I've been seriously trying to land my first paying AI automation client for about a month now. A little about me: 9 years in IT, from QA Engineer to Head of QA. Now I'm building AI automations with n8n, APIs, and Claude. Here's what I've tried so far: 🔹 Upwork — Sending 1–2 proposals a day, $40/hr, Freelancer Plus. Had a Rising Talent badge. Still no client. 🔹 Fiverr — Just getting started. Not sure whether offering cheap services to get the first reviews is a smart move. 🔹 LinkedIn — Connecting with business owners, asking about their processes and what they'd like to automate. Some really interesting conversations! 🔹 Networking — Talking to local business owners, getting introductions, meeting people. Lots of "sounds interesting, let's stay in touch." But nothing paid yet. I'm learning a lot, but sometimes it feels like I'm doing everything except the one thing that matters -- getting a client. 😂 So I have a question for those who've already made it past this stage: Where did your FIRST paying client actually come from? Was it Upwork, Fiverr, LinkedIn, networking, referrals, or something completely different? And what made the difference? A better portfolio? Lower prices? A free demo? Just talking to more people? I'd genuinely love to hear your stories, especially if you started from zero without an existing audience or connections. 👍 Give this a like if you're also struggling to find your first clients. At least we'll know we're not alone! 😄 And if you've already figured it out, please share what worked. Your experience might save someone months of trial and error. 🙌

For those of you who have built automation workflows beyond the testing stage, what has been your biggest challenge when moving them into production? Is it handling errors, managing API limits, dealing with unexpected data, keeping workflows running reliably, or something else? I’ve been experimenting with AI-powered workflows and integrations, and one thing I’m learning is that making a workflow work once is very different from making it reliable in real-world use. What practices or tools have helped you make your workflows production-ready? I’d love to hear your experiences, lessons learned, and any tips for someone moving from experimenting to building reliable automation systems.

Your idea already has competition... even if nobody sells the exact app you’re imagining. The competition might be a spreadsheet, an assistant, a group chat, or simply putting up with the problem. Imagine software that organizes subcontractor documents. The current solution might be someone checking an inbox every morning and maintaining a folder. Before designing a dashboard, understand that routine: - How much time does it take? - What gets missed? - Who notices the mistakes? - What happens afterward? - Why hasn’t the team changed it already? “Doing nothing” can be a strong competitor when switching feels harder than the problem. Write down the current workaround for your idea. Include what it does well. People have reasons for using it. Then identify the specific improvement that could justify changing their routine. What would your product replace today?

Not every business is ready for automation, and taking on the wrong client costs more than missing a sale. Ready: they already get enquiries but reply slowly, they can name the problem in one sentence, and someone owns the follow-up. Not ready: they have no steady enquiries yet, or they want software to fix a process nobody has defined. A short chat before you build saves weeks of rework. What's your quickest way to tell if a new client is ready?

Two new voice-agent stacks shipped on the same day. They take opposite approaches, and the difference matters if you're building one. ElevenLabs — Eleven Realtime One speech-to-speech model hears and speaks. → Max starts talking in 0.57 s and scores 98.8% on Big Bench Audio → Mini is the cheaper option for natural chat. It handles interruptions and "mm-hmm" → Already on OpenAI's Realtime API? Change the endpoint and model name Mistral — Voxtral 3 A pipeline: listen → reason → speak, streamed together. → Each turn finishes in under a second → Swap the LLM, voice or language without rewriting the agent → Runs on Mistral's EU-hosted API from $0.04/min, or on your own servers For building AI voice receptionists, here's how to choose: Speech-to-speech when a fast, natural feel is what sells. Pipeline when the client needs control: their own LLM, their own data region, a specific language. Either way, the voice itself is no longer the bottleneck. The tool calls and the script are. Which would you pick for building AI voice receptionist?