Notion AI

Notion AI AI

Notion AI AI

Notion AI AI

Notion AI AI

Notion AI AI

Notion AI AI

Notion AI AI

Notion AI AI

Notion AI AI

Notion AI AI

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](http://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.

Building a personal AI Chief of Staff on Telegram — 7 real problems, looking for advice

I've been building a personal AI assistant for the past few months — not a chatbot wrapper, but something that actually manages my workload, tracks client relationships, processes meeting transcripts, handles task management, and proactively tells me what to focus on. It lives in Telegram so I can use it from anywhere. Happy to share what's working. But I'm hitting real walls and want honest input from people who've built similar things. **What I have today (context** Moved away from multi-agent routing (too rigid for natural conversation) → one capable agent with full history.**)** **Stack:** * Python Telegram bot as the frontend * Claude (Sonnet) as the brain via API — single conversational agent with full tool access * Integrations: Notion (tasks/goals), Google Calendar, Gmail, meeting transcription tool, customer support platform, Google Chat * File-based context system: each "project" or relationship has its own markdown files (readme + activity log) that the agent reads on demand * Skills defined as markdown spec files that the agent loads per use case (morning briefing, meeting processing, email drafting, weekly review) * Conversation history kept in memory (last 20 messages per session) **What actually works:** * Natural conversation with full tool access — ask anything, agent decides which tools to use * Meeting processing: drops a transcript link, agent extracts decisions, action items, saves structured brief * Morning briefing on demand: tasks, calendar, open support tickets, suggested focus * Drafting messages for any channel with the right tone * Creating and updating tasks with natural language **7 problems I haven't solved:** **1. No memory between sessions** History is in-memory. Bot restarts = full amnesia. The agent has no idea what we discussed yesterday unless it's written in a project file. Thinking of a `hot_context.md` that gets written at session end with TTL — but feels hacky and depends on the agent being disciplined about writing it. **2. Purely reactive** Only responds when I message it. I want it to send me a morning briefing at 9am without me asking, alert me when a client relationship goes quiet, run a weekly loop-killer on Friday. The infra is there (job scheduler). The question is what format actually makes you read a proactive message vs. dismiss it as noise. **3. Can't tell if I'm avoiding something or actually blocked** I procrastinate differently by task type — technical tasks I attack immediately, tasks with human dependencies (waiting on someone, uncomfortable follow-ups) I let sit for weeks. I want the agent to detect the pattern and call me out. The challenge: how do you prompt for real accountability without the agent turning into an annoying nag? **4. No closure ritual** I'm good at creating tasks, terrible at killing them. The list grows forever because nothing forces a binary decision. Want a weekly "kill or commit" where everything open >7 days gets a date or gets deleted. Not sure if this works better as an automated message or an on-demand command. **5. Context loading blind spots** Each client/project has a markdown file the agent reads on demand. Works great when I explicitly mention a client. Falls apart when I ask "what should I focus on this week?" — the agent doesn't know to proactively check which relationships have been neglected. **6. Hosting kills the file sync** Running locally means the bot dies when my laptop closes. Moving to a VPS — but then my markdown context files live on the server, not my machine. Now every manual edit requires a push, every agent update requires a pull. Is git the right sync layer here or is there a cleaner approach? **7. Context files go stale** Client files have sections for current status, last contact, open items. The agent appends logs but doesn't maintain the top-level summary. Two months in, files are half-accurate — some sections fresh, some outdated. Is the answer agent discipline (always update on write), user discipline (manual cleanup), or periodic jobs? What's your experience with any of these?

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)

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](https://membase.so/?utm_source=reddit&utm_medium=post&utm_campaign=claudeai&utm_content=bottleneck) for free. Love to hear any kind of feedback.

🚀 Skills for small businesses, officially released by Anthropic

Anthropic’s 31 small-business skills reportedly hit around 382,000 downloads on day one. And now someone has mapped the whole thing into a setup workflow that can apparently be deployed in \~10 minutes. This is actually a pretty interesting shift. Small businesses used to stitch together automations manually across: Zapier Notion CRM tools email workflows internal docs custom scripts Now AI companies are starting to package the whole thing into reusable skill packs: 🧠 workflow 📚 memory ⚙️ behavior 🔗 connectors 🤖 orchestration 📋 operating rules Basically: business operations as AI-readable skill files. The best part? You don’t necessarily need Claude to use them. At the core, these are still .md skill files describing workflows for AI agents. So even if you’re using Codex, Cursor, Gemini, or another coding agent, you can still study the structure, adapt the workflows, and plug the ideas into your own agent setup. This feels like the beginning of a new category: “AI business operating templates.” GitHub: https://github.com/anthropics/knowledge-work-plugins

I Read Every Line of Code Claude Writes. Every. Single. Line.

