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New drop: Options Market Depth V2 🔥 • Bookmap-style depth + trade flow • Stack charts horizontally, vertically, or grid. • Built on TradingView lightweight-charts Open source & free → code in next post. #Schwab

Nick

1,582 subscribers

11,255 просмотров • 1 год назад •via X (Twitter)

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New Llama.cpp UI is a blessing for the local AI world 🌎 - Blazing fast, beautiful, and private (ofc) - Use 150,000+ GGUF models in a super slick UI - Drop in PDFs, images, or text documents - Branch and edit conversations anytime - Parallel chats and image processing - Math and code rendering - Constrained generation with JSON schema supported Easy setup + open-source + community-built 🔥

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🔥 Phoenix is officially live on Solaris AI Flow. You can now trade Phoenix perps inside a Solaris AI workflow. No code. Drop a node onto the canvas, pick an operation, and wire it to anything: AI signals, price feeds, schedules, alerts. The full order-book DEX from Ellipsis Labs, now programmable. Built so you can trade with confidence: ✦ Paper mode is on by default. Every order is checked and simulated against the live order book, real depth and real slippage, but nothing is signed or broadcast. ✦ Paper behaves exactly like Live. If an order would be rejected on-chain, it is rejected in simulation too. No false fills, no surprises. ✦ Going Live is one switch, and it asks for confirmation before any real funds move. Build it. Test it. Trade it. A full perps strategy, proven on paper before a single dollar moves. 30 operations in one node: ✦ Read live markets, order book depth, candles, and funding rates ✦ Track your positions, collateral, and PnL, realized and unrealized ✦ Place limit, market, and stop-loss orders, plus conditional triggers ✦ Cancel orders, manage margin, and move collateral, all from the workflow No scripts. No backend. No terminal to babysit. Just a workflow that trades. You can try it for free. Demo + Link down below 👇

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Solaris AI

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Finley AI V2: The Ultimate Raiding Upgrade is Here! Raiding will never be the same. Finley AI transformed engagement with AI-powered, dynamic comment suggestions—and V2 takes it even further! 🔥 What’s New in Finley AI V2? 🔥 ✅ Full Customization – Adjust tone, humor, energy levels, and variation for replies that fit your style. ✅ Predefined Templates – Choose from Recommended, Professional, or Meme options. ✅ Precision Control – Decide on emojis, hashtags, cashtags, and X handles, or randomize for a natural mix. ✅ More Human-Like Replies – Enable typos and set a desired length range for realistic interactions. ✅ Unmatched Flexibility – No two comments look the same, keeping engagement fresh and authentic. With Finley AI V2, every raid is sharper, more strategic, and algorithm-friendly. Say goodbye to repetitive, shadowban-prone comments—this is the future of high-impact raiding. Best of all? It’s FREE for all RaidSharksBot communities! The next level of raiding is here—are you ready?

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RaidSharks

34,776 просмотров • 1 год назад

🚀 Update Next Scene V2 only 10 days after last version, now live on Hugging Face 👉 🎬 A LoRA made for Qwen Image Edit 2509 that lets you create seamless cinematic “next shots” — keeping the same characters, lighting, and mood. I trained this new version on thousands of paired cinematic shots to make scene transitions smoother, more emotional, and real. 🧠 What’s new: • Much stronger consistency across shots • Better lighting and character preservation • Smoother transitions and framing logic • No more black bar artifacts Built for storytellers using ComfyUI or any diffusers pipeline. Just use “Next Scene:” and describe what happens next , the model keeps everything coherent. 🧩 Try it directly in ComfyUI, or check the thread to launch it on fal . Open-source, no restrictions, made for filmmakers, animators, and dreamers. ComfyUI #AIcinema #LoRA #Flux #Qwen #ComfyUI #AIart #GenerativeVideo you can test on comfyui or to try on you can go here : and use my lora link : start your prompt with "Next Scene:" and lets go !!

