1290 posts · Page 67 of 108. ← Blog
The prebuilt orchestration builders in agent-framework-go — Sequential, Concurrent, Group Chat — plus wrapping a whole workflow as one agent.
The Sequential, Concurrent, and Group Chat orchestration builders in agent-framework-go, plus wrapping a whole workflow as one nestable agent.
SHAP is the most important interpretability method in practice today, because it answers "how much did each feature contribute to this prediction?" with a principled, mathematically fair answer borrowed from game theory. Where LIME gives an intuitive but unstable local explanation, SHAP gives a grounded one with guarantees — and it unifies local and global interpretability into one framework. This post explains the idea behind SHAP without the heavy math, and why it became the field's default.
SHAP is the field's default: it answers 'how much did each feature contribute?' with a fair, game-theoretic answer (Shapley values), fixing LIME's instability with real guarantees and unifying local and global interpretability. The catch: exponential cost (TreeSHAP fixes it for trees), and it's attribution, not causation.
If the income statement is a movie of what happened over a period, the balance sheet is a photograph — a snapshot of what a company owns and owes at a single moment. It answers a different question than "did we make money?": it answers "what is the financial position right now?" And it rests on one elegant equation that always balances, by definition — an equation that, once you understand it, makes the whole document readable.
If the income statement is a movie of what happened over a period, the balance sheet is a photograph — a snapshot of what a company owns and owes at a single moment. It rests on one elegant equation that always balances by definition, and once you understand it, the whole document becomes readable.
A working guide to Go's channels — unbuffered rendezvous vs buffered capacity, send/receive/close semantics, directional types in APIs, and how `select` multiplexes, times out, and disables cases with a nil channel.
A working guide to Go's channels — unbuffered rendezvous vs buffered capacity, send/receive/close semantics, directional types in APIs, and how `select` multiplexes, times out, and disables cases with a nil...
Modeling borrower hardship as an explicit state machine, so relief, delinquency, and accounting never disagree.
Modeling hardship: payment holidays, term extensions, re-aging, and how restructuring interacts with delinquency and accounting.
Lay out a two-tier retail CBDC — central-bank ledger, intermediaries, retail wallets, and the offline mode that makes engineers nervous.
Lay out a two-tier retail CBDC: central-bank ledger, intermediaries, retail wallets, and offline modes.
Instrument auth rate, settlement lag, and decline-reason breakdowns instead of raw uptime.
Instrument the payment SLOs that actually matter: auth rate, settlement lag, and decline-reason breakdowns.
Protect PINs with DUKPT key-per-transaction derivation and point-to-point encryption all the way to the HSM.
Protect PINs with DUKPT key-per-transaction derivation and point-to-point encryption to the HSM.
Post 18 of 26 in "Google ADK, Concept by Concept" — retrieval tools, grounding metadata, rendering citations, and the retrieve→augment→generate loop.
Grounding answers in real data: retrieval tools, grounding metadata returned with responses, rendering citations from that metadata, and the retrieve-augment-generate RAG pattern in ADK.
How one router function plus a `path_map` dict lowers to exactly one edge firing per step.
A conditional edge is a router function plus a path map: the router reads state and returns a key, the path map turns that key into the next node. This is how branching (and, next post, loops) are expressed.
The final lesson ties the whole Go tutorial into one small product: an assistant that answers questions about your docs — grounded, cited, and refusing to guess.
The capstone: a grounded DocQA agent that answers only from embedded docs via a search_docs tool, cites sources, and refuses to guess — with an optional reviewer.
Agents as graph executors in Go: switch routing, fan-out/fan-in barriers, and mixing typed function nodes with agent nodes.
Agents as Go workflow executors: switch routing, fan-out and fan-in barriers, and mixing typed function nodes with agent nodes in one graph.