Occupy Mars

Genie3 generates videos. We generate 𝟯𝗗 𝘄𝗼𝗿𝗹𝗱𝘀 you can actually use. Launching tomorrow — Tencent #HYWorld 2.0, an engine-ready World Model🚀 This isn't a video. It's a real 3D scene, all generated & editable. One image in. A whole 3D world out. 🔥Open-source tomorrow

Open source skills for App Store Optimization (ASO) and app marketing have crossed over 700⭐️. I have released several new skills. Powered by real-time data. If you want to boost your app's visibility and profits, check it out @appeeky github.com/Eronred/aso-sk…

AI now can visualise your design concept in 1 min

3 ideas you can build with the macOS apps plugin in Codex: 👾 Menu bar game Use build-macos-apps to build a menu bar Tetris. Clicking the menu bar icon should start a game instantly. Make it fully keyboard-playable, add shortcuts to restart & keep the full playfield visible.

introducing Autoreason, a reasoning method inspired by @karpathy's AutoResearch which extends the strategy for subjective domains the paper was co-written with Hermes Agent by @NousResearch, using a research-paper-writing skill developed while writing it paper + results below

how to use autoreason for marketing karpathy's autoresearch works when you have a number to optimize. conversion rate, pass rate, something measurable. but most marketing decisions dont have that whats the right positioning? is this landing page copy good? does this email hook or does it just exist? autoreason solves that. say you need positioning for a product launch 1. you write the initial positioning (or an agent does). this is candidate A 2. a fresh critic agent reviews A and tears it apart. whats generic, what a competitor could say word for word 3. a separate author agent reads that critique and writes candidate B from scratch. no access to A, only the critique 4. a synthesizer reads both A and B and creates a third option AB that pulls from each 5. all three go to a blind judge panel. three fresh agents score unchanged A, synthesis AB, and revision B via borda count. they dont know which is which 6. winner becomes the new A. loop repeats 7. when A survives two rounds without getting replaced, youre done. thats your output every role is a fresh isolated agent. the critic has no channel to the author, the judges never see the critic's reasoning. nothing leaks between rounds so you dont get the usual yes-man feedback loop where one agent just agrees with itself your value prop goes through adversarial review instead of one agent's first take. landing page copy gets tested against agents trying to beat it. brand voice docs get refined through structured debate instead of a single prompt. ad briefs get sharpened round by round, each pass stripping whatever is generic this is different from asking an AI to "make this better" because autoreason builds in disagreement. agent B is competing with agent A, the judges are blind, what survives that is stronger than what comes out of a single conversation now add a knowledge layer. feed the critic and judges real performance data from past campaigns. without that data the loop debates from general copywriting principles. with it the loop debates from your results what goes into the knowledge layer: > past campaign performance. open rates, CTR, conversion by segment, what moved revenue > winning copy and losing copy. the subject lines that hit 38% open rate and the ones that sat at 12% > audience research. what your customers say in reviews, support tickets, reddit threads > competitor positioning. how they describe themselves, where your messaging overlaps, where youre distinct > brand voice rules. the specific words, tone, and patterns that sound like you vs sound like anyone example: you run this on email subject lines. the critic can now say "this reads like the subject lines that averaged 12% open rate for us, not the ones that hit 38%" instead of arguing from gut feel. the whole loop gets anchored to your numbers every campaign result goes back into the knowledge base. the next run has better evidence to work with. the loop gets better the more you use it because the data it argues over is accumulating

Gemini now generates designs for your apps while they’re building! pick between 5 themes, hit apply, and get beautiful designs out of the box. now live in AI Studio

