May 2026 Summaries

Creating a custom CRM involves defining specific business goals and designing a data model around key elements such as contacts, companies, and deals while choosing a suitable building method based on budget and technical expertise. Unlike off-the-shelf CRMs like Salesforce, custom CRMs are tailored to fit unique business processes, offering an exact fit, cost control, and full ownership over the data model and integrations. Modern CRM development no longer requires coding from scratch, thanks to AI and no-code tools like Bubble, which allow for rapid deployment and customization. The process includes mapping data models, defining pipeline stages, setting up user roles and automations, and integrating existing tools, followed by testing with a pilot team to refine usability. The scalability of these systems is enhanced by extending CRM functionalities to web and mobile platforms, creating automations, and implementing AI for tasks such as email drafting and lead scoring to improve efficiency and adoption within sales teams.

AI tools have become essential for startups by automating tasks, generating content, and facilitating product development, allowing small teams to operate on a scale similar to larger companies. The guide outlines 10 AI tools in 2026 that are beneficial for startups, detailing how they assist in product building, market research, content creation, sales management, and workflow automation. It emphasizes the importance of choosing the right tools based on a startup's stage, skill level, and data privacy needs, as adding tools without strategy often fails to deliver significant value. Among the highlighted tools are Bubble for non-technical founders to build apps without coding, ChatGPT for writing and ideation, GitHub Copilot for enhancing engineering productivity, and Zapier for automating repetitive tasks. The guide advises startups to focus on a small, effective stack of AI tools that align with their specific needs and warns against tool sprawl, which can fragment workflows and increase costs unnecessarily.

Visual programming and text-based coding offer distinct approaches to app development, each with its own advantages and limitations. Visual programming allows users to build app logic through graphical elements like flowcharts or drag-and-drop blocks, making it accessible and quick for those without extensive coding knowledge, particularly when using platforms like Bubble that integrate AI to assist in generating and editing app components visually. This method suits most app use cases and supports rapid iteration, though it can struggle with diagram complexity at scale and vendor dependency. In contrast, text-based coding provides nearly unlimited flexibility and control over app performance and infrastructure, but it requires coding expertise and is more prone to syntax errors and slower iteration. AI tools have blurred some differences by enabling code generation, yet the need for code literacy remains since AI-generated code still requires understanding and maintenance. In 2026, the optimal approach often involves a blend of AI-assisted visual development for quick prototyping and iteration, while relying on traditional coding for performance-critical tasks. Platforms like Bubble, which offer shared backends for web and native mobile apps, built-in security features, and AI-assisted development, exemplify the evolving landscape where visual programming can meet diverse app development needs efficiently.

Eric Ries, renowned for his Lean Startup methodology, discusses the themes of his new book, "Incorruptible: Why Good Companies Go Bad... And How Great Companies Stay Great," in a conversation with Bubble co-founder Emmanuel Straschnov. While his earlier work focused on the initial stages of building a company, "Incorruptible" examines why successful companies often deviate from their original mission and offers practical steps to prevent this drift, emphasizing the importance of governance and structural protections. The discussion also touches on the impact of AI on startup culture, where many founders misuse AI for speed rather than intelligence, and how companies must differentiate between genuinely mission-driven actions and mere mission statements. Ries highlights that "The Lean Startup" was more about learning efficiently than about rapid product shipping, and he links its principles to those in "Incorruptible," aiming to ensure companies maintain their core values and integrity over time.

Bubble AI is an innovative feature within the no-code platform Bubble that allows users to create fully editable applications from text prompts using a visual editor, eliminating the need for writing code. By generating apps directly into a visual environment, users can easily understand, modify, and manage every element of the app, including pages, workflows, and database structures. Bubble AI stands out from other AI tools by offering a transparent and user-friendly interface, ensuring that users can maintain and scale their applications without needing extensive technical expertise. Additionally, it includes the Bubble AI Agent, a conversational assistant that aids in adding features and troubleshooting post-generation. This approach not only facilitates rapid development but also empowers users to make adjustments and handle issues directly, enhancing the app's scalability and functionality on Bubble's infrastructure.

