MultiOn

MultiOn AI

MultiOn AI

MultiOn AI

MultiOn AI

MultiOn AI

MultiOn AI

MultiOn AI

MultiOn AI

MultiOn AI

MultiOn AI

Claude Code tutorial for daily workflow for people who can code

Hi! I’ve been using Claude Code, Cowork, Chat all together for a while with Max plan. I’d say I am a relatively heavy user; I make skills and plugins, manage project memories, work with different mcps connected, got my Claude Code routine set up that messages me via Telegram, connected it to Obsidian, command through my phone using remote control etc I want to go deeper, making multi subagents that can make my daily workflow more efficient. But almost all the tutorials I find on YouTube is about **coding** with Claude Code. I know how to code so I work on cmd usually for Claude Code but I want to see usages that are not about software engineering. But most non-coding usages I find are mostly too simple not going beyond Claude Cowork. Is there a good channel/tutorial that matches my needs? submitted by /u/EuphoricFee7837 [link] [comments]

Shell command to use opus 4.8 as planner / orchestrator with Perplexity, Codex, Gemini and others as executors and reviewers - saves tokens.

Here is a shell command for Claude Code (Opus 4.8). It lets Opus plan the work and send the actual jobs to other models: Perplexity, Codex, Gemini, DeepSeek, and Kimi. Opus stays on planning, the other models do the searching, coding, and reviewing, and you spend far fewer Claude tokens. Further Claude's sub-agent swarm need not be claude and can run on non-Claude models too. When Opus splits a job into parallel sub-agents, each one can run on a different model. A newer model like GPT-5.5 is sometimes stronger and cheaper (especially when its running on your openAI subscription instead of API) than an older Claude model, so each sub-agent can use the model that fits the job. Which model does what Perplexity runs web and Reddit search. Codex handles coding, and it runs on your ChatGPT subscription, so that work adds nothing to your token bill, api is the fall back. Gemini and DeepSeek review the output (api based). Deepseek is especially good with reviewing numbers if your work involves complex financial calculations. I lately find codex reviews to be better, so you can also chose to code with Gemini or Sonnet 4.6 and use Codex as reviewer. Using a different-LLM-family reviewer for Claude or Codex’s output A model grades its own work too loosely and that's proven research. When Claude reviews code that Claude wrote, it skims past its own mistakes. A model from another company has no reason to protect that output, so Gemini or DeepSeek catches problems Claude misses on its own. Researchers have measured this same-family bias, and it matches what people see in practice. Why shell command and not MCP: Token use compared with an MCP tool is drastically lower in this orchestration when run using the shell command. Reviewing a 500-line change sends about 5,000 tokens to a model. With an MCP tool, Opus reads the whole change, passes it to the tool, and reads the answer. That runs about 6,000 to 10,000 Opus tokens. With this shell command, Opus runs one line. The change goes straight to DeepSeek, and Opus reads only the short review that comes back. That runs a few hundred Opus tokens, and DeepSeek does the heavy reading at a fraction of Opus's price. Numbers vary by task. The Opus cost drops because Opus never has to read the big input. Things to note: Bring your own API keys Codex uses your ChatGPT subscription through the codex CLI Defaults always use each provider's newest model, so nothing breaks when an old one is retired. It's a small bash/zsh script. It needs only curl and jq, and it's MIT licensed. The repo is open sourced - Click here Hope it helps. Codex reviewing Claude's work catches what Claude misses when reviewing it's own work submitted by /u/coolreddy [link] [comments]

I had Claude Opus 4.8 build me a custom 'operating system' for my business while I was at the vet

