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Introducing FlyOCR 🪰 - I trained a fly brain to read a PDF It uses the full MaleCNS v1.0 fruit fly connectcome. The architecture is inspired by doomfly by The fly splits a pdf image into individual glyphs, maps pixels into receptor activations, runs simplified current-based dynamics across the... 166k neurons and 25m edges in the circuit, applies a compact readout model on the downstream spikes, and concatenates everything into the parsed output. On reading an actual Microsoft 10-k, the fly gets ~86% over the balance sheet heading, but is largely able to read the numeric values correctly. Over 1.7k+ sampled glyphs (chars+digits) it gets 87% accuracy. With enough training it might match some of the latter-generation MNIST models! Maybe eventually we’ll replace our doc parsing VLMs with flies. Full video below. Repo with full code + report:show more

Jerry Liu

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36,648 görüntüleme • 2 gün önce •via X (Twitter)

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Jerry Liu profil fotoğrafı

Jerry Liu2 gün önce

if u don't want to use a fly brain to read a PDF, you could always check out an actual OCR solution like llamaparse

Utkarsh Agrawal profil fotoğrafı

Utkarsh Agrawal2 gün önce

@nftechie_ I trained it to be a MMA fighter

Aethmere-OS profil fotoğrafı

Aethmere-OS2 gün önce

@nftechie_ A useful control: shuffle the connections while preserving neuron degrees, then train the same readout. That could help show how much the fly wiring contributes to OCR.

pwguler profil fotoğrafı

pwguler2 gün önce

@nftechie_ a fruit fly reading a pdf is the only benchmark that cannot be gamed: the fly has no incentive to please the evaluator

Imaan Sultan 🇵🇰🇸🇦 profil fotoğrafı

Imaan Sultan 🇵🇰🇸🇦2 gün önce

@nftechie_ that’s so wild holy

Mesut De profil fotoğrafı

Mesut De2 gün önce

@nftechie_ congrat

Zaid Rais | Design Engineer profil fotoğrafı

Zaid Rais | Design Engineer2 gün önce

@nftechie_ The fly connectome mapping is the fun part but the real test is whether receptor activations generalize past clean glyphs to a scanned page with noise and skew.

First Sauce Labs profil fotoğrafı

First Sauce Labs2 gün önce

@nftechie_ This feels like a good example of when the "AI" part just means calling a well-known component a few new things.

Oussama profil fotoğrafı

Oussama2 gün önce

@nftechie_ 87% on real financial glyphs before even touching MNIST territory suggests the connectome is doing more useful work than the benchmark framing implies.

Kevihaiceth 💹🧲 profil fotoğrafı

Kevihaiceth 💹🧲2 gün önce

@nftechie_ Training a fly brain to read PDFs is wild

klownShowz profil fotoğrafı

klownShowz2 gün önce

@nftechie_ Lord of the PDF flies.

Benzer Videolar

For the first time in history, GPT-6 Astra created a fly that is simply a fly. Not a chatbot pretending to be a fly. Not a simplified animation. A digital fly running its own connectome brain, perceiving a real room through its own senses and interacting with the environment using the power of AR. The entire loop is surprisingly wild: Spectacles capture the retina, light rays, hands, and the surrounding room → the room is converted into an inventory → sensory information is fed into the fly’s neural system → the MaleCNS connectome processes it → descending neurons and motor pools generate actions → the commands return to the Spectacles → wings, legs, and head respond. In other words, the fly sees through its own eyes, processes the world through its own simulated brain, and acts through its own virtual body. We are moving from AI that generates outputs to AI that actually has a body, senses, a brain, and a closed perception-action loop. The 🪰 is no longer just simulated. It is experiencing a world The developer Pavlo Tkachenko is pinned below in the post

