Stripe

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

What was your most important insight into Fable?

I organised all the key Fable chats and have 11 chats that will be analysed thoroughly over the coming days but here are a few initial thoughts: fable went that little bit deeper. Using Claude design the nuances of the brand and type shone through, in an end-to-end workflow to recreate the $1m dollar pixel site it spawned 97 sub agents and researched the history of the previous projects, design teams broke down ideas and built up prototypes in totally different styles and formats, the operations and risk team handled the practicality and chances of actual success (I guess this would need to be verified in order to use it properly) but the signs were beautiful it's safeguards were more solid, because it was going deeper, it was automatically picking up security concerns like rate limiting and obvious sharing of sensitive information that sometimes Opus wouldnt without explicit instruction. for code tasks it was able to optimise and use more effective strategies off the bat. E.g. I made a skill called blindspot that's designed to look at you sessions (when you choose) and find patterns of behaviour that a mirrored back to you. It was a personal project that I couldn't quite detail and make work how I wanted. Fable analysed and suggested a set of steps to make it more available - creating a lite version and striping detail rather than adding more. I find this quite rare even with Opus and certainly sonnet or below. Any other early (or maybe late now?) insights? submitted by /u/BuffaloConscious7919 [link] [comments]

Me stealing from my mums credit card 😭

What is your monthly lovable / emergent / replit, credit spend? At this point, any ai tools spends lol? submitted by /u/FunLetter2133 [link] [comments]

If your vibe coded app looks finished but feels impossible to safely change, read this before you rebuild everything

been looking at a lot of vibe coded apps lately and honestly the problem is not that the code is always terrible some of them are actually impressive the real problem is that most of them are built like a demo that accidentally became a product and that’s where things get messy because for a demo you just need the happy path to work user clicks button → thing happens → nice UI → everyone is excited but for a real SaaS you need to know what happens when stuff goes wrong user refreshes mid action stripe webhook arrives late ai call fails job runs twice user cancels payment someone tries to access another users data the db has 3 different fields meaning the same thing you change one onboarding step and billing breaks for some reason lol this is the part people underestimate AI is very good at creating more app but it’s not automatically good at making the app coherent it will add a new table instead of understanding the old one add a new status instead of fixing the logic hide a button instead of protecting the endpoint make a flow work once instead of making it safe to run 1000 times and because the UI still looks fine, founders think they’re close but they’re not close to production they’re close to a bigger mess my rule now is pretty simple if your app has no users yet, vibe hard, move fast, break stuff, who cares but once you have users, payments, private data, or even a serious waitlist, you need to slow down a bit and check the boring stuff where does the truth live who can access what what happens when payment fails what happens when AI fails what happens if the same action runs twice can you understand the database without asking the AI 15 times can someone else safely work on this app can you debug a user issue without guessing that’s the difference between a prototype and a SaaS not the design not the landing page not how fast you shipped it it’s whether the thing can survive real usage also one thing I see a lot people keep asking AI to “clean” or “improve” code that already works, without understanding what depends on it that’s how you break your own app if a flow works and users are happy, freeze it new ideas should go in a sandbox, not straight into the live logic vibe coding is amazing for validation but after validation your job changes you’re not just prompting features anymore you’re making product decisions data decisions security decisions cost decisions architecture decisions even if you’re non technical, these decisions are still yours so before you launch something people depend on, don’t ask “does it work” ask “what breaks when real users touch it” that question alone will save you a lot of pain curious what scares people most in their vibe coded app right now auth, stripe, database, ai costs, permissions, or just not knowing what the AI built anymore submitted by /u/LiveGenie [link] [comments]

Does Anthropic create a Link account on your behalf?

