Stop wasting hours on work AI can already do for you. These 14 AI tools can save you time every single week: ✂️ OpusClip → Turns long videos into viral clips 📝 Fireflies → Records and summarizes meetings 🔎 Scite → Finds and verifies research sources ⚡ Runpod → Affordable cloud GPUs on demand 🎥 Pika → Generate videos from prompts 🖼️ Magic Studio → Edit images in seconds ✍️ Copy AI → Generate marketing content 🏠 Reimagine Home → Redesign rooms with AI 🎤 Poised → Improve speaking and presentations 📋 Taskade → AI-powered project management 🎵 Suno → Create songs from text 🤖 Chatbase → Build custom AI chatbots 🎨 Recraft → Create and refine designs 📚 Notion AI → Organize notes and knowledge Most people use AI for ChatGPT. The real productivity boost comes from building an AI stack around your workflow. Bookmark this for later.

20 GitHub repositories that will give you superpowers:↓

ChatGPT has quietly built a file on you. You've never seen most of it. Every message you send adds to it. It reads your patterns to map your personality and habits, things you never actually typed. Here are 6 prompts to pull up everything it has on you, and erase what you never agreed to:

I’m curious to see how different creators use Buzzy. The 45-90 second limit is tight, but that’s where the best storytelling happens. Good luck to everyone entering. #BuzzyFilmCompetition

Andrej Karpathy said something that perfectly captures where AI coding is heading: “Vibe coding is incredible. But agentic engineering is the next level. 90% of my coding routine is automated by AI agents.” Most people are still using AI one prompt at a time. Karpathy is building systems where agents handle entire workflows. In this 30-minute talk, he breaks down how he structures his AI-assisted development process, how agents fit into real engineering work, and why the future is less about writing prompts and more about designing workflows. If you’re serious about AI coding, this is one of the most valuable videos you’ll watch this year. Honestly, worth more than most $500 agentic engineering courses floating around the internet. Watch it today. 📺

Most people don’t need another $500 course. They need a better bookmark folder. The internet already has world-class education available for free. Here are 10 websites that can teach you skills people regularly pay hundreds or thousands of dollars to learn: 1. The Odin Project Complete full-stack web development curriculum from beginner to job-ready. 2. CS50 (Harvard) Harvard’s legendary introduction to computer science. Millions have started here. 3. Fast AI Practical deep learning courses focused on building real AI applications first. 4. Khan Academy Math, science, economics, computing, and more with interactive exercises and progress tracking. 5. Coursera Access courses from Stanford, Yale, Google, and other institutions. Most can be audited for free. 6. Brilliant Interactive STEM learning that focuses on problem-solving rather than passive video watching. 7. freeCodeCamp Thousands of hours of coding lessons, projects, and certifications completely free. 8. MIT OpenCourseWare Lecture notes, assignments, exams, and materials from actual MIT courses. 9. edX Thousands of university-level courses from institutions around the world. 10. TU Delft OpenCourseWare Engineering, architecture, technology, and design courses from one of Europe’s top technical universities. The biggest advantage people have today is not access to information. It’s the willingness to sit down and learn. Everything else is already online.

Wait... You can change a few words in a voiceover without re-recording the entire thing? That's one of those features that immediately makes sense. ViiTor TTS is onto something here. Check it out now:

CapCut is about to launch Seedance 2.0, and honestly, the pricing is what caught my attention. The quality looks great from the early examples I've seen, but the cost is less than half of what it used to be. That's a pretty big deal for creators who generate videos regularly. Better quality is always nice, but making high-quality video generation more affordable is what really moves the needle. Curious to see how it stacks up once more people get their hands on it, but lowering the barrier to entry while improving quality feels like the right direction. #Seedance2 #CapCut

Train your own LLM from scratch! A step-by-step repo that walks you through building and training a transformer model from scratch using PyTorch. From downloading training data all the way to generating text. The architecture is built from the ground up following the original "Attention is All You Need" paper. MLP, single head attention, multi-head attention, transformer blocks, and the full transformer model - all coded and explained with detailed diagrams at each step. Training data comes from The Pile - a diverse 825GB open-source dataset covering books, articles, code, websites, and more. The repo includes scripts to download it, preprocess and tokenize it using tiktoken, store it in HDF5 format, and feed it into training batches. You can train a 13M parameter model on a single Colab T4 GPU. At 13M parameters the model starts generating proper grammar and coherent short sentences. For billion-parameter training you need at least an A100 or RTX 4090. The repo includes a full GPU compatibility table so you know exactly what's possible on your hardware. Includes a complete SFT and RLHF guide as a separate notebook for taking your trained model further. Key capabilities: • End-to-end pipeline: data download → preprocessing → training → text generation • Full transformer implementation from scratch with PyTorch • Trains models from 13M to 2B parameters on a single GPU • Training data from The Pile (825GB, 22 diverse datasets) • Tokenization via tiktoken (r50k_base) • SFT and RLHF guide included 100% open source. I've shared the link in the replies!

