Journal
Occasional writing on technology culture, digital life, work, money, education, and public issues. These articles stay live and sourced, but sit apart from the core engineering library.
Find the guide that matches the decision in front of you. The core tracks stay close to backend systems, TypeScript, fintech, AI-assisted coding, and the work of getting hired.
Browse all 33 topics498 articles
Latest journal articles
Page 1 of 17AI infrastructure stocks: how to separate chips, power, clouds, and real estate
AI infrastructure is a chain of businesses, not one stock category. Learn how chips, power, cloud, and data-center real estate differ.
Why AI companies use several cloud partners instead of one
Multi-cloud AI infrastructure can improve capacity and bargaining power, but it adds software and operational complexity. Here is the tradeoff.
AI infrastructure financing: debt, equity, leases, and customer commitments
AI infrastructure can be funded with debt, equity, leases, or customer commitments. Learn what each structure changes for risk and delivery.
AI cloud margins explained: power, hardware, and utilization
AI cloud margins depend on more than GPU prices. Learn how power, depreciation, financing, networking, and utilization shape the business.
How to evaluate an AI partnership announcement without falling for the headline
A plain-English framework for separating funding, contracts, capacity, and delivered products when an AI partnership announcement arrives.
Why AI data centers need power contracts before they need more GPUs
Power delivery is often the slowest part of an AI data-center build. Learn how electricity contracts, substations, and cooling shape usable compute.
How to read AI-company earnings when capex is doing the talking
A practical checklist for reading AI-company earnings when data-center spending, financing, contracts, and cash flow matter more than one headline metric.
AI infrastructure depreciation: when expensive GPUs become an earnings problem
GPU depreciation can turn AI infrastructure spending into a future earnings test. Learn what depreciation means and which numbers investors should compare.
OpenAI and SB Energy's 1.2-gigawatt lease: how the structure works
OpenAI and SB Energy's 1.2-gigawatt data-center lease shows how AI companies can secure capacity without owning every part of the physical build.
Stargate's 10-gigawatt AI plan: what a data-center target actually means
Stargate's 10-gigawatt target sounds abstract. Here is what it means for power, hardware, construction risk, cloud capacity, and AI investors.
What $500 billion of third-party AI capital actually means
A simple guide to NVIDIA's claim about mobilizing more than $500 billion of third-party AI infrastructure capital, including what the number means and what it does not.
NVIDIA's indirect AI customers: why revenue concentration is hard to see
How indirect customers can hide concentration in AI hardware revenue, why cloud providers complicate the picture, and what investors should look for in company filings.
OpenAI's $122 billion funding round: what changed after the headline
What OpenAI says its $122 billion funding round is for, how the valuation and partner network should be read, and what the announcement does not prove about future profits.
NVIDIA's $36 billion AI-cloud commitments explained
What NVIDIA disclosed about its AI-cloud partner commitments, how the new model works, and why investors should separate supplier support from delivered customer demand.
OpenAI's multi-cloud strategy explained: why Azure still matters
OpenAI works with several cloud and chip partners. Learn what Azure still does and why more suppliers reduce one risk while adding another.
How investors can detect artificial AI growth without becoming conspiracy theorists
A calm, evidence-based checklist for testing AI growth claims, circular-financing theories, customer demand, cash flow, and infrastructure utilization.
AI data-center spending: real demand or capacity built ahead of customers?
How to tell the difference between customer demand, contracted capacity, construction, and unused AI data-center supply without relying on hype.
CoreWeave, NVIDIA, and AI cloud debt: who carries the risk?
A beginner-friendly guide to the debt, customer concentration, contracts, and supplier relationships behind AI cloud infrastructure.
Vendor financing in AI: when a supplier helps customers buy its own products
How vendor financing works in AI infrastructure, why suppliers may support customer funding, and which questions separate productive growth from concentrated financial risk.
AI infrastructure contracts versus cash paid: how to read the difference
A plain-English guide to backlog, take-or-pay contracts, and cash paid in AI infrastructure so you can separate future demand from current revenue.
AI investment scams using real NVIDIA and OpenAI headlines
Scammers use genuine AI funding and chip announcements to sell fake investments. Learn how to verify the claim, the company, and the payment request before sending money.
OpenAI’s $110 billion funding round: what the money is meant to fund
What OpenAI publicly announced about its 2026 funding round, how compute partnerships fit into the plan, and what readers should not assume from the headline number.
NVIDIA investing in AI customers: strategic growth or concentration risk?
A plain-English analysis of why NVIDIA may invest in AI companies that buy its hardware, what the strategy can achieve, and where investors should look for risk.
AI circular financing explained: scam, bubble, or normal business?
Why AI companies, chipmakers, cloud providers, and investors keep appearing in the same deals, and how to separate real demand from risky circular financing claims.
Cardinality budgets for metrics labels
A metric label creates a time series for each value combination, so user IDs, URLs, and error messages can overwhelm storage and query performance.
Database migration observability: know what the deploy is waiting on
Production migrations need timing, lock-wait, progress, and rollback signals so a quiet terminal does not hide blocked traffic.
Kubernetes readiness during graceful shutdown
A terminating pod should become unready before it stops accepting work, then drain within the platform grace period.
Trace sampling: keep rare failures without storing every request
Head sampling controls volume early while tail sampling can retain slow or failed traces after seeing the full outcome.
Docker BuildKit secrets: credentials without image layers
BuildKit secret mounts expose credentials only to the selected build step, avoiding ARG and COPY patterns that persist secrets in history.
pull_request_target security: never run untrusted checkout code
pull_request_target has base-repository privileges, so checking out and executing fork code in that workflow can expose secrets and write tokens.