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> be a solo developer > hear that B2B clients pay $35,000/month for traffic data > assume you need a massive team > assume you need proprietary AI models > assume you need venture capital > open VS Code > write a single Python script > pairing an open-source... model with a high-ticket commercial concept > 6 core features. 0 frame drops. 0 lag > powered by YOLOv8, Supervision, and ByteTrack > install and optimize the pipeline in minutes > define a custom Polygon Zone to filter out all noise > system follows a military-like routine every single day > tracks bounding boxes and calculates real-time telemetry > doesn’t even use raw screen pixels for calculation > one single feature (Perspective Transform via OpenCV) converts pixels to real meters > generates high-value Excel traffic reports for smart cities > $0 in VC funding > same accuracy as an enterprise infrastructure team > follow the detailed guide below to repeatshow more

Ridark

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20,935 views • 1 month ago •via X (Twitter)

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A 21-year-old solo developer stopped chasing AI trends and locked in a $35,000/month B2B niche using a single Python script He built a production-ready traffic monitoring Micro-SaaS that smart cities and businesses pay big money for He didn't build complex AI agent teams He just paired an open-source model with a high-ticket commercial concept Every single day, his system follows a military-like routine: tracks the bounding boxes, calculates telemetry, and locks in the data For 4 to 6 steps straight, the pipeline is completely optimized. Zero frame drops, zero lag. Just pure automated tracking under the hood He doesn't even use raw screen pixels for calculation to keep his speed metrics clean from perspective distortion Out of 6 core features he launched, 3 handle the baseline detection and tracking using YOLOv8, Supervision, and ByteTrack But one single feature (Perspective Transform via OpenCV) converts pixels to real meters and generates high-value Excel traffic reports for clients His main secret? He utilizes a custom Polygon Zone to filter out noise and ships a ready-to-sell B2B product immediately instead of overcomplicating the setup

A 21-year-old solo developer stopped chasing AI trends and locked in a $35,000/month B2B niche using a single Python script He built a production-ready traffic monitoring Micro-SaaS that smart cities and businesses pay big money for He didn't build complex AI agent teams He just paired an open-source model with a high-ticket commercial concept Every single day, his system follows a military-like routine: tracks the bounding boxes, calculates telemetry, and locks in the data For 4 to 6 steps straight, the pipeline is completely optimized. Zero frame drops, zero lag. Just pure automated tracking under the hood He doesn't even use raw screen pixels for calculation to keep his speed metrics clean from perspective distortion Out of 6 core features he launched, 3 handle the baseline detection and tracking using YOLOv8, Supervision, and ByteTrack But one single feature (Perspective Transform via OpenCV) converts pixels to real meters and generates high-value Excel traffic reports for clients His main secret? He utilizes a custom Polygon Zone to filter out noise and ships a ready-to-sell B2B product immediately instead of overcomplicating the setup

Ridark

29,326 views • 1 month ago

> be a mr robot > hear that bug bounties pay $15,000 per vulnerability > assume you need a security degree > assume you need expensive tools > assume you need a team > open GitHub > find Claude-BugHunter > free. MIT license. 51 skills. 15 commands > 574 patterns from real HackerOne reports > install in 3 minutes > describe the target in plain English > Claude auto-loads the specialist skill > finds a vulnerable pattern > /triage - 7-question validation gate > PASS > /report - clean HackerOne submission generated > $20/month in tools > same methodology as a professional security team > follow the detailed guide below to repeat

> be a mr robot > hear that bug bounties pay $15,000 per vulnerability > assume you need a security degree > assume you need expensive tools > assume you need a team > open GitHub > find Claude-BugHunter > free. MIT license. 51 skills. 15 commands > 574 patterns from real HackerOne reports > install in 3 minutes > describe the target in plain English > Claude auto-loads the specialist skill > finds a vulnerable pattern > /triage - 7-question validation gate > PASS > /report - clean HackerOne submission generated > $20/month in tools > same methodology as a professional security team > follow the detailed guide below to repeat

Ridark

112,799 views • 1 month ago

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. If you are a Python developer, there is an open-source alternative! Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. Quix Streams is all Python, and it's open-source! The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. Here is what the video covers: • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) • How to efficiently stream that data into Kafka • How to optimize Kafka producer settings like batch size and compression for maximum throughput A few other notes about Quix Streams: • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. Thanks to the Quix team for collaborating with me on this post.

