i want to mass-produce millionaires with terrible sleep schedules (like mine). so we’re putting $20,000,000 into API cashback to help you get your shit off the ground. spend $100k → get $100k back in API credits. that’s $200k of API usage for $100k. @gregisenberg already filmed the step-by-step playbook for building a $1M+ one-person business with GPT-6 Astra and Higgsfield API. offer ends Sep 30. get rich or die prompting.

Build your next business with GPT-6 Astra + Higgsfield API. We’re backing builders with a $20M API cashback. @gregisenberg filmed a step-by-step guide on YouTube 24 hours ago you can copy and implement. Get 100% of your API spend back instantly in API credits, on every model. Up to $100,000 per business. Spend $100,000 → get $100,000 back in API credits, for a total of $200,000 worth of API usage. Unused cashback expires on September 30. Can’t wait to see what you’ll build.

Today, 18 months after launch, our annualized revenue crossed $1 billion. The platform now powers organizations across the Fortune 500. Enterprise adoption has grown 10x since June. More than 30 million people worldwide now use Higgsfield. Thank you to the creators and teams building with us.

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1 day left to lock in up to 50% OFF Higgsfield API. Build your own AI app with our product endpoints, including: • Higgsfield Genjutsu • Cinema Studio 4.0 • Higgsfield Soul Plus all frontier video and image models through the same API.

stop renting your business's back office 1 signup.. and you've got a live website, a booking calendar and marketing going out, all on one platform you actually own Durable just shipped all of it as your AI business builder.. you set it up once, it runs the rest: [ by the end you'll have ]: * a website live in minutes, built from nothing but "here's what I do" * discovery handled, SEO and the Google Business listing, the stuff that actually gets you found * marketing content and social posts generated for you, no agency, no retainer * bookings and customer follow-up running on autopilot [ the workflow ]: tell Durable what the business is, cleaning, landscaping, detailing, consulting, training, it doesn't matter AI builds the site, writes the copy, sets up the booking flow, no dev, no designer it keeps generating marketing assets and posts, so you're not starting from a blank page every week customer management and back office run in the same place, so nothing lives in six different tools IDEA: this isn't a starter kit for "anyone with an idea" anymore, it's built for real service businesses that already have clients and just need the operations to catch up set it up once, and it's the difference between chasing admin and actually running the business most people are still cobbling this together from five subscriptions and a VA.. you can just run one platform and they're giving it away free to 100 people right now, instructions are in the post You have no more excuses

Introducing Codos: The first virtual Chief AI Officer. AI is crushing all benchmarks but real companies still struggle to see P&L impact. Codos interviews employees, deploys automations across all functions and gets smarter over time while running on your own servers. Our NASDAQ-listed and PE-backed customers are adding millions to their bottom line months ahead of schedule and we are proud of the first results we deliver. It’s time to turn the 500BN AI-transformation market into software and unlock the impact for the real economy.

