1290 posts · Page 84 of 108. ← Blog
How to keep books in more than one currency — transaction-date versus settlement-date rates, unrealized and realized FX gain/loss, and the period-end revaluation run that keeps the balance sheet honest.
Teaches how to keep books in multiple currencies with a functional/reporting currency: transaction-date vs settlement-date rates, unrealized/realized FX gain-loss postings, and period-end revaluation runs.
Turning ledger cutoff, accruals, trial-balance assembly, and out-of-balance detection into a controlled, repeatable pipeline instead of a month-end fire drill.
Teaches how to automate the financial close: ledger cutoff/freeze, accrual and reversing entries, trial-balance assembly, out-of-balance detection, and soft-close vs hard-close controls.
How two Foundry agents cooperate inside a cyclic graph workflow where one of them owns the loop control.
Two Foundry agents cooperate in a cyclic workflow: a SloganWriter drafts, a FeedbackProvider critiques and owns loop control via YieldOutput vs SendMessage.
Turn a plain Python function into something the model can call, and watch the tool-call loop close itself.
Turn a plain Python function into a tool the model can call. The @tool decorator, the tool-call loop, multiple tools, and what the model actually sees.
Before neural networks, computer vision spent decades building an ingenious toolkit: detect edges, find corners, describe local patches, match features across images. These classical methods are no longer state of the art for recognition, but they're far from obsolete — they still power real systems, they illuminate what deep learning automated, and understanding them makes you a better vision engineer. This post covers how vision worked before deep learning, and why it still matters.
Before neural networks, vision hand-engineered features: edges, corners, invariant descriptors (SIFT). These still power matching, panorama stitching, and 3D/SLAM — and reveal exactly what CNNs later automated. Their ceiling on recognition is what the deep-learning breakthrough overcame.
The gap between "multimodal AI exists" and "I built something useful with it" is smaller than it's ever been — you can call a vision-language model, a speech recognizer, or an image generator with an API and a few lines of code. But building well with multimodal AI means knowing what these models can and can't reliably do, how to combine them, and where the pitfalls are. This closing post is the practical one: how to actually build multimodal applications, and where the whole field is heading — toward general, any-to-any models.
The gap between 'multimodal AI exists' and 'I built something useful with it' is smaller than ever — you can call a vision-language model, a speech recognizer, or an image generator with a few lines of code. But building well means knowing what these models can and can't reliably do, how to combine them, and where the pitfalls are. Plus the any-to-any future.
How a central counterparty turns a web of bilateral trades into a single, mutualized book of risk — and what your clearing systems must do to survive the margin cycle.
Novation to a central counterparty, initial vs variation margin, margin calls, and the default waterfall (margin, default fund, skin-in-the-game).
How Coinbase revived a dormant status code so software agents can pay for what they use, one request at a time.
Coinbase's x402 revives the dormant HTTP 402 Payment Required status code so agents pay for resources machine-to-machine.
Two ways to move interbank money, and the engineering tradeoff that decides which one you build: settle every payment gross and pay in liquidity, or net at a window and carry settlement risk.
Teaches the engineering tradeoffs between gross real-time settlement and deferred net settlement: liquidity vs finality, queue/gridlock resolution in RTGS, and settlement-risk windows in DNS.
Reconciling correspondent-bank balances with mirror-account bookkeeping, camt.053 matching, value-date breaks, and unreconciled-item aging.
Teaches how to reconcile correspondent-bank nostro/vostro balances: mirror-account bookkeeping, expected-vs-actual statement (camt.053) matching, value-date breaks, and unreconciled-item aging.
How matched-leg submission, net pay-in scheduling, and conditional simultaneous settlement remove Herstatt risk from cross-currency trades.
Teaches how payment-versus-payment settlement eliminates Herstatt (principal) risk in FX: matched trade submission, pay-in schedules, simultaneous conditional settlement, and pay-out with net funding.
This lesson teaches how to build a cyclic workflow where a Writer and Critic loop until approval, using `AddSwitch` to route on structured output.
A cyclic Writer-Critic-Summary workflow: the Critic emits a structured CriticDecision, AddSwitch routes on Approved, and Context state caps the revision loop at maxIterations.