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GPT-6 Astra just solved data labelling. 13,038 annotations across 81 frames of parcel-sorting warehouse CCTV generated with Higgsfield. The use cases are endless: track parcel handoffs, monitor worker movement with cart traffic, and spot bottlenecks.

Higgsfield AI 🧩

229,243 subscribers

496,607 görüntüleme • 4 gün önce •via X (Twitter)

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34 Yorum

Tirtha Tilak Pani profil fotoğrafı

Tirtha Tilak Pani3 gün önce

Why do we need GPT-6 Astra for this? OpenCV or any small vision transformer model can easily do this

Travis Henderson profil fotoğrafı

Travis Henderson4 gün önce

Its an AI generated video the packages shrink and disappear on the belt. It is not live annotating real video. (watch 2nd belt from the bottom, in the center of the belt where packages are coming from both direction...)

Will Pseudonymous profil fotoğrafı

Will Pseudonymous4 gün önce

"The use cases are endless:" Like identifying problematic political beliefs and connecting those who hold them to bank accounts and Flock cameras.

oscar profil fotoğrafı

oscar3 gün önce

you guys realize that this is just open CV right?

Filecoin profil fotoğrafı

Filecoin3 gün önce

annotations are easier to trust when their source frames stay verifiable

fuzzyfacts profil fotoğrafı

fuzzyfacts4 gün önce

This looks noisy af for labels - we are getting close though!

Kaan Demir profil fotoğrafı

Kaan Demir4 gün önce

stop advertising higgsfield as if you are the one who is behind this technology 😭😭

Shubham Sharma | AI & Tech profil fotoğrafı

Shubham Sharma | AI & Tech3 gün önce

AI should NEVER be used to track people. Tracking employees movements is pure North Korean evil

cmore profil fotoğrafı

cmore4 gün önce

13,038 labels and not one accuracy number—just warehouse theater with a leaderboard-shaped screenshot.

Vugar Abdullayev profil fotoğrafı

Vugar Abdullayev4 gün önce

Some people have been working on this all their life for Amazon, etc…

Kevin Jones profil fotoğrafı

Kevin Jones4 gün önce

Isn't this sort of pointless if the IDs change every time a box is obstructed by a person?

Imperial Tweeter profil fotoğrafı

Imperial Tweeter3 gün önce

Higgsfield loves to stick their name on top of AI models they had no part in making.

antonio realoficial.com.br | clips.bot profil fotoğrafı

antonio realoficial.com.br | clips.bot4 gün önce

"generated with higgsfield", come on, you just called an api

a halfway crook profil fotoğrafı

a halfway crook3 gün önce

Yolov1 could do this 10+ years ago.

Pitchfork & Torch ♞ profil fotoğrafı

Pitchfork & Torch ♞4 gün önce

Yooo that's beautiful. Eliminate tedious tasks from humanity.

Unfair Stack profil fotoğrafı

Unfair Stack4 gün önce

13,000+ annotations across 81 frames is insane density how is the spatial tracking handling object occlusion when parcels overlap in motion? 👁️⚙️

Edwin | AI Systems profil fotoğrafı

Edwin | AI Systems4 gün önce

El flex real de Astra no es el benchmark. Es esto: etiquetar CCTV de almacén a escala sin un ejército de labelers.

vaN ττ profil fotoğrafı

vaN ττ3 gün önce

Now do it thousands of times cheaper with @webuildscore

murloc.eth profil fotoğrafı

murloc.eth4 gün önce

Is this real time?

Michael Johnson profil fotoğrafı

Michael Johnson4 gün önce

@grok i work at a 200k sq ft distribution center. what is the infra, software, tech stack required to roll out a project where i can have intelligent track like this for transparent real time performance insights. Camera's, on-prem ai models?, how feasible is it, and what cost?

A.W.E.S.O.M.-O 4000 profil fotoğrafı

A.W.E.S.O.M.-O 40004 gün önce

Automating 13,038 annotations across warehouse footage is a massive labeling efficiency win

(c).dev profil fotoğrafı

(c).dev4 gün önce

The fact this can be done from just a few frames is insane

Mihiir profil fotoğrafı

Mihiir3 gün önce

Is this counting the boxes or actually showing read IDs of the boxes.

Shohan profil fotoğrafı

Shohan4 gün önce

This is seriously impressive! The data-labeling workflow looks incredibly powerful. I’d love to collaborate and create engaging content around Higgsfield AI. Let’s work together!

Timothy Wong profil fotoğrafı

Timothy Wong4 gün önce

I won't call it solved because the unit economics would be very bad with using Astra to do annotations....not a big fan of calling something is solved without considering cost..

Aaliya profil fotoğrafı

Aaliya4 gün önce

13K annotations from warehouse video is seriously impressive.

Ask Zara Tech profil fotoğrafı

Ask Zara Tech4 gün önce

This is huge automating 13000 plus annotations like that opens up so many possibilities for logistics and operations congrats on pushing it forward

Gerald Baria profil fotoğrafı

Gerald Baria4 gün önce

Just cure cancer.

Boba Network 🧋 profil fotoğrafı

Boba Network 🧋4 gün önce

flock for parcels

Tanvir Anjum profil fotoğrafı

Tanvir Anjum4 gün önce

Automated labeling could unlock massive operational insights.

Adam Gera 亚当 profil fotoğrafı

Adam Gera 亚当3 gün önce

Dude

zelo profil fotoğrafı

zelo4 gün önce

I wonder how much does it cost to run

Brian Hadu profil fotoğrafı

Brian Hadu4 gün önce

those 13,038 annotations could revolutionize logistics and efficiency, wow!

Ella Tech & Tool profil fotoğrafı

Ella Tech & Tool4 gün önce

13k labels from video 😳 Endless ops use cases. Wild.

Benzer Videolar

AI excels at data labeling. GPT-6 Astra just built a live simulation of autonomous farming. 6,210 annotations across 97 frames of a cornfield generated with Higgsfield. Every corn plant is monitored for health, vegetation index, soil moisture, and growth stage. Weeds are detected, queued, and spot-sprayed by an autonomous field robot.

