Temporal
Trigger.dev
n8n.io
Amazon AWS
Apache Airflow
Molted
Aditya Protocol
e2b
Apache Karaf
Docker
Google App Engine
Amazon S3
AWS Elastic Beanstalk
Apache ServiceMix
Cisco CloudCenter
GlusterFS
Temporal Apache KarafNo features have been listed yet.
Based on our record, Temporal seems to be a lot more popular than Apache Karaf. While we know about 15 links to Temporal, we've tracked only 1 mention of Apache Karaf. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Two specific moves stand out in Duncan's account. The first is durable execution, via Temporal โ 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 / 14 days ago
We picked Temporal as the first reference engine on purpose. Temporal has the strictest execution model we know of โ a V8 sandbox, determinism constraints, replay-driven recovery. If our port contract holds up against that, easier engines โ an in-memory test double, a BullMQ queue, or JSON-first platforms like Inngest or Restate โ 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" โ a playbook that makes subsequent payments to the same provider faster and more reliable. - Source: dev.to / 4 months ago
Apache Karaf with OSGi works pretty nice using annotation based dependency injection with the declarative services, removing the need to mess with those hopefully archaic XML blueprints. Too bad it's not as trendy as spring and the developers so many of the tutorials can be a bit dated and hard to find. Karaf also supports many other frameworks and programming models as well and there's even Red Hat supported... Source: about 5 years ago
Trigger.dev - Trigger workflows from APIs, on a schedule, or on demand. API calls are easy with authentication handled for you. Add durable delays that survive server restarts.
Docker - Docker is an open platform that enables developers and system administrators to create distributed applications.
n8n.io - Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services.
Google App Engine - A powerful platform to build web and mobile apps that scale automatically.
Amazon AWS - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.
Amazon S3 - Amazon S3 is an object storage where users can store data from their business on a safe, cloud-based platform. Amazon S3 operates in 54 availability zones within 18 graphic regions and 1 local region.
