#Monorepos# make it easier to work across a #codebase#, but they don’t isolate projects the way separate repositories do. #AI# coding agents raise the stakes because they can have broad, persistent access. #GenAI# #AIAgents#

Cloning multi-gigabyte monorepos creates an infrastructure bottleneck. GitFarm serves Git as a Service via a gRPC API, delivering checkouts under 500ms and cutting resource overhead by 80%+: #UberEngandSci# #UberEngineering# #PlatformEngineering#

# Useful but Little-Known Features of Claude Agent SDK 🌍 Grant your agent access to files outside the working directory with the additionalDirectories option. Essential for monorepos, shared libraries, and any project spanning multiple directory trees. 📌 Title: Granting Access Beyond the Working Directory 🔗 URL: 🧩 Overview `additionalDirectories` is a `query()` option that allows agents to access paths outside the working directory (cwd). By default, agents can only operate on files within cwd. When you specify additional directories, the agent gains read/write access to files in those paths as well. In `acceptEdits` permission mode, edits within additional directories are also auto-approved. This is especially useful for monorepo packages, shared libraries, or cross-project workflows. 🛠 Usage Pass `add_dirs` (TypeScript: `additionalDirectories`) in the query options. ```python from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage # Grant access to multiple monorepo packages async for message in query( prompt="Update the shared utility functions and fix the imports in the web app", options=ClaudeAgentOptions( add_dirs=[ "/path/to/monorepo/packages/shared-utils", "/path/to/monorepo/packages/web-app", ], permission_mode="acceptEdits", # Auto-approve edits in additional dirs too allowed_tools=["Read", "Edit", "Write", "Grep", "Glob"], ), ): if isinstance(message, ResultMessage) and message.subtype == "success": print(message.result) # Access a shared library alongside the main project async for message in query( prompt="Check the shared library API and update our integration code", options=ClaudeAgentOptions( add_dirs=[ "/path/to/shared-library", ], allowed_tools=["Read", "Edit", "Grep", "Glob"], ), ): if isinstance(message, ResultMessage) and message.subtype == "success": print(message.result) ``` 🏗 Integration into Production Systems - In monorepo setups, list related package paths in `add_dirs` to enable cross-package changes with dependency awareness - Combined with `acceptEdits` mode, file edits (Edit, Write) and filesystem operations (mkdir, rm, mv, cp) in additional directories are auto-approved - Writes to protected paths still prompt even when included in additional directories 💡 Use Cases 📦 Monorepo development: modify shared packages and individual apps simultaneously 📚 Shared library reference: read external library source to verify correct API usage while coding 🔧 Centralized config management: access and update configuration directories outside the project ⚠️ Caveats - Access to paths outside both cwd and additional directories remains blocked or prompted - `bypassPermissions` mode approves all paths regardless of `add_dirs` settings - This expands the agent's security boundary — specify only the minimum necessary paths - `acceptEdits` auto-approval is scoped to cwd and additional directories only ✨ additionalDirectories enables agents to work seamlessly across multiple directory trees, making cross-project and monorepo workflows practical. #ClaudeAgentSDK# #AIAgent#

software was: you writing it then running it now, if anyone has used Hermes you understand that you don't actually design the software, your agent does in a way that it thinks is best. These are not traditional software in monorepos etc. They are scripts the agent loves to use, and they compose into epic workflows. The harness is guiding the code, not the other way around. soon everyone will realise this

