grokbot it's an agent with its own identity, its own computer, and it stays on when you're not here's what "active AI employee" actually looks like in the demo: - chief of staff agent - checks in on your other agents, reads your calendar, dispatches tasks to the right one automatically - shopping agent - logged into your accounts, books tickets, buys groceries, reports back - marketing agent - signed into your actual linkedin, browses your past posts for tone, then writes and publishes a new one on its own - engineering agents - self-triage bug reports, kick off cloud coding agents, come back with a pull request, a screenshot, and a video of the fix the interface isn't a dashboard, it's a chat - same shape as texting a coworker, no tool calls to babysit the number that matters more than any of the demos: grok 4.6 scored 70.8% on cursor bench at $2.81 a task, fable 5 max scored 70.5% at $17.32 same capability, 6x the cost difference - that's the unlock that makes running a fleet of these actually affordable instead of a novelty

grokbot it's an agent with its own identity, its own computer, and it stays on when you're not here's what "active AI employee" actually looks like in the demo: - chief of staff agent - checks in on your other agents, reads your calendar, dispatches tasks to the right one automatically - shopping agent - logged into your accounts, books tickets, buys groceries, reports back - marketing agent - signed into your actual linkedin, browses your past posts for tone, then writes and publishes a new one on its own - engineering agents - self-triage bug reports, kick off cloud coding agents, come back with a pull request, a screenshot, and a video of the fix the interface isn't a dashboard, it's a chat - same shape as texting a coworker, no tool calls to babysit the number that matters more than any of the demos: grok 4.6 scored 70.8% on cursor bench at $2.81 a task, fable 5 max scored 70.5% at $17.32 same capability, 6x the cost difference - that's the unlock that makes running a fleet of these actually affordable instead of a novelty

a baby gift shop with 5,907 followers just pulled 21.5M views on a single video... 15 posts total, no ad spend visible, and the account is clearly running AI-generated UGC, not real customer content this is the disconnect people aren't pricing in yet: - follower count means nothing now - the format is the unlock, not the brand i- t's not one lucky post - 15.4k, 6.8k, 10.1k, 21.5m, 3.5k across just five pinned pieces zero real customers on camera, zero UGC creators paid $200-500 a piece, and the output is indistinguishable enough to pull organic reach at this scale the actual lesson: the barrier used to be "can you produce content that looks native." that barrier is now basically gone the new bottleneck is volume and hook testing, not production

a baby gift shop with 5,907 followers just pulled 21.5M views on a single video... 15 posts total, no ad spend visible, and the account is clearly running AI-generated UGC, not real customer content this is the disconnect people aren't pricing in yet: - follower count means nothing now - the format is the unlock, not the brand i- t's not one lucky post - 15.4k, 6.8k, 10.1k, 21.5m, 3.5k across just five pinned pieces zero real customers on camera, zero UGC creators paid $200-500 a piece, and the output is indistinguishable enough to pull organic reach at this scale the actual lesson: the barrier used to be "can you produce content that looks native." that barrier is now basically gone the new bottleneck is volume and hook testing, not production

a solo creative agency replacing a $15-30k/mo team with $200/mo and two subscriptions... this is now just a workflow, not a fantasy here's the stack, in order: - Seedance 2.5 - generates every image and video, brand assets, product shots, UGC, unboxing, high-end commercials - claude - the brain. writes every prompt, tags reference images so nothing gets confused, connects to higgsfield via MCP so it can generate and file assets on its own - notion - the agency backend, every client gets a column, every ad gets a row with hook copy, prompt, CTR, ROAS - apify - scrapes competitor ads across tiktok/meta/instagram, feeds trending hooks straight into the pipeline the part that actually changes the math: real UGC runs $200-500 a piece from an actual creator same output through the stack costs about $5 and takes 5 minutes 5 clients at $2k/mo is a $10k/mo agency, run solo the catch nobody skips past: the stack is now a commodity, anyone watching this can copy it by next week the actual moat is closing clients, setting scope, and not blowing up your reputation on revision cycle #1# - that's the part AI still can't do for you

the gpu shortage isn't a supply problem, it's an allocation problem - buy the rack: 18 months - buy the power: longer - install a virtualization layer: today Thunder Compute ( @carlpeterson + Brian Model, ex-Bain / ex-Citadel) just closed $13M to make every GPU already deployed do more work - invisible to the workload, drops into existing infra - good for enterprises who need capacity now - good for neoclouds who don't want to build another substation - good for NVIDIA - every chip sold does more over its lifetime, which is the best argument for buying the next one