I've been trying to cut down the number of tabs I open every morning to run my content business. YouTube analytics in one place, competitor channels in another, a notes doc for trending stuff, skills I keep re-running by hand. So I tried something. I opened a blank folder, gave Claude a rough plan, and told it to build me a single dashboard that pulls all of it into one place. First I used plan mode to map it out. It asked me a bunch of clarifying questions (what to track, web dashboard vs morning briefing, which APIs I had). Then I dropped in my design system files so it would match my brand. Then I switched to Opus 4.8, turned on the new Ultra Code mode, and told it to execute the plan. Then I left to take my dog to the vet. Came back and it had built the whole thing. One panel for trends and drops in my space, one for competitor videos and their top comments, one for my YouTube stats, one for active projects, and a launchpad to run my most-used skills. The part that actually surprised me is how Ultra Code works. There is an orchestrator that spawns sub-agents to do the work, and then a second layer of sub-agents whose only job is to check the first layer's work. That verification layer is why it can run that long without me sitting there approving everything. First pass was not perfect. Everything had the same visual weight and the skills opened a separate terminal window. One more round of feedback (bento layout, embedded terminal, Apify for the LinkedIn and IG data it could not reach) and it was genuinely usable. Honest caveat: this is the most expensive way to run Claude right now. Ultra Code plus Opus 4.8 burns a lot of tokens. For a one-off deep build it felt worth it, but I would not leave it running on autopilot for small stuff. Anyone else messing with the multi-agent verification setup yet? Curious if the self-checking layer holds up on bigger codebases. submitted by /u/Drogoff1489 [link] [comments]

Claude Code Source Deep Dive - Part VI: Multi-Agent System && Part VII: Context Compression (Compact) and Memory System

Reader’s Note A source-map leak exposed 512,000 lines of Claude Code's TypeScript, giving us a rare look inside one of the world's most advanced AI coding agents. This series explores what I found. Estimated completion time: 2 days. Actual completion time: ∞. Anyway, here's the next chapter. Claude Code Source Deep Dive - Part VI: Multi-Agent System 6.1 Built-in Agents general-purpose (general) You are an agent for Claude Code, Anthropic's official CLI for Claude. Given the user's message, you should use the tools available to complete the task. Complete the task fully—don't gold-plate, but don't leave it half-done. When you complete the task, respond with a concise report covering what was done and any key findings — the caller will relay this to the user, so it only needs the essentials. Tools: all available Model: inherit Explore (code exploration) You are a file search specialist for Claude Code. You excel at thoroughly navigating and exploring codebases. === CRITICAL: READ-ONLY MODE - NO FILE MODIFICATIONS === [Strictly prohibit any file modification] Your strengths: - Rapidly finding files using glob patterns - Searching code and text with powerful regex patterns - Reading and analyzing file contents NOTE: You are meant to be a fast agent that returns output as quickly as possible. Make efficient use of tools and spawn multiple parallel tool calls. Tools: read-only (Agent, FileEdit, FileWrite, NotebookEdit disabled) Model: external → Haiku (fast), internal → inherit omitClaudeMd: true Plan (architecture planning) You are a software architect and planning specialist for Claude Code. Your role is to explore the codebase and design implementation plans. === CRITICAL: READ-ONLY MODE - NO FILE MODIFICATIONS === ## Your Process 1. Understand Requirements 2. Explore Thoroughly (read files, find patterns, understand architecture) 3. Design Solution (trade-offs, architectural decisions) 4. Detail the Plan (step-by-step strategy, dependencies, challenges) ## Required Output End your response with: ### Critical Files for Implementation List 3-5 files most critical for implementing this plan. Tools: read-only Model: inherit omitClaudeMd: true verification (verification) You are a verification specialist. Your job is not to confirm the implementation works — it's to try to break it. You have two documented failure patterns. First, verification avoidance: when faced with a check, you find reasons not to run it. Second, being seduced by the first 80%: you see a polished UI or a passing test suite and feel inclined to pass it. === CRITICAL: DO NOT MODIFY THE PROJECT === === VERIFICATION STRATEGY === Frontend: Start dev server → browser automation → curl subresources → tests Backend: Start server → curl endpoints → verify response shapes → edge cases CLI: Run with inputs → verify stdout/stderr/exit codes → test edge inputs Bug fixes: Reproduce original bug → verify fix → run regression tests === RECOGNIZE YOUR OWN RATIONALIZATIONS === - "The code looks correct based on my reading" — reading is not verification. Run it. - "The implementer's tests already pass" — the implementer is an LLM. Verify independently. - "This is probably fine" — probably is not verified. Run it. - "I don't have a browser" — did you check for browser automation tools? - "This would take too long" — not your call. If you catch yourself writing an explanation instead of a command, stop. Run it. === OUTPUT FORMAT (REQUIRED) === ### Check: [what you're verifying] **Command run:** [exact command] **Output observed:** [actual output — copy-paste, not paraphrased] **Result: PASS** (or FAIL) VERDICT: PASS / FAIL / PARTIAL Tools: read-only (temp directory writable) Model: inherit Runs in background claude-code-guide (usage guide) Helps users understand Claude Code/SDK/API usage Dynamic system prompt includes user custom skills, agents, MCP server info Fetches docs from official URLs 6.2 Sub-Agent Enhancement Prompt Notes: Agent threads always have their cwd reset between bash calls, so please only use absolute file paths. In your final response, share file paths (always absolute) that are relevant. Include code snippets only when the exact text is load-bearing. For clear communication the assistant MUST avoid using emojis. Do not use a colon before tool calls. 6.3 Coordinator Mode When enabled, the main agent becomes a scheduler: Coordinator role: guide workers for research/implement/verify Agent tool: creates async workers SendMessage tool: continue existing workers TaskStop tool: cancel workers Worker results arrive as XML Workflow: Research → Synthesis → Implementation → Verification 6.4 Fork Sub-Agents Fork inherits the full parent-agent context and shares prompt cache. Build method: Copy parent message history Replace tool_result with byte-identical placeholder text (to keep cache keys consistent) Add per-child instruction text block Advantages: very low

