Zencoder, a provider of AI agents integrated directly into developers’ environments, announced today that it has acquired Machinet, a developer of context-aware AI coding assistants with over 100,000 downloads in JetBrains IDEs. The strategic acquisition further cements Zencoder’s position in the rapidly expanding AI coding assistant market, while broadening its multi-integration ecosystem across popular development platforms.

Expanding the Developer Experience

Following the acquisition, Machinet users will gain access to a significantly enhanced developer experience, including:

• Enhanced JetBrains Integration

By combining Machinet’s specialized expertise in JetBrains IDEs with Zencoder’s existing support, developers can look forward to even more powerful tools tailored for these widely-used environments.

• Augmented Unit Testing

Machinet’s context-aware unit test generation technology will be integrated with Zencoder’s advanced testing agents, offering developers a more comprehensive and automated testing experience.

• Industry-Leading Customization

Machinet’s developer community will now benefit from Zencoder’s deep capabilities in understanding large codebases, adapting to team-specific coding styles, and aligning with organizational architecture patterns.

“This acquisition aligns perfectly with our mission to turn everyone into a 10x engineer by providing AI solutions that handle routine coding tasks and let developers focus on innovation,”

said Andrew Filev, CEO and Founder of Zencoder.

“By bringing our advanced coding agent to Machinet’s thriving JetBrains community, we’re fulfilling our mission to deliver the best AI coding experience regardless of development environment.”

Streamlined Transition for Customers

As part of the acquisition, Machinet’s domain and marketplace presence will be transferred to Zencoder. Current Machinet customers will receive detailed guidance on transitioning to Zencoder’s platform, which leverages its proprietary Repo Grokking technology and AI agents.

Existing Machinet users will now gain access to Zencoder’s full feature set, including:

• Advanced multi-file editing and refactoring capabilities
• Deep codebase understanding across repositories using Repo Grokking™
• Sophisticated self-repair mechanisms that automatically test and refine outputs
• Expanded integration with over 20 developer tools, including Jira, GitHub, and GitLab
• Access to Zencoder’s specialized coding and unit testing AI agents

Industry-Leading Performance

Earlier this year, Zencoder’s AI platform demonstrated benchmark-breaking performance:

• A 2x improvement over previous best results on SWE-Bench-Multimodal
• State-of-the-art results on the challenging “IC SWE (Diamond)” section of SWE-Lancer, outperforming top published results by 23%

With the integration of Machinet’s technologies, Zencoder aims to further enhance its capabilities, reinforcing its leadership position in AI-assisted software development.

Availability and Next Steps

Zencoder’s full suite of AI coding and testing tools, including the newly enhanced JetBrains integration, is now available through zencoder.ai, with subscription options ranging from free basic plans to comprehensive enterprise solutions.

Current Machinet users will be provided with detailed transition instructions in the coming weeks.

About Zencoder

Based in Silicon Valley, Zencoder offers powerful AI coding and testing agents designed to empower professional developers. Founded by serial entrepreneur Andrew Filev, Zencoder’s globally distributed team of over 50 engineers helps organizations accelerate innovation and ship impactful software faster. The company holds ISO 27001 certification, is SOC 2 Type II compliant, and is in the process of finalizing its ISO 42001 certification.

Snyk has introduced a groundbreaking solution to tackle the next wave of cybersecurity challenges. The company announced the launch of Snyk API & Web, a next-generation Dynamic Application Security Testing (DAST) tool engineered to secure modern, AI-powered applications.

Bridging the Security Gap in AI-Driven Development

Traditional DAST solutions have long struggled to keep up with the rapidly evolving architectures and increasing complexity of APIs brought by AI-centric development. Recognizing this critical need, Snyk acquired Probely in 2024, integrating its advanced DAST capabilities into Snyk’s broader security ecosystem—complementing existing offerings like SAST, SCA, container, and Infrastructure as Code security.

The result is a unified platform that delivers a holistic, end-to-end view of application security throughout the entire Software Development Life Cycle (SDLC).