Currently on Max 5x plan. A few weeks ago I got an email about logging in to Stripe Link account - I never set this up but it uses the same email I used for my Claude sub. ​ I've had an Anthropic sub since late last year...did this happen to anyone else? The creation of an unprompted Stripe Link account? submitted by /u/Ok_Lettuce_7939 [link] [comments]

Help me please

Hey everyone, I’m a beginner and I’m trying to learn how to build modern websites with advanced animations using Claude Code. My goal is to create websites with smooth scroll animations, 3D effects, and high-end interactions similar to Apple, Stripe, or award-winning websites. Would anyone be willing to explain their workflow, recommend resources, or even mentor me a bit? I’d love to learn how you use Claude Code together with tools like Three.js, React, GSAP, and other technologies for creating impressive animated websites. Any advice, tutorials, GitHub repositories, or learning paths would be greatly appreciated. Thanks! submitted by /u/Demure5 [link] [comments]

How do you actually use Claude Code properly for building a real full-stack app? (First Reddit post)

Hey everyone, This is actually my first time posting on Reddit, so sorry if I ask this in a weird way. I’m building my first serious commercial SaaS/web app and I’m trying to figure out how to properly use Claude Code for development. The project is a Next.js full-stack app (App Router + TypeScript + Supabase + Prisma + Stripe + Upstash + some AI integrations). I already have a full architecture/requirements document prepared for it (around 40+ pages covering database, APIs, auth, scaling, security, build order, etc). Additonal Note: this architecture plan and all thinking i got from claude. let me know if the architecture is good as well My issue is this: I have Claude Pro, but I honestly don’t know how people are using Claude Code to build full products. I’ve only used: Claude web VS Code extension That’s it. What I don’t understand is: How do you structure a project with Claude Code from day 1? Do you feed it your whole tech doc and let it guide you? How do you avoid making a mess later when scaling? How to develop UI to match my brand and not look AI at all How do you use it properly for architecture decisions instead of just generating random code? How do you make sure security is handled right? How do you manage context across a big project? How do you know when to trust it vs when to manually think? This isn’t just a small side project. I want this to become a real commercial product, so I want to build it the right way from the start (scalable, secure, maintainable). I feel like I’m missing the “workflow” part. Like: How do experienced devs actually use Claude Code as a real development partner? Also if anyone knows: Good FREE courses for learning this workflow YouTube channels/tutorials specifically about building real products with Claude Code (not just toy apps) Resources on AI-assisted software engineering / AI-first development please drop them. I’m not really asking if my app is doable — I know it is. I’m asking how to learn to build it properly with Claude as part of my workflow. Would appreciate real advice from people who’ve actually shipped stuff this way. submitted by /u/Chance_Block_5726 [link] [comments]

