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شاركنا إجابتك واحصل على هدية العيد : كرتون قشطة قيمر من لونا 🤩💫 للمشاركة: 1- اكتب إجابتك في التعليقات 2- منشن 3 من أصدقائك 3- تابع حساب لونا على الانستقرام Luna Nyberg.KSA أو تيك توك وإكس Luna Nyberg_KSA 4- اعد نشر البوست #لونا #منتجات_لونا Share your answer and win...

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لونا

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10,737 Aufrufe • vor 4 Monaten •via X (Twitter)

#لونا#منتجات_لونا
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oh Jared Mascola King Jared its time you got your file pulled in this post i will not use “Jokes” or “fake screenshots” to come at your character, i will use simple fact and not resort to faking police reports or doxxing as you have done previously 1. you have tried to spin things you knew were jokes or fake to suit your own narrative (per your own tweets) 2. you act like your an angel but are firends with Baka Not Nice (photo evidence attached) someone found GUILTY of putting his hands on a woman 3. you openly support Lil O Angel (Octavian) after after his allegations of beating women were brought to you 4. you faked racist and weird tweets about me and countless others (the video of your discord attached) 5. you found ZERO issue with akademiks GENUINELY talking sexually to a 15 year old boy (even told people they should move on) 6. your friends with Dom Lucre (Dom Lucre - Only & Confirmed backup.) someone who trademarked and distributed child porn (picture attached) 7. your yet to speak out against your friend Akademiks weird account which is still visible where he GENUINELY shared revenge porn (DJ AKADEMIKS) 8. you supported ovoanon after he threatened rape 9. you supported drakeiscooking after they threatened rape 10. Doxxed men and women using PimEyes (per your own tweets about others) 11. made comments about people’s bodies and frequently called Kendrick Lamars female fanbase “Ugly” whilst looking the way you do all because you don’t share their opinion 12. your friends with P Diddys publicist and were seen a photograph with her 13. made inappropriate and false statements about people engaging in disturbing cp/beastiality/being predators all while supporting someone seen on camera kissing a minor 14. you supported zaya after they threatened rape 15. you engaged in sharing pictures of people as minors including myself and other users (tweets are still visible) 16. you were in a discord server where you shared photos of peoples family members including children all of which you have proven you have saved in your device as number 15 has shown 17. you have two old and visible twitter accounts where you shared racist pictures •Jared• & @TheEpicalTweets 18. you harrased peoples family members to the point where someone made a GENUINE police complaint which your Dad Pete Mascola acknowledged via Facebook (picture attached) 🧵

ry

51,286 Aufrufe • vor 1 Jahr

how to CRACK the tiktok algorithm in 2026... the algorithm in 2026 is not judging your account. it's judging your first 500 views. everyone overthinks this. here's the actual method to crack it: every video starts from ZERO. doesn't matter if your last one hit 1M or your account has 200k followers. tiktok takes the new post, runs it through content moderation first, THEN drops it to a small cold test pool. that pool isn't random. it's people who already watch your exact niche, because tiktok's whole goal is keeping people on the app, so it shows your video to the people most likely to sit through it. then it reads 4 signals, in this order of weight: completion rate (did they finish it) rewatches (did they loop it) shares (did they send it to someone) velocity (how FAST all of that came in) clear the wave, it pushes ~5x bigger. clear that, 5x again. it keeps expanding until the numbers drop below the bar for that wave, then it stops. that's it. that's "going viral." it's just a video that kept clearing waves. nothing mystical. the part that breaks people's brains: this resets EVERY post. tiktok is not youtube shorts. on youtube the channel carries momentum. on tiktok the VIDEO carries itself. your last banger does not pre-load the next one. all followers do is get you shown FIRST in the test pool, which is a head start, not a guarantee. every video still earns its reach from scratch. now the myths that waste your time: "warm up a new account by scrolling for 2-3 days before posting." MYTH. tiktok has said this directly, there is no warm-up requirement. the only real device-level flag is ban evasion, and that only triggers if your device/IP is tied to a previously banned account. clean device = post day one, no penalty. "posting too much hurts the algorithm." also wrong. there is no volume throttle on your account. LOW volume is the real risk. every post is a fresh roll at the test pool. fewer posts = fewer rolls = fewer chances to hit a wave that clears. the accounts that grow fast post 1-3x a day, not once a week. "you need trending audio to go viral." no. trending audio helps discovery marginally but a strong hook on a silent slideshow will beat a weak video on the #1 sound every time. the sound doesn't save a bad first 2 seconds. what ACTUALLY moves it: the first 2 seconds are the whole game. that IS your completion rate. weak hook, the test pool bounces, the video dies in wave 1 and never recovers. front-load the payoff, cut the intro, no "hey guys." shares > likes, by a lot. a like is passive, it barely registers. a share tells tiktok "spread this" and it's the single signal that widens the wave hardest. build for the share: a take people want to send to someone, a "wait what" fact, a screenshot-able line. velocity beats total. 200 views in the first hour tells the algo more than 2,000 over a week. post when your specific audience is actually awake, check your analytics, don't guess "peak times." the format is disposable, the system isn't. 90% of your posts will die under 5,000 views, most under 300. that's not failure, that's the model working. you're not making one perfect video, you're feeding the test pool enough clean shots that a few clear all the waves. 10 accounts x 2 posts a day = 20 rolls daily. one hits 500k and pays for the other 19. and if monetization is the goal: creator rewards in 2026 is 10k followers + 100k views in the last 30 days, ROLLING (a viral month 6 months ago counts for nothing). only videos 60 SECONDS or longer earn a cent. and you have to be based in an eligible country to even apply: US, UK, germany, france, japan, korea, brazil, a few others. pakistan, india, most of MENA are not on the list. so "target US for higher RPM" isn't just a payout tip, geo is a hard gate on whether the program exists for you at all. stop trying to game it. feed it clean videos with a killer first 2 seconds and let the test pool do the sorting. the algo is dumber and more fair than people think.
0:10

