How do you test an AI agent before it touches real customer data?
A builder asks how to test an agent before giving it access to real customer data. The reliable pattern: test against a small set of made-up customers with known right answers, watch what the agent actually writes back rather than what it claims, and keep a separate verification check between the agent and the customer. Only after the fake set passes clean does the agent get a small slice of real traffic with a human watching.
Read the story →More stories
What framework should you pick to build an agent today?
A developer who already knows LangGraph and the OpenAI SDK asks which framework to choose for a first agent. The pragmatic answer: start with what you already know instead of learning a new tool. The framework matters less than two reliability habits — a verification step before the agent claims something is done, and a log of what it really did. Build one small agent end to end, and only switch frameworks if the current one fights you.
Read the source →How do you make an agent relentless instead of quitting at the first obstacle?
A builder complains that agents quit at the first obstacle and hand the work back. The fix is not asking the agent to try harder but giving it a loop it cannot exit early: a clear done-condition it can check itself, permission to try a different approach when one fails, and a rule that reporting what was tried and what blocked it beats saying I cannot. Agents quit when the goal is vague; a checkable finish line keeps them going.
Read the source →Muse main chat vs side chats: which one should hold what?
A Muse user is confused by main chat versus side chats: the main chat keeps everything and its replies cannot be deleted. The practical split: main chat carries long-term memory and context, so anything worth remembering goes there; side chats are scratch pads with fresh context for one-off tasks, drafts, and experiments you would rather not keep. Start the side chat before the task, not after.
Read the source →Muse main chat stuck repeating one old shell command for two days
A user reports that since November 8 their Muse main chat replaces almost every tool call with one old shell command from the same chat, looping uselessly. The likely cause is context stuck on the old command. The fastest fix is a fresh chat with only the needed context pasted in, or a compact or reset instruction at the top if it recurs.
Read the source →Business owners: what is the one task you would give an AI employee first?
A Facebook group asks business owners which single job they would hand to an AI employee working 24/7: sales, support, marketing, operations, or something else. A good first pick is the daily posting loop — the agent drafts, a human approves before anything goes live — followed by lead follow-up with everything logged and anything unusual flagged for a person. One task plus one approval tap, made boring and reliable, then expanded.
Read the source →Heyron: 6.4K MRR verified by Stripe, down six straight months from a 2.9K peak
Heyron, a hosted AI agent for solo founders that plugs into Gmail, Notion and GitHub for 9 a month, shows 6,409 MRR verified through Stripe by TrustMRR, down six months in a row from a 2,946 peak in April. It went viral on TikTok with a give-an-agent-00-to-make-money experiment, but the decline shows keeping customers is harder than attracting them. The lesson here is retention, not the headline MRR.
Read the source →Imposter AI: 0.6K MRR verified, a solo-built AI party game
Imposter AI is a solo-built AI party game, free with in-app purchases, earning 0,600 MRR verified through RevenueCat by TrustMRR, with 51,000 all-time revenue and 5,500 active subscriptions. Distribution came from TikTok, Instagram and app-store optimization. The numbers are processor-verified, but games are hit-driven, so this is proof a solo AI app can earn real money rather than a repeatable formula.
Read the source →Dropmagic: AI store builder for first-time sellers claims 20K MRR
Dropmagic is an AI platform that helps people who have never sold online build a store, with the AI handling store setup, products and content. Its founder reports 20,000 in monthly recurring revenue, but the figure is self-reported and not independently verified. It fits the lower-the-barrier-for-beginners trend, in a niche crowded by Shopify and Wix, which already ship built-in AI.
Read the source →Muse Ember pricing detail: rates triple past 200,000 input tokens
New pricing detail for Muse Ember: the headline /bin/bash.10 per million input tokens and /bin/bash.50 per million output tokens only apply up to 200,000 input tokens, and both rates triple above that threshold. Ember is also only available on the Meta Model API in Standard mode, with no Contributor-mode variant. Meta recommends pairing Ember for repetitive execution with Muse Spark or Wonder for planning.
Read the source →I want an agent to apply for jobs for me completely autonomously. What should I use?
A Reddit user wants an AI agent to fill out job applications on its own while still watching it work to catch mistakes. The thread is a good reminder that full autonomy is risky when the agent can invent a job title or apply for the wrong role. A supervised loop — agent drafts, human approves in batches — saves most of the typing while keeping the final call with the person.
Read the source →For those building AI agents, what has been the biggest pain so far?
A first-time poster building AI infrastructure asks practitioners what hurts most when building agents, naming reliability first: agents crashing mid-workflow and losing progress. The recurring answer from experience is that agents rarely fail loudly — they fail quietly and report success anyway. A separate verification step and a log of what the agent actually did matter more than framework or model choice.
Read the source →