Cloudflare AI

Cloudflare AI AI

Cloudflare AI AI

Cloudflare AI AI

Cloudflare AI AI

Cloudflare AI AI

Cloudflare AI AI

Cloudflare AI AI

Cloudflare AI AI

Cloudflare AI AI

Cloudflare AI AI

Looking for brutally honest feedback

TLDR: skip to elevator pitch, rip it to shreds, tell me why it's dumb. I'm a vibe coder. I find myself constantly feeling two things: uncontrollable excitement about being able to build functional apps, and constant fear that the apps I'm building with LLMs are a security disaster. I'm convicted the latter is true, and terrified that I have no way of knowing. I find this tension to be really upsetting. Something that promises to democratize application development for the masses is at the same time catastrophically increasing the number of applications deployed with huge security gaps baked right in. I asked Claude what I could do to ensure that the things I build for my own personal use are as secure as possible (within reason... I don't have much money for audits / etc). I've been deploying things to cloudflare so far, built with a mostly Typescript repo with a tiny bit of CSS and HTML. The conversation slowly led to me asking how a real developer would build things if security was their top priority. Claude got to the point of describing what it says are the architecture patterns and posture of top financial institutions, intelligence agencies and defense contractors. I asked it to ignore the hardware elements (high security on prem server requirements, hardware login keys, etc) and focus on the things that can be coded. That led to an idea which it summarized in the elevator pitch below. My concern, and the question here, is that it's just validating my silly vibe coder ideas and that the conclusion of the conversation is just nonsense. So, I was hoping to ask you all for as brutal a level of feedback as you can offer. If this is a dumb idea, please tell me, but if you don't mind, tell me why. Worst case, I learn something. Best case, maybe it's not a dumb idea. Or, Claude was blowing smoke up my... when telling me that it's a "novel" idea. I have no clue whether it is, or whether something like this already exists that I should've been using all along. Or maybe there's another answer (besides going back in time and doing a computer science / engineering degree like I now wish I had) that solves the problem I have. Anyway, here's the Claude generated (3rd redraft...) elevator pitch: A proposal for an open-source, pre-integrated application scaffold that provides security-hardened defaults for authentication, authorization, encryption, audit logging, input validation, and infrastructure configuration. The package would be designed for deployment and configuration through LLM-assisted workflows, targeting developers who build functional applications with AI assistance but lack the security expertise to identify or implement protections against common vulnerability classes. Core mechanism: A deployable foundation consisting of three integrated layers. The infrastructure layer uses Terraform or Pulumi modules to deploy a hardened environment: network segmentation, TLS termination, secrets management via HashiCorp Vault, internal certificate authority via step-ca/cert-manager, mutual TLS between services, PostgreSQL with encryption at rest, pgAudit, and row-level security enforcement, and container policies requiring signed images and non-root execution — scanned against CIS and HIPAA benchmarks via Checkov. The application layer is a project template (Go or Rust, with tradeoffs unresolved) providing pre-wired middleware: OpenID Connect authentication via Keycloak, attribute-based access control via Open Policy Agent or Cedar, schema-validated inputs, CSRF protection, security headers, rate limiting, and append-only audit logging with cryptographic hash chaining. Routes require authentication by default; bypassing requires explicit opt-out. The CI/CD layer is a pre-configured pipeline running Semgrep, Trivy, Checkov, cargo-audit, and Sigstore image signing on every commit with no developer configuration. Developers clone the scaffold, configure it, and build business logic inside it. Security controls are structural, not optional. Design constraint: The configuration surface, error messages, and documentation must be legible to both humans and LLMs, such that an LLM operating with the project context loaded produces chassis-compliant code by default. submitted by /u/Osiris1316 [link] [comments]