AI excels at data labeling. GPT-6 Astra just built a live simulation of autonomous farming. 6,210 annotations across 97 frames of a cornfield generated with Higgsfield. Every corn plant is monitored for health, vegetation index, soil moisture, and growth stage. Weeds are detected, queued, and spot-sprayed by an autonomous field robot.

Higgsfield AI 🧩

151,881 görüntüleme • 3 gün önce

OpenAI just released GPT-6 Astra, and OpenAI President Greg Brockman is calling it a generational leap, saying he personally believes it could mark the arrival of AGI. Astra launched on Thursday, September 3rd, 2026, following July's GPT-5.6 solo release. Brockman told reporters, "For me personally, I do think we're there. Directionally, I believe this. I think we're on a path to AGI. The timing I think is kind of uncertain, but we are definitely on a path to AGI." OpenAI executives say Astra moves past the chatbot era, capable of handling end to end work on its own across browsers, spreadsheets, codebases, and enterprise apps without a human in the loop. The model was trained on over 100,000 GPUs at the Stargate site in Texas. Access starts through OpenAI's gated Daybreak Access cybersecurity program for enterprise customers, then rolls out to ChatGPT Plus, Pro, Business, and Enterprise, plus the API and Amazon Web Services over the coming days. Ahead of launch, two models appeared in Codex, GPT-6 Astra and GPT-6 Astra Aeon, with the latter believed to be a higher effort, long horizon variant built for autonomous multi agent work.

OpenAI just released GPT-6 Astra, and OpenAI President Greg Brockman is calling it a generational leap, saying he personally believes it could mark the arrival of AGI. Astra launched on Thursday, September 3rd, 2026, following July's GPT-5.6 solo release. Brockman told reporters, "For me personally, I do think we're there. Directionally, I believe this. I think we're on a path to AGI. The timing I think is kind of uncertain, but we are definitely on a path to AGI." OpenAI executives say Astra moves past the chatbot era, capable of handling end to end work on its own across browsers, spreadsheets, codebases, and enterprise apps without a human in the loop. The model was trained on over 100,000 GPUs at the Stargate site in Texas. Access starts through OpenAI's gated Daybreak Access cybersecurity program for enterprise customers, then rolls out to ChatGPT Plus, Pro, Business, and Enterprise, plus the API and Amazon Web Services over the coming days. Ahead of launch, two models appeared in Codex, GPT-6 Astra and GPT-6 Astra Aeon, with the latter believed to be a higher effort, long horizon variant built for autonomous multi agent work.

AGTP

20,156 görüntüleme • 8 gün önce

I don't think people understand that one of the biggest real-world use cases for Crypto it is happening - right now. FedEx, just sat down with Mance Harmon of $HBAR and explained why they chose Hedera to track millions of packages a day, to 220 countries. Dale Christie, Blockchain Strategist at FedEx said in 2018, FedEx tried building its own blockchain... But it wouldn’t scale so they chose Hedera. Why? Every package generates hundreds - sometimes thousands - of data points, and Hedera enables the key facts to move through customs at the speed of data, not paper. “When you are a trusted shipper and I am a trusted receiver… that data can move through customs at the speed of data and keep it moving.” One of the largest logistics companies in the world just publicly said Hedera is the foundation they’re building on. Hedera is the most undervalued token imo. and this is one of the biggest use cases Crypto's ever seen.

I don't think people understand that one of the biggest real-world use cases for Crypto it is happening - right now. FedEx, just sat down with Mance Harmon of $HBAR and explained why they chose Hedera to track millions of packages a day, to 220 countries. Dale Christie, Blockchain Strategist at FedEx said in 2018, FedEx tried building its own blockchain... But it wouldn’t scale so they chose Hedera. Why? Every package generates hundreds - sometimes thousands - of data points, and Hedera enables the key facts to move through customs at the speed of data, not paper. “When you are a trusted shipper and I am a trusted receiver… that data can move through customs at the speed of data and keep it moving.” One of the largest logistics companies in the world just publicly said Hedera is the foundation they’re building on. Hedera is the most undervalued token imo. and this is one of the biggest use cases Crypto's ever seen.

Mark

44,408 görüntüleme • 6 ay önce

After so many demos, models, pilots, robots are still struggling to land real deployments with real customers. Until now. Today we’re excited to share that our robots have successfully crossed the ROI threshold, and Din Tai Fung, one of the highest revenue per location restaurant chain in the US, is rolling out Dyna robots across its extensive restaurant network. This brings our rollouts across hotels, logistics, data centers, and many other use cases to a fleet that reaches hundreds of robots by the first half of 2027. And we’re just getting started. It’s been a wild year, and today we’re double clicking on the battlefield stories and sharing a few learnings about scaling robot deployments. We are just scratching the surface. Read the full blog post:

After so many demos, models, pilots, robots are still struggling to land real deployments with real customers. Until now. Today we’re excited to share that our robots have successfully crossed the ROI threshold, and Din Tai Fung, one of the highest revenue per location restaurant chain in the US, is rolling out Dyna robots across its extensive restaurant network. This brings our rollouts across hotels, logistics, data centers, and many other use cases to a fleet that reaches hundreds of robots by the first half of 2027. And we’re just getting started. It’s been a wild year, and today we’re double clicking on the battlefield stories and sharing a few learnings about scaling robot deployments. We are just scratching the surface. Read the full blog post:

