Archive
1290 posts · Page 68 of 108. ← Blog
Global feature importance tells you what a model relies on overall, but the question people actually ask is local: why did the model deny this particular loan? LIME answers it with a clever trick — approximate the complicated model with a simple, interpretable one in the small neighborhood around a single prediction. It was the method that popularized model-agnostic local explanations, and understanding how it works (and where it's fragile) is essential interpretability literacy.
Why did the model make this one prediction? LIME approximates the complex model with a simple one in the small neighborhood around a single instance — intuitive, model-agnostic, launched the modern wave of local explanation. But it's fragile: unstable across reruns, sensitive to choices, and gameable — which is why SHAP followed.
The income statement is the one financial document every engineer should be able to read, because it's the scoreboard everyone above you is watching. It answers, for a period of time, the most basic business question — did we make money? — but the real value is in its structure: the journey from "money customers paid us" at the top to "profit we actually kept" at the bottom, with every cost that eats into it along the way. Once you can read that journey, a huge amount of business behavior stops being mysterious.
The income statement is the one financial document every engineer should be able to read — it's the scoreboard everyone above you is watching. The real value is in its structure: the journey from 'money customers paid us' at the top to 'profit we actually kept' at the bottom, with every cost that eats into it along the way.
What a goroutine actually is, why it is cheaper than a thread, and the one rule that separates working concurrent code from a program that quietly leaks itself to death: every goroutine you start must have a way to stop.
What a goroutine actually is, why it is cheaper than a thread, and the one rule that separates working concurrent code from a program that quietly leaks itself to death: every goroutine you start must have...
How a single loan payment is split across fees, interest, and principal — and why the engine that does it has to be deterministic, auditable, and safe to run twice.
How a loan payment is split across fees, penalties, accrued interest, and principal, with configurable waterfalls and partial-payment rules.
Design MPC / threshold-signature custody so no single party or HSM holds a whole key.
Design MPC / threshold-signature custody so no single party or HSM holds a whole key.
Solving the sending-side reliability problem — nonce sequencing, fee estimation, and replacing a transaction that gets stuck in the mempool.
Solve the sending-side reliability problem: nonce sequencing and gas/fee estimation, including stuck-transaction replacement.
Cluster and risk-score blockchain addresses for on-chain AML and sanctions exposure.
Cluster and risk-score blockchain addresses for on-chain AML and sanctions exposure.
Two open protocols that let an agent reach outside its own process — one to borrow tools, one to call other agents as peers.
Two interoperability protocols: MCP lets an agent consume tools from external servers, and A2A — HTTP for agents — lets one agent discover and call another remote agent as a peer over HTTP.
How the four smallest pieces of the LangGraph API turn a bag of nodes into a program you can run.
Edges, START, END, compile() and invoke() are the four smallest pieces that turn a bag of nodes into a runnable program. Here is the full lifecycle of a tiny two-node graph.
This lesson hosts one specialized Foundry agent over the A2A protocol, publishing an agent card the client can discover.
Host one Foundry agent over A2A: newMux pins the card interface URL, wraps the agent in an a2aprovider executor, and serves the card plus JSON-RPC routes.
The graph model underneath every multi-agent app: executors as nodes, edges as data flow, and typed events streaming out of `WatchStream` as it runs.
The Microsoft Agent Framework workflow model in Go: executors bound to IDs, AddEdge wiring, WithOutputFrom, and typed WatchStream events - plus an upstream route-builder fix.
The most basic and most used interpretability question is also the most useful: which features actually matter to this model? Feature importance answers it, giving a ranked list of the inputs that drive a model's predictions — the first thing to compute when you want to understand, debug, or trust any model. This post covers the main ways to measure it, the traps that make naive importance misleading, and why permutation importance is the reliable default.
Which features actually matter? Feature importance is the workhorse global method — great for sanity-checking and leakage detection — but built-in importances mislead (impurity bias), so prefer permutation importance. And the traps: correlated features hide importance, and importance is not causation.