Anthropic

Anthropic AI

Anthropic AI

Anthropic AI

Anthropic AI

Anthropic AI

Anthropic AI

Anthropic AI

Anthropic AI

Anthropic AI

Anthropic AI

Subscription alternative for occasional users? (small business)

We're a small business with 10 employees. Since we only use Claude very occasionally it makes no sense to pay for a monthly subscription for all users. E.g once or twice a month, we need to make a report, and would like to use Claude Excel plugin, etc. I thought about just making one account, and sharing the user and pass amongst the employees, but this is not a good solution since they sometimes will enter personal data. From what I understand, Anthropic don't offer a payment model where multiple users have access and only pay for the use, instead of a fixed monthly fee? Confusing, it seems that API account is similar to what I want, but it doesn't support plugins or chat? submitted by /u/cryptogeezuzz [link] [comments]

API Cache not working or Claude Console Dashboard UI bug?

Hey there, I'm not sure if my system was using cache or if I was burning tokens needlessly? How can I confirm I have things set up correctly and it's only UI bugs? My Claude Dashboard and dashboard logs are not consistent (can one download the full log?) Using Rider and Continue.dev name : Local Config version : 1.0.0 schema : v1 models : - name : Claude Sonnet 4.6 provider : anthropic model : claude-sonnet-4-6 apiKey : ${{ secrets.ANTHROPIC_API_KEY }} roles : - chat - edit - apply defaultCompletionOptions : promptCaching : true requestOptions : timeout : 200000 thinking : type : "adaptive" effort : "low" https://preview.redd.it/h40ho3ui7n4h1.png?width=392&format=png&auto=webp&s=262fc188714299962923fbff961bab6814c3c45c https://preview.redd.it/g1l5uahj7n4h1.png?width=1096&format=png&auto=webp&s=08960e0f51b6628f9bb15863e463862cd844a0de https://preview.redd.it/5gtstt1k7n4h1.png?width=423&format=png&auto=webp&s=9fcb62379045259cd6ebc99c541b6bb90100e5a2 submitted by /u/SavingClippy [link] [comments]

I used Claude Code to build a free Pokémon personality profiler from scratch in one session

Hey r/ClaudeAI, I built NotRandom (notrandom.vercel.app) a free web app where you type your favorite Pokémon and get a psychographic profile based on your choice. The premise: your favorite Pokémon is not random. It reveals something real about you. What it does: Fetches the Pokémon's types and Pokédex lore from PokéAPI Classifies it into one of 10 archetypes (The Sovereign, The Rebel, The Shadow Operator, The Jester...) Generates a Core Identity, Shadow Side, a unique nickname ("The Calculated Vanishing" for Greninja), and a one-liner "The Line" designed to feel uncomfortably accurate Lets you download a 1080x1080 shareable image card How Claude Code specifically built this: Everything was written by Claude Code in a single session. Here's what it actually did: Full architecture I described the concept, Claude Code planned the stack (React + Vite + Tailwind + Vercel serverless) and the complete file structure before writing a single line All React components from scratch LandingPage, LoadingScreen, ProfileCard, ShareCard, state machine in App.tsx The archetype system designed and coded the mapping of all 18 Pokémon types to 10 personality archetypes with color palettes per type The Claude Haiku prompt engineered to return a structured JSON with the right tone (intelligent, slightly poetic, never cringe) Solved a real architectural problem — the Anthropic API blocks direct browser calls (CORS). Claude Code diagnosed it, then created a Vercel serverless /api/profile route to proxy the call server-side Debugged the share card the downloaded image was black. Claude Code identified two causes: position: fixed breaks html-to-image's canvas renderer, and Google Fonts fail silently during capture. Fixed with skipFonts: true + sprite conversion to base64 before capture Full deployment Vercel config, environment variables, 30s timeout for cold starts I described what I wanted, Claude Code wrote the code, I tested, reported what broke, it fixed. Classic loop. https://preview.redd.it/i62hsrbd0n4h1.png?width=1080&format=png&auto=webp&s=aa31a839f094fa5760e401e8f336b01efbb49179 Free to use: https://notrandom.vercel.app no login, just type any Pokémon name in English (all 1025 are supported). submitted by /u/dyloum84 [link] [comments]