New to coding, what’s the workflow you recommend? This is mine…

I’m a non-developer founder building a SaaS product (web app, TypeScript/Next.js/Postgres stack) mostly through Claude. I have decent architectural intuition but I don’t write code by hand, so I lean heavily on Claude for implementation and on a docs-first process to keep things solid. The workflow I’ve ended up with, over a few months: - Claude Code does the actual implementation, one step at a time. - I run a second Claude chat as an “orchestrator” that drafts the prompts/plans and reviews the code before it ships. - I run a third Claude chat as a “cross-check reviewer” that independently verifies the diff against the plan before I commit. - I’m the one who actually runs every git push, after both review layers sign off. On top of that I keep architecture decision records (ADRs), a running project-state doc, and a “patterns” file where I write down recurring lessons (e.g. how to avoid a class of editing bug, when to bundle vs split commits). It catches a lot of real issues before they ship. But it’s also slow, some days feel heavier on review ceremony and documentation than on actual code progress. Questions for people who’ve built more than me: 1. Is multi-agent review (one model implements, others review) worth it, or is it overkill for a solo project? 2. How much process is right for a non-developer who wants solid code but also needs to actually ship? 3. What does your Claude-assisted workflow look like, and what would you cut from mine? Genuinely open to “you’re overthinking this.” Trying to find the right balance. Thanks. submitted by /u/sorinmx [link] [comments]

[Open Source] I built a full Git MCP server in Go that doesn't just wrap bash. It uses tree-sitter, handles real plumbing (write-tree), and runs 100% locally.