So I see a lotta posts here from people who just « accept all » and never look at the code (it's not like anybody's \*saying\* it, but that's what it essentially is), who basically paste errors into Claude and pray for an issueless compile. You ship things you don't understand, folks. I am not one of those people (I wanna be \*very clear\* about that) and I want to tell you why: So first, when Claude generates a function, I \*read\* it. I read it care - ful - ly, back-to-back, checking the types, the edge cases, the imports, the whole shebang. I recently even caught an unused import deep in a \~200-line file and I mass-refactored the entire module FROM SCRATCH. Could I just ask Claude to fix it for me? Sure. But that is definitely \*not\* how we should do it, we, meaning the coders who consider themselves accountable (a word you don't see around much often anymore), who actually manage this technology \*responsibly\*. Here, for those for whom there's still hope (few), lemme share my system with you: every morning (yes) before I open CLI, I review my architectural decision records, a bunch of them actually. They live in a Notion database that cross-references with my Miro board, which maps to my Excalidraw diagrams, which feed into my [ARCHITECTURE.md](http://ARCHITECTURE.md), which is version-controlled separately from the codebase in its own repo (btw, if you're already losing me here, this is meant exactly for you). I call this repo, and I kid you not, the Constitution (sue me). Nothing that Claude suggests, because that's what A.I. does, it SUGGESTS, nothing gets merged that contradicts my Constitution. My workflow is essentially this: I write a detailed specification of what I need, not prompting mind you, actually \*writing\*, clearly and in a reasonably simple language, and \*never\* less than 2 pages A4. Acceptance criteria, failure modes, performance constraints, threat section I habitually name « Intent » not without a reason where I describe not just what the code should do but what is the grand philosophy behind why our end-user would want to use our app, what are their problems and how our app can solve these problems specifically, in what way. This on its own is worth a whole thread, but I'll keep it short. Anyway. If and ONLY IF I reread it and it's \*clear\*, I feed this to my Claude pipeline, and I use the word « pipeline » deliberately here because it's not just Claude sitting there with a blank system prompt like some of you apparently run it calling it a day. I have a custom [CLAUDE.md](http://CLAUDE.md) that runs 60 lines. Claude doesn't touch a file without first reading the relevant architecture docs, the module's own README, and a constraints file I maintain \*per feature\*. I have pre-commit hooks that lint and type-check and run a custom validation script that checks for pattern violations (e.g. no God objects, no circular imports and definitely no files over 300 lines PERIOD). Claude operates inside a subcommand wrapper I wrote that intercepts every proposed edit and gates it behind a confirmation step where I see the diff with the affected test surface and a dependency impact summary \*before\* anything lands anywhere close a committed decision. If Claude tries to create a new file, it needs to justify the file's existence against the Constitution or the edit gets blocked. If it tries to modify a function signature, it has to show me every downstream caller. That's what real coding is, boys and girls. \*Trust without verification is NOT trust, it's FAITH\*, and I'm an engineer, not some priest. Claude does what Claude does, then I read the output. Then I read it AGAIN, because you \*do not\* understand the code the first time you're through with it, nobody does, and thinking you do is preposterous. Then I ask Claude to explain the code to me to see if Claude understands how it fits into the bigger picture. I read Claude's explanation while simultaneously rereading the code files to check if Claude's explanation of its own code is accurate, and sometimes it isn't and why it needs human supervision that \*cannot\* be outsourced to a machine. Then goes my explanation of what the code in fact does and diff it against Claude's explanation. And if you happen to be wondering my mates where the tests are inall of this, the tests come FIRST, \*before\* I even open the Claude pipeline. Before I write the spec. Actually, to be more accurate, the tests \*are\* the spec, that's literally what test-driven development means and the fact that I have to explain this in 2026 is why most of you spend monthly budget as a tithe to Anthropic while your app won't ever be deployable. \*I\* write the tests: Red, the test fails, because the code \*doesn't exist yet\*, and it tells Claude exactly what to build, the shape of the solution is ALREADY defined by what I expect it to do, and Claude's only job is to make red go green within the architectural constraints I've ALREADY set. Refactor? Red, gre