For the first time in history, GPT-6 Astra created a fly that is simply a fly. Not a chatbot pretending to be a fly. Not a simplified animation. A digital fly running its own connectome brain, perceiving a real room through its own senses and interacting with the environment using the power of AR. The entire loop is surprisingly wild: Spectacles capture the retina, light rays, hands, and the surrounding room → the room is converted into an inventory → sensory information is fed into the fly’s neural system → the MaleCNS connectome processes it → descending neurons and motor pools generate actions → the commands return to the Spectacles → wings, legs, and head respond. In other words, the fly sees through its own eyes, processes the world through its own simulated brain, and acts through its own virtual body. We are moving from AI that generates outputs to AI that actually has a body, senses, a brain, and a closed perception-action loop. The 🪰 is no longer just simulated. It is experiencing a world The developer Pavlo Tkachenko is pinned below in the post

Zentrix⌚️

313,652 görüntüleme • 2 gün önce

For the first time ever, a fruit fly brain has been trained to launch a memecoin. Not "AI inspired by a brain" — the real connectome. 165,122 neurons, 10,228,000 synaptic connections, every one traced from an actual male Drosophila by electron microscopy. Janelia FlyEM + Google Research, released CC-BY. How it works: Screenshots of the live launchpad go in through 892 retinotopic hex columns into L1 and L2 — the neurons sitting directly behind a fly's photoreceptors. All 165,122 integrate. The cursor comes back out of the descending neurons a fly actually walks with: DNa02's left-right asymmetry steers, DNa01 drives forward, DNp09 clicks. The wiring is never trained. Only synaptic gains — you don't get to invent connections the fly doesn't have. Proof its eye is doing real work: in light mode it filled 0 of 3 form fields. In dark mode, 2 of 3. Its retina is a luminance map, so invert the contrast and it goes blind. And the honest split: the fly types the description and the ticker. The rig does the name, the pair and the tax. CA: 0x4eb990547bce4a982432ca88cf5fae7eed1a2d35

For the first time ever, a fruit fly brain has been trained to launch a memecoin. Not "AI inspired by a brain" — the real connectome. 165,122 neurons, 10,228,000 synaptic connections, every one traced from an actual male Drosophila by electron microscopy. Janelia FlyEM + Google Research, released CC-BY. How it works: Screenshots of the live launchpad go in through 892 retinotopic hex columns into L1 and L2 — the neurons sitting directly behind a fly's photoreceptors. All 165,122 integrate. The cursor comes back out of the descending neurons a fly actually walks with: DNa02's left-right asymmetry steers, DNa01 drives forward, DNp09 clicks. The wiring is never trained. Only synaptic gains — you don't get to invent connections the fly doesn't have. Proof its eye is doing real work: in light mode it filled 0 of 3 form fields. In dark mode, 2 of 3. Its retina is a luminance map, so invert the contrast and it goes blind. And the honest split: the fly types the description and the ticker. The rig does the name, the pair and the tax. CA: 0x4eb990547bce4a982432ca88cf5fae7eed1a2d35

Fruit fly dev

756,578 görüntüleme • 5 gün önce

Scientists used 200,000 human brain cells to play DOOM. The cells had never seen a computer. They learned in real time. They got better. Separately: an entire fruit fly brain, 139,000 neurons, 50 million connections, was copied into a laptop. It predicts real brain responses with 90%+ accuracy. The biological computing timeline: 2021 → neurons learn Pong 2022 → fruit fly brain fully mapped 2024 → entire fly brain simulated on a laptop 2026 → human brain cells play DOOM A GPU needs ~700W to run an LLM. The brain runs the whole show on 20W. We're spending trillions on building the next giant AI training computer but what if the solution to our energy needs was just copying the technology already running in our heads?

Scientists used 200,000 human brain cells to play DOOM. The cells had never seen a computer. They learned in real time. They got better. Separately: an entire fruit fly brain, 139,000 neurons, 50 million connections, was copied into a laptop. It predicts real brain responses with 90%+ accuracy. The biological computing timeline: 2021 → neurons learn Pong 2022 → fruit fly brain fully mapped 2024 → entire fly brain simulated on a laptop 2026 → human brain cells play DOOM A GPU needs ~700W to run an LLM. The brain runs the whole show on 20W. We're spending trillions on building the next giant AI training computer but what if the solution to our energy needs was just copying the technology already running in our heads?

