Pains

Users on expensive clusters want to pre-compile models to avoid paying compilation costs at runtime, but torch.compile requires actually executing the Python program to discover compilable regions, making straightforward ahead-of-time compilation impossible. This is compounded by graph breaks and unknown input metadata.

Models on Hugging Face Hub are community-contributed without formal vetting, leading to inconsistent quality, bugs, biases, and security issues. Models that work for research may not be suitable for production business use.

TensorFlow forces non-idiomatic Python patterns, requiring session handlers and TensorFlow-specific equivalents for basic operations like loops. This creates verbose, un-Pythonic code and makes the framework feel like a language within a language.

Electron updates frequently, introducing breaking changes that disrupt existing functionality. Many apps bundle multiple Electron versions or fall behind on updates, forcing developers to juggle compatibility issues and regularly refactor code to maintain stability.

Tight deadlines and unexpected shifts in requirements add stress and pressure, causing scope creep that impacts final product quality and efficiency, leading to missed deadlines, compromised quality, and developer burnout.

iOS apps suffer from multiple performance issues: retarded network requests, excessive CPU usage, unoptimized UI calibration, and memory leaks. These issues can break apps and be detrimental to business outcomes.

Developers, particularly juniors using Entity Framework or NHibernate, make multiple separate database calls instead of batching queries. Each database call carries significant time overhead, degrading application performance.

Codex excels at simple boilerplate code but struggles with complex logic requiring deep contextual understanding across multiple steps. It frequently produces incomplete or incorrect code when handling lengthy functions or workflows.

33% of respondents cite securing applications and integrating third-party tracing systems as pain points. Security has emerged as the #1 concern for DoK workloads, driven by complexity of securing distributed data workloads and regulatory compliance.

Integrating Three.js with Next.js creates compatibility issues that are difficult to resolve, breaking expected library functionality and causing significant frustration during development.

Large Terraform applies trigger API throttling (429 errors) when hitting per-account or per-region cloud provider limits. Additionally, eventually-consistent cloud services may not reflect changes immediately, causing subsequent API calls to fail or return stale data.

Developers are blocked waiting for QA test results after committing code, facing waits from 10 minutes to 10+ hours depending on test suite size. This creates productivity bottlenecks and forces developers to context-switch or risk introducing rework.

Memory leaks are nearly inevitable in JavaScript development without conscious effort to prevent them. Multiple common patterns inadvertently cause memory to accumulate, making leak prevention difficult and error-prone.

Claude Code (the competing tool) experienced three infrastructure bugs between August and September 2025 that caused intermittent performance dips, frustrating users who relied on its precision and suggesting the tool gives up on tough problems.

Terraform lacks a robust, built-in testing framework for HCL. Unit testing is difficult and integration testing (spinning up real infrastructure) is slow and expensive. Teams resort to embedding validation scripts or hacks, leading to accidental infrastructure drifts reaching production.

Claude Code recently started ignoring 'accept all edits' mode, requiring human intervention at every turn even for allowlisted operations like reading files or listing directories. This breaks automation and requires constant user approval during planning phases.

Most teams take days or weeks to deploy code from commit to production, while elite teams achieve sub-day deployments. The bottleneck typically stems from specialized deployment knowledge residing with individual team members, creating single points of failure and reducing deployment velocity.

npm suffers from slow installation times and high resource consumption, particularly in large-scale projects with many dependencies. This impacts developer productivity and build times, with developers finding alternatives like Yarn and pnpm faster.

Claude Code's desktop VS Code extension burns through token quota significantly faster than the CLI counterpart. A task consuming a small fraction on CLI can use 20%+ on desktop, triggering context compression and errors. This forces developers to manage tasks meticulously to preserve quota until end of day.

Moving resources between state files or across state boundaries is not supported by terraform mv or other built-in tools. Teams must manually edit JSON state files or write custom tooling to reorganize infrastructure across projects or environments.

Managing many resources in a single Terraform state increases blast radius (a single error affects many resources), slows plan/apply execution, and prevents new changes if any resource drifts. Resource drift in one resource blocks the entire apply.

C++20 modules were introduced but cross-platform support remains extremely limited and too complicated for average developers. Existing implementations lack practical usability.

A Microsoft study found that industry-leading AI coding models, including Claude 3.7 Sonnet and o3-mini, struggle to reliably debug software. Models need adequate test case coverage to be effective; without it, they become lost.

Unlike other IaC tools, Terraform lacks a stack concept for managing collections of related resources. It only operates at the root module level, making it difficult to manage large, complex environments with multiple resource groupings without custom orchestration.