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Lovis Odin

43,276 просмотров • 9 месяцев назад

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3 STOCKS A DAY

64,414 просмотров • 1 год назад

I just built a Claude Cowork skill that turns your Google Ads data into a visual performance dashboard in 60 seconds 🤯 One prompt → campaign breakdowns, CPA trends, spend vs conversions charts, and hourly conversion patterns, all rendered as an interactive HTML dashboard you open in Chrome. All inside Claude Cowork. Perfect for DTC brands and agencies who are pulling Google Ads data into spreadsheets every week, manually building charts, and spending an hour formatting a report that's outdated by the time you send it. If you're managing Google Ads and your weekly reporting workflow looks like this — export a CSV, open Google Sheets, build a pivot table, copy the numbers into a slide deck, manually create charts, format everything, realize you forgot a campaign, start over ... This skill does the whole thing in one prompt: → Connects to your live Google Ads data via MCP → Pulls spend, conversions, CPA, ROAS, CTR across every campaign → Builds an interactive HTML dashboard → Summary cards at the top: total spend, total conversions, avg CPA, avg ROAS → Bar chart comparing spend vs conversions by campaign → CPA trend line over the last 30 days → Campaign table ranked by performance, color-coded green/yellow/red → Opens in Chrome: hover over charts, compare campaigns, screenshot for your team No spreadsheets. No manual chart building. No hour-long formatting sessions. What you get: → A visual dashboard from live data in under 60 seconds → Campaign performance you can actually see, not just read in a table → CPA trends that show you where things are heading, not just where they are → A dashboard you can screenshot and drop into Slack, a client report, or a team standup → Reusable — run it weekly and the data updates automatically One prompt. Live data. A finished dashboard you open in your browser. I put together a playbook with the full skill file, the setup, and the exact prompts to customize the dashboard for your account. Want it for free? > Like this post > Comment "DASH" And I'll send it over (must be following so I can DM)

I just built a Claude Cowork skill that turns your Google Ads data into a visual performance dashboard in 60 seconds 🤯 One prompt → campaign breakdowns, CPA trends, spend vs conversions charts, and hourly conversion patterns, all rendered as an interactive HTML dashboard you open in Chrome. All inside Claude Cowork. Perfect for DTC brands and agencies who are pulling Google Ads data into spreadsheets every week, manually building charts, and spending an hour formatting a report that's outdated by the time you send it. If you're managing Google Ads and your weekly reporting workflow looks like this — export a CSV, open Google Sheets, build a pivot table, copy the numbers into a slide deck, manually create charts, format everything, realize you forgot a campaign, start over ... This skill does the whole thing in one prompt: → Connects to your live Google Ads data via MCP → Pulls spend, conversions, CPA, ROAS, CTR across every campaign → Builds an interactive HTML dashboard → Summary cards at the top: total spend, total conversions, avg CPA, avg ROAS → Bar chart comparing spend vs conversions by campaign → CPA trend line over the last 30 days → Campaign table ranked by performance, color-coded green/yellow/red → Opens in Chrome: hover over charts, compare campaigns, screenshot for your team No spreadsheets. No manual chart building. No hour-long formatting sessions. What you get: → A visual dashboard from live data in under 60 seconds → Campaign performance you can actually see, not just read in a table → CPA trends that show you where things are heading, not just where they are → A dashboard you can screenshot and drop into Slack, a client report, or a team standup → Reusable — run it weekly and the data updates automatically One prompt. Live data. A finished dashboard you open in your browser. I put together a playbook with the full skill file, the setup, and the exact prompts to customize the dashboard for your account. Want it for free? > Like this post > Comment "DASH" And I'll send it over (must be following so I can DM)

Mike Futia

38,811 просмотров • 4 месяцев назад

$Was waiting in line at the post office. Guy next to me saw my screen. "What are you tracking?" "Markets." "So like stocks?" "No. Open-source. Anyone can see how it works." He laughed. "So you're just staring at code?" I didn't argue. I turned the screen. One wallet returned 74% in 7 weeks. Another moved \$5.8M in volume. One more took $400 to $4,200. He stopped scrolling his phone. "That's... real?" Exactly. Then I showed him the repos. First: 86M+ trades. Every position ever taken on Polymarket. Free download. Second: Market making bot. Both sides of the book. Runs through Google Sheets. Third: ML + heuristics. Flagged that $400 → $4,200 before it moved. He pulled out his phone. Asked for the links. His exact words: "My brother has been looking for this." Profile: Most people think you need a finance degree or a fund. You don't. You just need to read the code and start.$

Was waiting in line at the post office. Guy next to me saw my screen. "What are you tracking?" "Markets." "So like stocks?" "No. Open-source. Anyone can see how it works." He laughed. "So you're just staring at code?" I didn't argue. I turned the screen. One wallet returned 74% in 7 weeks. Another moved \$5.8M in volume. One more took $400 to $4,200. He stopped scrolling his phone. "That's... real?" Exactly. Then I showed him the repos. First: 86M+ trades. Every position ever taken on Polymarket. Free download. Second: Market making bot. Both sides of the book. Runs through Google Sheets. Third: ML + heuristics. Flagged that $400 → $4,200 before it moved. He pulled out his phone. Asked for the links. His exact words: "My brother has been looking for this." Profile: Most people think you need a finance degree or a fund. You don't. You just need to read the code and start.

