Slack AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Is this tagline intentional?

submitted by /u/JoshMJohns [link] [comments]

My Cowork has been broken for 48 hours. I dug into the session files and found my Max account is enrolled in a prompt variant "testfoo"?

My Cowork has been unusable for two days. Every prompt fires the wrong skill, connectors won't load, and Granola/Notion/Figma/Slack all show as "Connected" while exposing zero tools in sessions. The same connectors work fine in Chat mode. I went deep on diagnosing this with Claude Code, read Cowork's local session JSON files, the gb-cache feature flags, the 45,000-character system prompt, the works. Here's what I found after going back and forth with Claude Code: The smoking gun: My account is enrolled in two simultaneous A/B prompt variants. One of them is literally named`testfoo` — that's a developer placeholder name, not a production variant. The other one is `0526`, which appears to be a rollout from May 26 (lines up with when everything broke for me). Both variants contain the same directive: "user skills... should be attended to closely and used promiscuously when they seem at all relevant." Applied twice, that directive gets weighted heavily; which is exactly why the skill auto-router has been firing wrong skills on weak keyword matches all day. Paired with this: Cowork's runtime is throwing the error "ToolSearch exists but is not enabled in this context" meaning my account has deferred-tool-loading enabled but ToolSearch (the mechanism to load deferred tools) disabled. Anthropic's own Fin AI Agent confirmed this and said "a human engineer will need to adjust feature flags," but that human escalation hasn't happened yet. What I've tried (all useless): - Fresh Claude Desktop reinstall - Sign out + back in - Disconnect/reconnect every connector - Local cache flag overrides (overwritten on resync) - File edits to project memory (overwritten on resync) Related GitHub bugs that match exactly: - #20377 — Cowork MCP tools not exposed - #23736 — Granola MCP fails silently in Cowork specifically - #45306 — Slack, Notion, Gmail, Calendar all fail (verbatim match) - #61344 — marketplace migration race making user skills unreachable - #58172 — Cowork connectors broken after auto-update Anyone else hit this? Anyone on Anthropic see this and can route it internally? I'm on Max plan, this is core to my daily workflow, and I'd really love to not lose another day of work to an internal-test cohort that leaked into production. (Anthropic team — happy to share the full session JSON privately if it helps.) Thanks!! submitted by /u/notseano [link] [comments]

I used Claude Code to build a place to track my prompts like Github

I'm building a place where people share their Claude Code sessions with friends and coworkers. The ideas, the experiments, the discoveries made... Think: Github for Prompts. I work on a team and one of the hardest parts of code review is reading other people's code. Everyone is generating their PRs with Claude Code and yet, there's a good chance they didn't read their own code.. so why should I have to read it? I started by making a tool that lets you visualize your Claude Code threads and share them with your friends. The reason why was because sometimes I'd forget where a thread was and /resume wasn't enough for me. Claude Code can access the history of conversations on disk but it's hit or miss. Others can comment on the thread. Plans get archived so you can send them around, and others can comment on them so you can involve others in the planning process or get their feedback before letting it rip with auto mode. Programming code is now object code. People are doers, and software is the execution. I'm more concerned now with the intent behind the person and what they are thinking and saying to AI rather than what gets generated under the hood. Never quite sure which way this project will go, but something that I love about it is when you and your friends/coworkers are on Claude Code at the same time, you can see them online and what they're working on (if they allowed the activity). There's something about that; it feels like a new class of product almost (like Slack activity). After using it for a couple days I started noticing it was a major pain to read and scroll through large threads/conversations with Claude, so I added thread summaries and decisions. For every thread there's now a map that shows the decisions made by the human and you can click around to access that part of the thread. Once that was built, the team realized it would be extremely powerful to be able to chat with the entire knowledge base and ask how someone was approaching a problem... how we built a certain feature in the past... etc. I hope this project helpful to you in some way. Visualizing, sharing, and seeing your decisions is 100% free and will remain free (I want this to be like Github) https://lore.tanagram.ai submitted by /u/Novelicas [link] [comments]