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. If you are a Python developer, there is an open-source alternative! Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. Quix Streams is all Python, and it's open-source! The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. Here is what the video covers: • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) • How to efficiently stream that data into Kafka • How to optimize Kafka producer settings like batch size and compression for maximum throughput A few other notes about Quix Streams: • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. Thanks to the Quix team for collaborating with me on this post.

Santiago

89,230 views • 2 years ago

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

zostaff

230,194 views • 3 months ago

AGENCIES ARE CHARGING $50,000 FOR HEAVY, CLUNKY AUTOMATION, WHILE SOLO OPERATORS ARE SELLING THE SAME OUTCOME FOR $5k AND BUILDING IT IN JUST 3 HOURS Most people think building an AI agency requires an expensive stack of tools like n8n, Make, and Zapier to justify high monthly retainers They spend weeks pitching and designing overly complex “spaghetti” workflows for clients In reality, the better approach is to build an AI Operating System directly in VS Code using local Markdown files The cost gap is insane. You pay $20/month for a Claude account and spend a few hours wiring up ClickUp and Google Workspace APIs Then you package that same architecture as a $5k one-time setup and a $2k monthly retainer Right now, founders are paying $15k + for enterprise AI setups that a single operator can build with almost zero overhead And if you want a practical AI OS playbook, I’ve attached an article below Save this post and the article so you can come back later and learn how to build a full AI operating system in just 3 hours

AGENCIES ARE CHARGING $50,000 FOR HEAVY, CLUNKY AUTOMATION, WHILE SOLO OPERATORS ARE SELLING THE SAME OUTCOME FOR $5k AND BUILDING IT IN JUST 3 HOURS Most people think building an AI agency requires an expensive stack of tools like n8n, Make, and Zapier to justify high monthly retainers They spend weeks pitching and designing overly complex “spaghetti” workflows for clients In reality, the better approach is to build an AI Operating System directly in VS Code using local Markdown files The cost gap is insane. You pay $20/month for a Claude account and spend a few hours wiring up ClickUp and Google Workspace APIs Then you package that same architecture as a $5k one-time setup and a $2k monthly retainer Right now, founders are paying $15k + for enterprise AI setups that a single operator can build with almost zero overhead And if you want a practical AI OS playbook, I’ve attached an article below Save this post and the article so you can come back later and learn how to build a full AI operating system in just 3 hours

Bonsai 🌳

10,885 views • 2 months ago

The corporate system wants you trading time for a paycheck. The alternative is building automated leverage. You do not need a team of engineers. You just need the right open-source architecture. Here are 10 GitHub repos to automate your workflows, replace manual labor, and direct your own reality: 1. n8n Bypass expensive SaaS subscriptions. Build custom AI automation workflows that run on your own servers. 2. Ollama Stop sending your private data to massive API providers. Run heavy AI models locally on your own machine. Complete privacy. 3. Open Interpreter Let language models control your computer. Automate the repetitive corporate tasks they pay you to do manually. 4. Aider An AI pair programmer that lives in your terminal. Stop writing boilerplate code and focus strictly on the architecture. 5. Dify An open-source LLM app development platform. Build and deploy functional AI agents in minutes, not months. 6. Flowise A drag-and-drop UI to build customized LLM flows. You do not need to be a senior developer to build massive leverage. 7. Supabase Spin up a Postgres database, authentication, and instant APIs. Own your backend entirely. 8. Auto-GPT Give an AI an objective and let it execute. It browses the web, writes code, and chains thoughts together autonomously. 9. Outline An open-source knowledge base for your personal leverage. Stop losing your documentation in arbitrary corporate systems. 10. NocoDB Turn any database into a smart spreadsheet. Keep your data on your own infrastructure and stop paying for convenience. The secret to tech survival? Stop playing by their rules. Build your own systems and take your leverage with you.

