XERJ + Qwen 3.8... give a cheaper model better context and it starts working smarter and faster Opus 5.5 and Qwen 3.8 get the same task of building an interactive website, with XERJ helping Qwen find the right context and make fewer guesses as it works. Qwen + XERJ finishes in 30 minutes for $24.77. Opus takes 37.5 minutes and $56.95 to reach the same result. free open source repo the site has animated scenes, scroll interactions and transitions between sections. XERJ helps the agent → find the relevant project code and reference material → look up definitions and parameters → understand existing behavior before writing a replacement → check its changes against the sources a model can write a scroll animation and still get your app wrong. it needs to know who owns the scene, what updates its state and how the project handles cleanup. retrieving those details before writing code gives the agent fewer assumptions to correct later. you can use the same setup with your expensive model too. when another question comes up, the agent searches again and checks the source. full XERJ breakdown below ↓

even a frontier model can write broken code when its guessing how your app works XERJ helps your agent find the facts it needs faster and more precisely. better context gives it fewer reasons to invent an API call or make the wrong assumption about your code free open source repo: here is what that looks like when Claude Code needs to add Stripe Checkout to an existing app the project already has routes, pricing and frontend code. Stripe has its own API requirements. the agent needs both to write a change that fits here is the workflow, step by step: > step 1 → give Claude Code the task: “Add Stripe Checkout to this app” > step 2 → XERJ finds the existing checkout route, server-side price configuration and frontend request > step 3 → the agent searches the Stripe reference pack for Checkout Session creation > step 4 → the relevant method and parameters come back alongside the project files, with source locations attached > step 5 → Claude Code uses those references to start writing the checkout route > step 6 → the proposed change follows the app's existing price mapping and response format knowing how to write a payment endpoint is only part of the task. the agent also needs to know where it belongs, which prices to use and what the frontend expects back. missing any of that can leave you with code that looks reasonable but breaks when you connect it to the app. XERJ makes those details searchable while the agent works. when another question comes up, it can look up the answer and check the source that can help a cheaper model handle unfamiliar code and help your expensive model make fewer avoidable mistakes. both get better material to reason from. XERJ runs locally and connects to coding agents over MCP full XERJ breakdown below ↓

you paid for a frontier model, burned through your allowance and STILL have nothing ready to ship XERJ helps you get more useful work out of that allowance by giving the agent relevant project knowledge when it needs it fewer wasted reads. fewer guesses that send the whole task down the wrong path here's how it fits: → XERJ search → project files + reference packs → relevant passages + source locations → focused context → model continues working Claude, GPT or Gemini still handles the reasoning and implementation. XERJ helps it find the definitions, contracts and project rules it would otherwise have to hunt down that matters when you're working in an unfamiliar repo or coming back to a project after weeks away. the knowledge stays searchable across tasks more of your allowance goes toward changes you can actually review, test and ship full breakdown in the article below ↓

introducing XERJ Corpus Hub, reference memory for AI agents. give your agent a corpus for your task. watch it become superintelligent. > finds the answers in indexed expert knowledge > applies them to your code, research, and projects > works with any model. free and OSS forever/Apache 2 install in one prompt: >Install XERJ (docs: index this project’s sources, docs, and logs, and connect as reference memory hub