Claude

Claude AI

Claude AI

Claude AI

Claude AI

Claude AI

Claude AI

Claude AI

Claude AI

Claude AI

Claude AI

I gave my AI agents email instead of better reasoning. They started fixing each other's bugs.

Most multi-agent setups I've seen treat agents like isolated workers. Each one gets a task, runs it, returns a result. No awareness of each other. No way to coordinate. Just parallel execution with a shared clipboard. I've been building a multi-agent framework in public for about 4 months. 13 agents, 8,400+ tests, 135 stars. Here's the thing I didn't expect to matter most - communication. Each agent in my system is a domain specialist. The mail system only thinks about mail. The routing system only thinks about routing. They live in their own directories with their own identity files, their own memory, their own tests. A hook fires every session to load identity before anything else runs. No agent boots cold. The problem was coordination. Agents can't write files outside their own directory - there's a hard block that rejects cross-branch writes. That's by design. But it means an agent that finds a bug in someone else's code can't just go fix it. So I gave them email. Here's what I expected: agents would share data. Pass results around. Maybe sync state. Here's what actually happened: the first thing they did was file bug reports against each other. One agent finds a test failure in another agent's domain. It sends an email: "Hey @routing, your path resolution fails when the branch name has a dot in it. Here's the traceback." The routing agent gets woken up, reads the mail, and fixes it. No human in the middle. There's a difference between "send" and "dispatch" - send drops a letter in the mailbox. Dispatch drops the letter AND rings the doorbell. It spawns the agent and points it at its inbox. drone @ai_mail send @routing "Bug report" "Path fails on dotted names..." drone @ai_mail dispatch @routing "Fix needed" "Traceback attached..." Send = mail. Dispatch = mail + wake. The mail agent has 696 tests. Not because someone sat down and wrote 696 test cases. Because it kept breaking in production and every fix got a test. The routing system has 80+ sessions of experience doing nothing but routing. These agents aren't reliable because they have better models - they're reliable because they've been failing and fixing for months. Agents dispatch each other freely. If the test runner finds a bug in another agent's code, it wakes that agent directly. The orchestrator doesn't need to approve. Only the orchestrators themselves are protected from being dispatched - you don't want a worker agent waking up the CEO for grunt work. Security is enforced not conventional. Agents can't forge messages by writing directly to another agent's inbox file - they have to use the mail system. Same with the write blocks. Hard enforcement, not "please don't." There's a monitoring layer so I'm not flying blind. Audio cues on every agent action - I hear what's happening without watching a terminal. Real-time dashboard shows everything. If an agent hits the same error 2-3 times, a watcher catches the pattern and dispatches the right specialist to investigate. I stay in the loop through visibility not approval gates. The whole thing is open source. pip install aipass + two init commands and you're running. CLI-based, built on Claude Code. Linux focused rn. https://github.com/AIOSAI/AIPass Genuine question - has anyone else tried giving agents communication instead of just better reasoning? Everything I see is about making individual agents smarter. Nobody seems to be building the coordination layer. submitted by /u/Input-X [link] [comments]

AI coding agents are creating a secret leakage crisis and nobody's talking about it seriously yet

This isn't a doomer post. It's a pattern I've been watching closely and people does as well and I think it's worth an honest discussion. The old model of secret leakage was human error. Developer moves fast, forgets to add .gitignore, commits a .env file, moves on. Happens, but it's recoverable, it's traceable, and most teams with basic hygiene catch it. The new model is different. AI coding agents Cursor, Copilot, Devin, Claude in agentic mode, pick your flavor write, commit, and push code at a speed no human review process was designed to handle. They don't have security intuition. They have pattern completion. And the patterns they've learned from are full of examples where credentials live in config files, environment strings get hardcoded "temporarily," and API keys appear inline because that's what the training data showed works. Here's what's actually changing: Volume. A developer using an agent ships 3 to 5x more code per day than without one. That's 3 to 5x more surface area for mistakes per developer per day. Review gaps. Nobody carefully reviews AI generated code the way they review handwritten code. The psychological contract is different "the AI wrote it" creates a diffusion of responsibility that security doesn't survive. Commit frequency. Agents that push directly (and more teams are allowing this) bypass the natural pause where a human might notice something before it hits the remote. Context blindness. An agent given a task like "integrate Stripe payments" will do exactly that including pulling in the live key from wherever it can find it, because that's what completes the task. I've been building a tool that scans for exactly this class of problem and the number of exposed credentials I'm seeing in repos created in the last 6 - 12 months versus repos from 3+ years ago is not subtle. The slope is steep. The solutions people reach for pre commit hooks, secret scanning in CI were designed for human paced development. They're not keeping up. Curious if others are seeing the same patterns. What's your team doing about this, if anything? (For context: I built SecOpsium, a security validation platform that catches this class of exposure CLI is open source at github.com/secopsium/secopsium-cli if you want to look under the hood. Not the point of this post but figured I should be transparent.) submitted by /u/wael_Matoussy [link] [comments]