AI-Powered Innovations for Modern Applications

Snyk API & Web introduces several forward-thinking features designed specifically for the AI era:

• AI-Driven API Testing: Leveraging in-house fine-tuned Large Language Models (LLMs), the platform automates API discovery and vulnerability scanning—detecting complex issues like Broken Object Level Authorization (BOLA) more efficiently.
• Code-Informed Dynamic Testing: By correlating DAST results with SAST findings, Snyk provides deeper context for vulnerabilities, supporting more accurate prioritization and enabling AI-powered auto-remediation via DeepCode AI Fix.
• CI/CD-Ready: Designed with DevOps in mind, the tool offers seamless CI/CD integration, allowing developers to perform self-service scans within pipelines, guided by organizational AppSec policies.

Strategic Vision from the Top

“Our vision is to empower developers and AppSec teams to secure their entire application surface without slowing down innovation,”

said Geva Solomonovich, Snyk CIO.

“Snyk API & Web is the culmination of that vision—bringing together the speed of AI with the depth of full-lifecycle security.”

Momentum and Market Response

Since integrating Probely, Snyk has reported a 245% quarter-over-quarter growth in Annual Recurring Revenue (ARR) for DAST services—a clear signal of strong market demand for modern, AI-augmented security tools.

Looking forward, the company plans to deepen its AI capabilities, broaden API coverage, and provide even richer context for faster, more accurate vulnerability remediation.

In a significant stride towards bolstering software supply chain security, Docker has introduced the Docker Maintained Community Packages (MCP) Catalog and Toolkit. This initiative is designed to offer developers and organizations a repository of secure, high-quality open-source packages maintained directly by Docker, addressing the growing concerns over the integrity and trustworthiness of software components in containerized applications.

The Docker MCP Catalog serves as a curated collection of frequently used open-source packages, selected based on community usage patterns and relevance to contemporary development workflows. By standardizing these images under Docker’s stewardship, the company provides a more trustworthy alternative to packages that are often sourced from less vetted or anonymous contributors.

Complementing the catalog is the Docker MCP Toolkit, an open-source suite of utilities aimed at simplifying the processes of building, testing, and verifying Docker images. This toolkit not only assists developers in reproducing Docker-maintained packages but also empowers contributors to create similarly secure and reliable images tailored to their specific use cases.

“Security remains a top priority for the open source and container ecosystems,”

stated Docker in its official blog post.

“By offering both the catalog and toolkit, we aim to reduce uncertainty in the software supply chain and help teams ship with confidence.”​

Docker’s MCP initiative aligns with broader industry trends emphasizing software supply chain integrity, especially in the wake of high-profile vulnerabilities and dependency attacks. It also supports best practices such as Software Bill of Materials (SBOMs) and digital signing, both of which are integrated into the MCP offerings.

The project is anticipated to see widespread adoption among developers seeking vetted base images, DevOps teams aiming to enhance CI/CD hygiene, and organizations striving to meet compliance standards related to open-source usage.​

Docker encourages community feedback and contributions to the MCP initiative via GitHub, promoting transparency and collaboration in building a more secure container ecosystem.

As threats to the software supply chain continue to evolve, Docker’s MCP Catalog and Toolkit represent a timely and proactive measure towards fortifying one of the most critical layers of modern application infrastructure.

Microsoft has announced a series of significant updates to the Microsoft 365 Developer Program, aiming to provide developers with a more robust, secure, and accessible environment. These enhancements, shaped by community feedback, are set to roll out over the coming months.

Streamlined Tenant Provisioning

A new and improved tenant provisioning flow will become the default for all new qualified members of the Microsoft 365 Developer Program. These tenants are commercially enabled with add-on options. Existing members will have the option to transition by expiring their current tenant and provisioning a new one.​

Support for Commercial Add-ons

Later this year, members will be able to purchase additional subscriptions on their development tenants provisioned through the new experience, including Microsoft 365 Copilot licenses.​

Improved Tenant Ownership and Management

Microsoft is enabling clearer identification of tenant owners, making it easier for developers to manage and secure their environments.​

Option to Transition to Paid Subscriptions

Developers wishing to move beyond the Developer Program will have the ability to convert their development tenant into a standard paid Microsoft 365 subscription.​

Future Improvements on the Horizon

Microsoft is exploring additional ways to make the Microsoft 365 Developer Program more inclusive, flexible, and valuable for a broader global developer base. These improvements are currently in planning, with more details to be shared as the roadmap is finalized.​

What’s Next

A comprehensive update detailing the next wave of changes is expected by September 2025. Currently, no other changes are planned for existing Developer Program members beyond the enhancements mentioned.

