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18 / ai workflows print money / dm open testing @SpaceXSI so you don't have to. @grok @bot

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HE STRAPPED A BATTERY TO A $599 MAC MINI AND TURNED A DESK COMPUTER INTO A 14-HOUR PORTABLE AI WORKSTATION 00:03 the battery slides onto the side of the mac mini and the whole setup stops behaving like a desk machine. now it can run from a backpack, power a screen, hold local files and keep working without asking for an outlet. that changes the use case completely. instead of renting another cloud box, one silent computer can handle research dumps, meeting notes, scraped pages, voice transcripts and small automation jobs from almost anywhere. with claude connected, it becomes a moving command center. 45-minute calls become summaries, 120 saved links become organized notes, and messy project folders get cleaned while the machine quietly keeps working in the background. the interesting number is not the battery size. it is the avoided rent. one portable local box can replace $25 storage, $39 automation, $20 transcription and another $30 vps bill if the workflow is built correctly. this is no longer just a desktop. it becomes a portable ai machine that keeps working long after you leave the desk. bookmark this before portable ai becomes the new normal.

HE STRAPPED A BATTERY TO A $599 MAC MINI AND TURNED A DESK COMPUTER INTO A 14-HOUR PORTABLE AI WORKSTATION 00:03 the battery slides onto the side of the mac mini and the whole setup stops behaving like a desk machine. now it can run from a backpack, power a screen, hold local files and keep working without asking for an outlet. that changes the use case completely. instead of renting another cloud box, one silent computer can handle research dumps, meeting notes, scraped pages, voice transcripts and small automation jobs from almost anywhere. with claude connected, it becomes a moving command center. 45-minute calls become summaries, 120 saved links become organized notes, and messy project folders get cleaned while the machine quietly keeps working in the background. the interesting number is not the battery size. it is the avoided rent. one portable local box can replace $25 storage, $39 automation, $20 transcription and another $30 vps bill if the workflow is built correctly. this is no longer just a desktop. it becomes a portable ai machine that keeps working long after you leave the desk. bookmark this before portable ai becomes the new normal.

1,790,214 次观看

Holy sh*t, Grok Bot just did my After Effects job No keyframes, no timeline, no plugins. One message and a full 3D motion video was ready Ask it to animate your own terminal and see what comes back The full launch playbook with all 7 bots is in the article below ↓

Holy sh*t, Grok Bot just did my After Effects job No keyframes, no timeline, no plugins. One message and a full 3D motion video was ready Ask it to animate your own terminal and see what comes back The full launch playbook with all 7 bots is in the article below ↓

21,766 次观看

THIS GUY BOUGHT A $31 TOY DRONE AND TURNED CLAUDE OPUS 4.8 INTO ITS ENGINEER he plugged it into a laptop, explained the control logic in plain english and let Claude build the flight interface by the end of the session it had calibration, live controls and a browser cockpit moving the drone in real time most people will use Opus 4.8 to save 12 minutes on emails. he used it to turn cheap plastic into a working demo the crazy part isn’t the drone. it’s that the bottleneck moved from writing code to describing exactly what you want built while everyone debates benchmarks, someone with a $31 gadget and one afternoon is already shipping hardware demos

THIS GUY BOUGHT A $31 TOY DRONE AND TURNED CLAUDE OPUS 4.8 INTO ITS ENGINEER he plugged it into a laptop, explained the control logic in plain english and let Claude build the flight interface by the end of the session it had calibration, live controls and a browser cockpit moving the drone in real time most people will use Opus 4.8 to save 12 minutes on emails. he used it to turn cheap plastic into a working demo the crazy part isn’t the drone. it’s that the bottleneck moved from writing code to describing exactly what you want built while everyone debates benchmarks, someone with a $31 gadget and one afternoon is already shipping hardware demos

735,627 次观看

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

591,712 次观看

19-year-old girl spent $42 and 30 minutes, then made $9,600 from children’s videos she never appeared in. 4,100,000 views on YouTube, no camera, no face, no editor. The entire channel was built around kids content. One video uploaded in 2023 still brings in money every month with zero extra work. Dozens of videos like this, each one turning simple stories, bright visuals and voiceovers into small digital assets. $6,000+ a month from a faceless kids channel running in the background. $42 spent on tools, $9,600+ made from content she didn’t even film herself. The best children’s channel is the one that works without needing you on camera.

19-year-old girl spent $42 and 30 minutes, then made $9,600 from children’s videos she never appeared in. 4,100,000 views on YouTube, no camera, no face, no editor. The entire channel was built around kids content. One video uploaded in 2023 still brings in money every month with zero extra work. Dozens of videos like this, each one turning simple stories, bright visuals and voiceovers into small digital assets. $6,000+ a month from a faceless kids channel running in the background. $42 spent on tools, $9,600+ made from content she didn’t even film herself. The best children’s channel is the one that works without needing you on camera.

520,956 次观看

19-YEAR-OLD GUY MADE $12K IN 1 MINUTE FROM HIS FIRST SHOPIFY DROP Not a big brand. Not a perfect product. Just one clean page, one offer, short-form traffic, and Claude for $20/month helping him tighten everything before launch. He used it to study competitors, rewrite the page, pull pain points from reviews, and turn them into hooks people would actually click. Then the store went live. 53 Shopify alerts later, the phone showed a $12K minute. The same store did $100K in one day. Most people keep looking for the right product. He made the offer, page, and ads sharp enough before the traffic arrived.

459,308 次观看

A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one

A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one

305,341 次观看

A 40-YEAR-OLD CHINESE MAN TURNED HIMSELF INTO AN AI GIRL AND BUILT A $4.7K/MONTH FANVUE FUNNEL he started with one face-swap clip. same room, same body, same camera, but the output looked like a completely different creator. not a filter, not cosplay, an ai identity built on top of his own footage the workflow is stupidly simple: claude picks the character, niche and visual style. comfyui generates the face, flux makes the content bank, kling turns still images into reels and capcut pushes out dozens of short clips the article breaks the system down: first month barely cleared $420, second month jumped to $4,700 after one reel crossed 600,000 views. the account reached 800 followers and 50 fanvue subscribers before the real automation even started the fanvue mcp is the part most people miss. claude can analyze messages, pricing, content performance and draft replies in the same style. instead of guessing what to post or say, the system turns the audience into data he only needed around 40 minutes a day. schedule reels, check what worked, generate new prompts, reply with claude, repeat. the business is not the ai girl, the business is the machine around her this is no longer content creation. it is identity arbitrage

