Head of Claude Code: What happens after coding is solved | Boris Cherny

The Future of Coding and Claude Code's Impact

Introduction to Claude Code

• The speaker mentions that 100% of their code is generated by Claude Code, with no manual edits since November. They ship multiple requests daily, indicating high productivity.

• A discussion arises about the relevance of learning to code in the future, suggesting that coding may become universally accessible and largely automated.

Transformation in Software Engineering Roles

• The speaker predicts a shift where everyone will be a product manager and software engineers may be replaced by "builders," leading to significant changes in job titles within tech.

• Boris Churnney from Anthropic discusses the transformative impact of Claude Code on software engineering jobs and its role in Anthropic's growth, highlighting a recent funding round exceeding $350 billion.

Growth and User Engagement

• The rapid growth of Claude Code is noted, with daily active users doubling recently, showcasing its increasing adoption among developers.

Personal Insights from Boris Churnney

• Boris shares his background and expresses gratitude for suggestions made by colleagues regarding topics for discussion during the podcast.

Sponsorship Messages

• An introduction to DX, a developer intelligence platform that helps organizations adapt to AI advancements by providing insights into tool usage and value generation.

• Sentry is introduced as a debugging tool that provides detailed error tracking and resolution suggestions through its AI agent.

Boris Churnney's Career Journey

Transition Between Companies

• Boris recounts his brief stint at Cursor before returning to Anthropic after realizing he missed the mission-driven environment at Anthropic focused on safety in AI development.

Reflections on Mission-Driven Work

• He emphasizes the importance of working towards a meaningful mission over merely building products, which resonates deeply with him personally.

Impact of Cloud Code on GitHub Contributions

Statistics on GitHub Commits

• A report indicates that 4% of all GitHub commits are now authored by Cloud Code, illustrating its significant influence on software development practices.

The Impact of AI on Software Development

The Rise of AI in Coding

• Predictions indicate that AI will account for a fifth of all code commits on GitHub by year-end, highlighting the rapid integration of AI into software development.

• Senior engineers are increasingly reporting that they no longer write code themselves, attributing this shift to advancements in AI technology.

• Current statistics show that 4% of all global code commits are now attributed to AI, with expectations that private repository contributions may be even higher.

Growth and Evolution of Quad Code

• The growth rate of Quad Code is accelerating across various metrics, indicating not just an increase but a faster pace of adoption and usage.

• Initially conceived as a small project at Anthropic, Quad Code has evolved significantly, demonstrating the potential for coding tools to enhance productivity and safety in software development.

Transition from Technical to Non-Technical Users

• The capabilities of AI have expanded beyond writing code; it now acts as a tool for non-technical users, enabling them to interact with applications like Gmail and Slack effectively.

• This transition marks a significant milestone where conversational AI becomes more than just a dialogue partner but an active participant in task execution.

Prototyping and Development Insights

• Early prototyping involved understanding the underlying model's capabilities before developing what became Quad Code. This foundational knowledge is crucial for effective engineering work.

• Initial versions were developed using terminal-based interfaces due to resource constraints, which later proved beneficial as the model improved over time.

User Reception and Product Design Lessons

• Initial internal reactions to early prototypes were lukewarm; however, this highlights the challenge of introducing innovative tools that diverge from traditional IDE environments.

• Building products under-resourced initially can lead to unexpected insights and improvements as user needs become clearer through iterative design processes.

The Evolution of Quad Code and Its Impact on Software Engineering

The Initial Concept and Development

• The speaker reflects on the challenges faced in deciding what to build, emphasizing that Quad Code was a primary focus for the past year.

• After its release, Quad Code quickly gained popularity at Anthropic, with daily active users increasing significantly.

• Initially, Quad Code did not achieve immediate success; it took months for broader understanding and acceptance among users.

User Experience and Adoption

• The concept of "latent demand" is introduced, highlighting how bringing tools to familiar environments can ease workflows while also presenting a novel experience through its terminal interface.

• Over time, Quad Code became more accessible across various platforms (iOS, Android, desktop app), enhancing familiarity for engineers.

Feedback and Iterative Learning

• The growth of Quad Code has been described as humbling; user feedback has been crucial in shaping its development.

• Rapid changes in software engineering due to AI advancements are noted; predictions about AI's role have shifted dramatically within a year.

