July 2026 Summaries

In July 2026, Together AI and Y Combinator introduced a dedicated GPU cluster to streamline compute access for YC founders, facilitating the development of AI models requiring significant resources. This advancement highlights a simultaneous race for both compute and data, where the traditional barriers of entry like code, infrastructure, and architecture are no longer sustainable moats due to their replicability. Instead, the true competitive advantage lies in the unique intersection of compute, data, and the proprietary "recipe" that informs the training of AI models, as these weights encapsulate decisions and experiences that cannot be easily duplicated. The text underscores the urgency of securing both compute and unique data sources early, as these elements are rapidly consolidating, and emphasizes that the strategic focus should be on the unique combination of these resources and the insights they generate. The narrative suggests that AI-driven companies must prioritize acquiring differentiating data and developing unique models to sustain a competitive edge, with the understanding that future opportunities to build such moats may diminish as the market dynamics evolve.

Automated web data collection, or web scraping, is crucial for various applications like generative AI and market intelligence, but it must be performed responsibly and legally. Key legal considerations include the type of data being collected, its geographic location, the method of collection, and its intended use. Bright Data has played a pivotal role in defining legal boundaries through notable court victories against Meta and X Corp, establishing that collecting publicly accessible data does not violate terms of service or copyright laws. The legal landscape around web scraping is complex, influenced by factors like privacy laws, copyright, and the Computer Fraud and Abuse Act, with distinctions made between public and private data access. Bright Data emphasizes compliance with privacy laws, ethical data collection practices, and using ethically sourced proxies, while advising adherence to legal frameworks like GDPR and avoiding unauthorized access to restricted data.

AI agents increasingly require reliable web access to perform real-world tasks, and exposing enterprise-grade web tools through a Command Line Interface (CLI) offers a familiar and effective method due to the extensive training of Language Learning Models (LLMs) on shell usage. The Bright Data CLI provides AI agents with enterprise-level web access, enabling them to search, scrape, discover, and automate browser interactions by leveraging a robust infrastructure of over 400 million residential IPs designed for high-scale workloads. This CLI tool supports AI workflows by providing a flexible and lightweight interface for tasks that involve web data retrieval and browser automation, making it highly compatible with modern AI solutions. Additionally, the Bright Data CLI offers a free tier with 5,000 requests per month, facilitating easy exploration and integration for AI agents with shell access. By using the CLI, AI agents can effectively perform complex tasks that require web access, although other integration methods like the Bright Data Web MCP and Agent Skills also offer complementary solutions for structured execution and improved decision-making.

Recent takedowns of malicious networks built on hijacked home devices have highlighted the dangers of non-consensual use of internet infrastructure, contrasting sharply with the operations of Bright Data, a web data infrastructure platform. Bright Data emphasizes its commitment to transparency and consent, sourcing IPs from real users who opt in with full knowledge and can opt out at any time without personal data being collected. The platform's network is built on four tiers of transparency: sourcing, vetting, governance, and accountability, ensuring legitimate use cases and blocking malicious activities. Bright Data's services underpin various economic and public-interest initiatives, including brand protection, ad verification, cybersecurity, and AI data lifecycle management, benefiting over 20,000 customers, including major corporations and non-profit organizations. The company adheres to strict governance principles, including independent audits, to maintain trust and demonstrate compliance, distinguishing itself from non-consented networks by prioritizing user consent and operational transparency.

Web data serves as a crucial resource for AI development, financial markets, and business strategies, yet the industry faces challenges from unethical practices and unreliable vendors. Bright Data distinguishes itself by prioritizing ethical data collection and transparency, having completed a second Compliance and Ethics Audit by PwC, making it the only company in its sector to undergo such rigorous independent verification. This audit ensures that their processes, such as consensual SDK sourcing and GDPR compliance, meet high ethical standards. The text underscores the risks associated with using unaudited vendors and positions Bright Data as a leader in establishing industry standards for transparency and accountability. Rony Shalit, with 14 years of experience in risk management and compliance, supports this initiative as the Chief Compliance and Ethics Officer at Bright Data.

