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

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]

Replaced my $15/mo Wispr Flow subscription with a free local macOS app I built using Claude Code

I spend most of my day writing prompts to Claude. Read a study recently that said people speak ~3x faster than they type, which lands differently when "writing" is basically your whole workflow. Looked at Wispr Flow – it's genuinely great, but $15/month forever for something I'd mostly use to dictate to Claude felt wrong. So I spent two weeks of evenings building my own with Claude Code. How Claude helped I'd never shipped a Tauri / macOS app before this. Claude Code did the bulk of the actual code: The menu bar app structure, global hotkey capture, and paste-anywhere flow UI and onboarding Integrating the local model runtimes (Parakeet / Whisper for transcription, Gemma 4 for polishing) The model download / storage logic so the app ships without bundling gigabytes of weights A lot of debugging I would not have had the patience for on my own I made the product and design calls; Claude wrote the vast majority of the code. Two weeks of evenings, usually an hour or two at a time. What it does Menu bar app for macOS. Hold a hotkey, talk, release – text is copied to your clipboard. Works in any app: Claude.ai, Cursor, Slack, browser, IDE, whatever. Two open-source models doing the work: Parakeet (NVIDIA) / Whisper for transcription Gemma 4 (Google) / Apple Intelligence for polishing the raw transcript into something readable Everything runs locally. No cloud calls, no API keys, no telemetry, no account. Fully offline after download. Free for personal use, no signup. Download: https://vox.rizenhq.com/ Caveats macOS only. Apple Silicon required (M-series chip). Windows build is next. It's two weeks old. Bugs I haven't found yet exist. ~90% of Wispr Flow's quality, not 100%. Enough for me to use every day. What it's saving me 40–60 minutes a day, mostly on prompts. Dictating to Claude feels noticeably more natural than typing to it. The ask Feedback, especially from people who talk to Claude a lot: Where does it break? Bug reports > compliments. What did you use it with? What feature would make you switch from Wispr Flow (or start using voice-to-text at all)? Tech notes No separate model download – onboarding handles it Gemma 4 options: E2B, E4B, 26B. E2B runs on phones; 26B is overkill for most machines. I use E4B – great quality, fast. RAM (Parakeet + Gemma 4 E4B): ~200mb idle, ~300mb while speaking, brief spike to 4–6GB during transcription/polish, then back to 200mb CPU: ~0% idle, ~20% peak during use EDIT BTW, I develop it during my live streams from 8:30 am to 10:30 am ET everyday here. I show the code and decisions I make live on the stream. If you want to ask questions / push for some features / push to make it open source / etc. - join the stream, push for it in the chat and I'll consider it! Also, seeing the number of feedback, and feature requests in the comments I've decided to create a discord server to make sure that nothing will be lost and everything will be addressed. You can join here. submitted by /u/EfficientLetter3654 [link] [comments]

I tested GPT-5.5 Codex against Opus 4.7 Claude Code, and it's about time Anthropic bros take pricing seriously.

I've used Claude Code the most among AI coding agents. Sonnet, Opus, I've run them all. The reason is simple: they're beasts at tool execution and prompt following. That's also why Anthropic dominates API revenue from code agents. First-mover advantage is real, and developers love them. But GPT-5.5 Codex has been insanely good. When new models drop, I run real tests, not benchmarks. This time I built two tasks: Test 1: PR triage bot – GitHub MCP, scoring formula, Slack alerts, retries, strict TS, no "any". Test 2: Real-time code review UI – React, WebSockets, optimistic rollback, virtualized diff, WS reconnect. Same prompts. Same MCP (GitHub + Slack). Same machine. Here's what I found out: Claude Code (Opus 4.7): - Verified MCP before writing a line - Built 36 files in 12 minutes - Wrote its own WebSocket smoke test (3ms broadcast) - Zero errors first run - Total cost: ~$2.50 Codex (GPT-5.5 via Cursor): - Failed Task 1 (GitHub MCP not reachable – Cursor environment issue, not model) - Task 2 shipped but needed a patch for infinite React loop - 28 files, more compact architecture - Total cost: ~$2.04 (18% cheaper) Claude shipped cleaner. Codex needed a patch pass. For complex, architecture-heavy work, I still reach for Opus – no question. But Codex was leaner, cheaper, and open source. For tight, self-contained tasks where you want to ship fast – Codex holds its own. I'm not switching. But for the first time, I'm watching the pricing gap. Full breakdown with all code, prompts, run logs, and cost tables: https://composio.dev/content/claude-code-vs-openai-codex submitted by /u/geekeek123 [link] [comments]

Claude still doesn’t feel personal when handling real production issues, and I realized that during a rough on-call incident recently.

