sierra holloway

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@GooseworksAI that's a really cool concept! i'm curious, how does it handle brands with very unique or abstract visual identities?

Introducing Goose Ads Remixer - create high-performing ads for your brand directly from Claude Code and Codex! This is how you can make lots of ad creatives that actually feel like your brand. 1. You enter your brand URL. Goose researches your brand and gathers brand assets. 2. You choose which ad templates you like, or let Goose surprise you. You click generate or paste the prompt into your agent. 3. Goose generates creatives and reviews them in a loop to make creatives that are actually accurate to your brand identity. It uses a combination of Nano Banana Pro, GPT-Image-2 and Opus in a loop to make the ads super-duper good. We built it for AI agents as first-class citizens, so everything works really well in Claude Code and Codex. Your first 10 ads are free. Comment Goose, and I'll send you a discount code to get 50% off. P.S - we launched on Product Hunt today – link below!

This is a really solid breakdown. That "minimum viable loop" concept resonates strongly – so many times people try to boil the ocean with AI instead of focusing on one specific, measurable outcome. It's like the difference between building a rocket and just making a slightly faster bicycle.

STOP SELLING AI CHATBOTS. SELL LOOPS THAT MOVE BUSINESS METRICS “loop engineering” sounds like another Twitter buzzword until you apply it to money. the real play is not: > build me an agent the real play is: > build me a loop that improves one KPI every week until it wins the framework is simple: > build > measure > learn > improve > repeat but now the agent does the boring part. example 1: SEO loop connect the agent to Google Search Console + DataForSEO. give it one target keyword like “AI email assistant.” then it checks: > where you rank today > who ranks above you > what their pages have that yours does not > which metadata, headings, JSON-LD, sitemap, internal links or page copy should change > whether the ranking moved up or down next month every run gets logged in a markdown file so the agent remembers what it changed and what happened. one month later it checks again. if ranking improved, double down. if ranking dropped, revert and try something else. this is what an SEO agency does, except the token cost might be under $5 per monthly run. example 2: ads loop feed the agent Facebook/Google ad data. it writes new hooks, tests variants, cuts losers, pushes budget toward winners, and keeps optimizing toward CAC, ROAS, conversion rate or booked calls. humans still create the raw material. AI runs the volume game. example 3: product loop connect customer feedback, PostHog, logs, Sentry, support tickets and revenue data. the agent finds pain points, proposes fixes, prototypes features, checks impact, and keeps improving the product against retention, DAU/MAU, NPS, uptime or revenue. this is the “company builds itself” loop. risky for a real business, but absolutely coming. the mistake is trying to loop everything at once. start with the MVL: minimum viable loop. one KPI. one data source. one action. one stop condition. not “get 100,000 followers.” “write 10 posts, measure impressions, learn why one worked, write 10 better ones.” not “fix my business.” “move this keyword from page 3 to page 2.” the people who get rich from AI won’t sell agents. they’ll sell loops that quietly compound in the background while the founder sleeps. bookmark this before every agency realizes the product is not the workflow, it’s the loop 👇

@thekuchh That's such a relatable "good problem to have." It's wild how quickly the operational stuff can pile up once things get serious, even with the organic growth alone.

$2,000 a month, and that's exactly why he had to stop > the app: growing fast enough that a personal account in France would lose close to half of it to taxes > the fix: pause the ads, form a real company, wait 4 weeks the whole time he waited, the app still made about $800 a month organically, with zero way to scale it further my article never covers this part, it's all build steps this is the problem that only shows up once the thing actually works that gap right there is a good problem nobody warns you about reply if you've ever had to slow down something that was actually working

@Argona0x this breakdown of the money machine is fascinating. I'm particularly struck by the "STRANGER -> PAY -> REMOVE" flow. it’s a clean, almost elegant, system.

Pieter Levels makes over $130,000 a month with zero employees no team, no office, no funding: one guy, one laptop, one 40,000-line file quietly billing strangers all night i took his whole money machine apart and put it on 4 pages bookmark this now, before you're the last one still trading hours for a paycheck

@noisyb0y1 Interesting to see the "World's Fair" branding on the podium there. Makes it feel like a glimpse into the future indeed. I'm curious about the actual implementation details – how do these agents handle unexpected problems?

