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This Indian Developer made 41.7 Lakh in roughly 2 months. He builds RL environments for AI labs. Here’s what that actually means. You don’t write answers for the AI. You set its exam. Take a real GitHub repo with a real bug. Pin it inside Docker so it runs...

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Arsh Goyal

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38,423 görüntüleme • 20 gün önce •via X (Twitter)

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He didn't go through prime intellect, he applied to different freelancing job boards and suggests it's once in a while task not replacement of normal job.

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something fresh for me - rl environments

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I got this opp. back in Feb/March but they rejected me , I fumble on some of things in interview Pay was around 40 to 70 dollars/hrs Honestly great way to earn as part time contractor or if an std then go crazy on it

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Benzer Videolar

Hiring RL Engineer! Started off as a curious project at Lossfunk to push the boundaries of LLMs in social reasoning - we are now building RL environments, data, and benchmarks to simulate more real-world scenarios. If you want to train SoTA RL models over multi-GPUs (H200s/B200s) to unlock next AI frontier, this is for you.
1:21

Hiring RL Engineer! Started off as a curious project at Lossfunk to push the boundaries of LLMs in social reasoning - we are now building RL environments, data, and benchmarks to simulate more real-world scenarios. If you want to train SoTA RL models over multi-GPUs (H200s/B200s) to unlock next AI frontier, this is for you.

Satpal Singh Rathore

46,010 görüntüleme • 1 yıl önce

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?
26:21

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

Pat Grady

66,449 görüntüleme • 1 ay önce

Just found an open-source tool that lets you see how well an AI can actually use a computer. it’s called OSWorld 2.0. you give the AI a real task on a computer, then watch to see if it can figure everything out and finish it. it gives you: → real computer tasks, not simple questions → tasks that can take many steps to finish → the same computer setup for every AI → websites made for testing → all the files and tasks for free this is also the environment used to test computer-use systems from OpenAI and Anthropic. so if you’re building AI agents, researching computer use, or just want to see what these models can actually do, you can run the same tests yourself instead of relying only on benchmark scores. the labs publish the scores. this repo is where the actual tests come from. bookmark this.
0:25

Just found an open-source tool that lets you see how well an AI can actually use a computer. it’s called OSWorld 2.0. you give the AI a real task on a computer, then watch to see if it can figure everything out and finish it. it gives you: → real computer tasks, not simple questions → tasks that can take many steps to finish → the same computer setup for every AI → websites made for testing → all the files and tasks for free this is also the environment used to test computer-use systems from OpenAI and Anthropic. so if you’re building AI agents, researching computer use, or just want to see what these models can actually do, you can run the same tests yourself instead of relying only on benchmark scores. the labs publish the scores. this repo is where the actual tests come from. bookmark this.

MIKE

13,429 görüntüleme • 1 gün önce

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA
1:21

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Flexion

72,555 görüntüleme • 1 ay önce

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,549 görüntüleme • 3 ay önce

A fun way to try the new Claude Fable. I asked it to train a bot using RL to navigate 3D worlds created with SpAItial AI. This is all running in the browser including RL training! Link to demo and code in comments What you can do: -> Train a bot from scratch, watch it learn in minutes, then drop it into a world it has never seen and it keeps navigating obstacles. -> The worlds are real scenes generated from a text prompt or a photo via the SpAItial AI API (you get a splat + a collision mesh). -> Mesh is made into a 2D walkability + height grid that the RL environment runs on. -> The trick to generalization: the policy only ever sees egocentric inputs. A 16-ray lidar, the goal's body frame, and its speed. No global coordinates, no map. So it learns navigation, not a memorized world.
1:05

A fun way to try the new Claude Fable. I asked it to train a bot using RL to navigate 3D worlds created with SpAItial AI. This is all running in the browser including RL training! Link to demo and code in comments What you can do: -> Train a bot from scratch, watch it learn in minutes, then drop it into a world it has never seen and it keeps navigating obstacles. -> The worlds are real scenes generated from a text prompt or a photo via the SpAItial AI API (you get a splat + a collision mesh). -> Mesh is made into a 2D walkability + height grid that the RL environment runs on. -> The trick to generalization: the policy only ever sees egocentric inputs. A 16-ray lidar, the goal's body frame, and its speed. No global coordinates, no map. So it learns navigation, not a memorized world.