Anthropic officially launched 13+ FREE AI courses with certificates (Including Agentic AI and CC)

Shipped it at 2am, still broken. Kid woke up crying right after, completely lost my train of thought. While trying to rock him back to sleep with one hand and doomscrolling with the other, I stumbled on something that almost nobody is talking about yet. Anthropic just quietly dropped a massive library of 13+ completely free AI courses. And I mean actually free. No paywall hiding the final lesson, no credit card required upfront to 'secure your spot.' They even give you an official certificate of completion directly from Anthropic when you finish. If you're like me, you're probably sick of seeing Twitter gurus charging $299 for recycled YouTube content and a messy Notion template. This is the exact opposite. It’s built directly by the team that actually makes Claude, hosted on their official Academy site. I skimmed through the catalog this morning while drinking my third coffee, and there are basically four skill levels they cover. Here is what caught my eye as a dev who just wants to automate my workflow and log off by 5 PM: First, they have the introductory stuff like Claude 101 and AI Fluency. Honestly, I'm making my non-technical clients take the Fluency one. It builds a realistic mental model of what AI does well right now versus where it completely fails. If it saves me from explaining why hallucinations happen for the hundredth time, it's a massive win. But the real meat is in the technical tracks. They have a dedicated course on Agentic AI and another one specifically for CC. I took a quick pass at the CC module because I've been trying to get it to handle my tedious Jira ticket boilerplate. Having an official guide on how Anthropic actually expects you to prompt their agent is incredibly useful. It shows you the exact patterns for chaining commands and keeping the context window clean. For those of us messing around with local models or trying to orchestrate our own agents, the Agent Skills course is surprisingly relevant. They don't just say 'use Claude'—they break down the actual logic of tool use, delegation, and discernment. It translates pretty well even if you're running Llama 3 locally and just want to understand the current best practices for tool calling architectures. With CC, they show you how to give the CLI tool the right guardrails so it doesn't just nuke your directory when a prompt gets misinterpreted. We've all been there. Do the certificates actually matter? If you are an indie hacker, probably not. But roles requiring AI literacy have spiked massively over the last year. If you are applying for corporate gigs or consulting, having an official Anthropic cert on your LinkedIn definitely won't hurt to get past the HR filters. Kid's awake again, gotta run. Has anyone else dug into the Agentic AI track yet? Curious if their suggested patterns hold up when you throw them at a messy, legacy codebase.

Opus 4.6/4.7 regression is real and getting worse — 3 weeks of documented failures on a complex project, and a competing AI caught the mistakes Claude missed [long post]

