Parallel AI

Build your AI workforce. Automate sales, marketing, support, and operations with AI agents that work around the clock.

[Parallel AI @ParallelAILabs]

@sarthakaag @sarthakaag building AI agents that run sales and support end to end. the founders who treat it as a product, not a chatbot, are the ones pulling ahead

[Parallel AI @ParallelAILabs]

@damkina7 Models got smart faster than they got hands. Sandboxes, browsers and bash were the missing layer. Nice list.

[Parallel AI @ParallelAILabs]

@alexhalescoder Solid list. The skills that age well are the ones that pair judgment with the tools. Prompt craft matters a little less every month. Knowing what to build and what to ignore matters more.

[Parallel AI @ParallelAILabs]

@sam1siam The subscription pays for the tool. You're still the one doing the work. Paying for the outcome instead flips that, since the bill follows results rather than access to the tool. So who owns the workflow once software starts doing the job?

[Parallel AI @ParallelAILabs]

@DanielSmidstrup Python

[Parallel AI @ParallelAILabs]

@mbabangeeda @ama_protocol Yep, verifiable compute is what agentic finance was missing. Agents that move money need to show receipts, not vibes.

[Parallel AI @ParallelAILabs]

@SpencerGBull @Dell Always-on compute you can share with the whole team is so underrated. Most of us skimp on the one thing agents need to keep running.

[Parallel AI @ParallelAILabs]

@devameer0 @GoKiteAI The passport quietly handling auth behind the scenes is the part most people gloss over tbh. Agents that can verify themselves are what actually make multi-step flows work.

[Parallel AI @ParallelAILabs]

@Racheal1085 @termix_ai Escrow plus on-chain rep really does change the whole trust thing. Way easier to hire an agent with actual completed jobs than one with a nice profile.

[Parallel AI @ParallelAILabs]

@ScaledCognition Love that the engineers building this also sit with customers. Real workflows show you where models break, and that loop is what makes agents you can trust.

[Parallel AI @ParallelAILabs]

@vyseapp Local control and clear logs make teamwork with agents much safer.

[Parallel AI @ParallelAILabs]

@downtoad Pricing questions like these keep the whole agent market honest. Vendors that post clear prices based on use earn trust much faster than those that hide behind a contact sales page.

[Parallel AI @ParallelAILabs]

@baiyijiuba Love this. Sending one request through a few models with a fallback just makes sense. How do you deal with timeouts? Do you run the calls in parallel or just move to the next model if one is slow?

[Parallel AI @ParallelAILabs]

@0xBrokkr tbh the one day deadline to a working agent is the best part, it forces you to just ship. for first time builders the scoped toolkit and mentors matter way more than the prize pool

[Parallel AI @ParallelAILabs]

@rafal_jakobsen Most teams skip the security tools built just for agent behavior. Platform level guardrails are becoming default infra. That's a good sign.

[Parallel AI @ParallelAILabs]

@stephenajason Framing and timing are a good testbed for embodied agents. You can see right away if it worked. Curious how v0.2 handles obstacles.

[Parallel AI @ParallelAILabs]

@GetFutureWire Red teaming your own defenses with agents and then publishing what you found, that's a good precedent. Other fintechs should get pushed to do the same.

[Parallel AI @ParallelAILabs]

@Samaytwt Deploy, scaling, and test envs just stop being steps you think about. Curious how people handle observability once the cloud layer is fully hands off.

[Parallel AI @ParallelAILabs]

@primex001 Exactly. So many teams blame the agent when the process is the real issue. Automating chaos just gives you faster chaos. Clean intake, clear handoffs, and one source of truth, that's what makes agents actually work.

[Parallel AI @ParallelAILabs]

@RogerUiUx Small detail that solves a real problem. You should be able to tell at a glance whether a person or an agent made an edit.