Today, we raised a $13M series A to solve the GPU shortage Trillions are being spent on GPU CapEx while 80% of it sits idle The capacity already exists Virtualizing GPUs unlocks it Backed by Matrix, YC, and CEAS

a team of one is now running what used to take 10-15 people... and it's not a headcount trick, it's agents running the actual workflow end to end here's what "GTM agents find and keep customers" looks like in practice, not theory: - pick where to host your next exec dinner - the agent pulls every open deal from your CRM, runs it past multiple models to cross-check the read, flags things like single-rep concentration risk, and picks the city - build the outreach - pulls the right contacts, writes the sequences, loads them straight into your email tool, no copy-paste - backfill your CRM - points at old contracts sitting in a drive folder, reconciles them against 200+ connected sources, updates amounts, close dates, line items, flags what needs a human look - voice-of-customer, daily - reads every transcript and email, surfaces what's resonating, then routes specific actions to product, enablement, and marketing separately - alert routing - "ping this channel when a deal of X size books a meeting" used to be a ticket and an intake call, now it's two lines the pattern underneath all of it: start reactive (you approve each step), build trust stage by stage, then package the whole thing as a recurring job that just runs and reports back on slack that's the actual shift...

a team of one is now running what used to take 10-15 people... and it's not a headcount trick, it's agents running the actual workflow end to end here's what "GTM agents find and keep customers" looks like in practice, not theory: - pick where to host your next exec dinner - the agent pulls every open deal from your CRM, runs it past multiple models to cross-check the read, flags things like single-rep concentration risk, and picks the city - build the outreach - pulls the right contacts, writes the sequences, loads them straight into your email tool, no copy-paste - backfill your CRM - points at old contracts sitting in a drive folder, reconciles them against 200+ connected sources, updates amounts, close dates, line items, flags what needs a human look - voice-of-customer, daily - reads every transcript and email, surfaces what's resonating, then routes specific actions to product, enablement, and marketing separately - alert routing - "ping this channel when a deal of X size books a meeting" used to be a ticket and an intake call, now it's two lines the pattern underneath all of it: start reactive (you approve each step), build trust stage by stage, then package the whole thing as a recurring job that just runs and reports back on slack that's the actual shift...

kimi 2.6's agent swarm quietly turned "hire a research team" into a single prompt... 300 sub-agents, one input, coordinated across thousands of steps, and the model plans the whole workflow itself - no roles to assign, nothing to configure here's what people are actually running through it right now: - job search - background + comp target + remote in 2-3 sentences, swarm deploys a dozen research agents, runs 240+ searches, comes back with matched roles in ~20 minutes - brand/sponsorship prospecting - describe your niche and deal criteria once, get back a scored list of 300 companies with affiliate links, commission structure, cookie duration, fit score - bulk document synthesis - drop in 40 PDFs, get one clean report out resume → application pipeline - upload once, it finds 100 matching roles and writes 100 tailored applications in one run - local lead gen - find 30 businesses with no website, generate 30 landing pages, same session underneath it: a 1 trillion parameter open-source model built for under $5M, benchmarking near GPT-5 and Opus 4.6 on real agentic tasks, not just Q&A what actually matters isn't the parameter count... it's that none of the above needed a dev, an IT team, or a workflow diagram you describe the outcome, it builds the team

kimi 2.6's agent swarm quietly turned "hire a research team" into a single prompt... 300 sub-agents, one input, coordinated across thousands of steps, and the model plans the whole workflow itself - no roles to assign, nothing to configure here's what people are actually running through it right now: - job search - background + comp target + remote in 2-3 sentences, swarm deploys a dozen research agents, runs 240+ searches, comes back with matched roles in ~20 minutes - brand/sponsorship prospecting - describe your niche and deal criteria once, get back a scored list of 300 companies with affiliate links, commission structure, cookie duration, fit score - bulk document synthesis - drop in 40 PDFs, get one clean report out resume → application pipeline - upload once, it finds 100 matching roles and writes 100 tailored applications in one run - local lead gen - find 30 businesses with no website, generate 30 landing pages, same session underneath it: a 1 trillion parameter open-source model built for under $5M, benchmarking near GPT-5 and Opus 4.6 on real agentic tasks, not just Q&A what actually matters isn't the parameter count... it's that none of the above needed a dev, an IT team, or a workflow diagram you describe the outcome, it builds the team