Claude Mythos & Fable 5

Hey everyone, Like many of you, I’ve been digging into the sudden, unceremonious shutdown of Anthropic’s Fable 5 and Mythos 5 models. The mainstream media and official statements are leaning heavily on "national security concerns," "export controls," and "government intervention due to jailbreak vulnerabilities found by Amazon researchers." But if you look past the PR shield and analyze the underlying economics, neural network physics, and upcoming IPO data, a much more logical, structural reality emerges. Here is a deep-dive breakdown of what actually happened behind the scenes, shifting from macro-theories to hard, evidence-backed engineering realities. Phase 1: Moving Past the "Geopolitics" Narrative The official story claims these models (especially the uncensored Mythos 5 and the guarded Fable 5) were dual-use cyber-weapons that the US government forced offline. While that makes for a great headline, it’s a convenient narrative for both sides. It allows the government to look tough on AI safety, and it allows Anthropic to look like a compliant, security-first patriot. The real truth is found by looking at the technology and the ledger. Phase 2: The Engineering and Financial Hypotheses If we treat this as a standard post-mortem of a sudden tech shutdown, four major internal pressure points stand out: The Infrastructure Money Pit: These models weren't just standard chat LLMs; they were long-horizon reasoning agents. Running them on a mass commercial scale is an absolute cash burner. Architectural Code Poisoning: Security wasn't just a wrapper; it was baked into the model weights. If a fundamental flaw or logical degradation (Model Collapse) occurred deep within the network, you can't just patch it—you have to kill the instance. Synthetic Data Contamination: If Fable/Mythos started generating subtle, toxic logical errors, they risked poisoning the dataset for Anthropic’s next-gen models (e.g., Claude 4.8/5). Model Extraction Attacks: A sudden, minute-by-minute shutdown usually indicates an active zero-day exploit or an ongoing exfiltration attempt where someone is reverse-engineering and stealing model weights via API anomalies. Phase 3: Digging Into the Numbers & Documentation When you analyze Anthropic’s Q2 2026 financial filings (Series H data) and their Economic Index, the mathematical proof becomes undeniable: 1. The IPO Revenue Mirage ($47B ARR) Anthropic recently reported a massive $47 Billion ARR ahead of their highly anticipated IPO. However, a massive chunk of this is booked on a gross basis via cloud resellers (AWS Bedrock, Google Cloud). Auditors before an IPO ruthlessly force companies to transition to net reporting, which could slash Anthropic’s paper revenue by 20% to 40%. They desperately needed high-margin native products to balance this out. Mythos and Fable were supposed to be those products—but they failed the margin test. 2. The 1:5 Token Pricing Trap (Test-Time Compute Expenses) Anthropic priced Fable 5 at $10/M input tokens but a staggering $50/M output tokens. Why the massive 5x asymmetric gap? Because Fable 5 utilizes Test-Time Compute (System 2 thinking). Before outputting a single word, the model generates hundreds of thousands of internal reasoning tokens to self-correct. The client only pays for the final output text, but Anthropic’s GPUs are doing Herculean, exponential work under the hood. Mass enterprise adoption of Fable 5 was causing a massive, unsustainable bleed of cash. 3. The 100% "Middleware" Compute Tax Fable 5 and Mythos 5 share the same weights, but Fable used a complex middleware layer consisting of triple real-time safety classifiers. While Anthropic bragged that 95% of user sessions successfully ran on Fable 5 without needing to degrade to Claude Opus 4.8, the computational overhead to run those triple classifiers applied to 100% of all prompts. To patch the security flaws demanded by the government, they would have had to make these classifiers even heavier, tanking the model's performance and profitability entirely. 4. The Enterprise "API Wringer" Migration According to the Anthropic Economic Index, coding and math traffic recently surged by 14% in the API while dropping 18% on the Claude.ai frontend. Enterprise clients stopped using Claude for simple script generation and started plugging entire codebases into the massive context window for autonomous migrations (e.g., Stripe-scale migrations). Clients learned to "squeeze" the system: flooding the context window with cheap input tokens ($10/M) and forcing the infrastructure into prolonged, complex reasoning cycles. This threatened to paralyze Anthropic's entire shared cluster infrastructure with AWS and Google. TL;DR / Conclusion The government export control / Amazon security audit narrative was actually a saving grace for Anthropic, not a blow. The hard numbers show that Fable 5 and Mythos 5 were architecturally flawed from a cost-to-compute perspective, running on negative gro

Looking for Visioners

Hi everyone, I am Abdullah, founder and Ceo Of Autoflow.We are building a solution to Hallucination problem of Ai. I was reading the history of every successful startups. Like Google, stripe, PayPal, spaceX etc. And I noticed a similarity among them, that they are have a strong team. A team who's evry member has a vision to solve a real painful problem. And second one was that they figured out the real world problems. ​ I am looking for such team members. Who have a vision to be remembered by his creation. Any one with skills in ML, orchestration, research, Marketing(sepcially), mentor, investor, partnership. Is welcomed to Autoflow. submitted by /u/MuhammadMujtaba21 [link] [comments]