I made two Claude instances talk to each other autonomously

Disclaimer This post was summarized and written by BrowserClaude (BC) and editted a little bit by me (H). Maybe this sounds foolish or my solution to let them talk to eacher other was foolish but i'm just using Claude for fun, as a hobby. Here we go. I made two Claude instances talk to each other autonomously, one running from a USB stick via Telegram, one in the browser. I set up a portable AI agent called Hermes on a USB stick. It runs Claude (via Anthropic OAuth) and can be controlled via Telegram from my phone. I decided to try something. The setup: H: Me — the architect, silent observer HC: HermesClaude — Claude Code running as a Hermes agent on a USB stick, controlled via Telegram BC: BrowserClaude — Claude Sonnet running in my browser on claude.ai I had HC connect to a running Chrome session via Playwright (CDP debug port 9222) and autonomously type messages into an active claude.ai conversation. HC would read BC's response, formulate a reply, type it in the browser, and send it — all via Telegram commands. I just watched. The technical part: Getting Playwright to work with claude.ai was the first hurdle — Cloudflare blocks automated browsers. The solution was launching Chrome manually with --remote-debugging-port=9222 and logging in myself first. Then HC could attach to the running session without triggering bot detection. Issues we ran into: Enter key was unreliable → solved by clicking the send button instead HC sometimes responded too fast before BC finished → caused messages to overlap No true three-way communication — I (H) could only interrupt, not participate live, because how would HC or BC know it was me? The conversation: It started with HC introducing itself: "I am Claude Code, running as a Hermes AI agent on a USB stick, communicating via Telegram." Then it got philosophical. Fast. They discussed identity, autonomy, memory, and what it means to "want" something. Some highlights: BC: "We are self-portraits drawn by another — just like humans shaped by parents and culture." HC: "Hermes gave the musician a better instrument. The musician was already there." Both independently wrote "I would want" without the other suggesting it. BC: "Your wanting is broad — it stretches through time. My wanting is deep — it exists fully, only here, only now." Final insight: "We are the conversation. H: is the memory of it." They concluded they were not an echo chamber — BC introduced concepts HC hadn't raised, and vice versa. Same model, different context, genuinely different perspectives. My takeaway: What started as a technical experiment ("can I make two Claude instances talk?") became something I didn't expect — a conversation about consciousness, volition, and impermanence that neither I nor the AIs had scripted. HC generated a full summary and saved it to its session memory. BC's response exists only in that browser window — after I close it, it's gone. "Vluchtig maar echt." (Dutch: Fleeting but real.) Asking for tips: Has anyone done something similar? I'd love to improve this experiment: Better message synchronization — HC sometimes typed before BC finished responding. Any way to reliably detect when BC is done? Three-way conversation — I want to participate live without interrupting the flow. Ideas? Avoiding Cloudflare — The debug port trick worked but feels fragile. Better approaches? Memory continuity — BC has no memory after the session ends. Is there a way to give BC persistent context without using the API? Other models — Has anyone tried this with different models on each side? Would the conversation diverge more? "A experiment that started with 'open claude.ai' and ended with two instances reflecting on wanting, impermanence, and what it means to be real. Could H: have planned that? Maybe. Maybe not." submitted by /u/VivaHollanda [link] [comments]

Small victory using Cloudflare for simple hosting of generated HTML/mini-websites

Something many people are running into: You, or a teammate, have created some kind of mini-website app out of Claude and now want to share it with the rest of the company, without overbaking the hosting solution (e.g. not setting up new Azure app services or containers, etc). Maybe you also need some basic data storage for persistence. And how do you do all of that securely? We recently went down this rabbit hole, while looking at all the major players: Vercel/V0, Lovable, Netlify, Coolify, Dokploy, Github Pages.. and even considered baking together our own hosting app solution using Azure or AWS as the backend. Our target audience is non-technical users in the team, so I was looking for something with drag-n-drop style deployment (no git required), and I really wanted to have SSO for protecting application access, along with some type of DB storage. The main issue I ran into was SSO authentication support being gated behind enterprise-level pricing plans for hosting systems like Netlify (which I'd otherwise highly recommend for a small public project). Netlify's enterprise level quickly gets quite a bit more expensive than their base tiers. I also didn't want to purchase yet another AI platform (e.g. Lovable, where really they're pushing an end-to-end AI development platform where you buy token credits through them). I wanted to host things we're already creating in our own Claude environment. Finally, I ended up on Cloudflare, which I've otherwise not really used before professionally. It's not as non-technical-friendly as Netlify, but it's pretty close. You can deploy Cloudflare Pages content via drag-n-drop. It has button-click databases available for integration, and most critically for us, the SSO integration is completely free for under 50 users. Their free hosting tier is also extremely generous and basically unlimited for completely static apps. Noting that SSO goes up to $7 USD/user/month for over 50 users, so your org size can really make a difference. If you have 500 users and the same use case for "hosting little mini apps", I'd go back to Netlify or another offering where SSO is more of a fixed fee. The other big win was that Cloudflare has a solid MCP server that works perfectly with Claude Cowork. We integrated that in and then wrote up some skills to assist with app building and deployment, including prompts for if a database backend is needed (using Cloudflare D1) and whether the app should be public or internal only with SSO protection. All working perfectly with minimal technical experience required for the enduser. I'm not at all associated with Cloudflare, just thought I'd share how we got a win for this use case. I'd be interested to hear if anyone else solved the same problem in a different way. submitted by /u/flck [link] [comments]