A 40-YEAR-OLD CHINESE MAN TURNED HIMSELF INTO AN AI GIRL AND BUILT A $4.7K/MONTH FANVUE FUNNEL he started with one face-swap clip. same room, same body, same camera, but the output looked like a completely different creator. not a filter, not cosplay, an ai identity built on top of his own footage the workflow is stupidly simple: claude picks the character, niche and visual style. comfyui generates the face, flux makes the content bank, kling turns still images into reels and capcut pushes out dozens of short clips the article breaks the system down: first month barely cleared $420, second month jumped to $4,700 after one reel crossed 600,000 views. the account reached 800 followers and 50 fanvue subscribers before the real automation even started the fanvue mcp is the part most people miss. claude can analyze messages, pricing, content performance and draft replies in the same style. instead of guessing what to post or say, the system turns the audience into data he only needed around 40 minutes a day. schedule reels, check what worked, generate new prompts, reply with claude, repeat. the business is not the ai girl, the business is the machine around her this is no longer content creation. it is identity arbitrage

196,560 次观看

THIS GUY TURNED HIMSELF INTO AN AI GIRL IN ONE SECOND. THE WORKFLOW BEHIND IT CAN RUN A $5,000/MONTH FANVUE PAGE IN 40 MINUTES A DAY 00:01 he raises his arms and instantly turns into an AI girl sitting in the same chair, ready to stream without ever showing his real face Claude creates the name, personality, captions and replies. one prompt can generate a full week of content while keeping the same character across TikTok and Fanvue ComfyUI with Flux builds the face and photo library. Kling 3.0 animates the images, then CapCut turns them into 30 to 50 short videos in one afternoon three daily streams and one clip reaching 400,000 views can send 70 buyers into a $15 Fanvue subscription. paid messages and tips are what push the page past $5,000 he stays behind the camera, Claude runs the brain and the AI girl becomes a business that works every day

THIS GUY TURNED HIMSELF INTO AN AI GIRL IN ONE SECOND. THE WORKFLOW BEHIND IT CAN RUN A $5,000/MONTH FANVUE PAGE IN 40 MINUTES A DAY 00:01 he raises his arms and instantly turns into an AI girl sitting in the same chair, ready to stream without ever showing his real face Claude creates the name, personality, captions and replies. one prompt can generate a full week of content while keeping the same character across TikTok and Fanvue ComfyUI with Flux builds the face and photo library. Kling 3.0 animates the images, then CapCut turns them into 30 to 50 short videos in one afternoon three daily streams and one clip reaching 400,000 views can send 70 buyers into a $15 Fanvue subscription. paid messages and tips are what push the page past $5,000 he stays behind the camera, Claude runs the brain and the AI girl becomes a business that works every day

133,196 次观看

18-YEAR-OLD GUY DID $412K IN SALES FROM ONE SHOPIFY STORE IN 3 MONTHS No warehouse, no big team, no “perfect product.” Just one Shopify store, short-form ads, and a system for fixing what was leaking every week. The screen shows $412,503 in sales, 4,085 orders, and a 4.58% conversion rate. Not because he found some magic product, but because the store kept getting sharper while everyone else was still copying the same pages. Claude helped him break down competitor listings, find patterns in bad reviews, clean up the offer, write simple ad scripts, and check the numbers every Sunday. Most people keep hunting for the next product. He kept fixing the page, the angle, and the ads until the traffic finally started buying.

18-YEAR-OLD GUY DID $412K IN SALES FROM ONE SHOPIFY STORE IN 3 MONTHS No warehouse, no big team, no “perfect product.” Just one Shopify store, short-form ads, and a system for fixing what was leaking every week. The screen shows $412,503 in sales, 4,085 orders, and a 4.58% conversion rate. Not because he found some magic product, but because the store kept getting sharper while everyone else was still copying the same pages. Claude helped him break down competitor listings, find patterns in bad reviews, clean up the offer, write simple ad scripts, and check the numbers every Sunday. Most people keep hunting for the next product. He kept fixing the page, the angle, and the ads until the traffic finally started buying.

144,208 次观看

A 19-year-old Chinese student spent $20 on Claude and built a YouTube automation system making $7,400/month. Found 12 small channels getting 300,000+ views from the same lazy formats, copied the structure, not the videos. Titles, thumbnails, pacing, hooks, upload schedule. 30 minutes later, Claude turned the winning patterns into 40 new video ideas. AI tools made the visuals, voiceovers and captions. CapCut packaged everything into Shorts. He didn’t edit a single video manually. Started testing 20-30 uploads a day across multiple channels. Most failed, but the winners kept pulling views automatically. Month 1 - $430 from test videos. Month 3 - $2,800. Month 6 - $7,400+ every month from channels running while he sleeps. University makes you pay $100,000 to learn theory for 4 years. He spent $20, opened 4 browser windows and let YouTube data tell him what to make next.

A 19-year-old Chinese student spent $20 on Claude and built a YouTube automation system making $7,400/month. Found 12 small channels getting 300,000+ views from the same lazy formats, copied the structure, not the videos. Titles, thumbnails, pacing, hooks, upload schedule. 30 minutes later, Claude turned the winning patterns into 40 new video ideas. AI tools made the visuals, voiceovers and captions. CapCut packaged everything into Shorts. He didn’t edit a single video manually. Started testing 20-30 uploads a day across multiple channels. Most failed, but the winners kept pulling views automatically. Month 1 - $430 from test videos. Month 3 - $2,800. Month 6 - $7,400+ every month from channels running while he sleeps. University makes you pay $100,000 to learn theory for 4 years. He spent $20, opened 4 browser windows and let YouTube data tell him what to make next.

137,223 次观看

JEV ENGINEERING JUST CUT A CLAUDE LOOP FROM $765/MONTH TO $3.02 the expensive part isn’t always writing code. a busy overnight loop can burn ~600 decisions just asking what to do next, whether a command is safe, or if the task is finished. trigger → Jev → allow / stop / wake → Claude only builds when needed at roughly 4,000 tokens per decision, those checks can cost ~$25.50 a night on Fable 5.1. Jev handles the same decision layer at $0.042 per 1M input tokens with output free. over 30 nights that moves the decision bill from ~$765 to $3.02. batch multiple questions into one call and it falls again to about $1.08/month. that’s the part i care about: Claude keeps the expensive work that actually needs intelligence, while Jev handles the hundreds of tiny judgment calls around it.

