With some fear that corporate data could be revealed by messy AI applications, sandboxing was high on the agenda, and Matt Brockman, an AI engineer at enterprise sandboxing business E2B, explained that there really wasn’t much to be frightened of. - Source: dev.to / 4 days ago

E2B is the sandbox. It gives the agent its own computer to do work. - Source: dev.to / 18 days ago

If you've used E2B, Daytona, Modal sandboxes, or Cloudflare Sandboxes, the shape is familiar: REST API, Python and JS SDKs, exec / files / snapshot primitives. Here's what the Python SDK looks like:. - Source: dev.to / about 2 months ago

E2B | SF, Prague, Remote | Eng, GTM, and Operations | https://e2b.dev/ E2B is building infrastructure for AI agents, and has quickly become the open source standard for agentic workflow sandboxes. Customers include Perplexity, Groq, Manus, and more. We are experiencing explosive growth and hiring for several technical and non-technical functions as we prepare to 3x the team this year. - Distributed Systems Engineer. - Source: Hacker News / 2 months ago

Sandbox: Since we are using Gemma 4 E2B, you should ideally provide an E2B.dev API key if you want the agent to execute code in a secure, cloud-hosted sandbox. If you want it 100% local, select Local Terminal. - Source: dev.to / 3 months 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