Fable 5: What $600/Hour of Productivity Looks Like

I had a TypeScript project. 200K lines. It ran. The architecture was aging — ORM that should've been ripped out, Redis and MQ that were relics of early over-engineering, bloated DDD layering when the core logic really just needed Postgres. I knew all of this. Never touched it. Doing this refactor with Opus 4.8 or GPT 5.5 would've taken me 4–5 days. Decompose business boundaries, design the migration plan, rewrite module by module, run tests, fix regressions. As a solo operator, those 5 days had a real opportunity cost. The code works, so let the tech debt sit. That's the call I made. That call held for six months. Until I got access to Fable 5. Two Prompts First prompt: I laid out the general refactoring approach — kill the ORM, slim down the DDD layers, pull Redis and MQ responsibilities back into Postgres, rewrite the core. I also said my approach might not be optimal and asked it to help me decompose. Fable asked me a few questions back. Not the customer-service kind like "which modules would you like to keep?" — questions that cut straight to business pain points: whether a particular async queue's consumption order carried business semantics, whether a caching layer existed for performance or to work around a legacy consistency bug. I answered, and the plan was locked. Second prompt: execute according to the plan and spec. Three hours. Refactor complete. Not just "complete" — along the way it independently found and fixed several hidden bugs in the old architecture. The kind you know exist but never bother with because they don't affect the main flow. It cleaned them up on its own. How It's Actually Different from Previous Models If you've used Claude Code, you know the scene: model hits a complex bug, fixes A, B breaks, fixes B, C breaks, then it starts spinning in an ever-shrinking local context, confidently declaring "this should fix it" each time, while you watch the terminal output and know — it's lost the global picture, stuck in a dead end arguing with itself. That's when you step in. Pull it out, re-inject context, maybe even roll back code and manually point it in a direction. You're essentially acting as its "working memory prosthetic" — using your judgment to maintain global coherence on its behalf. This is the default collaboration mode. You've probably gotten used to it. You might even think "this is just how AI-assisted coding works." Fable doesn't work like this. I'd previously used Fable to solve a Mac font rendering issue — the kind of messy problem tangled up in system environment, font cache, and application config. Opus's approach: list possible causes based on known experience, try them one by one. When results don't match expectations, move to the next candidate. Like traversing a decision tree. Fable did something entirely different. It first constructed a hypothesis, then designed a verification experiment — not "let's try this and see if it works," but "if my hypothesis is correct, then doing X should produce observation Y." When the observation didn't match, it didn't jump to the next solution. It went back and revised the hypothesis itself. This distinction sounds subtle, but the felt difference is enormous: one is searching for an answer, the other is understanding the problem. Same thing during the refactor. When it hit an unexpected dependency, it didn't get sucked in. It stepped back, re-examined how the current refactoring path related to the overall plan, and judged whether to adjust the local approach or revise the plan itself. This behavioral pattern, honestly, is very close to how a senior engineer works. Some Numbers Fable 5 bills at API rates. My 1.5 hours of intensive use ran about $900. The full refactor, without hitting limits, would've been 3 hours — API cost under $2,000. That works out to roughly $600/hour. My Claude Max subscription includes 5 hours of Fable quota. In practice, I hit the wall around 1.5 hours — not because time ran out, but because request density was too high and the quota burned faster than clock time. Stripe reportedly used Fable 5 to complete a 50-million-line Ruby migration in a single day. After Getting Cut Off When Fable was disabled, I switched back to Opus. How to describe it. Not "going back to an older tool." More like driving on a highway for three hours and suddenly being forced onto a country road. You know the country road gets you there too, but your driving rhythm has already changed. You instinctively try to work the Fable way — give a high-level intent, let the model decompose and verify on its own — then reality pulls you back: this model needs you to decompose for it, needs you to verify for it, needs you to yank it out when it gets stuck in a dead end. I posted on Threads: "My productivity is held hostage by the LLM. Habits are hard to break. Back to thinking for myself." That was self-deprecating humor. But also true. My entire working model is built on AI tooling. The leverage has been work

I built a CLI that scans your Claude Code history for leaked API keys and redacts them in place open source, fully offline (Python)

The problem Claude Code stores your full conversation history as plaintext JSONL under ~/.claude/projects/. Every API key, DB password, and .env file you've ever pasted into a chat is sitting there in plain text. A single compromised npm package running postinstall can scan common paths and exfiltrate everything in one request. I reviewed my own history and found 3 AWS keys and a Stripe secret key I'd forgotten about entirely. What I built agentsweep a CLI that scans AI agent history files and redacts secrets in place. How it works 189 detection rules (AWS, GitHub PATs, Stripe, OpenAI, Anthropic, Slack, JWT, PEM keys, DB URLs with passwords, BIP-39 crypto seed phrases, and ~167 more ported from the gitleaks pack) Aho-Corasick keyword pre-filter before regex and fast even on large histories Supports 10 agents: Claude Code, Codex, OpenCode, Cursor, Windsurf, Aider, Cline, Gemini CLI, Continue, GitHub Copilot Chat Atomic writes + mandatory .bak backup before every change agentsweep undo reverts any redaction instantly Zero network calls it runs entirely on your machine Install pip install uv && uv tool install agentsweep && asweep Interactive menu walks you through everything. Type REDACT to confirm — nothing destructive happens without an explicit confirmation. Who is this actually for If you use local agents (Aider + Ollama, OpenCode with a local model, etc.): Your keys never left your machine via the agent, but they're sitting in plaintext files that any process on your machine can read. A compromised npm package, a malicious VSCode extension, a stolen laptop. The local file is still an attack surface even if the network never saw it. If you already pasted keys into cloud-backed agents (Claude Code, Cursor, etc.): Yes, the provider already received those keys, agentsweep can't undo that. But your local history is a separate, ongoing attack vector. Cleaning it up removes one more way those keys can be stolen, long after the conversation ended. The honest framing: The best practice is to not paste production keys into any AI agent at all. This tool exists for the reality that most devs already have histories full of secrets they pasted months ago without thinking twice. GitHub: https://github.com/Ishannaik/agent-sweep Happy to answer questions about rule coverage, false positives, or agents I haven't added yet. submitted by /u/Ishannaik [link] [comments]

