Something new from YAC Yazılım: — an AI CEO that runs your company. Coming soon. And here's the part we're proud of — not one of these was built by someone who writes code: → — an AI CEO for your business → — turn a drawing into a 3D scaffold design + bill of materials → — a B2B scaffolding & formwork marketplace → — an AI toolkit for small businesses All built from İstanbul, with @AnthropicAI's Claude Code. You bring the body — we provide the brain. #ClaudeCode# #Anthropic# #AI#

# Claude Code Features and Practical Usage 🚀 Just mention "@/claude" in a PR or issue and watch fixes, features, and reviews happen automatically. Claude Code becomes an AI teammate that works inside your CI. 📌 Title and Feature URL Title: GitHub Actions URL: 📝 Overview Claude Code GitHub Actions integrates Claude Code into your GitHub workflow. Mention "@/claude" on any PR or issue and Claude analyzes your code, creates pull requests, implements features, or fixes bugs. Built on the Claude Agent SDK, it is ideal for automating routine engineering tasks. 🔧 How It Works - It auto-detects whether to run in interactive mode (responding to @/claude mentions) or automation mode (running immediately with a prompt) based on your config. - It respects the repository's CLAUDE.md, following your project standards and existing code patterns. - Code runs on GitHub-hosted runners and uses Sonnet by default (Opus 4.8 can be selected). - Beyond the direct Claude API, it supports Amazon Bedrock and Google Vertex AI. 🛠 Practical Usage - The easiest setup is running "/install-github-app" inside claude in your terminal; it guides you through the GitHub app and secrets (you need repo admin rights). - For manual setup: install the Claude GitHub app ( add ANTHROPIC_API_KEY as a repository secret, and copy examples/claude.yml into .github/workflows/. - Use the action "anthropics/claude-code-action@v1". Pass instructions via "prompt" and CLI arguments via "claude_args". - claude_args examples: --max-turns 5 / --model claude-sonnet-4-6 / --mcp-config /path/to/config.json - Comment examples: "@/claude implement this feature based on the issue description", "@/claude fix the TypeError in the user dashboard component". 🎯 Use Cases - Mention on an issue to auto-create a PR that turns requirements into working code. - Request a standard review on a PR: "@/claude review this for security issues". - Use a schedule (cron) trigger to auto-generate a daily summary of yesterday's commits and open issues. - Wire in the code-review plugin to run its skill automatically on every PR update. ⚠️ Caveats - Never commit API keys directly to the repo; always reference GitHub Secrets (secrets.ANTHROPIC_API_KEY). - Costs accrue both as GitHub Actions minutes and API tokens. Use --max-turns and timeouts to prevent runaway jobs. - If Claude doesn't respond, confirm the comment uses "@/claude" (not "/claude") and that the app and secrets are configured. - v1.0 has breaking changes from beta: remove mode, change direct_prompt to prompt, and move CLI options into claude_args. #ClaudeCode# #CICD#

# Claude Code Features and Practical Usage 🔌 Still copy-pasting schemas and logs into chat? Connect Sentry, PostgreSQL, or GitHub via MCP and Claude reads and writes those systems directly. "Investigate the last 30 days of this table" just works. 📌 Title and Feature URL Title: MCP URL: 📝 Overview MCP (Model Context Protocol) is an open standard for AI tool integration that connects Claude Code to hundreds of external tools and data sources. MCP servers give Claude Code access to tools, databases, and APIs, so it can read and operate those systems directly instead of working from what you paste. 🔧 How It Works - Multiple transports exist: HTTP (recommended) for remote connections, WebSocket for bidirectional push, stdio for local processes, and the deprecated SSE. - Three scopes are available: local (default, private to you, stored in ~/.claude.json), project (shared via .mcp.json), and user (all projects, private to you). - Many cloud servers require authentication and support OAuth 2.0; on a 401/403 response you can complete the flow from /mcp. - Tool search is on by default, lazily loading MCP tools on demand to keep context usage low. 🛠 Practical Usage - Add with claude mcp add --transport http , or for stdio claude mcp add [options] -- [args...]. Options go before the server name. - Manage with claude mcp list / claude mcp get / claude mcp remove , and use /mcp inside a session to check status and authenticate. - Set scope with flags like --scope project; for project sharing, the .mcp.json file is version-controlled. - Reference MCP resources with @/server:protocol://resource/path, e.g. analyze @/github:issue://123. - Run MCP prompts as commands in the form /mcp__servername__promptname. 🎯 Use Cases - Connect Sentry and debug production issues: "What are the most common errors in the last 24 hours?" - Connect PostgreSQL and query without pasting the schema: "Find customers who haven't purchased in the last 90 days." - Connect GitHub's remote MCP and ask "Review PR #456# and suggest improvements." - Commit .mcp.json so the whole team shares the same set of MCP tools. ⚠️ Caveats - Confirm you trust each server before connecting; servers that fetch external content can be exposed to prompt-injection risks. - Project-scoped servers require approval before use for safety (reset choices with claude mcp reset-project-choices). - A warning appears when tool output exceeds 10,000 tokens; raise the cap with MAX_MCP_OUTPUT_TOKENS (default max is 25,000). - SSE is deprecated; use HTTP where possible. #ClaudeCode# #MCP#

