
Temporal
Trigger.dev
n8n.io
Amazon AWS
Apache Airflow
Molted
Aditya Protocol
e2b
Packer
Terraform
Puppet Enterprise
Rancher
Red Hat OpenShift
HHVM
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Juju
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Based on our record, Temporal should be more popular than Packer. It has been mentiond 15 times since March 2021. 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
If you have just upgraded to Ubuntu 22.04, and you suddenly experience either errors when trying to ssh into hosts, or when running ansible or again when running the ansible provisioner building a packer image, this is probably going to be useful for you. - Source: dev.to / over 3 years ago
I am already using Hashicorp Packer at work and for personal projects and I wanted to test This idea out by wrapping it a single Packer Template file. This reduces the level of maintaining a lot of small scripts, Dockerfiles and configurations and the user can simply trigger a couple of Commands to get a minimalist OS at the end of the process. - Source: dev.to / almost 4 years ago
And while it is a slight increase in complexity, it can be an overall net gain in functionality, configurability and reliability. Much like Packer is far more reliable and practical than manually making VM images sitting in front of a terminal, even though making the initial configuration takes some time. Source: almost 4 years ago
Hashicorp Packer provides a nice wrapper / abstraction over the QEMU in order to boot the image and use it to set it up on first-boot. Instead of writing really long commands in order to boot up the image using QEMU, Packer provided a nice Configuration Template in a more Readable fashion. - Source: dev.to / almost 4 years ago
Packer seemed like the perfect tool for the job. I have never used it before and wanted to get familiar with the tool. It doesn't come with ARM support out of the box, but there are two community projects to fill that niche. - Source: dev.to / over 4 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.
Terraform - Tool for building, changing, and versioning infrastructure safely and efficiently.
n8n.io - Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services.
Puppet Enterprise - Get started with Puppet Enterprise, or upgrade or expand.
Amazon AWS - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.
Rancher - Open Source Platform for Running a Private Container Service