1046 posts · Page 40 of 88. ← Blog
How a buy-side smart order router turns one parent order into dozens of child orders, scores venues in real time, and then proves it did the right thing.
How a buy-side SOR splits a parent order across venues, scores liquidity/fees/latency, and proves best execution with TCA.
How three cryptographically signed mandates turn an agent's purchase into a non-repudiable audit trail.
Google's open Agent Payments Protocol (AP2).
How to engineer SEPA SCT and SDD flows: mandate lifecycle storage, pre-notification timing, FIRST/RCUR sequence types, and the R-transaction taxonomy modeled as an explicit state machine.
Teaches how to engineer SEPA SCT and SDD flows: mandate lifecycle storage, pre-notification timing, FIRST/RCUR sequence types, and the R-transaction taxonomy (reject/return/refund/reversal/revocation) as a state machine.
How to parse legacy MT103/MT202 fields, map them to MX pacs equivalents during coexistence, and thread a gpi UETR end-to-end so a cross-border payment stays trackable across correspondent hops.
Teaches how to parse legacy MT103/MT202 fields, map them to MX pacs equivalents during coexistence, and thread a gpi UETR end-to-end so a cross-border payment is trackable across correspondent hops.
How to engineer for 24x7 irrevocable instant credit — synchronous ISO 20022 messaging, request-for-payment flows, and idempotent liquidity checks with no batch cutoff.
Teaches how to build for 24x7 irrevocable instant credit: synchronous ISO 20022 request/response, request-for-payment (RfP) flows, credit-transfer timeouts, and idempotent liquidity checks at the rail with no batch cutoff.
TypeScript's type system is itself a small programming language — you can compute new types from existing ones. The built-in utility types (`Partial`, `Pick`, `Omit`, `Record`) are the everyday face of this; underneath, `keyof`, mapped types, conditional types, and template literal types are the primitives that make them possible. Learning to derive types instead of hand-writing them is what separates fluent TypeScript from annotation-copying.
TypeScript's type system is itself a small programming language — you can compute new types from existing ones. The built-in utility types (Partial, Pick, Omit, Record) are the everyday face; underneath, keyof, mapped types, conditional types, and template literals are the primitives. Learning to derive types instead of hand-writing them is what separates fluent TypeScript.
The conversations we most want to avoid are usually the ones that matter most — telling a colleague their work isn't good enough, raising a concern with your manager, addressing a conflict that's festering, giving feedback that might not be welcome. Avoiding them feels safer in the moment and is almost always worse over time: problems grow, resentment builds, and the unsaid thing poisons the working relationship. Emotional intelligence doesn't make hard conversations easy, but it makes them possible to have well — which is a learnable skill that changes everything downstream.
The conversations we most want to avoid are usually the ones that matter most — telling a colleague their work isn't good enough, addressing a festering conflict, giving unwelcome feedback. EQ doesn't make hard conversations easy, but it makes them possible to have well — a learnable skill that changes everything downstream.
From-scratch Go for the two mechanisms that turn an LLM from a text generator into a component you can wire into real software — schema-constrained JSON and function calling — both spoken over the same OpenAI-compatible chat JSON.
Getting reliable machine-readable output from an LLM in Go: structured output (json-schema mode, decode into a typed struct, validate with a bounded retry) and tool/function calling (the full round-trip loop, decoding tool arguments, returning results tied to the call id).
A hybrid allocator where a quantitative core owns the money, a language model only whispers tilts, and hard constraints plus a human bound everything before a single order goes out.
A hybrid allocator: quantitative signals set the baseline (mean-variance / risk parity) while an LLM proposes tilts from qualitative context, constrained by risk limits and human review.
The 40-year-old tag=value protocol still carrying most of the world's equity orders — and how to build an engine that survives a dropped connection.
How FIX session layer (logon, heartbeat, sequence-number gap-fill, resend) and application layer (NewOrderSingle, ExecutionReport) actually work, and how to build a resilient FIX engine.
How AI agents that discover, choose, and pay on your behalf break the assumptions baked into every checkout, and the protocol stack rushing in to fix them.
What agentic commerce is: AI agents that discover, select, and pay on a user's behalf.
How to treat pain, pacs, and camt as one typed, schema-driven domain instead of a pile of XML you concatenate by hand.
Teaches how to model, validate, and generate ISO 20022 XML payment messages (pain.001 initiation, pacs.008 interbank, camt.053 statements) with schema-driven typing, structured references, and idempotent message identifiers.