📝 TL;DR 📝 fal, working with Black Forest Labs, launched FLUX 3 Max, an AI video model that turns text, a still image, or a set of reference frames into short clips in roughly the time it takes to read this paragraph. For creators and small businesses, faster generation means more tries per hour and cheaper experiments, though the speed claims come from the companies themselves and the early discount is temporary. 🧠 Overview 🧠 AI video has had a patience problem. Many models take minutes to produce a short clip, which makes trial and error slow and costly. FLUX 3 Max is aimed squarely at that bottleneck, pairing a quality ranking near the top of a public leaderboard with generation times measured in seconds. The model was post-trained by fal Research, the research team at the fal platform, on top of Black Forest Labs' FLUX 3 foundation. It is a first version, and the teams say optimization work continues. Speed is the selling point here, so the useful question for our community is whether faster also means good enough to publish. 📜 The Announcement 📜 fal announced FLUX 3 Max on October 19, 2026, and it is available through fal's platform now. It supports text-to-video, image-to-video, and a keyframes mode, with output at 720p. Black Forest Labs also introduced the model on X with the pitch that it is fast enough to try anything. The headline numbers come from fal's press release. A 10-second clip takes about 10.95 seconds, compared with 277.80 seconds for Seedance 2.5 at what fal says is the same reported quality, which is where the 22 times faster claim comes from. Against the original FLUX 3, fal reports an 11.5 times speedup and a 50 point gain on a public ranking called the Artificial Analysis Video Arena, where people vote blind between two clips. That leaderboard lists the model at 1,138 points, close to the 1,139 fal quotes.

I talk a lot about using AI to handle my content, my systems, and the techy work I'd rather not do. Today showed me what all of that is for. Here's what I made today: a painted canvas, two trays of hand-painted seashells, and a plate of crispy breadsticks. AI didn't paint those shells. But AI is part of why I had an afternoon free to paint them. My hot take is that the goal isn't to do more. It's to hand off the draining stuff so you get your hands back for the things that make you feel human. What would you do with an extra afternoon each week if AI took one task off your plate?

A startup research example for Hermes Agent: merge two fictional interview notes and ask for a summary. One customer wants a cheaper version; the other needs more capability. Keeping statements attributed lets the founder see a disagreement instead of receiving a made-up consensus. Keep an older source beside its revision. Identify what would need to change in the draft before calling it current. Would you keep conflicting quotes together or separate the segments? What would that additional check change in your decision?

Love 💕 Serve All The Nation

Igor's post on moving from daily scrambling to a weekly rhythm got me thinking about a habit from my book, The AI-Powered Future of Work. It fits neatly inside a weekly review. Most people ask the wrong question about AI and their job. "Will AI replace me?" is a prediction. The better question is an audit: which of my tasks does AI substitute for, and which does it complement? In the book I call this the Task Exposure Audit. For each recurring task in a typical month, record four things: 1. Its share of your working time 2. Its type: codified, judgement or mixed 3. Its AI status: already done by AI, partly done or not yet 4. Your response: automate, supervise, develop or protect Codified tasks have clear rules, known inputs and checkable outputs. Judgement tasks rely on the kind of experience that cannot be written down as a rule. AI is extraordinarily good at the first and much less reliable at the second. Why bother? Stanford's Digital Economy Lab updated its "Canaries in the Coal Mine?" study in August 2026, using US payroll data. Employment for 22 to 25-year-olds in the most AI-exposed jobs is now 19% below where it would have been had it kept pace with less-exposed peers. The declines are concentrated where AI is used to automate work, not where it complements people. Same technology, opposite outcomes. The book recommends a full audit at least once a year and every time your tools change. Here is a lighter weekly version I would add on top, which goes beyond what the book sets out: Each Friday, ask one question: which task did AI change for me this week? Then decide whether to automate it fully, supervise it or move that saved time towards judgement work you want to grow. Over a month, those small notes become your audit. Over a year, they become a clear picture of where your value is moving. The goal is not to protect every task. It is to make sure your time keeps moving towards the work only you can do. What is one task AI changed for you this week?