#benchmarking

[quotelr]

How do you visualize the success of those parts of the world falling under your leadership? What about the success of your organisation, your family and all the people related to the two institutions? How fulfilling are the results of this personal evaluation? What can you start doing about it, today?

Archibald Marwizi, Making Success Deliberate

[frog707]

Please hold

The project to convert my buildscripts to Kotlin is on hold because I have an EXCITING NEW PROJECT.

Earlier this month (June 2024) Mazhar Akbar drew my attention to his performance comparison between JMonkeyEngine and Godot on a physics-intensive workload. The comparison favored Godot by a large margin. I was skeptical at first, but gradually I became convinced that, in order to level the field, JMonkeyEngine needs a new physics engine, one based on Jolt Physics instead of Bullet.

So now I'm coding all-new JVM bindings for Jolt. Jolt is an open-source software project of some complexity (about 100,000 lines of C++ code), so this could take awhile. Please hold. But not your breath.

I'm having a blast!

[jcmarchi]

Top 10 AI Practice Management Solutions for Healthcare Providers (January 2025)

New Post has been published on https://thedigitalinsider.com/top-10-ai-practice-management-solutions-for-healthcare-providers-january-2025/

Top 10 AI Practice Management Solutions for Healthcare Providers (January 2025)

AI practice management solutions are improving healthcare operations through automation and intelligent processing. These platforms handle essential tasks like clinical documentation, medical imaging analysis, patient communications, and administrative workflows, letting providers focus on patient care.

Today’s healthcare organizations can choose from various AI solutions tailored to specific operational needs. Some platforms focus on complete practice management with scheduling, billing, and EHR functionality. Others specialize in areas like medical scribing, imaging standardization, or clinical decision support. Each system applies AI technology differently – from processing patient conversations for automated documentation to analyzing medical images for faster diagnosis.

Here are some of the leading AI in healthcare solutions demonstrating practical applications in the industry:

Carepatron is an all-in-one practice management system that combines electronic health record (EHR) capabilities with administrative tools, designed specifically for healthcare and wellness providers. The platform serves practitioners across multiple disciplines, from counselors and therapists to physicians and chiropractors.

The system integrates five essential components to transform daily practice operations. The scheduling system manages online bookings and sends automated reminders to reduce no-shows. For clinical documentation, practitioners can access specialized tools and templates to streamline their note-taking and patient intake processes. The platform’s payment processing handles billing securely, while a dedicated client portal app maintains open communication channels with patients. Throughout these functions, AI automation works to reduce manual tasks and optimize common workflows.

What sets Carepatron apart is its emphasis on customization. The platform understands that each practice operates differently, so it allows providers to tailor their workflows and systems to match their preferred way of working. This customization extends to the client experience, where the platform creates seamless interactions through improved communication channels and client-centric processes.

Key features of Carepatron:

Integrated online scheduling system with automated appointment reminders and booking management

Clinical documentation suite featuring customizable templates and streamlined intake processes

Secure digital payment processing and billing management tools

Client communication portal with dedicated mobile app access

AI-powered automation for routine administrative tasks and workflow optimization

Visit Carepatron →

QuickBlox delivers specialized HIPAA-compliant communication tools for healthcare providers, focusing on secure telehealth interactions. The platform integrates real-time messaging, video consultations, and patient management features into a single, secure ecosystem for medical practices.

The system centers on three core components working in concert. The HIPAA-compliant chat system enables real-time messaging, group discussions, and secure file sharing, all protected by robust security measures. For virtual consultations, the platform provides high-quality audio and video calls with screen sharing capabilities, supporting both one-on-one and group sessions. These functions are anchored by a comprehensive user management system that controls access to sensitive information and maintains secure connections between patient records and user profiles.

The platform streamlines healthcare operations by replacing multiple communication tools with a single solution, reducing both costs and management complexity. Its focus on security and HIPAA compliance ensures that all patient data and communications are protected according to healthcare industry standards. The platform includes features for in-app appointment booking and scheduling capabilities to support care coordination through its communication framework.