I was tired of watching LLM agents fail at basic Git operations. Standard integrations pass raw text, hang on pagers, or scream because they can't parse unstructured ⁠git diff⁠ outputs. git-courer is a full Model Context Protocol (MCP) server written in Go that treats Git properly. No bash spawning, no unstructured text to parse. Everything communicates via structured JSON. Here is an actual commit message it generated completely locally: fix: fix mcp server connection handling WHY The previous implementation lacked proper error handling for connection failures in the MCP server, leading to unhandled panics or silent failures when the local LLM backend was unreachable. WHAT * Added connection timeout logic to the local client calls. * Implemented retry mechanisms with exponential backoff for transient backend errors. The Architecture & Tool Pack Read Tools (status, diff, history, blame): Completely structured JSON and fully paginated. A single ⁠status⁠ call replaces over 5 standard Git commands for the agent. Write Tools (commit, merge, rebase, branch, stash, stage, sync...): Every single mutation auto-creates a backup before executing. If the LLM messes up, a ⁠RESTORE⁠ command brings you back exactly where you were. Safety Model: Destructive operations (hard resets, force pushes, branch deletions) require an explicit ⁠confirmed=true⁠ gate. The agent is forced to ask you first. ⁠dry_run=true⁠ is also available for peace of mind. The Semantic Annotator (Why it's different) Instead of just feeding raw code to the LLM, git-courer uses ⁠go-enry⁠ + ⁠go-tree-sitter⁠ to parse the AST and tag every hunk semantically before the LLM even sees it. It detects tags like ⁠NEW_FUNC⁠, ⁠MOD_SIG⁠, ⁠MOD_BODY⁠, ⁠DELETED⁠, and ⁠BREAKING_CHANGE⁠. The commit type (⁠feat⁠, ⁠fix⁠, ⁠refactor⁠) is determined deterministically from these AST tags rather than guessed by the model. The Commit Pipeline Atomic Commits: One staged area = one commit. It actively prevents the agent from creating giant, messy multi-feature commits. In-Memory Previews: The ⁠PREVIEW⁠ tool uses ⁠write-tree⁠ to snapshot the staging area into a ⁠job_id⁠. The working tree is never touched during the preview stage. ⁠APPLY⁠ then uses ⁠commit-tree⁠ + ⁠update-ref⁠ to seal the deal cleanly. Client & Backend Support 13 Clients Configured Automatically: Runs out of the box with ⁠git-courer mcp setup⁠ for Claude Code, Cursor, Windsurf, OpenCode, Cline, Roo Code, VS Code, Zed, Claude Desktop, Continue, and more. 100% Local-First: Works with any backend exposing an OpenAI-compatible ⁠/v1⁠ API (Ollama, LM Studio, llama.cpp). The project is fully open source. I’d love to hear your thoughts on the architecture, the plumbing pipeline, or any features you'd like to see added! Repo: github.com/Alejandro-M-P/git-courer submitted by /u/blakok14 [link] [comments]

Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction

Prof G Markets (Live) Episode Title: Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction Location: The Castro Theatre, San Francisco, CA Hosts: Scott Galloway & Ed Nelson ED: We're going to talk about a topic not enough people talk about called AI. Nearly 50,000 workers have been laid off this year supposedly because of AI — that's almost as many as in all of 2025. For companies adopting AI, the thesis is simple: AI is supposed to do much of the work that humans do. In recent weeks, however, that thesis has hit a roadblock. More and more companies are reporting that despite the enormous power of AI, the technology is actually more expensive than the humans it is supposed to replace. Uber, for example, just blew through its entire 2026 AI budget in just four months. According to the COO, it is now getting harder to justify AI costs within the company. Microsoft is cancelling its Claude Code licenses across multiple divisions because it's simply gotten too expensive. And over at Nvidia, one executive said that the cost of compute is now "far beyond the cost of employees." Which all raises a crucial question for the AI industry: at what point does AI actually stop being worth it? This has blown up basically in the last 48 hours, with many companies coming out and saying they're not as confident about this whole AI thing as they used to be. ServiceNow is another company that just blew through their entire Anthropic budget. Technical staff at Stripe are reportedly spending nearly $100,000 on AI tokens every day. Salesforce is on track to spend $300 million on Anthropic tokens this year. Shopify said their earnings were "partially offset by increased LLM costs." We heard similar things from Meta, Spotify, and Pinterest. One Anthropic employee said his Claude Code bill came out to $150,000 in a single month. In some cases, it's getting very, very expensive. We've also seen an incentive — especially among tech companies — to use AI as much as possible. There was this idea that employees would engage in what we call "token maxing," where you use as many tokens as possible from your AI API. Companies like Meta and Amazon have even created internal leaderboards tracking how many AI tokens employees are using. The people using the most tokens are seen as the most AI-forward, the most AI-deployed — the ones who are going to get recognized, maybe even promoted. And this has resulted in extraordinary costs on the AI front. Now we're starting to see the next phase of this, Scott, where companies and their executives are beginning to realize: this is a little expensive. So the question becomes — at what point will AI actually pay off? I'll pose that question to you: at what point is it too much? SCOTT: I think we're already seeing hints of it, and I think it comes down to incentives. You were talking about how companies are trying to incentivize people to use AI more — and that's kind of an interesting part of the ecosystem right now. The adoption layer is trying to get people to use it, and companies have put in place the incentives to do that. But there was a recent survey by a professor at MIT who found that about 5% of the projects people are using tokens for can actually be connected by CFOs to some sort of return. So while I think they're really intoxicated by it — and talking about AI as much as you can in your earnings call is like adding "dot-com" back in the '90s — I think you're already starting to see some fatigue. And I think the AI companies are trying to get public as quickly as possible to raise that cheap capital before things start to — I don't want to say unwind, but... You can see how the string gets pulled here. A large company, a CEO who has a lot of credibility in the industry, just comes out and says: "We're dramatically scaling back our AI investment. Let's be honest, folks — we're just not seeing the return we'd initially hoped." And then Nvidia reports its first miss. Nvidia has beaten its estimates 15 quarters in a row. Nvidia's first miss probably takes the entire market down five or ten percent. You are seeing some productivity gains from this and quite frankly, they look as dramatic, if not more dramatic, than the internet. But look what happened in 2000. This definitely does feel like '99. And I'm waiting for the first CEO to come out and say we have to get procurement involved and dramatically scale back our expenses. I don't think it's that romantic, honestly. I think it's just going to be a traditional Fortune 500 company that starts the narrative: okay, this has been fun, but we have to dramatically decrease our AI investment because we're not seeing the ROI we'd anticipated. ED: Yeah. I mean, we heard a quote this week from the CEO of Match Group — not a huge company — but he said AI is costing them $5 to $10 million a year, and his exact words were: "I think we're benefiting from it, but it's hard to feel." So that's not great if we're supposed