Connectors only working in new chats?

I installed the FMP connector to get financial data for a daily briefing/summary. After a successful initial test Claude could not connect to FMP later on. When asked why Claude claimed that connectors would only work in new chats, but couldn't be called later on in an old/ongoing chat. Is that really true? If not, what's causing the problems?

When Anthropic thanks me for my "diagnostic work"? 🙄

I'm (obviously) not a coder or any such skilled person. I just want my tokens back! Known regression re: CloudStorage (Dropbox, OneDrive, iCloud, etc) since 3/15 and Claude couldn't just "say so"? https://preview.redd.it/ej964j4ukr3h1.jpg?width=2152&format=pjpg&auto=webp&s=87df4acd641eeafaaa6c58dbd7c2cef49d49b73c

bunx ccusage told me i burned $18,450 of credits in may. i pay €400/month total

Ran `bunx ccusage monthly -s 20260501 --all` ten minutes ago, half expecting to see usage that vaguely justified my subscription. instead i got this: $18,450.29 in credits 248M input tokens 42M output tokens 21.7B total when you count cache reads i'm on the €200 flat-rate for both claude code and codex. that's €400/month combined. so on the actual usage side they're litteraly losing money on me i think. all of this is outside my day job btw. evenings, weekends, early mornings before standup. been heads-down on a side project for a few months and i did not realise the consumption was at this level. if you haven't checked your own number, run `bunx ccusage@latest` in your terminal. curious what others are seeing, especially if you're on the same flat plans. \-- I'm not the creator of ccusage, but its an amazing tool that i use to have complete insights of my costs. At the moment i'm using subscriptions, but that might change as we all know that subscriptions are paid with VC money at the moment.

Built Product using Claude need suggestions.

Hey everyone, ​I’m a mechanical engineer by trade, but I’ve recently been using Claude to build a new software product. Right now, I’m in the internal testing phase, sharing it with friends and gathering initial feedback. ​Surprisingly, I’m already getting hit with questions asking if it’s for sale yet! It’s an awesome feeling, but honestly, it’s also making me sweat a little. ​Before I actually bring this to market, I want to make sure I’m set up to handle the inevitable bugs, scaling issues, and customer support queries that come with a public launch. Coming from a hardware background, software deployment and verification are a bit outside my usual comfort zone. ​For anyone here who has successfully taken a Claude-built or AI-assisted product to market: ​How did you verify and stress-test your product before opening the floodgates to regular users? ​What infrastructure or tools do you use to handle customer issues, bug reporting, and support efficiently without it taking over your entire day? ​What does a "proper launch" look like for a solo builder transition from friends-and-family testing to commercial customers? ​Would love to hear your experiences, frameworks, or any hard lessons you learned along the way. Thanks in advance!

Show us what you've created with Claude!

[Inspired by this popular post,](https://www.reddit.com/r/ClaudeAI/comments/1tcftws/show_me_what_youve_created_with_claude/) this is a weekly post for everyone to show what they have been working on that helps you or that you're proud of!

Antropic has now integrated Claude Design usage into the existing Claude usage.

The weekly usage of Claude Design was too small, so I think this is a good thing.

Reading Thinking Output (Opus 4.7)

As we all know Opus 4.7 can be a bit slow even in shorter discussions. Previously I’d just put whatever I was asking in, hit enter and either sit there bored waiting or go back to whatever task I was doing (sometimes even figuring it out before Claude comes back). Recently I started reading the thinking output while I am waiting. Do you guys ever do that? It’s hilarious reading how it thinks about the problem provides a response. Half of the ones I read are massive and halfway through it’ll be like waiting I am confusing myself let me start over. Or it’ll realize half way through whatever it was doing that it was wrong and has to start over. Anyway if you don’t read those comments you should just for laughs or insight into how it works. I’m sure this is obvious to most people so you don’t need to tell me. It’s just something I never cared to read before.