how to CRACK the tiktok algorithm in 2026... the algorithm in 2026 is not judging your account. it's judging your first 500 views. everyone overthinks this. here's the actual method to crack it: every video starts from ZERO. doesn't matter if your last one hit 1M or your account has 200k followers. tiktok takes the new post, runs it through content moderation first, THEN drops it to a small cold test pool. that pool isn't random. it's people who already watch your exact niche, because tiktok's whole goal is keeping people on the app, so it shows your video to the people most likely to sit through it. then it reads 4 signals, in this order of weight: completion rate (did they finish it) rewatches (did they loop it) shares (did they send it to someone) velocity (how FAST all of that came in) clear the wave, it pushes ~5x bigger. clear that, 5x again. it keeps expanding until the numbers drop below the bar for that wave, then it stops. that's it. that's "going viral." it's just a video that kept clearing waves. nothing mystical. the part that breaks people's brains: this resets EVERY post. tiktok is not youtube shorts. on youtube the channel carries momentum. on tiktok the VIDEO carries itself. your last banger does not pre-load the next one. all followers do is get you shown FIRST in the test pool, which is a head start, not a guarantee. every video still earns its reach from scratch. now the myths that waste your time: "warm up a new account by scrolling for 2-3 days before posting." MYTH. tiktok has said this directly, there is no warm-up requirement. the only real device-level flag is ban evasion, and that only triggers if your device/IP is tied to a previously banned account. clean device = post day one, no penalty. "posting too much hurts the algorithm." also wrong. there is no volume throttle on your account. LOW volume is the real risk. every post is a fresh roll at the test pool. fewer posts = fewer rolls = fewer chances to hit a wave that clears. the accounts that grow fast post 1-3x a day, not once a week. "you need trending audio to go viral." no. trending audio helps discovery marginally but a strong hook on a silent slideshow will beat a weak video on the #1 sound every time. the sound doesn't save a bad first 2 seconds. what ACTUALLY moves it: the first 2 seconds are the whole game. that IS your completion rate. weak hook, the test pool bounces, the video dies in wave 1 and never recovers. front-load the payoff, cut the intro, no "hey guys." shares > likes, by a lot. a like is passive, it barely registers. a share tells tiktok "spread this" and it's the single signal that widens the wave hardest. build for the share: a take people want to send to someone, a "wait what" fact, a screenshot-able line. velocity beats total. 200 views in the first hour tells the algo more than 2,000 over a week. post when your specific audience is actually awake, check your analytics, don't guess "peak times." the format is disposable, the system isn't. 90% of your posts will die under 5,000 views, most under 300. that's not failure, that's the model working. you're not making one perfect video, you're feeding the test pool enough clean shots that a few clear all the waves. 10 accounts x 2 posts a day = 20 rolls daily. one hits 500k and pays for the other 19. and if monetization is the goal: creator rewards in 2026 is 10k followers + 100k views in the last 30 days, ROLLING (a viral month 6 months ago counts for nothing). only videos 60 SECONDS or longer earn a cent. and you have to be based in an eligible country to even apply: US, UK, germany, france, japan, korea, brazil, a few others. pakistan, india, most of MENA are not on the list. so "target US for higher RPM" isn't just a payout tip, geo is a hard gate on whether the program exists for you at all. stop trying to game it. feed it clean videos with a killer first 2 seconds and let the test pool do the sorting. the algo is dumber and more fair than people think.

Sulfur

46,775 Aufrufe • vor 2 Monaten

this FREE MCP just replaced my entire $12K/month brand research team (I literally made this FREE) while you're downloading n8n workflows you'll never actually use... this thing creates detailed psychological profiles from any Twitter account or website in 30 seconds - zero installation - no python - no custom code - just pure intelligence here's what's actually happening: most people think AI automation has to be complicated they're trying to become overnight developers building complex workflows that break after one week burning hours on setups they'll abandon in 3 days meanwhile professionals are using simple tools that actually solve business problems you don't need to become a developer to use AI intelligently you just need the right tools this MCP gives you what $500/hour consultants charge for: → complete psychological profiles of any Twitter account → brand DNA extraction (colors, fonts, layouts) → content strategy insights normally reserved for agencies → analysis detailed enough to recreate entire brands think about what this replaces: expensive consultant fees ✓ manual competitor research ✓ hours of brand analysis ✓ complex automation nightmares ✓ the setup is embarrassingly simple: step 1 → click link in comments step 2 → get your MCP code step 3 → add to claude settings step 4 → analyze any brand instantly total time: 30 seconds we're scaling this to analyze 1,000+ tweets soon adding LinkedIn, YouTube, TikTok, Instagram next early access is completely free right now while everyone else is trying to learn python... you'll be extracting million-dollar brand insights in under a minute comment "MCP" + repost this + like + bookmark (you must follow or I CANT DM) i'll drop the link in your DMs don't sleep on this every day you wait is premium intelligence you're leaving on the table
0:18

this FREE MCP just replaced my entire $12K/month brand research team (I literally made this FREE) while you're downloading n8n workflows you'll never actually use... this thing creates detailed psychological profiles from any Twitter account or website in 30 seconds - zero installation - no python - no custom code - just pure intelligence here's what's actually happening: most people think AI automation has to be complicated they're trying to become overnight developers building complex workflows that break after one week burning hours on setups they'll abandon in 3 days meanwhile professionals are using simple tools that actually solve business problems you don't need to become a developer to use AI intelligently you just need the right tools this MCP gives you what $500/hour consultants charge for: → complete psychological profiles of any Twitter account → brand DNA extraction (colors, fonts, layouts) → content strategy insights normally reserved for agencies → analysis detailed enough to recreate entire brands think about what this replaces: expensive consultant fees ✓ manual competitor research ✓ hours of brand analysis ✓ complex automation nightmares ✓ the setup is embarrassingly simple: step 1 → click link in comments step 2 → get your MCP code step 3 → add to claude settings step 4 → analyze any brand instantly total time: 30 seconds we're scaling this to analyze 1,000+ tweets soon adding LinkedIn, YouTube, TikTok, Instagram next early access is completely free right now while everyone else is trying to learn python... you'll be extracting million-dollar brand insights in under a minute comment "MCP" + repost this + like + bookmark (you must follow or I CANT DM) i'll drop the link in your DMs don't sleep on this every day you wait is premium intelligence you're leaving on the table

