Before I built my rig. I used paperspace.com and parsec. you'll probably have to request that they unlock a better gpu server for you though. If you need any help just shoot me a message. Its like 50 cents an hour. Source: over 3 years ago

There are several tier-two clouds that offer GPUs but I think they generally fall prey to the many of the same issues you'll find with AWS. There is a new generation of accelerator native clouds e.g. Paperspace (https://paperspace.com) that cater specifically to HPC, AI, etc. workloads. The main differentiators are:. - Source: Hacker News / over 3 years ago

Guess you've never heard of paperspace.com :) Their systems (depending on the configuration ofc) work great with ESO and they run windows and it's parsec compatible. Source: almost 4 years ago

Something else to look into for a Windows machine would be Paperspace. It can be a little flaky at times, but you get a Windows machine in the cloud which works from a web browser. Even a pretty good one only costs $7 a month for storage 50¢ an hour to run. If you need a Windows machine in a hurry this is definitely your cheapest option. Source: almost 4 years ago

Have you ever tried Paperspace (https://paperspace.com)? I've spent many hours gaming using their Windows offerings, although always strategy games so the latency hasn't been noticeable. I'm not sure how well it would work for FPS (probably reasonably, to be honest). They have a large number of general computing/graphics-specific machines you can spin up, and you can either pay per hour or per month. I've also... - Source: Hacker News / over 4 years 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 / 13 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