I run Devin Review and CodeRabbit on every PR. PDF spec edge cases and CSS layout corner cases are exactly the kind of thing where having a second pair of eyes matters, and as a solo maintainer I don't have human reviewers. Both tools have caught real issues, especially around pagination edge cases. - Source: dev.to / 3 months ago

Navigate to coderabbit.ai and click the "Get Started Free" button. CodeRabbit supports sign-up through four Git platforms:. - Source: dev.to / 4 months ago

Install CodeRabbit from coderabbit.ai and connect your repositories. - Source: dev.to / 4 months ago

Open coderabbit.ai in your browser and click the "Get Started Free" button. - Source: dev.to / 4 months ago

Alternatively, you can start at coderabbit.ai, click "Get Started Free," and select Azure DevOps as your platform. This path takes you through CodeRabbit's onboarding flow which guides you through the Marketplace installation and PAT setup together. - Source: dev.to / 4 months ago

Two specific moves stand out in Duncan's account. The first is durable execution, via Temporal — Mercury replaced fragile cron-and-database state machines with workflow code whose failure semantics are platform-handled (replay, retry, timeout, cancellation). Mercury open-sourced its hs-temporal-sdk, which wraps Temporal's official Rust Core SDK via FFI and provides a Haskell-native API. The dovetail with Haskell's... - Source: dev.to / 20 days ago

We picked Temporal as the first reference engine on purpose. Temporal has the strictest execution model we know of – a V8 sandbox, determinism constraints, replay-driven recovery. If our port contract holds up against that, easier engines – an in-memory test double, a BullMQ queue, or JSON-first platforms like Inngest or Restate – plug in through the same two interfaces. We're shipping Temporal first; the rest is... - Source: dev.to / about 1 month ago

The trick is to find whatever metadata channel the queue already gives you and use that and thankfully, almost every mature queue has one (probably because of this scenario). SQS has message attributes, Temporal has context propagators built into the SDK, and Hatchet (which we use to run our workflows) has a metadata field called additionalMetadata. - Source: dev.to / 3 months ago

A typical production stack for teams using Claude or Gemini as the reasoning layer includes an LLM provider API, an orchestration layer (n8n, Temporal, or a custom Python service), application infrastructure (a server running the orchestration code), and a data layer (a database for storing results). Each boundary introduces a failure point. When the LLM provider changes its rate limits, as OpenAI did repeatedly... - Source: dev.to / 3 months ago

The core is a browserclaw agent loop wrapped in a Temporal workflow. The AI navigates to your provider's payment page, identifies form fields from the snapshot, fills in your payment details, and submits. Every successful payment generates a "biller skill" — a playbook that makes subsequent payments to the same provider faster and more reliable. - Source: dev.to / 4 months ago