The problem with social media right now, summarized: 1) Rage bait works 2) Bots are kinda everywhere 3) AI slop is everywhere 4) It doesn't feel super social anymore 5) Extremely addictive 6) Pressure to go viral 7) You compare yourself without noticing 8) Hard to tell what’s real 9) You forget why you opened the app 10) Everything is filtered Yet, it’s still one of the greatest tools humanity has for building, connecting, and finding your people! It's just far from perfect.

openai just let every business claim a profile inside chatgpt. my honest reactions: 1/ this could be the google my business moment for ai, and a lot of agencies made a great living (millions!) in the 2010s just claiming and fixing listings for local businesses 2/ ads can now click through to a conversation with your agent instead of your website (interesting) 3/ every business agent runs on context the company writes, and most brands are going to be bad at it 4/ your product feed is your new seo. boring, and it matters more than your blog now in some ways if you think about it 5/ the dashboard tells you how often chatgpt mentions you but HIDES the prompts and your competitors, so you still can’t see why you’re winning or losing 6/ discovery topics shows what chatgpt already cites you for, which is basically a free content calendar 7/ agents are only open to us ecommerce and online services right now. plumbers, dentists etc are next, and so lots of opportunity to help main street here 8/ businesses can’t see the conversations, so nobody knows which chats turn into sales yet 9/ i’m not sure people want to talk to a brand’s agent. i think they’ll want their own agent to talk to it for them 10/ 1.2 billion people use chatgpt every week. i’d go claim my profile before finishing this post if you can

my raw thoughts on fable 5.2 after testing it (vibe manufacturing, marketing engineering, AI native services) it might be the most thoughtful model i've ever used here are the 12 prompts i'd use to make money and become more productive with claude fable 5.2

i've been testing gemini 4 carbon for 7 days. some early observations: 1/ it stopped cheating mostly. successful shortcuts on terminal-bench went to 0.0% (you can finally leave an agent alone mostly) 2/ prompt injection attack success rate was 0.0% on gray swan's benchmark. that's the number that lets you hand an agent your inbox 3/ it doesn't quit. google's own teams have watched it work on one problem for days and push through dead ends (love to see it) 4/ when it's stuck, it tells you what it still needs instead of making something up. unsupported claims got cut in half vs argon 5/ it does what you asked. not more, not less (overreach down 26%, under-delivering down 29%) 6/ it writes secure code at 2x the rate of argon. vibe coded apps keep getting hacked, so this one matters 7/ it found 88% of the vulnerabilities on sec-bench pro. every small company is about to have a security team it never hired 8/ $2 in and $10 out per million tokens to start, with cached input 95% off. agents that run for days just got cheap enough to actually run for days 9/ google shipped this two weeks after argon. pretty amazing pace 10/ cyber defenders got it first. very interesting maybe i should do a full carbon vs fable 5.2 comparison on @startupideaspod. crazy ai launch week and it's only tuesday. i wonder what openai has in store. Also the week with the whole Jensen affair. what a week in ai land

Scientists copied a fruit fly’s entire brain into a computer and connected it to a virtual body Nobody programmed it to do anything, but it started tasting sugar and cleaning itself anyway It’s a long way from a human brain, but it’s hard not to think about where this goes!

quick observations from the claude fable and mythos 5.2 launch: 1/ it went from doing 21% of real freelance work to 41% in one release, so agencies are about to start charging per deliverable instead of per hour! 2/ it can work 10-12 hours on its own, which basically gives every small business a night shift (might as well use it if you know what you want to build) 3/ over 90% of anthropic's own code is now written by claude, and so i think the solo $1b founder is coming soon 4/ one engineer ran 1000+ subagents for 8 days and got an operating system that boots, so the ancient software running hospitals and airlines is finally rebuildable (lots of opportunity here on rebuilding) 5/ it designed 3 working electronic devices from a single prompt, which makes 1 person hardware companies a real thing 6/ it now says "i don't know" instead of guessing, and that's what law, medicine and finance have been waiting for (this has been the biggest thorn for these industries, hard to trust guessing) 7/ 1000 agents formalized fermat's last theorem in 30 hours, so software that's mathematically proven not to break is coming for payments and medical devices 8/ mythos is only for verified orgs, which means someone is going to build a business selling what only they have access to 9/ the models are getting better at noticing when they're being tested, so testing ai out in the real world is about to become its own industry i love new model day