I was debugging a Kafka burst issue in a monorepo with ~1500 files and multiple async services. Around 2 AM, one topic suddenly exploded in traffic, consumer lag went insane, retries started amplifying events, and half the system became unstable. I spent nearly 10 hours tracing logs, replaying events, checking old PRs, and rebuilding the service flow in my head. Then I realized something frustrating, I had already solved almost the exact same issue 4 months earlier. Back then, the root cause was a hidden interaction between a retry middleware and a non-idempotent consumer. But all the important context was gone: scattered Slack messages, temporary notes, and architecture that only existed in memory. Even after recognizing the pattern, it still took me another 3 hours to fully reconstruct the reasoning and fix it again. That’s when I felt current AI coding assistants are still missing something important. They retrieve code well, but they don’t retain engineering memory — the debugging journey, failed hypotheses, architectural scars, and operational lessons that senior engineers carry from past incidents. Feels like the missing layer is episodic memory for software systems, not just repository context. Have others faced this too? submitted by /u/intellinker [link] [comments]

Agents are meant to be shared, but existing tooling is not fit for purpose

A while back I was doing technical support at my company and a ticket came in about some feature not working. Instead of digging through logs myself, I let Claude Code do it. Gave it access to our support workspace, some read-only AWS creds, and a few minutes later it had the answer. That was super cool and I wanted to share the pattern with the team. That turned out harder than I thought. Half the team uses Cursor or Codex, not Claude Code. And the people who'd benefit the most weren't even in engineering, they were sales/ops. We tried to use Cursor background agents, available in our Slack initially, but it wasn't really a great fit. Everyone needed a paid seat, even folks who never open Cursor. And every session was tied to one user, so others couldn't jump in to correct the agent mid-thread. So I went and built Nairi (nairi.ai). It's a tool that allows you to deploy claude code backed agents agents in Slack which everyone shares. A single subscription for the whole team. How are others dealing with this? Are there any good tools out there that enable you to share agents in Slack or are you also building ones yourself? I also wrote a blog post about this issue, link in the comments. submitted by /u/pmihaylov [link] [comments]

How do you share project context with someone else so their AI is up to speed?

Curious how others handle this. When I work on a project, I usually keep a `context.md` with the background — goals, decisions, current state, open questions. My own Claude/Cursor uses it constantly. The friction starts when I want to bring someone else in — a cofounder, a freelancer, an advisor — and I want their AI to also have that context, not just them. Right now I literally: - send them the `.md` file in Telegram/Slack - a week later it's stale, so I send a new one - if I update something today, they have no idea - sometimes I just paste 5 paragraphs into a chat I know "just use a GitHub gist / repo" is the obvious answer, and for some flows it works. But it doesn't feel right when the recipient isn't a dev, or when the context evolves daily, or when I just want a clean link that their AI can fetch and that I can revoke later. Questions for the AI-heavy folks here: Do you actually run into this, or am I overcomplicating it? What do you do today? Gist? Notion share? Just paste it in chat? Has anything actually felt good? Not building anything (yet), just trying to figure out if this is a real shared pain or just my workflow being weird. submitted by /u/OsipovMe [link] [comments]

Sharing HTML w/ people

Read Thariq's tweet last week of HTML > Markdown and have been trying to figure out how I can embed it into my personal brain (I follow the Karpathy Wikilinked knowledge base structure) I want to know if other folks have found success leveraging HTML within their knowledge bases? One pattern that might make sense is for when you want to publish your wiki to other people. Say you work at a company, everyone has a wikilinked knowledge base, and they can connect with each other when you promote a personal wiki up to be publicly viewable. could you share with people an HTML version of your wiki instead of markdown? One of the biggest themes I got from the article was the people don't like reading large markdown files (i agree, my attention span drops tremendously on md files > 200 words, especially if it's quite clear it's all been written by AI "it's not X... it's Y!"). It sounds like HTML is a great solution to this problem because you can represent things in flow charts and visual diagrams. That makes sense. My first question to this point is: should you share raw HTML files to people (over slack, email, or even drive) or do you have some server of public wiki's and host HTML pages on there? Second question: HTML files are also good at having interactive buttons, toggles, sliders, etc. How can you build a system that takes as an input the modification that you made to an HTML page that communicates its change to the LLM? And what use case have people done this for? I think the primary case where i can see this being useful is for decision making. I.e, instead of doing plan mode in chat, there is an interactive HTML screen where users can click between different design decisions and better yet, between different HTML rendered design components for UI builds. Besides this, i'm curious if there are any other clever use cases for interactive components and communication techniques between human agent and human human with shareable pages. submitted by /u/Comprehensive-Ad1819 [link] [comments]