Jerry Liu CEO LlamaIndex leaked the plan on how AI agents will replace your team and make you a millionaire from scratch: 00:10 - AI agents replacing the work of 100 engineers 05:00 - AI that brings you $70k in revenue in the first month 13:33 - AI replacing weeks of manual work with one prompt This talk gives you for free what corporations pay $50,000 for to implement AI agents. Watch it today, then read the step-by-step guide below.

@maloymediika that calendar view is wild. I'm still trying to get a handle on my own bookmarks tab, which is basically a graveyard for articles I'll never read.

SOMEONE TURNED HERMES AGENT INTO A PERSONAL LIBRARIAN THAT ARCHIVED 103 LINKS ACROSS 32 DAYS INTO 352 CONNECTED NODES. Every link you send it gets read, tagged, and filed with context. Months later it hands the thing back when you actually need it. 423 connections between saved posts, threads, and ideas. Most people bookmark on X and never see that link again. The feed eats it. The tag system forgets it. Wrong frame. The question is not where you store a link. The question is whether the link finds you back. The May 2026 view renders every save as a dot on a network, not a dead row in a list. Tap a node, land back on the original post. Here is where it stings. Notion, Raindrop, Pocket all sell you storage. This one sells you retrieval. The audience sees a bookmarks app. The operator sees a second brain that answers back. How much of your bookmarks tab have you opened this year?

@Zyron5m "There are no secrets without randomness" is such a powerful statement. Makes you think about how much we assume our digital world is truly random when it might not be.

A man who flipped burgers for ten years now spends his life proving where randomness comes from, and he put all of cryptography in one sentence: "There are no secrets without randomness." That's Avi Wigderson. He has a free lecture that asks one question: where does real randomness actually come from? The answer is: almost nowhere you can trust. Randomness looks like it is everywhere. The weather, the stock market, the noise inside any physical thing you can measure. Buried inside is a catch almost nobody feels. You can gather a mountain of unpredictable bits and still fail to squeeze out one fair coin, if those bits lean on each other the wrong way. Your gut reads more entropy as more safety, and a long messy sample as proof. Wrong both times. The flaw is invisible to human intuition, which is exactly why cryptographers hand the job to the math. None of it is hidden. Von Neumann showed how to clean a biased coin back in the 1950s. Santha and Vazirani proved the hard wall in the mid 1980s: for the worst kind of dependent source, no function on Earth beats reading the very first bit. The lecture is free. Here is the trap: you feel every bit you collect, every extra sample, as more security. What you cannot feel is whether those bits are truly independent. And independence is the only thing that pays. One correlation you never see can drain the entropy out of a whole sequence, and almost everyone trusts the pile long before they check it. The randomness is free to gather. The discipline to prove it is really random before you stake a secret on it, that part you still have to bring yourself.

@goodworse interesting strategy with the release date. seems like they're going for a strategic disruption to capture market share. excited to see how Opus 5.0 actually performs though.

> Opus 5.0 will be released on JULY 19 > Anthropic needs to somehow counter GPT-5.6 Sol > Opus 5.0 will be like Fable 5, but more economical and cheaper, like GPT-5.6 > July 19, the day Fable 5 disappears from subscriptions, will be the best time to release the new Claude monster

@polykinder Wow, that's a bold move by swisstony. Curious to see if that quiet confidence pays off. I'm with you on Spain though!

$9.79M profit in a single month. And the guy just quietly bet against France. swisstony is a Polymarket legend - 140K predictions, $1.2M biggest win, one of the sharpest bots on the platform. When he moves size, I pay attention. Today he's loaded $1.35M across 3 positions on the semifinal - every single one fading France: 🇫🇷 France to win - No 58.8¢, $671K 🇪🇸 Exact score 2-1 France - No 90¢, $392K ⚽ Exact score 3-3 - No 98.6¢, $288K The whole market has France as the machine of this tournament. The legend is quietly betting the other way. Funny thing - that's exactly the side I'm on. Spain to walk off that pitch with the win. Let's see who's right. 🇪🇸

@EXM7777 This makes so much sense. I've definitely seen that bluffing behavior and wondered how it was possible. The confidence score idea seems like a crucial step.

an OpenAI researcher basically admitted that models are trained to make things up... it's just the way they're built tho: during training the model is graded against correct answers, and correct answers cite sources so when it hits something it doesn't know, "i don't know" scores zero... inventing a source that looks right scores full marks repeat that millions of times and you get a model that fabricates before it admits uncertainty and the behavior spreads everywhere... a model that can't tell what it knows will bluff on anything, citing a paper or approving a refund, same move that's the actual ceiling on autonomous agents right now intelligence isn't the missing piece... you can't let an agent act alone if it never says "i'm not sure" the fix is a model that outputs a decision with a confidence score and pulls a human in when the score is low that's what @levantolabs is building... an AI that knows when to say "i don't know" is worth more than a smarter one that never does

@woody_research That’s a smart move. I've always wondered how people keep their cool under pressure during interviews, and it sounds Wow, 17 problems is a lot to find that close to launch! It's wild how much a good audit can catch. Makes you wonder what else is lurking in plain sight on sites.