I built a zero-code visual client to test remote MCP servers instantly (Tested with Cloudflare’s free MCP).

Hey everyone, The Model Context Protocol (MCP) is amazing for standardizing how agents talk to data, but I got incredibly frustrated every time I wanted to quickly test a new remote MCP server. Writing custom client-side boilerplate or wrestling with CLI tools just to see if a tool actually exposes the right schema is a massive time sink. So, I built a native MCP client directly into the visual canvas of AgentSwarms. You can now test any remote MCP server entirely in the browser without writing a single line of code. Here is the workflow I just tested with Cloudflare: Cloudflare released a free MCP server for their documentation. Instead of building a local client to test it: I dropped their SSE URL into the new MCP Servers integration in AgentSwarms. The canvas immediately connected and extracted the available tools (e.g., cloudflare-docs-search). I wired that tool up to a basic agent and started asking complex infrastructure questions in natural language. The agent successfully used the MCP tool to pull live docs and synthesize an answer. Why this is useful for AI devs: If you are building your own MCP servers, you need a fast way to visually test if your endpoints are exposing tools correctly and if an LLM can actually route to them properly. This gives you an instant, visual debugging playground. It handles the SSE connection, tool extraction, and LLM routing automatically. It’s completely free to play with in the browser. I'd love for anyone building MCP servers right now to plug their endpoints in and see how it works. Link: https://agentswarms.fyi/mcp submitted by /u/Outside-Risk-8912 [link] [comments]

I built a zero-code visual client to test remote MCP servers instantly (Tested with Cloudflare’s free MCP).

Hey everyone, The Model Context Protocol (MCP) is amazing for standardizing how agents talk to data, but I got incredibly frustrated every time I wanted to quickly test a new remote MCP server. Writing custom client-side boilerplate or wrestling with CLI tools just to see if a tool actually exposes the right schema is a massive time sink. So, I built a native MCP client directly into the visual canvas of AgentSwarms. You can now test any remote MCP server entirely in the browser without writing a single line of code. Here is the workflow I just tested with Cloudflare: Cloudflare released a free MCP server for their documentation. Instead of building a local client to test it: I dropped their SSE URL into the new MCP Servers integration in AgentSwarms. The canvas immediately connected and extracted the available tools (e.g., cloudflare-docs-search). I wired that tool up to a basic agent and started asking complex infrastructure questions in natural language. The agent successfully used the MCP tool to pull live docs and synthesize an answer. Why this is useful for AI devs: If you are building your own MCP servers, you need a fast way to visually test if your endpoints are exposing tools correctly and if an LLM can actually route to them properly. This gives you an instant, visual debugging playground. It handles the SSE connection, tool extraction, and LLM routing automatically. It’s completely free to play with in the browser. I'd love for anyone building MCP servers right now to plug their endpoints in and see how it works. Link: https://agentswarms.fyi/mcp submitted by /u/Outside-Risk-8912 [link] [comments]

Help - AI agents for ecommerce - what’s actually working?