The corporate system wants you trading time for a paycheck. The alternative is building automated leverage. You do not need a team of engineers. You just need the right open-source architecture. Here are 10 GitHub repos to automate your workflows, replace manual labor, and direct your own reality: 1. n8n Bypass expensive SaaS subscriptions. Build custom AI automation workflows that run on your own servers. 2. Ollama Stop sending your private data to massive API providers. Run heavy AI models locally on your own machine. Complete privacy. 3. Open Interpreter Let language models control your computer. Automate the repetitive corporate tasks they pay you to do manually. 4. Aider An AI pair programmer that lives in your terminal. Stop writing boilerplate code and focus strictly on the architecture. 5. Dify An open-source LLM app development platform. Build and deploy functional AI agents in minutes, not months. 6. Flowise A drag-and-drop UI to build customized LLM flows. You do not need to be a senior developer to build massive leverage. 7. Supabase Spin up a Postgres database, authentication, and instant APIs. Own your backend entirely. 8. Auto-GPT Give an AI an objective and let it execute. It browses the web, writes code, and chains thoughts together autonomously. 9. Outline An open-source knowledge base for your personal leverage. Stop losing your documentation in arbitrary corporate systems. 10. NocoDB Turn any database into a smart spreadsheet. Keep your data on your own infrastructure and stop paying for convenience. The secret to tech survival? Stop playing by their rules. Build your own systems and take your leverage with you.

Katyayani Shukla

16,964 views • 3 months ago

This 22-year-old American developer showed how he makes $14,200 a month using just a dual-GPU setup and Cohere’s new open-weights model. He doesn't have a powerhouse dev team. No massive VC funding. No expensive enterprise infrastructure. He built a "localized AI workflow" powered by the newly dropped Command A+ model → an ultra-efficient system he calls “algorithmic mining.” The system works like this: 1 open-weights model → Apache 2.0 license integration → Command A+'s Mixture of Experts (MoE) processes complex data at lightning speed → Quantized architecture runs smoothly on just 2 GPUs → Automated agents handle enterprise-level tasks while he sleeps. 3 recurring revenue streams from a 72-hour implementation: > $49/month Essential API access > $99/month Scale tier for small teams > $199/month Custom enterprise pipeline > Delivered with near-zero hosting latency > Total setup time: 3 days He didn't wait for massive cloud budget approval. He took a highly practical, open-source-friendly model, spun it up locally, and built the exact kind of agents usually reserved for Fortune 500 companies. As the developer put it perfectly: In the open-weight era, efficiency and raw speed are the ultimate leverage.

This 22-year-old American developer showed how he makes $14,200 a month using just a dual-GPU setup and Cohere’s new open-weights model. He doesn't have a powerhouse dev team. No massive VC funding. No expensive enterprise infrastructure. He built a "localized AI workflow" powered by the newly dropped Command A+ model → an ultra-efficient system he calls “algorithmic mining.” The system works like this: 1 open-weights model → Apache 2.0 license integration → Command A+'s Mixture of Experts (MoE) processes complex data at lightning speed → Quantized architecture runs smoothly on just 2 GPUs → Automated agents handle enterprise-level tasks while he sleeps. 3 recurring revenue streams from a 72-hour implementation: > $49/month Essential API access > $99/month Scale tier for small teams > $199/month Custom enterprise pipeline > Delivered with near-zero hosting latency > Total setup time: 3 days He didn't wait for massive cloud budget approval. He took a highly practical, open-source-friendly model, spun it up locally, and built the exact kind of agents usually reserved for Fortune 500 companies. As the developer put it perfectly: In the open-weight era, efficiency and raw speed are the ultimate leverage.