JEV ENGINEERING JUST CUT A CLAUDE LOOP FROM $765/MONTH TO $3.02 the expensive part isn’t always writing code. a busy overnight loop can burn ~600 decisions just asking what to do next, whether a command is safe, or if the task is finished. trigger → Jev → allow / stop / wake → Claude only builds when needed at roughly 4,000 tokens per decision, those checks can cost ~$25.50 a night on Fable 5.1. Jev handles the same decision layer at $0.042 per 1M input tokens with output free. over 30 nights that moves the decision bill from ~$765 to $3.02. batch multiple questions into one call and it falls again to about $1.08/month. that’s the part i care about: Claude keeps the expensive work that actually needs intelligence, while Jev handles the hundreds of tiny judgment calls around it.

25,081 次观看

18-year-old built a faceless brainrot channel and pulled almost $200,000 in 28 days. 88,783,873 views on YouTube, never appeared on camera once, no studio, no editor, no personal brand. One format repeated hundreds of times: short clips, loud captions, fast pacing, simple hooks and loops designed to make people rewatch. The channel gained 543,000 subscribers in a month. One video crossed 16,000,000 views while even the weaker uploads got more attention than most creators get in a year. Claude turns trends into scripts, AI voice reads them, CapCut packages everything before the trend dies. Old laptop, cheap AI tools, almost $200,000 in estimated revenue. Most people laugh at brainrot content. The algorithm only counts watch time.

83,562 次观看

THIS GUY SAW A $430 AI BILL AND BUILT HIS OWN AI LAB UNDER HIS DESK INSTEAD RTX 5090 + RTX 4090, 56GB VRAM, 128GB RAM, Proxmox and local Qwen / DeepSeek / Llama models running without API keys while everyone else is still paying every time they test a prompt. The best part of the setup: api_key: “not-needed”. His agents can scan GitHub, Reddit and RSS feeds, read notes, test ideas overnight and break without turning into another invoice. If something fails, he fixes the config, not the credit card limit. Most people rent AI by the token. He is turning a desk setup into a private machine that works even when the dashboard is closed.

THIS GUY SAW A $430 AI BILL AND BUILT HIS OWN AI LAB UNDER HIS DESK INSTEAD RTX 5090 + RTX 4090, 56GB VRAM, 128GB RAM, Proxmox and local Qwen / DeepSeek / Llama models running without API keys while everyone else is still paying every time they test a prompt. The best part of the setup: api_key: “not-needed”. His agents can scan GitHub, Reddit and RSS feeds, read notes, test ideas overnight and break without turning into another invoice. If something fails, he fixes the config, not the credit card limit. Most people rent AI by the token. He is turning a desk setup into a private machine that works even when the dashboard is closed.

69,445 次观看

19-YEAR-OLD GUY BOUGHT A $90K BMW M3 AFTER HIS SHOPIFY STORE HIT $287K IN 30 DAYS The Shopify app shows $287K in sales, 50+ orders waiting, and a +4,533% jump in the last 30 days. A few clips later, he is filming a white BMW M3 at the gas station. His setup was simple: one Shopify store, short-form videos, a few products to test, and a $20/month Claude subscription helping him fix the page before sending more traffic. Claude helped rewrite the offer, find better angles from reviews, study competitors, and turn weak product benefits into ads people actually click. Most people keep searching for the perfect product. He kept improving the page, the hook, and the offer until the store started doing numbers. That’s the part people miss: the money didn’t come from one lucky product. It came from testing faster than everyone else.

19-YEAR-OLD GUY BOUGHT A $90K BMW M3 AFTER HIS SHOPIFY STORE HIT $287K IN 30 DAYS The Shopify app shows $287K in sales, 50+ orders waiting, and a +4,533% jump in the last 30 days. A few clips later, he is filming a white BMW M3 at the gas station. His setup was simple: one Shopify store, short-form videos, a few products to test, and a $20/month Claude subscription helping him fix the page before sending more traffic. Claude helped rewrite the offer, find better angles from reviews, study competitors, and turn weak product benefits into ads people actually click. Most people keep searching for the perfect product. He kept improving the page, the hook, and the offer until the store started doing numbers. That’s the part people miss: the money didn’t come from one lucky product. It came from testing faster than everyone else.

46,135 次观看

A 17-year-old guy skipped school and made around $9,300 in one month on YouTube. He used Claude to find trending formats for kids’ and ASMR videos, generate scripts instantly, and automate production. Editing, subtitles, and clips were done in minutes without filming a single frame. Four channels were launched simultaneously, each optimized for maximum reach. He spent just $35 on software and tools to make it all work. In one month, the channels totaled over 11.3 million views, generating roughly $9,300 from ads and small sponsorships. Revenue flows automatically while he tests new video ideas. Claude also helped him track performance, optimize content, and double output efficiency within the month.

A 17-year-old guy skipped school and made around $9,300 in one month on YouTube. He used Claude to find trending formats for kids’ and ASMR videos, generate scripts instantly, and automate production. Editing, subtitles, and clips were done in minutes without filming a single frame. Four channels were launched simultaneously, each optimized for maximum reach. He spent just $35 on software and tools to make it all work. In one month, the channels totaled over 11.3 million views, generating roughly $9,300 from ads and small sponsorships. Revenue flows automatically while he tests new video ideas. Claude also helped him track performance, optimize content, and double output efficiency within the month.

41,429 次观看

holy sh*t xai basically built an app store for grok bot instead of hard-coding every workflow, this repo lets you keep adding new capabilities to grok through plugins and reusable integrations [official xai marketplace - 200+ stars] 1/ add new tools without rebuilding the agent 2/ plug in reusable workflows and commands 3/ extend grok with external services and dev tooling the interesting part is that grok bot stops being one fixed assistant and turns into something you can keep upgrading around your own workflow this is probably the repo to study if you want to build on top of grok instead of just prompting it

holy sh*t xai basically built an app store for grok bot instead of hard-coding every workflow, this repo lets you keep adding new capabilities to grok through plugins and reusable integrations [official xai marketplace - 200+ stars] 1/ add new tools without rebuilding the agent 2/ plug in reusable workflows and commands 3/ extend grok with external services and dev tooling the interesting part is that grok bot stops being one fixed assistant and turns into something you can keep upgrading around your own workflow this is probably the repo to study if you want to build on top of grok instead of just prompting it