RO2⚡️

12,797 görüntüleme • 3 ay önce

This is the Claude Code moment for Reinforcement Learning. Every frontier lab knows the secret: post-training is where the magic happens. If you can eval a task, you can use RL to benchmax your model. But building and scaling the RL loop has remained a dark art reserved for the top 1% of AI Labs. Until today. Introducing Optimus — an autonomous RL Agent Harness, and the first of many we are about to ship Iacon Autonomics. We are removing the manual grind of RL and adding taste and relentlessness to the process. Just hand Optimus a codebase, an API key, and a goal. It spins up a fleet of sub-agents to run hundreds of parallel experiments. It learns from its own history, autonomously building an evolutionary tree of runs where every failure sets the next batch up to win. Train LLMs, agents, robots in sim, game npcs and much more. If you can score it, Optimus can train it. Stop doing manual RL. Let the agents cook.
1:57

This is the Claude Code moment for Reinforcement Learning. Every frontier lab knows the secret: post-training is where the magic happens. If you can eval a task, you can use RL to benchmax your model. But building and scaling the RL loop has remained a dark art reserved for the top 1% of AI Labs. Until today. Introducing Optimus — an autonomous RL Agent Harness, and the first of many we are about to ship Iacon Autonomics. We are removing the manual grind of RL and adding taste and relentlessness to the process. Just hand Optimus a codebase, an API key, and a goal. It spins up a fleet of sub-agents to run hundreds of parallel experiments. It learns from its own history, autonomously building an evolutionary tree of runs where every failure sets the next batch up to win. Train LLMs, agents, robots in sim, game npcs and much more. If you can score it, Optimus can train it. Stop doing manual RL. Let the agents cook.

Dev Shah

13,730 görüntüleme • 26 gün önce

THIS GUY JUST BUILT A DRONE THAT TRACKS TARGETS WITH A LASER USING CLAUDE > No robotics team > No engineering degree > No budget He used Claude Code to write the entire system in Python - a drone that locks onto a target, follows it with a laser and corrects its own aim automatically after every attempt. The AI gets more accurate the longer it runs because it learns from every single shot. He's still improving it and this is only the beginning. You have the same tool he used. The article below is about how to actually use it to its full potential.
0:39

THIS GUY JUST BUILT A DRONE THAT TRACKS TARGETS WITH A LASER USING CLAUDE > No robotics team > No engineering degree > No budget He used Claude Code to write the entire system in Python - a drone that locks onto a target, follows it with a laser and corrects its own aim automatically after every attempt. The AI gets more accurate the longer it runs because it learns from every single shot. He's still improving it and this is only the beginning. You have the same tool he used. The article below is about how to actually use it to its full potential.

Anatoli Kopadze

276,199 görüntüleme • 4 ay önce

PhD Students – How to write a literature review 10x faster? Meet 𝐀𝐧𝐬𝐰𝐞𝐫𝐓𝐡𝐢𝐬 - a tool for quick literature reviews. 𝐇𝐨𝐰 𝐀𝐧𝐬𝐰𝐞𝐫𝐓𝐡𝐢𝐬 𝐰𝐨𝐫𝐤𝐬? 1. Go to 2. Click on 𝐴𝐼 𝑊𝑟𝑖𝑡𝑒𝑟 and enter your review topic 3. Now click on 𝐶𝑟𝑒𝑎𝑡𝑒 𝑂𝑢𝑡𝑙𝑖𝑛𝑒 4. Once outline is created, use 𝐴𝑔𝑒𝑛𝑡𝑖𝑐 𝐴𝐼 to write 5. Start with writing the abstract 6. Once it writes abstract, review & accept/reject it 7. After abstract, write Introduction section 8. While writing, you can do the following as well. ➝ Paraphrase the text ➝ Highlight any text ➝ Check for (AI) plagiarism ➝ Refine and contextualize the writing ➝ Insert references in any style you want 9. Once done, download the paper in WORD/PDF/Latex This way you have more control over what you write. It is not blind use of AI to write for you.
1:36