The funding news was shared in an unconventional post on WunderGraph’s official blog, reflecting the team’s transparent and developer-first culture. In parallel, the company issued an official statement to TechCrunch offering additional insight into the strategic partnership with eBay and the broader vision for the product.

“Our investment in WunderGraph’s highly performant open source platform will help boost eBay’s API ecosystem and enable our teams to work faster and smarter in building products that help our sellers thrive,” said Bryan Woodruff, VP of Seller Experience Engineering at eBay.

Founded in 2020 by CTO Dustin Deus, CEO Jens Neuse, COO Björn Schwenzer, and CCO Stefan Avram, WunderGraph has maintained its U.S. incorporation from the outset. While most of the founding team is based in Germany, Miami-based Avram joined in 2022 to provide local leadership in the U.S.

Tackling API Sprawl With GraphQL

GraphQL, initially developed by Meta (then Facebook) in 2012 and open sourced in 2015, is a data query language for APIs designed to make data fetching more efficient. Rather than over-fetching or under-fetching data as is common with traditional REST APIs, GraphQL allows clients to request precisely the data they need. This efficiency has made it a cornerstone of modern software architecture, especially as companies adopt microservices.

However, with the proliferation of APIs, managing and orchestrating them at scale becomes increasingly complex. That’s where WunderGraph steps in. The company originally offered a software development kit (SDK) to help unify disparate APIs—including REST, SOAP, and databases like MySQL. In 2023, it secured a $3 million seed round to build what it dubbed a “GitHub for APIs,” a collaborative platform for discovering and sharing APIs.

At the same time, Apollo was making significant headway in GraphQL Federation, an approach to help multiple development teams collaborate on large, distributed applications. But Apollo’s shift in late 2021 from an open source MIT license to a proprietary Elastic License created an opening for competition.

“Our data showed that some people were really looking for an open source alternative to Apollo Federation,” Neuse told TechCrunch. “We figured our current approach is not working, so let’s just put out an open source alternative to Apollo Federation.”

In response, WunderGraph launched Cosmo in late 2023—a fully open source GraphQL federation platform.

Strategic Alignment With eBay

WunderGraph serves as the main maintainer of Cosmo and offers services like hosting, premium support, and integration assistance for analytics, authentication, and observability. While large enterprises can build their own in-house solutions, many prefer to rely on externally supported products like Cosmo, backed by service-level agreements (SLAs).

This is where the collaboration with eBay becomes pivotal. eBay gains the customization and openness of a flexible GraphQL federation solution, while WunderGraph benefits from eBay’s real-world design input and use case validation.

“I would say we are experts in federation, but we don’t have experience in eBay-scale problems,” Neuse explained. “By having this very close relationship, they taught us everything in terms of how we need to build our product so that it can be integrated into companies like eBay.”

This includes making Cosmo modular enough for companies to adopt only the components they need—something that aligns with enterprise concerns about vendor lock-in. According to Neuse, open source is the only viable path forward for widespread adoption:

“This market needs to be as wide as possible. How can we attract everybody? It must be open source. We cannot limit how people use it.”

Looking Ahead

With $7.5 million in fresh capital, WunderGraph aims to expand its 20-person team and enhance its open source GraphQL federation with tools tailored for collaboration and governance—critical for supporting distributed teams at enterprise scale.

“Open source is the future of API management, and enterprises are demanding transparency, flexibility, and control,” said co-founder Stefan Avram. “We’re building the essential plumbing for the world’s biggest platforms, and this funding allows us to scale while keeping our commitment to open source development.”

NVIDIA has unveiled a suite of cutting-edge technologies to accelerate humanoid robot development, featuring NVIDIA Isaac GR00T N1—the world’s first open and fully customizable foundation model for advanced humanoid reasoning and skills.

The other technologies include simulation frameworks and blueprints such as the NVIDIA Isaac GR00T Blueprint for generating synthetic data, as well as Newton, an open-source physics engine — under development with Google DeepMind and Disney Research — purpose-built for developing robots.