Ridark

211,874 views • 2 months ago

A SOLO DEVELOPER IS USING CLAUDE CODE TO HUNT BUG BOUNTIES ON PLANES. THE SETUP COSTS $20/MONTH. THE PAYOUTS RUN TO $15,000 PER FINDING. Companies are paying thousands of dollars for finding vulnerabilities in their systems. > The Math: > 1 valid low-severity bug = $200–$1000 > 1 valid high-severity bug = $1,000–$15,000 > 1 critical RCE on a top program = tens of thousands > DeFi audits on Immunefi = some of the largest payouts in the field > The Stack: > 51 skills: auto-load based on what you're testing > 574 patterns: curated from real disclosed HackerOne reports > /triage: 7-question gate that validates before you file > /report: generates clean submissions for every major platform > Burp MCP: Claude reads your HTTP traffic directly > The Cost: $20/month Claude plan. Everything else free. > You don't need a security degree. You don't need expensive tools. You don't need a team. > You need the bundle and 3 minutes to install it. Full setup, every command, legal targets to practice on below

A SOLO DEVELOPER IS USING CLAUDE CODE TO HUNT BUG BOUNTIES ON PLANES. THE SETUP COSTS $20/MONTH. THE PAYOUTS RUN TO $15,000 PER FINDING. Companies are paying thousands of dollars for finding vulnerabilities in their systems. > The Math: > 1 valid low-severity bug = $200–$1000 > 1 valid high-severity bug = $1,000–$15,000 > 1 critical RCE on a top program = tens of thousands > DeFi audits on Immunefi = some of the largest payouts in the field > The Stack: > 51 skills: auto-load based on what you're testing > 574 patterns: curated from real disclosed HackerOne reports > /triage: 7-question gate that validates before you file > /report: generates clean submissions for every major platform > Burp MCP: Claude reads your HTTP traffic directly > The Cost: $20/month Claude plan. Everything else free. > You don't need a security degree. You don't need expensive tools. You don't need a team. > You need the bundle and 3 minutes to install it. Full setup, every command, legal targets to practice on below

kiosa

109,639 views • 1 month ago

Zerve has been the best platform for writing data science code for a while. It's 100x better than Jupyter notebooks. Now, they have a specialized, fine-tuned AI assistant who knows all about data science code and how to use the platform. This is a killer feature. A generalist model will never be better than a specialized one. Watch the video. I promise you'll be jealous in 3 minutes. Some other features, in case you've never used Zerve: 1. It's web-based 2. You can share and work with your team on the same canvas 3. You can deploy your code with a click 4. Blocks are the center of attention. Promotes code modularity 5. You can combine multiple languages in a single canvas 6. You can run code blocks in parallel Zerve organizes your code as a DAG. Each block inherits the state of every block before it. Everything runs in a serverless architecture. By the way, you can use Zerve for FREE. Visit this link: Thanks to the team for partnering with me on this post.

Zerve has been the best platform for writing data science code for a while. It's 100x better than Jupyter notebooks. Now, they have a specialized, fine-tuned AI assistant who knows all about data science code and how to use the platform. This is a killer feature. A generalist model will never be better than a specialized one. Watch the video. I promise you'll be jealous in 3 minutes. Some other features, in case you've never used Zerve: 1. It's web-based 2. You can share and work with your team on the same canvas 3. You can deploy your code with a click 4. Blocks are the center of attention. Promotes code modularity 5. You can combine multiple languages in a single canvas 6. You can run code blocks in parallel Zerve organizes your code as a DAG. Each block inherits the state of every block before it. Everything runs in a serverless architecture. By the way, you can use Zerve for FREE. Visit this link: Thanks to the team for partnering with me on this post.

Santiago

27,942 views • 1 year ago

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

Santiago

324,602 views • 2 years ago

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

Dami-Defi

11,668 views • 2 months ago

A/B testing was the gold standard for product decisions for 15 years. The best AI companies abandoned it. For a generation of PMs, experimentation meant designing a controlled test, allocating traffic, waiting two weeks for statistical significance, and hoping you had enough sample size to learn something. That loop trained an entire discipline to think in cycles of weeks. Evals compressed that loop to minutes. Three components: a set of inputs your product needs to handle, a task that generates outputs, and a scoring function that produces a number between 0 and 1. You run it on your laptop. No production traffic. No two-week wait. No data engineering pipeline. The math on what this changes is staggering. Teams running evals are doing 12.8 experiments per day. That's roughly 384 per month. A traditional A/B testing team runs maybe 3. Over a quarter, one team has explored 1,150+ variations. The other has explored 9. That learning gap compounds every single week. Ankur Goyal built the eval platform behind Vercel, Replit, Ramp, and Notion. $800M valuation. He ran an eval from scratch on this episode, went from a score of 0 to 0.75 in under 20 minutes. That's a PM shipping a measurable quality bar before writing a single line of product code. The PM experimentation skill used to be about statistics: sample sizes, confidence intervals, traffic allocation. Now it's about judgment: can you encode what "good" means as a number between 0 and 1? That's a product sense question, not a math question. The cost of experimentation dropped 100x. The question is whether your team's experimentation rate did too.