PhD Students – How to write a literature review 10x faster? Meet 𝐀𝐧𝐬𝐰𝐞𝐫𝐓𝐡𝐢𝐬 - a tool for quick literature reviews. 𝐇𝐨𝐰 𝐀𝐧𝐬𝐰𝐞𝐫𝐓𝐡𝐢𝐬 𝐰𝐨𝐫𝐤𝐬? 1. Go to 2. Click on 𝐴𝐼 𝑊𝑟𝑖𝑡𝑒𝑟 and enter your review topic 3. Now click on 𝐶𝑟𝑒𝑎𝑡𝑒 𝑂𝑢𝑡𝑙𝑖𝑛𝑒 4. Once outline is created, use 𝐴𝑔𝑒𝑛𝑡𝑖𝑐 𝐴𝐼 to write 5. Start with writing the abstract 6. Once it writes abstract, review & accept/reject it 7. After abstract, write Introduction section 8. While writing, you can do the following as well. ➝ Paraphrase the text ➝ Highlight any text ➝ Check for (AI) plagiarism ➝ Refine and contextualize the writing ➝ Insert references in any style you want 9. Once done, download the paper in WORD/PDF/Latex This way you have more control over what you write. It is not blind use of AI to write for you.

Faheem Ullah

46,322 görüntüleme • 5 ay önce

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!
3:40

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,225,587 görüntüleme • 2 yıl önce

iit's wild that this still isn't the default way people use AI. Andrew Ng, a co-founder of Google Brain, gave a talk that crossed 2 million views: stop feeding your AI a folder of notes, build it a graph it can actually think with. his point is simple, stop making your AI read the whole vault to answer one thing. give it a graph, a router that points, an index that scores, edges that link, and it walks straight to the answer. it compounds, cheaper and faster the more you add. here's the whole thing: > point Claude at your notes and have it write a tiny router, your CLAUDE.md > build an index, one line per note, so it always knows where an answer lives > keep each note to one idea, and link the ones that depend on each other > make retrieval run as code, so it scores and opens the right files before the model thinks > tell it to prove the graph beats a flat folder on speed and cost, then keep the index fresh on every write set it up once and your AI stops guessing. it already knows your world before you type a word. full 12-step guide below. bookmark this.
0:23

iit's wild that this still isn't the default way people use AI. Andrew Ng, a co-founder of Google Brain, gave a talk that crossed 2 million views: stop feeding your AI a folder of notes, build it a graph it can actually think with. his point is simple, stop making your AI read the whole vault to answer one thing. give it a graph, a router that points, an index that scores, edges that link, and it walks straight to the answer. it compounds, cheaper and faster the more you add. here's the whole thing: > point Claude at your notes and have it write a tiny router, your CLAUDE.md > build an index, one line per note, so it always knows where an answer lives > keep each note to one idea, and link the ones that depend on each other > make retrieval run as code, so it scores and opens the right files before the model thinks > tell it to prove the graph beats a flat folder on speed and cost, then keep the index fresh on every write set it up once and your AI stops guessing. it already knows your world before you type a word. full 12-step guide below. bookmark this.