4nzn

13,522 Aufrufe • vor 1 Jahr

I think I've stumbled onto the future of building startups. It wasn't supposed to happen this way. It's 2 AM. I'm editing a podcast, questioning every life decision that led me here. I've already burned through hundreds of thousands on this thing since 2021. Zero monetization. Just burning cash. My business partner's probably thinking I've lost it. We're juggling 6 businesses, and here I am, playing wannabe Joe Rogan. Then it hit me. Not during the podcast. In the darn comments section. I start sorting comments by "contains question" using this AI creator tool called VidIQ. "How do you validate ideas?" "What tools do you use?" "Can you dive deeper on XYZ topic?" These questions keep popping up. Over and over. That's when the lightbulb went off. What if I could turn this into a lead magnet machine? Find questions. Answer them with free stuff. Rinse. Repeat. I team up with Design Scientist to crank out 2 lead magnets a month. (Tried doing it myself first. But it was hard lol) We start pumping out things like "6 Tools I Use to Find Startup Ideas." Suddenly, I'm drowning in subscribers. 10,000 to 20,000 a month. On autopilot. Now, you're probably thinking, "Cool story, bro. But how's this a big idea?" Clarity of what to build is probably one of the most valuable ways to build products people want. You have to understand a niche's problem better than they even know them. Problem: what's the roadblock keeping founders stuck? Segment: group these founders by their specific obstacles. Product: build the bridge that gets them over their hurdle. I use ConvertKit like a scalpel, dissecting these segments. Not by age or location. By the problems they're desperate to solve. Suddenly, I'm staring at a treasure map of founder pain points. And that's when you can build startups to solve their problems. Instead of being a lead factory, you become a startup factory. You use tools like v0/replit/cursor to prototype like a madman. And it makes your life less stressful as a founder. Because you know people are lined up to buy the products. I'm so convinced this is the future of startup building, I've bet $1M+ of my own cash on it. Building startups to solve people's problems. And cool part is this blueprint can be replicated in any niche. The best SaaS ideas aren't in some Silicon Valley incubator. They're hiding in your "free" content. Think of it like this (Isenberg's formula?): (Engaged Audience) x (Targeted Lead Magnets) x (Problem-Centric Segmentation) = Product-Market Fit on Demand Here's the step-by-step: 1. Use AI/software to categorize every single audience interaction by problem type. Build a heat map of pain points. 2. Create ultra-specific lead magnets for each major problem cluster. Think "5-Step Framework for Validating SaaS Ideas" not "Generic Startup Guide". I also use free communities as lead magnets. 3. Forget demographics. Segment by the problem they're trying to solve. Use ConvertKit to build dynamic segments that would make Zuck jealous. 4. Use AI to build rapid prototypes for top problem clusters. Test with your segmented lists for instant feedback. Your next cash-flowing business is probably stuck in a comment somewhere, just waiting for you to notice it. I accidentally built a startup factory at 2am. Happy I did. Sharing in case this is useful to anyone out there. The future of startups: 1. Be a content factory 2. Be a lead factory 3. Be a startup factory
0:10

I think I've stumbled onto the future of building startups. It wasn't supposed to happen this way. It's 2 AM. I'm editing a podcast, questioning every life decision that led me here. I've already burned through hundreds of thousands on this thing since 2021. Zero monetization. Just burning cash. My business partner's probably thinking I've lost it. We're juggling 6 businesses, and here I am, playing wannabe Joe Rogan. Then it hit me. Not during the podcast. In the darn comments section. I start sorting comments by "contains question" using this AI creator tool called VidIQ. "How do you validate ideas?" "What tools do you use?" "Can you dive deeper on XYZ topic?" These questions keep popping up. Over and over. That's when the lightbulb went off. What if I could turn this into a lead magnet machine? Find questions. Answer them with free stuff. Rinse. Repeat. I team up with Design Scientist to crank out 2 lead magnets a month. (Tried doing it myself first. But it was hard lol) We start pumping out things like "6 Tools I Use to Find Startup Ideas." Suddenly, I'm drowning in subscribers. 10,000 to 20,000 a month. On autopilot. Now, you're probably thinking, "Cool story, bro. But how's this a big idea?" Clarity of what to build is probably one of the most valuable ways to build products people want. You have to understand a niche's problem better than they even know them. Problem: what's the roadblock keeping founders stuck? Segment: group these founders by their specific obstacles. Product: build the bridge that gets them over their hurdle. I use ConvertKit like a scalpel, dissecting these segments. Not by age or location. By the problems they're desperate to solve. Suddenly, I'm staring at a treasure map of founder pain points. And that's when you can build startups to solve their problems. Instead of being a lead factory, you become a startup factory. You use tools like v0/replit/cursor to prototype like a madman. And it makes your life less stressful as a founder. Because you know people are lined up to buy the products. I'm so convinced this is the future of startup building, I've bet $1M+ of my own cash on it. Building startups to solve people's problems. And cool part is this blueprint can be replicated in any niche. The best SaaS ideas aren't in some Silicon Valley incubator. They're hiding in your "free" content. Think of it like this (Isenberg's formula?): (Engaged Audience) x (Targeted Lead Magnets) x (Problem-Centric Segmentation) = Product-Market Fit on Demand Here's the step-by-step: 1. Use AI/software to categorize every single audience interaction by problem type. Build a heat map of pain points. 2. Create ultra-specific lead magnets for each major problem cluster. Think "5-Step Framework for Validating SaaS Ideas" not "Generic Startup Guide". I also use free communities as lead magnets. 3. Forget demographics. Segment by the problem they're trying to solve. Use ConvertKit to build dynamic segments that would make Zuck jealous. 4. Use AI to build rapid prototypes for top problem clusters. Test with your segmented lists for instant feedback. Your next cash-flowing business is probably stuck in a comment somewhere, just waiting for you to notice it. I accidentally built a startup factory at 2am. Happy I did. Sharing in case this is useful to anyone out there. The future of startups: 1. Be a content factory 2. Be a lead factory 3. Be a startup factory

