FeatBit Blog
Practical notes on progressive delivery, experimentation, self-hosting, AI-native release engineering, and the operating habits behind safer software launches.
articles on building, releasing, and improving software.
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Compare FeatBit, Unleash, and Flagsmith when migrating legacy CloudBees Feature Management or separating runtime release control from a delivery suite.
A practical release guide for small teams building with AI: choose a useful app, start with selected users, watch release health, and restore a working release.
They care that the product just works. What that asks of the people shipping it: smaller risks, useful evidence, and a way back.
Coordinate promotion and product delivery with feature flags: define the promised audience, control implementation risk, measure outcomes, and preserve a useful fallback.
Turn a large AI-built feature into compatible, reviewable changes. Follow a concrete TypeScript sequence with intent records, safe defaults, and release boundaries.
Use three travel features to separate deployment from release, verify targeting and stable cohorts, and contain one new path without exposing the others.
Recover one understandable module boundary at a time. Preserve behavior, verify a replacement contract, and control migration without treating feature flags as a cure for technical debt.
AI coding can accelerate implementation while shifting work into review, QA, operations, and maintenance. A practical framework for measuring and controlling the whole delivery system.
Choose feature containment, code rollback, or a verified hotfix based on the actual failure boundary. Includes a decision table, runbook, and reproducible SDK lab.
Define what no-review coding actually removes, then assign verification, human judgment, exposure limits, and rollback responsibility by the risk of the change.
Evaluate AI coding across tool spend, authoring, review, QA, rework, incidents, and customer outcomes. Includes a measurement template and a checked illustrative cost model.
Go beyond passing tests and healthy rollouts. Define a feature hypothesis, eligible population, actual exposure, business outcomes, guardrails, and an evidence-based release decision.