KingWilliam

15,099 görüntüleme • 1 ay önce

Boris Cherny runs Claude Code at Anthropic. His line should stop you mid-scroll: "I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops." The skill moved. From writing prompts to building the loop around them. A loop that works has three things. Drop one and it burns money in circles. 1. A finish line it can prove. Tests pass or they fail. "Looks done" is not a finish line. 2. A checker that did not write the code. A model grading itself always says yes. 3. Brakes set before you start. A step limit. A money ceiling. A stop condition. That is the whole game. The loop does the work. You design the cage it runs in. Full breakdown below.
0:37

Boris Cherny runs Claude Code at Anthropic. His line should stop you mid-scroll: "I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops." The skill moved. From writing prompts to building the loop around them. A loop that works has three things. Drop one and it burns money in circles. 1. A finish line it can prove. Tests pass or they fail. "Looks done" is not a finish line. 2. A checker that did not write the code. A model grading itself always says yes. 3. Brakes set before you start. A step limit. A money ceiling. A stop condition. That is the whole game. The loop does the work. You design the cage it runs in. Full breakdown below.

shmidt

16,085 görüntüleme • 2 ay önce

I’m reverse-engineering the Google AI mouse pointer and making it Open Source! It sees your screen. You can freeform paint anything and ask AI to modify it. It understands the app or window you mean. It can click, type, edit, navigate, and even write + execute code. A tiny cursor becoming a real computer-use agent. Made with the help of Cua which is an AMAZING reference for computer use Sorry Google DeepMind but I had to do this heyclicky Started a revolution and we will be the ones to win Also credits to Omkar Satpute for the help Feel free to modify it , update it and make it your own, we gotta beat them at this. Link to the repo in the comments:
1:31

I’m reverse-engineering the Google AI mouse pointer and making it Open Source! It sees your screen. You can freeform paint anything and ask AI to modify it. It understands the app or window you mean. It can click, type, edit, navigate, and even write + execute code. A tiny cursor becoming a real computer-use agent. Made with the help of Cua which is an AMAZING reference for computer use Sorry Google DeepMind but I had to do this heyclicky Started a revolution and we will be the ones to win Also credits to Omkar Satpute for the help Feel free to modify it , update it and make it your own, we gotta beat them at this. Link to the repo in the comments:

Milind S

215,672 görüntüleme • 4 ay önce

Local AI 101: open models, Hugging Face, and businesses to build (38 min masterclass) I still think cloud AI is the default for most things, and honestly it should be, the frontier models are the strongest and easiest to use. But something shifted in the last 4-5 months. You can now run genuinely good open models directly on your own laptop, or even your phone. And once you actually try it, it changes how you think about what AI is even for. LOCAL AI, CLEARLY EXPLAINED: 1. The model is the brain doing the thinking. Gemma, Llama, Mistral, and Qwen are the main families, and each is better at different things, some at reasoning, some at coding, some small enough to run on a phone. 2. Hugging Face is the warehouse where you find them. You go there to see what each model is good at, check the license, and grab the compressed versions that run on a normal computer. 3. The software is what runs the model on your machine. Start with LM Studio if you're not technical, it feels like a normal app where you search, download, and start chatting. Ollama is the one you reach for when you want to plug a model into your own apps. 4. The workflow is the actual product you build on top of it all. That's what I'm ideating around for some businesses to create. I think local AI just made a specific kind of business way easier to start. Find an industry that: 1. Sits on sensitive data they'd never paste into ChatGPT 2. Does the same review over and over 3. Runs on software from 2003 Then build a local AI tool that does that review on their own machine, so the data never leaves the building! Take home health agencies. Nurses write visit notes all day, and if a note is missing a detail, the billing gets denied or the audit flags it. Here's how I'd start: 1. Find 5 small agencies. Offer to review a batch of their notes for them. 2. Run the notes through Gemma locally (free, private, no cloud). Read every output yourself. 3. Write down the 20 issues that keep showing up: missing vitals, vague med changes, notes that don't support the billed level. 4. That list of 20 is your checklist. The checklist is the product. 5. Turn it into a local desktop app that flags those 20 things before a note gets submitted. You just went from a service anyone could offer to a product nobody else has, and you learned exactly what to build by doing the work by hand first. Same recipe works for restoration contractors (draft the damage report on-site before the tech leaves) and wealth advisors (catch the compliance landmine in a client email before it sends). Basically the framework is sensitive data, repeated review, ancient software. I think there are tons of businesses like this! Almost none of it clicked for me until I actually started using local AI. So if you take one thing from this, go run a model on your own machine once. Also a fun thing to try with your friends. Feel free to send this to a friend. The episode is live for free on The Startup Ideas Podcast (SIP) 🧃 (thanks to Google for sponsoring today's episode and supporting local AI) I feel like local AI one of those things you need to try for it to really click. Run one model on your own machine and you'll see what I mean! I go way deeper in the full 38 minute masterclass, the models, the setup, and the businesses to build. Link below. LINK TO WATCH: OR WATCH BELOW ON X What do you think of local AI?
38:45