Lunar

13,003 просмотров • 2 месяцев назад

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 просмотров • 5 месяцев назад

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

Jonathan Stephens

17,712 просмотров • 7 месяцев назад

Introducing the Buy-Side Equity Research SOP on xBubble. State a ticker. Get a research memo built to the standard of an internal buy-side note. Thesis first, every number sourced to the original filing. One request in. A full equity deep dive out. What comes back isn't a news recap. It's the structure a real analyst writes: ✅ Where the company sits in its value chain ✅ Competitive map, edge, and key risks ✅ Filing-level financials + management read ✅ SOTP valuation, Bull / Base / Bear per share ✅ The catalysts to watch over the next 3 to 6 months Every claim is traceable. Each data point carries its original source and access date: SEC filings (10-K, 10-Q, 8-K), official IR decks, earnings call transcripts. You can check the work. That's the point. Here's what makes this more than one good report. The SOP isn't fixed. Bubble Engine builds it, tests it, and keeps generating stronger ones. Need a version tuned to a sector, a market, or your own thesis style? It generates that too. You don't get a template. You get a research engine. Buy-side research used to take 15 to 20 hours per company. Now it's one request. The edge was never about access. It was about depth. xBubble puts the depth on your side. Work mode → Buy-Side Equity Research SOP.

Introducing the Buy-Side Equity Research SOP on xBubble. State a ticker. Get a research memo built to the standard of an internal buy-side note. Thesis first, every number sourced to the original filing. One request in. A full equity deep dive out. What comes back isn't a news recap. It's the structure a real analyst writes: ✅ Where the company sits in its value chain ✅ Competitive map, edge, and key risks ✅ Filing-level financials + management read ✅ SOTP valuation, Bull / Base / Bear per share ✅ The catalysts to watch over the next 3 to 6 months Every claim is traceable. Each data point carries its original source and access date: SEC filings (10-K, 10-Q, 8-K), official IR decks, earnings call transcripts. You can check the work. That's the point. Here's what makes this more than one good report. The SOP isn't fixed. Bubble Engine builds it, tests it, and keeps generating stronger ones. Need a version tuned to a sector, a market, or your own thesis style? It generates that too. You don't get a template. You get a research engine. Buy-side research used to take 15 to 20 hours per company. Now it's one request. The edge was never about access. It was about depth. xBubble puts the depth on your side. Work mode → Buy-Side Equity Research SOP.

DAPPOS

427,279 просмотров • 1 месяц назад

🔥 MASS REPORT - Saturday🔥 17 minutes to our date with 9star Youtube Channel [21:00 BKK] I hope you’re all hanging in there. It’s Saturday, our day, the next manipulations by Siripun detected. As I wrote in my previous post, we’re focusing on views-there’s no option for this snake to get anything for free, only through hard work and she giving none, Please don’t engage with that post about live, or about GFD. Don’t drive engagement where it’s lacking, and we don’t want to spread something that tarnishes other people’s image. so as always you know what to do 🔥I invite you to join our activities.🔥 💥 MASS REPORT 💥 Report drop here: new templates below NINESTAR MANIPULATES TRUTH #StandAgainstNinestar #BoycottNineStarSeries #FayeYoko #ฝ้ายโยโกะ

🔥 MASS REPORT - Saturday🔥 17 minutes to our date with 9star Youtube Channel [21:00 BKK] I hope you’re all hanging in there. It’s Saturday, our day, the next manipulations by Siripun detected. As I wrote in my previous post, we’re focusing on views-there’s no option for this snake to get anything for free, only through hard work and she giving none, Please don’t engage with that post about live, or about GFD. Don’t drive engagement where it’s lacking, and we don’t want to spread something that tarnishes other people’s image. so as always you know what to do 🔥I invite you to join our activities.🔥 💥 MASS REPORT 💥 Report drop here: new templates below NINESTAR MANIPULATES TRUTH #StandAgainstNinestar #BoycottNineStarSeries #FayeYoko #ฝ้ายโยโกะ

Magda/Mag/Magsss ||✊|| FY🤍💛

13,901 просмотров • 6 месяцев назад

My ex texted me out of nowhere. Asked what I've been up to. "Building trading systems." She laughed. "Like crypto?" "No. Open-source. Anyone can see how it works." She didn't believe me. She never did. So I sent her a screenshot. One wallet returned 76% in 2 months. Another moved $6.1M in volume. One more turned $900 into $7,400. She went quiet. "That's... from code?" Exactly. Then I sent her the repos. First: 86M+ trades. Every position ever taken on Polymarket. Free. Second: Market making bot. Both sides of the book. Google Sheets execution layer. Third: ML + heuristics. Flagged that $900 → $7,400 before anyone noticed. She replied the next day: "My brother wants to start." Profile: Most people think you need a finance degree or a fund. You don't. You just need to read the code and start.

