1290 posts · Page 83 of 108. ← Blog
How a custodian actually safekeeps client assets — account structures, the custody network, settlement instructions, and the books-and-records engine that keeps it all honest.
How a custodian holds assets in omnibus vs segregated accounts, the CSD/sub-custodian network, settlement instructions, and asset-servicing (income, proxy, tax).
Architecting platform balances, commission splits, delayed payouts, and negative-balance recovery without ever losing a cent.
Teaches how to architect marketplace money movement: platform vs connected-account balances, fee/commission splits, delayed payouts, negative-balance handling, and merchant-of-record vs facilitator models.
How to turn contracts into performance obligations, recognition schedules, and ledger journals — and why billing is never the same event as revenue.
Teaches how to build a rev-rec engine: performance obligations, deferred-vs-recognized schedules, ratable recognition over time, and how billing events differ from recognition events in the ledger.
How to build escrow and conditional hold/release — segregated ledger accounts, release conditions and approvals, partial releases, and expiry auto-refund that never strand money.
Teaches how to build escrow and conditional hold/release: segregated escrow ledger accounts, release conditions/approvals, partial releases, and expiry auto-refund with double-entry safety.
An `LlmAgent`, a `Runner`, a `Session`, and a CLI that runs it all — the four pieces the other 25 concepts sit on top of.
The smallest ADK agent and the machinery around it: an LlmAgent, the Runner, a Session, and the adk CLI (adk web / adk run) that runs your agent with a dev UI or REPL — no server code.
How a multi-agent group chat pauses to ask a human for approval before one gated tool is allowed to run.
A QA and DevOps agent collaborate in a group chat where DeployToProduction is gated by tool.ApprovalRequiredFunc, pausing the workflow for human approval.
How a stateless agent remembers: sessions carry one conversation, context providers carry knowledge across all of them.
Microsoft Agent Framework agents are stateless. Sessions carry one conversation; context providers carry memory across all of them. Here is the mental model in real code.
The convolutional neural network is the architecture that finally solved computer vision — and it did so with an idea as elegant as it is powerful: slide a small learnable filter across the image, everywhere, looking for patterns. CNNs automated the feature engineering that classical vision did by hand, learning a hierarchy from edges to objects directly from pixels. This post explains how convolution works and why it was exactly the right inductive bias for images.
The CNN solved vision by learning features instead of hand-designing them. Convolution slides a small learnable filter everywhere — local connectivity and parameter sharing give translation invariance for free — and stacking layers builds a hierarchy from edges to objects. The right inductive bias for images.
No word makes an engineer wince quite like "sales." It conjures the pushy car salesman, the manipulative closer, the person who'll say anything to hit quota — everything a truth-valuing technical person recoils from. But that caricature is bad sales, and confusing it with sales itself leaves engineers unable to do something essential: help another person understand that what you've built genuinely solves their problem. Done honestly, that isn't manipulation. It's a service, and it's a learnable skill.
No word makes an engineer wince quite like 'sales' — it conjures the pushy, manipulative closer. But that caricature is bad sales, and confusing it with sales itself leaves engineers unable to do something essential: help another person understand that what you've built genuinely solves their problem. Done honestly, that's a service, and a learnable skill.
Two ways to borrow against securities — a lending fee or a repo rate — and the daily collateral servicing that keeps both alive.
Loan vs repo mechanics, collateral schedules and haircuts, daily mark-to-market and margin, recalls, and rehypothecation risk.
How Visa and Mastercard are reshaping tokenization so an AI agent can pay on your behalf — with scoped credentials, agent-aware identity, and the network doing what it has always done: authenticate, authorize, tokenize.
How the card networks are adapting tokenization for agents.
Designing an account hierarchy, dimensions, and posting rules that turn product events into balanced journal entries.
Teaches how to design a chart of accounts and sub-ledger-to-GL posting model: account hierarchy, dimensions/segments, contra accounts, and how product events map to balanced journal entries.