Available now, GR00T N1 is the first of a family of fully customizable models that NVIDIA will pretrain and release to worldwide robotics developers — accelerating the transformation of industries challenged by global labor shortages estimated at more than 50 million people.

“The age of generalist robotics is here,”

said Jensen Huang, founder and CEO of NVIDIA.

“With NVIDIA Isaac GR00T N1 and new data-generation and robot-learning frameworks, robotics developers everywhere will open the next frontier in the age of AI.”

GR00T N1 Advances Humanoid Developer Community

The GR00T N1 foundation model features a dual-system architecture, inspired by principles of human cognition.

• “System 1” is a fast-thinking action model, mirroring human reflexes or intuition.
• “System 2” is a slow-thinking model for deliberate, methodical decision-making.

Powered by a vision language model, System 2 reasons about its environment and the instructions it has received to plan actions. System 1 then translates these plans into precise, continuous robot movements. System 1 is trained on human demonstration data and a massive amount of synthetic data generated by the NVIDIA Omniverse™ platform.

GR00T N1 can easily generalize across common tasks — such as grasping, moving objects with one or both arms, and transferring items from one arm to another — or perform multistep tasks that require long context and combinations of general skills. These capabilities can be applied across use cases such as material handling, packaging and inspection.

Developers and researchers can post-train GR00T N1 with real or synthetic data for their specific humanoid robot or task.

In his GTC keynote, Huang demonstrated 1X’s humanoid robot autonomously performing domestic tidying tasks using a post-trained policy built on GR00T N1. The robot’s autonomous capabilities are the result of an AI training collaboration between 1X and NVIDIA.

“The future of humanoids is about adaptability and learning,”

said Bernt Børnich, CEO of 1X Technologies.

“While we develop our own models, NVIDIA’s GR00T N1 provides a significant boost to robot reasoning and skills. With minimal post-training data, we fully deployed on NEO Gamma — advancing our mission of creating robots that are not just tools, but companions capable of assisting humans in meaningful, immeasurable ways.”

Among the additional leading humanoid developers worldwide with early access to GR00T N1 are Agility Robotics, Boston Dynamics, Mentee Robotics and NEURA Robotics.

NVIDIA, Google DeepMind and Disney Research Focus on Physics

NVIDIA announced a collaboration with Google DeepMind and Disney Research to develop Newton, an open-source physics engine that lets robots learn how to handle complex tasks with greater precision.

Built on the NVIDIA Warp framework, Newton will be optimized for robot learning and compatible with simulation frameworks such as Google DeepMind’s MuJoCo and NVIDIA Isaac™ Lab. Additionally, the three companies plan to enable Newton to use Disney’s physics engine.

Google DeepMind and NVIDIA are collaborating to develop MuJoCo-Warp, which is expected to accelerate robotics machine learning workloads by more than 70x and will be available to developers through Google DeepMind’s MJX open-source library, as well as through Newton.

Disney Research will be one of the first to use Newton to advance its robotic character platform that powers next-generation entertainment robots, such as the expressive Star Wars-inspired BDX droids that joined Huang on stage during his GTC keynote.

“The BDX droids are just the beginning. We’re committed to bringing more characters to life in ways the world hasn’t seen before, and this collaboration with Disney Research, NVIDIA and Google DeepMind is a key part of that vision,”

said Kyle Laughlin, senior vice president at Walt Disney Imagineering Research & Development.

“This collaboration will allow us to create a new generation of robotic characters that are more expressive and engaging than ever before — and connect with our guests in ways that only Disney can.”

NVIDIA and Disney Research, along with Intrinsic, announced an additional collaboration to build OpenUSD pipelines and best practices for robotics data workflows.

More Data to Advance Robotics Post-Training

Large, diverse, high-quality datasets are critical for robot development but costly to capture. For humanoids, real-world human demonstration data is limited by a person’s 24-hour day.

Announced today, the NVIDIA Isaac GR00T Blueprint for synthetic manipulation motion generation helps address this challenge. Built on Omniverse and NVIDIA Cosmos Transfer world foundation models, the blueprint lets developers generate exponentially large amounts of synthetic motion data for manipulation tasks from a small number of human demonstrations.