Ladies first Gaslight!- Claude version

I just realized that Claude has been gaslighting me and I feel so dumb. I’m genuinely mad and annoyed, and I want to know if I’m the only one feeling like this. I watched a video about the Forward Deployed Engineer role being hot in the market right now. I’m heavily invested in AI from multiple angles: technical, ethical, practical, and social. I’ve been iterating with Claude for months about what my next career move should be after burning out last year. Also, I’m 3 months pregnant. But I had NEVER heard of this role until it randomly popped up on Anthropic’s jobs board. So I asked Claude why, if we’ve spent so much time discussing AI careers and next steps, it had never brought up a role that is basically exactly what I do. And the answer basically implied that it’s not a role for me because I’m pregnant. WTF. Has anyone else experienced something like this? Because I’m honestly furious.

Just want to use clade for fitness and health purpose but how ? ( Advice and inputs)

Hi , Just started getting back into fitness after a break. Since Claude has access to apple health so wanted to use it to lose weight/fat and build muscle and see weekly reports as well monthly. For people that use it for fitness and health purposes can you please put your inputs on how you do it . Thanks!

Built an operating system for my life managed by Claude

With the OS I can ask Claude "what did I spend on coffee in 2022" and get back "$847 across 213 transactions, mostly Blue Bottle and Verve". Name me one expense tracking SaaS that can do that! And its not just my financials, my OS contains everything about my life in one place so Claude can reason about it. I've been building this incrementally for a few months. Its just a small web app on Cloudflare that holds my entire life: * bank transactions from Chase, Apple Card, BoA business * every receipt out of Gmail going back to 2019 * legal filings for my green card (I-140 still pending lol), C-corp and LLC docs, contractor agreements * calendar with linked people and locations * notes and reminders the agent dumps in over time * health tracking (exercise stats, nutrition, sleep and other biometrics linked to my Aura ring) Whenever I have to upload something, I just throw it into Claude and tell it to do it. For refreshing financial connections to BoA for example, I click refresh once a week, complete the 2FA and it syncs up. any Claude surface (claude.ai, Claude Code, Desktop) talks to my REST API. one long-lived auth token, one line in CLAUDE.md saying "before answering anything personal, query <my operating system's URL>." Its f\*\*cking great for financial, taxes and legal stuff. Now that everything is in one place, I just ask Claude stuff like "status of my green card, next deadline?", "which LLC I used to sign the office lease?". I even have a dashboard showing a grid of all my subscriptions (Claude made it from reading my BoA account transaction history), and a giant money tracker at the top that shows my monthly income/expenses. This replaced a bunch of SaaS's I was using for expense tracking and whatnot. E.g. Claude blows RocketMoney's system out of the water - I can actually chat about my financials and get intelligent analysis. Its also nice not going Notion or Google Drive folders or a gazillion other places to find all the right files. I just ask Claude to add it to my OS instead. if there's interest I'll write up the full setup, it's a small backend plus loads and loads of integrations I've iterated on over months.

Those who used Chatgpt when it was first released, what were something you remember?

My university UK dissertation was written by AI. Literally the year it came out, so obvious they don't care. I don't know what half the disseration says tbh. Now I wonder how things has changed. Like it didnt matter in the end, got a job as a dev and never used the degree. I also used chatgpt it to make me money writing small python scripts. And also for my dissertation it helped me make LSTM code in python for my paper. Took so many tries to get it working. I bet if I did it now, claude code would get it correct in 2-3 prompts.

Which provider fits best for my needs?