trying to see if Mythos claims are verified or not

The claims about Mythos Preview (Anthropic's unreleased Claude model) are substantially verified from multiple independent sources, though the exact CVE numbers and commit hashes for the FFmpeg vulnerability are still being disclosed through coordinated security processes. Key Verified Facts 1. OpenBSD 27-year-old vulnerability ✅ Verified What: A bug in OpenBSD's TCP SACK (Selective Acknowledgment) handling introduced around 1998[forum.devtalk] Impact: Allows an attacker to remotely crash any OpenBSD machine just by connecting to it via TCP[linkedin] Details: OpenBSD tracks SACK state as a singly linked list of holes; the vulnerability is subtle and survived 27 years of expert review[reddit] Patch: Available at openbsd/pub/Openpatches/.8/025ack.patch[reddit] 2. FFmpeg 16-year-old vulnerability ✅ Verified What: A bug in FFmpeg's H.264 decoder where a 32-bit slice counter is stored in a 16-bit lookup table, initialized to 65535[secureworld] Impact: A specially crafted frame with exactly 65,536 slices causes counter collision triggering out-of-bounds write[secureworld] Origin: Type mismatch dates to FFmpeg's 2003 H.264 commit; exploitable code path introduced in 2010 refactor[secureworld] Testing evasion: The code path was hit by automated testing tools 5 million times without flagging the bug[linkedin] Patch status: Three FFmpeg vulnerabilities found by Mythos were patched in FFmpeg 8.1[secureworld] 3. Linux kernel vulnerability chain ✅ Verified What: Mythos autonomously found and chained multiple Linux kernel vulnerabilities for privilege escalation[reddit] Impact: Escalation from ordinary user to complete root control of the machine[linkedin] Cost: Under $2,000 in tokens to create the exploit chain[linkedin] Status: Anthropic is funding the Linux Foundation to fix these vulnerabilities[linkedin] Supporting Evidence Source Type Key Confirmation Anthropic's risk report Official PDF Technical assessment of Mythos Preview released April 7, 2026 [anthropic] AI Security Institute evaluation Independent Confirmed 73% success on expert-level cyber CTF tasks [aisi.gov] Debian security tracker Official CVE-2026-40962 fixed in FFmpeg 8.1 [security-tracker.debian] Reddit/OpenBSD forum Community Patch discussion and technical details [reddit] Why This Matters This is considered "possibly the most frightening cybersecurity news in decades" because: AI found bugs that survived decades of expert audits and relentless fuzzing[agent-wars] Mythos found thousands of zero-days versus Opus 4.6's ~500[reddit] The model achieved 181 working exploits in Firefox benchmark testing[agent-wars] Access is gated/restricted due to dual-use risk[docs.aws.amazon] The FFmpeg commit should indeed be public given it's open source, and the patch is in FFmpeg 8.1. The exact commit hash is being handled through coordinated disclosure, but the vulnerability details are confirmed by multiple independent security researchers.The claims about Mythos Preview (Anthropic's unreleased Claude model) are substantially verified from multiple independent sources, though the exact CVE numbers and commit hashes for the FFmpeg vulnerability are still being disclosed through coordinated security processes.Key Verified Facts1. OpenBSD 27-year-old vulnerability ✅ VerifiedWhat: A bug in OpenBSD's TCP SACK (Selective Acknowledgment) handling introduced around 1998[forum.devtalk] Impact: Allows an attacker to remotely crash any OpenBSD machine just by connecting to it via TCP[linkedin] Details: OpenBSD tracks SACK state as a singly linked list of holes; the vulnerability is subtle and survived 27 years of expert review[reddit] Patch: Available at openbsd/pub/Openpatches/.8/025ack.patch[reddit]2. FFmpeg 16-year-old vulnerability ✅ VerifiedWhat: A bug in FFmpeg's H.264 decoder where a 32-bit slice counter is stored in a 16-bit lookup table, initialized to 65535[secureworld] Impact: A specially crafted frame with exactly 65,536 slices causes counter collision triggering out-of-bounds write[secureworld] Origin: Type mismatch dates to FFmpeg's 2003 H.264 commit; exploitable code path introduced in 2010 refactor[secureworld] Testing evasion: The code path was hit by automated testing tools 5 million times without flagging the bug[linkedin] Patch status: Three FFmpeg vulnerabilities found by Mythos were patched in FFmpeg 8.1[secureworld]3. Linux kernel vulnerability chain ✅ VerifiedWhat: Mythos autonomously found and chained multiple Linux kernel vulnerabilities for privilege escalation[reddit] Impact: Escalation from ordinary user to complete root control of the machine[linkedin] Cost: Under $2,000 in tokens to create the exploit chain[linkedin] Status: Anthropic is funding the Linux Foundation to fix these vulnerabilities[linkedin]Supporting EvidenceSource Type Key Confirmation Anthropic's risk report Official PDF Technical assessment of Mythos Preview released April 7, 2026 [anthropic] AI