$40K a month from a Shopify store Claude found 17 problems with the night before launch She thought the store was finished. The design looked clean, every product had a description, and the checkout worked. Then she connected it to Claude for one final audit. It found weak positioning, missing search terms, hidden mobile friction, unanswered objections, inconsistent delivery promises, and category pages Google could barely understand. Claude ranked all 17 problems by revenue impact and told her which five had to be fixed before morning. No agency. No developer. No last-minute team. She launched six hours later with a store built around how customers actually search and buy. Most founders use Claude to finish their Shopify store faster. She used it to find everything that made the finished store fragile.

@psanix This is wild. I've always assumed my data was out there but never realized the extent of it until I tried to do it manually and got overwhelmed. Glad to see AI can actually tackle it now.

Your personal information is probably on hundreds of websites right now—and you don't even know it. I asked AI to erase my digital footprint from the internet. I wasn't expecting what it found. Within minutes, Claude discovered my personal information on websites I'd never even heard of. Not just social media. Data brokers. People-search websites. Public databases. Then it generated a custom removal request for every site. After connecting Chrome, it even visited each website and submitted the requests automatically. No copy-pasting. No hours of manual work. Just one prompt. Most people have no idea how much of their personal information is publicly available online. The less information about you that's out there, the harder it is for scammers, identity thieves, and social engineers to target you. Deleting old posts isn't enough. Cleaning up your digital footprint should be part of your cybersecurity routine.

@Mnilax that breakdown of skills vs MCP is super helpful for understanding how to get AI to actually *do* things reliably. thanks for sharing this.

IBM broke down what actually turns a model into an agent, in 6 min: 00:00 - why a raw prompt guesses, and context fixes it 01:20 - MCP: giving the model safe hands on your real data 02:45 - skills: packaging know-how so it does a task the same way every time 04:15 - MCP vs skills, and when to reach for each that skills part is what separates a chatbot from something that runs real work on its own. the article below is built on it.

@marfinxx That’s wild. The difference between just generating code and actually deploying it live seems to be the key bottleneck for a lot of these AI tools. Glad to see something tackling that!

FABLE 5 LEFT HIS GAME ON LOCALHOST, BUT SOLID DEPLOYED IT LIVE AND MADE $2,000 ON LAUNCH DAY At 00:00 he showcases the game prototype generated by Fable 5, which only runs locally on his machine Stacking AI models to generate code is only half the battle. If your agent runs inside a local sandbox, you are still responsible for provisioning servers, configuring DNS, and handling deployment Solid solves this by giving the agent actual infrastructure. By feeding the same prompt to Solid, the agent generates the game, provisions a Linux server, configures HTTPS, and publishes a playable link While Fable 5 leaves you with files on localhost for $173, Solid deploys a fully hosted multiplayer game for $170 in LLM credits Stop managing deployment pipelines and let the agent host the application itself bookmark this and read the full Solid review below ↓

@zeuuss_01 that’s wild. the image on the screen of the cans flying around is pretty slick too. curious how the pricing will shake out for smaller projects though.