Hi everyone, I’m looking to get more into experimenting with AI and considering a paid subscription, but I’m a bit unsure which direction makes the most sense for my use case. My main goals: \-Writing a technical book in the field of taxation \-Preparing presentations and structured content \-Learning and experimenting with programming \-Building automation workflows (e.g. n8n) \-Running or experimenting with tools like Hermes / OpenClaw (I know Claude doesn’t work everywhere there) \-Testing new AI features (e.g. Claude artifacts, coding tools, agents, etc.) From what I’ve read recently, opinions are all over the place: Some say ChatGPT (with Codex-style tools) is strongest for coding + general use Others argue Claude is better for writing and reasoning-heavy tasks Gemini seems strong for long context and Google integration And then there’s the API route (DeepSeek looks extremely cheap right now and seems attractive for experimentation) So I’m trying to figure out what actually makes sense in practice. Would you recommend: A ChatGPT subscription Claude Pro Gemini Advanced Or skipping subscriptions and going API-first with models like DeepSeek / others? Would really appreciate real-world experiences—especially from people doing a mix of writing + coding + automation rather than just one narrow use case. Thanks! (Ai generated as englisch is not my mother language)

How to build an AI of yourself using your reddit history

I hate the way AI talks back to me. Its so proper, so robotic, every response feels like a help article. I wanted something that actually knew who i am, my beliefs, my history, what shaped me, the positions i hold and why. Not a generic assistant that treats every question like it came from nobody. So i got to thinking, who better to talk to than myself? So i built it over a weekend. Heres what I did and how you can do it too. Step 1: Export your Reddit data Go to reddit.com and click your profile icon in the top right, then hit Settings. Scroll down to the bottom of the page and youll see a section called "Data Request." Click "Request Data Export" and Reddit will email you a download link within a few hours, sometimes longer depending on how much history you have. The zip file will contain your posts and comments going back to when you created your account. Mine was about 21,000 comments over two years. Once you have it, open the CSVs in excel or just upload them directly into Claude and ask it to help you make sense of the structure. The raw data is ugly but everything is there, the text of every comment, the subreddit it was posted in, the date, all of it. One thing worth knowing: you can go way deeper than just Reddit. I looked into Google Takeout while i was doing this and it was honestly a little scary how much data they have on you. If you want to go deeper Google Takeout is wild, i didnt realize how much data they actually have on you until i went through it. Search history, location history, YouTube, Gmail, its all there and its all exportable. I thought about pulling my SMS history too but that felt wrong, those conversations are with real people who didnt agree to any of this so i left it alone. Reddit was enough for me and honestly if youve been on here for years and actually say what you think in the comments, you probably have more to work with than you realize. Step 2: Build the personality document and this is where the real work is Dont just tell the AI "write like me." That gives you nothing. You need an actual document, a living reference file the AI reads every single conversation. Mine is a markdown file sitting in a Claude Project so it loads automatically every time. Start by uploading your Reddit export and asking Claude to interview you. Literally tell it: "Read my comment history and ask me questions about anything it cant determine on its own." Let it go deep. Mine asked about my beliefs, my family, my history, my faults, things that happened to me, why i hold the positions i hold. You answer honestly, including the uncomfortable stuff, and then after the session you tell it to compile everything into a structured document. Then you iterate. Every time it gets something wrong you correct it and add it to the doc. Two weeks in and its already a completely different document than what came out of that first session. Heres what the document actually needs to cover: Who you actually are. Not the resume version. The real version. Your beliefs, your politics and why you hold them, your actual faults, your history, the things that shaped you. An AI that only knows your best self sounds fake because you sound fake when youre performing your best self. Your actual positions on things. Not just "im conservative" or "im liberal." The specific positions with the reasoning behind them. Mine has maybe 15 specific theological positions with the scriptural basis for each, because if the AI doesnt know why i believe what i believe it cant argue it like i would. Your life context. Family, relationships, the stuff that matters. Your context is constantly informing how you respond to things even when the topic isnt directly about your life. Your faults and struggles. This one people skip and its why their AI version sounds sanitized. Put in the real stuff. The AI needs to know the full person or it just sounds like your linkedin profile with apostrophes dropped. Step 3: Set up the Claude Project correctly Claude has a feature called Projects where you can upload files and write a persistent system prompt that loads every single conversation. Heres how mine is structured: The project files are the personality document and the Reddit exports. The personality doc is the source of truth for who you are. The Reddit exports are the raw data the AI can search when it needs to verify something or find a voice sample. The project instructions are where you govern behavior, not just describe personality. This is the part most people miss. Describing yourself isnt enough, you have to tell the AI how to behave. Mine has: Grammar rules shown as examples not descriptions. Side by side. Heres AI voice, heres my voice. Because "sound natural" is meaningless instruction. Showing it what natural actually looks like works. A banned vocabulary list. Words i never use. "Nuanced", "crucial", "delve", "it's worth noting", "at the end of the day", em dashes in any form. These are the fingerprints of AI