1290 posts · Page 82 of 108. ← Blog
How an AI agent turns a shopper's intent into a settled purchase, and where ACP and AP2 plug into the same eight-stage skeleton.
A complete walkthrough: product discovery via an AI surface → cart assembly → user approval/mandate → delegated payment token → merchant checkout → authorization → fulfillment → receipts/webhooks.
How decline-code classification, backoff scheduling, retry budgets, and network-token refresh recover subscription revenue without hammering the rails.
Teaches how to build a smart retry/dunning system: decline-code classification (hard vs soft), backoff and retry-window scheduling, retry-budget limits, and network-token refresh to recover subscription revenue.
Computing loan amortization you can reproduce to the cent — annuity math, day-count conventions, the per-installment principal/interest split, and prepayment recomputation.
Teaches how to compute loan amortization deterministically: EMI/annuity vs reducing-balance formulas, day-count conventions, principal/interest split per installment, and prepayment/rescheduling recalculation.
How to compute interest that earns every day, tracks the accrued-but-unbilled balance, survives mid-period rate changes, and posts to the ledger with idempotent replay-safe jobs.
Teaches how to build a daily interest accrual engine: accrual basis and compounding, accrued-but-unbilled tracking, rate changes mid-period, and idempotent daily accrual jobs that post to the ledger.
description, instruction, generation params, and structured output — the dials on almost every agent you'll build
The four knobs on almost every ADK agent: description (for delegation), instruction with {state} templating, generation params, and structured output — Python Pydantic model vs Go genai.Schema.
How to collapse a whole multi-agent workflow into a single agent so callers use it exactly like any leaf agent.
A concurrent French+English workflow is hosted as one agent via agentworkflow.NewAgent, with IncludeOutputsInResponse surfacing the merged output.
Typed results from `response_format`, consuming a stream event by event, and sending an image alongside text.
Three dials on one run() call: typed results via response_format, consuming a stream event by event, and sending an image alongside text.
Once convolution proved it could crack vision, the race was on to build better CNNs — and the story of how they improved is a compact lesson in deep learning itself. From AlexNet to VGG to ResNet, each landmark architecture solved a specific problem that was blocking progress, and the solutions (depth, small filters, skip connections) became foundational ideas used far beyond vision. This post traces that evolution and the principles it taught.
From AlexNet (which launched deep learning) to VGG (small filters, deep) to ResNet (skip connections that made very deep networks trainable), each landmark solved a specific blocker. ResNet's residual connection generalized everywhere — including every transformer block — so the arc is a compact lesson in deep learning itself.
Sales looks, from the outside, like a matter of personality — some people just have "the gift." But effective modern sales is far more a process than a talent: a repeatable sequence of stages that moves a prospect from stranger to customer, tracked through a pipeline, and improved like any system. This reframe is good news for engineers, who are naturally suited to thinking in processes and systems. Understanding the sales process and pipeline turns the mysterious art of selling into something structured and learnable.
Sales looks like a matter of personality — some people just have 'the gift.' But effective modern sales is far more a process than a talent: a repeatable sequence of stages that moves a prospect from stranger to customer, tracked through a pipeline, and improved like any system. Good news for engineers, who think in processes.
What actually makes Python distinctive — the design values that shape the language, how it runs, and the everyday tools you'll live in. The first post in a series that treats readability and correctness as features, not afterthoughts.
What actually makes Python Python — the design values that shape the language, how it runs, and the everyday tools you'll live in. The first post in a series that treats readability and correctness as...
Why Go is shaped the way it is, and how its toolchain — go run, build, test, fmt, vet, mod, doc — turns a small language into a fast, predictable team workflow.
Why Go is shaped the way it is, and how its toolchain — go run, build, test, fmt, vet, mod, doc — turns a small language into a fast, predictable team workflow.
When software spends money on your behalf, the merchant has to answer two questions before the charge clears: which agent is this, and what did the human actually let it do.
How a merchant verifies WHICH agent is acting and WHAT it may do.