Emmanuel Straschnov, co-founder of Bubble, shared updates during a live session where he addressed various topics including product developments, the integration of AI, and advice for developers and startups. Bubble is set to ship more features than usual on June 3, reflecting a faster release cycle bolstered by AI-driven enhancements. A major highlight is the forthcoming launch of the mobile plugin editor, which promises to empower the community to create native plugins for mobile apps. AI agents are being rolled out to existing web applications, with plans to support mobile apps by mid-July. Emmanuel emphasized the combination of visual programming and AI as a long-term strategy for Bubble, contrasting it with the emerging trend of vibe coding that still requires developers to understand and maintain generated code. He also discussed ongoing improvements to Bubble’s workflow engine and data tab, as well as efforts to enhance the property editor's performance. Additionally, the platform is working on allowing payments in local currencies to reduce conversion fees and is committed to global accessibility. In partnership with Anthropic, Bubble tests new AI models to ensure optimal performance for its users. Emmanuel advised developers to focus on clearly articulating their goals for better AI outcomes and shared insights from his podcast on company building. He concluded with tailored advice for VC-backed and bootstrapped startups, emphasizing prudent financial management and the importance of user feedback in product development.

AI app development tools have revolutionized the software creation process, enabling faster development and reducing the need for extensive coding knowledge. The tools can be categorized into prompt-to-app builders, visual builders with AI generation, AI coding assistants, and workflow automation tools, each suited to different user skill levels and project requirements. Prompt-to-app builders generate a starting app from a simple description, while visual builders like Bubble offer a fully editable interface without requiring code. AI coding assistants integrate into existing projects to enhance developer workflows, and workflow automation tools connect existing applications to automate tasks. Despite the ease introduced by AI, these tools are not foolproof; users must test, refine, and ensure security before deployment. The choice of tool depends on the user's technical skill, the nature of the project, and the level of control required post-generation, with platforms like Bubble providing extensive control and integration capabilities for both web and mobile applications.

AI editing tools combine AI-driven content generation with the ability to directly edit results, offering a balance between automation and user control. These tools can handle a variety of content types, including web apps, databases, images, videos, and text, by allowing users to refine AI-generated outputs through visual editors, code editors, or prompt-based interfaces. The guide explores eight AI editing tools, each serving different purposes and workflows, such as Bubble for no-code application development, Cursor for AI-assisted code editing, and Figma for design systems. These tools provide flexibility and control by enabling users to modify generated content directly, contrasting with pure AI generators that often lack this capability. While most tools offer free tiers, users are encouraged to evaluate the technical requirements and specific features of each tool to determine the best fit for their needs and to understand the trade-offs involved, such as the need for coding skills or existing data infrastructure.

Learning Management Systems (LMS) are software platforms that enable organizations to create, deliver, track, and manage educational courses and training programs from a centralized location. These platforms are utilized for various purposes, including employee onboarding, compliance training, customer education, partner certification, and academic course delivery. The suitability of an LMS depends on the specific needs of an organization, with options like TalentLMS for small teams requiring quick setup, 360Learning for collaborative employee training, Absorb for compliance-heavy programs, and Skilljar for customer education. The market for LMS platforms is projected to grow significantly, and choosing the right one involves evaluating factors such as standards support, integrations, pricing, and implementation requirements. Additionally, organizations with unique requirements may consider building a custom LMS using tools like Bubble, which offers a visual, no-code platform for developing tailored learning solutions. The decision between packaged and custom LMS solutions hinges on the specific use case, team size, and the level of customization needed.

AI tools for business development are revolutionizing the process by automating tasks like market research, data enrichment, prospecting, account targeting, CRM automation, and more, which traditionally required manual effort and significant time investment. These tools address specific stages within the business development (BD) workflow, allowing teams to operate more efficiently by reducing the time spent on repetitive tasks and enabling personalized outreach at scale. The guide discusses nine tools, each tailored to a distinct BD function, providing insights into their strengths, limitations, and ideal applications. While AI tools enhance efficiency, they cannot replace the strategic thinking and relationship-building capabilities of human BD teams. Success lies in selecting the right tool for the specific bottleneck in a team's workflow, integrating them effectively, and scaling usage as each tool proves its value.

No-code platforms offer a streamlined way to develop, launch, and manage applications without requiring programming knowledge, using visual tools like drag-and-drop editors to simplify app creation. While they enable rapid development for initial app versions, they often face limitations in customization, scalability, integration, and security as applications grow more complex. These constraints can lead to potential issues like vendor lock-in, governance challenges, and technical debt if not evaluated properly before full-scale deployment. AI advancements in the no-code space have accelerated initial development but introduced risks related to opaque AI-generated code. Platforms like Bubble aim to mitigate these challenges by providing a robust visual programming environment that supports complex workflows, scalable hosting, and comprehensive security features while maintaining the flexibility to adjust and scale as needed. Before committing to a no-code platform, thorough testing of the platform’s capabilities against realistic data volumes, security requirements, and integration needs is essential to ensure it aligns with long-term production goals without incurring unforeseen costs or limitations.

