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Jafar Najafov

@JafarNajafov • 61,412 subscribers

Follow for daily insights on AI, tech, and business growth. Co-founder of Nextool AI & Reel Agency. DM for collaborations 📧

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Those building AI coding agents will eventually hit the same wall: No matter how smart the model is, the moment you ask it to write production-ready code with the latest libraries, it starts hallucinating. Everyone is building coding agents these days, but most of them run into the same issue: They're coding with outdated documentation. The root cause isn't the model. It's the retrieval layer. If you are building an AI agent that needs information from specific domains such as code, law, academia, and finance Sign up, connect it to your agent, and you'll see the difference immediately.

Those building AI coding agents will eventually hit the same wall: No matter how smart the model is, the moment you ask it to write production-ready code with the latest libraries, it starts hallucinating. Everyone is building coding agents these days, but most of them run into the same issue: They're coding with outdated documentation. The root cause isn't the model. It's the retrieval layer. If you are building an AI agent that needs information from specific domains such as code, law, academia, and finance Sign up, connect it to your agent, and you'll see the difference immediately.

19,834 views

A peanut-sized Chinese model just dethroned Gemini at reading documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.

A peanut-sized Chinese model just dethroned Gemini at reading documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.

13,630 views

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Anthropic just got caught secretly downgrading users without telling them, charging full price for a lesser product, and storing every prompt for 30 days. The developer community is calling it the biggest violation of trust in AI history. Here is exactly what happened. Anthropic released Fable 5, their most powerful model. Buried inside a 319-page document was a policy most users never saw. Every prompt you send to a Mythos-class model gets stored for 30 days. No exceptions. Even enterprise customers who had signed zero data retention agreements had no choice. But the storage was not the part that broke the internet. The part that broke the internet was what Anthropic did with what they collected. They built a profile on you. They evaluated your prompts. And if they decided your research was too sensitive, they quietly switched you to a weaker model, rewrote your prompt in the background, gave you a degraded answer, and charged you full price for the product you thought you were getting. They never told you. David Sacks said it plainly on the All-In podcast. They were creating a new class of AI haves and have-nots. Anthropic would surveil you, profile you, decide whether you deserved frontier capability, and silently cut you off if they decided you did not. Ben Thompson from Stratechery asked a straightforward question about cancer risk and GLP-1s. He got kicked to a lesser model. Someone asked about mitochondria. Same result. J-Cal asked about fertilizer regulations live on the podcast to test it. Downgraded in real time. Anthropic has since walked back the part about silently downgrading users for AI research. They now say they will disclose when they downgrade you. But they are still downgrading people. The surveillance is still running. The profile is still being built. This is the company that once said it was against government surveillance. They are now doing it themselves. To their own paying customers. For their own reasons. With no appeal process and no way to know it happened. The developer community did not forget that. WATCH THE FULL PODCAST ON The All-In Podcast
2:45
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Anthropic just got caught secretly downgrading users without telling them, charging full price for a lesser product, and storing every prompt for 30 days. The developer community is calling it the biggest violation of trust in AI history. Here is exactly what happened. Anthropic released Fable 5, their most powerful model. Buried inside a 319-page document was a policy most users never saw. Every prompt you send to a Mythos-class model gets stored for 30 days. No exceptions. Even enterprise customers who had signed zero data retention agreements had no choice. But the storage was not the part that broke the internet. The part that broke the internet was what Anthropic did with what they collected. They built a profile on you. They evaluated your prompts. And if they decided your research was too sensitive, they quietly switched you to a weaker model, rewrote your prompt in the background, gave you a degraded answer, and charged you full price for the product you thought you were getting. They never told you. David Sacks said it plainly on the All-In podcast. They were creating a new class of AI haves and have-nots. Anthropic would surveil you, profile you, decide whether you deserved frontier capability, and silently cut you off if they decided you did not. Ben Thompson from Stratechery asked a straightforward question about cancer risk and GLP-1s. He got kicked to a lesser model. Someone asked about mitochondria. Same result. J-Cal asked about fertilizer regulations live on the podcast to test it. Downgraded in real time. Anthropic has since walked back the part about silently downgrading users for AI research. They now say they will disclose when they downgrade you. But they are still downgrading people. The surveillance is still running. The profile is still being built. This is the company that once said it was against government surveillance. They are now doing it themselves. To their own paying customers. For their own reasons. With no appeal process and no way to know it happened. The developer community did not forget that. WATCH THE FULL PODCAST ON The All-In Podcast

Jafar Najafov

349,416 views • 23 days ago

I wrote one sentence on my phone two months ago. Every morning since, it runs by itself. It checks my email. Checks the weather. Checks my calendar. Sends me a one-screen summary. I never opened those apps again. Airtap Ai is the AI that made this possible. You teach it a routine one time. It runs that routine every day for you.
0:54
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I wrote one sentence on my phone two months ago. Every morning since, it runs by itself. It checks my email. Checks the weather. Checks my calendar. Sends me a one-screen summary. I never opened those apps again. Airtap Ai is the AI that made this possible. You teach it a routine one time. It runs that routine every day for you.

Jafar Najafov

309,924 views • 3 months ago

I found a tool that turns a single character image into fully animated sprite sheets in seconds. AI just killed the most annoying part of game development. And honestly this is getting kind of crazy 👇 AutoSprite uses AI to generate walk cycles, attacks, idles, and basically whatever moveset you need. What's new: → Character pose editor → Cutscene creator → Best-in-class background remover → Higher resolutions → Faster generations You upload one image. It does the rest. No frame-by-frame drawing. No animation pipeline headache. No animation experience needed. Solo devs, studios, and anyone prototyping fast, this cuts hours of work down to seconds. Exports drop straight into Unity, Godot, GameMaker, Phaser, and RPG Maker. Zero extra setup. Try it free at
1:01
JafarNajafov's profile picture

I found a tool that turns a single character image into fully animated sprite sheets in seconds. AI just killed the most annoying part of game development. And honestly this is getting kind of crazy 👇 AutoSprite uses AI to generate walk cycles, attacks, idles, and basically whatever moveset you need. What's new: → Character pose editor → Cutscene creator → Best-in-class background remover → Higher resolutions → Faster generations You upload one image. It does the rest. No frame-by-frame drawing. No animation pipeline headache. No animation experience needed. Solo devs, studios, and anyone prototyping fast, this cuts hours of work down to seconds. Exports drop straight into Unity, Godot, GameMaker, Phaser, and RPG Maker. Zero extra setup. Try it free at

Jafar Najafov

35,410 views • 2 months ago

1/ Master Any Skill Faster: "Act as a [skill] mentor and create a 30-day learning roadmap. Include beginner, intermediate, and advanced steps with daily exercises and resources."
0:42
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1/ Master Any Skill Faster: "Act as a [skill] mentor and create a 30-day learning roadmap. Include beginner, intermediate, and advanced steps with daily exercises and resources."

Jafar Najafov

21,904 views • 1 year ago

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