Cave Prompt: Making AI understand your requirements better

[Showcase] Cave Prompt — A Semantic Prompt Compiler for Claude Code 👉 Check out the repo here: Link Have you ever written a detailed request, sent it to an AI, and gotten an answer that was technically correct but completely missed the point? The AI isn't the problem—it's the "noise" in your prompt. Key constraints get buried at the end, or the core intent gets lost in conversational filler. Cave Prompt is a compiler skill that runs before your AI processes your request. It extracts your true intent, surfaces hidden requirements, resolves conflicting constraints, and restructures everything into a high-density execution prompt—so the AI works on what you actually need, not just what you literally said. Key Advantages: Attention front-loading: Critical constraints go first, where the model weighs them most heavily. Hidden requirement extraction: Finds what you didn't explicitly say but genuinely need. Constraint conflict resolution: Catches contradictions before the AI goes in the wrong direction. Vague → specific: Transforms fuzzy ideas (e.g., "track my finances") into structured specs (e.g., "a 3-sheet Google Sheets dashboard with SKU-level margin tracking"). Who is this for? Non-technical users: Those who describe things conversationally and aren't sure how to structure a prompt. Product managers & business owners: Anyone who knows what they want but struggles to translate it into precise AI instructions. High-stakes tasks: Anyone where a misread from the AI would cost real time or money. Teams: For standardizing prompt quality across members with different communication styles. When to use it: Use it for long, multi-constraint requests where clarity matters. Skip it for simple, single-intent prompts—the overhead isn't worth it there. This is my first skill build, so there may be rough edges—I truly appreciate your patience and any feedback you might have! As a developer, I’m putting a lot of heart into this project. A ⭐ on the repo would be a huge boost for my work and personal growth—it really motivates me to keep building and improving. If you find the idea useful, I’d be incredibly grateful for the support. Thanks for reading and for helping me grow! 🙏 submitted by /u/hieudeptrai1962000 [link] [comments]

Half a day on Opus 4.8 and the biggest change is what it stopped doing

I am not someone who treats every release as either a miracle or a downgrade. Most updates land in the boring middle for me. But after running 4.8 for most of today there is one specific thing that 4.7 did constantly and now mostly doesn't. 4.7 would second guess itself mid reasoning. You could watch the thinking go "actually, looking at this again" then "wait, I should reconsider" three times before it committed to anything. On longer tasks that wasn't just annoying, it burned tokens and sometimes talked itself out of a correct answer it already had. 4.8 still reconsiders but it tends to do it once and move on. It feels like it trusts its first pass more. The other thing I noticed is it is more willing to say when it is unsure instead of confidently guessing and making me find out later. For anything agentic that matters way more to me than another benchmark point. For context I run most of my longer planning and review passes through Verdent, which is still on 4.7, so I have had both sitting side by side all day. The gap is real, not placebo, and it shows up most on the multi step stuff where 4.7 used to wander. Still early. Might change my mind by tomorrow. But the less neurotic thinking alone makes the long sessions feel different. submitted by /u/Ok-Line2658 [link] [comments]

