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here's how to make money with jev on autopilot it's a super fast decision model, so it picks an answer in a fifth of a second and tells you how sure it is - put instant pricing on a business that has none - sell the same thing to... show more

Chris

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90,674 просмотров • 8 дней назад •via X (Twitter)

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grok bot + hermes can generate unlimited leads on autopilot now - reads 3 months of what buyers actually say - turns that data into ads in their own words - runs them through whop ads, funded by sales - finds who needs the trade and renders the fix - reaches them by email, sms or post daily - builds a dashboard where you track all of it works if you sell a product, own a business, or want to sell leads to businesses that do just wrote a full guide on how you can set this up:

grok bot + hermes can generate unlimited leads on autopilot now - reads 3 months of what buyers actually say - turns that data into ads in their own words - runs them through whop ads, funded by sales - finds who needs the trade and renders the fix - reaches them by email, sms or post daily - builds a dashboard where you track all of it works if you sell a product, own a business, or want to sell leads to businesses that do just wrote a full guide on how you can set this up:

Chris

140,723 просмотров • 15 дней назад

hermes + grok bot + claude = lead gen on steroids it finds businesses whose site can't book, call or capture a lead, fixes all of it, and renders a walkthrough demo with an ai avatar - pulls every business in one trade, one city - opens each site and counts what's missing - rebuilds it with booking, a form and tap to call - edits the whole demo video on its own - an ai avatar explains it while clicking through - runs on hermes or grok bot, on a schedule just wrote a whole guide on how you can set this up:

Chris

16,631 просмотров • 10 дней назад

my grok bot monitors the internet 24/7 and builds systems that make money on autopilot - pulls the problems out of what people wrote - only surfaces what more than one source confirmed - scores it and puts it on the dashboard - spins up a new agent when i approve - that agent builds it and sells it with whop - shows me what needs approving everyday everything from finding the customer to selling them just wrote a whole guide on how you can set this up exactly:

my grok bot monitors the internet 24/7 and builds systems that make money on autopilot - pulls the problems out of what people wrote - only surfaces what more than one source confirmed - scores it and puts it on the dashboard - spins up a new agent when i approve - that agent builds it and sells it with whop - shows me what needs approving everyday everything from finding the customer to selling them just wrote a whole guide on how you can set this up exactly:

Chris

50,983 просмотров • 1 месяц назад

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Armagan Amcalar

365,108 просмотров • 5 дней назад

here's how to make $100,000 with grok bot in the next 6 weeks grok bot finds the streets that already decorate and mails every house a card with the lights rendered onto their own roof here's the system you can sell to christmas light installers: - pulls every residential address on those streets - checks satellite and street view data for imagery - renders the lights onto that exact house - prints the card and mails it out itself - calls route to the installer paying the retainer - swap the list and the machine runs again just wrote a full guide on how you can set this up:

here's how to make $100,000 with grok bot in the next 6 weeks grok bot finds the streets that already decorate and mails every house a card with the lights rendered onto their own roof here's the system you can sell to christmas light installers: - pulls every residential address on those streets - checks satellite and street view data for imagery - renders the lights onto that exact house - prints the card and mails it out itself - calls route to the installer paying the retainer - swap the list and the machine runs again just wrote a full guide on how you can set this up:

Chris

16,103 просмотров • 1 месяц назад

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

John Turner

21,029 просмотров • 11 дней назад

The cheapest way to sell an app right now is a face and a screen I'm running it and the conversion is stupid It's one video doing two jobs → A reaction that buys you the first second → Your app running on the screen behind it Neither half has to explain the other That's the whole thing The screen recording never changes You only ever swap the reaction on top An account with 1,584 followers did 908K views and 66.5K saves with this Ten minutes of filming is a month of ads