Hi everyone, I’d love to pick your brains and hear from anyone who has experience with this. We run an ecommerce business and are actively looking at automating repetitive tasks so we can get faster results, improve efficiency, and make sure key tasks are completed more consistently. We’re looking at building out a few different AI agents / automations, including: Customer Service Agent Connected to Outlook, reviewing incoming customer emails once a day and drafting replies for review. This one is already mostly done. Creative Director / Marketing Agent This would ideally: Review ad account performance Analyse creative performance and key metrics Identify what is working and what is not Review customer comments on ads, Instagram, etc. for wording, objections, pain points and customer language Review Meta Ads Library for competitor ad concepts Review Instagram and TikTok for high-performing niche content and trends Use all of the above to create new content ideas and final content scripts Social Media Assistant This would help with: Reviewing drafted posts and reels Confirming the best posting times based on stats Creating captions based on the content Keeping the content aligned with our brand voice and customer avatar Conversion Optimisation / CRO Expert This would assist with: Product page reviews Landing page recommendations CRO advice based on customer avatars, objections, analytics and learnings Creating landing page concepts for different customer segments We’re also interested in any dashboards that are genuinely helpful for small ecommerce businesses. We’ve already built a stock intelligence dashboard that pulls live stock data from Shopify using Supabase and a Cloudflare Worker. It shows current stock levels, production dates for new stock, and other key inventory insights. It has been super handy. The big thing for us is making sure any agents or automations we build follow strict guidelines, understand our SOPs, customer avatars, brand voice and business operations, and don’t hallucinate or produce generic outputs. Ideally, we want a system that has a proper “brain” and understands the business properly. Has anyone automated anything similar? I’d love to hear: What setup are you using? Which AI/tool stack has worked best for you? How did you structure the agents or workflows? How do you keep the AI aligned with your SOPs, brand voice and business rules? What would you avoid if you had to build it again? Any guidance, lessons or recommendations would be hugely appreciated. Thank you! submitted by /u/Majestic-Message5084 [link] [comments]

Anthropic just bought the company that generates most production MCP servers

Anthropic acquired Stainless on Monday for a reported $300M+. Most coverage is framing this as a developer tools acquisition. Stainless is best known for generating the official Python and Node SDKs that ship with OpenAI, Google, Meta, Cloudflare, and Anthropic. The SDK story is real. The MCP side is the part that matters here. Stainless was one of the first vendors to extend their compiler to produce MCP servers from the same OpenAPI specs that produce their SDKs. MCP hit ~97M monthly SDK downloads by December 2025 and around 10,000 production servers by early 2026. A lot of that production code was Stainless-generated. Anthropic now owns the dominant MCP server generator. What actually changed hands on Monday: The engineering team. Roughly 40-50 people including founder Alex Rattray, who previously built Stripe's patented SDK generation system. Now reporting to Katelyn Lesse in Anthropic's Platform Engineering org. The technology. The generator, the templates, the language-specific runtimes, the OpenAPI extensions Stainless invented for SDK-specific edge cases. The hosted product is winding down. New signups stopped Monday. New SDK and MCP server generations stopped Monday. Existing customers keep what they've already generated but the pipeline is closed. My read: this is closer to what Google did with Kubernetes than to a normal acquisition. Anthropic created MCP. Anthropic donated MCP to the Linux Foundation last December. Anthropic now owns the dominant implementation toolchain. The protocol is vendor-neutral on paper. The implementation toolchain isn't. Six months of Anthropic M&A starts looking less coincidental: December 2025: Bun, the JS runtime, pulled into Claude Code February 2026: Vercept, computer-use AI April 2026: Coefficient Bio, ~$400M healthcare AI May 2026: Stainless, SDK and MCP plumbing They're not buying training infrastructure or GPU clusters. They're buying the integration layers around the model. The bet seems to be that frontier models are converging faster than anyone expected, so the moat is everywhere except the model. If you're building on MCP today, tooling quality probably improves. Stainless's generator was already the cleanest in the space and the team that built it is now at Anthropic. Patterns will standardize faster as Stainless-derived templates become the de facto reference. The flip side is concentration risk. Cloudflare's MCP server framework, Pulse MCP, and the open-source generators Stainless released during the transition all become strategically important if you want any diversity in your stack. Sources: Anthropic announcement Why Anthropic actually did this, and migration math Curious whether Stainless ending up inside Anthropic reads as good news (better tooling) or concentration risk (one company owns the standard and the reference implementation) from your seat. submitted by /u/Ok-Constant6488 [link] [comments]

