiny library, makes the model write Python code instead — "agents that think in code." That one design choice, plus a ruthless commitment to minimalism, is what the whole library is about, and it turns out to matter more than it sounds.
Most agent frameworks have the model call tools by emitting JSON. smolagents, Hugging Face's deliberately tiny library, makes the model write Python code instead — 'agents that think in code.' That one choice, plus ruthless minimalism, is the whole library.
Most agent frameworks ask you to design the workflow — the steps, the branches, the orchestration. Strands Agents, AWS's open-source SDK, makes the opposite bet: give the model a goal and tools, and let it drive. That model-driven philosophy is the whole point, and understanding it is understanding why Strands feels different from everything else.
Most agent frameworks ask you to design the workflow. Strands Agents, AWS's open-source SDK, makes the opposite bet: give the model a goal and tools, and let it drive. That model-driven philosophy is the whole point.