Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Reduced a Docker image from 1.37GB → 168MB. Just by using multi-stage builds, slim base images, and cleaning unused dependencies. Small optimizations = faster CI/CD and safer containers.

Anupam

2,182 subscribers

157,277 views • 5 months ago •via X (Twitter)

Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Turn images into decentralized videos with Eternal AI Upload an image and tweet: Eternal AI create video <your prompt> It'll reply with a short video using your image, powered by Wan2.1 by Wan, stored on Filecoin, and verifiable onchain with @Base.

Turn images into decentralized videos with Eternal AI Upload an image and tweet: Eternal AI create video <your prompt> It'll reply with a short video using your image, powered by Wan2.1 by Wan, stored on Filecoin, and verifiable onchain with @Base.

Eternal AI

44,364 views • 1 year ago

You are using Docker wrong. I promise! Here are the two things you are missing out: • Your Dockerfiles are using a single stage. This is very slow. • You aren't caching steps as much as you could. This is also slow. For a couple of years now, Docker has been using BuildKit as its primary engine, which has added support for multi-stage Dockerfiles. Here's the deal: A single-stage Dockerfile (the one you are likely using) forces every step to depend on the previous one. That means: • Everything runs sequentially • The generated image is huge • Any change will skip cached layers • No opportunities to parallelize steps Changing one line early in the Dockerfile renders everything after it useless. There's a better way: Start using multi-stage builds. I recorded the attached video to illustrate a comparison between a slow, single-stage build and a fast, multi-stage build. Here is what you need to do: Split your Dockerfile into independent stages using multiple FROM statements. BuildKit will turn those stages into a dependency graph and run independent stages in parallel. There are several benefits to this: 1. The build process will be way faster. Parallelizing independent stages will save a massive amount of time. 2. You'll get way better caching. Each stage has its own set of layers. If something changes in your build stage, your final stage doesn't get invalidated. get clean final images. You don't need to ship tooling, compilers, or temporary files. You'll only copy to the final image what you need. This has been a massive upgrade to the way I structure my Dockerfiles.

You are using Docker wrong. I promise! Here are the two things you are missing out: • Your Dockerfiles are using a single stage. This is very slow. • You aren't caching steps as much as you could. This is also slow. For a couple of years now, Docker has been using BuildKit as its primary engine, which has added support for multi-stage Dockerfiles. Here's the deal: A single-stage Dockerfile (the one you are likely using) forces every step to depend on the previous one. That means: • Everything runs sequentially • The generated image is huge • Any change will skip cached layers • No opportunities to parallelize steps Changing one line early in the Dockerfile renders everything after it useless. There's a better way: Start using multi-stage builds. I recorded the attached video to illustrate a comparison between a slow, single-stage build and a fast, multi-stage build. Here is what you need to do: Split your Dockerfile into independent stages using multiple FROM statements. BuildKit will turn those stages into a dependency graph and run independent stages in parallel. There are several benefits to this: 1. The build process will be way faster. Parallelizing independent stages will save a massive amount of time. 2. You'll get way better caching. Each stage has its own set of layers. If something changes in your build stage, your final stage doesn't get invalidated. get clean final images. You don't need to ship tooling, compilers, or temporary files. You'll only copy to the final image what you need. This has been a massive upgrade to the way I structure my Dockerfiles.

Santiago

67,776 views • 9 months ago

I asked Kling 3.0 to create a short film from just a single image... and the result blew my mind! here's how I made this video using Multi-Shot 👇

I asked Kling 3.0 to create a short film from just a single image... and the result blew my mind! here's how I made this video using Multi-Shot 👇

TechHalla

77,818 views • 6 months ago

Skyfall is a new Mars mission concept by NASA using six small helicopters instead of one rover. Using a mid-air Skyfall maneuver, each explores independently, capturing images and scanning below the surface. It aims for faster, wider exploration, targeting a 2028 launch.

Skyfall is a new Mars mission concept by NASA using six small helicopters instead of one rover. Using a mid-air Skyfall maneuver, each explores independently, capturing images and scanning below the surface. It aims for faster, wider exploration, targeting a 2028 launch.