Key features of QuickBlox:

Real-time messaging platform with delivery status tracking, typing indicators, and secure push notifications

Comprehensive file sharing system for secure exchange of medical documents and images

In-app appointment booking capabilities

Advanced push notification system for delivering test results and critical updates

HIPAA-compliant hosting infrastructure with Business Associate Agreement support

Visit QuickBlox →

(Freed AI)

Freed AI serves as an intelligent medical scribe that transforms clinical documentation through real-time AI processing. The system combines voice recognition, automated note-taking, and EHR integration to help healthcare providers focus more on patient care and less on paperwork.

The system works by actively listening during patient encounters, processing conversations through advanced AI algorithms to generate accurate medical notes as the visit unfolds. Healthcare providers can interact with the system through voice commands, retrieving information and updating records without interrupting their patient interactions. This automation extends beyond basic note-taking – the system connects directly with existing EHR platforms, ensuring all documentation syncs automatically and eliminates redundant data entry. Each practice can customize their experience through specialty-specific templates and workflows that match their established protocols.

The impact of this automation reaches across the entire practice workflow. By removing the burden of manual documentation and administrative tasks, healthcare providers gain significant time back in their day for direct patient care. The system’s precision in documentation helps minimize costly errors, while its comprehensive data capture capabilities support more informed clinical decision-making. For teaching institutions, the platform serves an additional purpose by providing medical students with practical exposure to modern healthcare documentation methods.

Key features of Freed AI:

Real-time AI note-taking system that processes and documents patient encounters as they happen

Voice-activated command interface for hands-free interaction with patient records

Direct integration with major EHR systems for automatic data synchronization

Customizable workflow templates tailored to specific medical specialties

HIPAA-compliant security framework protecting patient information

Visit Freed AI →

(Praxis EMR)

Praxis EMR stands apart with its AI-powered Concept Processor technology. Rather than using traditional templates, the system learns and adapts to each physician’s unique way of thinking and documenting, creating a personalized experience that evolves with use.

The system’s intelligence stems from its neural network-based Concept Processor, which observes and learns from every interaction. As physicians document patient encounters, the AI identifies patterns in their clinical reasoning and documentation style. This creates an increasingly sophisticated understanding of how each provider thinks and works, allowing the system to anticipate and suggest appropriate documentation based on past behaviors. The result is a completely template-free environment where providers can practice medicine their own way while maintaining consistency and efficiency.

The platform also includes a sophisticated query engine that helps providers extract meaningful insights from their patient data, supporting better clinical decision-making.

Key features of Praxis EMR:

AI-powered Concept Processor that learns and adapts to individual physician practice patterns

Template-free documentation system that preserves clinical thinking processes

Automated quality reporting system for regulatory compliance

Custom practice guideline creation tools for consistent care delivery

Advanced query engine for extracting patient data insights

Visit Praxis EMR →

AdvancedMD provides a cloud-based medical office platform that runs on AWS, integrating practice management, EHR, and patient engagement tools. The system serves various healthcare settings, from individual providers to large groups and billing services.

The platform connects every aspect of practice operations through a unified workflow. Front desk staff access scheduling and billing tools through an integrated interface, while providers use specialty-specific templates for clinical documentation. The system automates routine tasks like appointment reminders and claims processing to reduce administrative work. For billing operations, the platform includes claims scrubbing tools and revenue cycle management options that can be handled either in-house or through external services.

The platform emphasizes patient engagement through multiple channels. Patients can schedule appointments and access health information through a dedicated portal. The system supports telemedicine visits and electronic intake forms, while providers can use mobile apps for secure access to practice data. For practice oversight, the platform generates detailed analytics on financial performance and clinical metrics.

Key features of AdvancedMD:

AWS-based cloud infrastructure with multi-factor authentication security

Integrated scheduling and billing system with automated claims processing

Specialty-specific clinical templates and documentation tools

Patient portal with self-scheduling and electronic forms

Comprehensive analytics suite for practice performance monitoring

Visit AdvancedMD →

Augmedix offers AI-powered medical documentation that captures and processes doctor-patient conversations in real-time. The system combines ambient AI technology with specialized documentation workflows to handle EHR data entry while doctors focus on patient care.

The system processes medical conversations through advanced AI models tailored to specific specialties like emergency medicine and oncology. These models understand clinical terminology and context to generate accurate medical notes. The platform adapts to each provider’s documentation preferences and integrates with existing EHR systems. All data processing adheres to HITRUST certification standards and HIPAA compliance requirements.