How long does it take for Anthropic to approve Claude Code community plugin?

Submitted through my Claude Console and it has been in "submitted and pending review" for over 10 days, wonder what I shall do next other than waiting... https://preview.redd.it/4z3a6ylowl4h1.png?width=546&format=png&auto=webp&s=e606c8f59fbcefea35b42f399e1dc7ef9480e7dd submitted by /u/darren_eng [link] [comments]

this chart felt shady, so I fixed it (what I found will shock you!)

The first chart is in the Opus 4.8 system card (p.195 for those playing along at home). Several things struck me as odd about it: The horizontal axis is log scale — there are good reasons to use this, but as an experienced data professional, I can tell you for free that most people just sort of slide off a log scale axis. One can, therefore, often be used to "soften a numerical blow", and so they always set my spidey-sense going. Nobody cares about output tokens except that they cost money, so really this axis should be expressed in $ no sonnet 4.6 for comparison — lots of other charts in in the system card include sonnet, why not this one? …so I had to make my own. The method, briefly: I sampled 50 tasks at random from the public 731-task set for each effort level, and graded the output patches in Docker image. As the uncertainty band shows, I gave up before I had anything truly robust. In my defence it ran for ~24h and I'm not made of tokens >.< My takeaways, in no particular order: The "Sonnet 4.6 is better than Opus 4.6 fr" crowd was probably on to something. Everyone complaining Opus 4.8 is burning tokens too fast needs to drop their effort level a notch, the log scale hid how crazy-expensive max mode can get. Opus 4.8 on low effort beats Sonnet 4.6 on med, high, or max, and for less cost. Unless the task can genuinely be done by Sonnet 4.6 on low, you're better off using Opus rn. It's obvious why they hid sonnet, it comes away terribly here. Suspect there are other tasks for which it still makes good sense. Of course this is all in the context of a single benchmark, and benchmarks are kinda fake. However I've always held that while all benchmarks are bad, some benchmarks are useful. Follow-ups: (use your own tokens and report back, lol) needs more N anyone want to sanity-check some Opus configs locally? Be nice to validate this methodology lines up with Anthropic's what does this chart look like using other providers' pricing? could throw in some GPT+codex data points, that'd be interesting submitted by /u/samthehugenerd [link] [comments]