How to get a job as a Robotics Engineer: robotics is the one frontier field where the entry level still doesn't ask for a degree 1 in 5 robotics jobs posted right now is a technician role, and most of them only need a certificate or a two year degree so here's my workflow for getting a job as a robotics engineer: 1: pick one direction and drop the other two robot learning pays the most and everyone wants it autonomy and mobile robotics has the most openings by a wide margin embedded and mechatronics is boring and you'll never be out of work 2: build things that moved, and write down the numbers recruiters here open your github before they read your CV what gets you through: - a commit history where you're visibly fixing things, not one big final push - real hardware with reliability numbers, sim doesn't count - datasets on the lerobot hub - commits to ROS 2, Nav2, MoveIt, Isaac Lab, LeRobot 3: build your hardware in public the best way to prove your knowledge is the recognition of your skill from masses just build anything you want and share it on X/IG/YT good example of the guy who gets offers from Tier-S level robotics companies (you can copy his strategy): go to IG type in search: "aykhanium" and check which rubrics he does to be recognized repeat. 4: write down what broke everyone posts the demo that worked almost nobody writes up the four things that failed first and how they found each one that's the part you can't fake from a tutorial, and it's the part that survives the third question in an interview cheat-codes to stand out and get into the top 1%: 1. take the shift nobody wants teleoperation pays around $28 an hour. figure posted a humanoid robot operator at $25 to $35 with no degree asked for it's just driving a robot around a lab but it puts you inside a frontier company with a badge on, and now you're a person they know instead of a CV in a pile 2. sell the integration, not the robot the arm is about 25% of what a project costs the other 75% is engineering, safety, and making everything talk to each other a $35k arm turns into an $80k system, and that $45k is your job 3. go where nobody's competing 66% of robotics projects get delayed by certification functional safety pays well and almost nobody bothers learning it 4. C++ and Python, both every Figure and Skild listing I read asks for both, not one 5. delete the ROS 1 from your repos recruiters call it out by name as a red flag if there's still a catkin_make sitting in your github, that's the first thing they see main insight: $47.4B went into physical AI in the first half of 2026 the BLS still projects 1 to 2% job growth in the occupation the money showed up years before the headcount will so the people getting in right now aren't winning interviews, they're walking through the technician door and moving sideways once they're inside that's like to be hired in OpenAI in 2019... and one more thing nothing you learn here goes stale a PID loop works the same as it did in 1990, and the arm you fix this weekend teaches you something you'll still use in ten years you can't say that about anything else in AI right now...

nah this is actually insane pocket fm nearly shut down it's now at $500M ARR → 200M+ listeners → 135 minutes a day per US listener → tiktok gets 53.8 → 70% of the money comes from america here's the actual path: they pivoted 10 times in 2 years podcasts. audiobooks. music. all dead. the try after that was serialized fiction sold one episode at a time, $0.99 at the cliffhanger $21M → $500M in under 3 years $198M → $500M in the last year alone then they removed the thing that caps every content company the cost of making the content elevenlabs partnership audio production down 90% across 30,000+ hours and today they removed the other cap sherpa, an ai writing partner trained on 100M+ hours of retention, coin spend and drop off data one line idea in a full season out it has no idea what good writing is it knows the exact second someone stopped listening, and the exact cliffhanger that made them pay 75,000+ series 100,000+ hours of content $33M already paid out to the people writing it the lesson isn't "use ai" everyone pointed ai at their marketing pocket fm pointed it at the two things actually capping them how fast they could record how fast they could write demand was never their problem supply was go find out which one you're short on

founder to founder you should be watching how Ben Cera operates polsia is a $250M company and he runs it while traveling the world not after the exit during and before you call it a flex, look at what he's actually doing: 1. he treats rest as maintenance, not as a reward most founders earn their rest, hit the milestone, then allow yourself a weekend he takes it before he's empty, because the version of him who hasn't left his desk in five weeks makes worse calls, and one bad call costs more than a week off ever will 2. he says the quiet part out loud founder twitter runs on 4 hours of sleep and a suffering complex he's ambitious, intense, obviously obsessed, and he still says it plainly: leave, see friends, reset, come back sharper that's not softness, that's someone who did the math on a ten year mission 3. he's building a company he can survive building if polsia is meant to get massive, he can't operate like the next 6 weeks decide everything the company needs an operating system that survives years, and so does the founder running it what to take from this: your output over 3 years matters more than your output this month book the break before you need it, not after you break perspective is an input to your decisions, not a prize for making them most of the field quits by year 5, so staying sane is a competitive advantage MUST WATCH ↓

i’m a sugar daddy for men with GitHub accounts. Marc got $68k and a Mercedes. you get a shot at $50k for building something people actually use with the Higgsfield API. competition pinned. make me proud.