Key features of Augmedix:

Real-time conversation capture and processing for medical documentation

Specialty-specific AI models for accurate clinical terminology

Direct EHR integration for seamless note synchronization

HITRUST-certified security protocols for data protection

Customizable documentation workflows for different practice needs

Visit Augmedix →

Enlitic focuses on transforming raw medical imaging data into standardized, actionable information through AI-powered solutions. The platform processes imaging studies to create consistent clinical data that integrates across different healthcare IT systems.

The system operates through two main applications. ENDEX handles data standardization, converting varied medical imaging formats into uniform nomenclature while maintaining clinical relevance. This standardization enables intelligent image routing and consistent display protocols across systems. ENCOG manages data privacy, using AI to identify and anonymize Protected Health Information within imaging studies while preserving essential clinical data. Together, these applications create a framework for healthcare organizations to better use their imaging archives.

The platform’s standardization capabilities directly impact workflow efficiency and data value. Radiologists spend less time manually adjusting display protocols and study descriptions, as the system automatically normalizes imaging data using computer vision and natural language processing (NLP). For healthcare organizations, this standardized data opens new possibilities for research databases and real-world evidence platforms, potentially creating additional revenue streams from previously static archives.

Key features of Enlitic:

AI-powered medical imaging standardization with consistent clinical nomenclature

Automated Protected Health Information anonymization

Intelligent image routing and display protocol optimization

Cross-system clinical content integration

Real-world evidence database creation tools

Visit Enlitic →

(Corti)

Corti creates AI systems that support healthcare professionals during patient consultations through real-time assistance and automated documentation. The platform processes clinical conversations to provide decision support while reducing administrative work.

The system’s AI analyzes patient interactions as they happen, offering contextual suggestions and insights that act as a second opinion for medical staff. For documentation, the platform automates note-taking and medical coding tasks, allowing providers to focus on patient care. The AI draws from extensive training on real patient data, including audio recordings and medical records, to ensure accuracy in its suggestions and documentation.

Key features of Corti:

Real-time AI analysis of patient consultations with contextual suggestions

Automated medical documentation and coding

Quality assurance tools for staff performance monitoring

Decision support system trained on extensive medical data

Performance benchmarking and improvement tracking

Visit Corti →

Merative provides data-driven healthcare solutions that span clinical decision support, data management, imaging, and analytics. The platform helps healthcare providers make evidence-based decisions while optimizing their workflows through integrated technology.

The system’s core includes several interconnected components. Micromedex serves as a comprehensive drug database, delivering evidence-based insights at the point of care. When combined with DynaMed’s disease content in DynaMedex, it creates a unified resource for care teams. For imaging needs, the Merge suite provides cloud-based enterprise imaging solutions with specialized tools for radiology and cardiology workflows. The platform also includes Zelta for clinical trial data management and Truven Health Insights for healthcare analytics.

Organizations using Merative’s solutions have achieved measurable improvements in care delivery. In Sonoma County, California, the platform helped reduce emergency department visits by high utilizers by 32%. The system’s analytics tools assist organizations in optimizing their benefits programs and improving population health outcomes through data-driven insights.

Key features of Merative:

Evidence-based drug and disease content database for clinical decision support

Cloud-based enterprise imaging system with specialty-specific workflows

Clinical trial data management platform

Healthcare analytics suite for population health management

Real-world evidence tools using longitudinal claims data

Visit Merative →

(Viz.ai)

Viz.ai’s AI-powered platform analyzes medical imaging data across multiple specialties to accelerate disease detection and treatment coordination. The system processes CT scans, EKGs, and echocardiograms through FDA-cleared algorithms to support fast clinical decision-making.

The platform’s Viz.ai One solution integrates disease detection with care coordination capabilities. When the AI identifies a suspected condition in medical imaging, it automatically alerts the relevant care team members. This immediate notification system enables faster team activation and treatment initiation across neurology, cardiology, vascular medicine, trauma, and radiology departments. The platform maintains round-the-clock clinical specialist support and implementation assistance to ensure consistent operation.

Beyond direct patient care, Viz.ai collaborates with pharmaceutical and medical device companies to develop specialized solutions. The system helps achieve faster access to clinical trials and innovative treatments through its automated detection and coordination features. All implementations include comprehensive support from implementation experts and a dedicated customer success team.