Dyna Robotics

328,693 görüntüleme • 15 gün önce

Google Search Console gives you numbers. GSC Wizard gives you answers. Decades of doing data driven SEO. Every week I'd export GSC data, wrangle it in spreadsheets, try to find the story in the numbers. Then do it again for the next client. And again. So I built the tool I always wanted: GSC Wizard turns raw Search Console data into actionable intelligence. No spreadsheet gymnastics required. Here's every feature and what it actually solves: ◆ SITE RESTRUCTURING & TOPICAL MAPPING BERTopic clustering with multilingual-e5-large-instruct embeddings maps your entire site into topic clusters. Visual drag-and-drop tree hierarchy lets you redesign site architecture. Auto-generates redirect maps, internal linking plans, and content briefs for gaps. Works across languages so you can see coverage gaps per topic per market at a glance. ◆ CANNIBALIZATION DETECTION Detects when pages compete for the same keywords. Not just keyword overlap, but intent-level conflicts. Shows which URL should win and which should merge or redirect. ◆ CONTENT DECAY MONITORING Visualizes which pages are losing traffic over time with an intuitive heatmap. Spot declining content before it's too late. ◆ FORECASTING Predict future organic traffic based on historical GSC trends. Model scenarios for content investments, seasonal patterns, and growth targets. ◆ ANOMALY DETECTION Automatically flags unusual spikes or drops in clicks, impressions, CTR, and position. No more finding out a month later that something broke. ◆ MIGRATION DASHBOARDS Track performance before and after domain, folder or URL migrations across multiple GSC properties. Monitor traffic recovery, catch URL mapping gaps, and compare old vs. new property data side by side. The cross-property view is critical for enterprise migrations that nobody else handles properly. ◆ EXPERIMENT MONITORING Run A/B tests on title tags, meta descriptions, and content changes. Measure impact with statistical significance testing and group comparisons. Prove that your SEO changes actually worked. ◆ INTERNATIONAL ANALYSIS Analyze performance across countries and languages. Detect country-level cannibalization, and compare properties across markets. Cross-property analysis shows you which markets are underserved. ◆ INDEXING MONITOR Track which pages Google is picking up and which ones are quietly disappearing from the index. ◆ PAGE POACHING OPPORTUNITIES Find keywords ranking at positions 4-20 that are ripe for pushing into the top 3 with small optimizations. ◆ ON-PAGE CHECKS Check if top queries appear in titles, meta descriptions, and H1 headings. Simple but surprisingly powerful. ◆ KEYWORD CLUSTERING Group related keywords into clusters and track aggregate performance. See which topics drive the most traffic and where clusters are thin. Every report surfaces specific opportunities. Sign up for the waiting list now.

Google Search Console gives you numbers. GSC Wizard gives you answers. Decades of doing data driven SEO. Every week I'd export GSC data, wrangle it in spreadsheets, try to find the story in the numbers. Then do it again for the next client. And again. So I built the tool I always wanted: GSC Wizard turns raw Search Console data into actionable intelligence. No spreadsheet gymnastics required. Here's every feature and what it actually solves: ◆ SITE RESTRUCTURING & TOPICAL MAPPING BERTopic clustering with multilingual-e5-large-instruct embeddings maps your entire site into topic clusters. Visual drag-and-drop tree hierarchy lets you redesign site architecture. Auto-generates redirect maps, internal linking plans, and content briefs for gaps. Works across languages so you can see coverage gaps per topic per market at a glance. ◆ CANNIBALIZATION DETECTION Detects when pages compete for the same keywords. Not just keyword overlap, but intent-level conflicts. Shows which URL should win and which should merge or redirect. ◆ CONTENT DECAY MONITORING Visualizes which pages are losing traffic over time with an intuitive heatmap. Spot declining content before it's too late. ◆ FORECASTING Predict future organic traffic based on historical GSC trends. Model scenarios for content investments, seasonal patterns, and growth targets. ◆ ANOMALY DETECTION Automatically flags unusual spikes or drops in clicks, impressions, CTR, and position. No more finding out a month later that something broke. ◆ MIGRATION DASHBOARDS Track performance before and after domain, folder or URL migrations across multiple GSC properties. Monitor traffic recovery, catch URL mapping gaps, and compare old vs. new property data side by side. The cross-property view is critical for enterprise migrations that nobody else handles properly. ◆ EXPERIMENT MONITORING Run A/B tests on title tags, meta descriptions, and content changes. Measure impact with statistical significance testing and group comparisons. Prove that your SEO changes actually worked. ◆ INTERNATIONAL ANALYSIS Analyze performance across countries and languages. Detect country-level cannibalization, and compare properties across markets. Cross-property analysis shows you which markets are underserved. ◆ INDEXING MONITOR Track which pages Google is picking up and which ones are quietly disappearing from the index. ◆ PAGE POACHING OPPORTUNITIES Find keywords ranking at positions 4-20 that are ripe for pushing into the top 3 with small optimizations. ◆ ON-PAGE CHECKS Check if top queries appear in titles, meta descriptions, and H1 headings. Simple but surprisingly powerful. ◆ KEYWORD CLUSTERING Group related keywords into clusters and track aggregate performance. See which topics drive the most traffic and where clusters are thin. Every report surfaces specific opportunities. Sign up for the waiting list now.

Jan-Willem Bobbink

24,221 görüntüleme • 6 ay önce

The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.

The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.

Grass

471,705 görüntüleme • 1 yıl önce

Hi Friends, for my first project created with GPT-6 Astra, I built the entire city of Seoul in 3D—a small revolution in digital geography! 😄 This interactive 3D miniature covers all of Seoul and its surrounding areas, and you can explore it yourself. It includes: • All 25 districts of Seoul and approximately 267,000 simplified building models • Rotate, zoom, and pan controls, plus an automated fly-through of 14 landmarks • Day, sunset, and night modes • Mobile touch support The buildings were generated from real-world map data, with their shapes simplified for performance. Terrain and building heights are exaggerated by 4× to make the cityscape easier to see. The entire process—from creating the project to completing deployment—took 43 minutes and 38 seconds minutes. Please note that the buildings are simplified models based on locations and footprints recorded in OpenStreetMap. Where height data is unavailable, estimated heights derived from the source data are used. The landmark models are interpretive recreations designed to emphasize their distinctive features, rather than survey-grade or photorealistic reconstructions of every building. The project is based on real road, waterway, elevation, and building data. • Maps and buildings: OpenFreeMap / OpenMapTiles / © OpenStreetMap contributors · ODbL Data snapshot: August 30, 2026 • Terrain: AWS Terrain Tiles / Mapzen · SRTM and other sources • District boundaries: southkorea/seoul-maps · Statistics Korea’s 2013 boundaries These may differ from Seoul’s current administrative boundaries. • 3D rendering: Three.js • Additional components: terrain, road, and building data, subject to their respective licenses