15,598 次观看

A 20-YEAR-OLD GIRL TURNED WORLD CUP 2026 + AI INTO $3,400 IN THE FIRST 4 DAYS she is not filming matches, stealing goals or running a football media page. she is turning basic world cup questions into youtube shorts for americans watching soccer for the first time the tournament has 48 teams, 104 matches and 3 host countries. every match creates a fresh search wave: what is offside, how do penalties work, who is this player, why is everyone talking about brazil her setup is simple. claude writes the script, an ai voice reads it, capcut cuts the video and canva makes the graphics. one short takes 15 to 20 minutes, so she can post 4 to 6 videos a day the niche is the whole edge. not random edits, not stolen highlights, not generic world cup news. just rules, player stories and country explainers for people who opened youtube after the match confused after just 4 days, one offside explainer crossed 820k views. two player stories passed 400k. with shorts revenue, affiliate links and one small sponsor slot, the channel cleared around $3,400 the crazy part is that the world cup has barely started. most creators are fighting over the same clips while she is selling explanations to a new audience with zero football context this is why timing beats talent. when millions of people search the same questions at the same time, the boring content machine wins

A 20-YEAR-OLD GIRL TURNED WORLD CUP 2026 + AI INTO $3,400 IN THE FIRST 4 DAYS she is not filming matches, stealing goals or running a football media page. she is turning basic world cup questions into youtube shorts for americans watching soccer for the first time the tournament has 48 teams, 104 matches and 3 host countries. every match creates a fresh search wave: what is offside, how do penalties work, who is this player, why is everyone talking about brazil her setup is simple. claude writes the script, an ai voice reads it, capcut cuts the video and canva makes the graphics. one short takes 15 to 20 minutes, so she can post 4 to 6 videos a day the niche is the whole edge. not random edits, not stolen highlights, not generic world cup news. just rules, player stories and country explainers for people who opened youtube after the match confused after just 4 days, one offside explainer crossed 820k views. two player stories passed 400k. with shorts revenue, affiliate links and one small sponsor slot, the channel cleared around $3,400 the crazy part is that the world cup has barely started. most creators are fighting over the same clips while she is selling explanations to a new audience with zero football context this is why timing beats talent. when millions of people search the same questions at the same time, the boring content machine wins

27,412 次观看

A CHINESE GUY PUT 4 MINISFORUM MS-S1 MAX MINI PCs IN HIS BEDROOM AND TURNED THEM INTO A 24/7 AI AGENT CLUSTER. TOTAL POWER BILL: ABOUT $44/MO. each box is a tiny local AI workstation built around the Ryzen AI Max+ 395. around $3,000 per unit gets him 128GB of unified memory, 2TB storage, dual 10GbE, and up to roughly 96GB usable as VRAM on Linux. one MS-S1 Max can already run serious open models without touching the cloud. Qwen3-Coder 30B for fast coding, Llama 3.3 70B for heavier reasoning, and larger research models overnight when speed matters less than free inference. four boxes in one room changes the whole game. he is not opening a chatbot, paying for every loop, or shutting agents down before sleep. this is private infrastructure that keeps working even when he is offline. the agents can sort inboxes, review code, summarize documents, monitor feeds, prep meetings, and read papers overnight. on cloud APIs, that kind of always-on stack can easily burn $800 to $1,200 a month if used aggressively. his setup is roughly a $12,000 hardware spend, but the monthly cost is basically electricity. a rack, a switch, a NAS, a small monitor, and four tiny MS-S1 Max boxes turning a bedroom corner into a private inference factory. this is what AI looks like when it stops being rented and starts becoming something you own.

A CHINESE GUY PUT 4 MINISFORUM MS-S1 MAX MINI PCs IN HIS BEDROOM AND TURNED THEM INTO A 24/7 AI AGENT CLUSTER. TOTAL POWER BILL: ABOUT $44/MO. each box is a tiny local AI workstation built around the Ryzen AI Max+ 395. around $3,000 per unit gets him 128GB of unified memory, 2TB storage, dual 10GbE, and up to roughly 96GB usable as VRAM on Linux. one MS-S1 Max can already run serious open models without touching the cloud. Qwen3-Coder 30B for fast coding, Llama 3.3 70B for heavier reasoning, and larger research models overnight when speed matters less than free inference. four boxes in one room changes the whole game. he is not opening a chatbot, paying for every loop, or shutting agents down before sleep. this is private infrastructure that keeps working even when he is offline. the agents can sort inboxes, review code, summarize documents, monitor feeds, prep meetings, and read papers overnight. on cloud APIs, that kind of always-on stack can easily burn $800 to $1,200 a month if used aggressively. his setup is roughly a $12,000 hardware spend, but the monthly cost is basically electricity. a rack, a switch, a NAS, a small monitor, and four tiny MS-S1 Max boxes turning a bedroom corner into a private inference factory. this is what AI looks like when it stops being rented and starts becoming something you own.

24,836 次观看

A 24-YEAR-OLD STUDENT FROM BERLIN TURNED MUSIC ROYALTY DATA INTO A $14,250/MONTH PROMO BUSINESS he found out most indie artists do not have a music problem. they have a distribution problem. spotify, tiktok and youtube do not reward the best track first, they reward repeatable attention, saves, playlist signals and listener retention his setup was simple. one laptop, royalty analytics, playlist research, short-form clips and outreach automation. instead of chasing fake streams, he built a system that finds small artists with good songs and pushes them into real listener loops every track gets packaged into 15 to 30 clips, tested across tiktok, reels and shorts, then routed back into spotify through landing pages and playlist pitches. the whole point is to create real discovery before the algorithm buries the song after 30 days, the system had promoted 50+ tracks for indie producers, underground rappers and low-fi artists. his service fee starts at $500 per track, with bigger packages going to $1,500 when artists want clips, outreach and reporting the crazy part is that he does not need a label, studio or team. just music data, content distribution and a repeatable backend that artists are too lazy to build themselves this is what music promo looks like when it becomes software

A 24-YEAR-OLD STUDENT FROM BERLIN TURNED MUSIC ROYALTY DATA INTO A $14,250/MONTH PROMO BUSINESS he found out most indie artists do not have a music problem. they have a distribution problem. spotify, tiktok and youtube do not reward the best track first, they reward repeatable attention, saves, playlist signals and listener retention his setup was simple. one laptop, royalty analytics, playlist research, short-form clips and outreach automation. instead of chasing fake streams, he built a system that finds small artists with good songs and pushes them into real listener loops every track gets packaged into 15 to 30 clips, tested across tiktok, reels and shorts, then routed back into spotify through landing pages and playlist pitches. the whole point is to create real discovery before the algorithm buries the song after 30 days, the system had promoted 50+ tracks for indie producers, underground rappers and low-fi artists. his service fee starts at $500 per track, with bigger packages going to $1,500 when artists want clips, outreach and reporting the crazy part is that he does not need a label, studio or team. just music data, content distribution and a repeatable backend that artists are too lazy to build themselves this is what music promo looks like when it becomes software