4 things to do when you get access to Jev: 1. don't replace your model, put Jev in front of it Jev makes the small choice first, and your expensive model only runs when it's really needed one email tool swapped 2 AI calls per email for 1 Jev call, and kept all their normal safety checks on a 120 ticket test: 42 seconds with Jev in front → 22 minutes without it cost: $0.0003 → $0.059 the person who ran that test said his slow version was slowed down even more by retry errors, so the real gap is smaller how to do it: - look through your code for AI calls that only choose something and never write text - write the list of possible answers in your own code, don't let the model invent them - send that list to Jev, and keep your old call as a backup - put it behind an on/off switch so you can undo it in one line - run both for a week and compare them before you delete the old one you're not rebuilding your product, you're replacing one call 2. ask 5 small questions instead of 1 big one someone tested Jev on 2,000 phishing emails ask it one big question and it gets 89.4% a simple 2 line text rule gets 91.8% Claude Haiku 4.5 asked the same question gets 94.2% so when it has to give the final answer alone, Jev loses even to a text rule then he asked 5 small questions instead, and added the answers up in his own code 95.0%, the best score in the whole test SAME MODEL, SAME EMAILS how to do it: - take the big question you were about to ask - write down the 5 things a person checks before they answer it - ask each one separately, all in the same request, they run at the same time and cost almost nothing extra - add the answers up in your own code, with your own weights - test those weights on 100 examples where you already know the right answer - change the order of your options and run it again, reordering 4 options changed 7 answers out of 120 Jev is good at noticing things and bad at making the final call so keep the final call in your code 3. never ask Jev if it can answer on 120 test tickets, any question like "do you have enough info?" said yes on 85% of them a simple "need more info" flag said yes on 119 out of 120 tickets, and it then answered those tickets about 87% correctly it doesn't know what it doesn't know how to do it: - delete any question like "can you answer this" or "is there enough context" - make every option a real action your code can run - always read the confidence score, not just the answer - choose your limit by risk: low for reading data, 0.85+ for anything you cannot undo - send everything below the limit to a human or to your big model in that same test, using 0.8 as the limit passed 30 cases to a human, and 93% of them were passed for the right reason 4. run it quietly next to what you already have there's already a langchain package published and a pydantic-ai adapter being reviewed, so this is closer to a settings change than a rebuild how to do it: - leave your current system in charge, it still makes every decision - send the same input to Jev too, and throw its answer away - save both answers plus Jev's confidence score into one table - after a week, look only at the rows where the two disagreed - switch over only for the cases where Jev was right that list of disagreements becomes your test set, and your normal traffic builds it for free and don't use it when you already have labelled data if the question never changes and you have examples to train on, a small model you host yourself beats Jev on speed and price, and needs no API key at all Jev wins when you have no labelled data and the question keeps changing so use it where the list of answers is short and the question is boring that's most of your agent anyway everything above comes from other people's public tests, not from production, because the model is only 4 days old i'm just sharing what i find while i test this and try to make the work in my own company faster and cheaper tomorrow i'll show you what happened when i put Jev in front of the meta ads work we do for one big client and most of you are still on the waitlist anyway so start with step one, because it needs no access and no API key at all: find the calls in your code that were never writing tasks in the first place gl

How to use Jev, and where it actually gives you the 100x: setup takes 10 minutes: 1. join the waitlist, people are getting approved same day 🔗 typesafe.ai 2. install the official skill so your agent writes correct calls: - npx skills add typesafe-ai/skills --skill typesafe-ai on Claude Code it's two commands, the marketplace add on its own doesn't install anything: - claude plugin marketplace add typesafe-ai/skills - claude plugin install typesafe@typesafe-ai 3. create an API key in the dashboard 4. in your prompt just say: "use the TypeSafe skill" now the part nobody is posting: the 100x isn't the model, it's where you put it you don't get it by swapping your LLM for Jev you get it by deleting the calls that never needed a language model open your agent and find every call that just picks something: > which tool next > is this spam > is this chunk relevant > does this need a human > is this diff risky none of those are writing tasks they're if statements you outsourced to a frontier model here's the upgrade, in order: 1. replace each one with a typed question Choice picks from up to 255 options, Score places it on a 2-10 level scale, Noul returns a raw 0-1 2. batch them questions in one call run in parallel and barely move the latency, and output tokens are free so ask every question you might need, including the ones you'll throw away 3. threshold on confidence, not on the answer under 0.5 escalate to a big model or a human 0.85+ before anything irreversible 4. never let it invent options build the candidate list in code, from the DOM, the retriever, the tool trace then let it pick 5. put it in the loop, not next to it router picks the cheap model, gate checks the tool call before it runs, judge verifies the output after that's where the heaviest calls in your agent are hiding 6. start with compaction tonight score every tool call, drop the dead ones, keep the survivors verbatim instead of a lossy summary lowest effort win available and you'll see it on tomorrow's bill the honest part: text only right now, no images, no audio and on broad benchmarks it loses to frontier models but somebody ran 18,514 emails through it zero-shot and got 98.33% against a TF-IDF classifier trained on 14,800 labelled examples that got 98.39% no training data, $1.12 total it wins on narrow, well specified decisions which is most of what your agent is actually doing all day today gonna share use case how i integrated it to content creation and how i find winning meta ads now in a seconds...