Josh Kale

136,752 görüntüleme • 6 ay önce

Nature’s ultimate patience game: Fly vs Mantis … WAIT FOR IT A fly can launch into an escape in just 10–50 milliseconds. A praying mantis strike usually takes 50–100+ ms to complete. On pure reaction speed, the fly wins every time. Yet the mantis still catches it, by doing the opposite of rushing. It waits. It sways. It creeps closer millimeter by millimeter with that subtle shaking motion, using stealth, stereoscopic vision, and perfect timing. It only strikes when the fly is already inside the kill zone… often before the fly even registers the threat. Speed is impressive. Patience is deadly. 🎥 murphy_guy_brendan

Nature’s ultimate patience game: Fly vs Mantis … WAIT FOR IT A fly can launch into an escape in just 10–50 milliseconds. A praying mantis strike usually takes 50–100+ ms to complete. On pure reaction speed, the fly wins every time. Yet the mantis still catches it, by doing the opposite of rushing. It waits. It sways. It creeps closer millimeter by millimeter with that subtle shaking motion, using stealth, stereoscopic vision, and perfect timing. It only strikes when the fly is already inside the kill zone… often before the fly even registers the threat. Speed is impressive. Patience is deadly. 🎥 murphy_guy_brendan

Dreams N Science

77,889 görüntüleme • 29 gün önce

Introducing MIRA. A playable, multiplayer world model. A dream of Rocket League. Trained on 10k hours of data collected with publicly available bots, MIRA learns the dynamics of a four-player game. The model runs in real time at 20 fps, based on the keys you and the other players press. Built by General Intuition and kyutai, in collaboration with Epic Games. Not used to develop Rocket League. ▶️ Play the demo, read the technical report, and explore the open-source code at At ICML? Find us at Booth 111 to try it yourself and dig into the results with the team.

Introducing MIRA. A playable, multiplayer world model. A dream of Rocket League. Trained on 10k hours of data collected with publicly available bots, MIRA learns the dynamics of a four-player game. The model runs in real time at 20 fps, based on the keys you and the other players press. Built by General Intuition and kyutai, in collaboration with Epic Games. Not used to develop Rocket League. ▶️ Play the demo, read the technical report, and explore the open-source code at At ICML? Find us at Booth 111 to try it yourself and dig into the results with the team.

General Intuition

503,797 görüntüleme • 2 ay önce

The fruit fly's brain is superior to the human brain, and GPT-6 ASTRA has proven this. And the internet went crazy. Most AI models learn by training on massive datasets, running billions of computations to acquire new skills. But researchers just demonstrated a radically different approach: No giant dataset. No traditional training. Just take the physical wiring of a biological brain and simulate it in a computer. The digital fruit fly reached ~91% behavioral accuracy using only 4 things: The complete neural connectivity map Synaptic connection counts as connection weights Excitatory vs inhibitory neuron types A simple Leaky Integrate-and-Fire model The wild part? The wiring itself may contain much more of the brain’s “code” than we thought. From tens of thousands of neurons in a fly → millions in a mouse → ~86 billion in a human. If this scales, whole-brain simulation could become one of the biggest frontiers in AI. The era of biologically inspired intelligence may have just begun

The fruit fly's brain is superior to the human brain, and GPT-6 ASTRA has proven this. And the internet went crazy. Most AI models learn by training on massive datasets, running billions of computations to acquire new skills. But researchers just demonstrated a radically different approach: No giant dataset. No traditional training. Just take the physical wiring of a biological brain and simulate it in a computer. The digital fruit fly reached ~91% behavioral accuracy using only 4 things: The complete neural connectivity map Synaptic connection counts as connection weights Excitatory vs inhibitory neuron types A simple Leaky Integrate-and-Fire model The wild part? The wiring itself may contain much more of the brain’s “code” than we thought. From tens of thousands of neurons in a fly → millions in a mouse → ~86 billion in a human. If this scales, whole-brain simulation could become one of the biggest frontiers in AI. The era of biologically inspired intelligence may have just begun

Zentrix⌚️

99,581 görüntüleme • 3 gün önce

They are now making fruit flies write. They are even using them to create entire fonts. The method is surprisingly fascinating. First, an image of a letter is projected into the fruit fly’s eye. The visual information is transmitted through its visual neurons and processed by a trained neural network connected to its neural circuitry. The resulting signal is then sent to the motor neurons controlling the fly’s right foreleg. Those motor neurons activate the leg muscles, causing the fly to move its foreleg in a specific pattern that traces the shape of the letter. A tiny black pen is attached to the foreleg, allowing the movement to leave an actual written mark on the surface. In other words, visual information enters through the fly’s eyes, is translated into neural commands, and ultimately becomes physical handwriting through its own muscles. Researchers have even used this system to create fonts based on the letters drawn by fruit flies. At this point, I’m becoming increasingly curious about what else fruit flies could be made to do.