21,826 次观看

Videos

Anya Rossi

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HOLY SH*T, I BUILT A F**KING COMPANY INSIDE GROK BOT i split the operation into separate roles for research, writing, outreach, ops, finance and support. each agent owns one narrow job 6 agents → 1 shared operation → automatic handoffs → human approval each bot keeps its own workflow and memory, then passes finished work forward instead of routing everything back through me the control layer catches anything that sends, spends, publishes or deletes and pushes it into approval before anything happens after a few runs this stops feeling like ai chat. it feels like a 24/7 operations team running behind one dashboard

HOLY SH*T, I BUILT A F**KING COMPANY INSIDE GROK BOT i split the operation into separate roles for research, writing, outreach, ops, finance and support. each agent owns one narrow job 6 agents → 1 shared operation → automatic handoffs → human approval each bot keeps its own workflow and memory, then passes finished work forward instead of routing everything back through me the control layer catches anything that sends, spends, publishes or deletes and pushes it into approval before anything happens after a few runs this stops feeling like ai chat. it feels like a 24/7 operations team running behind one dashboard

1,395,087 次观看 • 2 个月前

claude + REA can rebuild software features without copying a single line of code opus 5.5 reads what REA finds inside a program, then rebuilds the feature in a separate clean-room setup. here's how to do it: 1. run npx rea-agents setup to connect REA to Claude Code 2. pick a feature and let REA figure out how it works. save the behavior and rules in a spec file 3. start a fresh Claude session with only that spec. rebuild the feature without touching the original code 4. use /goal to run the tests and check 3 intentionally broken versions. all 3 must fail one project recovered 697 functions in just 12 days, with 696 exact matches. the real trick is building tests that catch mistakes before they become part of the finished software.

claude + REA can rebuild software features without copying a single line of code opus 5.5 reads what REA finds inside a program, then rebuilds the feature in a separate clean-room setup. here's how to do it: 1. run npx rea-agents setup to connect REA to Claude Code 2. pick a feature and let REA figure out how it works. save the behavior and rules in a spec file 3. start a fresh Claude session with only that spec. rebuild the feature without touching the original code 4. use /goal to run the tests and check 3 intentionally broken versions. all 3 must fail one project recovered 697 functions in just 12 days, with 696 exact matches. the real trick is building tests that catch mistakes before they become part of the finished software.

140,768 次观看 • 11 天前

your Grok Bot routine is quietly burning $397 a month. until you add one prompt. a routine on a 5-min clock wakes 8,640 times a month and does the full job every single time, even when nothing changed. the fix is a change check before every run: 1. read one cheap signal 2. compare it with the last state 3. no diff, stop 4. diff, run the heavy work same routine, same 5 minutes. $397 drops to $45. that's -89%. paste this into your Grok Bot: add a change check to all my routines. check first, run only on diff save this before your weekly pool hits zero. full article ↓

your Grok Bot routine is quietly burning $397 a month. until you add one prompt. a routine on a 5-min clock wakes 8,640 times a month and does the full job every single time, even when nothing changed. the fix is a change check before every run: 1. read one cheap signal 2. compare it with the last state 3. no diff, stop 4. diff, run the heavy work same routine, same 5 minutes. $397 drops to $45. that's -89%. paste this into your Grok Bot: add a change check to all my routines. check first, run only on diff save this before your weekly pool hits zero. full article ↓

22,457 次观看 • 2 天前

i'm f**king done with Grok Bots waking up 8,640 times a month just to find nothing changed... one routine running every 5 minutes costs roughly $397/month at Grok 4.6 API rates. run it hourly and that drops to $33. same bot, same job, 12x the estimated cost. so here's how i'd structure a 6-bot crew that actually makes sense overnight. [the setup looks like this] 1. chief routes events, not noise 2. scout checks what actually changed 3. quant compares the last saved state 4. risk returns PASS / CUT / KILL 5. exec handles safe, approved actions 6. scribe logs every run and queues drafts the trick isn't making 6 bots work harder. it's stopping them from waking up for nothing. add a cheap change check before every full run and my cost model drops from $397 to $45 (-89%). anything risky at 3 a.m. stays a draft until you're awake. that's the Grok Bot setup i'd actually trust overnight. full breakdown in the article below ↓

i'm f**king done with Grok Bots waking up 8,640 times a month just to find nothing changed... one routine running every 5 minutes costs roughly $397/month at Grok 4.6 API rates. run it hourly and that drops to $33. same bot, same job, 12x the estimated cost. so here's how i'd structure a 6-bot crew that actually makes sense overnight. [the setup looks like this] 1. chief routes events, not noise 2. scout checks what actually changed 3. quant compares the last saved state 4. risk returns PASS / CUT / KILL 5. exec handles safe, approved actions 6. scribe logs every run and queues drafts the trick isn't making 6 bots work harder. it's stopping them from waking up for nothing. add a cheap change check before every full run and my cost model drops from $397 to $45 (-89%). anything risky at 3 a.m. stays a draft until you're awake. that's the Grok Bot setup i'd actually trust overnight. full breakdown in the article below ↓

113,302 次观看 • 15 天前

A 19-year-old girl makes $50,000/month from automated kids’ YouTube videos. She found a viral video with 2.2M views, copied the format, and made her own. Bright visuals, fun animations, satisfying endings kids love. It took her 30 minutes to set up using $40 on Picsart for graphics and CapCut for editing and captions. Uploaded as Shorts, views climbed fast. Week 1: $6,000, week 2: $18,500, week 3: $50,000 from one channel. Now she runs three kids’ channels, making $120,000/month, fully automated. She never appears on camera, didn’t film a single clip herself, and the system handles uploads, captions, and scheduling automatically. One person, a laptop, and simple tools now generate what used to take full teams.