Space and Technology

119,438 views • 3 months ago

🦞 OpenClaw 3.7 JUST DROPPED and it's HUGE. ✅ Context Engine Plugin API - Say goodbye to context loss. Plugins like lossless-claw can now preserve full memory without touching core logic. ✅ 30% Faster with LongCat - New official integration solves the Google/Anthropic ban problem. Faster tasks, stable API, zero account risk. ✅ Threads Survive Restarts - Discord/Telegram bindings now persist. Your conversations stay connected even after reboot. ✅ Docker Slim + i18n - Multi-stage builds, Spanish locale support, iOS App Store prep. OpenClaw is going mainstream. Your AI agent just got a serious upgrade. The AI OS war has begun.

🦞 OpenClaw 3.7 JUST DROPPED and it's HUGE. ✅ Context Engine Plugin API - Say goodbye to context loss. Plugins like lossless-claw can now preserve full memory without touching core logic. ✅ 30% Faster with LongCat - New official integration solves the Google/Anthropic ban problem. Faster tasks, stable API, zero account risk. ✅ Threads Survive Restarts - Discord/Telegram bindings now persist. Your conversations stay connected even after reboot. ✅ Docker Slim + i18n - Multi-stage builds, Spanish locale support, iOS App Store prep. OpenClaw is going mainstream. Your AI agent just got a serious upgrade. The AI OS war has begun.

Boxmining

127,735 views • 5 months ago

A dream doesn't become reality overnight. it takes courage, sacrifice, and relentless belief. From a small room to a global stage, every note tells a story of hope. 🎤✨ Made with FlovaAI using GPT Image 2 and Seedance 2.0 #Flovaai #FlovaCPP

A dream doesn't become reality overnight. it takes courage, sacrifice, and relentless belief. From a small room to a global stage, every note tells a story of hope. 🎤✨ Made with FlovaAI using GPT Image 2 and Seedance 2.0 #Flovaai #FlovaCPP

Zyra AI

103,753 views • 28 days ago

the most powerful image agent has arrived. flowith image agent doesn’t just make better images. It builds perfect visual workflows. - zoom-worthy details - text that actually works - search-aware creative intelligence - images to videos with seedance 2.0 from one prompt to a complete campaign workflow. like, comment, and retweet for 20 random membership drops!

the most powerful image agent has arrived. flowith image agent doesn’t just make better images. It builds perfect visual workflows. - zoom-worthy details - text that actually works - search-aware creative intelligence - images to videos with seedance 2.0 from one prompt to a complete campaign workflow. like, comment, and retweet for 20 random membership drops!

Flowith

24,658 views • 4 months ago

I was able to create a prompt that helps me generate bulks of images using flow agent. this is a 61 image I generated in one go just by using one character image and it used the style across every 61 prompt And everything was done on mobile.. Yt automators this is for you😎

I was able to create a prompt that helps me generate bulks of images using flow agent. this is a 61 image I generated in one go just by using one character image and it used the style across every 61 prompt And everything was done on mobile.. Yt automators this is for you😎

Peter Buildweb | Al

20,966 views • 1 month ago

Introducing Wan2.6 - A native multimodal model that turns your ideas into breathtaking videos and images! · Starring: Cast characters from reference videos into new scenes. Support human or human-like figures, enabling complex multi-person and human-object interactions with appearance and voice consistency. · Intelligent Multi-shot Narrative: Turn simple prompts into auto-storyboarded, multi-shot videos. Maintain visual consistency and upgrade storytelling from single shots to rich narratives. · Native A/V Sync: Generate multi-speaker dialogue with natural lip-sync and studio-quality audio. It doesn’t just look real - it sounds real. · Cinematic Quality: 15s 1080p HD generation with comprehensive upgrades to instruction adherence, motion physics, and aesthetic control. · Advanced Image Synthesis and Editing: Deliver cinematic photorealism with precise control over lens and lighting. Support multi-image referencing for commercial-grade consistency and faithful aesthetic transfer. · Storytelling with Structure: Generate interleaved texts and images powered by real-world knowledge and reasoning capabilities, enabling hierarchical and structured visual narratives.

