Ohjelmistoteollisuus ei ole viimeisen kahden vuoden aikana vain kehittynyt. Se on kääntynyt ympäri. Vuosikymmenten ajan kehittäjät on opetettu kääntämään vaatimuksia koodiksi rivi riviltä, funktio funktiolta. Työ on ollut hidasta, tarkkaa ja syvästi inhimillistä. Nyt tämä malli murenee tehottomuutensa alle.

https://etn.fi/index.php/opinion/18714-agenttikoodaus-muuttaa-myoes-sulautetun-kehityksen

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Client-Side vs. Server-Side Processing in Windsurf
Client-Side Processing
User Interface Rendering: All UI elements, animations, and visual feedback are handled locally.
Local Code Indexing: Windsurf performs local indexing of your codebase to understand its structure and provide context-aware suggestions.
Text Editor Operations: Basic text editing, cursor movements, and selection handling happen locally.
Syntax Highlighting: Real-time syntax highlighting is typically processed locally.
Local Caching: Windsurf caches responses and code snippets locally to improve performance.
Request Preparation: Packaging your code context, queries, and history before sending to the server.
File System Operations: Reading local files, monitoring changes, and managing the workspace structure.
Server-Side Processing
AI Model Execution: The core AI models (like SWE-1, Claude, or GPT models) run on Windsurf’s servers.
Code Analysis: Deep semantic understanding of code, pattern recognition, and complex code analysis.
Code Generation: Creating new code snippets, completing functions, or refactoring suggestions.
Natural Language Processing: Understanding your queries and generating human-like responses.
Authentication & Authorization: User account management and access control.
Rate Limiting: Managing the number of requests users can make within certain timeframes.
Telemetry & Analytics: Processing usage data to improve the service (if enabled).
Performance Implications
When you experience “deadline exceeded” errors or “this request is taking longer than expected” messages, the bottleneck is typically on the server side, particularly with the AI model processing. The client is waiting for the server to complete its work, but the server takes too long and eventually times out.

Large conversation histories impact both sides:

Client: Larger memory usage, slower UI rendering
Server: More context to process, longer inference times, higher token usage
This is why clearing conversation history can sometimes help with performance issues, as it reduces the processing load on both the client and server sides.