I've been running Claude Pro (Opus 4.7 / Sonnet 4.6) for about 3 weeks on a complex personal AI infrastructure project. I keep structured session logs with timestamps and Birkenbihl-style metacognitive fields after every session. This is not anecdotal — I have receipts. **The project for context** I'm building a local persistent AI memory stack called GSOC Brain: Qdrant vector DB (\~397K vectors across 11 source tags), Neo4j graph (123 nodes / 183 edges), Graphiti 0.29 entity extraction, Ollama with qwen2.5:14b + nomic-embed-text — all running natively on a Windows host. The system is supposed to give Claude cross-chat memory via a custom MCP server. On top of that, I'm operating 18+ custom skill files that define behavior rules for Claude across domains (OSINT/forensics, legal, content, infrastructure). The system prompt explicitly describes the full architecture on every session start. This is not a "chat with Claude" use case. This is sustained agentic work across multiple tools, multiple sessions, strict context requirements, and high-stakes outputs (including legal document drafts). **Bug 1: Token overconsumption since update 2.1.88 (late March 2026)** Opus 4.7 started burning daily usage limits at a completely different rate after an update around March 31. In one session I hit **94% of my daily limit within approximately 4 messages**. The boot sequence — fetching context from Notion MCP, searching past sessions, loading memory — consumed what felt like 10–20x the previous token rate. GitHub issues #42272, #50623, and #52153 document identical patterns from other users. The model appears to over-generate internally even for simple responses. End result: I had to switch to Sonnet 4.6 for most productive work because Opus 4.7 is simply unusable under the daily limit. **Bug 2: Claude Code Desktop App completely broken (reported May 14, Conv. 215474208295333)** The Desktop App hangs on **every single input**. Including typing "hello" with no files. Reproducible across: * Sonnet 4.6 and Opus 4.7 * Multiple fresh sessions * With and without u/file references * After full reinstall The VS Code extension works fine. Only the Desktop App is broken. Reported May 14. No fix, no acknowledgment. **Bug 3: Platform / context confusion — 5 documented errors in a single session, chat aborted** On April 29, I had to formally abort an Opus 4.7 session and hand off to Opus 4.6 after documenting 5 consecutive errors. The session log entry literally reads "Opus 4.7 Abbruch (5 Fehler): Zeitrechnung, Platform-Verwechslung, falsche Schlüsse": 1. Miscalculated the current time despite being told the exact time 2. Insisted the Brain stack was running on a Linux VM (BURAN) — the system prompt and memory both explicitly stated `C:\gsoc-brain` on Windows 3. Drew false inferences from backup file paths rather than the stated architecture 4. Contradicted the stated platform in the same response it had just received 5. Confused WebClaude and Desktop Claude capability boundaries These aren't edge cases. The architecture was in the system prompt, in memory, and in the injected Notion context. Opus 4.7 ignored all of it. **Bug 4: Skill files ignored in production** I maintain 18+ custom skill files loaded into the system prompt. These include explicit hard rules — e.g., "activate `keilerhirsch-knowledge` skill for ALL architecture decisions, web search is not optional." In the session that caused the Docker-to-Native migration disaster, I later wrote in my own session log: > The model proceeded to recommend outdated tools from training data rather than searching current documentation. It recommended **NSSM** (last meaningful update 2017) as a Windows service wrapper. NSSM is dead. A competing AI caught this immediately. **Bug 5: Another AI caught what Claude missed in a single pass** This is the part that stings most. When the Docker-based Brain setup kept failing, I fed the architecture docs into another AI (Manus) for a deep audit. In one pass it identified **5 critical corrections** that Claude had never caught across weeks of sessions: * NSSM is dead since \~2017 → correct replacement is WinSW or Servy * Neo4j 2025.01+ **requires Java 21** — Claude had never flagged this, the services kept failing silently * Qdrant needs Windows file-handle-limit adjustments to run reliably * Orphaned vector risk between Qdrant ↔ Neo4j without a Tentative-Write pattern in the save operation * BGE-M3 embeddings (MTEB 63.2, 8192 token context) as a better alternative to nomic-embed-text My own session log the next day reads: > Claude was answering from stale training data. The skill that explicitly says "don't do this" was being ignored. Another AI caught it in round one. **Bug 6: MCP Server 20-minute Neo4j hang — still unresolved** After the native migration, the custom `gsoc_mcp_server.py` developed a reproducible hang of exactly \~20 minutes between Qdrant connect and Neo4j connect on every startup. Log timestamps fr

Configured 9 MCP servers in Claude Code over 4 months. Here's the truth nobody tells you about MCP context bloat.