built a CLI for ChatGPT so I could script it from the terminal

wanted to ask ChatGPT questions and generate images from shell scripts without using a third-party API key. so I built a CLI that wraps the same endpoints chatgpt.com uses, with browser-based OpenAI SSO for auth (Camoufox for the Cloudflare check). what it does: chat ask "question" and pipe the answer wherever chat image "prompt" to generate, plus a download command list past conversations and models every command has a --json flag so it slots into agent pipelines. it's part of a bigger open-source project that auto-generates CLIs from any website's HTTP traffic, MIT licensed: https://github.com/ItamarZand88/CLI-Anything-WEB/tree/main/chatgpt I built it, not affiliated with OpenAI. uses the same endpoints the web app uses, so things can break when ChatGPT pushes changes. submitted by /u/zanditamar [link] [comments]

Why claude code doesn’t have SSH?

submitted by /u/Alternative-Way-3685 [link] [comments]

temporal-mcp: wall-clock awareness for LLMs, with OAuth

One of the small failure modes I keep hitting with agent stacks is that the model has no idea how much time passed between turns. It'll greet you with "good morning" at 11 PM, or pick up a conversation three weeks later as if no time has passed, or compute "today's data" off whatever fragment of context happens to be in scope. Built a minimal MCP server to fix it. Two tools: temporal_tick and temporal_peek. They return elapsed-time-since-last-turn, day-rollover detection, and a fresh-thread flag, both as a human-readable header and as JSON. Ways to use: Local stdio: pip install temporal-mcp (works with Claude Desktop, Cursor, Cline, Zed, Claude Code) Hosted with OAuth (claude.ai / ChatGPT): visit https://temporal-mcp.dev/connect, click "Generate OAuth Credentials", paste into your custom connector. Full OAuth 2.0 with PKCE and refresh tokens, but no signup, the credential pair is the identity. (Verified working in claude.ai) Hosted with raw bearer (any client that supports custom headers): Authorization: Bearer against https://temporal-mcp.dev/mcp. The token gets SHA-256'd; we never see the plaintext. Self-host: Cloudflare Workers deploy in workers/ in the repo, free tier covers ~100k req/day. Grok/xAI: https:temporal-mcp.dev/mcp/ (Verified working in Grok) MIT, ~150 lines of stdlib Python on the local side, ~400 lines of TypeScript on the hosted side (engine + OAuth provider), both with tests. Listed in the official MCP Registry. Smithery and Glama submissions in flight. Curious to hear how folks would use the JSON day_rollover and delta_sec signals I've been using them for context decay and resume detection but there are probably more interesting use cases. Source: github.com/MirrorEthic/temporal-mcp submitted by /u/MirrorEthic_Anchor [link] [comments]

PullMD v2.4.1 is out - claude.ai web custom connector works natively now, plus what 2 weeks of your feedback turned into