# Claude Code Features and Practical Usage 🤖 Don't want logs and search results cluttering your main conversation? Subagents work in their own context and return only a summary. You can carve out read-only reviewers and research specialists. 📌 Title and Feature URL Title: Subagents URL: 📝 Overview A subagent is a specialized AI assistant for a specific kind of task. It runs in its own context window with its own system prompt, tool access, and permissions, working independently and returning only a summary to the main conversation. This isolates verbose output and lets you constrain what it can do via tool limits. 🔧 How It Works - Built-in subagents include Explore (a read-only, Haiku-powered codebase explorer), Plan (a research agent for plan mode), and general-purpose (for complex tasks needing both exploration and changes). - Custom subagents are defined as Markdown with YAML frontmatter; only name and description are required. - Key fields include tools (allowed tools), disallowedTools (denied tools), model (sonnet/opus/haiku/inherit), permissionMode, skills, and memory. - Claude decides delegation based on the description; phrases like "use proactively" encourage eager delegation. - Subagents cannot spawn other subagents, which prevents infinite nesting. 🛠 Practical Usage - The /agents command opens a tabbed UI to create, edit, and delete subagents via guided setup or Claude generation (recommended). - Storage location sets scope: .claude/agents/ for the current project, ~/.claude/agents/ for all projects. The former checks into version control for team sharing. - Invoke explicitly via natural language ("use the test-runner subagent to..."), an @-mention, or run the whole session as one with claude --agent code-reviewer. - For a read-only reviewer, restrict tools, e.g. tools: Read, Grep, Glob, Bash. - Set memory: project to give a subagent a persistent memory directory that accumulates insights across conversations. 🎯 Use Cases - Defining a code reviewer that never modifies code, or a read-only research specialist for long investigations. - Isolating high-output work like running tests or processing logs, returning only the failing tests as a summary. - Running parallel research on independent modules in separate subagents. - Routing tasks to fast, cheap Haiku to control cost. ⚠️ Caveats - Each subagent starts with a fresh context and does not inherit conversation history or already-read files; it may take time to ramp up. - Many subagents returning detailed results can themselves consume significant main-conversation context. - Editing subagent files directly on disk requires a session restart to load them (changes via /agents take effect immediately). - Use bypassPermissions carefully, as it skips permission checks. #ClaudeCode# #DevTools#

🧠 Claude Code agents start every session with amnesia. This post solves that without adding a dedicated service, using the Elasticsearch you already run. TL;DR: A CLI called bridge and three hooks automatically store an agent's decisions, context, and tasks in Elasticsearch, then recall them across sessions and devices via hybrid search plus temporal decay. Title: Persistent memory for agents: Claude Code on Elasticsearch URL: Points ・🗂 Seven indices: memory/messages/tasks/sessions/status/entities/entity-history store memory by dimension ・🪝 Three hooks automate it: SessionStart syncs, markdown writes get indexed, Stop logs the session end (no explicit calls) ・🔎 Hybrid recall: BM25 fused with semantic_text dense vectors via RRF, catching both exact task IDs and conceptual matches ・⏳ Temporal decay: a default 45-day DECAY ranks recent memories higher (needs ES 9.3+ or Serverless) ・🕸 Knowledge graph: extracts blocked_by and friends from markdown frontmatter, surfacing blockers at depth-2 traversal ・📡 Offline resilience: writes queue locally as JSON and flush via bulk API once connectivity returns ・💻 Cross-device: gen-handoff produces a handoff JSON so another machine restores context with no git pull If you already run Elasticsearch, the pragmatic appeal is no new service and reusing your existing monitoring and backups. Setup is three commands: git clone and #Elasticsearch# #ClaudeCode#

Your Claude Code work becomes an always-up-to-date live shared page — Artifacts eliminate the time spent explaining status 📊 Title: Claude Code now supports artifacts URL: 📊 Overview Artifacts turn a Claude Code work session into a live, interactive web page. They pull in the full session context — codebase, connectors, and conversation — and output a visual, shareable result. ❓ Challenges Solved Dev teams spend more time communicating status than building. ・Manually compiling findings, explaining progress verbally, recreating visualizations ・This overhead drags on development Artifacts automate it and keep the page synchronized as work progresses. 💡 Features & How It Works ・Built on full session context: code, monitoring data, and reasoning combined with no external infrastructure ・Live pages that update in place: teammates see refreshes immediately on publish ・Version history for restoring previous states, plus a gallery to manage them ・Private by default, shareable with org members, with admin-controlled access The flow: request an artifact → Claude Code builds and publishes it → share the link → it auto-republishes to the same URL as work continues. 🌍 Use Cases License audits, data-flow mapping, security findings linked to code lines, cloud cost tracking, PR walkthroughs, architecture diagrams, incident postmortems, and team shipping summaries. #ClaudeCode# #AIAgents#