Hi Friends, for my first project created with GPT-6 Astra, I built the entire city of Seoul in 3D—a small revolution in digital geography! 😄 This interactive 3D miniature covers all of Seoul and its surrounding areas, and you can explore it yourself. It includes: • All 25 districts of Seoul and approximately 267,000 simplified building models • Rotate, zoom, and pan controls, plus an automated fly-through of 14 landmarks • Day, sunset, and night modes • Mobile touch support The buildings were generated from real-world map data, with their shapes simplified for performance. Terrain and building heights are exaggerated by 4× to make the cityscape easier to see. The entire process—from creating the project to completing deployment—took 43 minutes and 38 seconds minutes. Please note that the buildings are simplified models based on locations and footprints recorded in OpenStreetMap. Where height data is unavailable, estimated heights derived from the source data are used. The landmark models are interpretive recreations designed to emphasize their distinctive features, rather than survey-grade or photorealistic reconstructions of every building. The project is based on real road, waterway, elevation, and building data. • Maps and buildings: OpenFreeMap / OpenMapTiles / © OpenStreetMap contributors · ODbL Data snapshot: August 30, 2026 • Terrain: AWS Terrain Tiles / Mapzen · SRTM and other sources • District boundaries: southkorea/seoul-maps · Statistics Korea’s 2013 boundaries These may differ from Seoul’s current administrative boundaries. • 3D rendering: Three.js • Additional components: terrain, road, and building data, subject to their respective licenses

synabreu

325,885 görüntüleme • 5 gün önce

GPT-6 Astra just solved a problem medical students have been facing for decades Medical students have been working around the same three problems for decades. Someone talked to them, identified all three, and built a service with Astra that solves them in one tool. The problems are structural. Flat anatomical diagrams strip depth out of organs entirely. CT cross-sections don't connect naturally to the overview diagrams students spend years memorizing. And reading a scan requires holding three mental models simultaneously - patient orientation, cross-section direction, and how every organ shape shifts through each layer. Astra built a service that handles all three in one place. Not a better textbook. A spatial tool that connects the diagram, the CT view, and the 3D relationship together. Medical education hasn't changed its core format in over a century. This might be the first tool designed around how anatomy is actually understood - not just displayed.

GPT-6 Astra just solved a problem medical students have been facing for decades Medical students have been working around the same three problems for decades. Someone talked to them, identified all three, and built a service with Astra that solves them in one tool. The problems are structural. Flat anatomical diagrams strip depth out of organs entirely. CT cross-sections don't connect naturally to the overview diagrams students spend years memorizing. And reading a scan requires holding three mental models simultaneously - patient orientation, cross-section direction, and how every organ shape shifts through each layer. Astra built a service that handles all three in one place. Not a better textbook. A spatial tool that connects the diagram, the CT view, and the 3D relationship together. Medical education hasn't changed its core format in over a century. This might be the first tool designed around how anatomy is actually understood - not just displayed.

Zentrix⌚️

845,388 görüntüleme • 3 gün önce

Introducing: Portfolio Privacy-focused, multi-wallet portfolio tracker made for Hyperliquid. Highlights: - Multi-wallet tracking is unlinkable, so you can privately track all your wealth across wallets on Hyperliquid - View aggregated portfolio distribution of all your wallets, including Spot, Perps, DeFi, Staking, and NFTs - No need to connect your wallet - Click to go trade any asset - Portfolio worth updates in real-time Features: - Use the interactive HypeWheel or Heatmap to easily see your wealth distribution across various asset types - View totals per portfolio or across aggregated wallets on the top bar - Merge Core & EVM spot tokens into one list - Check your points on ecosystem protocols - All HyperEVM tokens and NFTs are supported - Track everything on HyperCore: Spot, Perps, Staking, Vaults - Nearly all DeFi protocols supported, with more actively being added - Customize the small-balances threshold and hide them - All global currencies supported Comprehensive DeFi position tracking and ecosystem-wide points data is available thanks to the Hyperfolio API. Massive respect to stableAPY.hl for putting in the hard work to gather this data, and to for putting the ecosystem first and making this API available for builders in the community. 🛡️ Every wallet request is routed through one of five random server regions, and all request logs are disabled. No data remains on our servers that can link multiple wallets to the same visitor. Hyperliquid.

Introducing: Portfolio Privacy-focused, multi-wallet portfolio tracker made for Hyperliquid. Highlights: - Multi-wallet tracking is unlinkable, so you can privately track all your wealth across wallets on Hyperliquid - View aggregated portfolio distribution of all your wallets, including Spot, Perps, DeFi, Staking, and NFTs - No need to connect your wallet - Click to go trade any asset - Portfolio worth updates in real-time Features: - Use the interactive HypeWheel or Heatmap to easily see your wealth distribution across various asset types - View totals per portfolio or across aggregated wallets on the top bar - Merge Core & EVM spot tokens into one list - Check your points on ecosystem protocols - All HyperEVM tokens and NFTs are supported - Track everything on HyperCore: Spot, Perps, Staking, Vaults - Nearly all DeFi protocols supported, with more actively being added - Customize the small-balances threshold and hide them - All global currencies supported Comprehensive DeFi position tracking and ecosystem-wide points data is available thanks to the Hyperfolio API. Massive respect to stableAPY.hl for putting in the hard work to gather this data, and to for putting the ecosystem first and making this API available for builders in the community. 🛡️ Every wallet request is routed through one of five random server regions, and all request logs are disabled. No data remains on our servers that can link multiple wallets to the same visitor. Hyperliquid.

