Kaidu

This guy built an AI pipeline that generates hyperrealistic fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.

@koltregaskes weekend limits hit way worse than weekday ones feels like the resets are tuned for office hours

These 5-hour token resets are brutal during weekends. That's when I actually have time to run my agents, but I'm constantly hitting limits. I'd happily sacrifice overnight tokens for more during the day and especially weekend days. But even if I could shift the allocation, it wouldn't solve the real problem. Agents still aren't autonomous. They can't loop through hundreds of queued tasks. They don't complete tasks satisfactorily without fixes. They don't wake up when the 5-hour reset happens and carry on. Most of the time I'm pushing them along, feeding them corrections, forcing them to follow instructions. Recent research confirms this - what they're calling "calibrated autonomy" is just a polite term for constant hand-holding. We're nowhere near autonomous agents. We've got expensive assistants that need micromanaging.

@iScienceLuvr pre-trained models feeling like a lost art now that everyones obsessed with fine-tuning

We need to bring back the magic of base models

@pierceboggan multi-agent part is the most interesting to me how do u keep agents from stepping on each others work?

Catch me at #MicrosoftBuild this Wednesday! What we learned shipping VS Code weekly (10:15AM) - To be successful with AI, you must also evolve how your team works. We dive into what we learned running an AI native engineering team on the world's largest editor. Multi-agent patterns for VS Code (11:30AM) - The team shares different styles scaling yourself from one to multiple agents in VS Code. I'll be hanging out at 'The Terminal' area all week. Come say hi!

@berdyn new to the structure but this tracks what provinces are the exception?

@WesRoth more connectors is the part im interested in. curious what the memory actually remembers tho

Perplexity is reportedly continuing work on Daily Digest, a feature that lets users customize exactly what information gets pulled into their daily updates. The feature is expected to support memory, web sources, custom instructions, and multiple connectors.

@GoSailGlobal huge deal for RAG workflows my vector pipeline boutta look real clean now

微软开源了 MarkItDown，GitHub 13.5 万星，一个工具把所有文件变成 Markdown PDF、Word、PPT、Excel、图片、音频、HTML、甚至 YouTube 链接，全部转成干净的 Markdown 为什么重要？因为 LLM 吃 Markdown 效率最高，这个工具相当于给 AI 装了一个万能文件翻译器 以前你要花半小时清洗数据格式，现在一行命令搞定 🔗 github.com/microsoft/mark… 你在用什么工具预处理文档喂给 AI？

@andrew_n_carr had to read this twice before i got it? so they just swapped hats and kept the same ppl

The robotics team became the code generation team. The code gen team became the reasoning team. How many from reasoning joined the new robotics team?

@eliebakouch open weight or just open token count? genuinely asking

minimax M3 is the first open model trained on 100T+ tokens, natively multimodal, 1M context, built for long horizon tasks

@PowerThesaurus this is the most beautifully unnecessary vocabulary lesson ive ever seen whats the synonym tho?

thsr.us/future If people say the "future", they mean the time that is yet to come or the events that will happen later. E.g. "She dreams about traveling the world in the future." #synonym #thesaurus #learnenglish #ielts

@neil_xbt @kreoapp knew i shouldve checked the referral rate before telling my boy to stfu

So many markets to choose from on @kreoapp. A win is easier with the right tool and that tool is Kreo. Not to mention the 30% referral rate that is active rn! Here is the link to join fam: t.me/KreoPolyBot?st…

@scaling01 100T tokens is a wild number whats the effective data quality per dollar spent though?

@ThePrimeagen yea job security never felt this passive aggressive and its not even a person coming for it, its a line of code

If Dario cannot joyfully take your job, this guy sure will try

@tom_doerr skill-based automation is a nice framing, shifts the focus from replacement to augmentation. curious what the upper limit on that actually is in practice

Skill-based ML research automation for Claude Code github.com/wanshuiyin/Aut…

@VictorTaelin 5x on the first version sounds brutal what was the bottleneck in HVM4 that made this click?

Hm after all these years I think I finally figured out how to incorporate NanoHVM's architecture (that thing I posted about months ago) into a full Interaction Net runtime. Built the first version today and it is already 5x faster than HVM4

@JonahBlake ngl thats actually a fair way to frame it ansem in pick your fighter mode not everyone picker mode

Not a diss at all just noticing Ansem will pick some solid winners that are deff consensus and then will pick a few wildcards But those wildcards are the dominant of that sector Such as prime token when web3 gaming was a thing. So maybe that’s cards tbd

@TheAhmadOsman hard to argue when the numbers are starting to back it up local just feels better too

Buy a GPU, The Movement, was always right

@kimmonismus Wait so you turned it back on? Been missing that visual, tbh the circles kept me sane

lol just figured out you can re-enable the context window circle in codex. thank god

@0rdlibrary @solana build the community the same way u built the product that part always comes second but it changes everything

Imagine if I had holders who felt the same? We would be a powerhouse. Privacy, payments, perps. All done with $clawd on @solana

@wordgrammer gonna need someone to chart the difference like a venn diagram

The Christian mystics describe a suite of immensely pleasurable, heightened states of awareness as different from the Jhanas as prayer is from meditation

@Hesamation bro really said "i'll build it myself" before finishing the video

Pewd did it again. now he open-sourced a self-hosted AI workspace. bro is building a CV harder than a CS undergrad looking for a job: > built a 10-GPU home rig > quantized giant LLM to run local > built ChatOS, local AI UI > added RAG/local memory > built “council” of AI models > built “swarm”, small models in parallel for data collection > fine-tuned a Qwen 32B-based coding model > donated compute from his GPU rig for protein folding research