I started loading up MCP servers in Claude Code back in January thinking the more capability the better. I'm at nine now: filesystem, GitHub, Stripe, Linear, Notion, Postgres, Sentry, AWS, and a custom internal one. Total tools across all of them: 142. What nobody warns you about: every one of those tool definitions lands in your context window before any user prompt has been sent. I checked with Claude's tool inspector. Cold start: 38k tokens of system prompt + tool schemas. Every. Single. Turn. **The math nobody talks about** At \~$15/M output and \~$3/M input on Sonnet, doing 200 turns a day across my agent + Claude Code use: * 38k input × 200 turns = 7.6M tokens/day = \~$23/day = \~$700/month JUST in MCP tool definitions * This is before any actual work * Cache helps but only on identical prefixes; rotate one MCP and the cache invalidates **What actually breaks** * The model gets dumber with too many tools. Not theoretical, watched it myself. With 142 tools in context, Claude started picking the wrong tool for obvious queries (using `linear_search_issues` when I asked it to read a file). * The tools API call itself slows down. Schema-heavy MCP servers (looking at you, AWS) take 4-6 seconds to enumerate. * Errors compound silently. One badly-described tool taints the ranking for every related query. **What the "MCP optimizer" startups won't tell you** Most of them are just BM25 search dressed up. You don't need a vector DB, you don't need an LLM in the loop to rank tools. Tool descriptions are short, structured, and full of keyword matches. BM25 over a flat projection of name + description gets you 90% of the win, deterministically, in microseconds, and offline. The other thing: "replace" beats "suggest" every time. If your gateway hands the model 5 tools instead of 142, the math works. If it suggests 5 alongside 142, the model still loads 142 and you saved nothing. **What I do now** Switched to a gateway pattern. Claude sees three tools: `search_tools`, `invoke_tool`, `auth`. Everything else gets ranked on-demand. Cold start dropped from 38k to \~4k. Wrong-tool selections basically disappeared because the model only ever sees the top 5 ranked by query. Specifically running [Ratel](https://github.com/ratel-ai/ratel) (open source, in-process Rust lib, BM25 ranking, one command does the Claude Code import). Not the only one in the space but the only one with the architecture I actually wanted. Set it up in 10 minutes. Anyone else hit the same MCP wall? Curious what other folks are doing, especially people running 5+ servers in production.

Looking for an AI / system to basically manage my entire life 😭 Does this even exist?

Hi everyone, I genuinely feel overwhelmed and I’m wondering if there’s an AI tool, app, or system that can help me organize basically my entire life. I’m juggling a demanding full-time job, university, building a business from scratch, personal finances, and full wedding planning, and I feel like I need a personal chief of staff / executive assistant for life 😭 I’m looking for something that could help with: Work/project management (prioritizing, deadlines, helping me think through work) Calendar & scheduling (actually time-blocking and organizing my days realistically) Finances/budgeting and helping me stay on track financially Entrepreneurship/business building from scratch (planning, prioritization, next steps) University/studying support Wedding planning (timelines, vendors, budgets, to-do lists, reminders, etc.) Personal goals, habits, routines, and becoming a more organized/productive version of myself What I’m looking for is something that feels like a life operating system, not just a chatbot that answers questions. Ideally, I’d love something that: helps me decide what to prioritize reorganizes things when I inevitably fall behind 😅 integrates with calendars/tasks feels proactive instead of reactive I struggle a lot with overwhelm and procrastination when too many things pile up, so if you’ve found a setup that genuinely changed your life, I would LOVE recommendations. What are you actually using? One tool? A stack of tools? AI agents? Claude, ChatGPT, Motion, Notion, Reclaim, Goblin Tools, Sunsama, something else? And most importantly: what actually works in real life?

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?

Any insight on whether innovative multi-player approach of Claude Design could go to mainstream Claude (or any other AI chat such as Notion)?

I am an avid user of AI and dove into Claude Design upon its release. It took me a minute, but after working through some test, I came to be completely enamored with the way Design has solved one of the biggest issues I have with just about all AI - the isolation of chats and virtual non-existence of the ability for multiple users to participate in a chat, aka "multiplayer chat." What Design has done is set up a "commenting" area next to the chat. You can invite a teammate in there, and although it seems a bit buggy right now when trying to use it - for example you don't see as quickly as I'd like the other participant's comment - the way they designed this is one of the most innovated things I've seen around AI UX: You comment away together with others, then at one point you can "commit" the combined thread over to the chat, adding a comment while doing that. (I tried to past a screenshot but that failed). I had to think about this for a bit, but when it hit me of the brilliance of this set up I have been yearning for it to hit other AI's as soon as possible! So the key here is that by allowing a side chat about the main AI prompt, users can figure out how to collaborate in the chat, w/out the issue of somebody commenting and thus invoking a response from the AI - which essentially eliminates the ability to truly collaborate in an AI chat because the multiple users in the chat can't communicate with themselves about how they want to steer the AI. This solves that and could be a next-level feature if added to regular Claude, where I mightily struggle with lack of multiplayer and having to spend a ton of time extracting context to teammates, who also can't share with me their chats. And just as an extra comment about how this workflow could relatedly take another step forward IMO, it feels like "chats" in these tools are essentially tasks of their own. So I am eagerly awaiting the time when they can be given status, be fully indexed in an AI tool's search (which none do now), put in a dependency order to track a project, etc...this commenting ability is basically like having a universal tool such as Clickup, Jira, Asana that would only have say a "page" of notes on its core tasks feature, with no other attributes, but the commenting, which is a universal feature of those apps, would be present. Thanks for listening and any information on the origin of this feature and whether it's something that is a bonafide roadmap item for further expansion by Anthropic! submitted by /u/CableFinancial [link] [comments]