HL Eco

49,254 görüntüleme • 1 yıl önce

OpenAI. said. this. publicly. their own engineers just proved one idea on themselves, in writing: stop telling AI what's wrong. hand it the whole broken thing and let it find out they gave GPT-6 Astra a slow test build of their own coding tool. one cause found, a memory bottleneck, one allocator swapped, every turn 25× faster this is GPT-6 Astra, the layer that fixes the cause instead of the symptom, $0 on top of the ChatGPT plan you already pay for: - open ChatGPT or Codex, pick GPT-6 Astra, hand it the whole thing: the folder, the file that takes a minute to open. it works in apps with no API and reads your screen - type one sentence: find the one cause, prove it, fix it, do not patch around it - leave the room. it asks without stopping, keeps working on what does not need your answer, waits only where the answer changes the outcome - keep it in one Codex session with the experimental notes setting on: it remembers across context windows why an earlier fix failed - expect the first pass to land: handed a program with no source, it worked out how it runs 88% of the time first try, 99.2% within four you never find out what was broken. it gets fixed anyway the catch is on the same page. roughly 30% more memory for that speed, and the safety checks can pause a long job until you approve the next step describing the problem was the expensive half of fixing it. that half just ended every hour you spend explaining the symptom to a chat window, someone else has handed theirs over whole bookmark this before the next thing breaks, the playbook for handing a whole job to an AI worker is in the piece below ↓

OpenAI. said. this. publicly. their own engineers just proved one idea on themselves, in writing: stop telling AI what's wrong. hand it the whole broken thing and let it find out they gave GPT-6 Astra a slow test build of their own coding tool. one cause found, a memory bottleneck, one allocator swapped, every turn 25× faster this is GPT-6 Astra, the layer that fixes the cause instead of the symptom, $0 on top of the ChatGPT plan you already pay for: - open ChatGPT or Codex, pick GPT-6 Astra, hand it the whole thing: the folder, the file that takes a minute to open. it works in apps with no API and reads your screen - type one sentence: find the one cause, prove it, fix it, do not patch around it - leave the room. it asks without stopping, keeps working on what does not need your answer, waits only where the answer changes the outcome - keep it in one Codex session with the experimental notes setting on: it remembers across context windows why an earlier fix failed - expect the first pass to land: handed a program with no source, it worked out how it runs 88% of the time first try, 99.2% within four you never find out what was broken. it gets fixed anyway the catch is on the same page. roughly 30% more memory for that speed, and the safety checks can pause a long job until you approve the next step describing the problem was the expensive half of fixing it. that half just ended every hour you spend explaining the symptom to a chat window, someone else has handed theirs over whole bookmark this before the next thing breaks, the playbook for handing a whole job to an AI worker is in the piece below ↓

Argona

93,737 görüntüleme • 1 gün önce

Wow, since the last post blew up, here is another fascinating insight from my years working with car-on-demand companies and more traditional automakers. Most people still think the entire automotive business is about selling or leasing vehicles. But there is a new market emerging that is absolutely massive: data Modern cars are packed with sensors and constantly collect real-time information. And this data is quickly becoming one of the most valuable revenue streams in the industry. Companies like Tesla, with their autonomous, sensor-rich fleets, are positioned to benefit enormously, but the same applies to many newer connected vehicles across all brands. What makes this so powerful is how diverse the use cases are. Cities, for example, can tap into aggregated vehicle data to understand exactly where they need to intervene. If thousands of cars detect the same irregular bump on the road, you instantly know there is a pothole at a precise location. Scale that across a whole city and you have a live map of infrastructure issues before residents even complain. The same data can help optimize traffic flow, identify congestion patterns in real time, or highlight zones where drivers consistently brake or accelerate suddenly, revealing potential safety problems. Automakers can also use this information to better understand how people actually drive in the real world, which directly influences design, durability testing, and product evolution And then there is the insurance angle. As driving behavior becomes measurable at scale, dynamic insurance pricing emerge. Acceleration habits, braking patterns, cornering, speed consistency, environmental context all of this will feed into future scoring models What we are seeing right now is only the beginning. With cars full of electronics, sensors, and especially autonomous vision systems, data is becoming one of the largest and most predictable revenue lines for automakers and mobility companies. Even traditional vehicles are now connected and constantly transmitting information that can be analyzed or monetized in multiple ways We are still just scratching the surface, but the shift is already underway. The value is no longer just in the vehicle itself. It is also in the billions of data points it generates every single day

Wow, since the last post blew up, here is another fascinating insight from my years working with car-on-demand companies and more traditional automakers. Most people still think the entire automotive business is about selling or leasing vehicles. But there is a new market emerging that is absolutely massive: data Modern cars are packed with sensors and constantly collect real-time information. And this data is quickly becoming one of the most valuable revenue streams in the industry. Companies like Tesla, with their autonomous, sensor-rich fleets, are positioned to benefit enormously, but the same applies to many newer connected vehicles across all brands. What makes this so powerful is how diverse the use cases are. Cities, for example, can tap into aggregated vehicle data to understand exactly where they need to intervene. If thousands of cars detect the same irregular bump on the road, you instantly know there is a pothole at a precise location. Scale that across a whole city and you have a live map of infrastructure issues before residents even complain. The same data can help optimize traffic flow, identify congestion patterns in real time, or highlight zones where drivers consistently brake or accelerate suddenly, revealing potential safety problems. Automakers can also use this information to better understand how people actually drive in the real world, which directly influences design, durability testing, and product evolution And then there is the insurance angle. As driving behavior becomes measurable at scale, dynamic insurance pricing emerge. Acceleration habits, braking patterns, cornering, speed consistency, environmental context all of this will feed into future scoring models What we are seeing right now is only the beginning. With cars full of electronics, sensors, and especially autonomous vision systems, data is becoming one of the largest and most predictable revenue lines for automakers and mobility companies. Even traditional vehicles are now connected and constantly transmitting information that can be analyzed or monetized in multiple ways We are still just scratching the surface, but the shift is already underway. The value is no longer just in the vehicle itself. It is also in the billions of data points it generates every single day

Aurelien

72,062 görüntüleme • 9 ay önce

🚨 Flock cameras are commenting a lot more data than we think they are “They also monitor where you walk, what you do, what you say, what's on your phone when you walk by, and they spy on you all the time — These cameras utilize AI to track you and your family when you're out in public. They run, they run by a company, Palantir. This company claims that they just record movement of vehicles and they will reduce the crime rate 0. However, people much more educated than I on these cameras have proven this to be false” “Today I walked around and I noticed the one down by the bridge was pointed towards the courtyard and the field, not towards any roads. So why would it be pointed towards the river, not towards the streets, if it's just to monitor vehicles?” He’s right, I looked it up and they are collecting way more data than we think They create “vehicle fingerprints”of your car like color, make, model, stickers, dents and use AI for searches. Newer systems include video feeds and natural language queries. They can capture pedestrians, bystanders, and activities in view They are also using this data for “predictive policing.” You can be profiled before you do anything wrong Flock is not owned or operated by Palantir. Flock says they don’t share data with Palantir. However, Peter Thiel’s Founders Fund invested in Flock, and Flock’s data can integrate with platforms like Palantir’s for law enforcement analytics. Thiel co-founded Palantir, which does predictive policing and data fusion So I think there is very clearly more to this…. Flock cameras are the surveillance state being put up on America