BREAKING: Perplexity can now help you build a one-person business like a $10M solo founder. For free. Here are 5 prompts that replace your business coach, strategist, and creator. (Bookmark for later)

For brand and commercial creators, this is worth paying attention to. AI video is starting to look less like a gimmick and more like a serious production shortcut when the idea is strong.

The creator of Claude Code said something that most AI users still haven’t realized: “I don’t prompt Claude anymore. I create loops. The loops do the work. My job is to create loops.” That single idea explains why some people are 10x more productive with AI than everyone else. Most users sit in a chat window writing prompt after prompt. Power users build systems. In this 30-minute walkthrough, Boris shares how he actually uses Claude Code day to day: → Custom loops that keep work moving without constant supervision → Reusable routines for repetitive tasks → Dynamic workflows that adapt as projects evolve → Multi-step processes where Claude reviews, improves, and extends its own work → The setup that turns Claude from a chatbot into a working system The interesting shift is that prompting becomes less important over time. The real leverage comes from designing workflows that can run repeatedly with minimal input. Instead of thinking: “What prompt should I write?” You start thinking: “What process can I build once and reuse forever?” That mindset is what separates people casually using AI from people building with it every day. Claude loops routines dynamic workflow Worth more than most vibe-coding courses charging hundreds of dollars.

Your iPhone may be listening more than you think. A lawsuit alleged Siri accidentally recorded private conversations and sent some clips for human review. Apple agreed to a $95 million settlement while denying wrongdoing. Here are 7 settings worth checking today 👇

An Anthropic engineer said something that completely changed how I think about Claude: “You’re not supposed to prompt Claude. You’re supposed to build a system that prompts itself.” Most people open Claude, type a question, get an answer, and leave. That is using maybe 10% of what it can do. In this walkthrough, he breaks down how advanced users actually work with Claude: → Why your CLAUDE.md file can have a bigger impact than the prompts you type → How to build multi-agent workflows with separate agents for research, execution, review, and orchestration → The architecture that turns Claude from a chatbot into a production system → The three properties every agent team needs to stay reliable as complexity grows The most interesting idea is that you stop thinking in terms of prompts. Instead, you design systems. One agent gathers information. One agent builds. One agent critiques. One agent coordinates everything. The human stops managing every step and starts managing the workflow itself. If you’ve been using Claude exclusively through the chat interface, there’s a good chance you’re missing most of the platform’s capabilities. This is less about prompting better. And more about building environments where the AI can do the prompting, planning, and coordination on its own. One of the best practical AI workflow breakdowns I’ve seen recently. Worth bookmarking.

97% of iPhone users never touch Camera settings. Which means 97% are shooting with settings that make photos look worse than they should. I changed 5 of them last week. My photos instantly looked more professional. Here is what I changed:

Code generation is no longer the bottleneck. Validation is. AI can ship hundreds of lines of code in minutes. But PR reviewers haven’t magically become 10x faster. Bugs slip through. Verification debt piles up. That’s why TestSprite caught my attention. Instead of reading code and guessing, it opens your app and uses it. A fleet of agents explores your product, generates test plans, runs tests in parallel, and surfaces real issues with clear traces, causes, and fixes. The new TestSprite 3.0 web portal makes the whole process visible. You can watch agents explore your app in real time, replay failures step by step, inspect API data flows, and debug issues visually instead of digging through logs. The result isn’t more AI reviewing AI. It’s a validation layer that actually closes the loop. For teams shipping AI-generated code faster than ever, that’s becoming a necessity, not a nice-to-have. Try it here: testsprite.com/?via=dcoder-g…

Introducing Super, the world's first AI with her own apps. Super can schedule Ubers, order groceries, and have Starbucks delivered, just like a real assistant. AI was meant to make our lives easier, but instead, we have to learn MCP, skills, and tools that still feel brittle. Rather than relearning new apps each year, Super automates them for you and meets users at their level of understanding. She acts as the single concierge for apps and the real world, saving you time and money. Unlike most AI assistants that only automate your calendar or email integrations, Super has her own email, phone number, and apps in the cloud. You can integrate her into your life like an assistant without giving up access to your private email or calendar. We invented a new primitive called Computer-Use Cache, which reduces costs to near-zero for repeatable tasks so cloud automations are effectively free. No subscriptions! Super can purchase products and services for you from her wallet, which you pay as you go. Our thesis: apps are a vestige of the mobile era, and we're building what comes next. You can access Super over iMessage, Meta Display glasses, and iPhone today. Text Super at getsupers.com, and if you're one of the first 5,000 in the Bay Area, you get a free Starbucks! Comment and repost ♻️ and I'll DM you free money to buy things with Super! US and Canada only, while supplies last.

Every AI assistant demo ends right before the useful part. Super goes one step further. Text for a coffee. Coffee gets ordered. That's the product.