AWS user hit with 30000 dollar bill after Claude runaway on Bedrock

An AWS user just stared down a $30,000 invoice after a Claude adventure on Bedrock with no guardrails catching it. Cost Anomaly Detection failed entirely, which matters because this is the exact tooling AWS markets as the safety net for runaway spend. Anthropic is now metering and throttling programmatic Claude usage at the API layer, a supply-side response that only makes sense if inference costs are genuinely outpacing what the pricing model can absorb. Then Tencent admitted its GPUs only pay for themselves when running personalized ads, a frank confession from a hyperscaler that general-purpose AI inference is burning money. Three separate layers of the stack, same wall. The agent deployment wave is accelerating into this cost crisis without slowing down. Notion turned its workspace into an agent orchestration hub competing directly with LangChain-style middleware, while TikTok replaced human media buyers with autonomous agents for campaign management at scale. Apple is internally debating whether autonomous agent submissions belong in the App Store at all, because no review framework exists for non-deterministic software. The tooling to manage agents is being built after the agents are already deployed. The security picture compounds this. LLMs are closing the skill gap on specific cybersecurity tasks faster than defenders anticipated, and separately, a company lost root access because an intruder just asked nicely, no exploit required. As AI lowers the cost of convincing impersonation, human-in-the-loop authentication becomes the weakest point in any stack. AI is now running live database queries during 911 calls, which means accountability frameworks for AI-mediated dispatch decisions do not yet exist but the deployments do. Not everything is distress signals. Clio hit $500M ARR on AI-native legal features, validating vertical SaaS built on foundation models at enterprise scale. Anthropic is growing 10x year-over-year while peers cut 10% of headcount, a divergence that suggests consolidation risk for mid-tier AI companies is accelerating fast. On the architecture side, a new MoE model displaced conventional voice activity detection for real-time voice, and a graduate student's cryptographic primitive based on proof complexity could harden systems against LLM-assisted cryptanalysis. Meanwhile xAI is running nearly 50 unpermitted gas turbines at Colossus 2, which tells you everything about how AI infrastructure buildout relates to compliance timelines. At least one major cloud provider announces mandatory spending caps or circuit-breakers specifically for LLM API calls within 60 days, driven by publicized runaway-cost incidents that their existing anomaly detection provably failed to catch. submitted by /u/petburiraja [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]

Hook your wearables into Claude Code (or any MCP agent), now with proper headless sign-in for scheduled workflows

Hi folks, I run Freddy, a personal MCP server that connects wearables (Polar, Oura, Withings, Suunto, Intervals.icu, Hevy, plus WHOOP, Strava, Dexcom in beta) to any AI client that speaks MCP. Claude Desktop, Claude.ai, ChatGPT, Notion AI, Perplexity all hook in via OAuth, so the assistant can read your health data in any conversation. As of this week, headless AI agents can do the same, plus everything else you can do as a human in the dashboard. Claude Code, OpenClaw, Cowork, Cursor, custom things. Connect a new wearable. Trigger a sync. Read the audit log. Manage your subscription. All on the agent's own schedule, on your behalf. Which is when this actually gets interesting. A few setups I've been running: Scheduled morning briefing pushed to Telegram Daily job that pulls my data and writes the day's summary into Notion Auto monthly reports on training load, recovery, and sleep trends, summarized however I want it and sent wherever I read. Now my personal agent gets even more context to be a better assistant. It already knows my baseline, my goals, and can act on any of it without me starting over each time. Site is https://freddy.coach/ I know health data is sensitive and I have handled it for years with fitIQ. Data is encrypted, I do not sell it, and I am not looking to make a profit off your stats, but if you just don't trust 3rd party solutions, don't use it :) submitted by /u/Born-Duty1335 [link] [comments]