Using the first components available for the blueprint, NVIDIA generated 780,000 synthetic trajectories — the equivalent of 6,500 hours, or nine continuous months, of human demonstration data — in just 11 hours. Then, combining the synthetic data with real data, NVIDIA improved GR00T N1’s performance by 40%, compared with using only real data.

To further equip the developer community with valuable training data, NVIDIA is releasing the GR00T N1 dataset as part of a larger open-source physical AI dataset — also announced at GTC and now available on Hugging Face.

NVIDIA GR00T N1 training data and task evaluation scenarios are now available for download from Hugging Face and GitHub. The NVIDIA Isaac GR00T Blueprint for synthetic manipulation motion generation is also now available as an interactive demo on build.nvidia.com or to download from GitHub.

The NVIDIA DGX Spark personal AI supercomputer, also announced at GTC, provides developers a turnkey system to expand GR00T N1’s capabilities for new robots, tasks and environments without extensive custom programming.

The Newton physics engine is expected to be available later this year.

NVIDIA has introduced the open Llama Nemotron family of models, equipped with advanced reasoning capabilities to offer developers and enterprises a robust foundation for building AI agents that operate autonomously or collaboratively to tackle complex challenges.

Built on Llama models, the NVIDIA Llama Nemotron reasoning family delivers on-demand AI reasoning capabilities. NVIDIA enhanced the new reasoning model family during post-training to improve multistep math, coding, reasoning and complex decision-making.

This refinement process boosts accuracy of the models by up to 20% compared with the base model and optimizes inference speed by 5x compared with other leading open reasoning models. The improvements in inference performance mean the models can handle more complex reasoning tasks, enhance decision-making capabilities and reduce operational costs for enterprises.

Leading agent AI platform pioneers — including Accenture, Amdocs, Atlassian, Box, Cadence, CrowdStrike, Deloitte, IQVIA, Microsoft, SAP and ServiceNow — are collaborating with NVIDIA on its new reasoning models and software.

“Reasoning and agentic AI adoption is incredible,”

said Jensen Huang, founder and CEO of NVIDIA.

“NVIDIA’s open reasoning models, software and tools give developers and enterprises everywhere the building blocks to create an accelerated agentic AI workforce.”

NVIDIA Post-Training Boosts Accuracy and Reliability for Enterprise Reasoning

Built to deliver production-ready AI reasoning, the Llama Nemotron model family is available as NVIDIA NIM™ microservices in Nano, Super and Ultra sizes — each optimized for different deployment needs.

The Nano model delivers the highest accuracy on PCs and edge devices, the Super model offers the best accuracy and highest throughput on a single GPU, and the Ultra model will provide maximum agentic accuracy on multi-GPU servers.

NVIDIA conducted extensive post-training on NVIDIA DGX™ Cloud using high-quality curated synthetic data generated by NVIDIA Nemotron™ and other open models, as well as additional curated datasets cocreated by NVIDIA.

The tools, datasets and post-training optimization techniques used to develop the models will be openly available, giving enterprises the flexibility to build their own custom reasoning models.

Agentic Platforms Team With NVIDIA to Enhance Reasoning for Industries

Agentic AI platform industry leaders are working with the Llama Nemotron reasoning models to deliver advanced reasoning to enterprises.

Microsoft is integrating Llama Nemotron reasoning models and NIM microservices into Microsoft Azure AI Foundry. This expands the Azure AI Foundry model catalog with options for customers to enhance services like Azure AI Agent Service for Microsoft 365.

SAP is tapping Llama Nemotron models to advance SAP Business AI solutions and Joule, the AI copilot from SAP. Additionally, it is using NVIDIA NIM and NVIDIA NeMo™ microservices to promote increased code completion accuracy for SAP ABAP programming language models.

“We are collaborating with NVIDIA to integrate Llama Nemotron reasoning models into Joule to enhance our AI agents, making them more intuitive, accurate and cost effective,”

said Walter Sun, global head of AI at SAP.

“These advanced reasoning models will refine and rewrite user queries, enabling our AI to better understand inquiries and deliver smarter, more efficient AI-powered experiences that drive business innovation.”