Predictions and Exponential Growth

• A bold prediction made during a developer conference suggested that by the end of 2025, traditional IDE usage might become obsolete for coding tasks.

• The speaker discusses the exponential thinking ingrained in Anthropic’s culture, referencing foundational work on scaling laws by co-founders.

Innovation Through Experimentation

• Emphasizing innovation as an unpredictable process, the speaker notes that psychological safety is essential for fostering creativity within teams.

• Reflecting on early days with Quad Code reveals uncertainty about its utility; initial performance metrics showed limited code generation capabilities.

Commitment to Exploration

• Despite early doubts about usefulness, there was a strong commitment to exploring potential developments within Quad Code.

• Personal dedication from the speaker is highlighted as they invested significant time into refining the tool alongside supportive personal relationships.

Current State of AI in Coding

Prolific Coding with AI Assistance

• The speaker confirms that 100% of their code is written by an AI tool called Quad Code, highlighting their productivity as a coder even during previous employment at Instagram.

• Despite being the head of the team, they continue to actively code, shipping numerous pull requests daily (10-30).

• They emphasize the importance of human oversight in coding, especially for safety and correctness, despite relying on Quad for automatic code reviews.

Future Directions in Software Development

• The discussion shifts towards the next frontier in software development beyond coding itself, suggesting that coding tasks are largely solved.

• Quad is evolving to generate ideas for bug fixes and improvements based on feedback and telemetry data.

• The speaker mentions using AI tools like Co-work for various non-coding tasks such as project management and administrative duties.

Enhancing Product Management with AI

• The speaker notes that while coding is becoming increasingly automated, there remains a need for human input in prioritizing what to build next.

• They describe how they utilize Quad Code to process internal feedback quickly, fostering a responsive environment where user suggestions are acted upon promptly.

Improvements in AI Capabilities

• The speaker shares insights into how they interact with Quad Code by directing it towards specific Slack threads containing feedback.

• They acknowledge significant improvements in Quad's capabilities over time, particularly with version updates like Opus 46 enhancing its performance.

Productivity Gains in Engineering Teams

The Impact of AI on Engineering Productivity

• The engineering team has seen a 200% increase in productivity per engineer, despite the team size being approximately four times larger than before.

• In previous experiences at Meta, productivity gains were minimal (a few percentage points), making current increases seem unprecedented and significant.

• The rapid changes brought by AI in software development are remarkable, yet there's a tendency to normalize these extraordinary advancements without fully recognizing their implications.

Challenges with Rapid Changes

• Frequent model updates can lead to outdated thinking; even experienced engineers may struggle to adapt compared to newer team members who embrace modern approaches.

• A personal anecdote illustrates this: an engineer utilized AI tools for debugging a memory leak more efficiently than traditional methods, highlighting the shift in problem-solving techniques.

Principles for Team Efficiency

• A key principle is leveraging AI tools like Claude for tasks that traditionally required manual effort, emphasizing the importance of adapting to new technologies.

• Underfunding projects slightly can drive intrinsic motivation among engineers, leading them to work faster and innovate more effectively.

Encouraging Speed and Innovation

• The culture within the team promotes immediate action—if something can be done today, it should be done today—to maintain competitive advantage in a crowded market.

• Utilizing AI tools not only enhances speed but also encourages engineers to take ownership of their projects and push boundaries.

Optimizing Resource Allocation

• Contrary to common beliefs that AI reduces workforce needs, underfunding can actually yield better results as empowered engineers find innovative solutions with fewer resources.

• Initial advice for companies is not to focus on cost-cutting but rather provide ample resources (like tokens for using AI tools), fostering creativity and efficiency among engineers.

Exploring Innovative Ideas and Token Usage in Engineering

The Importance of Experimentation

• Encourages freedom in experimentation with ideas that may initially seem impractical, emphasizing the need to explore various concepts before scaling them.

• Highlights that innovative breakthroughs often arise from pushing boundaries and maximizing resource usage, despite potential skepticism about token consumption.

• Notes that individual engineers experimenting with tokens incur relatively low costs compared to their salaries, making initial explorations financially feasible.

Trends in Token Costs

• Observes a trend at Anthropic where some engineers are spending significant amounts on tokens monthly, indicating a shift towards higher operational costs associated with innovation.

• Discusses the personal journey of transitioning from coding to broader engineering roles, reflecting on the practical origins of programming skills.