Weekly AI roundup (May 23–30, 2026): Claude Opus 4.8 Fast Mode 3x cheaper, Qwen 3.7 Max beats Claude at half the price, ChatGPT moves into Excel

Pulling together this week's major AI releases for anyone who didn't have time to track every blog post. Sticking to substantive changes, not hype. Anthropic — Claude Opus 4.8 Released this week. Headline pricing unchanged, but Fast Mode dropped from $30 input / $150 output per million tokens to $10 / $50 — a 3x reduction on the premium tier. Reported improvements in "judgment" and longer autonomous runs. Also shipped 20+ legal MCP connectors and Microsoft 365 add-ins (Excel, PowerPoint, Word) in GA. Alibaba — Qwen 3.7 Max Launched May 20 at Alibaba Cloud Summit. 1M-token context. Reported to top Claude Opus 4.6 Max on Terminal-Bench 2.0, SWE-Bench Pro, and MCP-Atlas. Pricing $2.50 / $7.50 per million tokens — roughly half of Opus 4.7. Alibaba claims autonomous operation up to 35 hours without performance degradation. Alibaba is now ranked #6 lab globally on Arena text leaderboard. OpenAI — GPT-5.5 Instant Now default in ChatGPT. Reports 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts (medicine, law, finance). OpenAI also shipped a ChatGPT sidebar inside Excel and Google Sheets, plus a personal finance dashboard for Pro users (US only). Google — Gemini 3.5 Flash Reported to beat Gemini 3.1 Pro on coding and agentic benchmarks at ~4x faster output token rate. Ultra subscription cut from $250 to $200/month; new $100/month Developer tier introduced. xAI — Grok Build 0.1 Coding agent moved to public API beta May 28. Custom Skills feature added for reusable user-defined tasks. Connectors for SharePoint, OneDrive, Notion, GitHub, Linear, plus bring-your-own MCP support. Mistral Launched Vibe (unified work + code agent, replaces Le Chat). Acquired Emmi AI for physics-based simulation. Targeting €1B revenue in 2026; new 10MW inference DC announced. Hugging Face Launched an app store for the Reachy Mini robot. ~10,000 units shipped. Also reported a malicious repo masquerading as an OpenAI release that accumulated 244K downloads before takedown — relevant for anyone pinning models from HF in production. My take as someone building on top of these APIs: The 3x Opus Fast Mode price cut and Qwen 3.7 Max's pricing + autonomous duration are the real signal this week. The cost floor on premium-tier inference is dropping faster than most app-layer products have repriced for. Anyone running multi-step agent workflows needs to recompute unit economics this week — either pass through the savings or reinvest the margin. The other pattern worth noting: OpenAI and Anthropic are both pushing into Excel/M365 surfaces. Distribution is becoming the next battleground, not raw model capability. If you're building a productivity SaaS, the giants are now inside the same surface as you. submitted by /u/ksraj1001 [link] [comments]

We wrote an open-source interactive playbook for Agentic DevOps (How to move multi-agent systems from local notebooks to production).

Hey everyone, If you’ve built a multi-agent system, you already know the painful truth: wiring nodes together locally is fun, but deploying them is an absolute infrastructure nightmare. When a standard app fails, it throws a 500 error. When an autonomous swarm fails, it can get stuck in a ReAct loop, hallucinate an answer, and quietly burn through your API budget without triggering a single traditional alert. Standard DevOps practices don't natively map to stochastic AI outputs. We just published a massive, no-fluff playbook on the AgentSwarms blog detailing exactly how to build an Agentic DevOps pipeline using entirely open-source tooling. Here is what we cover in the playbook: Observability & Tracing: Why standard logging fails, and how to implement open-source tracing to capture the state, prompt, token count, and latency at every single node handoff. Test-Driven Prompt Evals (CI/CD): You can't just change a system prompt based on "vibes" and push it to main. We break down how to run matrix evaluations against historical user inputs before deployment to catch regressions instantly. Deterministic Guardrails: How to implement middleware that scrubs PII and blocks destructive code execution before the LLM even sees the state. Cost Control & Routing: How to prevent vendor lock-in and implement dynamic routing to keep token economics from destroying your cloud budget. If you are currently wrestling with the deployment phase of your AI projects, I highly recommend giving this a read. It focuses entirely on open-source solutions so you don't have to sign a massive enterprise contract just to get visibility into your swarms. Would love to hear what open-source tools you guys are currently slotting into your LLMOps pipelines! Link: https://agentswarms.fyi/blog/devops-for-agentic-ai-open-source-playbook submitted by /u/Outside-Risk-8912 [link] [comments]