Local AI 101: open models, Hugging Face, and businesses to build (38 min masterclass) I still think cloud AI is the default for most things, and honestly it should be, the frontier models are the strongest and easiest to use. But something shifted in the last 4-5 months. You can now run genuinely good open models directly on your own laptop, or even your phone. And once you actually try it, it changes how you think about what AI is even for. LOCAL AI, CLEARLY EXPLAINED: 1. The model is the brain doing the thinking. Gemma, Llama, Mistral, and Qwen are the main families, and each is better at different things, some at reasoning, some at coding, some small enough to run on a phone. 2. Hugging Face is the warehouse where you find them. You go there to see what each model is good at, check the license, and grab the compressed versions that run on a normal computer. 3. The software is what runs the model on your machine. Start with LM Studio if you're not technical, it feels like a normal app where you search, download, and start chatting. Ollama is the one you reach for when you want to plug a model into your own apps. 4. The workflow is the actual product you build on top of it all. That's what I'm ideating around for some businesses to create. I think local AI just made a specific kind of business way easier to start. Find an industry that: 1. Sits on sensitive data they'd never paste into ChatGPT 2. Does the same review over and over 3. Runs on software from 2003 Then build a local AI tool that does that review on their own machine, so the data never leaves the building! Take home health agencies. Nurses write visit notes all day, and if a note is missing a detail, the billing gets denied or the audit flags it. Here's how I'd start: 1. Find 5 small agencies. Offer to review a batch of their notes for them. 2. Run the notes through Gemma locally (free, private, no cloud). Read every output yourself. 3. Write down the 20 issues that keep showing up: missing vitals, vague med changes, notes that don't support the billed level. 4. That list of 20 is your checklist. The checklist is the product. 5. Turn it into a local desktop app that flags those 20 things before a note gets submitted. You just went from a service anyone could offer to a product nobody else has, and you learned exactly what to build by doing the work by hand first. Same recipe works for restoration contractors (draft the damage report on-site before the tech leaves) and wealth advisors (catch the compliance landmine in a client email before it sends). Basically the framework is sensitive data, repeated review, ancient software. I think there are tons of businesses like this! Almost none of it clicked for me until I actually started using local AI. So if you take one thing from this, go run a model on your own machine once. Also a fun thing to try with your friends. Feel free to send this to a friend. The episode is live for free on The Startup Ideas Podcast (SIP) 🧃 (thanks to Google for sponsoring today's episode and supporting local AI) I feel like local AI one of those things you need to try for it to really click. Run one model on your own machine and you'll see what I mean! I go way deeper in the full 38 minute masterclass, the models, the setup, and the businesses to build. Link below. LINK TO WATCH: OR WATCH BELOW ON X What do you think of local AI?