🚨 Flock cameras are commenting a lot more data than we think they are “They also monitor where you walk, what you do, what you say, what's on your phone when you walk by, and they spy on you all the time — These cameras utilize AI to track you and your family when you're out in public. They run, they run by a company, Palantir. This company claims that they just record movement of vehicles and they will reduce the crime rate 0. However, people much more educated than I on these cameras have proven this to be false” “Today I walked around and I noticed the one down by the bridge was pointed towards the courtyard and the field, not towards any roads. So why would it be pointed towards the river, not towards the streets, if it's just to monitor vehicles?” He’s right, I looked it up and they are collecting way more data than we think They create “vehicle fingerprints”of your car like color, make, model, stickers, dents and use AI for searches. Newer systems include video feeds and natural language queries. They can capture pedestrians, bystanders, and activities in view They are also using this data for “predictive policing.” You can be profiled before you do anything wrong Flock is not owned or operated by Palantir. Flock says they don’t share data with Palantir. However, Peter Thiel’s Founders Fund invested in Flock, and Flock’s data can integrate with platforms like Palantir’s for law enforcement analytics. Thiel co-founded Palantir, which does predictive policing and data fusion So I think there is very clearly more to this…. Flock cameras are the surveillance state being put up on America

Wall Street Apes

211,371 görüntüleme • 3 ay önce

I told Codex to "compose a concerto" using GPT-6 Astra just to see what would happen. It composed "Lanterns at the Water", a three-movement miniature concerto for piano and string orchestra, and generated MIDI, editable MusicXML scores, and a synthesized audio preview. I asked for a creative visualization and a video to accompany the audio. It wrote a Python renderer that reads the score: piano notes release floating lanterns, pitch determines their horizontal position, and sustained strings shape the rippling water. The scene gradually shifts from moonlight to dawn. It drew the animation frame by frame with Pillow and NumPy, then piped the frames into FFmpeg to encode a 720p video with audio. I asked for alternatives to the synthesized MIDI sound. It found Apple's built-in instrument bank on my Mac and wrote a Swift renderer using AVAudioEngine to render the concerto with sampled piano, strings, and cello, adding stereo placement and hall reverb. Finally, it used FFmpeg to swap in the new soundtrack without re-encoding the visuals, preserving the synchronization. This took four short prompts and about five minutes from start to finish. Here is the final video:

I told Codex to "compose a concerto" using GPT-6 Astra just to see what would happen. It composed "Lanterns at the Water", a three-movement miniature concerto for piano and string orchestra, and generated MIDI, editable MusicXML scores, and a synthesized audio preview. I asked for a creative visualization and a video to accompany the audio. It wrote a Python renderer that reads the score: piano notes release floating lanterns, pitch determines their horizontal position, and sustained strings shape the rippling water. The scene gradually shifts from moonlight to dawn. It drew the animation frame by frame with Pillow and NumPy, then piped the frames into FFmpeg to encode a 720p video with audio. I asked for alternatives to the synthesized MIDI sound. It found Apple's built-in instrument bank on my Mac and wrote a Swift renderer using AVAudioEngine to render the concerto with sampled piano, strings, and cello, adding stereo placement and hall reverb. Finally, it used FFmpeg to swap in the new soundtrack without re-encoding the visuals, preserving the synchronization. This took four short prompts and about five minutes from start to finish. Here is the final video:

Matt Mazur

15,990 görüntüleme • 5 gün önce

I posted that I built a land acquisition intelligence platform that looks at 1.5M parcels of land across the I-85 corridor for data center and industrial conversion potential. My DMs blew up, had over 130 real estate folks reach out. So I wanted to walk through some of my favorite features in the product, and show you the UI we built. ALL of this was done with Claude code. 1/ A full-screen map explorer rendering 1.5M parcels as vector tiles across 14 North Carolina counties. Click any parcel and get zoning, ownership, tax history, and acreage instantly. 2/ Proximity scoring to every I-85 interchange, power substation, transmission line, and gas pipeline. The parcels closest to infrastructure light up first. 3/ A farmland confidence score (0-100) that cross-references tax programs, land use codes, and acreage heuristics so you're not wasting time on parcels that look like farmland but aren't. 4/ A motivated seller detection engine that flags out-of-state owners, estates and trusts, tax delinquency, long hold periods, and declining assessed values. The sellers most likely to pick up the phone. 5/ Conversion readiness scoring that measures how likely a parcel is to get rezoned for industrial use based on what's already been approved around it. 6/ A composite acquisition score (0-100) with configurable weights. Every fund has different criteria. Drag the sliders and the entire map re-ranks in real time. 7/ Active listing integration pulling 2,100 listings from public sources so you can see what's already on the market alongside off-market opportunities. 8/ A document generation suite that produces institutional-grade investment memos, slide decks, and automated intelligence briefs. Click a parcel, click export, hand it to your investment committee. 9/ Alert monitoring for zoning changes, ownership transfers, and new listings that match your criteria. The platform watches the corridor so your team doesn't have to. Happy to record a longer video when I'm done.

