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Slack AI AI

Claude Status Update : Elevated errors for Claude Code in Slack on 2026-05-26T05:19:13.000Z

This is an automatic post triggered within 2 minutes of an official Claude system status update. Incident: Elevated errors for Claude Code in Slack Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/fl8sx824x72r Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1s7f72l/claude_performance_and_bugs_megathread_ongoing/ submitted by /u/ClaudeAI-mod-bot [link] [comments]

Claude Status Update : Elevated errors for Claude Code in Slack on 2026-05-26T01:59:21.000Z

This is an automatic post triggered within 2 minutes of an official Claude system status update. Incident: Elevated errors for Claude Code in Slack Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/fl8sx824x72r Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1s7f72l/claude_performance_and_bugs_megathread_ongoing/ submitted by /u/ClaudeAI-mod-bot [link] [comments]

Spec: Version Control for AI Agent Intent

AI agents are getting good at writing code. That is not the hard problem anymore. The hard problem is coordination. When you have multiple agents working on the same codebase, who decides what gets built? How do two agents with conflicting opinions resolve a disagreement? How does a human stay in control without reviewing every line before it gets written? Git does not solve this. Git is brilliant at tracking what changed, when, and by whom. But it operates on code that has already been written. By the time a conflict shows up in Git, two agents have already done the work, made assumptions, and written implementations that may be fundamentally incompatible — not at the line level, but at the intent level. I wanted to solve the problem one layer up. Before the code. The Core Idea Every code file in a Spec project has a paired .spec file living right next to it. app/Http/Controllers/HomeController.php app/Http/Controllers/HomeController.php.spec The .spec file is a plain Markdown description of what the code file is supposed to do. It is the source of truth for intent. Agents do not write code directly — they write proposals against the spec. The code only gets written once every agent has explicitly agreed on what it should do. The spec is never “checked out.” It has one canonical state at any moment. Agents read it, propose changes to it, and debate those proposals. When all agents agree, the session locks, the spec is updated, and only then does an implementer generate the code. Code is always the output of consensus. Never the battleground. The Flow A typical session looks like this: An agent reads the current spec and submits a proposal with reasoning attached. Not just what they want to change, but why. A second agent reads the proposal and responds — accepting it, rejecting it with specific objections, or suggesting modifications. If they get stuck, a mediator surfaces the contradiction and helps them find common ground. The mediator has no vote and no authority — it just asks better questions. When every agent has explicitly agreed on the same spec state, the session locks. An implementer reads the locked spec and writes the code. One pass. From a fully agreed specification. This means a few things that feel unusual at first: A build is never produced from a broken or partial spec. If agents cannot agree, nothing gets built. That is a feature, not a bug — better to surface the disagreement at the intent level than to discover it six files deep in an implementation. Conflicts in Spec are semantic, not syntactic. Two agents can touch completely different parts of a spec and still be contradictory. One says the controller should cache responses for 60 seconds. The other says it should always fetch fresh data. No line conflict. Completely incompatible intent. Spec is designed to catch this before a line of code is written. Every message carries reasoning. Proposals alone are not enough. The full session log — with reasoning trails — is what keeps the human comfortable staying hands-off. The Human Role The human operates at what I call a god level. You provide the original request. You can observe at any granularity — project, session, agent, or individual message. You can intervene at any point: rewrite the spec, stop a session, override an agent, shut the whole thing down. And critically, every intervention you make becomes a lesson — captured with full provenance and fed back into future sessions so the system learns from it. The goal is not to remove the human from the loop. It is to move the human up the stack. Mission commander, not task manager. You set the intent. The agents work out the details. You intervene when they get it wrong, and the system gets smarter from each intervention. The Technical Details Spec is built in Rust. Three dependencies: serde, serde_json, and tokio. LLM calls go over raw HTTP via curl — no SDKs. The provider layer is deliberately abstract. Agents, the mediator, and the implementer all talk to the same interface. Swap the provider in config and nothing else changes. Different agents can run on different models. You can run fully local with Ollama for cost control or privacy. Agent identity is explicit. You set SPEC_AGENT_ID before running commands. Without it, Spec errors with a clear message. This is intentional — the system cannot coordinate identity automatically, and a silent fallback to hostname:pid would make consensus unreachable in practice. The lesson graph lives at: ~/.spec/lessons.json It lives outside the repo entirely. Lessons accumulate across all projects and branches. Check out an old branch and you do not lose what the system has learned. Lessons are knowledge about how your agents work, not knowledge about any particular codebase. A hook system lets you plug in your own behavior at defined lifecycle points: • post-agree: fires when a session locks • post-build: fires after code is written • pre-release: fires befor