Lunar

14,814 просмотров • 2 месяцев назад

Claude Code + Google Stitch 2.0 is f*cking cracked 🤯 Google just dropped a free AI design agent that solves Claude Code's biggest weakness: frontend design. One screenshot of a high-converting landing page → a production-ready site for your brand in minutes. All inside Google Stitch + Claude Code. Perfect for DTC brands and agencies who are building advertorial pages and product launch pages for Meta but burning days on designer back-and-forth. If you're running Meta ads and need 5-10 different landing pages testing different hooks, angles, and offers — each one targeting a different audience and pain point — you know the bottleneck isn't the ads. It's the pages. Briefing designers, waiting for revisions, paying $2-5K per page. Stitch eliminates the design bottleneck: → Find a high-converting advertorial that's scaling on Meta → Screenshot it and drop it into Stitch (powered by Gemini 3.1) → Stitch redesigns it with your brand's colors, fonts, and imagery using Nano Banana 2 → Edit sections visually — headlines, CTAs, layouts — without touching code → Export the code and paste it into Claude Code → Claude builds the full production site and deploys to Vercel or Netlify in 60 seconds No designer. No $3K per landing page. No Claude Code frontend that looks like a template from 2019. What you get: → Designer-quality landing pages and advertorials built in minutes, not weeks → Visual editing so you actually see the design before you code it → Nano Banana 2 generating on-brand product imagery and hero shots → A repeatable system — new angle, new page, same pipeline Built 100% with Google Stitch 2.0 + Claude Code. I put together a full playbook showing the exact workflow: how to find winning pages, redesign them in Stitch, and deploy with Claude Code. Want it for free? > Like this post > Comment "STITCH" And I'll send it over (must be following so I can DM)

Mike Futia

125,816 просмотров • 4 месяцев назад

Claude Code can now watch & analyze ANY video 🤯 I built a skill that gives Claude the ability to watch any video file you drop in — UGC ads, competitor Meta ads, organic TikToks, screen recordings, anything. All inside Claude Code. Perfect for DTC brands and agencies who study competitor creative every week to figure out what's working and what to test next. Here's the problem: If you're studying competitor ads on Meta or hooks on TikTok, you're scrubbing through videos manually, pausing to write down hooks, screenshotting on-screen text, and trying to remember what made the ad land by the time you've watched 10 of them. This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: - Native video understanding via Gemini (not just transcripts) - Structured analysis — hook, angle, audience, pain point, CTA - Verbatim on-screen text and dialogue with timestamps - Hook variations generated directly from competitor ads - About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this and I'm giving away the skill for free. Want the skill? > Comment "CLAUDE" + > Like this post And I'll send it over (must be following so I can DM)

Claude Code can now watch & analyze ANY video 🤯 I built a skill that gives Claude the ability to watch any video file you drop in — UGC ads, competitor Meta ads, organic TikToks, screen recordings, anything. All inside Claude Code. Perfect for DTC brands and agencies who study competitor creative every week to figure out what's working and what to test next. Here's the problem: If you're studying competitor ads on Meta or hooks on TikTok, you're scrubbing through videos manually, pausing to write down hooks, screenshotting on-screen text, and trying to remember what made the ad land by the time you've watched 10 of them. This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: - Native video understanding via Gemini (not just transcripts) - Structured analysis — hook, angle, audience, pain point, CTA - Verbatim on-screen text and dialogue with timestamps - Hook variations generated directly from competitor ads - About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this and I'm giving away the skill for free. Want the skill? > Comment "CLAUDE" + > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

35,609 просмотров • 3 месяцев назад

My girlfriend came home Friday at 1am. I was on the couch. Laptop open. Three terminal panels scrolling. "You're still up?" "Just checking the bot." She dropped her bag. "What bot?" "The trading one. Open-source. Anyone can audit the code." She stared at me. "So you code for free?" "No. The code makes money." "Sure it does." I didn't argue. I opened my laptop. One wallet I was tracking turned $640 into $11,800 in 31 days. Another flipped 287 trades with 76% winrate. One more pulled $3.4M in volume in two weeks. She sat down next to me. Slowly. "That's... from a script?" Exactly. Then I showed her the repos. All free. All public. First: 86M+ trades on Polymarket. Every outcome since day one. Free to download. Second: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution. Third: ML + heuristics. I fed 14,000 wallets into Claude. One prompt. 4 minutes. Found 47 traders with 70%+ winrate. Bot mirrors them with 60-second delay. She went quiet for a long time. Then: "Why are you only telling me this now?" "We've been dating eleven months." "Exactly. Eleven months." I didn't have a good answer. She closed my laptop lid. Looked at me. "You're going to show me everything. Tomorrow morning. From the top. I'm not asking." Trade with him: Girlfriends don't ask about the money. They ask why you waited eleven months. Then they take over the conversation.