A 19-year-old girl makes $50,000/month from automated kids’ YouTube videos. She found a viral video with 2.2M views, copied the format, and made her own. Bright visuals, fun animations, satisfying endings kids love. It took her 30 minutes to set up using $40 on Picsart for graphics and CapCut for editing and captions. Uploaded as Shorts, views climbed fast. Week 1: $6,000, week 2: $18,500, week 3: $50,000 from one channel. Now she runs three kids’ channels, making $120,000/month, fully automated. She never appears on camera, didn’t film a single clip herself, and the system handles uploads, captions, and scheduling automatically. One person, a laptop, and simple tools now generate what used to take full teams.

1,446,005 次观看 • 5 个月前

A 20-year-old Japanese guy is turning kids’ AI videos into a $12,000/month main income. He didn’t hire animators or build a studio. He found a simple format: bright nursery characters, short stories, catchy songs, clean captions, and videos kids can replay nonstop. The numbers are wild. One channel he shows has 28.2M subscribers, 771 videos, and three Shorts with 32M, 3.4M, and 42M views. That’s around 77M views from just three videos. His math: 77M Shorts views can be roughly $15,400. The setup is simple: Claude for ideas, scripts, and structure. Then AI visuals, voice, and CapCut turn it into repeatable kids’ content. Kids don’t care who made the video. They care if it’s bright, simple, loud, and worth watching again. That’s the entire business.

A 20-year-old Japanese guy is turning kids’ AI videos into a $12,000/month main income. He didn’t hire animators or build a studio. He found a simple format: bright nursery characters, short stories, catchy songs, clean captions, and videos kids can replay nonstop. The numbers are wild. One channel he shows has 28.2M subscribers, 771 videos, and three Shorts with 32M, 3.4M, and 42M views. That’s around 77M views from just three videos. His math: 77M Shorts views can be roughly $15,400. The setup is simple: Claude for ideas, scripts, and structure. Then AI visuals, voice, and CapCut turn it into repeatable kids’ content. Kids don’t care who made the video. They care if it’s bright, simple, loud, and worth watching again. That’s the entire business.

588,844 次观看 • 5 个月前

A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid

A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid

459,617 次观看 • 4 个月前

Grok Bot users: you're paying for 94 out of 96 overnight runs that find nothing. one skill fixes it: /night-filter save it as a custom skill, then split the night between 4 bots: 1. SCOUT checks for changes every 5 min 2. QUANT kills the full run if nothing moved 3. EXEC does the real work 4. RISK blocks anything you can't undo my last night: 96 checks, 2 full runs, 0 irreversible actions. the expensive part was never the model. it was waking up for nothing. save this before your next overnight routine.

Grok Bot users: you're paying for 94 out of 96 overnight runs that find nothing. one skill fixes it: /night-filter save it as a custom skill, then split the night between 4 bots: 1. SCOUT checks for changes every 5 min 2. QUANT kills the full run if nothing moved 3. EXEC does the real work 4. RISK blocks anything you can't undo my last night: 96 checks, 2 full runs, 0 irreversible actions. the expensive part was never the model. it was waking up for nothing. save this before your next overnight routine.

35,255 次观看 • 12 天前

this is the most UNDERRATED Grok Bot setup right now 1 router + 4 specialist agents. no polling, no wasted runs, nothing fires until something actually needs action i switched after doing the math: a routine every 5 minutes costs 12x more than hourly. $397 a month vs $33 for the same job → webhooks, Slack, GitHub and email all hit one router → most events get ignored on purpose, zero wasted runs → 4 specialist agents wake up only when there's real work → risky actions stop at a draft before they reach you stop polling every 5 minutes. let events wake your agents. here's the exact setup, send it to your Grok Bot ↓

this is the most UNDERRATED Grok Bot setup right now 1 router + 4 specialist agents. no polling, no wasted runs, nothing fires until something actually needs action i switched after doing the math: a routine every 5 minutes costs 12x more than hourly. $397 a month vs $33 for the same job → webhooks, Slack, GitHub and email all hit one router → most events get ignored on purpose, zero wasted runs → 4 specialist agents wake up only when there's real work → risky actions stop at a draft before they reach you stop polling every 5 minutes. let events wake your agents. here's the exact setup, send it to your Grok Bot ↓

29,431 次观看 • 10 天前

xAI Grok Bot has a f**king brutal 3 a.m. problem: 96 wakeups while you sleep, and nobody is there to approve anything [the overnight rule] give your Grok Bot crew 3 hard stoplines: → SCOUT checks what changed before waking the full team → EXEC handles safe, reversible actions on its own → RISK leaves anything irreversible as a draft background approvals expire after ~10 minutes. your bots can't wait until breakfast. save this before your next overnight run. full guide below ↓

xAI Grok Bot has a f**king brutal 3 a.m. problem: 96 wakeups while you sleep, and nobody is there to approve anything [the overnight rule] give your Grok Bot crew 3 hard stoplines: → SCOUT checks what changed before waking the full team → EXEC handles safe, reversible actions on its own → RISK leaves anything irreversible as a draft background approvals expire after ~10 minutes. your bots can't wait until breakfast. save this before your next overnight run. full guide below ↓

34,631 次观看 • 14 天前

Grok Bot users: paste this ONE prompt NOW before your routines burn another $350 this month [check first, run only on diff] 1. every 5 min it compares a hash to the last run 2. nothing changed, it skips in milliseconds 3. real change, it drafts a plan and waits for your tap 4. non-urgent stuff waits until 07:00 in the video: 176 checks, 171 skipped, 4 real runs 1. full run every 5 min: $397/mo 2. hourly: $33/mo, but you're up to an hour late 3. every 5 min + check: $45/mo copy this into your Grok Bot: for every routine: hash check before the full run, skip if nothing changed, hold non-urgent diffs until 07:00, drafts before any send. show me the cost before and after, change nothing until I say go. wake up for changes, not for the clock

Grok Bot users: paste this ONE prompt NOW before your routines burn another $350 this month [check first, run only on diff] 1. every 5 min it compares a hash to the last run 2. nothing changed, it skips in milliseconds 3. real change, it drafts a plan and waits for your tap 4. non-urgent stuff waits until 07:00 in the video: 176 checks, 171 skipped, 4 real runs 1. full run every 5 min: $397/mo 2. hourly: $33/mo, but you're up to an hour late 3. every 5 min + check: $45/mo copy this into your Grok Bot: for every routine: hash check before the full run, skip if nothing changed, hold non-urgent diffs until 07:00, drafts before any send. show me the cost before and after, change nothing until I say go. wake up for changes, not for the clock