They are now making fruit flies write. They are even using them to create entire fonts. The method is surprisingly fascinating. First, an image of a letter is projected into the fruit fly’s eye. The visual information is transmitted through its visual neurons and processed by a trained neural network connected to its neural circuitry. The resulting signal is then sent to the motor neurons controlling the fly’s right foreleg. Those motor neurons activate the leg muscles, causing the fly to move its foreleg in a specific pattern that traces the shape of the letter. A tiny black pen is attached to the foreleg, allowing the movement to leave an actual written mark on the surface. In other words, visual information enters through the fly’s eyes, is translated into neural commands, and ultimately becomes physical handwriting through its own muscles. Researchers have even used this system to create fonts based on the letters drawn by fruit flies. At this point, I’m becoming increasingly curious about what else fruit flies could be made to do.

IGNIS

77,405 görüntüleme • 5 gün önce

I used Claude Code Skills + souped up PDF parsing to create an M&A deal comp agent 🤖 Given a directory of public M&A filings (DEF 14A), it parses and analyzes each pdf, and generates an Excel sheet with deal terms and comparables. 1️⃣ The native parsing is pypdf which sucks, so I swapped it out with LlamaIndex semtools. This uses LlamaCloud for parsing - letting it handle deeply complex financial tables, charts, and anything else you throw at it. 2️⃣ Claude has native skills to write Excel spreadsheets! Full prompt is in the video below 👇 LlamaCloud + Semtools access also takes < 5 mins to setup, come check it out! Some notes: - disclaimer: Some of the values in the Excel spreadsheet are 100x bigger because they’re formatted as percentages not raw values - We’re working on native Claude skill integrations too! Semtools: LlamaCloud:

I used Claude Code Skills + souped up PDF parsing to create an M&A deal comp agent 🤖 Given a directory of public M&A filings (DEF 14A), it parses and analyzes each pdf, and generates an Excel sheet with deal terms and comparables. 1️⃣ The native parsing is pypdf which sucks, so I swapped it out with LlamaIndex semtools. This uses LlamaCloud for parsing - letting it handle deeply complex financial tables, charts, and anything else you throw at it. 2️⃣ Claude has native skills to write Excel spreadsheets! Full prompt is in the video below 👇 LlamaCloud + Semtools access also takes < 5 mins to setup, come check it out! Some notes: - disclaimer: Some of the values in the Excel spreadsheet are 100x bigger because they’re formatted as percentages not raw values - We’re working on native Claude skill integrations too! Semtools: LlamaCloud:

Jerry Liu

46,748 görüntüleme • 11 ay önce

This is called a Bat Fly, and they cling exclusively to bats while feeding on their blood. There are actually 274 species of bat fly! Some have wings too! But most all of them are wingless and blind. However, i think the coolest and weirdest thing is that the females have a uterus! Most bugs lay eggs outside their body, but female bat flies produce a single egg inside their uterus that they nourish with essentially a bug milk while it goes through all its developmental stages, until RIGHT before they pupate into a full bat fly. When thats about to happen, the female will leave the host bat and give birth to the larvae in the dirt, where it has its final pupation and becomes an adult bat fly. I just think thats wild lol

This is called a Bat Fly, and they cling exclusively to bats while feeding on their blood. There are actually 274 species of bat fly! Some have wings too! But most all of them are wingless and blind. However, i think the coolest and weirdest thing is that the females have a uterus! Most bugs lay eggs outside their body, but female bat flies produce a single egg inside their uterus that they nourish with essentially a bug milk while it goes through all its developmental stages, until RIGHT before they pupate into a full bat fly. When thats about to happen, the female will leave the host bat and give birth to the larvae in the dirt, where it has its final pupation and becomes an adult bat fly. I just think thats wild lol