GREG ISENBERG

128,620 Aufrufe • vor 2 Jahren

Here's your blueprint for building in GTA 6 ecosystem before it goes mainstream: the ideas on what to build in GTA 6 ecosystem went crazy, now let me show you HOW first, understand the size of what's coming: 1: GTA 5 generated over $8 billion in lifetime revenue 2: GTA Online alone pulls in $800 million per year just from in-game purchases analysts predict GTA 6 will hit $3.2B in YEAR ONE and one firm says $7.6B in the first 60 days this will be the biggest entertainment launch in history. not gaming. entertainment and do you know what happens in GTA 6 now??? no dominant servers. no established tools. no go-to creators. no infrastructure. NOTHING the people who build now will own categories for the next decade Steps which you can do RIGHT NOW: Step 1: Pick your lane there are 4 and you only need one: - Builder: tools, bots, dashboards for server owners - Operator: run a server, community, or paid service - Creator: clips, guides, streams, newsletters - Seller: digital assets (scripts, lore packs, voice packs, skins) if you code or use n8n → Builder if you're good with people → Operator if you already make content → Creator if you can write or design → Seller --------- Step 2: Learn the stack while nobody's watching - Lua basics (the language behind GTA mod frameworks) - how FiveM servers work (the architecture transfers to GTA 6) - Discord bot development (every server needs one) - AI tools you can plug in (Claude API, ElevenLabs, n8n) you don't need to master any of this you need to be 2 months ahead of everyone else that is the ADVANTAGE --------- Step 3: Join the communities forming right now the Discord servers and X accounts building for GTA 6 are tiny today join now. contribute. help server owners for free the people who show up early become the trusted names when 100M players flood in, those names become the gatekeepers in potential it's yours 6-7 figs + successful own community as the game will be launched lol, I know even the guys who buy PS5 to play in GTA in 6 months... the biggest gaming launch which EVER existed --------- Step 4: Ship ONE thing before launch Builder → Discord bot kit for RP servers Operator → niche RP server (racing league, police sim, business world) Creator → daily GTA 6 newsletter or TikTok clip page Seller → AI character backstory packs or NPC voice packs one product. shipped. before anyone else has one --------- Step 5: Build in public for 90 days post weekly progress on X, Tiktok, Youtube the person documenting the ecosystem for 3 months before launch will look like a veteran on day one by release you will have: - one shipped product - a small audience that trusts you - relationships with 10-20 other builders - enough knowledge to move fast while everyone else is still googling basics --------- CONCLUSION every platform shift follows the same pattern Roblox made devs millionaires. Fortnite paid creators $700M+. FiveM turned one Australian dev into Rockstar's official partner GTA 6 is the next one and it's BIGGER than all of them the difference? right now you're reading this before 99.9% of the world even thinks about it most people will download the game, play for 2 weeks, and go back to scrolling a small number will build inside the ecosystem while it's empty and own their lane for years nobody is talking about this not on youtube. not on linkedin. not in any newsletter this is one of the biggest builder opportunities of the decade hiding behind a video game i'm writing a full article on this right now: "How to build and monetize in GTA 6 ecosystem using AI (RESOURCES)" if this post gets 1,000 LIKES ❤️ i'll publish it next week follow so you don't miss it gl
0:13

Here's your blueprint for building in GTA 6 ecosystem before it goes mainstream: the ideas on what to build in GTA 6 ecosystem went crazy, now let me show you HOW first, understand the size of what's coming: 1: GTA 5 generated over $8 billion in lifetime revenue 2: GTA Online alone pulls in $800 million per year just from in-game purchases analysts predict GTA 6 will hit $3.2B in YEAR ONE and one firm says $7.6B in the first 60 days this will be the biggest entertainment launch in history. not gaming. entertainment and do you know what happens in GTA 6 now??? no dominant servers. no established tools. no go-to creators. no infrastructure. NOTHING the people who build now will own categories for the next decade Steps which you can do RIGHT NOW: Step 1: Pick your lane there are 4 and you only need one: - Builder: tools, bots, dashboards for server owners - Operator: run a server, community, or paid service - Creator: clips, guides, streams, newsletters - Seller: digital assets (scripts, lore packs, voice packs, skins) if you code or use n8n → Builder if you're good with people → Operator if you already make content → Creator if you can write or design → Seller --------- Step 2: Learn the stack while nobody's watching - Lua basics (the language behind GTA mod frameworks) - how FiveM servers work (the architecture transfers to GTA 6) - Discord bot development (every server needs one) - AI tools you can plug in (Claude API, ElevenLabs, n8n) you don't need to master any of this you need to be 2 months ahead of everyone else that is the ADVANTAGE --------- Step 3: Join the communities forming right now the Discord servers and X accounts building for GTA 6 are tiny today join now. contribute. help server owners for free the people who show up early become the trusted names when 100M players flood in, those names become the gatekeepers in potential it's yours 6-7 figs + successful own community as the game will be launched lol, I know even the guys who buy PS5 to play in GTA in 6 months... the biggest gaming launch which EVER existed --------- Step 4: Ship ONE thing before launch Builder → Discord bot kit for RP servers Operator → niche RP server (racing league, police sim, business world) Creator → daily GTA 6 newsletter or TikTok clip page Seller → AI character backstory packs or NPC voice packs one product. shipped. before anyone else has one --------- Step 5: Build in public for 90 days post weekly progress on X, Tiktok, Youtube the person documenting the ecosystem for 3 months before launch will look like a veteran on day one by release you will have: - one shipped product - a small audience that trusts you - relationships with 10-20 other builders - enough knowledge to move fast while everyone else is still googling basics --------- CONCLUSION every platform shift follows the same pattern Roblox made devs millionaires. Fortnite paid creators $700M+. FiveM turned one Australian dev into Rockstar's official partner GTA 6 is the next one and it's BIGGER than all of them the difference? right now you're reading this before 99.9% of the world even thinks about it most people will download the game, play for 2 weeks, and go back to scrolling a small number will build inside the ecosystem while it's empty and own their lane for years nobody is talking about this not on youtube. not on linkedin. not in any newsletter this is one of the biggest builder opportunities of the decade hiding behind a video game i'm writing a full article on this right now: "How to build and monetize in GTA 6 ecosystem using AI (RESOURCES)" if this post gets 1,000 LIKES ❤️ i'll publish it next week follow so you don't miss it gl