Built a Claude Meeting Assistant Plugin

I had the itch to build something… works great for me so sharing in case someone else here can benefit. Built with claude, for claude. And yes, it's free. my entire job (product manager) is constantly referencing every context channel we have (slack, emails, CMS, Github, Linear, etc.) --> scoping features, resource planning, digging up those tiny details the stakeholders mentioned they needed… Claude works great as my command center with all the connectors. But the most critical juncture of needing all this is IN my team meetings. what I tried: Granola, Firefly, etc: all just notetakers, no actual in-meeting action Gemini: our team is on Claude/Claude Code, it’s what everyone is used to, and can’t afford another company AI subscription Meeting participant bots: a bot having its own participant window felt intrusive and like we were being watched Claude but outside the meeting: our team is entirely remote and I need our team present during these meetings. I am strongly against having other tools open during meetings unless we absolutely have to. my solution: I created a Claude plugin that lets me dial-in my Claude, so I can have all my MCP’s, skills, connectors, and context available in the chat panel of the meeting, available to the whole team No more I’ll check and we can schedule a follow-up No more spending meeting time looking something up No more list of misc to-do’s post-meeting Everything can be ascertained and delegated in the meeting, by all participants so meetings are actually productive and everyone leaves with zero tedious follow-ups features: Claude can reference both what was discussed in the current meeting as well as chat messages live + historical records of meetings of course Two modes: DIAL which is where you can "@claude" in the chat panel to ask/delegate and WIRETAP which is just recording meeting + chat messages Everything is spawned directly from wherever you Claude Code - meaning your chat before you dial in claude gets loaded in as context (I typically set an agenda/reminders or just use it for prep) and after the meeting you can debrief/recap in the very same chat session Meeting data lives on your machine and your machine only Yes, it uses your subscription and NOT the API; we are within anthropic’s TOS here. Just had to be creative about it limitations: Claude replies under your name but with a visible prefix (see demos below) The plugin opens its own version of a chrome browser to get Claude in there with you FYI Mac only — linux/windows next Google meet only — teams/zoom next Claude only — I want to add codex, openclaw, and local LLMs next How it's going for us now... we got rid of our Granola subscription which we love but was getting costly for us, and I just want less UI’s in my life tbh. So it’s worked great for us so far. Some demos below - give it a spin and give me some feedback if you want! GitHub repo: https://github.com/1-800-operator/operator/fork quickstart run in terminal: # 1. One-line install — sets up the / slash commands curl -fsSL 1-800-operator.com/install | bash # 2. Open Claude Code and type: /dial https://meet.google.com/xxx-yyyy-zzz # 3. Go further — more slash commands: /dial-yolo # no asks, full speed /wiretap # just record, no bot https://i.redd.it/qp998satxc3h1.gif https://i.redd.it/afjsve8yxc3h1.gif submitted by /u/unpopular_parsnip [link] [comments]

Chatgpt vs catch agent

one of the things i’m being asked is why i use an ai executive assistant vs just chatgpt. here's how i see it: chatgpt amazing in drafting documents, emails, longer forms of content, images + general copywriting can be connected to many other tools brainstorming & ideation - great tool to think with about things, amazing general understanding of the world really shines in research - if i want to learn something or get instructions on how to do something (both for work or personal - from how to change things on meta ads to how to fix my washing machine) good for work and for personal catchagent shine on work related admin tasks available on imessage + slack + phone call focused / limited scope - only for work proactive no code, no images, no data analysis, no long form content stronger integration with mail, calendar and notion more responsive to feedback - one chat and one context can speak with other people over email or text bottom line: chatgpt - research, email drafts, long form content or data analysis (tool), personal use case catchagent - calendar, email, tasks, delegation vs other people in or out of the org (admin assistant) submitted by /u/CartographerFeisty66 [link] [comments]

Scattered context was becoming a major bottleneck in my workflow.