A/B testing was the gold standard for product decisions for 15 years. The best AI companies abandoned it. For a generation of PMs, experimentation meant designing a controlled test, allocating traffic, waiting two weeks for statistical significance, and hoping you had enough sample size to learn something. That loop trained an entire discipline to think in cycles of weeks. Evals compressed that loop to minutes. Three components: a set of inputs your product needs to handle, a task that generates outputs, and a scoring function that produces a number between 0 and 1. You run it on your laptop. No production traffic. No two-week wait. No data engineering pipeline. The math on what this changes is staggering. Teams running evals are doing 12.8 experiments per day. That's roughly 384 per month. A traditional A/B testing team runs maybe 3. Over a quarter, one team has explored 1,150+ variations. The other has explored 9. That learning gap compounds every single week. Ankur Goyal built the eval platform behind Vercel, Replit, Ramp, and Notion. $800M valuation. He ran an eval from scratch on this episode, went from a score of 0 to 0.75 in under 20 minutes. That's a PM shipping a measurable quality bar before writing a single line of product code. The PM experimentation skill used to be about statistics: sample sizes, confidence intervals, traffic allocation. Now it's about judgment: can you encode what "good" means as a number between 0 and 1? That's a product sense question, not a math question. The cost of experimentation dropped 100x. The question is whether your team's experimentation rate did too.

Aakash Gupta

39,690 views • 4 months ago

We just raised $30M at a $500M valuation, bringing our total funding to $47M. Led by Craft Ventures , with Pace Capital , Chemistry , TruArrow, and others. But before anything else: this belongs to the community. ComfyUI started as one developer and one open-source repo. No roadmap. No company. Just creators who wanted real control over how they built with AI. That community is now: → 4 million users → 60,000+ community-built nodes → 150,000+ daily downloads Every number traces back to people who built in the open, for anyone to use. Here's where the funding goes: → Comfy Cloud: for teams and studios that need security and scale → Collaborative workflows: versioning and iteration built for how studios actually work → A better local experience: more seamless, more stable → Ecosystem reliability: making 60,000+ community nodes more dependable → Day-one model support: every major release, compatible at launch We are not building a walled garden. We are building open infrastructure, built to last. Thank you genuinely, The ComfyUI Team

We just raised $30M at a $500M valuation, bringing our total funding to $47M. Led by Craft Ventures , with Pace Capital , Chemistry , TruArrow, and others. But before anything else: this belongs to the community. ComfyUI started as one developer and one open-source repo. No roadmap. No company. Just creators who wanted real control over how they built with AI. That community is now: → 4 million users → 60,000+ community-built nodes → 150,000+ daily downloads Every number traces back to people who built in the open, for anyone to use. Here's where the funding goes: → Comfy Cloud: for teams and studios that need security and scale → Collaborative workflows: versioning and iteration built for how studios actually work → A better local experience: more seamless, more stable → Ecosystem reliability: making 60,000+ community nodes more dependable → Day-one model support: every major release, compatible at launch We are not building a walled garden. We are building open infrastructure, built to last. Thank you genuinely, The ComfyUI Team