Key features of Viz:

FDA-cleared AI algorithms for rapid disease detection in medical imaging

Automated care team alerts and coordination system

Multi-specialty support across neurology, cardiology, and radiology

24/7 clinical specialist availability

Integration tools for pharmaceutical and medical device partnerships

Visit Viz.ai →

The Bottom Line

These AI practice management solutions show the diverse ways healthcare organizations can apply automation to improve operations. From basic task automation to sophisticated clinical decision support, each platform addresses specific challenges in modern healthcare delivery. What unifies them is a focus on reducing administrative burden while enhancing care quality – whether through faster diagnostic workflows, more accurate documentation, or better-coordinated care teams. As healthcare technology continues to improve with AI, these systems show us just how it can serve as a practical tool for supporting healthcare professionals in their daily work rather than replacing human judgment.

[todayscroll]

‘Manipulative and disgraceful’: OpenAI’s critics seize on math benchmarking scandal

Reports suggest OpenAI is about to make a huge announcement about AI agents. But its last big announcement is already looking slightly questionable. Read More

[devleader]

Who doesn't want to nearly double the performance of their code? How the heck did I speed up CSharp collection initialization by 87%?! Benchmarking, comparing, and analyzing all thanks to BenchmarkDotNet. Disclaimer: These benchmarks aren't meant to persuade you into writing code a certain way, I just want you to realize that you can benchmark your code and make informed decisions for yourself. Read the article here: https://www.devleader.ca/2024/03/31/collection-initializer-performance-in-c-double-your-performance-with-what/ #Benchmarking #Performance #DotNet #CSharp

[juaniitaalopezz]

ICYMI: Measuring and Tracking Operational Performance https://kamyarshah.com/measuring-and-tracking-operational-performance/

[grapheneai]

Contact us at GrapheneAI to get ahead and make better decisions with benchmarking insights. 

[projectmanagertemplate]

Benchmarking is an ongoing process that adds structure and accountability to project management. By carefully selecting KPIs, gathering data, and comparing performance against benchmarks, project managers can make informed decisions to boost efficiency and drive project success. Take the time to incorporate benchmarking into your project management practices. it’s an investment in your team’s performance and your project’s ultimate success. Thanks for reading Project Benchmarking Tips to Track and Enhance Performance.

[govindhtech]

Application Performance Benchmarking Focused On Users

How to compare the application performance from the viewpoint of the user

How can you know what kind of performance your application has? More importantly, how well does your application function in the eyes of your end users?

Knowing how scalable your application is is not only a technical issue, but also a strategic necessity for success in this age of exponential growth and erratic traffic spikes. Naturally, giving end customers the best performance is a must, and benchmarking it is a crucial step in living up to their expectations.

To get a comprehensive picture of how well your application performs in real-world scenarios, you should benchmark full key user journeys (CUJs) as seen by the user, not just the individual components. Component-by-component benchmarking may miss certain bottlenecks and performance problems brought on by network latency, external dependencies, and the interaction of multiple components. You can learn more about the real user experience and find and fix performance problems that affect user engagement and satisfaction by simulating entire user flows.

This blog will discuss the significance of integrating end-user-perceived performance benchmarking into contemporary application development and how to foster an organizational culture that assesses apps immediately and keeps benchmarking over time. Google Kubernetes Engine (GKE) also demonstrates how to replicate complicated user behavior using the open-source Locust tool for use in your end-to-end benchmarking exercises.

The importance of benchmarking

You should incorporate strong benchmarking procedures into your application development process for a number of reasons:

Proactive performance management: By identifying and addressing performance bottlenecks early in the development cycle, early and frequent benchmarking can help developers save money, speed up time to market, and create more seamless product launches. Furthermore, by quickly identifying and resolving performance regressions, benchmarking can be incorporated into testing procedures to provide a vital safety net that protects code quality and user experience.

Continuous performance optimization: Because applications are dynamic, they are always changing due to user behavior, scaling, and evolution. Frequent benchmarking makes it easier to track performance trends over time, enabling developers to assess the effects of updates, new features, and system changes. This keeps the application responsive and consistently performant even as things change.