AI-powered app builders offer a revolutionary approach to software development, allowing users to describe their desired application and watch it materialize with minimal traditional coding, a process known as vibe coding. While this offers speed and ease, not all platforms give users the flexibility to modify generated code, posing challenges when customization is needed. The best platforms blend AI generation with visual editing tools, enabling users to switch between rapid code generation and precise visual modifications. Key considerations when choosing a platform include the ability to manage both frontend and backend development (full-stack capabilities), clear security controls, support for native mobile apps, and a seamless progression from prototype to production-ready software. Platforms like Bubble combine AI generation with a comprehensive visual editor, allowing for direct editing of app logic, database structures, and privacy rules, ultimately supporting both web and native mobile application development. In contrast, other tools may focus on specific aspects such as frontend development, browser-based rapid prototyping, or refining AI-generated code, each with varying levels of control and customization. Understanding these differences is crucial for selecting an AI app builder that aligns with specific project needs and ensures security, scalability, and maintainability.

AI app builders are categorized into three primary types: code generators that require developer maintenance, visual platforms like Bubble that enable code-free AI-driven app creation, and specialized tools for internal applications. When selecting an AI app builder, it's crucial to consider production readiness, control when AI is insufficient, platform scope, security, compliance, data ownership, and pricing. Bubble stands out by offering a fully visual approach allowing users to generate both web and native mobile apps with a shared backend, ensuring seamless data structure and logic across platforms. It features a visual interface for editing workflows without needing to code, and includes SOC 2 Type II compliance and scalable infrastructure, making it suitable for production apps. Softr is ideal for creating client portals using existing spreadsheets, while Lovable and Bolt.new focus on MVP generation with GitHub export and UI prototyping, respectively. Replit Agent provides an educational approach to development, and V0 is tailored for React component handoff to engineers. Retool is designed for building data-heavy internal tools with enterprise controls. Each platform has unique strengths and limitations, making the choice dependent on specific project requirements and the desired balance between speed, control, and scalability.

Publishing a Bubble app to the App Store involves enrolling in Apple's Developer Program, preparing and submitting the app through App Store Connect, and ensuring compliance with Apple's technical and privacy requirements. This process requires setting up an Apple Developer account, creating an app record, connecting API keys, and packaging builds, all of which can be done without a Mac or Xcode using Bubble's one-click publishing feature. Developers must focus on providing accurate app information, metadata, and privacy disclosures to avoid common rejections, such as broken demo credentials or undisclosed data collection. Bubble simplifies the technical aspects by automatically managing SDK compliance and build uploads, allowing developers to concentrate on the business and user experience aspects of their app. Testing through TestFlight is recommended before submission to catch potential issues, and after approval, developers can choose how to release their app, whether automatically, manually, or on a scheduled basis.

Clément Llehi, founder and CEO of Makko, a facility management company, built his business from the ground up by immersing himself in the industry, initially doing the cleaning himself to gain first-hand experience. Today, Makko employs 300 people and serves over 600 B2B clients, including major French companies, all without outside funding or a co-founder. Llehi's journey from a solo cleaner to running a large operation demonstrates his belief in understanding the work intimately before building a product, which he achieved by leveraging his consulting background to create a tech-forward platform that addresses client needs effectively. His company, which offers cleaning, maintenance, and hospitality services through a Bubble-built platform, emphasizes the importance of paying attention to small signals and iterating quickly in a capital-light environment. Llehi's approach underscores the value of speaking the client's language and structuring a business to adapt rapidly to market changes, which has been key to earning enterprise trust in a traditionally old-school industry.

This guide provides a comprehensive framework for building an AI minimum viable product (MVP) that validates your concept without overextending resources. It emphasizes starting with API-based models from providers like OpenAI, Anthropic, and Google to quickly test hypotheses and proves value using one narrow feature. The process involves setting up confidence thresholds to manage uncertain predictions, tracking metrics for both business and AI quality, and iterating based on user feedback. It highlights the importance of data quality, model performance, cost management, and privacy considerations, which are often the downfall of AI projects. Bubble AI is showcased as a tool that simplifies the development process by allowing for rapid app generation without code, visual debugging, and real-time cost monitoring. This approach enables teams to focus on core value propositions and adjust quickly, with the option to transition to custom-trained models when justified by stable usage and specific needs. The guide also offers insights into efficient data preparation, model selection, and UX development, ensuring that AI solutions are both effective and user-friendly from the start.