The MuleSoft Anypoint Platform is a cloud-based enterprise integration platform designed to help organizations build and manage applications, APIs, and data sources across cloud and on-premises environments. It offers features such as API design, integration tools, prebuilt connectors, and deployment flexibility. A notable limitation of AI-powered applications, including those using MuleSoft, is their reliance on static training data, which can lead to outdated insights. To address this, Bright Data’s Web MCP can be integrated with MuleSoft to provide real-time web scraping, search, and data extraction capabilities, enabling access to up-to-date and contextual information. The tutorial outlines how to connect a MuleSoft agent network to the Web MCP using Anypoint Code Builder, enhancing the platform's ability to handle dynamic web data. With the integration of Web MCP, MuleSoft's capabilities are expanded to include live web access and automated browsing, making it more effective for real-world applications.

PromptQL is an AI-native collaborative workspace designed to unify enterprise data, team knowledge, and workflows without relying on traditional pre-modeled dashboards or static semantic layers. It operates by learning from real interactions and converting them into reusable operational knowledge, which is particularly useful in environments with frequently changing business logic and distributed knowledge. A key feature is its integration with external data sources, facilitated by the Bright Data Web MCP connector, which allows real-time web search, scraping, and data retrieval. This integration addresses limitations of LLMs by providing access to real-time external data, enabling PromptQL to perform tasks like competitive pricing monitoring or brand reputation analysis. The Bright Data Web MCP connector uses a global proxy network to ensure reliable web access, supporting a variety of enterprise use cases that require up-to-date information from the web. PromptQL thus enhances enterprise decision-making by combining internal data with fresh external signals, producing actionable insights grounded in current information.

Quantum AI represents an emerging field that combines artificial intelligence with quantum computing, aiming to leverage the unique properties of qubits, such as superposition and entanglement, to solve complex problems beyond the reach of classical AI systems. While today's AI models rely heavily on large web datasets for training, quantum AI does not primarily depend on such datasets due to the current limitations of quantum hardware in processing structured web data. Instead, hybrid quantum-classical models are seen as the most practical approach, using quantum computing for specific tasks where it offers advantages, such as optimization and probabilistic modeling, while classical systems handle data collection and preprocessing. Companies like Bright Data facilitate this integration by providing APIs that enable reliable web data retrieval, supporting the development of quantum-enhanced AI applications. Despite being in the research phase, quantum AI holds potential for significant advancements in AI training, optimization, and machine learning, promising improvements in computational performance and energy efficiency.

Amazon Nova Act, an AWS SDK designed for building browser agents in Python, emphasizes splitting responsibilities between the agent and infrastructure to tackle the challenges of web access in AI applications by 2026. The key challenge is not the AI model itself but reliably accessing the live web, handling compliance, and geo-targeting, which is where Bright Data’s web-access layer complements Nova Act. Nova Act focuses on executing multi-step browser actions and decisions, while Bright Data handles access to real-time web data, compliance, and scalability. The infrastructure layer utilizes a network of residential IPs for geo-targeting and CAPTCHA management, offering a compliant and reliable access framework. Organizations increasingly rely on structured web data for AI, with 97% using real-time data and 90% facing access restrictions. The agent is best suited for tasks requiring complex decision-making, such as form submissions and navigating multi-step processes, whereas the data layer handles bulk structured data retrieval more efficiently. This architecture, combining Nova Act's decision-making capabilities with Bright Data’s robust web-access infrastructure, addresses the demands of accessing and extracting structured data from the web while ensuring compliance and reliability.

Grok Build, developed by SpaceXAI, is an open-source, terminal-based AI coding agent designed to streamline and automate development tasks directly from the command line interface (CLI). It features a full-screen terminal UI, an extensible agent runtime, and supports both interactive and headless modes for various workflows. While Grok Build excels in understanding codebases and automating tasks, its built-in knowledge is inherently limited by its training data cutoff. To overcome this limitation, it can be enhanced with live web access capabilities through integration with Bright Data's Web MCP and Agent Skills, which provide tools for web search, scraping, and browser automation. This integration allows Grok Build to access real-time web data, ensuring up-to-date responses and supporting complex workflows, such as vulnerability assessments and web data extraction. The Bright Data infrastructure enhances Grok Build with enterprise-grade web capabilities, offering a scalable solution with high success rates and global reach, ultimately turning it into a more robust and intelligent CLI assistant for developers.