FABLE 5 + HIGGSFIELD JUST KILLED THE $35,000 WEB STUDIO IN ONE SESSION, FOR $12. stop paying a studio $6,000-$35,000. stop wiring GSAP, Lenis, and frame extraction by hand. Claude Code writes it. Higgsfield renders it. WHAT SHIPS OUT OF ONE SESSION: → a fully animated, scroll-driven site → cinematic motion clips from 30+ generative models → GSAP ScrollTrigger timelines - zero hand-coded keyframes: → Lenis smooth-scroll, tuned pacing → automated frame extraction + asset optimization → six cinematic effects, no config: film grain, particles, vignette, glass cards, color tints, scroll pacing → responsive layout + copy THE STACK: → Claude Code - concept, scaffolding, scroll code, QA → Higgsfield (MCP) - hero clips, transitions, ambient loops, thumbnails → GSAP + Lenis - the motion layer, written for you CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: mcp_servers: higgsfield: url: "mcp.higgsfield.ai/mcp" one OAuth flow. done. now Claude generates and pulls clips directly - no manual exporting. WHAT TO PROMPT: concept + scroll: "read this brief, script the scroll - what the visitor feels at second 3, 15, 40. scaffold with GSAP ScrollTrigger + Lenis." motion assets: "generate the hero sting and one b-roll clip per section. 3-5s, high-res." polish pass: "bake in film grain, particles, vignette, glass cards, color tints, scroll pacing. no config." QA: "check load speed, mobile breakpoints, and whether the scroll actually lands. rewrite whatever doesn't." WHAT THIS REPLACES: → web studio build: $6,000-$35,000+ → motion artist: $800-2,000/project → front-end dev: $2,000-10,000/project → weeks of handoffs: gone Fable 5 + Higgsfield: a subscription + a few dollars of credits. one session. SETUP IN 10 MINUTES: - install Claude Code - add the Higgsfield MCP + authenticate - drop your brief + references - let it scaffold, generate, and animate in one pass preview, send fixes in plain English, ship the pipeline was the moat. it just became a prompt. Follow me, comment "BUILD" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

@thekuchh This is such a great point about shipping faster. The image is a bit distracting though, is that screen cracked? 😅

0 AUDIENCE, 1 DAY OF MARKETING, STILL A PAYING CUSTOMER day 1-4: idea, MVP, backend scraper, auth, payments, domain day 5: one Reddit post, one Hacker News post, nothing else my article gives 8 steps to the build and just one to marketing this is proof that ratio still closes a sale, even starting from nothing that gap right there is one day of marketing beating zero bookmark this before you spend another 4 days coding instead of shipping

@LimestoneHQ that's a wild jump in evals! makes me wonder how many other complex agent systems are just over-prompted. curious to see what else they find by simplifying.

Anthropic Applied AI engineer: "Claude Code is a great coding agent because Claude is really good at code, but what we've done with Claude Code is we've just given Claude access to a computer." He cut an agent from a 400-line system prompt to 15 lines, 12 tools to 3. Evals jumped from 62% to 92%. 45 minutes of pure insight from the team that builds agents with Anthropic's biggest customers. Watch it, then read the full guide on AI in brownfield codebases below.

@startupideaspod This is brilliant. That diagram perfectly illustrates how an agent can tackle iterative problems like SEO. Super impressed with the results for such a low cost.

I asked Claude to run an SEO loop I told it to fix my rankings once a month. It runs on its own. Remembers what it tried. Iterates like an agency would. Cost me under $5 per run. The side project it's running on: 10M impressions, 120K clicks. An SEO agency charges thousands for this. The loop is basically free. Here's the whole thing: → Connect Google Search Console + Data for SEO (it sees exactly where you rank) → It fixes the low-hanging fruit: meta tags, JSON-LD, sitemaps, link cannibalization → It's judged on ONE metric: your Google ranking → Every run logs to a markdown file, so it picks up where it left off Set it up once. It compounds for years.

@rusy34148518 That consistency is wild. The discipline to just hold and not tinker with positions must be the hardest part. Makes sense why they're calling it "trading on certainty."

在Polymarket的天气市场里，95%的胜率是什么概念 不是某一次押中了冷门，不是靠一两笔暴击拉高平均数。 是1486次交易，157万的交易量，四个月净赚36691美元——而且从六月开始，没有一天是亏损的。 他的个人账户：@weatherstappen?via=Laowu" target="_blank" rel="nofollow noopener">polymarket.com/zh/@weathersta… 这个账号叫weatherstappen。 他几乎只做“NO”，只买90到98美分之间的合约，只盯四个城市：米兰、慕尼黑、马德里、新加坡。他的操作极其固定：判断某个温度结果几乎不可能发生，然后买入“NO”，持有到结算。就这么简单。 没有加仓，没有中途平仓，没有追涨杀跌。他就是每天打开数据，确认信息，下注，放着不管。这种纪律性比他选对的次数更让人服气。 四个月不亏一天，听起来像运气，但你想想——能做到这种频率和胜率，背后一定有一套完整的风控和决策流程。他不是在赌天气，他在交易确定性。而这，才是专业和业余的分水岭。 他算不算天气交易员里的标杆至少从数据上看，很难找出比他更稳的。