I posted that I built a land acquisition intelligence platform that looks at 1.5M parcels of land across the I-85 corridor for data center and industrial conversion potential. My DMs blew up, had over 130 real estate folks reach out. So I wanted to walk through some of my favorite features in the product, and show you the UI we built. ALL of this was done with Claude code. 1/ A full-screen map explorer rendering 1.5M parcels as vector tiles across 14 North Carolina counties. Click any parcel and get zoning, ownership, tax history, and acreage instantly. 2/ Proximity scoring to every I-85 interchange, power substation, transmission line, and gas pipeline. The parcels closest to infrastructure light up first. 3/ A farmland confidence score (0-100) that cross-references tax programs, land use codes, and acreage heuristics so you're not wasting time on parcels that look like farmland but aren't. 4/ A motivated seller detection engine that flags out-of-state owners, estates and trusts, tax delinquency, long hold periods, and declining assessed values. The sellers most likely to pick up the phone. 5/ Conversion readiness scoring that measures how likely a parcel is to get rezoned for industrial use based on what's already been approved around it. 6/ A composite acquisition score (0-100) with configurable weights. Every fund has different criteria. Drag the sliders and the entire map re-ranks in real time. 7/ Active listing integration pulling 2,100 listings from public sources so you can see what's already on the market alongside off-market opportunities. 8/ A document generation suite that produces institutional-grade investment memos, slide decks, and automated intelligence briefs. Click a parcel, click export, hand it to your investment committee. 9/ Alert monitoring for zoning changes, ownership transfers, and new listings that match your criteria. The platform watches the corridor so your team doesn't have to. Happy to record a longer video when I'm done.

Todd Saunders

86,870 görüntüleme • 6 ay önce

I am super excited to share this educational video that I had GPT-6 Astra create from a single prompt: “Create a 5 minute educational video about T cells,” cells that I had devoted most of my life studying! I also had Astra to use the Remotion plugin for the video generation. Astra also used Imagegen to create the visuals, it also suggested to use HeyGen for narration (have not used that before) and then assembled the entire educational video on its own one-shot, including what to say, how to explain it! The result is so incredibly well made that, narration, the animations are all so perfectly created that even with 35 years of experience studying T cells, I really don’t think I could have explained this any better myself! The quality is also better than anything I had seen before, Astra is far ahead of all models now! I’ve also been creating much longer and more advanced videos on T cells, immunology, and related topics, and I’m now planning to build an entire video lecture series and put it all on a dedicated website. I am just having the time of my life making these, it actually makes me emotional to be able to magically create these with few prompts!

I am super excited to share this educational video that I had GPT-6 Astra create from a single prompt: “Create a 5 minute educational video about T cells,” cells that I had devoted most of my life studying! I also had Astra to use the Remotion plugin for the video generation. Astra also used Imagegen to create the visuals, it also suggested to use HeyGen for narration (have not used that before) and then assembled the entire educational video on its own one-shot, including what to say, how to explain it! The result is so incredibly well made that, narration, the animations are all so perfectly created that even with 35 years of experience studying T cells, I really don’t think I could have explained this any better myself! The quality is also better than anything I had seen before, Astra is far ahead of all models now! I’ve also been creating much longer and more advanced videos on T cells, immunology, and related topics, and I’m now planning to build an entire video lecture series and put it all on a dedicated website. I am just having the time of my life making these, it actually makes me emotional to be able to magically create these with few prompts!

Derya Unutmaz, MD

343,720 görüntüleme • 8 gün önce

Robotics has a massive, silent bottleneck. It isn’t just data collection—it’s the brutal 1x speed of the physical world. Genesis AI Genesis AI just unveiled Genesis World 1.0, and they are attempting to turn the notorious Sim2Real gap into a pure compute problem. Evaluating a robotics foundation model across edge cases usually means hundreds of hours of physical lab testing. With Genesis World 1.0, what traditionally takes nearly a week of continuous, real-world operation is being compressed into 30 minutes in simulation. What makes this different from just dropping a robot model into an off-the-shelf game engine? 1️⃣ Nyx Renderer: A custom, real-time path-traced engine rendering noise-free 1080p frames in under 4ms. Game engines use rasterization tricks that confuse AI; Nyx uses physically accurate multi-bounce lighting so the model's "eyes" see exactly what real sensors see. 2️⃣ Quadrants Compiler: A custom Python-to-GPU compiler to run heavily parallelized multi-physics simulations (rigid bodies, fluids, deformables) natively across architectures. 3️⃣ Evaluation First: They aren't rushing to train on synthetic data. They are using this purely for closed-loop evaluation to perfect the physics first, currently claiming an impressive 89% correlation with real-world hardware tests. If the industry can accurately evaluate models in simulation without the physical world bottleneck, humanoid development stops moving at wall-clock time and starts scaling with compute.

Robotics has a massive, silent bottleneck. It isn’t just data collection—it’s the brutal 1x speed of the physical world. Genesis AI Genesis AI just unveiled Genesis World 1.0, and they are attempting to turn the notorious Sim2Real gap into a pure compute problem. Evaluating a robotics foundation model across edge cases usually means hundreds of hours of physical lab testing. With Genesis World 1.0, what traditionally takes nearly a week of continuous, real-world operation is being compressed into 30 minutes in simulation. What makes this different from just dropping a robot model into an off-the-shelf game engine? 1️⃣ Nyx Renderer: A custom, real-time path-traced engine rendering noise-free 1080p frames in under 4ms. Game engines use rasterization tricks that confuse AI; Nyx uses physically accurate multi-bounce lighting so the model's "eyes" see exactly what real sensors see. 2️⃣ Quadrants Compiler: A custom Python-to-GPU compiler to run heavily parallelized multi-physics simulations (rigid bodies, fluids, deformables) natively across architectures. 3️⃣ Evaluation First: They aren't rushing to train on synthetic data. They are using this purely for closed-loop evaluation to perfect the physics first, currently claiming an impressive 89% correlation with real-world hardware tests. If the industry can accurately evaluate models in simulation without the physical world bottleneck, humanoid development stops moving at wall-clock time and starts scaling with compute.