AI data collection is a crucial process for developing effective artificial intelligence systems, focusing on gathering, structuring, and preparing large volumes of data to train, fine-tune, and evaluate models. Distinct from ordinary data collection, it emphasizes scale, diversity, freshness, and structure to meet the demands of modern AI models. The collection process involves sourcing data from public web sources, APIs, first-party data, and synthetic data, employing methods such as web scraping, APIs, and crowdsourcing. An AI data collection pipeline typically includes stages like identifying sources, collecting data, parsing, cleaning, labeling, and formatting it into training, validation, and test splits, with a feedback loop to address gaps identified during model training. Bright Data provides infrastructure solutions that enhance the reliability and efficiency of this process, offering tools like a Web Scraper API, residential proxy networks, and ready-to-use datasets, while maintaining high compliance standards. The effectiveness of AI systems heavily relies on disciplined data collection practices that prioritize model needs, diversity, quality, and provenance, making reliable collection infrastructure essential.

Aider is an AI-powered pair programming tool that enhances coding capabilities by utilizing large language models (LLMs) to assist in coding, debugging, refactoring, and testing, supporting over 100 programming languages and integrating with Git and IDEs. The tool, gaining significant community traction with over 46k GitHub stars, faces limitations due to LLMs' outdated knowledge, prompting the integration of external tools like the Bright Data CLI for web grounding. The Bright Data CLI enhances Aider's functionality by providing robust web scraping and browser automation capabilities, overcoming challenges such as anti-bot protections that hinder Aider's built-in web scraping tool. Through a combination of Aider and the Bright Data CLI, developers can efficiently interact with websites, extract data, and generate web applications with current, real-time insights. This integration enables Aider to perform tasks such as analyzing competitors' websites, grounding LLMs with up-to-date documentation, and interacting with complex web applications, thereby enhancing its reliability and accuracy in development workflows.

ToolJet is an AI-powered, low-code platform designed to build full-stack internal applications, dashboards, workflows, and AI agents, enabling rapid development of tools like admin panels, CRMs, and data dashboards. It supports over 80 integrations and offers features such as workflow automation, enterprise security, and flexible deployment options, including cloud and self-hosting. By integrating Bright Data APIs, ToolJet applications can access real-time, structured web data from various online sources, enhancing the capabilities of enterprise-grade web apps. This integration facilitates the development of applications like market monitoring dashboards, e-commerce price tracking systems, and brand reputation platforms by providing access to reliable external data, thereby overcoming the challenges of collecting and using large-scale web data. The combination of ToolJet and Bright Data empowers enterprises to build scalable, data-driven applications that integrate both internal and external data sources, supporting a wide range of business use cases.

The article explores the evolving landscape of AI and machine learning (ML) training data, focusing on the roles of synthetic data and real-world web data. It highlights the increasing interest in synthetic data due to its scalability, privacy advantages, and cost-effectiveness, as opposed to the limited and expensive nature of real web data. While synthetic data is predicted to become more prevalent, real web data remains crucial due to its authenticity and natural distribution, which is essential for training robust AI models. The text suggests a hybrid approach, combining both data types to leverage the strengths of synthetic data's scale and edge-case coverage alongside the realism and comprehensive nature of real data. The discussion includes comparisons of data distribution, long-tail coverage, cost, privacy considerations, data quality, and overall model performance, ultimately emphasizing the significance of carefully balancing both types of data for optimal AI training outcomes.

Web scraping browser extensions are increasingly popular tools that allow users to extract structured data from web pages quickly and easily without the need for coding or server setup. These extensions, such as Thunderbit, Instant Data Scraper, and Web Scraper, cater to various user needs, from simple, one-click data grabs to more complex, structured scraping projects. The market for web scraping is expanding significantly, with projections indicating its value will reach $3.49 billion by 2031. Extensions typically operate within browsers like Chrome or Edge and are best suited for small-scale tasks due to limitations in handling large or blocked jobs. While many extensions offer free tiers, advanced features like cloud runs and scheduling often require paid subscriptions. For larger-scale operations requiring more robust infrastructure, such as proxy rotation and anti-bot handling, transitioning to a full Web Scraper API is recommended.