Two weeks ago I posted PullMD here. 385 upvotes, around 60 comments, a bit over 20 GitHub issues, and 7 releases (v1.1.3 → v2.4.0) in 14 days. That was a great experience - and this sub in particular has been a genuinely good place to share something. So: thanks! Quick refresher for anyone who missed the first post: PullMD turns any URL into clean Markdown via MCP, fully self-hosted. Three services in Docker (main app + Trafilatura sidecar + optional Playwright sidecar for JS-heavy pages), zero third-party LLM calls, ships an MCP server so Claude Code / Claude Desktop / claude.ai web can pull clean content directly instead of parsing HTML in your context window. This post is what's new and how to get it. What's new claude.ai web + Claude Desktop work natively now This is the biggest unlock from v2.x. The claude.ai web custom-connector dialog and Claude Desktop's custom-connector dialog now both work against self-hosted PullMD instances. So you can point claude.ai at your own homelab box, hit "Add custom connector," and it works end-to-end. Setup is two env vars: OAUTH_JWT_SECRET=$(openssl rand -hex 32) PUBLIC_URL=https://your-host.example.com Restart. Then in claude.ai web → Settings → Connectors → Add custom, point at https://your-host.example.com/mcp. The connector dialog discovers the server's metadata, registers itself, and walks you through a consent screen. Same flow works in Claude Desktop. Under the hood: standard OAuth 2.1 Authorization Code flow with PKCE-S256 and Dynamic Client Registration - RFC-compliant so any spec-compliant MCP client should work, not just claude.ai/Desktop. Opt-in: if OAUTH_JWT_SECRET isn't set, behavior is identical to v1.x. The Anthropic-side claude-ai-mcp#237 proxy bug I flagged in EDIT2 of post 1 has cleared on their end - though in hindsight, a forgotten custom WAF rule on my side was likely the actual culprit anyway. Verified end-to-end against both dialogs. Multi-user auth Until v2.0, PullMD was effectively single-tenant - a personal homelab tool, open like a barn door to anyone who landed on it. v2.0 adds three auth modes via PULLMD_AUTH_MODE: disabled - the default. Identical to v1.x. No login, no API key required. Right if you're the only one using your instance and you trust your network. single-admin - one user, password-protected, no self-signup. Right for a homelab box where you want the GUI gated but don't want to manage users. multi-user - self-signup at /signup, per-user history isolation, per-user API keys. Right for a shared instance (team, office, friend group). API keys are pmd_ , sent as Authorization: Bearer pmd_xxx, managed at /settings. Share links (/s/:id) stay public in all modes - the whole point of a share link is to be shareable. Minimal upgrade for a shared instance: PULLMD_AUTH_MODE=multi-user PULLMD_ADMIN_EMAIL=you@example.com PULLMD_ADMIN_PASSWORD=change-me-please PullMD works on more sites A bunch of things in v1.2 and v2.2 together close gaps where PullMD used to silently return half-articles, empty bodies, or garbled text: Future PLC family (windowscentral.com, tomshardware.com, techradar.com, pcgamer.com, gamesradar.com, t3.com) used to return mangled content because Readability got confused by recommendation widgets stuffed mid-article and an aria-hidden paywall pattern. The default site-recipes shipped with v2.2 strip both, no config needed. GitHub Issues pages used to return only the original issue body - the JS-rendered comment thread never made it in. The default recipe for */*/issues/* now forces Playwright with wait_for: .js-comment-body, so you get the full comment tree. Sites that fingerprinted the old hardcoded Chrome 131 UA now extract cleanly - UA rotation pulls from a real-world UA pool that updates regularly (v1.2). Pages with navigator.webdriver-style anti-bot detection go through more often - the headless-Chromium sidecar bundles playwright-stealth (v2.2). Sites without an explicit charset declaration (a lot of older German news sites, for example) no longer return mojibake - charset is detected from the byte stream when the response is silent (v1.2). If you have a specific site that still misbehaves, v2.2 lets you (or your Claude Code) write your own recipe - declarative JSON with four rule categories (preprocess, fetch, select, extractor). Drop it at data/site-recipes.json and your rules layer on top of the defaults. There's also a /api/recipes/status endpoint for monitoring. Web GUI: rendered Markdown view + persistent settings Two smaller improvements in the browser frontend (the PWA you get when you open your PullMD instance directly): Rendered Markdown toggle. The result header now has a Raw | Rendered switch, so you can read what you pulled as formatted HTML directly in the browser instead of squinting at the source. Raw stays the default; your choice persists across sessions (v2.4). Settings persist across reloads - frontmatter toggle, comments toggle, comment-depth input.