My girlfriend came home Friday at 1am. I was on the couch. Laptop open. Three terminal panels scrolling. "You're still up?" "Just checking the bot." She dropped her bag. "What bot?" "The trading one. Open-source. Anyone can audit the code." She stared at me. "So you code for free?" "No. The code makes money." "Sure it does." I didn't argue. I opened my laptop. One wallet I was tracking turned $640 into $11,800 in 31 days. Another flipped 287 trades with 76% winrate. One more pulled $3.4M in volume in two weeks. She sat down next to me. Slowly. "That's... from a script?" Exactly. Then I showed her the repos. All free. All public. First: 86M+ trades on Polymarket. Every outcome since day one. Free to download. Second: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution. Third: ML + heuristics. I fed 14,000 wallets into Claude. One prompt. 4 minutes. Found 47 traders with 70%+ winrate. Bot mirrors them with 60-second delay. She went quiet for a long time. Then: "Why are you only telling me this now?" "We've been dating eleven months." "Exactly. Eleven months." I didn't have a good answer. She closed my laptop lid. Looked at me. "You're going to show me everything. Tomorrow morning. From the top. I'm not asking." Trade with him: Girlfriends don't ask about the money. They ask why you waited eleven months. Then they take over the conversation.

Lunar

104,135 просмотров • 1 месяц назад

I just built a Meta ad policy checker in Claude Code that catches rejections BEFORE Meta does 🤯 Drop in your ad copy → it pulls Meta's LIVE Advertising Standards, checks every line against the actual policy text, and hands each ad a verdict: Cleared for launch, Fix before launch, or Grounded. All inside Claude Code. Perfect for media buyers and DTC brands who've had ads bounced — or an account restricted — and never got a straight answer why. If you're finding out about policy problems only after the rejection email, resubmitting the same ad and praying, losing days of delivery while the appeal sits in review, and every bounce quietly teaches Meta to trust your account a little less... This runs the review before Meta ever sees the ad: → Drop in your ad copy (one ad or a whole batch) → It reads each ad and figures out which of Meta's policies apply → Scrapes the live policy pages from Meta's Transparency Center → Flags the exact phrase that violates, with Meta's own policy quoted next to it → Rewrites the risky lines so the message survives but the violation doesn't → Renders a dashboard: every ad, every finding, every fix in one place No guessing which word killed the ad. No resubmit-and-pray loops. No stacking rejections on your account history. What you get: → A verdict on every ad before you spend a dollar → The violating phrase + the policy citation, side by side → Rewrites that keep the selling intent → A report you can hand straight to your team or client Built 100% in Claude Code. No API keys, no Meta login. I'm giving away the complete Claude skill file. Want the skill for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

I just built a Meta ad policy checker in Claude Code that catches rejections BEFORE Meta does 🤯 Drop in your ad copy → it pulls Meta's LIVE Advertising Standards, checks every line against the actual policy text, and hands each ad a verdict: Cleared for launch, Fix before launch, or Grounded. All inside Claude Code. Perfect for media buyers and DTC brands who've had ads bounced — or an account restricted — and never got a straight answer why. If you're finding out about policy problems only after the rejection email, resubmitting the same ad and praying, losing days of delivery while the appeal sits in review, and every bounce quietly teaches Meta to trust your account a little less... This runs the review before Meta ever sees the ad: → Drop in your ad copy (one ad or a whole batch) → It reads each ad and figures out which of Meta's policies apply → Scrapes the live policy pages from Meta's Transparency Center → Flags the exact phrase that violates, with Meta's own policy quoted next to it → Rewrites the risky lines so the message survives but the violation doesn't → Renders a dashboard: every ad, every finding, every fix in one place No guessing which word killed the ad. No resubmit-and-pray loops. No stacking rejections on your account history. What you get: → A verdict on every ad before you spend a dollar → The violating phrase + the policy citation, side by side → Rewrites that keep the selling intent → A report you can hand straight to your team or client Built 100% in Claude Code. No API keys, no Meta login. I'm giving away the complete Claude skill file. Want the skill for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