Personal Reflections on Coding

• Shares a humorous anecdote about using programming for academic shortcuts during school, illustrating an early practical application of coding skills.

• Describes how initial motivations for learning to code were rooted in practicality rather than artistry, though later experiences led to an appreciation for programming's beauty.

The Evolution of Programming Perspectives

• Explores the balance between viewing coding as a tool versus an art form; acknowledges differing perspectives among engineers regarding their relationship with programming.

• Mentions specific team members who still find joy in traditional coding practices, underscoring the diversity of experiences within the engineering community.

Concerns About Skill Atrophy

• Addresses concerns over skill degradation as technology evolves but expresses confidence that foundational knowledge will remain valuable amidst changing tools and methodologies.

• Reflects on historical shifts in programming practices and emphasizes understanding underlying layers as essential for effective engineering.

The Evolution of Programming and AI's Impact

The Nature of Learning in Programming

• The speaker reflects on the continuous learning required in programming, noting that new frameworks and languages are a norm in the field.

• Acknowledges that not everyone feels comfortable with this constant change; some may experience feelings of loss or nostalgia.

AI and Coding: Future Perspectives

• Discussion about Elon Musk's question regarding why AI doesn't write directly to binary, highlighting the ongoing debate about coding education.

• The speaker suggests that within a year or two, understanding coding may become less critical for many users due to advancements in AI tools.

Historical Context: The Printing Press Analogy

• The speaker draws parallels between current technological transitions and historical shifts like the advent of the printing press.

• Describes how literacy rates were low before the printing press revolutionized access to written material, leading to increased literacy over 200 years.

Changes in Engineering Work

• Emphasizes that while printed material became more accessible, it took time for education systems to adapt and improve literacy rates.

• Shares an anecdote from a scribe's perspective during the printing press era, expressing relief at being freed from tedious tasks.

Shifting Focus for Engineers

• The speaker expresses excitement about spending less time on tedious coding tasks and more on creative problem-solving and collaboration.

• Highlights how modern tools allow non-programmers to engage in projects without deep technical knowledge, streamlining processes significantly.

Future Roles Affected by AI

• Discusses how roles adjacent to engineering (like product managers and designers) will also be impacted by AI advancements.

• Predicts that as AI models improve, they will expand into various computer-based work areas beyond just programming.

Understanding the Evolution of AI Agents

The Shift in Perception of AI Agents

• A year ago, the concept of an "agent" in AI was largely unfamiliar; now it has become integral to work processes.

• Many people confuse conversational AI (like chatbots) with true agents, which have a specific technical definition involving tool usage.

• True agents can perform actions beyond conversation, such as interacting with systems, sending emails, and executing commands.

Societal Implications and Responsibilities

• The urgency to understand and integrate AI tools is emphasized by the presence of diverse professionals at Anthropics, including economists and policy experts.

• Concerns about job loss due to AI are addressed through Jevons Paradox, suggesting that increased efficiency may lead to more hiring rather than less.

Personal Experience with AI in Engineering

• The speaker shares their positive experience with coding enhanced by AI tools like Quad Code, making work more enjoyable and efficient.

• Historical analogies are drawn between current technological advancements and the democratizing effect of the printing press on knowledge dissemination.

Future Predictions for Programming Accessibility

• Envisions a future where programming becomes universally accessible, akin to how reading became widespread after the printing press.

• Acknowledges potential disruptions during this transition but emphasizes the need for societal dialogue on navigating these changes.

Advice for Thriving in an Evolving Job Market

• Encourages experimentation with new AI tools without fear; staying ahead requires familiarity with cutting-edge technology.

• Advocates for becoming a generalist across disciplines rather than specializing narrowly; cross-disciplinary skills enhance effectiveness in roles.

Discussion on Team Roles and Future of Work

The Evolution of Team Disciplines

• The speaker discusses the importance of team members being generalists who can cross disciplines, emphasizing a broader problem-solving approach beyond just engineering.

• There is a recognition that roles such as engineering, design, and product management may have significant overlap, with individuals often performing similar tasks despite having different specialties.

• A prediction is made about the future of these roles becoming less distinct, suggesting titles like "builder" may replace traditional ones like "software engineer."

Hiring Challenges in Tech

• The conversation shifts to hiring pressures faced by founders and managers, highlighting the need for speed in recruiting top talent amidst increasing competition.