Ronin

94,293 Aufrufe • vor 5 Monaten

i analysed 1,000 TIKTOK slideshows for consumer apps... here's what i found something that change how you run your app/saas campaign since. most people assume the slideshow with the most views brings in the most installs. i tracked every metric i could pull across 1000+ posts. views, saves, comment sentiment, slide count, where the app got mentioned in the sequence, caption length, niche. the data told a different story. the highest converting slideshows rarely broke 100k views. some sat under 20k. meanwhile some of the viral ones with 2m+ views converted under 0.05%. viral and profitable are two different games. here's the pattern that separated the winners. the format was almost always: content, content, content, content, ad warmup, app push. 4 to 6 slides that feel like normal lifestyle or niche content, no mention of the app at all. then one slide near the end where the product shows up, framed as part of the story instead of an ad. apps that opened with the product on slide 1 underperformed almost every time. the accounts winning were disguising the app inside content people were already scrolling for anyway. travel aesthetics, interior inspiration, "things nobody tells you about x" hooks, niche opinions. the second pattern was volume, not virality. accounts running 8-10 tiktoks, posting 2x a day, same slide formats with fresh variations each time. 90% of posts stayed under 5k views, most even under 300. a handful hit 50k-500k. a rare one crossed 1m. the accounts winning weren't making one perfect post, they were running the format enough times that the algorithm found the winners for them. the workflow behind it, if you want to copy it: research first. search your niche on tiktok, screenshot every top slideshow, save the captions somewhere. this is the raw material for everything after. pull matching visuals from pinterest for each slide type in that screenshot pile. figure out what slide 1 looks like, slide 2, slide 3. download a handful per slide type. not everything from pinterest can be reposted as is. run it through an image api like openai or gemini to generate variations that keep the same vibe. 5 slide types x 100 variations gets you 500 usable images fast. feed the competitor captions into claude code and have it write new caption variations in that same tone, keep the early slides content only, drop the app in near the end. claude code can overlay the captions onto the images directly using ffmpeg, then hand scheduling off to a tool that allows accounts to post automatically without you touching them daily. set this up once and you get months of content queued across every account, running the same proven format with fresh visuals each time. the accounts losing were treating every post as a one off. the accounts winning built a system and let volume do the work.
0:18

i analysed 1,000 TIKTOK slideshows for consumer apps... here's what i found something that change how you run your app/saas campaign since. most people assume the slideshow with the most views brings in the most installs. i tracked every metric i could pull across 1000+ posts. views, saves, comment sentiment, slide count, where the app got mentioned in the sequence, caption length, niche. the data told a different story. the highest converting slideshows rarely broke 100k views. some sat under 20k. meanwhile some of the viral ones with 2m+ views converted under 0.05%. viral and profitable are two different games. here's the pattern that separated the winners. the format was almost always: content, content, content, content, ad warmup, app push. 4 to 6 slides that feel like normal lifestyle or niche content, no mention of the app at all. then one slide near the end where the product shows up, framed as part of the story instead of an ad. apps that opened with the product on slide 1 underperformed almost every time. the accounts winning were disguising the app inside content people were already scrolling for anyway. travel aesthetics, interior inspiration, "things nobody tells you about x" hooks, niche opinions. the second pattern was volume, not virality. accounts running 8-10 tiktoks, posting 2x a day, same slide formats with fresh variations each time. 90% of posts stayed under 5k views, most even under 300. a handful hit 50k-500k. a rare one crossed 1m. the accounts winning weren't making one perfect post, they were running the format enough times that the algorithm found the winners for them. the workflow behind it, if you want to copy it: research first. search your niche on tiktok, screenshot every top slideshow, save the captions somewhere. this is the raw material for everything after. pull matching visuals from pinterest for each slide type in that screenshot pile. figure out what slide 1 looks like, slide 2, slide 3. download a handful per slide type. not everything from pinterest can be reposted as is. run it through an image api like openai or gemini to generate variations that keep the same vibe. 5 slide types x 100 variations gets you 500 usable images fast. feed the competitor captions into claude code and have it write new caption variations in that same tone, keep the early slides content only, drop the app in near the end. claude code can overlay the captions onto the images directly using ffmpeg, then hand scheduling off to a tool that allows accounts to post automatically without you touching them daily. set this up once and you get months of content queued across every account, running the same proven format with fresh visuals each time. the accounts losing were treating every post as a one off. the accounts winning built a system and let volume do the work.

