Most people make AI video the hard way. One tool for the image, one for the motion, one for the voiceover, one for captions, then something to glue it together. Every handoff is another login, another bill, another thing that breaks at 11pm. Fal.ai runs the whole chain through one API. I start with a single reference image, push it through an image-to-video model (Kling or Seedance) to get real motion, generate the voiceover with ElevenLabs, then stitch the whole thing with their ffmpeg endpoint. One provider, one key. I tested it this week on a faceless content channel. One still image turned into a believable animated clip with a deep voiceover, native 9:16, and the character stayed consistent across the whole motion. About 35 cents a clip. Worked first try. The real win is that you can script the entire pipeline end to end and run it as many times as you want for pennies. That's what makes a daily posting schedule actually doable instead of a nice idea. If you're making short-form content by hand right now, what's the one step eating most of your time? Editing, voiceover, or just coming up with the idea?

It's called andrej-karpathy-skills. Worth saying up front: it's not actually from Karpathy. Someone took his public rants about where LLMs fall over when they write code and turned them into a single CLAUDE.md you drop into any project. That's the whole repo. One file. 178k stars. The reason it's blowing up is the four rules it gives Claude: 1. Think before coding. Surface your assumptions first instead of charging in. 2. Simplicity first. No speculative features, no "while I'm here" extras. 3. Surgical changes. Touch only what the task needs, leave the rest alone. 4. Goal-driven execution. Give Claude the success criteria and let it loop until it hits them, instead of dictating every step. That last one is the bit most people sleep on. You stop micromanaging the steps and start describing what "done" looks like. Claude is genuinely good at looping toward a target. It's worse at following a 12-step recipe you wrote. Drop the file in, restart Claude Code, and you'll feel the difference on the next task. Repo here: https://github.com/multica-ai/andrej-karpathy-skills Have a read and tell me which of the four your projects break most often. Mine's number 2.

Here's the problem it solves. On a big repo, Claude greps your files every time you ask it something. It opens them, reads them, works out how they connect, and burns a load of tokens doing it. Then the session ends and it forgets the lot. Graphify reads your repo once and builds a knowledge graph. Claude queries that map instead of crawling files. Fewer tokens, more accurate answers, and it remembers the structure. How to try it this week: 1. Install it Grab the repo (github.com/safishamsi/graphify), paste the link into Claude Code, and say "install Graphify for me." It reads the repo and sets itself up. There are manual steps in the readme but you don't need them. 2. Build the map Run /graphify . in the folder you're working in. It pulls out your nodes (the pieces), edges (the connections) and communities (clusters of related code). It also shows you the most connected parts of the repo, so you get a tour before you've typed a prompt. 3. Make Claude actually use it Use graphify query or graphify explain so Claude answers off the map instead of guessing. Run graphify claude install to make it always on. 4. Keep it current Run graphify hook install and it rebuilds after every commit. That rebuild is free, no API cost, so the map never goes stale. What you get: Claude stops re-reading your whole project on every question. Cheaper, faster, and it actually knows how things are wired. Take 20 minutes this week, point it at a repo you actually work in, and ask it the same question with and without the map. Watch the token count. What codebase would you point it at first?

Anthropic just put Fable 5 in everyone's hands, so now you've got three coding models to choose from inside Claude Code. Most people leave it on default and never think about it. That's money left on the table in both directions: overpaying on simple jobs, underpowering the hard ones. Here's the rule I use. First, how to switch. Type /model in Claude Code and pick from the list. You can change it mid-session whenever the task changes. That's the whole mechanic; it takes two seconds. Now when to use which: 1. Haiku for the cheap, repetitive stuff Renaming files, simple edits, running through a checklist, quick lookups. Don't burn a frontier model on work a small model nails. It's faster and almost free. 2. Opus 4.8 as your daily driver This is where most of your real work lives. Building features, debugging, writing, planning. It's the best balance of smart and affordable, and it'll handle 90% of what you throw at it. 3. Fable 5 for the long, messy, high-stakes tasks Big multi-step builds where the cheaper models drift halfway through. Gnarly bugs nobody can crack. The work where one good output is worth more than the token cost. It's twice the price of Opus 4.8, so reach for it on purpose, not by accident. The simple version: match the model to the difficulty of the task, not the other way round. One timing note. Fable 5 is free on the paid plans until June 22. So the next couple of weeks are the cheapest chance you'll get to test it on your own real work and feel where it actually pulls ahead before it starts costing credits. Try this today: next time you start a session, ask yourself "is this a Haiku job, an Opus job, or a Fable job?" before you type the first prompt. What are you mostly running on right now, and have you noticed a difference when you switch up for the hard stuff?

It came out the same week they filed for IPO, so it's not a throwaway line. That's their actual development process. Forget the stat for a second. The useful part is that you can copy the setup behind it. Here's what makes Claude write the majority of your code well, instead of slop you have to babysit. 1. Plan before it writes a line. Don't ask for the feature. Ask for the plan first. Get it to lay out the files it'll touch and the approach, read it, correct it, then let it build. Most bad output comes from skipping this step. 2. Give it the context it needs. Claude writes badly when it's guessing. Point it at the actual files, the docs, the example you want it to match. The more relevant context in the window, the less it invents. 3. Make it check its own work. Have it write a test, run it, and fix what fails before it hands anything back. This one loop is the difference between code that looks right and code that actually runs. That's the whole game. Plan, context, verify. Same loop Anthropic runs internally, just smaller. Try it on one task this week. Pick something you'd normally write yourself, make Claude plan it first, and see how much further it gets. What's the biggest thing stopping Claude from writing good code for you right now? Drop it below.