Bridging the gap between development and production: A realistic evaluation of application performance in a production setting can be obtained as part of a development process by benchmarking real-world workloads, images, and scaling patterns. This facilitates seamless transitions from development to deployment and helps developers proactively address possible problems.

Benchmarking scenarios to replicate load patterns in the real world

Benchmarking your apps under conditions that closely resemble real-world situations, such as deployment, scalability, and load patterns, should be your aim as a developer. This method evaluates how well apps manage unforeseen spikes in traffic without sacrificing user experience or performance.

To test and improve cluster and workload auto scalers, the GKE engineering team conducts comprehensive benchmarking across a range of situations. This aids in the comprehension of how autoscaling systems adapt to changing demands while optimizing resource use and preserving peak application performance.Image credit to Google Cloud

Application Performance tools

Locust for performance benchmarking and realistic load testing

Locust is an advanced yet user-friendly load-testing tool that gives developers a thorough grasp of how well an application performs in real-world scenarios by simulating complex user behavior through scripting. Locust makes it possible to create different load scenarios by defining and instantiating “users” that carry out particular tasks.

Locust in one example benchmark to mimic consumers requesting the 30th Fibonacci number from a web server. To maintain load balancing among many pods, each connection was closed and reestablished, resulting in a steady load of about 200 ms per request.

from locust import HttpUser, task

Simulating these complex user interactions in your application is comparatively simple with Locust. On a single system, it can produce up to 10,000 queries per second. It can also expand higher through unconventional distributed deployment. With users who display a variety of load profiles, it allows you to replicate real-world load patterns by giving you fine-grained control over the number of users and spawn rate through bespoke load shapes. It is expandable to a variety of systems, such as XML-RPC, gRPC, and different request-based libraries/SDKs, and it natively supports HTTP/HTTPS protocols for web and REST queries.

To provide an end-to-end benchmark of a pre-release autoscaling cluster setup, it has included a GitHub repository with this blog post. It is advised that you modify it to meet your unique needs.Image credit to Google Cloud

Delivering outstanding user experiences requires benchmarking end users’ perceived performance, which goes beyond simply being a best practice. Developers may determine whether their apps are still responsive, performant, and able to satisfy changing user demands by proactively incorporating benchmarking into the development process.

You can learn more about how well your application performs in a variety of settings by using tools like Locust, which replicate real-world situations. Performance is a continuous endeavor. Use benchmarking as a roadmap to create outstanding user experiences.

Reda more on govindhtech.com

[ltslean]

How to Perform Gap Analysis? : A Step-by-Step Guide

[researchers-me]

Our primary focus is on market research and data analytics. We offer a range of Market Research Services, such as surveys, mystery shopping, benchmarking, and feasibility studies. Additionally, our analytics services include data analysis, reporting, and dashboard creation, which enables management to make informed decisions. We also assist companies in implementing analytics tools such as IBM Cognos and Power BI.

[jcmarchi]

NVIDIA AI Software Party at a Hardware Show

New Post has been published on https://thedigitalinsider.com/nvidia-ai-software-party-at-a-hardware-show/

NVIDIA AI Software Party at a Hardware Show

A tremendous number of AI software releases at CES.

Created Using Midjourney

Next Week in The Sequence:

We start a new series about RAG! For the high performance hackers, our engineering series will dive into Llama.cpp. In research we will dive into Deliberative Alignment, one of the techniques powering GPT-03. The opinion edition will debate open endedness AI methods for long term reasoning and how far those can go.

You can subscribe to The Sequence below:

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

📝 Editorial: NVIDIA AI Software Party at a Hardware Show

The name NVIDIA is immediately associated with computing hardware and, in the world of AI, GPUs. But that is changing so rapidly. In several editions of this newsletter, we have highlighted NVIDIA’s rapidly growing AI software stack and aspirations. This was incredibly obvious last week at CES which is, well, mostly a hardware show!

NVIDIA unveiled not only a very clear vision for the future of AI but an overwhelming series of new products, many of which were AI software-related. Take a look for yourself.