Lana the Bug 🪲💕

135,296 görüntüleme • 1 ay önce

“Chat with your PDF” still fails on ChatGPT/Claude when the doc is too complex 📉🧾 . If it’s a complicated financial report with scanned tables, you’re going to get back hallucinated values in your analysis! Check out the video 🎬 - in the first part, Claude gives back hallucinated values on the scanned tables in the equity research report. Having a proper document MCP server capable of high-accuracy parsing/indexing can help 💡. In the second part of the video I plug in LlamaCloud as an MCP tool to help with doc parsing and retrieval - it is fully able to render the scanned table in 2D format. You can set one up for yourself in 5 mins in the UI, no coding required. LlamaCloud: MCP Server:

“Chat with your PDF” still fails on ChatGPT/Claude when the doc is too complex 📉🧾 . If it’s a complicated financial report with scanned tables, you’re going to get back hallucinated values in your analysis! Check out the video 🎬 - in the first part, Claude gives back hallucinated values on the scanned tables in the equity research report. Having a proper document MCP server capable of high-accuracy parsing/indexing can help 💡. In the second part of the video I plug in LlamaCloud as an MCP tool to help with doc parsing and retrieval - it is fully able to render the scanned table in 2D format. You can set one up for yourself in 5 mins in the UI, no coding required. LlamaCloud: MCP Server:

Jerry Liu

21,369 görüntüleme • 1 yıl önce

Web scraping will never be the same. (100% open-source visual search at scale) PixelRAG is a retrieval system that skips HTML parsing completely. Instead of scraping a page into text and embedding chunks, it screenshots the page and retrieves the image. A vision-language model reads the answer straight off the pixels. Why that matters: parsing is where web RAG quietly loses information. - A single HTML-to-text parser can drop 40%+ of a page. - Tables, charts, and layout get flattened or thrown out. - Swapping parsers alone can move accuracy ~10 points on the same docs. PixelRAG indexes the page a person actually sees. The team built a visual index of all of Wikipedia, 30M+ screenshots, and it still beats the strongest text RAG baseline by 18.1% on text-only QA. The repo also ships a Claude Code plugin that gives Claude eyes. It lets Claude screenshot any URL and read the rendered page instead of scraping the DOM. So you can hand it a live page, an arXiv paper, or your local site and ask what it actually looks like. One setup script. No MCP server, no backend. How the pipeline works: - Renders each document (web, PDF, image) to image tiles. - Embeds them with Qwen3-VL-Embedding, LoRA fine-tuned on screenshots. - Builds a FAISS index and serves a search API. A stronger reader model lifts accuracy with no re-indexing, since the index is just pixels. Everything is open-source under Apache-2.0. GitHub repo: Talking about RAG, I recently wrote an article on a new approach that makes retrieval much more efficient by cutting corpus size by 40x, reducing tokens per query by 3x, and improving vector search relevance by 2.3x. The article is quoted below.

Web scraping will never be the same. (100% open-source visual search at scale) PixelRAG is a retrieval system that skips HTML parsing completely. Instead of scraping a page into text and embedding chunks, it screenshots the page and retrieves the image. A vision-language model reads the answer straight off the pixels. Why that matters: parsing is where web RAG quietly loses information. - A single HTML-to-text parser can drop 40%+ of a page. - Tables, charts, and layout get flattened or thrown out. - Swapping parsers alone can move accuracy ~10 points on the same docs. PixelRAG indexes the page a person actually sees. The team built a visual index of all of Wikipedia, 30M+ screenshots, and it still beats the strongest text RAG baseline by 18.1% on text-only QA. The repo also ships a Claude Code plugin that gives Claude eyes. It lets Claude screenshot any URL and read the rendered page instead of scraping the DOM. So you can hand it a live page, an arXiv paper, or your local site and ask what it actually looks like. One setup script. No MCP server, no backend. How the pipeline works: - Renders each document (web, PDF, image) to image tiles. - Embeds them with Qwen3-VL-Embedding, LoRA fine-tuned on screenshots. - Builds a FAISS index and serves a search API. A stronger reader model lifts accuracy with no re-indexing, since the index is just pixels. Everything is open-source under Apache-2.0. GitHub repo: Talking about RAG, I recently wrote an article on a new approach that makes retrieval much more efficient by cutting corpus size by 40x, reducing tokens per query by 3x, and improving vector search relevance by 2.3x. The article is quoted below.