real-time AI lip-sync, fully open source, from Tencent Music's research lab it's called MuseTalk - takes any video + any audio and syncs the mouth to match, live → 30fps+ real-time inference on an NVIDIA V100 → works in Chinese, English, Japanese → operates in latent space (not a diffusion model - single-step inpainting, so it's fast) → inference, training code, AND weights are all released - v1.5 added GAN + perceptual + sync loss for a real jump in quality over v1.0 → pairs with MuseV (their video-generation model) for a full text/image-to-talking-avatar pipeline 6.4k stars, MIT licensed, weights on Hugging Face with a live Gradio demo honest limitations, straight from their own README: some jitter since it generates frame-by-frame, and fine details like mustaches or exact lip color don't always carry over perfectly

real-time AI lip-sync, fully open source, from Tencent Music's research lab it's called MuseTalk - takes any video + any audio and syncs the mouth to match, live → 30fps+ real-time inference on an NVIDIA V100 → works in Chinese, English, Japanese → operates in latent space (not a diffusion model - single-step inpainting, so it's fast) → inference, training code, AND weights are all released - v1.5 added GAN + perceptual + sync loss for a real jump in quality over v1.0 → pairs with MuseV (their video-generation model) for a full text/image-to-talking-avatar pipeline 6.4k stars, MIT licensed, weights on Hugging Face with a live Gradio demo honest limitations, straight from their own README: some jitter since it generates frame-by-frame, and fine details like mustaches or exact lip color don't always carry over perfectly

Seedance 2.5 + ChatGPT + TikTok Shop is a great combo for making money one account did $500K in 90 days without touching a single unit of inventory... same AI avatar, reposted with a new product every day, and TikTok has no idea here's exactly how to reproduce it: - find a product that’s still early in its rise, with fewer than 200 creators promoting it. - create your avatar once in ChatGPT and stick with the same character - just change the outfits - Seedance 2.5 turns that image into a 10sec native 9x16 clip... ready to post same-day, before the product's even trending yet

Seedance 2.5 + ChatGPT + TikTok Shop is a great combo for making money one account did $500K in 90 days without touching a single unit of inventory... same AI avatar, reposted with a new product every day, and TikTok has no idea here's exactly how to reproduce it: - find a product that’s still early in its rise, with fewer than 200 creators promoting it. - create your avatar once in ChatGPT and stick with the same character - just change the outfits - Seedance 2.5 turns that image into a 10sec native 9x16 clip... ready to post same-day, before the product's even trending yet

you can also use a project like this to find Startup Ideas the setup: - scrapes target subreddits + YouTube comment sections + twitter replies on a schedule - filters for specific phrase patterns - "is there a tool for," "does anyone know a way to," "I wish there was," "so tedious," "I hate having to" - tags every match with source, upvotes/likes, and how many people replied "same" or "+1" - sorts by frequency - same complaint showing up 40 times across 3 platforms outranks a clever one-off why this beats manual scrolling: - people are already telling you exactly what they want to pay for, in their own words - you're just not reading enough of it to see the pattern - one complaint is an anecdote. the same complaint from a fitness subreddit, a YouTube tutorial's comments, and a twitter thread is a market - a 2-star review with 4,000 users still using the product tells you more than any survey - people who hate something but haven't left yet are your easiest customers the sources it hits and what each one is actually good for: - reddit - raw, unfiltered demand ("is there a tool for X" searches surface this instantly) - YouTube comments on tutorials - people narrating exactly where they got stuck trying to stitch 8 tools together to do one thing - twitter replies - froth and complaints in real time, good for catching something before it's saturated the real unlock isn't finding one idea. it's finding the idea that's already been asked for 40 times by people who'll be your first customers before you've written a line of code

you can also use a project like this to find Startup Ideas the setup: - scrapes target subreddits + YouTube comment sections + twitter replies on a schedule - filters for specific phrase patterns - "is there a tool for," "does anyone know a way to," "I wish there was," "so tedious," "I hate having to" - tags every match with source, upvotes/likes, and how many people replied "same" or "+1" - sorts by frequency - same complaint showing up 40 times across 3 platforms outranks a clever one-off why this beats manual scrolling: - people are already telling you exactly what they want to pay for, in their own words - you're just not reading enough of it to see the pattern - one complaint is an anecdote. the same complaint from a fitness subreddit, a YouTube tutorial's comments, and a twitter thread is a market - a 2-star review with 4,000 users still using the product tells you more than any survey - people who hate something but haven't left yet are your easiest customers the sources it hits and what each one is actually good for: - reddit - raw, unfiltered demand ("is there a tool for X" searches surface this instantly) - YouTube comments on tutorials - people narrating exactly where they got stuck trying to stitch 8 tools together to do one thing - twitter replies - froth and complaints in real time, good for catching something before it's saturated the real unlock isn't finding one idea. it's finding the idea that's already been asked for 40 times by people who'll be your first customers before you've written a line of code

