1290 posts · Page 74 of 108. ← Blog
How a RequestPort pauses a workflow, emits a request to a human, and feeds their answer back into the graph.
A RequestPort emits a RequestInfoEvent and suspends the workflow; the driver answers with SendResponse. A guess-the-number cycle shows the pause/resume mechanism.
The minimal loop in Go: a Foundry provider, an Agent with instructions, run collected and streamed — and what RunText hands back.
The minimal Microsoft Agent Framework loop in Go: a foundryprovider agent, DefaultAzureCredential, and one RunText you either Collect or range over as a Go 1.23 iterator.
When classical methods aren't enough — many related series, rich external drivers, complex nonlinear patterns — you can bring the full machine-learning toolkit to forecasting. But you can't just feed a time series to a standard model; you have to reframe forecasting as a supervised learning problem through the right features. This post is about that reframing, where ML genuinely helps, and the time-specific traps that make it easy to get wrong.
When classical isn't enough — many series, rich drivers, complex patterns — reframe forecasting as supervised learning via lag, rolling, calendar, and driver features, then train (often) gradient-boosted trees. Where ML beats classical (global models across many series), and the time-specific traps: feature leakage, awkward multi-step, and trees that can't extrapolate trend.
There's a role that sits exactly on the seam between engineering and the market — translating what was built into what customers understand and want — and it's the one technical companies most often do badly or not at all. Product marketing is that bridge. When it's missing, you get the classic technical-company failure: a genuinely great product described in terms only its builders understand, launched into silence, losing to a worse product that explained itself better. Product marketing is how good engineering becomes a product the market actually gets.
There's a role that sits exactly on the seam between engineering and the market — translating what was built into what customers understand and want — and it's the one technical companies most often do badly or not at all. Product marketing is that bridge, and its absence is why great products launch into silence.
How Go builds aggregate types from value semantics up — why a struct is a copy, when it stops being comparable, what embedding actually promotes (and what it deliberately doesn't), and how a backtick string in a field definition ends up steering `encoding/json`.
How Go builds aggregate types from value semantics up — why a struct is a copy, when it stops being comparable, what embedding actually promotes (and what it deliberately doesn't), and how a backtick string...
How a captured card transaction turns into money in a merchant's bank account — batching, gross-to-net fees, reserves, adjustments, and the T+N funding file.
From captured transactions to merchant bank account: batching, interchange/scheme-fee deduction, reserves/holdbacks, and T+N funding files.
Route a payment across multiple providers to maximize auth rate, with health-aware routing, failover, and normalized webhooks.
Route a payment across multiple PSPs to maximize auth rate, with health-aware routing, failover, and normalized webhooks.
Name-match the beneficiary before a push payment to stop authorized-push-payment (APP) fraud.
Name-match the beneficiary before a push payment to stop authorized-push-payment (APP) fraud.
Encode and decode EMVCo QR payloads (static vs dynamic), handle expiry, and reconcile QR-initiated payments.
Encode and decode EMVCo QR payloads (static vs dynamic), handle expiry, and reconcile QR-initiated payments.
before/after the agent, model, and tool steps — and the single short-circuit rule that turns them into guardrails
Callbacks are lifecycle hooks around the agent, model, and tool steps — before/after each — used for guardrails (short-circuit by returning a response), logging, and mutating requests and responses.
How an edge assigner delivers one message to a chosen subset of targets — a multi-way switch that may fall through to more than one case.
WithEdgeAssigner yields target indexes via an iter.Seq[int], so a single message reaches several branches at once — a long email goes to both assistant and summary, offline.
Why I learned the whole framework in Go by writing one runnable lesson per concept, against Azure AI Foundry, instead of reading the docs top to bottom.
I learned the whole Microsoft Agent Framework in Go by building one runnable lesson per concept against Azure AI Foundry. Here is the 12-track map.