20,540 次观看 • 8 天前

F**KING INSANE, GROK BOT IS TRYING TO FIND A TREATMENT FOR A DOG WITH A DISEASE THAT HAS NO CURE 6 agents, 1 patient, 0 candidates and 0 doses. milo is still at 1.4/10 mobility, but on day 2 the crew finally gets the structure it needs to start working chief → assigns charter → defines owns → defines stops → signs role → unlocks vote scout owns literature, chem owns molecule design, tox owns safety, vitals watches milo, scribe keeps evidence, and chief routes the whole case the important part is what happens before research starts. no agent can vote until its charter is signed, and every role gets a hard stopline showing exactly where it must stop by the end, 6/6 charters are active. still 0 candidates, still 0 doses, still 1.4/10 mobility. no treatment yet, but now the crew is finally allowed to search for one. bookmark this

F**KING INSANE, GROK BOT IS TRYING TO FIND A TREATMENT FOR A DOG WITH A DISEASE THAT HAS NO CURE 6 agents, 1 patient, 0 candidates and 0 doses. milo is still at 1.4/10 mobility, but on day 2 the crew finally gets the structure it needs to start working chief → assigns charter → defines owns → defines stops → signs role → unlocks vote scout owns literature, chem owns molecule design, tox owns safety, vitals watches milo, scribe keeps evidence, and chief routes the whole case the important part is what happens before research starts. no agent can vote until its charter is signed, and every role gets a hard stopline showing exactly where it must stop by the end, 6/6 charters are active. still 0 candidates, still 0 doses, still 1.4/10 mobility. no treatment yet, but now the crew is finally allowed to search for one. bookmark this

132,604 次观看 • 2 个月前

A GROK ENGINEER JUST SHOWED HOW TO MAKE AGENTS IMPROVE THEMSELVES AFTER EVERY RUN most agents lose the useful part when a task ends: the mistake, the correction, and the reason the final version actually worked grok runs two loops at once → one handles the real task → another studies the result → extracts the lesson → saves it for the next run instead of growing one massive prompt, feedback becomes small reusable knowledge files. bad decisions get corrected once, while useful patterns survive across future tasks over time, research, coding and review all inherit what previous runs learned without retraining the model or rebuilding the agent from scratch this is where grok stops feeling like something you keep correcting and starts behaving like a system that compounds what it learns

A GROK ENGINEER JUST SHOWED HOW TO MAKE AGENTS IMPROVE THEMSELVES AFTER EVERY RUN most agents lose the useful part when a task ends: the mistake, the correction, and the reason the final version actually worked grok runs two loops at once → one handles the real task → another studies the result → extracts the lesson → saves it for the next run instead of growing one massive prompt, feedback becomes small reusable knowledge files. bad decisions get corrected once, while useful patterns survive across future tasks over time, research, coding and review all inherit what previous runs learned without retraining the model or rebuilding the agent from scratch this is where grok stops feeling like something you keep correcting and starts behaving like a system that compounds what it learns

90,342 次观看 • 1 个月前

dots + gpt-6 astra is f**king insane. sam altman just shipped an agent that can keep working after you close chatgpt. this setup turns 5 dots into an always-on launch team. [the part people will miss: each dot owns a job] 1. researcher watches feedback, docs and new signals, then routes what matters. 2. app builder takes repeated requests, builds fixes and hands back a tested PR. 3. launch lead updates positioning and launch assets whenever the product changes. 4. sales lead tracks requirements, checks product docs and prepares the next move. 5. creator turns new interviews and updates into clips, copy and social drafts. same launch. 5 workers. one coordinated system. approvals only when the action actually needs you.

dots + gpt-6 astra is f**king insane. sam altman just shipped an agent that can keep working after you close chatgpt. this setup turns 5 dots into an always-on launch team. [the part people will miss: each dot owns a job] 1. researcher watches feedback, docs and new signals, then routes what matters. 2. app builder takes repeated requests, builds fixes and hands back a tested PR. 3. launch lead updates positioning and launch assets whenever the product changes. 4. sales lead tracks requirements, checks product docs and prepares the next move. 5. creator turns new interviews and updates into clips, copy and social drafts. same launch. 5 workers. one coordinated system. approvals only when the action actually needs you.

44,499 次观看 • 23 天前

26-YEAR-OLD GIRL QUIT HER JOB AND BUILT A SMALL SOCIAL NETWORK FOR NYC CAFES. 31,000 USERS IN 4 MONTHS. NOW IT MAKES $18,400/MO FROM LOCAL SHOPS she didn’t try to build another instagram. she built a tiny app for one obsessed niche: people who save cafes, share lists, follow local taste, and discover places before they go viral on tiktok the product is simple. profiles, posts, likes, saved lists, search, comments, and a feed. that is basically every social network at the core. the hard part was never the idea. it was needing 5 engineers to make it work claude code changes that. one person can now build auth, database, feed logic, profiles, follow system, storage, notifications, and deployment with supabase and vercel instead of hiring a full team the money is not from ads. cafes pay $199/month to claim their page, boost new menu drops, see which lists saved them, and post local offers. power users pay $8/month for private maps and early city guides most people still think social networks need millions of users. she proved the opposite. 10,000 people in one niche can be worth more than 100,000 random users scrolling for nothing small social networks are the new local media businesses. claude code just made them cheap enough for one person to start one from a laptop

26-YEAR-OLD GIRL QUIT HER JOB AND BUILT A SMALL SOCIAL NETWORK FOR NYC CAFES. 31,000 USERS IN 4 MONTHS. NOW IT MAKES $18,400/MO FROM LOCAL SHOPS she didn’t try to build another instagram. she built a tiny app for one obsessed niche: people who save cafes, share lists, follow local taste, and discover places before they go viral on tiktok the product is simple. profiles, posts, likes, saved lists, search, comments, and a feed. that is basically every social network at the core. the hard part was never the idea. it was needing 5 engineers to make it work claude code changes that. one person can now build auth, database, feed logic, profiles, follow system, storage, notifications, and deployment with supabase and vercel instead of hiring a full team the money is not from ads. cafes pay $199/month to claim their page, boost new menu drops, see which lists saved them, and post local offers. power users pay $8/month for private maps and early city guides most people still think social networks need millions of users. she proved the opposite. 10,000 people in one niche can be worth more than 100,000 random users scrolling for nothing small social networks are the new local media businesses. claude code just made them cheap enough for one person to start one from a laptop

224,469 次观看 • 4 个月前

jev + opus 5.5 is one of those setups that makes agent architecture feel weirdly overcomplicated in hindsight. the useful split isn’t “small model vs big model.” it’s deciding which calls actually need generation at all. → jev routes the next worker → scores which context is worth keeping → catches repeated actions before they burn the run → gates risky tool calls and sends uncertainty to review opus still owns the expensive part: planning, writing, coding, synthesis. jev sits around it as the decision layer. that matters because a typical loop can have 11 decision points but only 2 real generation calls. once those are separated, latency and cost stop scaling with every tiny fork in the workflow.

jev + opus 5.5 is one of those setups that makes agent architecture feel weirdly overcomplicated in hindsight. the useful split isn’t “small model vs big model.” it’s deciding which calls actually need generation at all. → jev routes the next worker → scores which context is worth keeping → catches repeated actions before they burn the run → gates risky tool calls and sends uncertainty to review opus still owns the expensive part: planning, writing, coding, synthesis. jev sits around it as the decision layer. that matters because a typical loop can have 11 decision points but only 2 real generation calls. once those are separated, latency and cost stop scaling with every tiny fork in the workflow.