ServiceNow is harnessing Llama Nemotron models to build AI agents that offer greater performance and accuracy to enhance enterprise productivity across industries.

Accenture has made NVIDIA Llama Nemotron reasoning models available on its AI Refinery platform — including new industry agent solutions announced today — to enable clients to rapidly develop and deploy custom AI agents tailored to industry-specific challenges, accelerating business transformation.

Deloitte is planning to incorporate Llama Nemotron reasoning models into its recently announced Zora AI agentic AI platform designed to support and emulate human decision-making and action with agents that include deep functional- and industry-specific business knowledge and built-in transparency.

NVIDIA AI Enterprise Delivers Essential Tools for Agentic AI

Developers can deploy NVIDIA Llama Nemotron reasoning models with new NVIDIA agentic AI tools and software to streamline the adoption of advanced reasoning in collaborative AI systems.

All part of the NVIDIA AI Enterprise software platform, the latest agentic AI building blocks include:

• The NVIDIA AI-Q Blueprint, which enables enterprises to connect knowledge to AI agents that can autonomously perceive, reason and act. Built with NVIDIA NIM microservices, the blueprint integrates NVIDIA NeMo Retriever™ for multimodal information retrieval and enables agent and data connections, optimization and transparency using the open-source NVIDIA AgentIQ toolkit.
• The NVIDIA AI Data Platform, a customizable reference design for a new class of enterprise infrastructure with AI query agents built with the AI-Q Blueprint.
• New NVIDIA NIM microservices, which optimize inference for complex agentic AI applications and enable continuous learning and real-time adaptation across any environment. The microservices ensure reliable deployment of the latest models from leading model builders including Meta, Microsoft and Mistral AI.
• NVIDIA NeMo microservices, which provide an efficient, enterprise-grade solution to quickly establish and maintain a robust data flywheel that enables AI agents to continuously learn from human- and AI-generated feedback. The NVIDIA AI Blueprint for building a data flywheel will offer a reference architecture for developers to easily build and optimize data flywheels using NVIDIA microservices.

The NVIDIA Llama Nemotron Nano and Super models and NIM microservices are available as a hosted application programming interface from build.nvidia.com and Hugging Face. Access for development, testing and research is free for members of the NVIDIA Developer Program.

Enterprises can run Llama Nemotron NIM microservices in production with NVIDIA AI Enterprise on accelerated data center and cloud infrastructure. Developers can sign up to be notified when NVIDIA NeMo microservices are publicly available.

The NVIDIA AI-Q Blueprint is expected to be available in April. The NVIDIA AgentIQ toolkit is available now on GitHub.

In a defining moment for the cybersecurity landscape, Wiz has announced its agreement to be acquired by Google, marking a significant milestone in its five-year journey to revolutionize cloud security. While the deal remains subject to regulatory review, the acquisition will see Wiz become part of Google Cloud upon completion, ushering in a new chapter in its mission to redefine how organizations secure the cloud.

A Vision Born in the Cloud

Founded just five years ago, Wiz was born out of a simple yet powerful ambition: to build a platform that security and development teams truly love. From the outset, the company focused on listening to its customers, learning from their pain points, and innovating to solve real-world challenges in cloud security. This customer-first approach has propelled Wiz to become a leading player in modern cloud security—supporting organizations from startups to large enterprises and public sector entities.

A Platform for the Modern Cloud Security Operating Model

Wiz’s growth has been fueled by a relentless drive to change the way security is done. Its comprehensive platform spans the full spectrum of cloud risk management—from prevention and detection to response—covering everything from public clouds to data, AI, and the entire development lifecycle. This full-stack approach makes Wiz a unified platform for modern cloud security, bridging the gap between security and development teams.

Shared Vision with Google Cloud

At the heart of this acquisition is a shared philosophy between Wiz and Google Cloud: cloud security must be simpler, smarter, and more accessible. Together, they aim to democratize security—making it available and scalable for every organization, across any cloud environment.

Crucially, Wiz will remain a multicloud platform, continuing its strong collaborations with AWS, Azure, Oracle, and the broader cloud ecosystem. The company’s open approach will be preserved and strengthened, ensuring that organizations can benefit from best-in-class security tools, no matter where they build and run their systems.