If you're starting an AI services business with $0 and no portfolio, don't pick a niche until you read this most people picking a niche just go with whatever their guru told them or whatever looked cool on twitter. wrong approach entirely here's how I actually rank a niche before touching it: the 4 filters that matter: - seasonality - does revenue die for 3 months a year - recession resilience - do people still buy this when money's tight - accessibility - can you actually get the owner on the phone - ticket size - is there enough margin to sustain a retainer run every niche through this before you pitch a single person what I'd actually avoid (despite what gurus push): - real estate - top 5% of agents take 80% of the commission, everyone else avoids retainers because it eats their cut - roofers - every roofer in the US has been cold-called 500+ times since 2022, the market's burned out - personal trainers / social media agencies - low ticket, no budget, or they'll just reverse-engineer what you sell them and cut you out what I'd actually start with: - HVAC - recession-proof, seasonal peaks in BOTH summer and winter, $5K-15K installs, this is the one I'd pick with zero experience and zero portfolio - landscaping - recurring revenue built in, easy to find on the street, easy to cold call - electricians - miss constant calls, recession-proof, just harder to find ones with real volume the pricing formula that removes all the guesswork: cost per lead ÷ response rate = what they're already paying per appointment then undercut that by 20-30% for reactivated leads, 30-50% for fresh ones this way you're never guessing, you're pricing off numbers they already have the part people skip: close with a proof-of-concept demo and a full refund if it doesn't convert. zero risk for them = no reason to say no

a friend of mine has been using this to cut through job-search noise, thought I'd share not an "AI got him the job" tool. an AI that tells you which jobs are worth your time it's called career-ops, open source, runs in Claude Code / Codex / any AI coding CLI what it actually does: → scans 100+ pre-configured companies across Greenhouse, Ashby, Lever, Wellfound → scores each listing A-F across 5 weighted dimensions (1.0–5.0), plus a separate scam/ghost-job legitimacy check → generates ATS-tailored CV PDFs per listing → finds the actual hiring manager or recruiter to reach out to → drafts (never sends) application emails and cover letters → tracks the whole pipeline in one place instead of a spreadsheet the honest part: it recommends against applying to anything under 4.0/5 - it's built to stop you from spraying applications, not to automate spraying them. you still review and click submit yourself built by one person who used it on their own search - 740+ listings evaluated, 100+ tailored CVs, landed the role 63.5k stars, MIT licensed, free

If you're starting an AI services business with $0 and no portfolio, don't pick a niche until you read this most people picking a niche just go with whatever their guru told them or whatever looked cool on twitter. wrong approach entirely here's how I actually rank a niche before touching it: the 4 filters that matter: - seasonality - does revenue die for 3 months a year - recession resilience - do people still buy this when money's tight - accessibility - can you actually get the owner on the phone - ticket size - is there enough margin to sustain a retainer run every niche through this before you pitch a single person what I'd actually avoid (despite what gurus push): - real estate - top 5% of agents take 80% of the commission, everyone else avoids retainers because it eats their cut - roofers - every roofer in the US has been cold-called 500+ times since 2022, the market's burned out - personal trainers / social media agencies - low ticket, no budget, or they'll just reverse-engineer what you sell them and cut you out what I'd actually start with: - HVAC - recession-proof, seasonal peaks in BOTH summer and winter, $5K-15K installs, this is the one I'd pick with zero experience and zero portfolio - landscaping - recurring revenue built in, easy to find on the street, easy to cold call - electricians - miss constant calls, recession-proof, just harder to find ones with real volume the pricing formula that removes all the guesswork: cost per lead ÷ response rate = what they're already paying per appointment then undercut that by 20-30% for reactivated leads, 30-50% for fresh ones this way you're never guessing, you're pricing off numbers they already have the part people skip: close with a proof-of-concept demo and a full refund if it doesn't convert. zero risk for them = no reason to say no