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1290 posts · Page 65 of 108. ← Blog
Subscription businesses changed what "revenue" means. When customers pay every month instead of once, a whole new vocabulary appears — MRR, ARR, churn, net revenue retention, the Rule of 40 — and these metrics, not the raw P&L, are how SaaS companies are actually judged. For any engineer working at or evaluating a subscription business (which is most software today), these are the numbers that matter, and the logic behind them explains why SaaS companies behave the way they do.
Subscription businesses changed what 'revenue' means. When customers pay every month instead of once, a whole new vocabulary appears — MRR, ARR, churn, net revenue retention, the Rule of 40 — and these metrics, not the raw P&L, are how SaaS companies are actually judged.
How Go's `context` package carries a cancellation signal, a deadline, and a small bag of request-scoped values across every API and goroutine boundary in a request — and the handful of rules that keep it from leaking or lying to you.
How Go's `context` package carries a cancellation signal, a deadline, and a small bag of request-scoped values across every API and goroutine boundary in a request — and the handful of rules that keep it...
Retiring the calendar-driven refresh and rebuilding customer due diligence as an event-driven state machine with regulator-grade audit evidence.
Moving from periodic review to event-driven pKYC: trigger events, risk re-scoring, refresh workflows, and audit evidence.
Treat agent output quality like a test. A baseline file plus a gate that fails the build on regression turns "the agents got worse" into a red X.
Agent systems rot because nobody catches quality regressions until users do. A committed baseline plus a gate that fails the build turns the agents got worse into a red X and a reviewable diff in the pull request.
How ADK skills bundle instructions, tools, and resources into folders an agent can browse and load on demand.
Skills package reusable capabilities — instructions, tools, resources, including file-based skills — so they can be discovered and attached to agents, promoting reuse across projects.
Watching a LangGraph run happen — the three things `.stream()` can show you, and why they fall out of the superstep model for free.
stream() exposes a run in three modes: values (full state after each node), updates (what each node changed), and debug (the raw event stream). Streaming falls out naturally from the superstep model.
The mirror image of backend tools: the server hosts the agent but the tools live on the client, so the handler must forward tool calls instead of running them.
The server side of AG-UI frontend tools: set DisableFuncAutoCall so the server forwards tool calls to the client to execute and awaits the result.
Wrapping Go agents in A2A, MCP, and AG-UI transports, then a full client/server app that ties the whole series together.
Wrap Go agents in A2A, MCP, and AG-UI transports, then run the end-to-end client/server capstone where a host agent uses remote specialists as tools.
The hardest interpretability frontier is the models that matter most right now: deep neural networks and large language models, with billions of parameters and no obvious place to look for "the reason." The attribution methods that work on tabular models strain here, and a new field — mechanistic interpretability — is trying to reverse-engineer what these networks actually compute. This post covers how we peer inside deep models and LLMs, and why it's both crucial and genuinely unsolved.
The hardest frontier: deep nets and LLMs with billions of parameters and no obvious place to find 'the reason'. Saliency maps (fragile), attention (not explanation), probing, and mechanistic interpretability's attempt to reverse-engineer circuits — plus the honest truth that we largely cannot yet explain why frontier LLMs behave as they do.
A company can grow revenue explosively, raise huge rounds, and dominate headlines — and still be doomed, if it loses money on every customer. Unit economics is the question underneath all the aggregate financials: does a single customer, on its own, make money? Get that right and scale is the amplifier of a good thing; get it wrong and scale just multiplies the losses. It's the most important economic idea for judging whether a business actually works — and the one flashy growth numbers most often hide.
A company can grow revenue explosively, raise huge rounds, and dominate headlines — and still be doomed, if it loses money on every customer. Unit economics is the question underneath all the aggregate financials: does a single customer, on its own, make money? It's the most important test of whether a business actually works.
What a data race actually is, why it is undefined behavior rather than "just a wrong number," the happens-before rules that make concurrent code correct, and when `sync/atomic` is the right tool — and when it quietly is not.
What a data race actually is, why it is undefined behavior rather than "just a wrong number," the happens-before rules that make concurrent code correct, and when `sync/atomic` is the right tool — and when...
Why per-transaction scoring misses organized fraud, and how modeling accounts, devices, cards, addresses, and IPs as a graph catches the whole ring.
Modeling accounts/devices/payments as a graph, connected-component and community detection, shared-attribute linking, and analyst review.