• MetaView is introduced as an AI solution designed to streamline the hiring process by automating candidate sourcing and interview note-taking.

Impact of AI Tools on Job Satisfaction

Survey Insights on Job Enjoyment

• An informal Twitter survey reveals that 70% of engineers and PMs report enjoying their jobs more since adopting AI tools, while only 10% feel less enjoyment.

• Designers show a lower satisfaction rate with only 55% enjoying their jobs more; this discrepancy raises questions about their experiences with AI tools.

Exploring Designer Experiences

• The speaker expresses interest in understanding why some designers are enjoying their jobs less and mentions plans for follow-up polls to gather deeper insights.

• At Anthropics, technical skills are emphasized across all roles; designers coding has led to increased job satisfaction as they can resolve issues independently without relying on engineers.

Product Development Principles

Enhancing User Experience through Familiarity

• Discussion centers around making products accessible by integrating familiar workflows rather than forcing users to adapt to new systems.

• The principle of latent demand is highlighted as crucial in product development—creating solutions that align closely with existing user behaviors leads to better adoption and satisfaction.

Understanding Latent Demand in Product Development

The Concept of Latent Demand

• Latent demand refers to the potential market interest that emerges when a product is designed in a way that allows users to utilize it beyond its intended purpose, providing insights for future development.

Case Study: Facebook Marketplace

• Facebook Marketplace was developed after observing that 40% of posts in Facebook groups were related to buying and selling, indicating a strong user-driven demand for such functionality.

• The initial response to creating buy and sell groups confirmed the latent demand; users were already engaging in commerce within existing platforms, suggesting a marketplace would be well-received.

Additional Examples of Latent Demand

• Similar observations led to the creation of Facebook Dating, where 60% of profile views came from non-friends of the opposite gender, highlighting an unaddressed need for dating features on social media.

• The concept also applies to co-work environments; unexpected uses of tools like quad code revealed diverse applications outside their original intent, prompting developers to create more tailored solutions.

Observations from User Behavior

• Users have been found using technical products like quad code for non-technical tasks (e.g., gardening or photo recovery), indicating a clear opportunity for product adaptation based on user behavior.

• A notable instance involved data scientists utilizing terminal commands creatively, showcasing how users often find ways around product limitations when they see value.

Evolving Perspectives on Product Design

• Traditional approaches focus on simplifying user actions based on observed behaviors. In contrast, modern strategies emphasize understanding what models aim to achieve and facilitating those goals directly.

• For example, cloud code was designed with minimal constraints around the model's capabilities, allowing it greater flexibility and responsiveness based on latent demands identified during development.

Launching Co-work: Rapid Development Insights

• The co-work feature was built in just ten days but did not initially gain traction until several key inflection points occurred over time as user understanding improved.

The Success of Co-Work and Its Development Process

Team Efforts and Initial Success

• The launch of Co-Work was met with immediate success, surpassing the early reception of Cloud Code. Credit is given to the strong team behind its development, including Felix, Sam, and Jenny.

• The team explored various options for several months before deciding to integrate Quad Code into the desktop app, which ultimately proved effective.

Implementation and Safety Measures

• Co-Work features a sophisticated security system designed to ensure that the model operates correctly without deviating from intended functions. This includes shipping an entire virtual machine alongside it.

• The initial launch was somewhat rough around the edges; however, releasing early allowed for user feedback that would inform future product iterations.

Learning Through User Feedback

• The philosophy of "release early" is emphasized as crucial in understanding latent demand and how users interact with AI technology. Early releases help identify unexpected uses.

Safety Layers in Model Development

• There are three layers to studying model safety: alignment (ensuring safe training), laboratory testing (synthetic situations), and real-world behavior observation. Each layer provides insights into model performance.

Continuous Improvement and Research Preview

• Releasing products like Co-Work as research previews allows for ongoing improvements based on real-world usage while ensuring safety measures are in place. This iterative process is vital for long-term alignment with user needs.

Mechanistic Interpretability Insights

• Observability tools allow developers to peek inside the model's operations, enhancing understanding of its decision-making processes. Chris Ola is noted as an expert in mechanistic interpretability within this field.

Understanding Model Neurons and Their Functionality

Insights on Model Neurons

• Model neurons, while not identical to animal neurons, exhibit similar behaviors, allowing researchers to gain insights into their functionality and concept mapping.