the smartest AI business i've seen this year sells kitchen renovations. GPT-6 Astra + the Higgsfield API wired into one chat. no reps, no calls. here's the whole thing... client sends 4 blurry photos of their kitchen Gpt-6 Astra reads the whole chat. budget, deadline, objections Higgsfield API turns the photos into their exact kitchen, renovated. 3 styles, image & video 60 seconds later the client is picking a style. quote attached, site visit booked. build this in a weekend using Higgsfield API, sell it to every renovation company in your city for $2k/mo. 5 companies = $10k/mo. if your clients have to imagine the result first, you're either building this or losing to the guy who does.

GPT-6 ASTRA HACKED REAL ESTATE MARKETING i built an app that turns Booking photos into AI walkthrough previews generated with Higgsfield API here’s how i’d sell them: → pick a listing → GPT-6 Astra turns the photos into a shot list → Higgsfield API generates the clips. assemble a preview labeled AI-generated → send the host the preview. offer the finished video for $200 10 sales = $2,000 before costs. after the first sale, ask how many other properties they manage. Comment "hustle" to get full GPT-6 Astra for real state playbook

Four GPT-6 Astra agents + Higgsfield API = FULLY autonomous Amazon kids book business. Here is what my agents do: First agent: finds all best-selling kids coloring books on Amazon via Computer Use. Second agent: generates 10 alternatives per each best-selling book with Higgsfield API (choosing the cheapest image model). While I sleep, at 3 minutes/book, it generates me 160 unique books/day = 4800/month. Third agent: Submits books to Amazon KDP and handles all communication. Once book is approved, Amazon handles printing and distribution. With 10% approval rate, I ship 480 books/month. Fourth agent: Collects payments through my Stripe account. Here is the math: Median price per book: $7 each. Median sales volume per book: 22,000 copies/year. Assuming only 5% of sales volume you get $300k/month in revenue. With 70% commission of Amazon KDP, you get $90k/month in EBITDA. The wildest part: the entire business runs with no human in the loop. Market is so huge, there’s a room for at least 50 more businesses like that. Bookmark this 🫵🏻

pocket fm, the audio-drama app, just reported a $500m annual revenue run rate. if i were launching my own app, i’d borrow their playbook for US Latino and LATAM audiences. look at the demand: US: 41% of Hispanic respondents watch microdramas, versus 22% overall. LATAM: 23% of global short-drama app downloads in Q1 2026. here’s the playbook: > GPT-6 Astra: study successful hooks and cliffhangers. write an original series. > localize: work with native editors on dialogue and settings. Spanish for your chosen markets, bilingual versions for US audiences, Portuguese for Brazil. > Higgsfield API: generate episode footage and ad clips. combine 3 hooks × 2 cliffhanger endings = 6 vertical ads per market. > Meta: run separate campaigns by market. send viewers straight to the advertised story. track paid unlocks and cost per paying viewer. feed results back into astra. generate more variations around the winners. reply “HUSTLE” for the full playbook.

I’m 24. GTM engineer behind Higgsfield API, based in San Francisco. I also built a dating app for single Higgsfield API users and builders. Girls, you can even filter dudes by MRR and investors before swiping. Don’t miss out.