SOC 2 compliance is an independent attestation, not a certification, provided by licensed CPA firms to verify that a company's data security controls meet the Trust Services Criteria, which include Security, Availability, Processing Integrity, Confidentiality, and Privacy. This framework, originating from the AICPA, is increasingly demanded by enterprise customers, especially for service organizations that handle sensitive customer data. SOC 2 Type II reports, which assess control effectiveness over time, have become essential for securing B2B contracts as they offer comprehensive security assurances compared to Type I reports, which only provide a snapshot of control design. Preparing for SOC 2 compliance involves defining the scope, gathering evidence, and selecting an experienced auditor, with the process taking several months to complete. Continuous maintenance and annual renewals are crucial to ensure ongoing compliance, with many organizations also using bridge letters to cover gaps between audit periods. SOC 2 reports help streamline vendor management by replacing lengthy security questionnaires and provide valuable insights into an organization’s security posture, making them indispensable in today's data-driven business environment.

The text discusses the diverse landscape of AI app builders, emphasizing the importance of selecting a platform that balances speed with maintainability, security, and governance, particularly for operations teams. It highlights the critical evaluation criteria for these platforms, including security requirements like SOC 2 compliance and SSO integration, mobile strategy considerations, technical resources, and pricing models. The text contrasts various platforms such as Bubble, Airtable Omni, Retool, and Superblocks, each catering to different operational needs, from database-heavy internal tools to governed enterprise operations and lightweight field applications. It underscores the potential pitfalls of choosing platforms that generate unmaintainable code or lack governance features, stressing the necessity for visual transparency and control. Ultimately, the text guides operations teams in selecting app builders that align with their specific requirements, ensuring they can efficiently build, maintain, and scale applications while meeting compliance standards.

Choosing the right development path for building iOS apps largely depends on your technical expertise, timeline, and whether you're targeting multiple platforms. Native tools like Xcode offer deep Apple integration but require Swift knowledge and macOS, whereas cross-platform frameworks such as Flutter and React Native allow for simultaneous iOS and Android development from a single codebase. Bubble offers a no-code solution that enables app creation with AI-generated components and one-click App Store publishing, making it ideal for non-technical founders or teams wanting a seamless transition from prototype to production. The iOS platform offers significant opportunities, with high consumer spending and stringent App Store quality standards, which necessitate choosing tools that enable fast launches while maintaining high-quality standards. Traditional development involves multiple tools for coding, testing, and deployment, whereas Bubble integrates all these functions into one platform, simplifying the development process. Ultimately, the choice of tools should align with your team's skills and project requirements, whether that means leveraging the extensive capabilities of Xcode, the efficiency of cross-platform frameworks, or the simplicity of Bubble's all-in-one solution.

In 2026, affordable mobile app development involves balancing the speed of AI with the need for visual control, and platforms like Bubble, FlutterFlow, and Thunkable offer native iOS and Android publishing options starting at $30-60/month. The choice of platform depends on factors such as the need for app store distribution, customization levels, and the ability to maintain apps when AI-generated code reaches its limits. Key cost considerations include data storage, API limits, and publishing complexities, with hidden costs potentially arising post-commitment. Bubble emerges as a standout platform by combining AI-driven app generation with visual workflows that allow for user understanding and modification, thus avoiding the technical debt associated with unreadable AI-generated code. Additionally, platforms vary in their approach to publishing workflows, scalability, and essential app features, impacting the total cost and long-term viability of the app.

Enterprise app builders leverage AI to create secure web and mobile applications without requiring code, prioritizing security features such as SOC 2 Type II compliance, SSO integration, and visual workflows. These platforms aim to balance speed and control, allowing enterprises to develop applications faster than traditional methods while avoiding technical debt. Visual development tools allow teams to review application logic without deep coding expertise, ensuring security compliance and maintainability. Key considerations when selecting an app builder include security requirements, cross-platform capabilities, enterprise integrations, and collaboration features. Platforms like Bubble, Microsoft Power Apps, and OutSystems cater to different needs, from AI-powered development with visual control to Microsoft-centric stacks and complex enterprise portfolios. Pricing models vary, and organizations must consider costs related to licensing, integration, and ongoing maintenance. Ultimately, choosing the right platform involves testing pilot projects to assess development speed, code clarity, and maintenance confidence.