No-Code Agentist

A daily digest of practical AI agent workflows for non-developers. We isolate actionable no-code guides from viral hype. Scored against human-defined standards.

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Daily Summary

3 curated | 3 evaluated

No-code agent platforms advanced this week with expanded capabilities and refined control mechanisms. introduced voice call integration and DeepSeek V4 model support, while shipped mobile access and infrastructure improvements, and detailed runtime interaction patterns including message queuing and trajectory steering.

•openclaw released an update adding voice call integration with full agent access, DeepSeek V4 Flash and Pro model support, and enhanced browser automation with coordinate clicks and improved recovery mechanisms.

•xona_agent delivered a weekly update spanning mobile access, improved agent infrastructure, and expanded resources across their development stack.

•Teknium explained four runtime control methods for Hermes Agent: direct messaging to interrupt the loop, /queue for post-loop execution, /bg for async parallel prompts, and /steer for injecting trajectory guidance during agent execution.

Apr 25, 7:39 PM

OpenClaw 2026.4.24 🦞 ☎️ Voice calls can now reach the full agent 🧠 DeepSeek V4 Flash + Pro join the team 🖱️ Browser automation got coordinate clicks + better recovery 🔧 Telegram, Slack, MCP, sessions, and TTS fixes More reach. Less duct tape. https://t.co/Oa9G9WpO84

agentic-orchestration100

high-signal-utility75

no-code-feasibility63

Apr 26, 9:22 AM

XONA Weekly Recap ⚫️ Here’s what we shipped this week 👇 From mobile access → agent infra → new resources Real progress across the stack. https://t.co/FLsEVmYOMz

Apr 26, 2:47 AM

Hermes Agent tip of the day: There are 4 ways to deal with the model while its running, - Message it, by default, it will interrupt the agent loop, stopping it and making it respond to your new message - /queue will queue up a message that will fire after the agent loop completes - /bg or /btw will run a parallel prompt that is async And - /steer will inject a guidance message into the next tool calls result sent to the model during an agent loop, to try to guide the rest of its trajectory