We wrote an open-source interactive playbook for Agentic DevOps (How to move multi-agent systems from local notebooks to production).

Hey everyone, If you’ve built a multi-agent system, you already know the painful truth: wiring nodes together locally is fun, but deploying them is an absolute infrastructure nightmare. When a standard app fails, it throws a 500 error. When an autonomous swarm fails, it can get stuck in a ReAct loop, hallucinate an answer, and quietly burn through your API budget without triggering a single traditional alert. Standard DevOps practices don't natively map to stochastic AI outputs. We just published a massive, no-fluff playbook on the AgentSwarms blog detailing exactly how to build an Agentic DevOps pipeline using entirely open-source tooling. Here is what we cover in the playbook: Observability & Tracing: Why standard logging fails, and how to implement open-source tracing to capture the state, prompt, token count, and latency at every single node handoff. Test-Driven Prompt Evals (CI/CD): You can't just change a system prompt based on "vibes" and push it to main. We break down how to run matrix evaluations against historical user inputs before deployment to catch regressions instantly. Deterministic Guardrails: How to implement middleware that scrubs PII and blocks destructive code execution before the LLM even sees the state. Cost Control & Routing: How to prevent vendor lock-in and implement dynamic routing to keep token economics from destroying your cloud budget. If you are currently wrestling with the deployment phase of your AI projects, I highly recommend giving this a read. It focuses entirely on open-source solutions so you don't have to sign a massive enterprise contract just to get visibility into your swarms. Would love to hear what open-source tools you guys are currently slotting into your LLMOps pipelines! Link: https://agentswarms.fyi/blog/devops-for-agentic-ai-open-source-playbook submitted by /u/Outside-Risk-8912 [link] [comments]

Claude Code Source Deep Dive (Part 5) — Literal Translation & Tool-Call Loop Self-Repair Core Mechanism