Mufasa

43,574 Aufrufe • vor 1 Monat

Dear Dr. Sagar Preet Hooda, IPS Director General of Police, Chandigarh DGP Chandigarh Police Subject: Request for intervention regarding FIR No. 44 dated 19-04-2026 registered by East Sector 26 Police Station, Chandigarh 1. I write this letter through X with the sincere hope that it reaches your attention. The present issue concerns FIR No. 44 dated 19-04-2026 registered by East Sector 26 Police Station, Chandigarh against Professor Madhu Kishwar Madhu Purnima Kishwar and a few others under Sections 66C, 66D and 67 of the Information Technology Act, 2000, and Sections 196, 318, 336(1), 336(3), 336(4), 340, 353 and 356 of the Bharatiya Nyaya Sanhita, 2023. The FIR was registered on the complaint of one Satinder Singh, who alleged that a video clip was being circulated by various social media users with misleading claims that Prime Minister Narendra Modi was receiving a facial massage from a woman. According to the complaint, several social media accounts amplified this allegedly false narrative. As understood, the complainant further stated that his preliminary verification revealed that the video was originally posted by one Pardeep Kaur Dhillon through Facebook, YouTube and Instagram accounts on 12 April 2026, and that the person in the video was actually Jaspal Singh Sarai, who was apparently receiving a facial massage from a woman. However, even a plain reading of the complaint does not disclose the commission of any offence, much less a cognizable offence. Surprisingly, the police registered the FIR invoking an extraordinary range of provisions relating to identity theft, cheating, forgery, promoting enmity, defamation, public mischief, and even obscenity. 2. Let us briefly examine the applicability of each provision invoked in the FIR: (i) Section 66C of the Information Technology Act, 2000 criminalises fraudulent or dishonest use of another person’s electronic signature, password, or unique identification feature. The fundamental question is: whose identity has Professor Madhu Kishwar allegedly stolen? Was it the identity of Satinder Singh? If not, what is his locus standi in lodging such a complaint? If the allegation concerns some other person, has that person complained? In the absence of any such complaint, how is an offence under this section made out? (ii) Section 66D of the Information Technology Act, 2000 relates to cheating by personation through a communication device or computer resource. Whom did Professor Madhu Kishwar allegedly impersonate? Who was deceived, and what wrongful gain or loss occurred? Did she impersonate Satinder Singh or any other person? If not, how does the complainant acquire locus standi under this provision? (iii) Section 67 of the Information Technology Act, 2000 criminalises publication or transmission of obscene material in electronic form. The complainant himself states that the video was originally posted by Pardeep Kaur Dhillon and depicted Jaspal Singh Sarai receiving a facial massage from a woman. The video, by any objective standard, contains no obscene or lascivious material. Neither individuals were nude, nor was any sexual act depicted. It was merely a professional facial massage. Professor Madhu Kishwar merely reposted the content on X. Therefore, the essential ingredients of Section 67 are entirely absent. (iv) Section 196 of BNS concerns promoting enmity between groups on grounds such as religion, race, language, place of birth, or residence, and acts prejudicial to communal harmony. How does reposting a video of a man receiving a facial massage — even assuming someone believed the man resembled Prime Minister Narendra Modi — amount to promoting enmity between groups or disturbing public harmony? The provision appears wholly inapplicable. (v) Section 318 of BNS deals with the offence of cheating. The essential ingredients of cheating require deception causing wrongful loss, delivery of property, or inducement to act or omit in a manner causing harm. How does reposting a social media video satisfy any of these ingredients? Even assuming, for the sake of argument, that Prime Minister Narendra Modi felt personally aggrieved, the complaint would have to come from him or an authorised representative. What is the locus standi of Satinder Singh or any unrelated third party in this matter? (vi) Sections 336(1), 336(2) and 336(3) of BNS These provisions deal with forgery involving false documents or electronic records. How does reposting a pre-existing video amount to creating a false document or forged electronic record? No document was fabricated. No electronic record was forged. Again, even assuming that Prime Minister Narendra Modi was the aggrieved person, why has no complaint been filed either by him or by any authorised representative? How does Satinder Singh acquire standing in such circumstances? (vii) Section 340 of BNS concerns fraudulent or dishonest use of a forged document or electronic record. When there is no forged document at all, the provision itself cannot apply. Further, the alleged victim has neither filed nor authorised any complaint. In such circumstances, invocation of Section 340 appears entirely untenable. (viii) Section 353 of BNS concerns statements or reports intended to incite mutiny, offences against the State, public disorder, or inter-community violence. How can reposting a video of a man receiving a facial massage — even if someone attributes resemblance to Prime Minister Narendra Modi — possibly amount to incitement of mutiny, public disorder, or offences against the State? The invocation of this section is wholly unsustainable. (ix) Section 356 of BNS concerns defamation. However, Section 222 of the Bharatiya Nagarik Suraksha Sanhita, 2023 clearly provides that defamation can be prosecuted only by the aggrieved person. If the aggrieved person is a public servant, proceedings can be initiated only by a Public Prosecutor with prior sanction of the Central or State Government as the case may be. Neither Satinder Singh nor any unrelated individual has the legal standing to initiate such proceedings. 3. Dr. Hooda Ji, permit me to share a version of a well-known satirical anecdote about policing, which bears a striking resemblance to the present case, where Professor Madhu Kishwar is being compelled to endure the ordeal of criminal prosecution despite the absence of the essential ingredients of the offences invoked against her. A man sees an old friend running down the street — terrified, panting, and sweating. He stops him and asks, “What is wrong? Why are you running?” The friend replies, “The police are arresting all bulls!” The man says, “But you are not a bull. Why are you running?” The friend shouts back, “We both know I am not a bull. But by the time I prove it to the police and the courts, I will have spent the rest of my life in jail!” Terrified by hearing this, the other man also starts running. 4. Dr. Hooda Ji, I have heard that you are a competent, upright and distinguished officer. It is the solemn responsibility of senior officers of the IPS to prevent abuse of the criminal justice process and to ensure that policing does not degenerate into a spectacle in which criminal law is stretched beyond recognition to harass individuals for expressing views that may inconvenience those in power. 5. I therefore request your kind intervention to prevent the continued harassment and persecution of Professor Madhu Kishwar for her views on public issues, and to direct immediate closure of the case. 6. This letter has also been converted into video format and attached.👇 With Regards
0:20