17,130 просмотров • 20 дней назад

I was at a coffee shop working on my laptop. The barista asked what I was doing. "Building a trading system." She laughed. "You're one of those crypto people." I said no. It's not crypto. It's open-source infrastructure. Anyone can see how it works. She didn't believe me. Most people don't. Copytrade wallet: Then she asked to see it. So I showed her. One wallet on Polymarket made $4.1M in 6 months. Another made $6.12M in a single day on sports. Another traded $36M in volume while I was sipping my coffee. She went quiet. "So people just... build robots to do this?" Exactly. Not robots. Systems. Open-source systems that anyone can download and run. Then I showed her the repos. First one: Every trade ever made. 86M+ trades. Free to download. Snapshot saves 2+ days of research. Second one: Market making bot. Both sides of the book. Gas optimized. Managed through Google Sheets. Third one: ML + heuristics. Flagged that $35K → $442K wallet before anyone noticed. The barista asked: "How do I start?" I said: open GitHub. Read the code. Start small. Most people think they need a PhD or a hedge fund to compete. They don't. They just need to open the terminal and read. She wrote down the repos. Asked if she could email me questions. I said yes. But first she should join the channel. Real alpha lives there.

I was at a coffee shop working on my laptop. The barista asked what I was doing. "Building a trading system." She laughed. "You're one of those crypto people." I said no. It's not crypto. It's open-source infrastructure. Anyone can see how it works. She didn't believe me. Most people don't. Copytrade wallet: Then she asked to see it. So I showed her. One wallet on Polymarket made $4.1M in 6 months. Another made $6.12M in a single day on sports. Another traded $36M in volume while I was sipping my coffee. She went quiet. "So people just... build robots to do this?" Exactly. Not robots. Systems. Open-source systems that anyone can download and run. Then I showed her the repos. First one: Every trade ever made. 86M+ trades. Free to download. Snapshot saves 2+ days of research. Second one: Market making bot. Both sides of the book. Gas optimized. Managed through Google Sheets. Third one: ML + heuristics. Flagged that $35K → $442K wallet before anyone noticed. The barista asked: "How do I start?" I said: open GitHub. Read the code. Start small. Most people think they need a PhD or a hedge fund to compete. They don't. They just need to open the terminal and read. She wrote down the repos. Asked if she could email me questions. I said yes. But first she should join the channel. Real alpha lives there.

Lunar

30,091 просмотров • 2 месяцев назад

Release: LichtFeld Studio v0.5.3 is out! With 316 commits merged into master, this release is a huge step forward for LichtFeld Studio. What's new in v0.5.3 • Vulkan viewer/rendering migration: New Vulkan viewport pipeline, pass graph, VkSplat renderer, Vulkan point-cloud renderer, 3DGUT/VkSplat support, improved alpha/depth composition, tighter CUDA/Vulkan interoperability, and device matching on multi-GPU systems. • RAD + LOD workflow: Added RAD file export/import, RAD LOD viewer, Spark-style GPU LOD selection, GPU-driven page prefetching, a bounded VRAM pool, out-of-core PLY-to-RAD LOD conversion, and RAD import/export speedups of approximately 3–5×. • HiGS / macro-tile inference: Added a macro-tile inference path for the Vulkan viewer, including macro sorting, batched rasterization, composition, and capacity management. • Asset Manager: Added and significantly enhanced the Asset Manager with thumbnails, SH information, faster synchronization, import-from-URL support, docked mode, data-loading popup integration, and general UI cleanup. • Viewport export: Integrated viewport export directly into the application as a toolbar/overlay tool, added fast render_view_u8-style readback paths, fixed high-resolution clipping issues, improved orthographic export parity, resolved 32K image/video export problems, and added post-export GPU resource cleanup. • Selection and tooling: Added and reworked selection toolbar controls, the Select menu, ring selection, color eyedropper, distance-from-center selection, faster point-cloud and zoomed-out selection paths, Vulkan measurement tool fixes, and drag-and-drop scene graph improvements. • UI/RmlUi platform work: Major RmlUi redesign efforts, hot reloading for RML/RCSS/Python UI files, reactive UI/store integration, viewport toolbar flyouts, improved histogram interactions, input settings enhancements, custom TRS gizmos, and numerous panel, tooltip, and localization fixes. • Windowing and UX: Added borderless window support, title bar drag/maximize/restore behavior, work-area-aware maximize functionality, resize responsiveness and performance improvements, and DPI/UI scaling fixes. • Training and data features: Added adaptive depth loss and depth gradients for the EWA rasterizer, mask loading/application fixes, a new combined Ignore+Segment mask mode, --add-splat, --freeze, improved checkpoint and training state handling, and training speed and VRAM optimizations. • COLMAP/equirectangular support: Added SPHERICAL/equirectangular camera model support and canonical EQUIRECTANGULAR handling, along with fixes for undistortion and camera export. This release will be available to all supporters as a Windows binary via approximately in about an hour. At the same time, LichtFeld Studio remains committed to being free and open source under GPLv3 and can also be built directly from source. Please consider supporting the ongoing development of LichtFeld Studio through a donation via the portal or the supporters page. Thank you to everyone who supports this project financially, contributes code, reports bugs, provides datasets, helps with the website, and contributes in countless other ways. A special thank you to our foundational sponsor Core11 and our Gold Sponsor Volinga, whose support has helped make the current state of the software possible. Thank you as well to every donor and to all of our new Bronze Sponsors. Looking ahead to v0.6 For the next major release, work will focus primarily on stability and user experience. This includes improved cleanup workflows and the ability to modify training parameters while training is in progress. I would also like to introduce a native .licht project format that allows users to save and restore their complete editor state. You can find links to our main sponsors below. Please also visit our website to discover all our Bronze Sponsors. Hint: We do not yet have a Silver Sponsor or Platinum 😉