Introducing Wan2.6 - A native multimodal model that turns your ideas into breathtaking videos and images! · Starring: Cast characters from reference videos into new scenes. Support human or human-like figures, enabling complex multi-person and human-object interactions with appearance and voice consistency. · Intelligent Multi-shot Narrative: Turn simple prompts into auto-storyboarded, multi-shot videos. Maintain visual consistency and upgrade storytelling from single shots to rich narratives. · Native A/V Sync: Generate multi-speaker dialogue with natural lip-sync and studio-quality audio. It doesn’t just look real - it sounds real. · Cinematic Quality: 15s 1080p HD generation with comprehensive upgrades to instruction adherence, motion physics, and aesthetic control. · Advanced Image Synthesis and Editing: Deliver cinematic photorealism with precise control over lens and lighting. Support multi-image referencing for commercial-grade consistency and faithful aesthetic transfer. · Storytelling with Structure: Generate interleaved texts and images powered by real-world knowledge and reasoning capabilities, enabling hierarchical and structured visual narratives.

Wan

3,847,733 views • 8 months ago

Code Arena can handle image inputs for agentic web dev tasks, reasoning through multi-step problems and using tools along the way. Watch how it works with Aryan Vichare in this clip. Find a link to the full walkthrough on how Code Arena creates sites and apps from images in thread.

Code Arena can handle image inputs for agentic web dev tasks, reasoning through multi-step problems and using tools along the way. Watch how it works with Aryan Vichare in this clip. Find a link to the full walkthrough on how Code Arena creates sites and apps from images in thread.

Arena.ai

19,933 views • 4 months ago

A common problem faced by researchers is finding and using information from scientific papers. 🔍 Gemini's capabilities mean it can accurately uncover knowledge within thousands of files including dense text & complex images. How could this help deliver breakthroughs faster? ↓

A common problem faced by researchers is finding and using information from scientific papers. 🔍 Gemini's capabilities mean it can accurately uncover knowledge within thousands of files including dense text & complex images. How could this help deliver breakthroughs faster? ↓

Google DeepMind

248,619 views • 2 years ago

Grok Imagine’s image editing literally blows my mind I just edited this entire image from scratch using Grok Imagine and its full set of editing tools The segment + precise editing tools are insanely useful Instead of regenerating the entire image, you can target exactly what you want to change while keeping everything else intact That level of control makes editing much faster and more practical Grok Imagine is becoming a seriously powerful image editor….not just an image generator

Grok Imagine’s image editing literally blows my mind I just edited this entire image from scratch using Grok Imagine and its full set of editing tools The segment + precise editing tools are insanely useful Instead of regenerating the entire image, you can target exactly what you want to change while keeping everything else intact That level of control makes editing much faster and more practical Grok Imagine is becoming a seriously powerful image editor….not just an image generator

X Freeze

4,138,260 views • 18 days ago

It's June 16. My birthday. So I built a Mini version of Kubernetes. You give it a container image → it schedules it, pulls it, runs it, and tracks its lifecycle. What it does: - Submit jobs via a REST API - Move jobs through SUBMITTED → RUNNABLE → RUNNING → SUCCEEDED / FAILED - Pull container images and run real Docker containers - Persist state in Postgres - Use BullMQ + Valkey for scheduling and execution Building this from scratch gave me a much deeper appreciation for how orchestration systems work behind the scenes. There's still plenty to improve, but seeing the control loop model in action was incredibly satisfying. Stack: Node.js · TypeScript · Express · BullMQ · Valkey · Postgres · Docker · Drizzle Not a bad way to spend a birthday. 🎂

It's June 16. My birthday. So I built a Mini version of Kubernetes. You give it a container image → it schedules it, pulls it, runs it, and tracks its lifecycle. What it does: - Submit jobs via a REST API - Move jobs through SUBMITTED → RUNNABLE → RUNNING → SUCCEEDED / FAILED - Pull container images and run real Docker containers - Persist state in Postgres - Use BullMQ + Valkey for scheduling and execution Building this from scratch gave me a much deeper appreciation for how orchestration systems work behind the scenes. There's still plenty to improve, but seeing the control loop model in action was incredibly satisfying. Stack: Node.js · TypeScript · Express · BullMQ · Valkey · Postgres · Docker · Drizzle Not a bad way to spend a birthday. 🎂