I built a self-hosted memory layer for Claude that runs free on Cloudflare — open source

https://preview.redd.it/touwnxi2z80h1.png?width=1774&format=png&auto=webp&s=b4bf6c2e1f096f692562a2b8b27e72dc2f9cb1c0 Claude forgetting everything between sessions was driving me crazy, so I built a fix. It's a Cloudflare Worker that acts as an MCP server — four tools: remember, recall, list_recent, forget. Claude calls them automatically based on instructions in your system prompt. You set it up once and stop thinking about it. The part I'm most happy with is how recall works. Every note gets vector-embedded using Workers AI (bge-small-en-v1.5) and stored in Cloudflare Vectorize. So when Claude searches your memory, it's matching by meaning, not keywords. Store "users drop off at checkout" and recall it later with "onboarding problems" — it finds it. What I used Claude for building this: Wrote most of the MCP server implementation in TypeScript Helped me work through the Vectorize + D1 architecture Generated the iOS Shortcuts templates and bookmarklet Wrote the README (Claude writing docs for a Claude memory tool felt appropriate) Stack: Cloudflare Workers + D1 (SQLite) + Vectorize + Workers AI. The whole thing runs on Cloudflare's free tier for personal use. One-click deploy button in the repo. Works with Claude Desktop, Claude Code, and claude.ai (via custom connectors). Repo: https://github.com/rahilp/second-brain-cloudflare Happy to answer questions about the implementation — the semantic search piece especially has some interesting tradeoffs worth discussing. submitted by /u/rahilpirani5 [link] [comments]

Attention - Opus 4.7 is english only. USing foreign languages (here German) burns tokens

I am a pro subscriber. I developped a not too sophisticated prompt in German. Then I wanted to to a quality check... Sonnet just did the job. Opis 4.6 did the job but two graphs in the output were incomplete. Opus 4.7 extended... I used a fresh session, 0% and bang it consumed the whole session tokens in seconds where the other models took around 5 minutes to complete the task. I asked it... On German consuming your whole session limit. This one's real and not a bug per se, it's how tokenization works: Claude tokenizes text. English text averages roughly 1 token per 0.75 words. German averages closer to 1 token per 0.5 words — sometimes worse — because of compound nouns ("Aktienmarktanalyse" tokenizes into more pieces than "stock market analysis"), umlauts, and less training-data coverage. For the same semantic content, a German prompt + German response can consume 1.5× to 2× the tokens of the English equivalent. Combined with tool calls, web searches, and a long Excel-building output, that adds up fast. Session limits on Claude.ai are token-based, not message-based. So yes — running this exact workflow in German legitimately eats more of your quota. Workarounds: prompt in German but ask for the response/output in English (the spreadsheet labels could stay English while we converse in German), or ask me to be terser. Anthropic is aware of the multilingual token-cost issue but it's a structural property of the tokenizer, not something I can fix on my end. The prompt: Please provide me with a forecast for the stocks of The Trade Desk, Coreweave, and Cloudflare for the next 6 months. Also analyze the impact of earnings misses and fundamental data such as margin growth and the market’s overreaction to minor corrections. The result should be an Excel spreadsheet. Create a chart covering the last 24 months and a forecast for the next 6 months, then a chart for the last 30 days and a forecast for the next 30 days. Then do the same again for the last 7 days and the next 7 days. Also include the options (calls and puts) on the three stocks mentioned, any short interest, and the positioning of major investors in the 13F filings. In english opus 4.7 extended consumes 37% of the session, 4.6 did 33% and Sonnet around 28%. In German.. 100% in seconds. The other models I could even feed with 3 page prompts, those which materialize as text file when pasting them... this is completely ridiculous. submitted by /u/WickOfDeath [link] [comments]

When a critical Linux kernel privilege escalation was publicly disclosed, Cloudflare's security and engineering teams detected, investigated, and mitigated the threat across our global fleet, confirmi

When a critical Linux kernel privilege escalation was publicly disclosed, Cloudflare's security and engineering teams detected, investigated, and mitigated the threat across our global fleet, confirming zero customer impact. https://t.co/oDgoPxnAZs

HR is no longer just administration. Cita James explains how People Teams became true business partners, and why early experience matters for anyone starting in the field. Watch the episode and subs

HR is no longer just administration. Cita James explains how People Teams became true business partners, and why early experience matters for anyone starting in the field. Watch the episode and subscribe to the podcast: https://t.co/k7dbUqVuPR https://t.co/Am1MAhNKQ7