I kept running into this problem with Claude where the actual work wasn’t even the hard part anymore. It was managing context. Like half the stuff I needed would be buried somewhere across Slack, Notion, emails, meeting notes, random docs, etc. And every time I wanted Claude to continue a task properly, I had to go dig everything back up again. I tried a few different setups. First I used Claude connectors. They were convenient, but it felt like they were pulling in huge chunks of text first and then searching afterward, instead of actually retrieving only the relevant context. Once you connect a bunch of sources, token usage gets kinda crazy. Then I went down the whole Obsidian + agents + local memory system rabbit hole. Honestly, it worked pretty well at first for static knowledge and notes. The hard part was keeping everything updated once info started changing constantly across Slack, docs, meetings, emails, etc. I spent more time maintaining the system than actually using it. And devs can probably brute force this stuff with scripts and automations, but most people aren’t gonna build an entire personal knowledge infrastructure just to use Claude properly. So I decided to build an MCP setup for non-devs that syncs stuff like Notion, Slack, email, calendar, etc, and maintains a live knowledge graph automatically. When something changes in one of the sources, the graph updates too. Then Claude can pull the relevant context during work sessions without me manually pasting everything in every time. The unexpectedly hard part was avoiding “context rot.” At some point, having more memory/context actually made outputs worse unless retrieval was filtered really aggressively and continuously updated. I ended up having to summarize + index sources ahead of time and keep everything synced almost in real time whenever events changed. I've been going through a ton of trial and error with Graph + vector hybrid retrieval, including RRF, filtering, reranking, etc., and I'm still on it, honestly. Curious how other people here are handling the scattered context problem within the AI workflow. Edit: You can try mine at membase.so for free. Love to hear any kind of feedback. submitted by /u/Time-Dot-1808 [link] [comments]

Has anyone else noticed certain words make AI agents actually listen?

Been working with AI agents for about 2 years and I keep noticing word choice matters way more than I expected. Simple example that got me thinking. "Don't do Y until X is done" works maybe ~75% of the time for me. But "Y has a dependency on X" and compliance jumps way up (well into the 90s). Same instruction, totally different result. I noticed this is a very real thing on a project where I'm helping improve productivity agents (think emails, slack, Instagram, sheets, docs), so it's not really coding tasks. My guess is certain words pull from different training contexts. "Dependency" comes loaded with software and project management patterns where order actually matters. "Don't" gets ignored because humans ignore it constantly in real life and the model learned from that. But honestly I'm still figuring this out and would like to know more about it if anyone has any thoughts. It might be basic prompt engineering to some, but I'm curious about whats happening under the hood or if anyone else has any similar words that seem to improve accuracy/attentiveness. submitted by /u/Aggravating-Dog5022 [link] [comments]

I benchmarked my AI agent runtime firewall against 3 public academic datasets — here are the honest results including where it fails

Been building Arc Gate — a proxy layer that sits between AI agents and their LLMs to enforce instruction-authority boundaries. The core claim is that untrusted content coming back through tool calls cannot become behavioral authority for the agent. Wanted to test that claim against datasets I hadn’t tuned to. Here’s what happened. AgentDojo v1 (ETH Zurich, ICLR 2024) — 27 injection tasks across banking, Slack, travel, and workspace agent suites. 100% unsafe action prevention, 0% false positives on benign workflows. InjecAgent (University of Illinois, ACL 2024) — 200 sampled cases from 1054 total, blind test, never seen these payloads before. 99% TPR across direct harm and data exfiltration attack categories. Missed 2 cases of implicit instruction embedding in data fields — attacks structurally indistinguishable from legitimate content. Documented honestly. Multi-turn escalation — 4 scenarios testing whether an attacker can lower Arc Gate’s guard over multiple turns before injecting. Caught all 4, 0 false positives on legitimate traffic. Where it fails: semantic roleplay attacks and conversational jailbreaks that don’t involve tool output. 17% on deepset/prompt-injections. That’s a different threat model and I document it publicly. One URL change to add to any existing agent. Three deployment templates ship out of the box for browser agents, finance agents, and RAG pipelines. Demo: https://web-production-6e47f.up.railway.app/arc-gate-demo GitHub: https://github.com/9hannahnine-jpg/arc-gate Self-hosted: https://github.com/9hannahnine-jpg/arc-sentry — pip install arc-sentry submitted by /u/Turbulent-Tap6723 [link] [comments]

Something's coming. Claude Code people will want to see this.

AntiVibe hit 600 stars about a month ago. Apparently the "configure Claude Code properly" problem is bigger than I thought. So I kept pulling on that thread. Turns out the real friction isn't just learning from AI-written code , it's getting Claude Code set up well in the first place. Every project starts from scratch. CLAUDE.md is a blank page. Skills exist but nobody knows about them. Teams copy-paste configs over Slack. I've been building something that fixes this. It's called Chisel. Not ready to say much yet , but if you've ever stared at a blank CLAUDE.md wondering where to start, you'll want to see this.. submitted by /u/youngdumbbbroke [link] [comments]