The cheapest way to sell an app right now is a face and a screen I'm running it and the conversion is stupid It's one video doing two jobs → A reaction that buys you the first second → Your app running on the screen behind it Neither half has to explain the other That's the whole thing The screen recording never changes You only ever swap the reaction on top An account with 1,584 followers did 908K views and 66.5K saves with this Ten minutes of filming is a month of ads

Adrià Martinez

30,658 просмотров • 1 месяц назад

I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link →

I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link →

Jarrod Watts

1,279,093 просмотров • 12 дней назад

here's a unique AI UGC format that you can use to sell your products... you can use an image generator like GPT-image-2 to create a still image of a person in the bottom right corner & a green-screen behind them (make sure to prompt it to look organic/hand-held & not perfect quality) then send that to Seedance 2.5 with what you want him to say - you can get unlimited Seedance 2.5 for 33 days on Higgsfield right now, so you can generate these in bulk then in a video editor, you can do a chroma key on the greenscreen background to make it see-through & put a screenshot of whatever the topic of the video is behind the subject there are endless ways you can use this to sell stuff, let your creativity run wild

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HERMES AGENT GEMMA 4 26B ON A MAC MINI NO API KEYS NO MONTHLY BILL NO ASKING PERMISSION TO USE YOUR OWN DATA a $700 box on your desk just replaced a $30/month subscription, runs a 26b model that outperforms what used to need a data center, texts you on telegram and never forgets a single thing you taught it the gap between people who own their ai and people who rent it is about to get very wide full breakdown on how this actually works ↓

HERMES AGENT GEMMA 4 26B ON A MAC MINI NO API KEYS NO MONTHLY BILL NO ASKING PERMISSION TO USE YOUR OWN DATA a $700 box on your desk just replaced a $30/month subscription, runs a 26b model that outperforms what used to need a data center, texts you on telegram and never forgets a single thing you taught it the gap between people who own their ai and people who rent it is about to get very wide full breakdown on how this actually works ↓

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Managed to get Ling Mini 16B (1.4B active) running on my iPhone Air. It runs very fast with MLX. It's a DWQ of Ling Mini quantized to 3 bits-per-weight. A 16B model running on an Air at this speed is pretty awesome:

Managed to get Ling Mini 16B (1.4B active) running on my iPhone Air. It runs very fast with MLX. It's a DWQ of Ling Mini quantized to 3 bits-per-weight. A 16B model running on an Air at this speed is pretty awesome:

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✨Tap here to find out how this tie can create a range of effects: 🪢 What you do with this tie depends entirely on your intention. You can wear it as a decorative flair, use it for extra sensations, or add it to a predicament to make things more challenging. All of the above? Also valid. Go explore out what it does for you!

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✨Tap here to find out how this tie can create a range of effects: 🪢 What you do with this tie depends entirely on your intention. You can wear it as a decorative flair, use it for extra sensations, or add it to a predicament to make things more challenging. All of the above? Also valid. Go explore out what it does for you!

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🚨SPACEX DIDN'T ACQUIRE CURSOR FOR TALENT A rocket company buying software for the largest startup acquisition in history Here's what almost nobody realizes: They didn't buy a talent or a coding app. They bought the data underneath it. Cursor's users generate ~150 million lines of code a day. Every keystroke is a record of how real engineers build and fix software The exact raw material to train an AI that can code. You can't buy that on a market. You have to own the thing that produces it. So SpaceX bought a permanent data firehose for Grok. The product is almost a side effect. The entire $60B was covered by a fraction of their market-cap gains since the IPO This is a catch-up bet with Anthropic and openAI You see how they are slowly building it don't you?