SOMEONE built an AI agent that sells pool installations on autopilot 10 "boring" cash-flowing startup ideas YOU can build on autopilot using the OpenClaw/Hermes etc: 1. find commercial buildings with flat roofs in sunny states and calculate their solar savings, render the install, mail the building owner a custom ROI report. become the broker between building owners and solar installers, take a cut of every deal or charge $$ 2. find shopify stores doing $1M+/yr with no international shipping and build them a localized storefront for their top non-US traffic countries, pitch a rev share to unlock revenue they're leaving on the table 3. find businesses paying for 10+ SaaS tools via public job postings and tech stack data and build a custom "consolidation audit" showing how to cut 40% of their software spend, sell the migration as a service 4. find commercial properties with high water bills using public utility data and render a xeriscaping or rainwater capture plan with projected savings, sell to property management companies at scale 5. find ecom brands running meta ads to products with 1-2 star reviews and build a better version of their top SKU with a manufacturer, launch against them with their own keyword data 6. find small banks and credit unions with websites from 2012 and render a modern site + mobile app with their branding, pitch it as a turnkey digital transformation. they have budget but no one's calling on them 7. find warehouses and industrial spaces near EV corridors with no charging infrastructure and model the revenue from installing chargers, pitch landlords a lease + install package 8. find franchisees posting complaints in public forums about their franchisor's tech and build a shadow operating system (POS, scheduling, inventory) that plugs into their existing franchise, sell directly to franchisees 9. find medical practices billing under specific CPT codes with low reimbursement rates and build an AI billing optimization engine that reclassifies and appeals claims, take a % of recovered revenue 10. find DTC brands with 100k+ instagram followers but no subscription offering and model their repeat purchase data from reviews, build a subscription flow with retention math, pitch it as a done-for-you program with rev share the framework is basically this use AI agents to surface a gap in public data, build the solution before anyone asks for it, and show up with the math already done. you use info arbitrage + OpenClaw is the ultimate wedge i dont know how long this lasts but i do know there's a ton of ways to make $$ using this and i won't hold back i'll be sharing more ideas here, @startupideaspod and @ideabrowser today is a beautiful day to be building

I took Clicky and I made it 5x Faster So I saw that @FarzaTV was using uses Claude's vision to find UI elements on screen, send a screenshot, wait for coordinates back. It works, but it's slow. I replaced that with OmniParser V2 by @Microsoft which is a local Object detection model trained specifically on UI elements. It runs on-device, detects every button, menu, and icon in 400ms, and gives me pixel-precise coordinates. No API call, no latency, no cost. The green highlights you see around the UI elements is the detection overlay and you can see as I am switching the screen it takes no time to detect and highlight which is pretty neat! With Local models improving by the day, Its the right direction for applications like these. Next up: video-synced tutorials where a YouTube tutorial pauses and waits for you to perform each action in the real app. I am not stopping!

Coconote acquired by Quizlet 🎉 $6.7M ARR, 2M+ users, 1B+ views on TikTok – exit after 18 months I got Brett & Zack to share all the sauce. Full vid 👇 00:00 Intro 01:23 Superwall Podcast Overview 02:17 Coco Note Introduction 03:23 Viral Growth Strategies 05:23 TikTok Success Story 07:32 Content Creation Insights 10:14 Creator Talent Development 15:04 Discovering and Nurturing Talent 20:33 App Monetization Tactics 25:25 Building a Lean Team 30:00 Conversion and Retention Strategies 38:39 Strategic Partnerships and Acquisitions 45:02 Future Innovations and Opportunities 51:00 Closing Remarks

Start building with Gemini Embedding 2, our most capable and first fully multimodal embedding model built on the Gemini architecture. Now available in preview via the Gemini API and in Vertex AI.

Markdown is the new Python

@Dimillian When you ask people to leave feedback, at least don't forget about them. It's been 3 days, and I'm still waiting for the link.

The feedbacks is amazing, thanks everyone, and keep it coming!

I’m diligently working on enhancing all the features of the Codex app to make it even better for you. If you have any suggestions or ideas for improvement, please feel free to share them with us here!

@Dimillian Still waiting for the intel version in dm, thx

🚨 First look of Codex Super App As i said in the quoted post use codex to patch the codex app More New Features coming , just use current tech to see into upcoming tech 👀 Btw Spud will be insane, now I can confirm 😁 Credit : @yashjitpal

Codex app would be a lot better at taxes if - clicking a pdf to open it, not in a code editor, worked - or if open in finder worked

@CryptoCabanel @thsottiaux Yeah, insane

@thsottiaux imagine paying for a subscription where the fees are changed during the paid monthly payment... they should throw a class action suit at you but I don't think you would understand

Codex App has achieved take-off internally. I can hear the fans