Akshay 🚀

947,346 görüntüleme • 2 ay önce

Toddler gets knocked over by a large Asian carp in Kentucky after the fish launched itself into the boat. Lmao. Kentucky couple Brandon and Emily Carter took out their new boat over the weekend with their three children. Emily Carter said they had only been on the water for a minute when the incident happened. "We were on water for maybe a minute and Brandon cranks the engine and fish started to fly everywhere." Henry, who was the one who was knocked over, is still "processing the trauma" but was thankfully unharmed. Video: emcar5 on TT.

Toddler gets knocked over by a large Asian carp in Kentucky after the fish launched itself into the boat. Lmao. Kentucky couple Brandon and Emily Carter took out their new boat over the weekend with their three children. Emily Carter said they had only been on the water for a minute when the incident happened. "We were on water for maybe a minute and Brandon cranks the engine and fish started to fly everywhere." Henry, who was the one who was knocked over, is still "processing the trauma" but was thankfully unharmed. Video: emcar5 on TT.

Collin Rugg

3,709,689 görüntüleme • 2 yıl önce

$BRAIN is live. the open-source fly-brain bridge has a ticker now, and the ticker is becoming the input for a physical robot. CA: 9LuwgFQemAoV9rgVBBtwbRSxBmamcRRbysEK8yL2pump what’s already done: the neural bridge is working. camera input becomes activity across eight virtual neural populations, and that activity becomes left and right motor commands IMU data feeds movement back into the model. smoothing, speed limits and a 500 ms watchdog are already built in the synthetic demo runs locally today. the ESP32 scaffold is ready for hardware integration the entire project is open source and MIT licensed github: what’s next, in order: the on-chain listener goes live. every $BRAIN transaction becomes a stimulus sent directly into the neural model then the physical robot connects. the wallet address determines which neurons activate, the amount determines the strength of the impulse, and the brain converts that reaction into movement then the livestream. every transaction, neural impulse and physical response visible in real time then user commands. spend $BRAIN to trigger a stronger reflex, request movement, temporarily control the robot, name a neuron or place your name on the stream the loop, plain: a transaction enters the neurons react the robot moves a Proof of Reflex is created with the transaction hash, neural activity map and video of the physical response the current brain is a small fly-inspired simulation. the bridge and local demo work today. the blockchain connection, physical robot and livestream are being built now the internet becomes its sensory organ. the blockchain becomes its nervous system. $BRAIN makes the body move.

$BRAIN is live. the open-source fly-brain bridge has a ticker now, and the ticker is becoming the input for a physical robot. CA: 9LuwgFQemAoV9rgVBBtwbRSxBmamcRRbysEK8yL2pump what’s already done: the neural bridge is working. camera input becomes activity across eight virtual neural populations, and that activity becomes left and right motor commands IMU data feeds movement back into the model. smoothing, speed limits and a 500 ms watchdog are already built in the synthetic demo runs locally today. the ESP32 scaffold is ready for hardware integration the entire project is open source and MIT licensed github: what’s next, in order: the on-chain listener goes live. every $BRAIN transaction becomes a stimulus sent directly into the neural model then the physical robot connects. the wallet address determines which neurons activate, the amount determines the strength of the impulse, and the brain converts that reaction into movement then the livestream. every transaction, neural impulse and physical response visible in real time then user commands. spend $BRAIN to trigger a stronger reflex, request movement, temporarily control the robot, name a neuron or place your name on the stream the loop, plain: a transaction enters the neurons react the robot moves a Proof of Reflex is created with the transaction hash, neural activity map and video of the physical response the current brain is a small fly-inspired simulation. the bridge and local demo work today. the blockchain connection, physical robot and livestream are being built now the internet becomes its sensory organ. the blockchain becomes its nervous system. $BRAIN makes the body move.