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A guy who turned CLAWD BOT into a SPORT EXPERT that MADE $63k per WEEK But how? He used a bot that does what no human can do manually: - Scans injury & lineup reports 28 min before tip-off - Checks back to back game schedules - Steam moves detection - Cross references 12 sportsbooks for line discrepancies in real time - Reads line movement in real time The bot processes all of this in seconds and tells you one thing: BET ON THIS & HERE'S WHY That's crazy how he trusted an AI to do its job and manage capital Is it luck or has AI really learned to predict properly? That's where you can bet on live sport -

A guy who turned CLAWD BOT into a SPORT EXPERT that MADE $63k per WEEK But how? He used a bot that does what no human can do manually: - Scans injury & lineup reports 28 min before tip-off - Checks back to back game schedules - Steam moves detection - Cross references 12 sportsbooks for line discrepancies in real time - Reads line movement in real time The bot processes all of this in seconds and tells you one thing: BET ON THIS & HERE'S WHY That's crazy how he trusted an AI to do its job and manage capital Is it luck or has AI really learned to predict properly? That's where you can bet on live sport -

Lorden

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so we already have an open source alternative to jev... and it's 6-7x faster?! it's a typed-decision classification system: no chat, no generated text, just fast yes/no, scoring, or choice answers. > runs in under 1gb of memory > free on hugging face > runs on a laptop, or even a phone there's a real demo where it plays snake live, making a fresh decision every single move. two honest limits, straight from the project itself: > only 512-1,024 token context, some cases won't fit > weaker generalization than jev out of the box

so we already have an open source alternative to jev... and it's 6-7x faster?! it's a typed-decision classification system: no chat, no generated text, just fast yes/no, scoring, or choice answers. > runs in under 1gb of memory > free on hugging face > runs on a laptop, or even a phone there's a real demo where it plays snake live, making a fresh decision every single move. two honest limits, straight from the project itself: > only 512-1,024 token context, some cases won't fit > weaker generalization than jev out of the box

Simplifying AI

45,608 просмотров • 8 дней назад

people often ask me how well does clipping work imo clipping has a 100% success rate because it's so new i don't think people truly understand what it is it's essentially just distributing a viral format at scale all we do is find a plethora of formats that go viral and convert extremely well you either find that yourself by doing reseach if ur niche is well developed or hire creators on a retainer to test out formats to find that if your niche doesnt have a lot of viral content it all really just comes down to finding viral formats consistantly and having clippers replicate content in that format hundreds of times once you find the winner, you put it on Content Rewards and you scale it up it's truly impossible to fail when a format is a winner and you have so much control over that you will obviously never push a format that doesn't convert so technically it has a 100% success rate

people often ask me how well does clipping work imo clipping has a 100% success rate because it's so new i don't think people truly understand what it is it's essentially just distributing a viral format at scale all we do is find a plethora of formats that go viral and convert extremely well you either find that yourself by doing reseach if ur niche is well developed or hire creators on a retainer to test out formats to find that if your niche doesnt have a lot of viral content it all really just comes down to finding viral formats consistantly and having clippers replicate content in that format hundreds of times once you find the winner, you put it on Content Rewards and you scale it up it's truly impossible to fail when a format is a winner and you have so much control over that you will obviously never push a format that doesn't convert so technically it has a 100% success rate

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I'M SHOCKED – ALMOST NOBODY IS USING GROK BOT THE RIGHT WAY. A LEAD ENGINEER AT SPACEX AI JUST DROPPED A 1-HOUR COURSE ON HOW IT'S ACTUALLY DONE. HERE'S ALL OF IT IN 60 SECONDS. The mistake almost everyone makes: they hand Grok Bot random one-off tasks. That's it. That's the whole reason it still feels like a chatbot to you. Here's the system he teaches instead – five parts: → ROLE – stop assigning tasks, create permanent roles. Chief of Staff, Inbox Manager, Researcher, Developer, Reviewer. A bot with a job title beats a bot with a to-do list → TOOLS – connect each bot to what it actually needs: Gmail, Slack, Calendar, Notion, GitHub. It does the work on its own persistent cloud computer → SKILL – teach it your workflow. Write the instructions, or just record yourself doing the job once – Grok Bot turns that demonstration into a reusable skill → ROUTINE – anything you repeat becomes a routine. It runs on a schedule or fires off an event, with your laptop closed → TEAM – group the bots together. The lead bot delegates, the specialists work in parallel, and you set approval rules before anything sends an email, moves a calendar or pushes code The one honest catch: a new bot still needs context and some hand-holding on its first few runs. But that's the entire system. Role → Tools → Skill → Routine → Team. Everyone else is still typing one-off prompts into Grok Bot and wondering why nothing compounds. Bookmark this & read the full breakdown in the article below ↓