NVIDIA NIM Microservices

NVIDIA’s NIM (NVIDIA Inference Microservices) is a significant leap forward in the integration of AI into modern software systems. Built for the new GeForce RTX 50 Series GPUs, NIM offers pre-built containers powered by NVIDIA’s inference software, including Triton Inference Server and TensorRT-LLM. These microservices enable developers to incorporate advanced AI capabilities into their applications with unprecedented ease, reducing deployment times from weeks to just minutes. With NIM, NVIDIA is effectively turning the once-daunting process of deploying AI into a seamless, efficient task—an essential advancement for industries looking to accelerate their AI adoption.

AI Blueprints

For developers seeking a head start, NVIDIA introduced AI Blueprints, open-source templates designed to streamline the creation of AI-powered solutions. These blueprints provide customizable foundations for applications like digital human generation, podcast creation, and video production. By offering pre-designed architectures, NVIDIA empowers developers to focus on innovation and customization rather than reinventing the wheel. The result? Faster iteration cycles and a smoother path from concept to deployment in AI-driven industries.

Cosmos Platform

NVIDIA’s Cosmos Platform takes AI into the realm of robotics, autonomous vehicles, and vision AI applications. By integrating advanced models with powerful video data processing pipelines, Cosmos enables AI systems to reason, plan, and act in dynamic physical environments. This platform isn’t just about data processing; it’s about equipping AI with the tools to operate intelligently in real-world scenarios. Whether it’s guiding a robot through a warehouse or enabling an autonomous vehicle to navigate complex traffic, Cosmos represents a new frontier in applied AI.

Isaac GR00T Blueprint

Robotic training just got a major upgrade with NVIDIA’s Isaac GR00T Blueprint. This innovative tool generates massive volumes of synthetic motion data using imitation learning, leveraging the capabilities of NVIDIA’s Omniverse platform. By producing millions of lifelike motions, Isaac GR00T accelerates the training process for humanoid robots, enabling them to learn complex tasks more effectively. It’s a groundbreaking approach to solving one of robotics’ biggest challenges—efficiently generating diverse, high-quality training data at scale.

DRIVE Hyperion AV Platform

NVIDIA’s DRIVE Hyperion AV Platform saw a significant evolution with the addition of the NVIDIA AGX Thor SoC. Designed to support generative AI models, this new iteration enhances functional safety and boosts the performance of autonomous driving systems. By combining cutting-edge hardware with advanced AI capabilities, Hyperion delivers a robust platform for developing the next generation of autonomous vehicles, capable of handling increasingly complex environments with confidence and precision.

AI Enterprise Software Platform

NVIDIA’s commitment to enterprise AI is reflected in its AI Enterprise Software Platform, now available on AWS Marketplace. With NIM integration, this platform equips businesses with the tools needed to deploy generative AI models and large language models (LLMs) for applications like chatbots, document summarization, and other NLP tasks. This offering streamlines the adoption of advanced AI technologies, providing organizations with a comprehensive, reliable foundation for scaling their AI initiatives.

RTX AI PC Features

At the consumer level, NVIDIA announced RTX AI PC Features, which bring AI foundation models to desktops powered by GeForce RTX 50 Series GPUs. These features are designed to support the next generation of digital content creation, delivering up to twice the inference performance of prior GPU models. By enabling FP4 computing and boosting AI workflows, RTX AI PCs are poised to redefine productivity for developers and creators, offering unparalleled performance for AI-driven tasks.

That is insane for the first week of the year! NVIDIA is really serious about its AI software aspirations. Maybe Microsoft, Google and Amazon need to get more aggressive about their GPU initiatives. Just in case…

🔎 AI Research

rStar-Math

In the paper “rStar-Math: Guiding LLM Reasoning through Self-Evolution with Process Preference Reward,” researchers from Tsinghua University, the Chinese Academy of Sciences, and Alibaba Group propose rStar-Math, a novel method for enhancing LLM reasoning abilities by employing self-evolution with a process preference reward (PPM). rStar-Math iteratively improves the reasoning capabilities of LLMs by generating high-quality step-by-step verified reasoning trajectories using a Monte Carlo Tree Search (MCTS) process.

BoxingGym

In the paper “BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery,” researchers from Stanford University introduce a new benchmark for evaluating the ability of large language models (LLMs) to perform scientific reasoning. The benchmark, called BoxingGym, consists of 10 environments drawn from various scientific domains, and the researchers found that current LLMs struggle with both experimental design and model discovery.