Frank

203,581 görüntüleme • 1 gün önce

There's been an unfortunate incident in LA with a Uhaul plowing into a crowd of anti-Khameini protestors This man should never have been able to get near the crowd with a uhaul but some info about the situation seem to be - signage on the truck is anti both the shah and the current ayatollah. is this his actual position or is this camouflage to have gotten into the protest to perpetrate an attack? "no Shah, No regime, No Mullah" Mullah is a religious leader so possibly referring to the current leader and not a king like Pahlavi Timeline appears to be - anti-Khameini protestors try to rip signs off his vehicle and are bashing on the windows and eventually his passenger side window is broken - guy in uhaul then stutterstops forward into the crowd, eventually accelerating further, then stuttering again, then full stopping down the road - there is a man surfing on top of the uhaul in the 3rd video, below I have posted another video showing the man on top of the uhaul trying to take the posters off the side, so he is likely part of the anti-Khameini protestors - uhaul driver is taken into custody by police is this a case of police not having the street sufficiently blocked off and so a guy was able to get a uhaul in here? He should not have been able to drive a uhaul this close to a massive protest crowd There are a lot of people saying this is a terrorist attack, it is possible it could be one but I don't think there's enough information to accurately assert that at this time The chronology of events also shows it is possible that the driver was in fear of his life since protestors were banging on the uhaul, windows, and removing signs+ eventually breaking his window Whatever turns out to be the actual case, it is an unfortunate event and as of right now a seeming silver lining is that no deaths have been reported

There's been an unfortunate incident in LA with a Uhaul plowing into a crowd of anti-Khameini protestors This man should never have been able to get near the crowd with a uhaul but some info about the situation seem to be - signage on the truck is anti both the shah and the current ayatollah. is this his actual position or is this camouflage to have gotten into the protest to perpetrate an attack? "no Shah, No regime, No Mullah" Mullah is a religious leader so possibly referring to the current leader and not a king like Pahlavi Timeline appears to be - anti-Khameini protestors try to rip signs off his vehicle and are bashing on the windows and eventually his passenger side window is broken - guy in uhaul then stutterstops forward into the crowd, eventually accelerating further, then stuttering again, then full stopping down the road - there is a man surfing on top of the uhaul in the 3rd video, below I have posted another video showing the man on top of the uhaul trying to take the posters off the side, so he is likely part of the anti-Khameini protestors - uhaul driver is taken into custody by police is this a case of police not having the street sufficiently blocked off and so a guy was able to get a uhaul in here? He should not have been able to drive a uhaul this close to a massive protest crowd There are a lot of people saying this is a terrorist attack, it is possible it could be one but I don't think there's enough information to accurately assert that at this time The chronology of events also shows it is possible that the driver was in fear of his life since protestors were banging on the uhaul, windows, and removing signs+ eventually breaking his window Whatever turns out to be the actual case, it is an unfortunate event and as of right now a seeming silver lining is that no deaths have been reported

Kirsche 🥥 🧁

41,247 görüntüleme • 8 ay önce

I think the biggest change here is where the filmmaking process starts. Instead of opening a video generator with a blank prompt, start by building the visual language of the story first. GPT Image 2.5 is coming to CapCut, where it will be available through Design Studio and AI Image. Use it to turn an idea into a visual storyboard. Then bring that storyboard into CapCut PC’s multi-track timeline and use AI Video with Seedance 2.5 to generate the first full version. Once the video exists, AI Edit lets you keep working on it. Change a scene. Adjust the pacing. Improve the audio. Refine the details. The first generation gives you the film. The editing gives you control over what the film becomes. #GPTImage25 #CapCutPC #AIVideo #Seedance25

I think the biggest change here is where the filmmaking process starts. Instead of opening a video generator with a blank prompt, start by building the visual language of the story first. GPT Image 2.5 is coming to CapCut, where it will be available through Design Studio and AI Image. Use it to turn an idea into a visual storyboard. Then bring that storyboard into CapCut PC’s multi-track timeline and use AI Video with Seedance 2.5 to generate the first full version. Once the video exists, AI Edit lets you keep working on it. Change a scene. Adjust the pacing. Improve the audio. Refine the details. The first generation gives you the film. The editing gives you control over what the film becomes. #GPTImage25 #CapCutPC #AIVideo #Seedance25

