Ever wondered how large language models like ChatGPT are actually built? Behind these impressive AI tools lies a complex but fascinating process of data preparation, model training, and fine-tuning. While it might seem like something only experts with massive resources can do, it’s actually possible to learn how to build your own language model from scratch. And with the right guidance, you can go from loading raw text data to chatting with your very own AI assistant.
FLORA aims to make generative creation accessible, removing the need for advanced technical skills or hardware. Drag, drop, and connect hand curated AI models to build your own creative workflows with a high degree of creative control.
With Gen-4, you are now able to precisely generate consistent characters, locations and objects across scenes. Simply set your look and feel and the model will maintain coherent world environments while preserving the distinctive style, mood and cinematographic elements of each frame. Then, regenerate those elements from multiple perspectives and positions within your scenes.
𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝘆 𝗚𝗲𝗻-𝟰 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴:
✨ 𝗨𝗻𝘄𝗮𝘃𝗲𝗿𝗶𝗻𝗴 𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 • Characters and environments 𝗻𝗼𝘄 𝘀𝘁𝗮𝘆 𝗳𝗹𝗮𝘄𝗹𝗲𝘀𝘀𝗹𝘆 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 across shots—even as lighting shifts or angles pivot—all from one reference image. No more jarring transitions or mismatched details.
✨ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗠𝘂𝗹𝘁𝗶-𝗔𝗻𝗴𝗹𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 • Generate cohesive scenes from any perspective without manual tweaks. Gen-4 intuitively 𝗰𝗿𝗮𝗳𝘁𝘀 𝗺𝘂𝗹𝘁𝗶-𝗮𝗻𝗴𝗹𝗲 𝗰𝗼𝘃𝗲𝗿𝗮𝗴𝗲, 𝗮 𝗹𝗲𝗮𝗽 𝗽𝗮𝘀𝘁 𝗲𝗮𝗿𝗹𝗶𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 that struggled with spatial continuity.
✨ 𝗣𝗵𝘆𝘀𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗙𝗲𝗲𝗹 𝗔𝗹𝗶𝘃𝗲 • Capes ripple, objects collide, and fabrics drape with startling realism. 𝗚𝗲𝗻-𝟰 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝘀 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗽𝗵𝘆𝘀𝗶𝗰𝘀, breathing life into scenes that once demanded painstaking manual animation.
✨ 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗦𝘁𝘂𝗱𝗶𝗼 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 • Outputs now blend effortlessly with live-action footage or VFX pipelines. 𝗠𝗮𝗷𝗼𝗿 𝘀𝘁𝘂𝗱𝗶𝗼𝘀 𝗮𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗮𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗚𝗲𝗻-𝟰 𝘁𝗼 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝘀𝗰𝗲𝗻𝗲𝘀 𝗳𝗮𝘀𝘁𝗲𝗿 and slash production timelines. • 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Gen-4 erases the line between AI experiments and professional filmmaking. 𝗗𝗶𝗿𝗲𝗰𝘁𝗼𝗿𝘀 𝗰𝗮𝗻 𝗶𝘁𝗲𝗿𝗮𝘁𝗲 𝗼𝗻 𝗰𝗶𝗻𝗲𝗺𝗮𝘁𝗶𝗰 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗶𝗻 𝗱𝗮𝘆𝘀, 𝗻𝗼𝘁 𝗺𝗼𝗻𝘁𝗵𝘀—democratizing access to tools that once required million-dollar budgets.
As point cloud processing becomes increasingly important across industries, I wanted to share the most powerful open-source tools I’ve used in my projects:
1️⃣ Open3D (http://www.open3d.org/) The gold standard for point cloud processing in Python. Incredible visualization capabilities, efficient data structures, and comprehensive geometry processing functions. Perfect for both research and production.
2️⃣ PCL – Point Cloud Library (https://pointclouds.org/) The C++ powerhouse of point cloud processing. Extensive algorithms for filtering, feature estimation, surface reconstruction, registration, and segmentation. Steep learning curve but unmatched performance.
3️⃣ PyTorch3D (https://pytorch3d.org/) Facebook’s differentiable 3D library. Seamlessly integrates point cloud operations with deep learning. Essential if you’re building neural networks for 3D data.
4️⃣ PyTorch Geometric (https://lnkd.in/eCutwTuB) Specializes in graph neural networks for point clouds. Implements cutting-edge architectures like PointNet, PointNet++, and DGCNN with optimized performance.
5️⃣ Kaolin (https://lnkd.in/eyj7QzCR) NVIDIA’s 3D deep learning library. Offers differentiable renderers and accelerated GPU implementations of common point cloud operations.
6️⃣ CloudCompare (https://lnkd.in/emQtPz4d) More than just visualization. This desktop application lets you perform complex processing without writing code. Perfect for quick exploration and comparison.