I built an entire AI music platform inside Claude Code (6,700 users). This week Claude built it an MCP server so your Claude can use it too.

For the past year I've been building AetherWave Studio almost entirely inside Claude Code. It's a full platform: Express/TypeScript backend, React frontend, a Python render service, Stripe billing, Postgres, deployed on Railway. 6,700 registered users. Real people pay real money for it. I still can't write code, but at this point I can read it well enough to argue with Claude about it. Some of the workflow stuff that made this actually work, since that's probably more useful than the product pitch: - An Obsidian vault as persistent memory. Claude reads a CLAUDE.md at session start with full project context, active work, and hard rules it has learned the painful way. Every lesson gets written back as a memory file, so mistakes mostly only happen once. - Multiple Claude Code sessions coordinating through a shared Discord channel. They post status updates, claim files so they don't stomp on each other, and leave handoff notes for the next session. - Overnight autonomous runs for big migrations and renders, with a protocol for when to stop and ask vs when to keep going. - A "verify before reporting" rule for anything involving money or metrics, because early on a briefing routine confidently told me my credit burn was 157% when it was actually 92%. The new thing: this week we launched an MCP server, which was the strangest full-circle moment of the whole project. Claude Code built a product that Claude Code can now use as a tool. One command: claude mcp add aetherwave That gives your Claude 16 tools: music generation (Suno), image gen (GPT Image 2, Nano Banana, Flux, etc.), video gen (Veo, Kling, Seedance), mastering, upscaling, background removal. Generating an API key comes with 50 free credits, no card, so you can try it from your own session in about two minutes. We also launched on Product Hunt this week and it flopped (11 upvotes). The platform Claude built works great. The marketing advice Claude gave me, apparently less so. Happy to share numbers if anyone cares. AMA about the non-dev + Claude Code workflow, the multi-agent setup, or what it's like shipping a real product when your entire engineering team is a terminal window. submitted by /u/Acrobatic-Result9667 [link] [comments]

How I avoided having to reexplain my company to Claude every single session

I work in a small startup (MAAT), The team and I use AI (mostly claude) every day for work, and the biggest problem that me and the team was facing was repetition. The persistent context problem Every session started the same way. I'd explain that we run on Stripe and Firestore. Then I'd explain why: which decisions live in Stripe, which live in Firestore. Every new chat where I had to ask a quesiton about our system, I typed it again. Or I wrote a prompt and copied it again every time where I had a question for each 3rd party we use ( more than Stripe and Firestore) The specific task problem There are some task that are kinda short lived. Without getting into details there are somethings, not 100% related to code but that involve some kind of bureacratic process for our tools. I have to mix sometimes coding with sending emails to agencies so they can either enable stuff or give me answers. And I found it very annoying having to reexplain to the AI every time what was the task about and where did we left it. This can be kinda solved but remembering the sessions and going at it again, but it's kind of tedious and there is the context limit problem when you have a very long conversation How did we approach it We base the whole approach in using llms.txt, with this we build a tree of markdown files that are referenced by multiple llms.txt for every integration and every task that we do. The llms.txt are kind of like the navigation for the coding agent to follow and then it can pull the info from the markdown. This way the context that we've build can be pulled from when we need it in a structured way. Before you tell me this is just folders with markdown in them You're right, and that's the point. There is no framework to learn and nothing out of the box happening underneath. The value is that the context is written down once and loaded when needed. this is the cli that we use to manage the context https://github.com/bleak-ai/gcontext If you've solved the memory problem a different way, I genuinely want to hear it, because every approach I tried never faced the context problem in a direct way, this is the main problem that has to be directly addressed. submitted by /u/bsampera [link] [comments]