ComfyUI

961,755 views • 3 months ago

Here is how you can install an open-source, enterprise-grade RAG system on your server (with the best document understanding I've seen.) First, something obvious to anyone trying to sell RAG in the market: You are crazy if you think companies will let their data travel to a hosted model. No one wants to send their data anywhere (those who do haven't found an alternative.) Every single company would rather have an air-gapped system with no internet access. GroundX is an open-source RAG system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network. (If the military wants to do RAG, this is precisely what they will be looking for.) I installed GroundX on my AWS account and recorded a video to show you how to use it. There are two services you can use: 1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base. 2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base. A quick note about the Ingest service: 99% of people think they need better "retrieval" mechanisms. I think they need better "ingestion." That's where this service comes in! Ingest "understands" your documents in a way I haven't seen before. After you try it, you'll realize why showing your LLM your raw documents is a bad idea. In the video, I use a free tool called X-Ray to test a document and understand how the Ingest service breaks it down. You can access this tool by signing up for a free GroundX cloud account and uploading your documents. You'll see a bit more about this in the video.

Here is how you can install an open-source, enterprise-grade RAG system on your server (with the best document understanding I've seen.) First, something obvious to anyone trying to sell RAG in the market: You are crazy if you think companies will let their data travel to a hosted model. No one wants to send their data anywhere (those who do haven't found an alternative.) Every single company would rather have an air-gapped system with no internet access. GroundX is an open-source RAG system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network. (If the military wants to do RAG, this is precisely what they will be looking for.) I installed GroundX on my AWS account and recorded a video to show you how to use it. There are two services you can use: 1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base. 2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base. A quick note about the Ingest service: 99% of people think they need better "retrieval" mechanisms. I think they need better "ingestion." That's where this service comes in! Ingest "understands" your documents in a way I haven't seen before. After you try it, you'll realize why showing your LLM your raw documents is a bad idea. In the video, I use a free tool called X-Ray to test a document and understand how the Ingest service breaks it down. You can access this tool by signing up for a free GroundX cloud account and uploading your documents. You'll see a bit more about this in the video.

Santiago

89,664 views • 1 year ago

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 views • 8 months ago

Nothing beats open-source! We now have a fully open-source coding agent that can compete with any proprietary solution out there: • 500+ available models • It doesn't train on your code • It doesn't charge a penny for tokens • It's open. You can change if you want. Kilo Code is the #1 app on OpenRouter right now, and the community is releasing new features on GitHub at rocket speed (about 20 releases in the last 30 days). I installed it in Visual Studio Code, but you can install it in pretty much any IDE. I need to spend more time with it, but here is a very interesting design choice: Most coding agents support two modes: an "agentic" mode for writing code and an "ask" mode for talking to the model without making any changes. Kilo Code supports 5 different modes: 1. Architect mode - Research, design, and planning 2. Debug mode - Problem solving and troubleshooting 3. Code mode - To write code. 4. Ask mode - To talk to the model. 5. Orchestrator mode - Combine every agent to solve problems. On paper, I really like this. I'll need more time to try out the specialized agents and see whether niching becomes an advantage. Here is the link to the website: Thanks to the Kilo for partnering with me on this post.

Nothing beats open-source! We now have a fully open-source coding agent that can compete with any proprietary solution out there: • 500+ available models • It doesn't train on your code • It doesn't charge a penny for tokens • It's open. You can change if you want. Kilo Code is the #1 app on OpenRouter right now, and the community is releasing new features on GitHub at rocket speed (about 20 releases in the last 30 days). I installed it in Visual Studio Code, but you can install it in pretty much any IDE. I need to spend more time with it, but here is a very interesting design choice: Most coding agents support two modes: an "agentic" mode for writing code and an "ask" mode for talking to the model without making any changes. Kilo Code supports 5 different modes: 1. Architect mode - Research, design, and planning 2. Debug mode - Problem solving and troubleshooting 3. Code mode - To write code. 4. Ask mode - To talk to the model. 5. Orchestrator mode - Combine every agent to solve problems. On paper, I really like this. I'll need more time to try out the specialized agents and see whether niching becomes an advantage. Here is the link to the website: Thanks to the Kilo for partnering with me on this post.

