21 articles tagged Artificialintelligence
AI codebases: two onboarding paths that actually work
Compare two ways to onboard developers into codebases containing AI-generated code, with failure modes, cost math, and a decision checklist.
13 min read · 15 June 2026
Why AI Interview Graders Reward Keywords Over Correct Design
AI interview graders score answers against training-data patterns, not correctness. Here is how the bias shows up and how to write answers that survive it.
12 min read · 14 June 2026
PR checklists are dead: AI agents do reviews now
How to split PR review into narrow automated agents that run checks, propose fixes, and escalate only the edge cases to humans.
12 min read · 14 June 2026
AI Interviews: Four Resilience Questions That Matter
AI screening interviews grade production resilience, not algorithm puzzles. Learn the four fault-injection questions and how to measure candidate fixes.
13 min read · 14 June 2026
AI agent postmortems: trace vs logs vs code
Traces, evaluation logs, and code debugging reveal different AI agent failures. Compare overhead, cost, and coverage, then pick the right tool.
13 min read · 14 June 2026
AI freelance rates 2026: which stacks still pay
AI code generation shifts effort from writing components to compliance, observability and edge cases. Here is how to price and instrument that hidden work.
12 min read · 13 June 2026
AI Static Analysis vs Rule-Based SAST: A Practical Comparison
How AI-assisted static analysis compares with rule-based SAST on scan latency, false negatives, cost, and ops, plus how to measure both on your own repos.
14 min read · 13 June 2026
AI-native apps: forget the old stack
LLM-backed services are token-bound, stateful and failure-prone. Learn the failure modes, how to measure them, and patterns that contain them.
10 min read · 12 June 2026
Designing an AI Alert Triage Layer That Reduces Pages
How to build an AI alert-triage service between Alertmanager and your pager: fingerprinting, LLM filtering, fail-open rules, and honest measurement.
14 min read · 12 June 2026
AI sales cycles: why dev tools now need 3 demos
Developer tools are increasingly bought by non-engineers who want proof in their own cluster. Here is how to structure three live demos that survive real infrastructure.
11 min read · 12 June 2026
AI rollouts live or die by flags
How to gate model versions, prompts, and safety filters behind feature flags, with a Lambda evaluator, SDK, timeout handling, canaries, and tests.
14 min read · 12 June 2026
Which AI wrappers made money in 2026
Most AI wrapper products fail on latency, cost, and churn rather than model quality. Here is a rubric, failure analysis, and code to build one that lasts.
10 min read · 12 June 2026
Self-healing agents vs rule-based deployment rollback
Comparing AI-agent and rule-based deployment rollback: failure modes, what to instrument, and a decision checklist for choosing between them.
12 min read · 11 June 2026
Turn AI coding telemetry into a salary negotiation case
Build a CLI that pulls AI coding-assistant session data, maps it to tickets, and produces defensible productivity numbers for pay talks.
12 min read · 11 June 2026
AI interviews broke system design: 3 patterns that
AI design tools produce complete-looking architectures that fail under real load. Three patterns, why they happen, and how to catch them before staging.
15 min read · 11 June 2026
Personal AI assistants: avoid vendor lock-in traps
How to structure a personal AI assistant as replaceable services: contracts, self-hosted embeddings, retries, failure modes, and a checklist to start.
15 min read · 11 June 2026
AI agents eat your budget 3 ways
Token inflation, retry storms and timeout math quietly inflate AI agent bills. Learn how to instrument, cap and verify each cost vector.
12 min read · 11 June 2026
7 code review traps when AI writes half your code
Seven review traps AI-generated code creates, how to detect each one, and a checklist you can add to your repo today.
12 min read · 11 June 2026
AI interviews now ask for this
AI-assisted coding has changed what engineering interviews probe: flaky tests, observability overhead, secret rotation, and auditing generated code.
16 min read · 10 June 2026
AI burnout: why output pressure rose even as tools got
Faster AI code generation shifts the bottleneck to review, QA and on-call. Here is how to instrument net time saved and stop review fatigue.
12 min read · 9 June 2026
AI code traps: Copilot vs Cursor smackdown
How to evaluate AI coding assistants on edge-case handling, latency, hardware and cost — with a decision checklist and a 30-minute first step.
12 min read · 9 June 2026