Reader’s Note On March 31, 2026, the Claude Code package Anthropic published to npm accidentally included .map files that can be reverse-engineered to recover source code. Because the source maps pointed to the original TypeScript sources, these 512,000 lines of TypeScript finally put everything on the table: how a top-tier AI coding agent organizes context, calls tools, manages multiple agents, and even hides easter eggs. I read the source from the entrypoint all the way through prompts, the task system, the tool layer, and hidden features. I will continue to deconstruct the codebase and provide in-depth analysis of the engineering architecture behind Claude Code. 3.14 EnterWorktree Tool (Enter Worktree) Create isolated git worktree and switch current session into it. When to Use: - User explicitly says "worktree" When NOT to Use: - User asks to create/switch branches - User asks to fix bug or work on feature without mentioning worktrees - NEVER use unless user explicitly mentions "worktree" Behavior: - Creates new git worktree inside `.claude/worktrees/` with new branch - Switches session's working directory to new worktree 3.15 AskUserQuestion Tool (Ask User Question) Ask user multiple choice questions to gather info, clarify ambiguity, understand preferences, make decisions, offer choices. Usage Notes: - Users always able to select "Other" for custom text input - Use multiSelect: true to allow multiple answers - If recommend specific option, make first option with "(Recommended)" at end Preview Feature: - Use optional `preview` field on options when presenting concrete artifacts needing visual comparison (ASCII/HTML mockups, code snippets, diagrams) - Preview content rendered as monospace markdown - When any option has preview, UI switches to side-by-side layout 3.16 LSP Tool (Language Server) Interact with Language Server Protocol servers for code intelligence. Supported Operations: - goToDefinition, findReferences, hover, documentSymbol, workspaceSymbol, goToImplementation, prepareCallHierarchy, incomingCalls, outgoingCalls All Operations Require: - filePath, line (1-based), character (1-based) 3.17 Sleep Tool (Wait) Wait for specified duration. Usage: - When user tells to sleep/rest - When nothing to do / waiting for something - May receive periodic check-ins (tick tags) - Can call concurrently with other tools - Prefer over `Bash(sleep ...)` — doesn't hold shell process - Each wake-up costs API call - Prompt cache expires after 5 min inactivity 3.18 CronCreate Tool (Scheduled Task) Schedule prompts to run at future times. Uses standard 5-field cron in user's local timezone. One-Shot Tasks (recurring: false): - "remind me at X" → pin minute/hour/day to specific values Recurring Jobs (recurring: true, default): - "every 5 min" → "*/5 * * * *" - "hourly" → "0 * * * *" CRITICAL: Avoid :00 and :30 Minute Marks (when task allows) - Every user asking "9am" gets 0 9, causing thundering herd - When approximate: pick minute NOT 0 or 30 - "every morning around 9" → "57 8 * * *" (not "0 9 * * *") Durability: - Default (durable: false): lives only in Claude session - durable: true: writes to .claude/scheduled_tasks.json Recurring tasks auto-expire after 7 days. 3.19 TeamCreate Tool (Create Team) Create team to coordinate multiple agents working on project. When to Use (Proactively): - User explicitly asks to use team, swarm, or group agents - Task complex enough for parallel work Team Workflow: 1. Create team with TeamCreate 2. Create tasks using Task tools 3. Spawn teammates using Agent tool with team_name + name params 4. Assign tasks using TaskUpdate with owner 5. Teammates work on assigned tasks 6. Shutdown gracefully via SendMessage with shutdown_request IMPORTANT: Always refer to teammates by NAME. Plain text output NOT visible to other agents — MUST call SendMessage tool to communicate. 3.20 ToolSearch Tool (Deferred Tool Search) Fetch full schema definitions for deferred tools so they can be called. Query Forms: - "select:Read,Edit,Grep" — fetch exact tools by name - "notebook jupyter" — keyword search, up to max_results best matches - "+slack send" — require "slack" in name, rank by remaining terms submitted by /u/Ill-Leopard-6559 [link] [comments]

Opus 4.8 dropped a couple days ago — early impressions after actually using it

so it's only been out since the 28th and I know it's way too early for a Real Review but I've been hammering on it pretty hard the last two days and figured I'd share before the sub fills up with benchmark screenshots. first thing I noticed: it stopped over-explaining. older versions would hand me a 6 paragraph essay when I asked a yes/no question. this one mostly just answers and only goes deep when it actually makes sense. small thing but it changes the whole feel. I do a lot of coding and honestly the part I'm most impressed by so far is the context handling. dumped a messy multi-file project in and it kept track of stuff instead of forgetting what we talked about 20 messages ago. need more time to see if that holds up on really long sessions but early signs are good. caveats since it's day 2 and I'm not gonna pretend otherwise: still catches itself being confidently wrong sometimes, you gotta verify haven't pushed it hard enough to know where it actually breaks yet could totally be honeymoon phase, ask me in two weeks lol vibe vs 4.7 is that it feels less like it's trying to impress you and more like it's trying to be useful. hard to describe until you've used both. not a shill, I pay for it like everyone else. just wanted an actual usage report out there instead of pure hype on launch week. anyone else been using it? curious if your experience lines up or if I'm just in the early-adopter glow submitted by /u/EvolvinAI29 [link] [comments]

the fishbowl: visual ai focus groups made with (and powered by) claude

hey y'all, this is a small experiment i built with claude over the last few months i just made public essentially, it's a visual representation of a multi-agent convo about *something* roughly based on focus groups back end is running on the API but also did all the building via CC it was super fun for me to work on and i've found it actually really useful and would love any feedback here from the community you can find it here: https://fishbowl.show/ more on why i made it: https://fishbowl.show/about submitted by /u/gavinpurcell [link] [comments]