Goose is an open-source, extensible AI agent designed to automate complex software development tasks, distinguishing itself from traditional code assistants by building full projects, writing and executing code, debugging, orchestrating workflows, and interacting with external APIs autonomously. Its integration with Bright Data's Web MCP enhances its capabilities by allowing real-time access to up-to-date information, addressing the static knowledge limitation shared by language models. This integration enables goose to leverage over 60 AI-ready tools for web data collection, structured data extraction, and automated web interactions, providing a more powerful AI experience. The setup, available as both a desktop application and a CLI tool, supports seamless connections to MCP servers, enabling users to extend goose’s capabilities with real-time web data access, thus enhancing productivity in software development tasks. The article also provides a step-by-step tutorial on configuring and testing the integration with Bright Data's Web MCP to ensure effective use of these enhanced features.

Amazon SageMaker is a comprehensive managed service designed to facilitate the building, training, and deployment of machine learning models and AI applications at scale, offering a unified environment that supports data access from various sources while ensuring enterprise-grade security. The blog post emphasizes the critical role of feature engineering in enhancing model performance by transforming raw data into meaningful metrics, with web data from platforms like Bright Data serving as a valuable resource due to its real-world activity representation. The text also highlights the challenges of working with web data, such as noise and inconsistency, and suggests using high-quality web data providers like Bright Data to overcome these issues. It provides a detailed guide on performing feature engineering in Amazon SageMaker, using a Glassdoor dataset to create features that improve a model's ability to predict high employee satisfaction. The tutorial demonstrates the workflow of retrieving web data, uploading it to Amazon S3, and applying feature engineering in SageMaker notebooks, culminating in training a predictive model using XGBoost. The blog concludes by suggesting ways to enhance model performance further, such as creating more derived features, transforming skewed distributions, and enriching data with external sources.

Bright Data skills offer AI coding agents enhanced capabilities by integrating structured tools and instructions for web search, scraping, and data extraction, thereby overcoming the limitations of static knowledge inherent in language models. These skills, compatible with over 40 AI coding solutions, are structured as folders containing scripts and metadata, allowing agents to perform tasks like web data retrieval, real-time search, and interaction with web pages more accurately. Installation can be done via the Vercel skills tool, Bright Data CLI, or manually, with each method offering varying levels of control. By providing access to Bright Data's extensive network infrastructure, these skills enable scalable operations with high uptime, thereby allowing coding agents to autonomously access up-to-date information and suggest relevant resources. Acquiring these skills involves setting up a Bright Data account and configuring API keys, which then allow agents to execute real shell scripts, connect to APIs, and handle complex tasks like pagination and error management. This integration not only enhances real-time web data access but also equips agents with the ability to propose best practices and enrich scripts with live data, making them valuable tools for developers seeking to expand the functionality of AI coding agents.

Oracle Generative AI Agents is a fully managed service within Oracle Cloud Infrastructure that facilitates the creation and deployment of AI agents capable of understanding natural language, maintaining conversation context, orchestrating tools, accessing enterprise data, and automating complex workflows. These AI agents are designed for a range of applications, including customer support, research, and content creation. A critical feature of these agents is their ability to access live web data for contextual insights—something they achieve by integrating with Bright Data, a platform that provides tools like the Web Unlocker API and SERP API to bypass anti-bot systems and extract real-time data from the internet. This integration allows Oracle AI agents to make business-ready decisions by retrieving up-to-date market data, overcoming the default limitations of large language models that lack real-time web connectivity. The setup process involves creating an Oracle Virtual Cloud Network, storing API keys securely, and defining custom tools within the AI agent to connect to Bright Data's services, thereby enhancing the agents' capabilities to deliver accurate, updated, and actionable insights.

Stagehand is an open-source browser automation framework developed by Browserbase that integrates natural language AI with deterministic code, aiming to balance the limitations of brittle selector-based tools and unpredictable AI agents. It allows users to perform browser actions through plain-English prompts and extract structured data into validated schemas. Stagehand's features include autonomous workflows, self-healing automation, and support for multiple LLM providers, making it suitable for creating custom automation pipelines. The framework is highly popular, with a robust developer community, and is enhanced when combined with Bright Data’s Browser API, which offers cloud-based, scalable, and stealth browser sessions to overcome challenges like bot detection and resource-intensive local browser management. The integration with Bright Data allows for seamless automation of remote browser instances, facilitating tasks such as web scraping and AI-driven data extraction even from sites with strong anti-bot measures, while maintaining high anonymity and global geo-targeting capabilities.