Pains

Deploying and maintaining AI systems is significantly more complex than traditional software. 47% of IT leaders find maintaining AI systems more challenging than conventional software, requiring complex architectures, regular updates, continuous monitoring, and iterative improvements based on real-world usage data.

ChatGPT cannot handle long conversations or large documents without hitting context length limits (a few thousand tokens). Users must truncate or summarize information, and when context is exceeded, ChatGPT forgets initial instructions or content, leading to quality drops mid-session.

ChatGPT experiences performance changes over time as it's updated (model drift), where improvements in one area inadvertently degrade another. Concept drift occurs when the model struggles with new slang, emerging technical terms, or shifts in cultural understanding. RLHF adjustments can cause over-correction leading to inconsistent behavior.

Developers lack Kubernetes expertise and want to consume infrastructure without delays, but provisioning new clusters is time-consuming and expensive. This creates bottlenecks where developers wait for ops to provision infrastructure rather than focusing on feature development.

Adopting serverless or edge computing platforms requires giving up significant control over infrastructure and deployment, creating trade-offs that developers must navigate.

Organizations use fragmented point-product security tools for S3, making it difficult to gain a holistic view of security posture and creating gaps in coverage.

DNS and security services running on physical appliances require monthly maintenance windows and weekend work, with global downtime coordination being disruptive for staff.

When Workers execute multiple database queries, each request must traverse globally (e.g., Australia to Europe repeatedly), combined with per-request connection establishment, resulting in significant latency penalties.

Cloudflare lacks sufficient DevOps integration and developer platform capabilities compared to competitors, making it less attractive for development teams seeking comprehensive developer tooling.

Reusing containers/sandboxes in serverless platforms requires worrying about data leaking between different uses, adding complexity and cost to the implementation.

Managing Cloudflare across many domains or clients is cumbersome due to missing bulk update functionality in the UI. Users are forced to rely on APIs or manual scripts, creating friction for agencies and multi-tenant deployments.

Cloudflare promises DNS firewall features that don't fully materialize in practice, forcing users who attempt migrations to pull back and maintain legacy solutions instead.

As developers use AI agents and LLMs with their development workflows, platforms struggle to keep AI-compatible APIs updated with framework changes. AI models often attempt to use unsupported or poorly-documented APIs, frameworks do not expose correct types, and there is incoherent documentation about what is safe for AI consumption, forcing developers to work around AI-generated code failures.

48% of IT and security leaders report difficulty supporting evolving user types and a growing number of users, creating operational burden and complexity.

Cloudflare's developer platform and products lack sufficient documentation, making it difficult for developers to understand and implement features effectively.

Ruby ecosystem suffers from a shortage of well-maintained, high-quality third-party libraries compared to Python, Node.js, and PHP ecosystems.

Ruby follows paradigms and standards strictly, making it difficult to change the core codebase. Many configured objects cannot be modified by developers, preventing required core changes without shifting to other tech stacks entirely.

Ruby's ecosystem is tightly tied to Rails and web development, limiting its applicability. Unlike Python (AI/ML/data science) or JavaScript (browser/serverless), Ruby has not made significant inroads outside web development.

Integrating search engines (particularly Elasticsearch) in Ruby applications is complex, with questions about this topic receiving longer response times on Stack Overflow. The work involves complex configuration and managing hierarchical relationships.

Developers create unnecessarily complex inheritance chains and abstraction layers that make code difficult to understand. Following a single business logic path requires jumping between ten or more different definitions, making the codebase hard to maintain and reason about.

Ruby's lack of type safety forces developers to write unit tests to enforce contracts and expectations instead of relying on the type system. This increases testing burden and reduces development velocity.

Development Environment and Infrastructure emerges as the most difficult category while also exhibiting above-average popularity, indicating widespread challenges in setting up and managing development environments for Ruby applications.

PHP's loose typing means variables do not have fixed data types, leading to unexpected errors and bugs that are difficult to pinpoint. As an interpreted language executed line-by-line at runtime, debugging becomes challenging, especially in complex codebases.

PHP doesn't allow modification of core language behavior, forcing developers to write entirely new scripts for customizations. Mistakes in code require starting from scratch or using unreliable workarounds, limiting flexibility compared to languages like Java or Ruby.