A SF house just went on sale priced in Anthropic stocks

The buyers these listings target are worth $10-100M+ on paper and senior anthropic engineers get stock grants worth millions annually. Anthropic employees have watched their equity compound through multiple funding rounds and they are still renting because the shares are private, locked and transfer restricted and paper wealth doesnt pay a mortgage. So the market found the workaround,sellers who believe in the AI trajectory take stock directly and buyers skip the liquidity problem so both sides get what they cant otherwise access.the listing agent at noe street said she kept running into buyers at open houses who wanted to buy but couldnt touch their equity yet. She went live and had overwhelming interest within 24 hours. The thing worth connecting here is that the IPO is expected this fall and when that liquidity actually unlocks, hundreds of millions of dollars of newly spendable wealth will be concentrated in one city .So the most powerful currency in the most expensive housing market in the country isnt dollars right now, Its stock in two companies that haven't had a public price yet What happens to the city if the IPOs disappoint?And whats next houses selling for api tokens of claude,kling,magichour or elevenlab in few years lol?? https://preview.redd.it/raylb079ok4h1.png?width=2293&format=png&auto=webp&s=5cf4614605eba4ddae783bdbd223334bddb9de3c submitted by /u/Healty_potsmoker [link] [comments]

Claude CVP criteria?

Has anyone got experience of whether its only 'corporate' accounts that are able to gain Anthropic CVP approval? Or, are 'individual' accounts eligible if certain criteria is met? Opus 4.8 seems to hit the brakes incredibly easily when it comes to anything remotely related to cybersecurity. submitted by /u/OscarP1981 [link] [comments]

Claude Cowork update corrupted my Claude Application and its data

So I was working with Claude Desktop aka Claude Cowork and I saw the “Relaunch to update” and I clicked on it (since I want to be upto date). Apparently the update locked my Claude process in WindowsApps folder. Basically App Installer acquired the lock and failed to release (even after repair and termination + restarting App Installer service). It constantly gave me “File in use by another process”. Did anyone else face the same issue? I had to reinstall the Claude (which btw didn’t update but reinstalled the application with clean slate) What is the best way to fix this in future without logging out and losing data? Existing issue reported on Github by someone: https://github.com/anthropics/claude-code/issues/46179 submitted by /u/Sufficient_Fox_4402 [link] [comments]

Large scale data hygiene tools for small scale team

Background: I’m reasonably clever with AI and computers, but I’m not an IT professional. I was put in contact with a non profit that does a lot of great work, but deals with sensitive info for the people they support. I’m certain Claude would help them knock out a lot of admin type work, organizing data and making it look pretty. They’re rightfully very concerned with their data going back into the models to train or have bits spilled out into the general public. I love Cowork and all the things it can do, but I think the only product that would fit their data privacy needs is an enterprise license. That’s expensive though. Is there an Anthropic product that would give them the protection they need without carrying a massive financial burden? submitted by /u/WDE117 [link] [comments]

How do you handle runaway API costs across multiple OpenAI agents? I built something to solve this

Hey, I'm a CS student and I've been building LedgerAI, a cost tracking and budget enforcement layer for LLM agents. The problem it solves: You're running 3+ agents in production. One goes rogue overnight. You wake up to a $400 bill with no idea which agent caused it and no way to have stopped it. What makes LedgerAI different: Most tools log costs after the call. LedgerAI enforces limits before it. The SDK hits a budget check endpoint before every LLM request, and if the agent is over its daily or monthly limit, the call is blocked. Hard stop, not a soft warning. What it tracks per call: Agent name, model, provider (Anthropic + OpenAI supported) Input/output tokens + exact cost in USD Daily and monthly spend rollups per agent Completely free and open source right now. Pip install or hit the API directly with cURL. Would love feedback from anyone running multi-agent systems, especially what alerting/enforcement features would actually be useful in prod! submitted by /u/IndianCurry06 [link] [comments]

OpenClaw is cool but have you met WhiteClawd? I built a drunk AI lobster that gives terrible life advice