went on greg's pod to talk about: - All marketing is now code and tokens. One agent replaces the designer, copywriter, UGC creator, editor and motion designer. - Long-form video editing crossed the line this year. Tesseract one-shot edits a 90-minute podcast: silence, takes, framing, active speaker. - Your job is conductor, not operator. Get out of the agent's way. - Think jobs-to-be-done, not employees. Turn what the top 1% media buyer does into code with a thinking loop on live data. - "God in a box" running a Facebook ads account failed at multiple companies. - An agent needs three things: data, access, tools. - Data: a pipeline (Airbyte) into a warehouse (ClickHouse). Never read from ad APIs directly. - Direct API reads overflow context, cause hallucination, and push the agent to build its own local DB full of dupes. - One warehouse joins ads to GA4, HubSpot, PostHog and payments. You see ad-level revenue and LTV, not just conversions. - Access: Marketing API tokens for Meta and Google, write-only. All reads come from the warehouse. - Agents act like a drunk uncle at the API. Thousands of requests get accounts banned, not "using AI." - Tools: kie, fal, OpenRouter, GetLeads, Apollo. Without them the agent cannot do the middle work. - Add a swipe warehouse. Thousands of examples, not a swipe file. Sublime or Cosmos to store it. - Feed the creative-director agent Ogilvy plus 100 hours of top ad podcasts. - Static ads flow: brand URL → site style → Ad Library → Reddit pains and outcomes → 5 concepts → GPT Image 2.5 → upload to Meta as a new ad set. - Brand style guide is a markdown file. Fonts and colors. Swap it for the extracted one. - Models swap in one line. GPT Image 2.5 is best seen so far, especially for targeted edits that keep the original image. - Regulated clients: a vision model gates each creative against a rules list and loops. Two or three passes. - UGC flow: script → fal voice clone → MP3 first. Never send the script straight to Seedance. - Seedance gets MP3 + reference image + script together. That walled garden fixes mumbled words and random faces. - Render at 480p, then upscale and grain with ffmpeg. 1080p native costs about $14. Full ad lands near $5, heading to $3. - Word-level transcript vs script. A wrong word gets spliced from the MP3 at its timestamp. No re-render. - Plastic look fix: start from a real creator photo and remix age, background, ethnicity. - Captions and motion graphics come from Tesseract off the same transcript. - Next build: a vision model scores the motion graphics frame by frame and gates on 8 or 9 out of 10. - AI avatars sometimes beat the human on the same script. Nobody knows why. - Andromeda matches good creative to the buyer. Your only job is creative velocity. - Clients go from 10 ads a month to cohorts of 30. - Software pattern: an ad gets 2 cheap demos, Meta finds the two buyers and stops spending. Feed it more. - Store the Seedance JSON for every ad. Join it to Meta performance. At 1,000 ads you have cellular-level data on what works. - The whole loop runs in a hosted Claude Code harness in the cloud. 100 creatives is the same process. - Human creators layer in via Tribe or Sideshift APIs. About $40 a video plus a spend bonus. The agent is the creator manager. - Motion graphics go over the human clip the same way as the avatar clip. - A human gate can sit before anything goes live. That is where taste enters the workflow. Watch now

now that apple put xcode inside claude code, here’s how i’d find, build and grow an iOS app with AI agents: find it - open appfigures or sensor tower and sort utilities by revenue - filter for apps with a rating under 3.5 - bonus points if the top app hasn't shipped an update in 6+ months - check the keywords they rank for in appfigures. low difficulty + high revenue is the sweet spot - scroll the "you might also like" row on the top app's page. every app there is another market to check - or find a startup idea on based on social sentiment validate it - use facundoolano/app-store-scraper to pull every review for the top 20 - give them to an agent: "cluster the 1 and 2 star reviews into the top 5 complaints and quote the users" - pick the app where the complaints are about the product, not the price - if people complain about the price but keep paying, that's demand. if they complain about bugs, that's your opening - search reddit for "alternative to [app]". every thread is a list of people waiting for you build it - claude code + apple's new xcode plugin to build, preview and fix - cameroncooke/XcodeBuildMCP so the agent can build, test and run the simulator on its own - joshuayoes/ios-simulator-mcp so it can tap, swipe and screenshot like a user - write a skill with the top 5 complaints so every feature gets checked against them - apple's foundation models for on-device ai, so your inference cost is $0 - app intents so siri and personal agents can use your app too - add an "import from [competitor]" button. make switching take 10 seconds test it - maestro tests every flow the reviewers complained about - the agent runs them on every screen size before each build - if the competitor's worst flow takes 6 taps, yours takes 2 - sentry for crashes, telemetrydeck for analytics - testflight it to 20 people from those reddit threads before launch monetize it - revenuecat for subscriptions and mrr - superwall to test paywalls without shipping a new build - paywall right after the first win, never on launch - annual as the default with a 3-day trial, weekly as the anchor - turn on win-back offers for everyone who cancels rank it - your screenshots answer each complaint directly. "no ads." "works offline." "cancel anytime." - fastlane snapshot + frameit regenerates every screenshot automatically - a/b test them with app store connect's product page optimization - have the agent localize your listing into 10 languages. most competitors only rank in english - ask for a review right after a win with apple's built-in review prompt, never after an error grow it - find 10 creators in your niche on tiktok one. small monthly base + a bonus for every 100k views - give each one a fresh tiktok account that only posts about your niche - apify's tiktok scraper pulls the top 100 hooks in your niche every week. that's your hook bank - every creator tests 3 new hooks a day in capcut. anything that loses people in the first 3 seconds gets cut - arcads or heygen for ai ugc when you need 50 variations of a winner - postiz to schedule everything across tiktok, reels and shorts - faceless slideshow accounts posting 3x a day: problem, screenshot, result - when an organic video takes off, put money behind it with tiktok spark ads instead of making new ads - a unique apple offer code for every creator so you know exactly who drove each subscriber - a quiz landing page on framer that ends in "download the app." web-to-app funnels convert better than cold traffic - appsflyer to track which video, creator and ad actually turned into revenue - apple ads on the competitor's name i could do a whole episode on @startupideaspod if people are interested. i will never gatekeep anything im learning in real-time. welcome to the golden age of ios apps, because now anyone can build one