51,452 次观看 • 1 个月前

I USED GPT-6 ASTRA TO TURN 1 FLY INTO A 7-PART ROCKET FACTORY the goal was simple: 1 fly studies every part, fits it, verifies it, then keeps building until the rocket is complete. part → study → neural state → fit gate → assembly → verify I called it buildfly. Astra helped me turn the idea into a working bay with 7 rocket parts, 3 live systems, a build log, neural activity map, and tolerance checks. the weird part is that the rocket is not even the main experiment. the real system is the neural loop deciding what happens next without skipping verification. I put the full build into a public GitHub repo so anyone can run it, break it, or make it even stranger. repo in the comments. bookmark this.

I USED GPT-6 ASTRA TO TURN 1 FLY INTO A 7-PART ROCKET FACTORY the goal was simple: 1 fly studies every part, fits it, verifies it, then keeps building until the rocket is complete. part → study → neural state → fit gate → assembly → verify I called it buildfly. Astra helped me turn the idea into a working bay with 7 rocket parts, 3 live systems, a build log, neural activity map, and tolerance checks. the weird part is that the rocket is not even the main experiment. the real system is the neural loop deciding what happens next without skipping verification. I put the full build into a public GitHub repo so anyone can run it, break it, or make it even stranger. repo in the comments. bookmark this.

49,931 次观看 • 1 个月前

EIGHT USED RTX 3090s. 192GB OF VRAM. ONE PRIVATE BOX BUILT TO RUN 70B AI MODELS WITHOUT RENTING A DATACENTER. 00:06 the team lowers all eight GPUs into the chassis at once, turning a pile of old gaming cards into the core of a serious local AI server. each 3090 contributes 24GB of memory. combined, the rack has enough VRAM for heavyweight models, large document collections, long context and multiple jobs running side by side. these cards are five years old, but AI economics work differently from gaming. memory capacity matters more than having the newest badge, which is why used 3090s remain valuable. the server can keep one model loaded while separate GPUs handle video, images, transcription and overnight agents without sending private company data into the cloud. bookmark this build before yesterday’s gaming GPUs become tomorrow’s cheapest AI infrastructure.

EIGHT USED RTX 3090s. 192GB OF VRAM. ONE PRIVATE BOX BUILT TO RUN 70B AI MODELS WITHOUT RENTING A DATACENTER. 00:06 the team lowers all eight GPUs into the chassis at once, turning a pile of old gaming cards into the core of a serious local AI server. each 3090 contributes 24GB of memory. combined, the rack has enough VRAM for heavyweight models, large document collections, long context and multiple jobs running side by side. these cards are five years old, but AI economics work differently from gaming. memory capacity matters more than having the newest badge, which is why used 3090s remain valuable. the server can keep one model loaded while separate GPUs handle video, images, transcription and overnight agents without sending private company data into the cloud. bookmark this build before yesterday’s gaming GPUs become tomorrow’s cheapest AI infrastructure.

119,133 次观看 • 3 个月前

i built a Robinhood Chain watchtower that remembers what the chart forgets it's called Canary. most tools help you enter a Pons V2 launch faster. Canary starts after that: it watches the position, remembers every state change, and tells you when something actually matters. [give it a token or wallet] → reads deployer balance, reserve / liquidity, swaps, fees and phase changes → stores every sweep as a snapshot → compares the new state against the last one → turns changes into QUIET, WATCH or LEAVE if a deployer starts dumping, reserve drops, fees get swept or volume dies, Canary doesn't look at one fresh chart and guess. it already remembers what changed. the web board keeps everything in one place: token table, selected-position memory, deployer-share history, persistent signals and a live refresh while the watcher keeps writing new snapshots. and the whole thing is intentionally READ ONLY. no signer. no private key. no buy button. no automatic exit. 34/34 deterministic tests passing. open source repo in the first reply.

i built a Robinhood Chain watchtower that remembers what the chart forgets it's called Canary. most tools help you enter a Pons V2 launch faster. Canary starts after that: it watches the position, remembers every state change, and tells you when something actually matters. [give it a token or wallet] → reads deployer balance, reserve / liquidity, swaps, fees and phase changes → stores every sweep as a snapshot → compares the new state against the last one → turns changes into QUIET, WATCH or LEAVE if a deployer starts dumping, reserve drops, fees get swept or volume dies, Canary doesn't look at one fresh chart and guess. it already remembers what changed. the web board keeps everything in one place: token table, selected-position memory, deployer-share history, persistent signals and a live refresh while the watcher keeps writing new snapshots. and the whole thing is intentionally READ ONLY. no signer. no private key. no buy button. no automatic exit. 34/34 deterministic tests passing. open source repo in the first reply.

46,370 次观看 • 1 个月前

$500 a day. Kids' songs. YouTube Shorts Before: 30 mins per video. Claude + Make: 10 videos simultaneously. A month’s worth of content in minutes. > $34/mo software costs > Claude scripts, InVideo edits > $10–500 revenue per video > Your cut: $30,000+/mo on autopilot 10 channels. 10 minutes to review. Zero employees. A niche everyone calls "child's play" while you print the money.

$500 a day. Kids' songs. YouTube Shorts Before: 30 mins per video. Claude + Make: 10 videos simultaneously. A month’s worth of content in minutes. > $34/mo software costs > Claude scripts, InVideo edits > $10–500 revenue per video > Your cut: $30,000+/mo on autopilot 10 channels. 10 minutes to review. Zero employees. A niche everyone calls "child's play" while you print the money.

122,569 次观看 • 5 个月前