Burning tokens on AI code review? 🔥 This tool turns your codebase into a structural graph so the AI reads only relevant files — cutting tokens by a median of 82x. Title: tirth8205/code-review-graph URL: 🔥 Overview A local-first code intelligence tool that persists a structural map (graph) of your codebase via Tree-sitter parsing. It lets AI assistants review by reading only contextually relevant files instead of the whole repository. ❓ Challenges Solved AI code review tools waste tokens by re-reading large parts of the codebase on every review. ・In large monorepos, context bloats and both cost and latency worsen ・Analyzing change impact required scanning the entire project 💡 Methodology & How It Works A three-stage pipeline. ・Parsing: Tree-sitter builds ASTs, extracting functions, classes, imports, and call relationships ・Graph storage: nodes and edges persist in SQLite (no external database) ・Analysis: on changes, blast-radius analysis traces affected callers, dependents, and tests, returning minimal context It supports many languages, incremental updates under 2 seconds, MCP integration (30 tools), a GitHub Action, and D3.js visualization. 📊 Experimental Results ・Token efficiency: 38x-528x reduction (median ~82x across 6 repos) ・Impact prediction F1 score: 0.71 average ・CLI example: full context 12,921 tokens → graph context 762 tokens (~94% saved) #CodeReview# #AIAgents#

# OpenCode Features and Practical Usage 📜 Tired of re-explaining your project's conventions every single session? `AGENTS.md` fixes that: write it once, and the agent always operates with your rules in mind. 🏷️ Title: AGENTS.md 🔗 URL: 📘 Overview `AGENTS.md` is the file you use to give OpenCode custom instructions. Document your project's conventions, architecture, and build steps there, and that content is always included in the LLM's context so the agent behaves the way your team expects. ⚙️ How It Works Rules live at two levels: ・Project-level: an `AGENTS.md` at the repo root, applied only within that directory tree. ・Global-level: `~/.config/opencode/AGENTS.md`, shared across all sessions and best for personal preferences. At startup OpenCode searches in order: local `AGENTS.md` or `CLAUDE.md` (walking up from the current directory) → global `~/.config/opencode/AGENTS.md` → the Claude Code compatibility file `~/.claude/CLAUDE.md`. The first match in each category wins. The design is close to Cursor-style rules and makes migration easy. 🛠️ Practical Usage To pull in external docs as instructions, list them in the `instructions` field of `opencode.json`, e.g. `"instructions": ["CONTRIBUTING.md", "docs/guidelines.md", ".cursor/rules/*.md"]`. Globs are supported. If writing from scratch feels like a chore, run `/init`: it scans important files, may ask targeted questions, and generates or improves `AGENTS.md` for you. Commit the result to Git so the whole team shares it. 💡 Use Cases Capture tacit knowledge like "commit messages in Japanese," "tests use pytest," or "never import this layer directly" in `AGENTS.md`, and both new teammates and the agent share the same assumptions, cutting down on review churn. In monorepos, `instructions` globs let you bundle per-package conventions. ⚠️ Caveats File references hand-written inside `AGENTS.md` are not expanded automatically. When you need multiple files reliably loaded, the `instructions` field of `opencode.json` is the dependable choice. An existing `CLAUDE.md` is recognized for compatibility, but consolidating new content into `AGENTS.md` keeps things tidy. Remote URL references carry a 5-second timeout. #OpenCode# #AGENTSmd#

Hacktron got from a compromised Codex account to OpenAI’s internal monorepo in under 72 hours. The bigger issue: coding agents can turn one compromised account into a much wider access problem.

@danielmarinq showed a "company brain," a 3D visualization of the entire @NexusLabs monorepo, marketing, strategy, engineering, all in one graph. the goal: automate 100% of the software development lifecycle

Coding agents broke OpenAI's Git. Codex sped up their engineers so much that by the end of last year the monorepo couldn't keep up. Slow pushes, failed merges, and reliability at times below 90%. The work multiplies. One engineer now has several changes heading to CI at once, and every push sets off more jobs fetching the same code. So they built a new Git backend called Pando. It went from kick-off to production in 10 weeks, designed by one engineer and written by teams of agents. Median push latency is down nearly 80%, and it now serves close to a petabyte of Git data a day. This is what agents do to infrastructure. Every agent is another worker pushing code, running tests and pulling data, and all of that lands on storage, networks and CPUs as well as GPUs. OpenAI hit the wall first because they use agents more than almost anyone, so at least the rest of us know what's coming. Every company that rolls out agents at scale is going to need a lot more infrastructure under them than it planned for