Pankaj Kumar

12,510 views • 2 months ago

GPT-Image-2 was just released... It's the best image model (By a wide margin) Heres how to use GPT-Image-2 in ChatGPT, OpenAI Playground and of Course - Codex. TIMESTAMPS 00:00 Intro 01:27 Initial Tests of GPT-Image-2 02:24 It can GENERATE working BARCODES? 03:40 11 Edits one Prompt 06:40 Creating Cartoons 07:40 Overlay Explanations 08:50 How to Get Started 10:51 IPhone Mockup Images (Amazing) 15:43 Using GPT-Image in OpenAI Playground 16:11 Generate images in 4k 17:44 The FIRST limitation... Its bad at counting 18:26 Using GPT-Image in Codex (Agentic Image Gen)

GPT-Image-2 was just released... It's the best image model (By a wide margin) Heres how to use GPT-Image-2 in ChatGPT, OpenAI Playground and of Course - Codex. TIMESTAMPS 00:00 Intro 01:27 Initial Tests of GPT-Image-2 02:24 It can GENERATE working BARCODES? 03:40 11 Edits one Prompt 06:40 Creating Cartoons 07:40 Overlay Explanations 08:50 How to Get Started 10:51 IPhone Mockup Images (Amazing) 15:43 Using GPT-Image in OpenAI Playground 16:11 Generate images in 4k 17:44 The FIRST limitation... Its bad at counting 18:26 Using GPT-Image in Codex (Agentic Image Gen)

Riley Brown

35,856 views • 4 months ago

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

305,663 views • 3 years ago

Turning Images Into Living Worlds with Victoria VR In our latest interview, the AI developer explains how we go from image to 3D using Gaussian splatting to turn a simple skybox into a fully dimensional world. We also show how improved prompt comprehension including embeddings, regex rules, and multi-metric validation, keeps player input sharp and reliable. If next-gen worldbuilding and AI-driven creation excite you, this one’s worth watching.

Turning Images Into Living Worlds with Victoria VR In our latest interview, the AI developer explains how we go from image to 3D using Gaussian splatting to turn a simple skybox into a fully dimensional world. We also show how improved prompt comprehension including embeddings, regex rules, and multi-metric validation, keeps player input sharp and reliable. If next-gen worldbuilding and AI-driven creation excite you, this one’s worth watching.

Victoria VR

18,772 views • 9 months ago

I've been mostly working on the internal systems lately for my #Dreamcast engine. The PC port gave me the debugging tools I needed to improve complex systems like the memory manager. • Audio is now working on the Dreamcast version • The renderer runs 20% faster on the Dreamcast • Heap allocation is 300% faster on average and 200% faster on worst case. While this had minimal impact on performance for the #Gamecube and PC versions, it was a significant bottleneck on the Dreamcast. • Increased real-time heap defragmentation throughput by using a new multi-regional heap structure, which also sets the foundation for DRAM bank-aware memory allocation. There's still plenty of room for more optimizations in Meese Engine. In fact, some optimizations found in Minecraft have yet to be implemented in this engine! #retrogamedev #indiegamedev

I've been mostly working on the internal systems lately for my #Dreamcast engine. The PC port gave me the debugging tools I needed to improve complex systems like the memory manager. • Audio is now working on the Dreamcast version • The renderer runs 20% faster on the Dreamcast • Heap allocation is 300% faster on average and 200% faster on worst case. While this had minimal impact on performance for the #Gamecube and PC versions, it was a significant bottleneck on the Dreamcast. • Increased real-time heap defragmentation throughput by using a new multi-regional heap structure, which also sets the foundation for DRAM bank-aware memory allocation. There's still plenty of room for more optimizations in Meese Engine. In fact, some optimizations found in Minecraft have yet to be implemented in this engine! #retrogamedev #indiegamedev

A Flock of Meese

294,839 views • 1 year ago