Santiago

73,062 views • 8 months ago

How a 24-year-old programmer from Portugal made $18,200 in a month on football betting He created an AI analyst that finds flaws in bookmakers' live lines in real time and delivers predictions with an 84% win rate. Costs: $0 (Used free APIs and Windsurf IDE) He launched a Python script that maps out match videos in real time: Top layer: A Computer Vision algorithm recognizes the positions of players from both teams (blue and pink dots) and the ball, instantly transferring them onto a 2D pitch layout. This allows the AI to track team formations and open spaces in high detail, things regular bettors completely miss. Bottom layer: Python code (written alongside the Windsurf AI assistant), where the SoccerPitchConfiguration class defines the field, penalty box, and center circle dimensions down to the centimeter for perfect player-distance calculations. The AI constantly correlates the real-time movement of players on the pitch with live bookmaker odds. The moment the algorithm detects that a team has pinned their opponent into a specific zone or exposed their flanks, while the bookmaker hasn't adjusted the odds yet, the script automatically fires a betting signal. First week: >Live bets placed: 142 > Won bets: 119 > Net profit: +$4,350 using a flat $50 stake The AI completely automated the entire cycle: Windsurf and Claude wrote the tracking code, and the algorithm autonomously parses live odds, calculates the mathematical expectation of value bets, generates player heatmaps, and spots hidden tactical anomalies. It runs 100% autonomously. Bookmark it and check out the article below 👇

How a 24-year-old programmer from Portugal made $18,200 in a month on football betting He created an AI analyst that finds flaws in bookmakers' live lines in real time and delivers predictions with an 84% win rate. Costs: $0 (Used free APIs and Windsurf IDE) He launched a Python script that maps out match videos in real time: Top layer: A Computer Vision algorithm recognizes the positions of players from both teams (blue and pink dots) and the ball, instantly transferring them onto a 2D pitch layout. This allows the AI to track team formations and open spaces in high detail, things regular bettors completely miss. Bottom layer: Python code (written alongside the Windsurf AI assistant), where the SoccerPitchConfiguration class defines the field, penalty box, and center circle dimensions down to the centimeter for perfect player-distance calculations. The AI constantly correlates the real-time movement of players on the pitch with live bookmaker odds. The moment the algorithm detects that a team has pinned their opponent into a specific zone or exposed their flanks, while the bookmaker hasn't adjusted the odds yet, the script automatically fires a betting signal. First week: >Live bets placed: 142 > Won bets: 119 > Net profit: +$4,350 using a flat $50 stake The AI completely automated the entire cycle: Windsurf and Claude wrote the tracking code, and the algorithm autonomously parses live odds, calculates the mathematical expectation of value bets, generates player heatmaps, and spots hidden tactical anomalies. It runs 100% autonomously. Bookmark it and check out the article below 👇

Ridark

371,888 views • 1 month ago

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,835 views • 2 years ago

We made a thing! Very happy to announce sqlcoder-pro and the Defog Alignment Platform. Available to use immediately without a wait-list, weights will be open-sourced very soon. The video does a quick show and tell comparison against ChatGPT (with gpt-4o). Read on for more details! TLDR 💪 equal (or better) performance on text-to-SQL as the most capable Claude-3.5 or GPT-4 models 🤝 You can use it today on a free plan/free trial, without a waitlist 🪽 self-hostable on a single RTX4090, with 2 second median generation times for SQL queries 🔁 exactly the same output every time, give the same prompt 👨🏻‍🏫 teachable and steerable: show the model what you want it to do 🛞 debuggable – you can understand WTF is going on inside the model, instead of treating it like a black box Let's dig into each of these one-by-one! Performance SQLCoder-8b-pro significantly exceeds the performance of our previous sqlcoder-8b model on Postgres text-to-SQL (from 88.2% to 90.2% accuracy - gpt-4o is at 87.6%, for reference). It is also better at following instructions. This was done via self-merges, hand crafted fine-tuning data, and adapting the training data to fit our tokenizer. Cost You can host this on the model on a single $3,500 RTX4090, and support ~5 requests/second via VLLM. If you're looking to host on the cloud instead, you can run it on a single L4 GPU that costs $300/mo on GCP Repeatability We have a dense 8b model with no MoE shenanigans. For the same prompt with temperature=0, you'll always get the same answer – which is critical in BI. Teachable In our alignment and feedback modes, you can give the model feedback on how it answered certain questions, and it will automatically adapt to the feedback. Debuggable You can use logprobs and attention scores to determine where, exactly is the model paying attention to inside a prompt + what it's getting confused by when generating outputs. Available today You can use Defog on the cloud today by going to docs[dot]defog[dot]ai, and getting an API key. Excited to hear what you think!