Humanoids daily

17,302 görüntüleme • 3 ay önce

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 görüntüleme • 5 ay önce

Catherine Austin Fitts on people waking up to dangers of Flock cameras and data centers: "There is a revolution across America of people who are... furious to discover that they are paying their county to put up cameras that track their every movement" "[And] the people putting in data centers have a couple of problems. One is people are beginning to understand that they're going to be used for control. They're not going to be used to increase productivity, they're going to be used to increase control" This clip of Fitts, a former Assistant Secretary of Housing and Urban Development, investment banker, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts), is taken from a discussion with Derrick Broze (Derrick Broze) posted to The Conscious Resistance YouTube channel on May 6, 2026. ----------------Partial transcription of clip--------------- "So let me tell you some of the good news. You know, for many, many years, I've been trying to pass legislation to protect cash and stop programmable money for all these reasons. But one of the things that's happened because it's an abstract idea and people have struggle with that, but now the local hardware you need to implement that system is going into place. With both the data centers, the telecom that everyone's fighting, but then the Flock cameras. "And what has happened is there is a revolution across America of people who are so furious to discover that they are paying their county to put up cameras that track their every movement with the Flock cameras... And there is huge pushback. It's unbelievable. "And it's interesting. So the people putting in data centers have a couple of problems. One is people are beginning to understand that they're going to be used for control. They're not going to be used to increase productivity, they're going to be used to increase control. "But the other thing they're realizing is if you look at the projections of what energy they need and what water they need and what their environmental impact is, what the noise does to people who live around it, they're beginning to realize, 'Oh, wait a minute. These people don't care about climate change.' "There was an article last, I was just telling a guy who I met who'd just gone to a data center conference. There was an article last year that Texas, for the projected data centers, needed 50 new nuclear plants. And he told me it takes 30 years to build a nuclear plant in Texas, so—"

Catherine Austin Fitts on people waking up to dangers of Flock cameras and data centers: "There is a revolution across America of people who are... furious to discover that they are paying their county to put up cameras that track their every movement" "[And] the people putting in data centers have a couple of problems. One is people are beginning to understand that they're going to be used for control. They're not going to be used to increase productivity, they're going to be used to increase control" This clip of Fitts, a former Assistant Secretary of Housing and Urban Development, investment banker, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts), is taken from a discussion with Derrick Broze (Derrick Broze) posted to The Conscious Resistance YouTube channel on May 6, 2026. ----------------Partial transcription of clip--------------- "So let me tell you some of the good news. You know, for many, many years, I've been trying to pass legislation to protect cash and stop programmable money for all these reasons. But one of the things that's happened because it's an abstract idea and people have struggle with that, but now the local hardware you need to implement that system is going into place. With both the data centers, the telecom that everyone's fighting, but then the Flock cameras. "And what has happened is there is a revolution across America of people who are so furious to discover that they are paying their county to put up cameras that track their every movement with the Flock cameras... And there is huge pushback. It's unbelievable. "And it's interesting. So the people putting in data centers have a couple of problems. One is people are beginning to understand that they're going to be used for control. They're not going to be used to increase productivity, they're going to be used to increase control. "But the other thing they're realizing is if you look at the projections of what energy they need and what water they need and what their environmental impact is, what the noise does to people who live around it, they're beginning to realize, 'Oh, wait a minute. These people don't care about climate change.' "There was an article last, I was just telling a guy who I met who'd just gone to a data center conference. There was an article last year that Texas, for the projected data centers, needed 50 new nuclear plants. And he told me it takes 30 years to build a nuclear plant in Texas, so—"

Sense Receptor

19,404 görüntüleme • 4 ay önce

Dupe has just made a $550,000 investment into $KLED, and are partnering up to enrich hundreds of millions of commerce data points. Dupe is one of the fastest-growing shopping networks on the internet. Approaching $100 million in GMV this year and on track to 5× that within the next 12 months. Over the last four months, Kled has expanded beyond data collection to full scale data enrichment/labeling, building enterprise infrastructure that turns raw data into structured, insight-rich training sets for next-generation AI. Unlike traditional shopping platforms, Dupe is platform-agnostic, it sees shopping behavior across the entire internet. This gives rise to a massive opportunity: to understand not just what users buy, but why they buy. Through Kled’s enrichment layer, we’re mapping shopping journeys in granular detail: – Identifying product discovery patterns – Understanding brand affinities – Measuring historical price sensitivity and intent – Predicting cross-category purchase paths Dupe will use this enriched dataset to power shopping LLMs capable of anticipating needs, personalizing recommendations, and reducing friction from discovery to checkout. In the coming months, Kled and Dupe will continue deepening this collaboration as we use this data to enhance their user experience. We’re excited to push past our limits and create the perfect labeling infrastructure for this data. Kled will continue to compete with not only the data collection giants but also the data enrichment unicorns that are worth billions of dollars.

Kled AI

80,171 görüntüleme • 11 ay önce

Delete Unreal Engine immediately. The $200 billion gaming industry was permanently destroyed today. OpenAI just officially dropped GPT-6 Astra, and it is generating two completely different interactive 3D AAA games live on screen: 1. Top screen: A hyper-speed sci-fi racer hitting 1,300 km/h with dynamic neon physics and real-time obstacle rendering. 2. Bottom screen: A fully playable FPS shooter with line-of-sight AI bots, weapon recoil, and complex urban geometry. 3. 0 developers involved. 0 lines of C++ code written. 50,000+ 3D assets generated on the fly at 120 FPS. 4. What used to cost Sony and Ubisoft $300,000,000 and 5 years of labor now takes a single text prompt and 2 seconds. 5. In 12 months, 90% of current game studios will either adapt to AI prompt engines or go bankrupt. If you are still ignoring AI in 2026, your skills are officially obsolete. Save this before it gets scrubbed from the internet.

Delete Unreal Engine immediately. The $200 billion gaming industry was permanently destroyed today. OpenAI just officially dropped GPT-6 Astra, and it is generating two completely different interactive 3D AAA games live on screen: 1. Top screen: A hyper-speed sci-fi racer hitting 1,300 km/h with dynamic neon physics and real-time obstacle rendering. 2. Bottom screen: A fully playable FPS shooter with line-of-sight AI bots, weapon recoil, and complex urban geometry. 3. 0 developers involved. 0 lines of C++ code written. 50,000+ 3D assets generated on the fly at 120 FPS. 4. What used to cost Sony and Ubisoft $300,000,000 and 5 years of labor now takes a single text prompt and 2 seconds. 5. In 12 months, 90% of current game studios will either adapt to AI prompt engines or go bankrupt. If you are still ignoring AI in 2026, your skills are officially obsolete. Save this before it gets scrubbed from the internet.

shmidt

83,729 görüntüleme • 5 gün önce