7️⃣ LAStools (https://lnkd.in/eRk5Bx7E) The industry standard for LiDAR processing. Fast, scalable, and memory-efficient tools specifically designed for massive aerial and terrestrial LiDAR data.
8️⃣ PDAL – Point Data Abstraction Library (https://pdal.io/) Think of it as “GDAL for point clouds.” Powerful for building processing pipelines and handling various file formats and coordinate transformations.
9️⃣ Open3D-ML (https://lnkd.in/eWnXufgG) Extends Open3D with machine learning capabilities. Implementations of state-of-the-art 3D deep learning methods with consistent APIs.
🔟 MeshLab (https://www.meshlab.net/) The Swiss Army knife for mesh processing. While primarily for meshes, its point cloud processing capabilities are excellent for cleanup, simplification, and reconstruction.
comfy-cli is a command line tool that helps users easily install and manage ComfyUI, a powerful open-source machine learning framework. With comfy-cli, you can quickly set up ComfyUI, install packages, and manage custom nodes, all from the convenience of your terminal.
C:\<PATH_TO>\python.exe -m venv C:\comfyUI_cli_install
cd C:\comfyUI_env
C:\comfyUI_env\Scripts\activate.bat
C:\<PATH_TO>\python.exe -m pip install comfy-cli
comfy --workspace=C:\comfyUI_env\ComfyUI install
# then
comfy launch
# or
comfy launch -- --cpu --listen 0.0.0.0
If you are trying to clone a different install, pip freeze it first. Then run those requirements.
# from the original env
python.exe -m pip freeze > M:\requirements.txt
# under the new venv env
pip install -r M:\requirements.txt
1 – Import your workflow 2 – Build a machine configuration to run your workflows on 3 – Download models into your private storage, to be used in your workflows and team. 4 – Run ComfyUI in the cloud to modify and test your workflows on cloud GPUs 5 – Expose workflow inputs with our custom nodes, for API and playground use 6 – Deploy APIs 7 – Let your team use your workflows in playground without using ComfyUI
As models continue to advance, so too must our measurement of their economic impacts. In our second report, covering data since the launch of Claude 3.7 Sonnet, we find relatively modest increases in coding, education, and scientific use cases, and no change in the balance of augmentation and automation. We find that Claude’s new extended thinking mode is used with the highest frequency in technical domains and tasks, and identify patterns in automation / augmentation patterns across tasks and occupations. We release datasets for both of these analyses.
Overview of Our Pipeline. We take 2D tracks and depth maps generated by off-the-shelf models as input, which are then processed by a motion encoder to capture motion patterns, producing featured tracks. Next, we use tracks decoder that integrates DINO feature to decode the featured tracks by decoupling motion and semantic information and ultimately obtain the dynamic trajectories(a). Finally, using SAM2, we group dynamic tracks belonging to the same object and generate fine-grained moving object masks(b).
For years, tech firms were fighting a war for talent. Now they are waging war on talent.
This shift has led to a weakening of the social contract between employees and employers, with culture and employee values being sidelined in favor of financial discipline and free cash flow.
The operating environment has changed from a high tolerance for failure (where cheap capital and willing spenders accepted slipped dates and feature lag) to a very low – if not zero – tolerance for failure (fiscal discipline is in vogue again).
While preventing and containing mistakes staves off shocks to the income statement, it doesn’t fundamentally reduce costs. Years of payroll bloat – aggressive hiring, aggressive comp packages to attract and retain people – make labor the biggest cost in tech. …
Of course, companies can reduce their labor force through natural attrition. Other labor policy changes – return to office mandates, contraction of fringe benefits, reduction of job promotions, suspension of bonuses and comp freezes – encourage more people to exit voluntarily. It’s cheaper to let somebody self-select out than it is to lay them off. …
Employees recruited in more recent years from outside the ranks of tech were given the expectation that we’ll teach you what you need to know, we want you to join because we value what you bring to the table. That is no longer applicable. Runway for individual growth is very short in zero-tolerance-for-failure operating conditions. Job preservation, at least in the short term for this cohort, comes from completing corporate training and acquiring professional certifications. Training through community or experience is not in the cards. …
The ability to perform competently in multiple roles, the extra-curriculars, the self-directed enrichment, the ex-company leadership – all these things make no matter. The calculus is what you got paid versus how you performed on objective criteria relative to your cohort. Nothing more. …
Here is where the change in the social contract is perhaps the most blatant. In the “destination employer” years, the employee invested in the community and its values, and the employer rewarded the loyalty of its employees through things like runway for growth (stretch roles and sponsored work innovation) and tolerance for error (valuing demonstrable learning over perfection in execution). No longer. …