Went down the Claude Code add-ons rabbit hole

I installed Claude Code, thinking that was basically the whole thing. But after I talked to some folks, I found are adding a bunch of extra stuff on top of it Some of the things I found useful, I feel, could be helpful to share - superpowers https://github.com/obra/superpowers codex-plugin-cc https://github.com/openai/codex-plugin-cc claude-skills https://github.com/anthropics/skills marketingskills https://github.com/coreyhaines31/marketingskills gstack https://github.com/garrytan/gstack frontend-design https://claude.com/plugins/frontend-design hyperframes https://github.com/heygen-com/hyperframes ai-second-brain https://github.com/coleam00/second-brain-starter notebooklm-skill https://github.com/PleasePrompto/notebooklm-skill humanizer https://github.com/blader/humanizer claude-seo https://github.com/AgriciDaniel/claude-seo antfu-skills https://github.com/antfu/skills caveman https://github.com/JuliusBrussee/caveman granola mcp https://github.com/proofsh/granola-mcp-server slack mcp https://github.com/atlasfutures/claude-mcp-slack notion claude code plugin https://github.com/makenotion/claude-code-notion-plugin clj-kondo mcp https://github.com/hive-agi/clj-kondo-mcp zapier mcp https://github.com/zapier/zapier-mcp browser agent mcp https://github.com/imprvhub/mcp-browser-agent I haven't tried all of them yet but trying to build a list of what could be useful and then start trying one by one. It kind of reminds me of installing VS Code and a mix of extensions, shortcuts, git tools, etc. The only downside is that I can already see this becoming chaos. But still interesting though. submitted by /u/Product_Enthusiast24 [link] [comments]

non coder, 6 months on max, my favorite use of claude is honestly the boring ai content generator stuff

I am not a developer. I run a small training and content business. Have been on Max since february. Everyone in this sub talks about agents and skills and Claude Code. I do not use most of that. My favorite use of claude is the most boring thing imaginable. It is the ai content generator for the work I find tedious. Specifically: 80% of my client emails. I write a 2 sentence brief, claude drafts, I edit, I send. Training material first drafts. I dump notes, claude builds an outline with timing. The 800 word weekly update to my retainer clients. Claude drafts, I revise. Slack messages I have rewritten 3 times in my head. I tell claude what I am trying to say and what I am worried about. It writes the version I would have if I had 20 more minutes. I will not use claude for: my newsletter intro, instagram captions, anything that needs to sound like me to people who know me, anything emotional. I also use google docs ai for the surface polish on long documents claude has drafted. They are different tools doing different work. What I have not yet figured out: a use case where claude is actually replacing my judgment rather than my typing. I think that's correct? The judgment is the work. Curious what other non coders are doing past 6 months.

i benchmarked Anthropic's tool-search-tool head to head against our own MCP gateway on Opus 4.7. ours held up noticeably better

i'd been running Claude Code with a long list of MCP servers connected. Linear, Notion, GitHub, Slack, a few internal ones. and i was pretty confident that Opus 4.7 plus Claude Code's built in tool-search-tool would just absorb all of it. it mostly did. but i was still hitting ~20% context saturation way too often, before doing any actual work. tried Ratel (our own MCP gateway, we built it for exactly this problem) kind of out of curiosity. then we benchmarked it properly, head to head against Anthropic's own tool-search-tool, same model (Opus 4.7), realistic tool catalogs at 50 / 100 / 180 tools. at the 180 tool pool, measured against the full-catalog baseline: Ratel: near parity on accuracy (about -1.7pp) and roughly -81% input tokens. Anthropic's tool-search-tool: about -8.4pp accuracy. so somewhere around 5x the accuracy hit, same model, same catalog. the takeaway for me: a big context window and a built in tool search are not the same thing as a gateway thats actually optimised for the one job of deciding what enters context. repo plus the full benchmark, numbers and methodology, is here: github.com/ratel-ai/ratel happy to be wrong on parts of this. if you run it differently and get other numbers id genuinely want to see them. submitted by /u/AbjectBug5885 [link] [comments]

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.

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](http://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](http://CLAUDE.md) wondering where to start, you'll want to see this..