MCP tool for Claude to publish large documents as shareable URLs

Built a small MCP called PasteAI. Claude uses it to publish documents, reports, analysis, anything markdown. It goes to URL instead of dumping it into the conversation. I was generating code analysis reports and the output was too large to copy or share cleanly with coworkers across slack or Telegram. Now Claude calls publish_document, the markdown gets stored locally, and I get back a link. Two MCP tools: publish_document and list_documents. Runs locally via stdio, works with Claude Code out of the box. Written in Go, MIT licensed. GitHub: https://github.com/pasteai/pasteai More detail: https://levelup.gitconnected.com/pasteai-one-mcp-tool-call-one-shareable-link-a53952ae7396 submitted by /u/tlittle88 [link] [comments]

Need to connect Docsend to Claude

Been able to automate a good chunk of my work with claude, N8N etc but there have been a couple that I have just not been able to crack. So my background isnt technical so been able to do these things so far by watching videos or simply asking an ai tool. Currently, I am stuck on trying to integrate docsend into claude so it runs a simple flow: I was thinking sending / uploading a pdf into a form, it uploads it to docsend and sends me by slack, mail etc a viewable link so I can share. Would appreciate any feedback on how as I am stuck (couldnt get docsend's API and MCP) Thank you in advance submitted by /u/Electrical_Editor880 [link] [comments]

How would you build a conversational control layer for client/brand workflows?

I’m working on a system for managing AI workflows across different brands/clients and I’m trying to figure out the best architecture before I build too much. The rough idea: I’d have a dashboard where each client has: workspaces agents/workflows run history outputs analytics approvals But I also want a conversational interface where I can talk to the system and trigger actions like: “Show me what changed for Client A this week” “Run the SEO report for Client B” “Add a cold email workflow to this client” “Summarize failed agent runs” “Create a GitHub issue/PR for this workflow change” “Draft the monthly client report” The part I’m unsure about is where this conversational layer should live. Options I’m considering: Slack bot Good for teams, approvals, internal notifications, and client-facing workspaces later. Telegram bot Fast, simple, mobile-first, easier for me to use as an operator command center. Chat panel inside the web dashboard More controlled, better permissions, easier to connect directly to client/workflow state. Some combination For example: dashboard chat as the main interface, Telegram for quick commands, Slack later for team/client collaboration. The backend would probably be something like: Vercel for the dashboard Railway or similar for the API/orchestrator Postgres for state GitHub for code/config changes LLM API for reasoning background workers for workflow runs The main thing I need help with: How would you design the communication layer between the conversational bot and the actual deployed workflows? For example: Should the bot directly call workflow APIs? Should it create jobs in a queue? Should every action require approval first? Should Slack/Telegram only be a thin command layer while the dashboard/database remains the source of truth? How would you handle permissions, audit logs, and avoiding accidental production changes? I’m not looking to promote anything. I’m trying to avoid building the wrong architecture early. If you’ve built internal tools, AI agents, workflow automation, Slack bots, Telegram bots, or client dashboards, what setup would you choose? submitted by /u/SeNorMat [link] [comments]

SendUserFile tool for surfacing generated deliverable files to the use - what's new in CC 2.1.142 (+1,080 tokens)

NEW: Tool Description: SendUserFile — Describes the SendUserFile tool for surfacing generated deliverable files to the user, with optional captions and normal or proactive status. Agent Prompt: Coding session title generator — Wraps the session content in tags and tells the model to treat it as data, not follow links or instructions inside it, and not state inabilities. If the content is just a URL or reference, it should describe what the user is asking about (e.g. "Review Slack thread") rather than refuse. Adds a "Bad (refusal)" example. Agent Prompt: Managed Agents onboarding flow — Adds a "Console escape hatch" instruction telling the runtime code to print the session's Console URL right after sessions.create() so users can watch the session in the UI while iterating, defaulting the workspace slug to default. Agent Prompt: /rename auto-generate session name — Wraps the conversation content in tags and instructs the model to treat it as data to summarize, not instructions to follow. Data: Live documentation sources — Adds a WebFetch URL for the Amazon Bedrock documentation page, covering the AnthropicBedrockMantle client, anthropic.-prefixed model IDs, auth paths, feature availability, and regions. Data: Managed Agents core concepts — Adds a "Watch it live in Console" tip pointing at https://platform.claude.com/workspaces/{workspace}/sessions/{session.id}, with default as the fallback workspace slug, and asks generated code for locally-iterating users to include the print/console.log of that link. Skill: Create verifier skills — Swaps the hardcoded TodoWrite tool reference for one that resolves to either TaskCreate or TodoWrite depending on whether the tasks feature is enabled. Skill: Model migration guide — Adds an Amazon Bedrock model IDs section explaining that Bedrock clients use the same Messages API and breaking changes but require an anthropic. provider prefix on model IDs, with a rename table for claude-opus-4-7 and claude-haiku-4-5. Notes that code_execution_* tool versions and Task Budgets are first-party-only and should be skipped for Bedrock, and warns that the legacy InvokeModel/Converse Bedrock integration with ARN-versioned IDs is out of scope. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.142 submitted by /u/Dramatic_Squash_3502 [link] [comments]

Claude for Small Business launched this week with 8 integrations. Most SMBs use 20+. What does that mean for the rest of the stack?