Release: LichtFeld Studio v0.5.3 is out! With 316 commits merged into master, this release is a huge step forward for LichtFeld Studio. What's new in v0.5.3 • Vulkan viewer/rendering migration: New Vulkan viewport pipeline, pass graph, VkSplat renderer, Vulkan point-cloud renderer, 3DGUT/VkSplat support, improved alpha/depth composition, tighter CUDA/Vulkan interoperability, and device matching on multi-GPU systems. • RAD + LOD workflow: Added RAD file export/import, RAD LOD viewer, Spark-style GPU LOD selection, GPU-driven page prefetching, a bounded VRAM pool, out-of-core PLY-to-RAD LOD conversion, and RAD import/export speedups of approximately 3–5×. • HiGS / macro-tile inference: Added a macro-tile inference path for the Vulkan viewer, including macro sorting, batched rasterization, composition, and capacity management. • Asset Manager: Added and significantly enhanced the Asset Manager with thumbnails, SH information, faster synchronization, import-from-URL support, docked mode, data-loading popup integration, and general UI cleanup. • Viewport export: Integrated viewport export directly into the application as a toolbar/overlay tool, added fast render_view_u8-style readback paths, fixed high-resolution clipping issues, improved orthographic export parity, resolved 32K image/video export problems, and added post-export GPU resource cleanup. • Selection and tooling: Added and reworked selection toolbar controls, the Select menu, ring selection, color eyedropper, distance-from-center selection, faster point-cloud and zoomed-out selection paths, Vulkan measurement tool fixes, and drag-and-drop scene graph improvements. • UI/RmlUi platform work: Major RmlUi redesign efforts, hot reloading for RML/RCSS/Python UI files, reactive UI/store integration, viewport toolbar flyouts, improved histogram interactions, input settings enhancements, custom TRS gizmos, and numerous panel, tooltip, and localization fixes. • Windowing and UX: Added borderless window support, title bar drag/maximize/restore behavior, work-area-aware maximize functionality, resize responsiveness and performance improvements, and DPI/UI scaling fixes. • Training and data features: Added adaptive depth loss and depth gradients for the EWA rasterizer, mask loading/application fixes, a new combined Ignore+Segment mask mode, --add-splat, --freeze, improved checkpoint and training state handling, and training speed and VRAM optimizations. • COLMAP/equirectangular support: Added SPHERICAL/equirectangular camera model support and canonical EQUIRECTANGULAR handling, along with fixes for undistortion and camera export. This release will be available to all supporters as a Windows binary via approximately in about an hour. At the same time, LichtFeld Studio remains committed to being free and open source under GPLv3 and can also be built directly from source. Please consider supporting the ongoing development of LichtFeld Studio through a donation via the portal or the supporters page. Thank you to everyone who supports this project financially, contributes code, reports bugs, provides datasets, helps with the website, and contributes in countless other ways. A special thank you to our foundational sponsor Core11 and our Gold Sponsor Volinga, whose support has helped make the current state of the software possible. Thank you as well to every donor and to all of our new Bronze Sponsors. Looking ahead to v0.6 For the next major release, work will focus primarily on stability and user experience. This includes improved cleanup workflows and the ability to modify training parameters while training is in progress. I would also like to introduce a native .licht project format that allows users to save and restore their complete editor state. You can find links to our main sponsors below. Please also visit our website to discover all our Bronze Sponsors. Hint: We do not yet have a Silver Sponsor or Platinum 😉

