Temporal Reviews

Amazon S3 Reviews

Temporal mentions (18)

• 23 TypeScript Tools for Making Software Explicit in the AI Era

  Temporal makes durable workflows, activities, retries, timers, failures, and long-running execution explicit. - Source: dev.to / 4 days ago

• Temporal in Production: Sharp Edges & Good Practices

  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 / about 1 month ago

• Your Agent Bills While It Waits. Here's the Fix.

  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 / about 1 month ago

• Compiler as Custodian

  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 / 2 months ago

• How we turned our workflow editor into a real SDK

  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

Amazon S3 mentions (214)

• Document Generation for Developers: Security, Compliance, and Build-vs-Buy Decisions for the Template-Plus-Data Pipeline

  TLS at the API boundary encrypts the payload in transit, but your application is responsible for what happens to the document after the response arrives. If you're writing the rendered PDF to disk, a message queue, or cloud storage, that persistence layer needs its own encryption at rest. An unencrypted file sitting in an Amazon S3 bucket with overly permissive ACLs falls outside what the API provider's TLS covers. - Source: dev.to / 3 months ago

• Dynamic Looping Comes to AWS SAM

  SAM CLI generates the SAMCodeUriServices mapping so that each collection value resolves to its own build artifact. At package time, those paths become Amazon S3 URIs. I don't need to manage any of this. - Source: dev.to / 3 months ago

• AIP-C01 last-minute revision: exam traps, memory hooks, and quick notes

  Fine-tuning adapts an FM to a specific use case with proprietary training data. Titan, Cohere, and Meta models support fine-tuning via Amazon Bedrock. Text models need labelled prompt-completion pairs; image models need Amazon Simple Storage Service (Amazon S3) paths linked to descriptions. Secure training data with Amazon Virtual Private Cloud (Amazon VPC) + AWS PrivateLink. - Source: dev.to / 4 months ago

• Why AWS Certified GenAI Developer stands apart from other AWS certs

  You need to understand vector stores for semantic and hybrid search using Amazon OpenSearch Service and Amazon Simple Storage Service (Amazon S3). Prompt caching helps reduce costs by reusing previously processed prompts. Amazon Bedrock Prompt Management simplifies the creation, evaluation, versioning, and sharing of prompts to help you get the best responses from foundation models. Flow orchestration with Amazon... - Source: dev.to / 4 months ago

• Fine-Tuning 14B SLMs for 3GPP Root Cause Analysis on Amazon SageMaker

  All fine-tuning used Amazon SageMaker Training Jobs — no instance provisioning, no SSH, no manual teardown. You provide a training script and an S3 dataset path, specify the instance type, and SageMaker handles the rest. - Source: dev.to / 6 months ago