Dear Dr. Sagar Preet Hooda, IPS Director General of Police, Chandigarh DGP Chandigarh Police Subject: Request for intervention regarding FIR No. 44 dated 19-04-2026 registered by East Sector 26 Police Station, Chandigarh 1. I write this letter through X with the sincere hope that it reaches your attention. The present issue concerns FIR No. 44 dated 19-04-2026 registered by East Sector 26 Police Station, Chandigarh against Professor Madhu Kishwar Madhu Purnima Kishwar and a few others under Sections 66C, 66D and 67 of the Information Technology Act, 2000, and Sections 196, 318, 336(1), 336(3), 336(4), 340, 353 and 356 of the Bharatiya Nyaya Sanhita, 2023. The FIR was registered on the complaint of one Satinder Singh, who alleged that a video clip was being circulated by various social media users with misleading claims that Prime Minister Narendra Modi was receiving a facial massage from a woman. According to the complaint, several social media accounts amplified this allegedly false narrative. As understood, the complainant further stated that his preliminary verification revealed that the video was originally posted by one Pardeep Kaur Dhillon through Facebook, YouTube and Instagram accounts on 12 April 2026, and that the person in the video was actually Jaspal Singh Sarai, who was apparently receiving a facial massage from a woman. However, even a plain reading of the complaint does not disclose the commission of any offence, much less a cognizable offence. Surprisingly, the police registered the FIR invoking an extraordinary range of provisions relating to identity theft, cheating, forgery, promoting enmity, defamation, public mischief, and even obscenity. 2. Let us briefly examine the applicability of each provision invoked in the FIR: (i) Section 66C of the Information Technology Act, 2000 criminalises fraudulent or dishonest use of another person’s electronic signature, password, or unique identification feature. The fundamental question is: whose identity has Professor Madhu Kishwar allegedly stolen? Was it the identity of Satinder Singh? If not, what is his locus standi in lodging such a complaint? If the allegation concerns some other person, has that person complained? In the absence of any such complaint, how is an offence under this section made out? (ii) Section 66D of the Information Technology Act, 2000 relates to cheating by personation through a communication device or computer resource. Whom did Professor Madhu Kishwar allegedly impersonate? Who was deceived, and what wrongful gain or loss occurred? Did she impersonate Satinder Singh or any other person? If not, how does the complainant acquire locus standi under this provision? (iii) Section 67 of the Information Technology Act, 2000 criminalises publication or transmission of obscene material in electronic form. The complainant himself states that the video was originally posted by Pardeep Kaur Dhillon and depicted Jaspal Singh Sarai receiving a facial massage from a woman. The video, by any objective standard, contains no obscene or lascivious material. Neither individuals were nude, nor was any sexual act depicted. It was merely a professional facial massage. Professor Madhu Kishwar merely reposted the content on X. Therefore, the essential ingredients of Section 67 are entirely absent. (iv) Section 196 of BNS concerns promoting enmity between groups on grounds such as religion, race, language, place of birth, or residence, and acts prejudicial to communal harmony. How does reposting a video of a man receiving a facial massage — even assuming someone believed the man resembled Prime Minister Narendra Modi — amount to promoting enmity between groups or disturbing public harmony? The provision appears wholly inapplicable. (v) Section 318 of BNS deals with the offence of cheating. The essential ingredients of cheating require deception causing wrongful loss, delivery of property, or inducement to act or omit in a manner causing harm. How does reposting a social media video satisfy any of these ingredients? Even assuming, for the sake of argument, that Prime Minister Narendra Modi felt personally aggrieved, the complaint would have to come from him or an authorised representative. What is the locus standi of Satinder Singh or any unrelated third party in this matter? (vi) Sections 336(1), 336(2) and 336(3) of BNS These provisions deal with forgery involving false documents or electronic records. How does reposting a pre-existing video amount to creating a false document or forged electronic record? No document was fabricated. No electronic record was forged. Again, even assuming that Prime Minister Narendra Modi was the aggrieved person, why has no complaint been filed either by him or by any authorised representative? How does Satinder Singh acquire standing in such circumstances? (vii) Section 340 of BNS concerns fraudulent or dishonest use of a forged document or electronic record. When there is no forged document at all, the provision itself cannot apply. Further, the alleged victim has neither filed nor authorised any complaint. In such circumstances, invocation of Section 340 appears entirely untenable. (viii) Section 353 of BNS concerns statements or reports intended to incite mutiny, offences against the State, public disorder, or inter-community violence. How can reposting a video of a man receiving a facial massage — even if someone attributes resemblance to Prime Minister Narendra Modi — possibly amount to incitement of mutiny, public disorder, or offences against the State? The invocation of this section is wholly unsustainable. (ix) Section 356 of BNS concerns defamation. However, Section 222 of the Bharatiya Nagarik Suraksha Sanhita, 2023 clearly provides that defamation can be prosecuted only by the aggrieved person. If the aggrieved person is a public servant, proceedings can be initiated only by a Public Prosecutor with prior sanction of the Central or State Government as the case may be. Neither Satinder Singh nor any unrelated individual has the legal standing to initiate such proceedings. 3. Dr. Hooda Ji, permit me to share a version of a well-known satirical anecdote about policing, which bears a striking resemblance to the present case, where Professor Madhu Kishwar is being compelled to endure the ordeal of criminal prosecution despite the absence of the essential ingredients of the offences invoked against her. A man sees an old friend running down the street — terrified, panting, and sweating. He stops him and asks, “What is wrong? Why are you running?” The friend replies, “The police are arresting all bulls!” The man says, “But you are not a bull. Why are you running?” The friend shouts back, “We both know I am not a bull. But by the time I prove it to the police and the courts, I will have spent the rest of my life in jail!” Terrified by hearing this, the other man also starts running. 4. Dr. Hooda Ji, I have heard that you are a competent, upright and distinguished officer. It is the solemn responsibility of senior officers of the IPS to prevent abuse of the criminal justice process and to ensure that policing does not degenerate into a spectacle in which criminal law is stretched beyond recognition to harass individuals for expressing views that may inconvenience those in power. 5. I therefore request your kind intervention to prevent the continued harassment and persecution of Professor Madhu Kishwar for her views on public issues, and to direct immediate closure of the case. 6. This letter has also been converted into video format and attached.👇 With Regards

