Guides, configurations, and tips for getting the most out of GPTCode.
The Problem Nobody’s Talking About You’re 3 hours into a new AI assistant session. You’ve already explained: Your microservices architecture (again) Which services use which databases (for the 10th time...
Why Your AI Coding Agent Needs End-to-End Encryption 🔐 You’re working on a proprietary codebase. Maybe it’s your startup’s core product, your company’s internal tools, or a client project under...
GPTCode now handles advanced Git operations autonomously: bisect for bug hunting, cherry-pick and rebase with conflict resolution, squash with AI-generated messages, reword for commit improvements, and intelligent merge conflict resolution.
In a single development session, GPTCode went from 14% autonomy to 59%, achieving 100% coverage of the MVAA (Minimum Viable Autonomous Agent) Critical Path. Here's how we did it in 21 commits, 8 phases, and 2,788 lines of code.
GPTCode isn't here to be 'better' than Cursor, Copilot, or the next AI coding unicorn. It's here to be different—transparent, hackable, and yours.
Anthropic released Claude Opus 4.5 with 80.9% SWEBench Verified, 66% OSWorld, and 50% fewer tokens than Sonnet. Here's what it means for GPTCode users.
A persistent REPL with conversation history, file context awareness, and token management. Natural follow-up questions that build on previous context.
Using dependency graphs and PageRank to achieve 5x token reduction while improving response quality. Smart context selection for AI coding assistants.
Intelligent model selection that automatically chooses the best model based on availability, cost, context window, and speed. Learn from usage patterns and optimize costs.
Anthropic's Tool Search and GPTCode's Agent Routing solve the same problem differently. Here's what we learned.
Anthropic's Tool Search and GPTCode's Agent Routing solve the same problem differently. Here's what we learned.
Shell hooks and a universal sink capture corrections from any CLI with two keystrokes. Your machine learns from you.
Foundation for autonomous execution: file validation prevents scope creep, explicit success criteria enable verification, telemetry tracks execution, model catalog centralizes capabilities.
gptcode do uses intelligent auto-recovery to find the optimal model path. Evaluates cost, speed, reliability across backends with real-time learning from execution history.
GPTCode's intelligence layer combines XGBoost, KAN networks, and unbiased sampling to optimize the speed vs intelligence tradeoff for autonomous coding tasks.
Master GPTCode's three-phase workflow: Research codebase context, plan implementation steps, and execute interactively or autonomously with built-in verification.
From autocomplete to agentic coding to autonomous engineering. Exploring the future of AI-assisted development and GPTCode's roadmap toward Phase 3.
Embedded ML models in GPTCode enable instant intent classification and complexity detection with zero API costs. 92% cheaper than commercial copilots.
Switch between multiple named configurations per backend. Create speed, quality, and free profiles to optimize your workflow without editing config files.
Learn how GPTCode uses RAG, smart retrieval, and context optimization to handle large repositories without dumping your entire codebase into the prompt.
Real-world coding benchmarks comparing models on HumanEval, AIME, and speed metrics. Find the best models for your budget and use case.
Switch between local Ollama models for privacy and cloud providers for power. Get the best of both worlds with GPTCode's flexible backend configuration.
Run GPTCode completely offline with Ollama. Your code never leaves your machine. Zero cost per token, full privacy, no network latency.
OpenRouter provides unified access to the best AI models from multiple providers through a single API, including Claude 4.5, Grok 4.1, and free tier models.
Deep technical analysis of Groq models with rigorously optimized profiles for budget and performance. Understand why router speed matters, editor output costs dominate, and 32B coding-focused models beat 70B generic ones.
Learn context engineering principles to make AI work effectively in production codebases. Proper context management enables handling 300k+ LOC repos with today's models.
AI coding assistants cost $20-30/month. GPTCode offers AI assistance with validation for $2-5/month with Groq or $0/month with Ollama. Radically affordable without compromise.