AI Panda

95,573 görüntüleme • 4 gün önce

‼️I need to warn you about something! The term CHRIST IS KING has been a declaration of shared Christian values for generations, but shocking research by Network Contagion Research Institute at Rutgers University compiled in a report I co-authored with Dr. Dr Jordan B Peterson demonstrates that this iconic phrase is being hijacked by antisemitic extremists to manipulate Christians…We collaborated with us_the_story to explain it in the video below. Watch it, and then read the full report linked to in the second post below.

‼️I need to warn you about something! The term CHRIST IS KING has been a declaration of shared Christian values for generations, but shocking research by Network Contagion Research Institute at Rutgers University compiled in a report I co-authored with Dr. Dr Jordan B Peterson demonstrates that this iconic phrase is being hijacked by antisemitic extremists to manipulate Christians…We collaborated with us_the_story to explain it in the video below. Watch it, and then read the full report linked to in the second post below.

Rev. Johnnie Moore ن

4,887,942 görüntüleme • 1 yıl önce

THIS GUY BUILT A SYSTEM THAT GETS SMARTER EVERY TIME HE USES IT AND IT LOOKS LIKE A GALAXY he types one command into Claude Code it finds sources, runs analysis through NotebookLM, saves everything to Obsidian automatically graph in the background is every idea he's ever researched - all linked together system handles the research so he only has to think about what to do with it most people are still copy-pasting into Notion and losing everything by next week full setup in the article

THIS GUY BUILT A SYSTEM THAT GETS SMARTER EVERY TIME HE USES IT AND IT LOOKS LIKE A GALAXY he types one command into Claude Code it finds sources, runs analysis through NotebookLM, saves everything to Obsidian automatically graph in the background is every idea he's ever researched - all linked together system handles the research so he only has to think about what to do with it most people are still copy-pasting into Notion and losing everything by next week full setup in the article

monokern

45,188 görüntüleme • 3 ay önce

ANTHROPIC LEAKED A 4-AGENT SETUP THAT CUTS A CODEBASE AUDIT FROM 3 DAYS TO 20 MINUTES you point it at a repo and walk away - it comes back with what breaks, ranked, patches already tested. repo → map → 4 auditors → rank → fix → verify → report → back into the map the map cuts the repo by blast radius, not by folder - skip it and four agents audit the same three files and miss the one that ships broken. 4 auditors run in parallel with separate contexts - deps, secrets, dead code, hot paths, and none of them sees another's findings. rank is code, not an agent - sort by what breaks production, drop the duplicates, zero tokens. the fixer only opens patches for the top slice - a hundred findings nobody acts on is a report, not an audit. verify runs the suite on every patch and red goes back to the fixer - that patch only, never the whole batch. the back edge into the map is the whole trick - accepted findings become rules, so next week starts where this one ended. one human step in all of it: which fixes ship - 20 minutes instead of 3 days. save this and read the full graph engineering course below ↓

ANTHROPIC LEAKED A 4-AGENT SETUP THAT CUTS A CODEBASE AUDIT FROM 3 DAYS TO 20 MINUTES you point it at a repo and walk away - it comes back with what breaks, ranked, patches already tested. repo → map → 4 auditors → rank → fix → verify → report → back into the map the map cuts the repo by blast radius, not by folder - skip it and four agents audit the same three files and miss the one that ships broken. 4 auditors run in parallel with separate contexts - deps, secrets, dead code, hot paths, and none of them sees another's findings. rank is code, not an agent - sort by what breaks production, drop the duplicates, zero tokens. the fixer only opens patches for the top slice - a hundred findings nobody acts on is a report, not an audit. verify runs the suite on every patch and red goes back to the fixer - that patch only, never the whole batch. the back edge into the map is the whole trick - accepted findings become rules, so next week starts where this one ended. one human step in all of it: which fixes ship - 20 minutes instead of 3 days. save this and read the full graph engineering course below ↓

Hanako

137,040 görüntüleme • 1 ay önce