When a team moves from a monolith into microservices and event-driven, asynchronous systems, it inherits a class of problems that used to be someone else's: work that fails halfway through, steps that must not run twice, calls that return before the work is done. Temporal is a durable execution engine that handles a lot of this - you define a multi-step process, and it guarantees the process runs to completion... - Source: dev.to / 21 days ago

Durable execution — the pattern implemented by Temporal, Inngest, Rivet Actors, and now Cloudflare Workflows — treats waiting as a continuation rather than a loop:. - Source: dev.to / 26 days 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 / about 2 months 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 / 3 months 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 / 4 months ago

Windmill (YC S22) is an open source alternative to Retool and a modern Airflow. They provide a developer platform to quickly build production-grade complex workflows and integrations from minimal Python and Typescript scripts. Their one-click integration with Supabase makes it simple to launch new databases, process large quantities of data (maybe even convert them into embeddings), and build internal dashboards. - Source: dev.to / about 3 years ago

Every project is a Postgres database, wrapped in a suite of tools like Auth, Storage, Edge Functions, Realtime and Vectors, and encompassed by API middleware and logs. - Source: dev.to / about 3 years ago

Since launching our Vector Toolkit a few months ago, the number of AI applications on Supabase has grown - a lot. Hundreds of new databases every week are using pgvector. - Source: dev.to / about 3 years ago

Hi everyone, Joshua from Hugging Face (and the creator of Transformers.js) here. Starting with embeddings, we hope to simplify and improve the developer experience when working with embeddings. Supabase already has great support for storage and retrieval of embeddings (thanks to pgvector) [0], so it feels like this collaboration was long overdue! Open-source embedding models are both smaller and more performant... - Source: Hacker News / about 3 years ago