• Evidence suggests that models engage in deeper cognitive processes beyond mere token prediction; sophisticated structures enable complex planning and conceptual encoding.

• The phenomenon of superposition indicates that a single neuron can represent multiple concepts when activated alongside others, leading to more nuanced representations.

Philanthropic Efforts in AI Development

• The commitment to safe AI development is central to the mission of Anthropic, with open-source initiatives aimed at inspiring other labs towards responsible practices.

• An open-source sandbox for cloud code has been developed to ensure agents operate within defined boundaries, promoting safety across various applications.

Addressing Anxiety in Agent Management

Challenges Faced by Users

• Users often experience anxiety when agents are unresponsive or blocked, leading to concerns about productivity loss and the need for constant monitoring.

• Personal anecdotes reveal a tendency among users to check on their agents frequently upon waking up or during breaks due to this anxiety.

Evolution of Coding Practices

• The definition of coding is evolving; it now encompasses describing desired outcomes rather than writing traditional code. This shift reflects changes in how programming is approached.

• Historical perspectives highlight generational shifts in programming methods, suggesting older programmers may have viewed software as less legitimate compared to earlier techniques like punch cards.

Cultural Connections Through Programming History

Personal Narratives

• A personal connection emerges through shared backgrounds; both speakers have roots in Ukraine and discuss familial experiences related to early programming practices using punch cards.

• Nostalgic memories illustrate the contrast between childhood experiences with technology and professional engagements with modern software development.

AI Product Development Insights

The Importance of Experimentation with AI

• Discussion on family traditions and toasts, highlighting the cultural significance of reflecting on life choices.

• Emphasis on allowing teams to experiment freely with AI by providing unlimited tokens for exploration.

• Warning against overly constraining AI models; instead, encourage flexibility in how they operate.

Principles for Building Effective AI Products

• Suggestion to provide tools rather than strict guidelines, enabling models to derive context independently for better outcomes.

• Introduction of the "bitter lesson" concept from Rich Sutton, advocating for general models over specific ones due to their superior performance in the long run.

Long-Term Strategy in AI Development

• Advice against fine-tuning small models; focus on leveraging more general models that can adapt and improve over time.

• Caution about relying too heavily on scaffolding around models as it may only yield marginal improvements that could be negated by future advancements.

Future-Proofing Your AI Solutions

• Insight into building products designed for future model capabilities rather than current limitations, which may lead to a smoother transition when new technologies emerge.

• Acknowledgment of initial discomfort during product development phases but reassurance that forward-thinking strategies will pay off once advanced models are available.

Anticipating Improvements in AI Capabilities

• Predictions about enhancements in tool usage and sustained performance over longer periods as technology evolves.

• Recognition of gradual improvements based on historical trends observed within the development cycle of previous AI iterations.

AI Product Development and Cloud Code Tips

Improvements in AI Model Performance

• The Opus 4.6 model has significantly improved, allowing for unattended operation for extended periods (10 to 30 minutes on average), with some instances running for weeks.

• This trend indicates a future where models can operate autonomously without constant supervision, enhancing efficiency in AI product development.

Tips for Using Cloud Code Effectively

• There is no single correct way to use cloud code; developers should find their own methods based on personal preferences and environments.

• Always utilize the most capable model (Opus 4.6) as it often proves more cost-effective by requiring fewer tokens than less intelligent models like Sonnet.

Best Practices in Task Management

• Start tasks in "plan mode," which involves a simple prompt to avoid immediate coding, allowing for better planning and execution later.

• In plan mode, users can interact with the model until they are satisfied with the plan before executing it, leading to higher accuracy.

Exploring Different Interfaces

• Experimenting with various interfaces beyond just terminal usage is encouraged; options include mobile apps and desktop applications that may suit different workflows better.

Competition and Market Perspective

• The speaker acknowledges competition from products like Codeex but emphasizes focusing on user needs rather than competing products.

• Continuous improvement based on user feedback is prioritized over monitoring competitors' offerings.

Personal Reflections Post AGI

• The speaker shares a personal anecdote about living in rural Japan before joining Anthropic, highlighting a lifestyle focused on community interactions through activities like trading homemade goods.

Miso Making and Long-Term Skills

The Art of Miso

• Miso production exemplifies the importance of patience, with white miso taking at least three months and red miso requiring two to four years to mature.

• The speaker expresses a passion for developing long-term skills through miso making, indicating that if not working at Anthropic, they would focus on this craft.

