Noah, a member of our community, shared a result worth breaking down. Noah reported making over $5,000 in a month from a voice-based AI receptionist. It answered calls, booked appointments and handled basic questions. He used ElevenLabs, Make and Google Calendar. Then he went on Indeed and looked for small businesses hiring receptionists. A few signed at around $1,500–$2,000 each. The useful part is how he found the problem. These businesses were already advertising that they needed help with calls and scheduling. He had a specific buyer, a visible problem and something he could demonstrate. That's the connection we work on inside AIA+'s Make Money with AI challenge: niche selection, an automation backend, clear positioning and the assets to show the offer to someone. If you've learned the tools but haven't connected them to a buyer yet, start here (guarantee your first client within 90 days): https://www.skool.com/theaiaccelerator/about

OpenAI released GPT-6 Astra and almost every video is reading the wrong number. They pull up the token price — $10 in, $50 out, identical to Claude Fable 5.1 — then go argue about AGI. The AGI argument is fun; it doesn't change anything about your business this week. Here's the moral up front: AI stopped getting cheaper per token and started getting cheaper per finished job. - The number that matters: cost per task. Same coding test, Astra scores slightly higher than Fable 5.1 and OpenAI's own line says it gets there at ~63% lower cost per task — roughly $7 a job against $19. And it's the same shape four times on the same page: terminal ~63% lower, CAD 86% lower, science 31% lower, reasoning 37% lower. It takes fewer tokens to get to done — it doesn't wander and doesn't retry six times. So you stop comparing model to model and start comparing it to the $22/hr person doing your follow-ups and proposals. - Two more worth knowing. On the scope test (does it go outside the lines to finish a hard job?) their last model ran at 48%; Astra hit 0%. 48% is a model you babysit, 0% is one you delegate to. And on computer use it scores higher than the last model at ~40 minutes a job instead of 75 — faster and more accurate, which normally doesn't happen. - The number that doesn't matter. 99.9% on ARC-AGI-3 is OpenAI's own harness; on the standard one it's 62.7 — still best in the world, just not 99. ARC Prize themselves said saturating their test wouldn't prove AGI. And it doesn't win everything: Fable still takes Humanity's Last Exam 65–57 and leads the Artificial Analysis index. My take: two things I'm changing before I even get access. My job moves from operator to reviewer — when a model runs on its own for a day and a half the bottleneck is how well you scoped it and how carefully you check what comes back. And patch your stack: this is the first model OpenAI rates critical for cyber, and it found real unknown vulnerabilities on its own in testing. Also, it's overkill for day-to-day — keep a cheaper model for routine work and point this at the hard thing.

Hi skool fams! I am a software engineer. I'd love to connect with Founders, builders, CTO or PMs currently building unique ideas. Mainly use Python/Django, React/Nextjs, C#/.NET, Gofast and serveral Typescript and Python libs for developing cutting-edge AI production such as Voice AI, AI assistants, Automation and LLM projects. Also take full responsibility for enhancing visual appeal and marketing effectiveness through UI/UX, website design, and front-end improvements. Let’s develop useful projects that can actually sell well to companies or customers. Also, if you have such an opportunity, please bring me on board.

The "spinning up messy automation scripts and broken AI agents without a clear roadmap" trap that kept my tech stack completely stuck in trial-and-error mode (and the AI Accelerator Q&A that finally made mastering systems click) Back when I first started trying to figure out how to build advanced artificial intelligence and automation architectures, I made the classic mistake of trying to piece everything together through random tutorials and guesswork—wasting weeks spinning my wheels while my projects went nowhere, but plugging into the AI Accelerator community completely changed everything by opening up a direct line to immediate value, expert answers, and master-level execution. Let’s open up the AI Accelerator Q&A: Since I'm new here and diving straight into mastering agents and automation, I'd love to see what everyone's tackling right now: what is the single best question or technical breakthrough answered in the community lately that completely changed how you build systems? Drop your favorite Q&A insights below and let's connect! 🤖⚡ (By the way, if you’re currently mapping out your system architecture and want a quick look at the exact floment frameworks we’re using to scale with AI, just drop me a message and I'll gladly share them with you.)

I'm on the $200 plan and still run out of credits every week — so here's the practical way around it. Claude Code running on free models through OmniRoute, roughly 1.5 billion free tokens a month, no card and no trial clock. I rebuilt my own company's site with it to prove it works. - The whole trick: keep the body, swap the brain. Claude Code is the app (reads files, runs commands, edits code); Opus is the brain. They ship in one box but they're only loosely attached — the address Claude Code sends requests to is just a setting in a file. Point it at OmniRoute instead of Anthropic and everything else stays identical. OmniRoute holds free API keys from a stack of providers (OpenRouter, OpenCode, NVIDIA NIM, OpenCode Zen) and falls through to the next one the moment one runs dry. These are published free tiers, not a loophole. - Why this and not the other "free" methods. The Google $300-credit trick is charging people — the credits don't cover LLM models and the comments are full of surprise $20–50 bills. Local models genuinely work but need real hardware. One free API key lasts about an hour. Here nothing expires, and if a provider kills its free tier you just add another to the bottom of the queue. That's a free setup, not a free model. My take: be honest about the ceiling. It won't build deep software with lots of files and moving parts anywhere near as well as Opus, it's slower, and some days a model just doesn't answer. For email drafts, landing pages, reports and small tools it's fine — and free. One hard rule: most of these free tiers require data collection to be on, so this is for your throwaway work, never client files. 📎 Full guide PDF pinned below — every install command, all four providers, the combo, the settings.json, and the honest limits. 👉 Hitting your Claude limits every week too? Drop it below. [Watch the video here ▶️]