MrNeRF

26,219 просмотров • 1 месяц назад

My girlfriend found a second laptop under my bed Saturday. I was still asleep. She was looking for a phone charger. She didn't say anything for ten minutes. I woke up. She was on the couch with both laptops open. "What are you doing?" "Comparing strategies." "Strategies for what?" "Trading. Open-source. Anyone can audit the code." She put the coffee down. "So you code for free?" "No. The code makes money." "Sure it does." I didn't argue. I sat next to her. One wallet I was tracking turned $1,500 into $24,200 in 16 days. Another flipped 298 trades with 85% winrate. One more pulled $7.4M in volume in four weeks. She stopped mid-sip. "That's... from a script?" Exactly. Then I showed her the repos. All free. All public. First: 86M+ trades on Polymarket. Every outcome since day one. Free to download. Second: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution. Third: ML + heuristics. I fed 14,000 wallets into Claude. One prompt. 4 minutes. Found 47 traders with 70%+ winrate. Bot mirrors them with 60-second delay. She went quiet for a long time. Then: "Why do you have a second laptop under the bed?" "Because one runs the bot. One shows the dashboard. I sleep better when they're not on the same machine." "You sleep better when there's trading software running while you sleep?" "Yeah." She closed both laptops. Looked at me. "You're going to show me everything. Tonight. From the top. I'm not asking." Try to trade: Girlfriends don't ask about the money. They ask why there's a second laptop under the bed. Then they take over the conversation.

My girlfriend found a second laptop under my bed Saturday. I was still asleep. She was looking for a phone charger. She didn't say anything for ten minutes. I woke up. She was on the couch with both laptops open. "What are you doing?" "Comparing strategies." "Strategies for what?" "Trading. Open-source. Anyone can audit the code." She put the coffee down. "So you code for free?" "No. The code makes money." "Sure it does." I didn't argue. I sat next to her. One wallet I was tracking turned $1,500 into $24,200 in 16 days. Another flipped 298 trades with 85% winrate. One more pulled $7.4M in volume in four weeks. She stopped mid-sip. "That's... from a script?" Exactly. Then I showed her the repos. All free. All public. First: 86M+ trades on Polymarket. Every outcome since day one. Free to download. Second: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution. Third: ML + heuristics. I fed 14,000 wallets into Claude. One prompt. 4 minutes. Found 47 traders with 70%+ winrate. Bot mirrors them with 60-second delay. She went quiet for a long time. Then: "Why do you have a second laptop under the bed?" "Because one runs the bot. One shows the dashboard. I sleep better when they're not on the same machine." "You sleep better when there's trading software running while you sleep?" "Yeah." She closed both laptops. Looked at me. "You're going to show me everything. Tonight. From the top. I'm not asking." Try to trade: Girlfriends don't ask about the money. They ask why there's a second laptop under the bed. Then they take over the conversation.

Lunar

11,812 просмотров • 1 месяц назад

Claude Code + ChatGPT Images 2.0 is f*cking cracked 🤯 I rebuilt my static ad system inside Claude Code on the new ChatGPT Images 2.0 model. One brand name + one URL = 40 production-ready static ads. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without briefing a designer or spending hours in Canva. If you're finding winning ad concepts on Meta and manually recreating them one at a time — copying prompts, pasting product details, tweaking aspect ratios, downloading, organizing... This system eliminates the entire loop: → Give Claude a brand name and URL → It researches the brand's fonts, colors, packaging, and photography style → Builds a Brand DNA document from scratch → Fills in 40 proven ad templates (headline, us vs them, testimonial, UGC, review cards, stat callouts) with brand-specific details → Fires every prompt to ChatGPT Images 2.0 with your product photos as reference → Downloads finished ads into organized folders with an HTML gallery No manual prompt filling. No Canva templates. No copy-pasting between tools. What you get: → 40 ad formats filled with your exact brand colors, fonts, and copy → Text that actually renders correctly (the new model handles dense copy, logos, and multi-language callouts cleanly) → Product photos passed as reference so the model matches your real packaging → A reusable system — new brand, new folder, same pipeline Built 100% in Claude Code with ChatGPT Images 2.0. I put together a DIY playbook showing the exact architecture so you can build this yourself in Claude Code. Want it for free? > Like this post > Comment "CHAT" And I'll send it over (must be following @learnwithella so I can DM)

Ismail Khan

19,773 просмотров • 3 месяцев назад