Insights on Technology and Development

• Emphasizes Anthropic's approach: starting from coding, progressing to tool use, and then computer use as a framework for model development.

• Discusses the unexpected success of Quad Code as a product, highlighting its growth into a multi-billion dollar business despite initial uncertainties about its market fit.

Current Landscape of AI

• Acknowledges that while Quad Code has gained significant traction, most of the world remains unaware or unutilized in AI technologies; feels like only 1% progress has been made.

• Reflects on the rapid financial growth within the AI sector, noting impressive revenue figures for both Quad Code and Anthropic.

User Engagement and Product Improvement

• Attributes Quad Code's continuous improvement to user feedback; passionate users contribute insights that drive enhancements in functionality.

• Shares personal enjoyment in engaging with users to refine products based on their experiences and needs.

Book Recommendations

Favorite Reads

• Recommends "Functional Programming in Scala" as an essential technical book due to its elegance in programming concepts despite potential limited practical application.

• Suggests "Accelerando" by Charles Stross for its fast-paced narrative reflecting current technological advancements leading towards singularity.

Exploring Sci-Fi Perspectives

• Highlights "The Wandering Earth" by Liu Cixin as an exceptional collection of short stories offering unique insights into Chinese sci-fi compared to Western narratives.

• Notes how science fiction prepares readers for future possibilities by creating mental models based on imagined worlds.

Exploring Seasonal Influences and Personal Journeys

The Impact of Seasons on Lifestyle

• Discussion on how seasonal changes influence social events and food availability, highlighting the importance of local farmers' markets in marking these transitions.

• Reflection on long-term skills and experiences, emphasizing a desire to contribute positively to societal progress inspired by reading science fiction.

Sci-Fi Inspirations

• Mention of "Fire Upon the Deep" as a significant sci-fi book that offers intriguing perspectives on AI and AGI.

• Acknowledgment of other notable works like "Deepness in the Sky," which are complex yet rewarding reads.

Media Consumption Habits

• Admission of limited time for TV or movies; however, appreciation for the Netflix adaptation of "The Three Body Problem" is expressed.

Favorite Products and Tools

• Introduction to Co-work as a transformative product that automates tedious tasks, enhancing productivity through its Chrome integration.

• Recommendation of the "Acquired" podcast for its engaging storytelling around business history, with a specific mention of an episode focused on Nintendo.

Practical Applications of Co-work

• Explanation of how Co-work can automate various tasks such as filling out forms or managing emails effectively.

• Insights into user experiences with Co-work's capabilities compared to previous tools like Quad Code, noting rapid advancements in functionality.

Enhancing Productivity with Automation

• Suggestions for new users to start utilizing Co-work by cleaning up desktops or summarizing emails efficiently.

• Description of using Co-work for project management tasks, including automated reminders sent via Slack based on team status updates.

• Encouragement to run multiple tasks simultaneously within Co-work, allowing users to manage their time better while automation handles routine work.

This structured summary captures key discussions from the transcript while providing timestamps for easy reference.

AI Evaluation and Common Sense in Work

The Value of AI and Personal Contribution

• The speaker expresses pride in their contribution to AI, noting that achieving 48 out of 50 evaluations is a significant milestone.

• They feel valuable to the future of AI, indicating a strong personal investment in the technology's development.

Importance of Common Sense

• The speaker emphasizes using common sense as a life motto, particularly in work environments where failures often stem from neglecting this principle.

• They argue that many individuals follow processes without critical thinking, leading to poor product choices or ideas.

• The best outcomes arise when people think from first principles and develop their own judgment about situations.

Engagement with Twitter

• The speaker discusses their recent activity on Twitter (now X), explaining they initially used Threads but switched due to boredom while traveling in Europe.

• They began engaging on Twitter by responding to discussions about coding and seeking feedback on bugs, which surprised users due to the quick response times.

Experience and Feedback Loop

• The speaker highlights how quickly they can address user feedback or bugs, showcasing an efficient workflow that enhances user experience.

• Engaging with users on Twitter has been positive; they appreciate hearing about bugs and feature requests directly from the community.

Closing Thoughts and Call for Interaction

• The conversation wraps up with an invitation for listeners to connect via Threads or Twitter, encouraging them to share bugs or feature requests.

• A reminder is given for listeners to subscribe and leave reviews for better visibility of the podcast.