GREG ISENBERG

29,524 görüntüleme • 5 gün önce

If I had to start from zero tomorrow and bring in new customers fast, here's exactly what I'd build. Top to bottom. I'd start with the ad. Open ChatGPT or Claude, paste in what you sell and who you sell it to, ask it to write you ten different hooks for a Meta ad. Pick the best three. Then jump into or one of the AI video tools and have it generate the visual. Thirty minutes, you've got a real ad ready to run. You run that ad to one landing page. Not your homepage. One page, one offer, one call to action. Headline that stops the scroll, a few lines of value, an email or phone field, a button. That's it. Build it in HighLevel in about twenty minutes, drag, drop, you're done. The second someone enters their email, they drop into your CRM, automatically tagged with where they came from. Now the part most people skip, the follow-up. The moment that form is filled, a sequence kicks off automatically. Email and SMS, spaced over five to seven days. First message thanks them and delivers what you promised. Then a story. Then social proof. Then an objection you know they have. Then the offer. They don't have to think about you. You're showing up in their inbox and their texts at exactly the right time. The last message has a calendar link or a buy button right inside it. They tap, they book, or they buy. Done. The whole flow runs on its own from the ad all the way to the sale. You build it once. It works while you're sleeping. AI does the creative work. The system runs the customer journey. You spend your time on strategy. That's the play.
1:09

If I had to start from zero tomorrow and bring in new customers fast, here's exactly what I'd build. Top to bottom. I'd start with the ad. Open ChatGPT or Claude, paste in what you sell and who you sell it to, ask it to write you ten different hooks for a Meta ad. Pick the best three. Then jump into or one of the AI video tools and have it generate the visual. Thirty minutes, you've got a real ad ready to run. You run that ad to one landing page. Not your homepage. One page, one offer, one call to action. Headline that stops the scroll, a few lines of value, an email or phone field, a button. That's it. Build it in HighLevel in about twenty minutes, drag, drop, you're done. The second someone enters their email, they drop into your CRM, automatically tagged with where they came from. Now the part most people skip, the follow-up. The moment that form is filled, a sequence kicks off automatically. Email and SMS, spaced over five to seven days. First message thanks them and delivers what you promised. Then a story. Then social proof. Then an objection you know they have. Then the offer. They don't have to think about you. You're showing up in their inbox and their texts at exactly the right time. The last message has a calendar link or a buy button right inside it. They tap, they book, or they buy. Done. The whole flow runs on its own from the ad all the way to the sale. You build it once. It works while you're sleeping. AI does the creative work. The system runs the customer journey. You spend your time on strategy. That's the play.

Neil Patel

39,323 görüntüleme • 1 ay önce

Blogging used to be simple. Write something helpful, rank on Google, and wake up to traffic. That worked for a long time. Then AI got good at summarizing it. A recent study tracked 100 once-successful blogs. The median one lost 85% of its Google traffic. But the split is what matters. The blogs getting destroyed right now are mostly describing things “best budgeting apps,” “how to start a podcast,” “top tools in 2026.” The ones still growing? They’re proving something. They didn’t write another roundup. They built the thing, tested it for real, spent the money, made the mistake, and showed what actually happened. That’s the new filter. Before you create your next piece of content, look at what people are searching for using then you can create content for what people actually want to know about. Then ask yourself one question: Can AI answer this without me? If the answer is yes, you’re writing something a machine can already summarize better and faster than you. If the answer is no, if it requires your experience, your specific results, your data, or your actual outcome that’s where the opportunity is. The future of content isn’t more articles. It’s proof that can’t be faked.
1:07

Blogging used to be simple. Write something helpful, rank on Google, and wake up to traffic. That worked for a long time. Then AI got good at summarizing it. A recent study tracked 100 once-successful blogs. The median one lost 85% of its Google traffic. But the split is what matters. The blogs getting destroyed right now are mostly describing things “best budgeting apps,” “how to start a podcast,” “top tools in 2026.” The ones still growing? They’re proving something. They didn’t write another roundup. They built the thing, tested it for real, spent the money, made the mistake, and showed what actually happened. That’s the new filter. Before you create your next piece of content, look at what people are searching for using then you can create content for what people actually want to know about. Then ask yourself one question: Can AI answer this without me? If the answer is yes, you’re writing something a machine can already summarize better and faster than you. If the answer is no, if it requires your experience, your specific results, your data, or your actual outcome that’s where the opportunity is. The future of content isn’t more articles. It’s proof that can’t be faked.

