1046 posts · Page 46 of 88. ← Blog
How the two Basel liquidity ratios are computed from raw positions, and why the hard part is a data pipeline, not a formula.
How the Liquidity Coverage Ratio and Net Stable Funding Ratio are computed: HQLA classification, outflow factors, and the reporting pipeline.
An autonomous agent injects its own plan-and-execute tools at runtime. If your gateway is fail-closed, you have to find and allowlist them — deliberately.
An autonomous harness injects its own tools at runtime, tools that don't exist at build time. Fail-closed governance is only complete when it turns that blind spot into a visible block, then allowlists the known-safe internals deliberately.
How ADK's config loader turns a declarative YAML file into a fully-built agent — and why treating an agent as data changes who gets to edit it.
Defining an agent declaratively in YAML and loading it via from_config — the loader reads, resolves, and validates the config into a built agent, so config-as-data works without writing code.
Lesson 2 of Harness Engineering in Go — a workflow that saves its progress after each step and picks up exactly where it died, proven by a test that kills a real subprocess mid-run.
Lesson 2: a workflow that checkpoints after every step and resumes from the last one after a crash, why at-least-once execution forces idempotent steps, and the atomic-rename store that survives a killed process.
Raising money looks, from the outside, like the goal — the headline, the milestone, the validation. It isn't. Funding is a tool with a specific purpose and a real price: you're selling pieces of your company, permanently, in exchange for capital to grow faster than your revenue alone would allow. Understanding what that trade actually is — when it's worth making, and what you're giving up — is the difference between funding that accelerates a business and funding that quietly takes it away from its founders.
Raising money looks like the goal — the headline, the validation. It isn't. Funding is a tool with a specific purpose and a real price: you're selling pieces of your company, permanently, for capital to grow faster than revenue alone would allow. Understanding that trade is what separates funding that accelerates from funding that takes the company away.
How a trade turns into a regulator-ready report — eligibility, enrichment, validation, submission, and the break management loop that keeps you compliant.
The trade/transaction reporting pipeline: eligibility, enrichment (LEI/UPI/UTI), validation, submission to the ARM/TR, and break management.
The full pipeline should run in CI with zero API keys and zero network. A deterministic classifier is the test double that makes an agentic system testable.
Put the seam at the router: same interface, a deterministic classifier for tests. The whole orchestration, routing, gateway, human-in-the-loop, and checkpointing, runs in CI with zero API keys and zero network.
How caching a large, stable prompt prefix cuts latency and cost — and the ADK config that decides when it pays off.
Context caching cuts latency and cost by caching large, stable context — system prompt, reference docs, tool definitions — so repeated calls don't re-send and re-process the same tokens.
The reference capstone — each term in the series, defined in plain English and grouped by what it does.
The capstone of the series: every LangGraph concept defined in one place — the graph model, state and reducers, persistence, human-in-the-loop, agents and tools, parallelism, streaming, and the surrounding ecosystem.
Lesson 1 of Harness Engineering in Go — why the input guardrail is a hard block, not a warning, and how a plain `net/http` handler wraps the model call so it tests without a running server.
Lesson 1: why the input guardrail is a hard block rather than flag-and-pass, why it counts runes instead of bytes, and how a plain net/http handler wraps the (stubbed) model call so it tests with httptest.
Financial literacy pays off in two moments: when you look at a business and can quickly tell whether it's healthy, and when you face a decision and can reason about it in the numbers. Both come down to synthesis — pulling the statements, metrics, and economics together into a judgment. This closing post is about that synthesis: reading a company's financial health at a glance, using finance to make and understand decisions, and the mindset that turns financial knowledge into better judgment.
Financial literacy pays off in two moments: when you look at a business and can quickly tell whether it's healthy, and when you face a decision and can reason about it in the numbers. Both come down to synthesis — pulling the statements, metrics, and economics together into a judgment.
How passive device and behavior signals become a trust score for auth and fraud — without turning into a surveillance liability.
How device signals and behavioral biometrics build a trust signal for auth and fraud, with privacy and false-positive trade-offs.