Cosmos World

In the paper “Cosmos World Foundation Model Platform for Physical AI,” researchers from NVIDIA introduce Cosmos World Foundation Models (WFMs). Cosmos WFMs are pre-trained models that can generate high-quality 3D-consistent videos with accurate physics, and can be fine-tuned for a wide range of Physical AI applications.

DOLPHIN

In the paper “DOLPHIN: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback,” researchers from Fudan University and the Shanghai Artificial Intelligence Laboratory propose DOLPHIN, a closed-loop, open-ended automatic research framework2. DOLPHIN can generate research ideas, perform experiments, and use the experimental results to generate new research idea.

Meta Chain-of-Thoguht

In the paper“Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought” researchers from SynthLabs.ai and Stanford University propose a novel framework called Meta Chain-of-Thought (Meta-CoT), which enhances traditional Chain-of-Thought by explicitly modeling the reasoning process. The researchers present empirical evidence of state-of-the-art models showing in-context search behavior, and discuss methods for training models to produce Meta-CoTs, paving the way for more powerful and human-like reasoning in AI.

LLM Test-Time Compute and Meta-RL

In a thoughtful blog post title“Optimizing LLM Test-Time Compute Involves Solving a Meta-RL Problem” from CMU explain that optimizing test-time compute in LLMs can be viewed as a meta-reinforcement learning (meta-RL) problem where the model learns to learn how to solve queries. The authors outline a meta-RL framework for training LLMs to optimize test-time compute, leveraging intermediate rewards to encourage information gain and improve final answer accuracy.

🤖 AI Tech Releases

NVIDIA Nemotron Models

NVIDIA released Llama Nemotron LLM and Cosmos Nemotron vision-language models.

Phi-4

Microsoft open sourced its Phi-4 small model.

ReRank 3.5

Cohere released its ReRank 3.5 model optimized for RAG and search scenarios.

Agentic Document Workfows

LlamaIndex released Agentic Document Workflow, an architecture for applying agentic tasks to documents.

🛠 AI Reference Implementations

Beyond RAG

Salesfoce discusses an enriched index technique that improved its RAG solutions.

📡AI Radar

NVIDIA released AI agentic blueprints for popular open source frameworks.

NVIDIA unveiled Project DIGITS, an AI supercomputer powered by the Blackwell chip.

NVIDIA announced a new family of world foundation models for its Cosmos platform.

Anthropic might be raising at a monster $60 billion valuation.

Hippocratic AI raised a massive $141 million round for its healthcare LLM.

Cohere announced North, its Microsoft CoPilot competitor.

OpenAI might be getting back to robotics.

Gumloop raised $17 million for its workflow automation platform.

[interestingsnippets]

As AI is commercialised and deployed in a range of fields, there is a growing need for reliable and specific benchmarks. Startups that specialise in providing ai benchmarks are starting to appear... to give researchers, regulators and academics the tools they need to assess the capabilities of AI models, good and bad. The days of ai labs marking their own homework could soon be over.

GPT, Claude, Llama? How to tell which AI model is best

[juaniitaalopezz]

Measuring and Tracking Operational Performance https://kamyarshah.com/measuring-and-tracking-operational-performance/

[malcified]

Today on #trialroom I’d like to share a sketch on #trustissues.

Trust (def): firm belief in the reliability, truth, or ability of someone or something.

The sketch may seem a bit dark, however, the actual stuff we do to each other in various settings to realize, snatch, force, betray, earn, build, and maintain trust is far insane.

The crazier and harder it gets to earn trust in the relationship, it can continue to get more extreme and dangerous to sustain such a relationship and it may sometimes be best to detach - temp or permanently.

“Trust testing” is a primal need and maybe unavoidable. However, the “true self” or the “real me” is a mental construct, since we keep evolving every moment and are as dynamic as the clouds - it may be impossible for us to realize our true self in a lifetime, so we can only share our perception of it with someone else, which will eventually change for ourselves and the other person. So holding ourself or someone else to it, maybe unhealthy. The root cause of abuse, addiction and other destructive behavior - of the self, other humans, insects, animals, ecosystem and the environment maybe related to trust issues.

Friendship is one of the greatest gifts of life and acceptance of ourself as a WIP, evolving, floating yet grounded being may allow us to accept others similarly.