Neil Patel

11,696 görüntüleme • 1 ay önce

Ben Affleck says AI can’t write meaningful movies because it produces average work “If you ask ChatGPT, Claude, or Gemini to write something, it’s really bad.” “The problem is that AI naturally goes to the average, and I can’t stand what it writes.” “It can be a useful tool for writers who need help solving small story problems or brainstorming ideas.” “I don’t think it’s going to write anything truly meaningful.” “I also don’t believe AI will be making complete movies on its own anytime soon.”
0:43

Ben Affleck says AI can’t write meaningful movies because it produces average work “If you ask ChatGPT, Claude, or Gemini to write something, it’s really bad.” “The problem is that AI naturally goes to the average, and I can’t stand what it writes.” “It can be a useful tool for writers who need help solving small story problems or brainstorming ideas.” “I don’t think it’s going to write anything truly meaningful.” “I also don’t believe AI will be making complete movies on its own anytime soon.”

_Chase😈

124,600 görüntüleme • 1 ay önce

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.
1:05

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

229,120 görüntüleme • 10 gün önce

Ken Griffin just asked the question everyone in AI is too scared to answer. Data center spending in the US this year alone is over $500 billion. Half a trillion dollars. To raise that kind of money, you have to make a promise. And the promise has to be big. "AI needs to be your savior almost. How else are you going to write $500 billion of checks in a single year?" He's not saying AI is fraud. He's saying the hype is structurally necessary. You can't fund a buildout at this scale without narrative that matches it. The real question is what AI actually delivers at the end. In some areas Griffin says it's going to be profound. Call centers. Software engineering productivity. Those are real, measurable, already happening. But in white collar work more broadly, he's more skeptical. A Harvard paper recently coined a term for it: AI Work Slop. Output that looks impressive on the surface. First few sentences read like genuine insight. Then you go deeper and it's all garbage. Griffin's colleague runs their commodities business. Got handed a report generated by an AI engine. First paragraph, genuinely good. The rest, useless. The model that can write a compelling opening can't yet think through the substance underneath it. This is the AI investing tension right now. The infrastructure spend is real. The hype is real. The productivity gains in specific verticals are real. But the blanket assumption that AI transforms every white collar job equally has not been proven yet.
1:20

Ken Griffin just asked the question everyone in AI is too scared to answer. Data center spending in the US this year alone is over $500 billion. Half a trillion dollars. To raise that kind of money, you have to make a promise. And the promise has to be big. "AI needs to be your savior almost. How else are you going to write $500 billion of checks in a single year?" He's not saying AI is fraud. He's saying the hype is structurally necessary. You can't fund a buildout at this scale without narrative that matches it. The real question is what AI actually delivers at the end. In some areas Griffin says it's going to be profound. Call centers. Software engineering productivity. Those are real, measurable, already happening. But in white collar work more broadly, he's more skeptical. A Harvard paper recently coined a term for it: AI Work Slop. Output that looks impressive on the surface. First few sentences read like genuine insight. Then you go deeper and it's all garbage. Griffin's colleague runs their commodities business. Got handed a report generated by an AI engine. First paragraph, genuinely good. The rest, useless. The model that can write a compelling opening can't yet think through the substance underneath it. This is the AI investing tension right now. The infrastructure spend is real. The hype is real. The productivity gains in specific verticals are real. But the blanket assumption that AI transforms every white collar job equally has not been proven yet.

Milk Road Macro

125,923 görüntüleme • 4 ay önce