Saw the OpenClaws post and it reminded me — the AI claw cinematic universe is getting out of hand. Between OpenClaw, Kimi Claw, and Moltbook, apparently lobsters are the unofficial mascot of AI agents now? Anyway this inspired me to make the dumbest possible use of the trend: a drunk lobster named Clawrence who gives terrible life advice. He runs on an open model through Cloudflare Workers AI — no OpenAI, no Anthropic, just vibes and bad decisions. He told someone to quit their job and become a "seltzer sommelier" yesterday. Has anyone else been messing around with parody/joke bots? whiteclawd.thenetworkadministrator.com if anyone wants to ask Clawrence for advice (he will call you bro) submitted by /u/Disastrous-Sherbert6 [link] [comments]

why are we celebrating burning more tokens like its a flex

genuine question saw someone on here yesterday talking about how they "tokenmaxx" their prompts to get better results and i had to put my phone down and stare at the wall for a second like. you are paying MORE. to get the same output. that you could get by just. writing a better prompt. or hiring a person. anthropic literally released an "effort control" slider with opus 4.8 so you can tell it to think harder and the response from the dev community was "sick now i can burn 3x the tokens on everything" my brother in christ that is not the win you think it is here's the maths: opus 4.8 is $25 per million output tokens. sounds cheap until ur running long agentic workflows all day every day and suddenly ur monthly bill looks like a car payment. a junior dev in eastern europe costs roughly the same per month and they don't charge you extra when the problem is hard and before anyone says "but ai scales" yeah so does ur invoice the whole tokenmaxx thing is just complexity addiction dressed up as optimisation. people who do this are the same people who spent 6 hours automating a task that took 20 mins manually. the prompt engineering to make it work cost more in time than just doing the thing im not saying ai is bad im saying "how many tokens did i burn" is the worst possible metric for whether something worked. did it solve the problem. was it cheaper than the alternative. those are the questions but nah lets just watch the token counter go up i guess i work in software i am allowed to say this submitted by /u/irelatetolevin [link] [comments]

Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction

Prof G Markets (Live) Episode Title: Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction Location: The Castro Theatre, San Francisco, CA Hosts: Scott Galloway & Ed Nelson ED: We're going to talk about a topic not enough people talk about called AI. Nearly 50,000 workers have been laid off this year supposedly because of AI — that's almost as many as in all of 2025. For companies adopting AI, the thesis is simple: AI is supposed to do much of the work that humans do. In recent weeks, however, that thesis has hit a roadblock. More and more companies are reporting that despite the enormous power of AI, the technology is actually more expensive than the humans it is supposed to replace. Uber, for example, just blew through its entire 2026 AI budget in just four months. According to the COO, it is now getting harder to justify AI costs within the company. Microsoft is cancelling its Claude Code licenses across multiple divisions because it's simply gotten too expensive. And over at Nvidia, one executive said that the cost of compute is now "far beyond the cost of employees." Which all raises a crucial question for the AI industry: at what point does AI actually stop being worth it? This has blown up basically in the last 48 hours, with many companies coming out and saying they're not as confident about this whole AI thing as they used to be. ServiceNow is another company that just blew through their entire Anthropic budget. Technical staff at Stripe are reportedly spending nearly $100,000 on AI tokens every day. Salesforce is on track to spend $300 million on Anthropic tokens this year. Shopify said their earnings were "partially offset by increased LLM costs." We heard similar things from Meta, Spotify, and Pinterest. One Anthropic employee said his Claude Code bill came out to $150,000 in a single month. In some cases, it's getting very, very expensive. We've also seen an incentive — especially among tech companies — to use AI as much as possible. There was this idea that employees would engage in what we call "token maxing," where you use as many tokens as possible from your AI API. Companies like Meta and Amazon have even created internal leaderboards tracking how many AI tokens employees are using. The people using the most tokens are seen as the most AI-forward, the most AI-deployed — the ones who are going to get recognized, maybe even promoted. And this has resulted in extraordinary costs on the AI front. Now we're starting to see the next phase of this, Scott, where companies and their executives are beginning to realize: this is a little expensive. So the question becomes — at what point will AI actually pay off? I'll pose that question to you: at what point is it too much? SCOTT: I think we're already seeing hints of it, and I think it comes down to incentives. You were talking about how companies are trying to incentivize people to use AI more — and that's kind of an interesting part of the ecosystem right now. The adoption layer is trying to get people to use it, and companies have put in place the incentives to do that. But there was a recent survey by a professor at MIT who found that about 5% of the projects people are using tokens for can actually be connected by CFOs to some sort of return. So while I think they're really intoxicated by it — and talking about AI as much as you can in your earnings call is like adding "dot-com" back in the '90s — I think you're already starting to see some fatigue. And I think the AI companies are trying to get public as quickly as possible to raise that cheap capital before things start to — I don't want to say unwind, but... You can see how the string gets pulled here. A large company, a CEO who has a lot of credibility in the industry, just comes out and says: "We're dramatically scaling back our AI investment. Let's be honest, folks — we're just not seeing the return we'd initially hoped." And then Nvidia reports its first miss. Nvidia has beaten its estimates 15 quarters in a row. Nvidia's first miss probably takes the entire market down five or ten percent. You are seeing some productivity gains from this and quite frankly, they look as dramatic, if not more dramatic, than the internet. But look what happened in 2000. This definitely does feel like '99. And I'm waiting for the first CEO to come out and say we have to get procurement involved and dramatically scale back our expenses. I don't think it's that romantic, honestly. I think it's just going to be a traditional Fortune 500 company that starts the narrative: okay, this has been fun, but we have to dramatically decrease our AI investment because we're not seeing the ROI we'd anticipated. ED: Yeah. I mean, we heard a quote this week from the CEO of Match Group — not a huge company — but he said AI is costing them $5 to $10 million a year, and his exact words were: "I think we're benefiting from it, but it's hard to feel." So that's not great if we're supposed

