AIGRAM – AI + Instagram Digital Income
Learn AI, Grow on Instagram & Build Your Digital Income 🚀
What it does
AIGRAM is a practical eBook for beginners who want to use AI and Instagram to build digital income. Learn AI tools, Instagram growth strategies, powerful prompts, digital product ideas, and simple steps to get started. Perfect for students, creators, freelancers, and anyone exploring the digital world. 🚀 📖 eBook: https://aigramincome.lovable.app 📸 Instagram: https://www.instagram.com/aigramincome
AI se Instagram par digital income banane ka complete system - 18 chapters, 200 AI prompts, 100 hooks, 50 DM scripts aur 30-day roadmap.
AI Foundation se leke Sales & Scaling tak — 18 chapters, 200 AI prompts, templates, scripts aur 30-day roadmap ke saath. Ek hi eBook me poora blueprint. Problem talent ki nahi — system ki hai. Ye eBook wahi system deta hai. Complete combo pack — har section apne aap complete aur actionable hai. Followers kam hain? Koi baat nahi - system zero se banaya gaya hai. Luck nahi - attract, convert, engage, monetize ka framework. Real readers, real results — system follow karne walon ka experience. “ 30-day plan follow kiya aur pehli baar mera reach 4x hua. DM scripts se 3 sales bhi ho gayi. ” “ Beginner tha, ab apna pehla digital product launch kar chuka hoon. Prompts library sabse kaam ki cheez…from aigramincome.lovable.app
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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