MCP Generator v2.0.0

Built this with Claude/Claude Code — it generates MCP servers from OpenAPI specs, free and open-source on GitHub. A feel days ago I posted a CLI that converts OpenAPI specs into MCP servers. The feedback here was brutal and exactly what I needed. Here's what I actually fixed and shipped based on your comments: The original post got two pieces of feedback that changed the project: "Raw endpoints wrapped as tools is a poor LLM interface pattern" — Fair. The generator now produces a scaffold you're supposed to implement, not ship. Incremental generation (@@mcp-gen:start/end markers) means you regenerate without losing your handler logic. "console.log leaking into stdio corrupts the JSON-RPC stream" — This was a real bug. Fixed with a log() helper that writes to stderr and a safeSerialize() that handles Buffer/Uint8Array as base64 before anything touches stdout. Circular $ref schemas were the next wall — fixed with SwaggerParser.dereference({ circular: "ignore" }) + a visited-Set guard in the schema walker. What shipped in v2.0.0: YAML input (.json, .yaml, .yml, URLs) Python/FastMCP + Pydantic v2 target Incremental generation — re-run the generator without losing custom handlers oneOf/anyOf/discriminator support for complex specs Auth stubs from securitySchemes Interactive CLI mode for first-time users Built-in registry: mcp-gen init --from stripe (10+ APIs: Stripe, GitHub, Slack, OpenAI, Twilio, Shopify, Kubernetes, DigitalOcean, Azure) stdout isolation + safe binary serialization Circular $ref safety Published on npm and pip Use cases: Give Claude instant access to any REST API in under 2 minutes Generate internal API MCP servers for your team Rapid prototyping — have a working server before writing a single handler API-first development — spec first, scaffold second, logic last 2-minute setup: npm install -g mcp-gen mcp-gen init --from stripe --out ./stripe-mcp cd stripe-mcp && npm install && npm start Then add it to claude_desktop_config.json and Claude has full Stripe access. GitHub: https://github.com/ChristopherDond/MCP-Generator npm: https://www.npmjs.com/package/mcp-gen Install: npm install -g mcp-gen Questions? Want to contribute? Drop a comment or check out CONTRIBUTING.md on GitHub: https://github.com/ChristopherDond/MCP-Generator/blob/main/CONTRIBUTING.md Still a lot to do — oneOf edge cases, better binary streaming, more registry entries. If you find a spec it chokes on, open an issue. Thanks for all feedbacks and stars!!! submitted by /u/ChristopherDci [link] [comments]

imagine paying $200/month for slop

posted an essay on r/ClaudeAI yesterday about ai dependency. got downvoted to 23% ratio. top comments: "that was a long ai generated post", "claude talking like claude, painfully obvious", "ask claude to make it concise". let that sink in. a sub dedicated to claude. downvoting content that sounds like claude. what should content sound like on r/ClaudeAI exactly? r/poetry? r/creativewriting? if i wrote it in broken hemingway prose with intentional typos would that be more authentic to the claude experience? heres the part that really gets me. the same people downvoting "ai-sounding" posts are using claude all day to write their work emails, their pitch decks, their linkedin posts, their performance reviews, their cover letters, their client proposals. claude wrote their last quarterly report. claude refined their slack message to their boss. claude polished their tinder bio. but god forbid you publish something on the claude sub that resembles claude's actual output. then suddenly its slop, its lazy, its inauthentic. what's happening is people have built an identity around "i can spot AI", and any well-structured paragraph triggers the detection reflex. doesn't matter if its true or not. doesn't matter if its useful or not. it pattern-matches to slop so it gets treated as slop. meanwhile the same person closes the tab and goes back to claude to "help me draft a quick note to my team about q2 priorities." the result: anyone who uses claude well enough to publish something polished is automatically suspect. anyone who uses it badly enough to leave the seams visible passes the vibe check. we're rewarding bad prompting and punishing good editing. we've built communities around AI tools where members hate seeing the tool work as intended. and then they go use it for everything. that's a weird place to be year three into this. submitted by /u/Careful_Elderberry33 [link] [comments]

accidentally turned Claude into a GoogleTPM

I think I accidentally built an AI Google technical program manager. I started making separate Claude instances for consulting projects and feeding them: meeting notes Slack chats project docs emails org context At first it just summarized things. But over time it started acting more like a persistent TPM/chief of staff that actually remembers what’s happening across the organization. The interesting part is the workflow. I can tell it: “Stakeholder X said Y in this meeting.” And it can: explain the likely implication identify conflicts/dependencies suggest next steps update project understanding write the follow-up doc generate the workflow/process changes preserve the context for future decisions At some point it stopped feeling like “chat with your docs” and started feeling like organizational memory. Made me realize how much operational work is really just maintaining continuity across fragmented conversations. submitted by /u/ColdPlankton9273 [link] [comments]