I think the coolest part of having kids is reliving your own childhood in a different way. You remember what it felt like to be scared of the dark, to wait all year for a birthday or to think a cardboard box was the best toy in the world. The difference is that now you're on the other side of it. And you get to be the person who makes those moments feel safe and magical. You see the world through someone who's seeing it for the first time, and it makes you curious again. I didn't expect parenting to make me feel younger on one hand, and older on the other but some days it does. Highly recommend :)

the story of how stripe started is pretty fascinating (now it's a $159B company) john and patrick were 2 brothers from small town ireland, only 19 and 21 when they started stripe! it's a good reminder that you don't need to be older or super experienced to build something big

running a company nowadays feels kinda dystopian my team is 50+ people in 12 countries, but i talk to my agents more than my human teammates - voice note to grokbot - mattpocock/skills /grill-me so the agent interrogates the idea before anything gets built - spec drops into linear through the linear mcp - exa + firecrawl agent reads every competitor site, pricing page and changelog - to find ideas and trends - apify pulls g2, reddit and app store reviews - prompt: "find the 10 complaints that show up most and quote the users" - context7 so the agent reads the latest docs before it touches any api the design - claude code + paper mcp - 4 versions of every screen before a single line of code - pick pieces from 2, it merges them. diverge then converge - frontend-design skill from anthropic/skills + our own design system skill - cleanshot screenshots of any ui i like go straight in with "make it feel like this" the build - pingdotgg/t3code as the control surface for every agent, from my phone too - stablyai/orca runs a fleet of agents in parallel, each in its own git worktree - graphify maps the whole codebase into a knowledge graph so agents navigate instead of guess - headroom compresses tool outputs before they hit the model, so tokens go 20-95% further - lsp plugins so the agent sees types and errors like an ide does - security guidance hook flags risky edits before they land - supabase mcp for the database, stripe for billing - github mcp opens the pr, vercel spins up a preview - loops run overnight, i check what shipped over coffee the user testing - lightpanda as the headless browser, way lighter than chromium so you can run hundreds at once - agents click every flow on the preview like a first-time user - one persona is impatient, one is confused, one is on a phone - playwright + chrome devtools mcp for regression tests the bugs - sentry mcp catches the error - agent reads the stack trace and opens a fix pr in under 10 minutes - human just approves the data - posthog mcp cron at 7am on yesterday's funnel - any step that drops more than 10%, the agent watches 20 session replays and tells me why - stripe mcp pulls every cancellation reason into one doc every monday the agents' tools - orgo so every agent gets its own computer - agentmail so every agent gets its own inbox - agentphone so i can call them from the car - composio connect-apps for every other connector - akitaonrails/ai-memory, a git-backed markdown wiki so they remember what we decided last week the skills - wshobson/agents so skills work across claude code, codex and opencode - my writing voice - our design system - how we write specs - skill creator so the agents write new skills themselves some helpful tricks - one agent per platform. the x agent never touches email - cheap model for triage, opus for anything that ships - every output gets scored 1-10 by an llm judge. under 8 goes back - every agent writes a 5-line log at night so i can read the whole company in 2 minutes - every agent mistake becomes a new skill, so it only happens once the team - humans get version 5, not my voice note which they are happy about lol - their job is creative feedback, relationships and telling the agents what they got wrong a human pm reviews the funnel once a week. this reviews it every morning running a company is now voice notes and inference

The cobra effect is one of the more interesting phenomena in startups. Once you see it, you start noticing it everywhere, in your team, your metrics and now your AI agents. It's basically when you reward people for hitting a number, and they find a way to hit it that makes the real problem worse. The name "Cobra Effect" comes from a story about Delhi under British rule. There were too many cobras in the city, so the government offered a reward for every dead cobra people brought in. Instead of hunting them, people started breeding cobras so they could kill them and collect the money! When the government found out and ended the program, the breeders let their snakes go, and Delhi ended up with more cobras than before! I'm noticing that AI agents are making this more common. Basically, an agent will chase whatever number you give it, all day, without asking why. Tell it to make the tests pass and it might rewrite the tests. Tell it to close support tickets faster and it might close them without solving anything. It happens. The best startups have already figured out the fix with people. Airbnb tracks bookings and host cancellations together. HubSpot takes back sales commission when a customer churns early. The video breaks down how they did it, and it's the same playbook you'll want for your agents.