Pains

Setting up production-ready Vite applications requires understanding and configuring multiple separate tools (testing, linting, formatting, bundling, scaffolding, task running), creating a steep learning curve and complex project setup.

The Root CA may be known on the system but not present in the specific application's trust store, causing certificate verification failures even though the CA is globally trusted.

Variable Rate Shading (VRS) produces poor results when combined with upscaling techniques and requires significant implementation complexity. Some upscalers will not produce good results when used together with VRS, forcing developers to disable VRS when using upscaling or sharpening filters.

Developers cannot customize server configurations, install specific software, or access server logs. This abstraction becomes limiting for applications requiring fine-tuned performance optimization or specific server-level requirements, preventing advanced troubleshooting and optimization.

SQLite lacks built-in support for efficient bulk insert operations, forcing developers to insert data row-by-row, which is extremely slow and tedious when dealing with large datasets.

Vercel enforces a 4.5MB request body limit, which becomes problematic for AI applications handling large payloads, file uploads, or complex data structures. This constraint requires workarounds like splitting requests or streaming uploads.

Gemini CLI lacks support for tool subcommands (e.g., git status vs git rm), forcing developers to grant all-or-nothing permissions for entire binaries like `git`, `gh`, `vercel`, or `supabase`. Users must constantly babysit permission requests instead of setting granular policies.

LangChain prioritizes building a proprietary ecosystem and investor interests (having received $30 million in funding) over developer needs. This misalignment restricts the framework's usefulness and adaptability while creating a business model that is not developer-centric.

LangChain doesn't define a common data format for LLM inputs/outputs, intermediate results, or knowledge from tools. Each component uses custom Python classes or schemas, hindering integration with other libraries and requiring adaptation or conversion when switching components.

Docker's documentation library doesn't keep pace with rapid releases and platform updates. Developers frequently struggle to find answers about changes in Docker until relevant documentation is finally available, creating frustration and delays.

ChatGPT requires an internet connection for all functionality with no offline mode. Users working in areas with unreliable connectivity or during travel cannot use the tool, and if the connection drops during output generation, the result is lost.

Python has a significant dependency on C implementations for heavy computational tasks, creating a gap between Python's ease-of-use and the complexity of leveraging performance-critical operations.

ChatGPT lacks proper citation handling, memory editing capabilities, privacy options, and cost visibility. These limitations make it difficult to verify sources, control what data is retained, and understand usage costs—preventing confident use for sensitive or high-stakes work.

Python's dynamic nature makes debugging difficult and time-consuming, especially in large codebases. Cryptic error messages and the need to trace through dynamically-typed code makes it hard to identify root causes of bugs without strong debugging tools.

AWS S3 documentation is difficult to understand, especially when integrating with other AWS services like Elemental MediaConvert. Developers must rely on external resources like AI, YouTube videos, or third-party aids to complete integrations.

When potential customers can't quickly understand pricing, tiers, and what they'll get, they abandon the product. Hidden fees, confusing tiers, and unexpected charges drive users to competitors with transparent pricing.

ChatGPT's GPT-5.x models decline requests at a higher frequency than previously, citing safety concerns for benign queries. Creative writing, hypothetical scenarios, and technical troubleshooting prompts trigger refusals that did not occur a year ago. Iterative RLHF tuning has made the model progressively more conservative.

Users seek customization to adapt features to their workflows but encounter rigid defaults lacking presets or templates. Tightly coupled frontend and backend architectures and weak user preference management limit modularity.

ChatGPT's knowledge is frozen at the point of its last training data update (late 2024 for current models). It has no inherent knowledge of events, discoveries, or data that emerged after that point, limiting utility for time-sensitive queries.

Rust's TOML configuration format, combined with complex namespace and feature flag systems, makes it easy to introduce subtle bugs and create fragile configurations, particularly problematic for projects with many optional dependencies.

ChatGPT cannot understand human emotions or provide genuine empathy. Its responses can come across as insensitive or cold in emotionally-driven conversations, potentially worsening situations requiring emotional support or crisis management, particularly in healthcare and education.

PostgreSQL backup and restore tools have counter-intuitive workflows: pg_dump by default does not include global objects like roles, so backups are incomplete unless users manually dump additional information. pg_dumpall doesn't support custom format, and pg_restore requires non-obvious flags like -C to create databases. File naming conventions (.backup) are inconsistent with documentation.

Users report that responses, particularly from advanced models, have become noticeably slower, sometimes taking unreasonable amounts of time to generate, impacting productivity and user experience.

ChatGPT excels at mimicking styles and remixing patterns but cannot create truly novel ideas. All outputs are derived from training data, making content inherently derivative. It struggles with originality, nuance, satire, irony, and emotional subtext crucial for engaging storytelling.