We made a thing! Very happy to announce sqlcoder-pro and the Defog Alignment Platform. Available to use immediately without a wait-list, weights will be open-sourced very soon. The video does a quick show and tell comparison against ChatGPT (with gpt-4o). Read on for more details! TLDR 💪 equal (or better) performance on text-to-SQL as the most capable Claude-3.5 or GPT-4 models 🤝 You can use it today on a free plan/free trial, without a waitlist 🪽 self-hostable on a single RTX4090, with 2 second median generation times for SQL queries 🔁 exactly the same output every time, give the same prompt 👨🏻‍🏫 teachable and steerable: show the model what you want it to do 🛞 debuggable – you can understand WTF is going on inside the model, instead of treating it like a black box Let's dig into each of these one-by-one! Performance SQLCoder-8b-pro significantly exceeds the performance of our previous sqlcoder-8b model on Postgres text-to-SQL (from 88.2% to 90.2% accuracy - gpt-4o is at 87.6%, for reference). It is also better at following instructions. This was done via self-merges, hand crafted fine-tuning data, and adapting the training data to fit our tokenizer. Cost You can host this on the model on a single $3,500 RTX4090, and support ~5 requests/second via VLLM. If you're looking to host on the cloud instead, you can run it on a single L4 GPU that costs $300/mo on GCP Repeatability We have a dense 8b model with no MoE shenanigans. For the same prompt with temperature=0, you'll always get the same answer – which is critical in BI. Teachable In our alignment and feedback modes, you can give the model feedback on how it answered certain questions, and it will automatically adapt to the feedback. Debuggable You can use logprobs and attention scores to determine where, exactly is the model paying attention to inside a prompt + what it's getting confused by when generating outputs. Available today You can use Defog on the cloud today by going to docs[dot]defog[dot]ai, and getting an API key. Excited to hear what you think!

Rishabh Srivastava

13,465 views • 1 year ago

I've used Claude Code to build 20+ projects in the last 6 months. Thousands of new users across them. And I've never written a single line of code. I just dropped a 24-min video with my top 10 tips for non-developers — the exact playbook I use every day to run multiple AI agents that handle work that used to take me a full week. This is the best beginner guide to learning and building with Claude Code out right now. Every tutorial I found assumes you're a developer. This one doesn't. I cover everything from first install to running multi-agent workflows — with live demos and real examples for every single tip. How I set up new projects, how I got Claude to match my writing style, how I automate repeatable workflows with one command, and how I run multiple agents working on different tasks at the same time. I also built a full resource repo to go alongside the video — curated video tutorials, the best skill libraries, plugin directories, MCP server guides, written docs, community links, and a starter template you can copy-paste into your first project today. Comment "GUIDE" and I'll send you the full guide with everything you need to learn Claude Code! (make sure we're connected so I can DM you)

I've used Claude Code to build 20+ projects in the last 6 months. Thousands of new users across them. And I've never written a single line of code. I just dropped a 24-min video with my top 10 tips for non-developers — the exact playbook I use every day to run multiple AI agents that handle work that used to take me a full week. This is the best beginner guide to learning and building with Claude Code out right now. Every tutorial I found assumes you're a developer. This one doesn't. I cover everything from first install to running multi-agent workflows — with live demos and real examples for every single tip. How I set up new projects, how I got Claude to match my writing style, how I automate repeatable workflows with one command, and how I run multiple agents working on different tasks at the same time. I also built a full resource repo to go alongside the video — curated video tutorials, the best skill libraries, plugin directories, MCP server guides, written docs, community links, and a starter template you can copy-paste into your first project today. Comment "GUIDE" and I'll send you the full guide with everything you need to learn Claude Code! (make sure we're connected so I can DM you)

JJ Englert

419,327 views • 5 months ago