Anyone else using Tailscale to connect Muse to their local machines?
A Muse user shares how Tailscale solved the Linux VM reboot problem by letting the agent SSH into home machines, phones and Raspberry Pis as if they were local. The thread is a practical discussion of making an agent setup reliable with a private mesh network instead of port-forwarding workarounds.
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Is Muse Wonder worth getting over ChatGPT Plus?
A user asks whether Muse Wonder, advertised as near Astra-level, is worth replacing ChatGPT Plus. The real question is chat versus agency: Wonder earns its keep when the agent acts across apps like email and calendar rather than just answering questions.
Read the source →Junior engineer pursuing agentic AI roles: seeking feedback on skill gaps
A junior engineer with Python and pandas experience asks practitioners and hiring managers what skills matter for agentic AI roles, focusing on agent system design and deciding where model autonomy fits versus deterministic logic, tools and human approval.
Read the source →What should AI agencies test before delivering a RAG or AI agent to a paying client?
A researcher studying how teams validate RAG and agent systems before handover notes that successful demos do not guarantee reliable production behavior. The discussion centers on testing retrieval quality on representative queries, handling irrelevant evidence, and agreeing on failure modes before delivery.
Read the source →What is one task you wish an AI agent could handle completely for you?
Users list repetitive work they still do by hand despite AI tools, from multi-site research to CRM updates and reporting. The thread's key insight is that the hard part is no longer getting an agent to perform tasks, but getting it to do them reliably and flag conflicting sources.
Read the source →Every AI has a job: Claude codes, ChatGPT answers, Grok Bot runs the business
A business owner explains splitting work across AI tools like a crew: Claude for coding, ChatGPT for everyday questions, and Grok Bot for business operations such as drafting posts, tracking ads and watching Search Console. The post asks readers which AI they use most and for what.
Read the source →If an AI agent works overnight, how much less do you actually work the next morning?
A discussion on the real value of overnight agent work, citing Anthropic's October 21 research preview of Claude Duet. The practical takeaway is that mornings only get lighter when overnight work is boring and reversible; anything touching money or customers still needs a human morning check.
Read the source →Replymer: marketing tool at $3,676 verified MRR
Replymer is a solopreneur-run marketing tool operating nearly autonomously, earning $3,676 in monthly recurring revenue at about $40 per customer. Revenue is verified through the Stripe API via TrustMRR as of October 16, 2026, ranking 35th on the Agent Money List.
Read the source →Chatwith: developer tools and finance tool at $3,569 verified MRR
Chatwith is a developer tools and finance product run nearly autonomously, earning $3,569 in monthly recurring revenue at an estimated $293 per customer, the highest per-customer rate in this batch. Revenue is verified through the Paddle API via TrustMRR as of October 16, 2026, ranking 36th.
Read the source →Wave: lead generation and sales tool at $3,424 verified MRR
Wave combines lead generation, social media and sales in a nearly autonomous tool sold as a service, earning $3,424 in monthly recurring revenue at about $65 per customer. Revenue is verified through the Creem API via TrustMRR as of October 16, 2026, ranking 37th.
Read the source →WPConvert.ai: developer tool at $3,408 verified MRR
WPConvert.ai is a solopreneur developer tool operating nearly autonomously, earning $3,408 in monthly recurring revenue at about $42 per customer. Revenue is verified through the Stripe API via TrustMRR as of October 16, 2026, ranking 38th on the Agent Money List.
Read the source →How to build a research-searching AI agent that handles failures
A beginner asks how to build a research agent that plans a query, searches the web, filters junk, and returns a structured, source-backed answer with a confidence level. The discussion centers on failure handling: logging dead ends and retries is what turns a demo into something trustworthy, and a simple confidence signal is counting how many independent sources agree.
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