Strapping a Rocket to Innovation

Joining Google Cloud isn’t just a strategic move—it’s an acceleration of Wiz’s mission. Google Cloud brings a legacy of engineering innovation in security, cloud-native technologies, and AI. From pioneering Kubernetes to setting new standards in Zero Trust and software supply chain security (SLSA), Google Cloud’s contributions have shaped the way organizations build and secure in the cloud. By aligning with this powerhouse, Wiz is poised to scale its innovations faster than ever before.

Wiz’s CEO likened the move to “strapping a rocket to our backs,” emphasizing how Google Cloud’s vast resources and AI expertise will turbocharge their development and reach.

Empowering Customers, Partners, and Employees

The benefits of this acquisition ripple outward:

• Customers will gain access to a richer, more integrated suite of cloud security, AI, and data solutions across all major cloud providers and hybrid environments.
• Partners will find renewed opportunity to collaborate with Wiz as it becomes an even more compelling choice for organizations of all sizes and sectors.
• Employees—the “Wizards”—celebrate a landmark achievement, ready to take their expertise to the next level by working alongside some of the brightest minds in cloud and AI.

A Future-Proof Security Mission

As cloud adoption and digital transformation accelerate, so too do the threats. Attackers are moving faster, powered by AI and advanced tools. Wiz and Google Cloud recognize the urgency of the moment—and the need to move even faster in response. This acquisition is not just about business synergy; it’s a bold move to future-proof cloud security and deliver the most advanced protection to organizations worldwide.

The message from Wiz is clear: the mission continues—stronger, faster, and more impactful than ever. With Google Cloud, Wiz is poised to build the most powerful security platform the world has ever seen.

To succeed in the tech industry, you need to be willing to learn and adapt – and with many big tech companies now offering free online courses, it’s never been easier to develop the skills you need to thrive. This is an excellent opportunity to learn from industry experts and enhance your skills without breaking the bank.

In this article, we’ll explore the best free courses offered by the leading tech companies, covering topics from artificial intelligence and machine learning to data science and cybersecurity.

Google – Google Developers Courses

Google offers a wide range of free courses on its Developers platform, covering topics such as:

Android Development

Learn to build Android apps with courses like “Android Basics” and “Android Fundamentals”.

Machine Learning

Dive into machine learning with courses like “Machine Learning Crash Course” and “TensorFlow tutorials”.

Cloud Computing

Explore Google Cloud Platform with courses like “Google Cloud Platform Fundamentals” and “Cloud Engineering”.

Microsoft – Microsoft Learn

Microsoft’s Learn platform provides free courses and tutorials on various topics, including:

Azure

Learn about Microsoft’s cloud computing platform with courses like Microsoft Azure AI Fundamentals: Computer Vision

Artificial Intelligence

Explore AI and machine learning with courses like AI-102 Course Introduction

Data Science

Develop data science skills with courses like Explore and analyze data with Python

Amazon – Amazon Web Services (AWS) Training and Certification

Amazon Web Services (AWS) offers free courses through its platform AWS Skill Builder .

Meta – Meta for Developers

Meta’s Developer platform offers free courses and tutorials on various topics, including:

Mobile App Development

Learn to build mobile apps with courses like “React Native” .

Web Development

Explore web development with courses like Programming with JavaScript.

Additional Resources:

• Coursera offers a wide range of courses from top tech companies, including Google, Microsoft, and IBM.
• edX provides free courses and certifications from leading institutions, including Harvard, MIT, and Microsoft.
• Udemy offers a variety of courses on tech topics, including AI, machine learning, and data science.

These free courses offer a valuable opportunity to learn from industry experts and stay up-to-date with the latest technologies.

So why wait? Start learning today and take your skills to the next level!

Photo: Freepik

The API now includes a file search tool that allows searching of up to 10,000 files per assistant and can allow developers to integrate knowledge extraction into their assistants. It works with OpenAI’s vector repository objects.

The company also introduced new controls for setting the maximum input and output token so that developers can more directly limit costs. They can also now choose how many last messages will be used for context shortening.

Other new features include the introduction of the Tool Choice option, which allows the selection of a relevant tool. Other updates in this release include more model configuration settings, new streaming and polling utilities, support for fine-tuned models, and more.