I'M SHOCKED – ALMOST NOBODY IS USING GROK BOT THE RIGHT WAY. A LEAD ENGINEER AT SPACEX AI JUST DROPPED A 1-HOUR COURSE ON HOW IT'S ACTUALLY DONE. HERE'S ALL OF IT IN 60 SECONDS. The mistake almost everyone makes: they hand Grok Bot random one-off tasks. That's it. That's the whole reason it still feels like a chatbot to you. Here's the system he teaches instead – five parts: → ROLE – stop assigning tasks, create permanent roles. Chief of Staff, Inbox Manager, Researcher, Developer, Reviewer. A bot with a job title beats a bot with a to-do list → TOOLS – connect each bot to what it actually needs: Gmail, Slack, Calendar, Notion, GitHub. It does the work on its own persistent cloud computer → SKILL – teach it your workflow. Write the instructions, or just record yourself doing the job once – Grok Bot turns that demonstration into a reusable skill → ROUTINE – anything you repeat becomes a routine. It runs on a schedule or fires off an event, with your laptop closed → TEAM – group the bots together. The lead bot delegates, the specialists work in parallel, and you set approval rules before anything sends an email, moves a calendar or pushes code The one honest catch: a new bot still needs context and some hand-holding on its first few runs. But that's the entire system. Role → Tools → Skill → Routine → Team. Everyone else is still typing one-off prompts into Grok Bot and wondering why nothing compounds. Bookmark this & read the full breakdown in the article below ↓

SCOTTY BEAM

97,697 просмотров • 1 месяц назад

Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Alright, now that we know what an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Google AI

22,487 просмотров • 10 месяцев назад

$We were taught the derivative as a formula to memorise. A definition to recite. A rule to apply. Something that "gives you the slope." But nobody told us what the formula was actually saying. Every symbol is a sentence. Every fraction is a question. Every limit is a story about getting closer and closer to something you can never quite touch. The top of the fraction? That's a change. A difference. A before and after. The bottom? That's how long you waited to see it. The limit? That's you, zooming in, refusing to settle for an approximation - chasing the truth all the way down to an interval so small it almost disappears. Put it all together, and you get the most honest question in calculus: How fast is something changing - right now, in this exact instant? Not on average. Not over a minute. Not eventually. Right now. That's it. That's the derivative. It's not a trick. It's not a rule. It's a beautifully precise way of asking a very human question: what's happening, in this moment? We spent years solving these. Maybe it's time we actually understood them.$

We were taught the derivative as a formula to memorise. A definition to recite. A rule to apply. Something that "gives you the slope." But nobody told us what the formula was actually saying. Every symbol is a sentence. Every fraction is a question. Every limit is a story about getting closer and closer to something you can never quite touch. The top of the fraction? That's a change. A difference. A before and after. The bottom? That's how long you waited to see it. The limit? That's you, zooming in, refusing to settle for an approximation - chasing the truth all the way down to an interval so small it almost disappears. Put it all together, and you get the most honest question in calculus: How fast is something changing - right now, in this exact instant? Not on average. Not over a minute. Not eventually. Right now. That's it. That's the derivative. It's not a trick. It's not a rule. It's a beautifully precise way of asking a very human question: what's happening, in this moment? We spent years solving these. Maybe it's time we actually understood them.

The Math Flow

22,498 просмотров • 4 месяцев назад