M. Nageswara Rao IPS (Retired)

17,299 Aufrufe • vor 4 Monaten

🚨 The CEO of Antrhopic said a one-person billion-dollar company will exist by 2026 sounds crazy until you see what non-technical people are doing with Claude Code right now $10-50k/mo selling automation pipelines that take 1-2 weeks to set up Some ideas almost nobody's running yet: 1. Proposal & SOW generator for agencies and consultancies every agency writes proposals from scratch or copy-pastes from old ones and forgets to change the "client name" Claude reads the prospect brief or discovery call transcript, generates: - branded proposal with scope, timeline, deliverables - quick win plan (how exactly we will do a good output) - SOW with payment milestones - pricing options (good/better/best) - follow-up email sequence charge $500/mo per agency agencies close 20-30% more deals when proposals go out same day.. you're selling speed and save them $1k+ on the guy who does it manually and anyway not quality, without personalisation 2. Job posting-to-intel pipeline for sales teams companies reveal everything in their job postings and don't realize it Claude monitors target account career pages daily, flags: - "Head of AI" posted = they're buying, not building - 3 DevOps roles = scaling infrastructure = budget unlocked - new VP of Sales = restructuring = old vendor contracts up for review package it as buying signals delivered to Slack every morning $500-1,500/mo per sales team this is data that Apollo and ZoomInfo don't sell 3. Support ticket-to-documentation pipeline for SaaS every SaaS with 1,000+ users has the same problem.. docs are 6 months behind the product Claude crawls your help center, pulls recent Zendesk/Intercom tickets, cross-references finds questions asked 200 times last month with no matching article drafts the missing docs in your existing format.. flags stale articles for update $1,500-3,000/mo retainer the ROI math: 30% of support tickets deflected = thousands saved per month.. pipeline pays for itself week one 4. Vendor contract review & renewal tracker mid-size companies have 50-200 active vendor contracts sitting in folders nobody opens Claude reads each contract, extracts: - renewal dates and auto-renewal traps - termination notice windows - price escalation clauses - SLA commitments vs what you're actually getting delivers a dashboard with "contracts expiring in 30/60/90 days" and flags where you're overpaying $1,000-2,000/mo per company CFOs will approve this before you finish the pitch.. one caught auto-renewal pays for a year of your service 5. Employee onboarding doc generator for HR teams every company with 20+ employees has the same problem.. new hire starts Monday and nobody has their docs ready Claude reads the role title + department, generates: - personalized welcome packet - 30/60/90 day plan with milestones - tool access checklist by role - manager-specific onboarding schedule - policy summaries tailored to their department charge $300-500/mo per company HR managers spend 4-6 hours per new hire on this.. companies hiring 5+ people a month will never cancel the pattern is always the same most people will use Claude Code to build apps a small number will use it to sell pipelines to businesses still running on manual labor only the second group builds real recurring revenue the niches are wide open right now because every developer thinks this work is "too boring" boring = no competition = you set the price screenshot this. save this. repost it to save a friend's next year
0:10

🚨 The CEO of Antrhopic said a one-person billion-dollar company will exist by 2026 sounds crazy until you see what non-technical people are doing with Claude Code right now $10-50k/mo selling automation pipelines that take 1-2 weeks to set up Some ideas almost nobody's running yet: 1. Proposal & SOW generator for agencies and consultancies every agency writes proposals from scratch or copy-pastes from old ones and forgets to change the "client name" Claude reads the prospect brief or discovery call transcript, generates: - branded proposal with scope, timeline, deliverables - quick win plan (how exactly we will do a good output) - SOW with payment milestones - pricing options (good/better/best) - follow-up email sequence charge $500/mo per agency agencies close 20-30% more deals when proposals go out same day.. you're selling speed and save them $1k+ on the guy who does it manually and anyway not quality, without personalisation 2. Job posting-to-intel pipeline for sales teams companies reveal everything in their job postings and don't realize it Claude monitors target account career pages daily, flags: - "Head of AI" posted = they're buying, not building - 3 DevOps roles = scaling infrastructure = budget unlocked - new VP of Sales = restructuring = old vendor contracts up for review package it as buying signals delivered to Slack every morning $500-1,500/mo per sales team this is data that Apollo and ZoomInfo don't sell 3. Support ticket-to-documentation pipeline for SaaS every SaaS with 1,000+ users has the same problem.. docs are 6 months behind the product Claude crawls your help center, pulls recent Zendesk/Intercom tickets, cross-references finds questions asked 200 times last month with no matching article drafts the missing docs in your existing format.. flags stale articles for update $1,500-3,000/mo retainer the ROI math: 30% of support tickets deflected = thousands saved per month.. pipeline pays for itself week one 4. Vendor contract review & renewal tracker mid-size companies have 50-200 active vendor contracts sitting in folders nobody opens Claude reads each contract, extracts: - renewal dates and auto-renewal traps - termination notice windows - price escalation clauses - SLA commitments vs what you're actually getting delivers a dashboard with "contracts expiring in 30/60/90 days" and flags where you're overpaying $1,000-2,000/mo per company CFOs will approve this before you finish the pitch.. one caught auto-renewal pays for a year of your service 5. Employee onboarding doc generator for HR teams every company with 20+ employees has the same problem.. new hire starts Monday and nobody has their docs ready Claude reads the role title + department, generates: - personalized welcome packet - 30/60/90 day plan with milestones - tool access checklist by role - manager-specific onboarding schedule - policy summaries tailored to their department charge $300-500/mo per company HR managers spend 4-6 hours per new hire on this.. companies hiring 5+ people a month will never cancel the pattern is always the same most people will use Claude Code to build apps a small number will use it to sell pipelines to businesses still running on manual labor only the second group builds real recurring revenue the niches are wide open right now because every developer thinks this work is "too boring" boring = no competition = you set the price screenshot this. save this. repost it to save a friend's next year

Ronin

66,151 Aufrufe • vor 5 Monaten

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!
0:12

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

Anshu

179,451 Aufrufe • vor 2 Monaten