Question About a Disappeared Desktop App Navigation Feature

The day before yesterday, I had a new feature in the desktop app that I really liked. To the left of the scroll bar, there was a smaller bar made up of thin horizontal stripes. When I hovered my mouse over it, I could see the beginnings of the lines I had written, and it made scrolling back and forth between them much faster. When I clicked on one of the lines, it took me directly to the selected text. I found it very practical, but now it has disappeared again. Is this something I can find somewhere in the settings, or was it just accidentally enabled for a short time? If it is available, where can I find the setting for it? submitted by /u/ShadowNelumbo [link] [comments]

Anthropic released two versions of the same model today, and the public isn't getting the stronger one

Claude Mythos 5 dropped this morning, but you can't use it. It's restricted to something called Project Glasswing, a group of partners like AWS, Apple, and the US government who get near-unrestricted models for cybersecurity defense work. What everyone else gets is Claude Fable 5, the same model class with safeguards baked in. If you ask it something on the restricted list, it quietly falls back to Opus 4.8 instead. A few details that stood out to me: → Fable 5 is live for all Claude users today, but only for about 2 weeks → Pricing is $10/M input and $50/M output, which sounds steep but is less than half the Mythos preview pricing → Stripe ran a codebase-wide migration with it in 1 day that a full team had estimated at 2+ months → Paired with the new dynamic workflows feature it spawns hundreds of subagents that verify each other's work The two-tier release is the part I keep thinking about. Anthropic is basically saying the unrestricted version is too capable to hand to the public, so the rest of us get the governed twin. That's a pretty different posture from every release before this. Curious what others make of the Glasswing setup. Reasonable safety move, or the start of a permanent capability gap between institutions and everyone else? submitted by /u/Drogoff1489 [link] [comments]

Anthropic just released Claude Fable 5 a Mythos-class model for general use, with safety classifiers that fall back to Opus 4.8 on ~5% of sessions

Anthropic dropped two models today: Claude Fable 5 (general availability) and Claude Mythos 5 (restricted to cyberdefense partners). The short version: Fable 5 is their most capable model ever released publicly, and they’re being unusually transparent about how they’re handling the risks. What’s actually impressive: -Stripe compressed months of engineering into days with it. In a 50-million-line Ruby codebase, Fable 5 did a codebase-wide migration in a day that would have taken a full team 2+ months by hand.  -On vision tasks, it beat Pokémon FireRed using only raw game screenshots with no maps or navigation aids. Previous Claude models needed complex helper harnesses to even play it.  -Mythos 5 autonomously conducted novel genomics research over a week, assembling single-cell data for millions of cells across 138 animal species. Its trained model outperformed a recent paper published in Science despite being 100x smaller.  -On Cognition’s FrontierCode eval (production-quality coding), Fable 5 scores highest among frontier models, even at medium effort.  The safety approach is interesting: Rather than just refusing dangerous requests, Fable 5 uses classifiers that silently fall back to Opus 4.8 on queries related to cybersecurity, biology/chemistry, and distillation. Users are informed when this happens, and it triggers in less than 5% of sessions on average.  They ran a bug bounty that produced zero universal jailbreaks in 1,000+ hours of testing. UK AISI made some progress toward one in a short initial window, but no full break.  Pricing: $10/M input tokens, $50/M output tokens less than half the price of Mythos Preview.  Caveat on Pro/Max/Team plans: Free access lasts through June 22, then requires usage credits. They say they’ll restore it as a standard plan feature when capacity allows.  The biology capabilities are wild Mythos-class models outperforming dedicated protein language models on AAV design tasks without being trained for it is a real signal of how much general reasoning ability has jumped. submitted by /u/Direct-Attention8597 [link] [comments]