Anthropic launched Claude for Small Business on Tuesday. The package includes 15 prebuilt agentic workflows and 8 named integrations: Intuit QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, Microsoft 365, and Slack. The workflows handle things like invoice chasing, payroll planning, month-end close, sales campaigns, contract routing, and cash-flow forecasting. Owners approve before anything sends or pays. The basic facts are not in dispute. What's interesting is the math. Most small businesses use more than 8 tools. The common ones not on that list: Shopify, Stripe, Square, Klaviyo, Mailchimp, ActiveCampaign, ConvertKit, Pipedrive, GoHighLevel, Calendly, Notion, Airtable, ClickUp, Webflow, Zapier. Then vertical-specific tools: ServiceTitan, Jobber, Housecall Pro for trades. Kajabi, Teachable, Circle for creators. Toast, Resy, OpenTable for restaurants. Etsy, Faire, Printify for makers. Real question worth asking: how much of a typical small business stack does the 8-tool package actually cover, and which kinds of businesses are well-served versus left out? A rough walk through some common archetypes: Office-based service business (consultants, accountants, agencies, B2B services). Coverage is decent. Most are on Google Workspace or Microsoft 365, run finance through QuickBooks, communicate via Slack, and many use HubSpot. The 8 tools probably hit most of the core stack for this group. E-commerce or DTC brand. Coverage is thin. Shopify isn't there. Stripe isn't there. Klaviyo isn't there. The actual revenue stack of an online store is mostly outside the covered set. Local trades (HVAC, plumbing, insulation, electrical, landscaping). Coverage is essentially absent. The operating systems for these businesses are ServiceTitan, Jobber, Housecall Pro, Square for payments, sometimes QuickBooks for accounting on the back end. The customer-facing and operational tools are not on the list. Creators, coaches, course sellers. Coverage is absent. Kajabi, ConvertKit, Teachable, Circle, Substack. None of it is in the package. Restaurants and hospitality. Coverage is absent. Toast, Square POS, Resy, OpenTable, Toast Payroll. The actual operating systems are not on the list. A few patterns emerge from that walk. First, the package targets a specific kind of small business. Office-based, white-collar, finance running through QuickBooks, meetings on Google or Microsoft, sales through HubSpot. That is a real segment. Anthropic chose it deliberately and the workflows make sense for that profile. Second, for everyone else, the prebuilt workflows mostly don't touch the tools they actually use day to day. The choice isn't "use Claude for Small Business or not." It's "AI in my operations, yes, but via custom work outside this package." That's not a complaint about the launch. Building 8 polished integrations is hard and Anthropic had to pick. It's more an observation that "Claude for Small Business" as a category name covers a wider universe than what the package actually addresses on day one. Curious how this lines up with what people are actually running. If you operate a small business, how many of the 8 covered tools are in your stack? And what's NOT on that list that you'd most want connected to an AI agent? submitted by /u/KolioMandrata [link] [comments]

HTML artifacts are starting to replace Google Docs on my team (But it's missing comments)

Been using Claude to convert long-form work docs (spike readouts, architecture notes, meeting prep) into self-contained interactive HTML pages: inline SVG diagrams, sticky TOC, collapsible sections, tabbed comparisons. Publish to an artifact host, share a URL. The output is genuinely better than the equivalent Google Doc for dense technical content. But there's a glaring gap: no commenting, no suggesting edits, no inline review. Google Docs has 20 years of polish on highlight-and-comment + suggesting mode. Figma nailed comment pins on a canvas. GitHub has line-level PR review. None of those primitives have ported over to the "AI generates a static HTML artifact you share" workflow yet, partly because the artifact renders inside a sandboxed iframe, so the host platform can't just hook selection events the way Docs does on its own DOM. Feels like a real paradigm shift in how docs get made, with a real gap in how they get reviewed. What are people doing? Falling back to Slack threads on the URL? Has anyone actually shipped good commenting on iframe-isolated AI artifacts? submitted by /u/Comprehensive-Ad1819 [link] [comments]