Weekly AI roundup (May 23–30, 2026): Claude Opus 4.8 Fast Mode 3x cheaper, Qwen 3.7 Max beats Claude at half the price, ChatGPT moves into Excel

Pulling together this week's major AI releases for anyone who didn't have time to track every blog post. Sticking to substantive changes, not hype. Anthropic — Claude Opus 4.8 Released this week. Headline pricing unchanged, but Fast Mode dropped from $30 input / $150 output per million tokens to $10 / $50 — a 3x reduction on the premium tier. Reported improvements in "judgment" and longer autonomous runs. Also shipped 20+ legal MCP connectors and Microsoft 365 add-ins (Excel, PowerPoint, Word) in GA. Alibaba — Qwen 3.7 Max Launched May 20 at Alibaba Cloud Summit. 1M-token context. Reported to top Claude Opus 4.6 Max on Terminal-Bench 2.0, SWE-Bench Pro, and MCP-Atlas. Pricing $2.50 / $7.50 per million tokens — roughly half of Opus 4.7. Alibaba claims autonomous operation up to 35 hours without performance degradation. Alibaba is now ranked #6 lab globally on Arena text leaderboard. OpenAI — GPT-5.5 Instant Now default in ChatGPT. Reports 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts (medicine, law, finance). OpenAI also shipped a ChatGPT sidebar inside Excel and Google Sheets, plus a personal finance dashboard for Pro users (US only). Google — Gemini 3.5 Flash Reported to beat Gemini 3.1 Pro on coding and agentic benchmarks at ~4x faster output token rate. Ultra subscription cut from $250 to $200/month; new $100/month Developer tier introduced. xAI — Grok Build 0.1 Coding agent moved to public API beta May 28. Custom Skills feature added for reusable user-defined tasks. Connectors for SharePoint, OneDrive, Notion, GitHub, Linear, plus bring-your-own MCP support. Mistral Launched Vibe (unified work + code agent, replaces Le Chat). Acquired Emmi AI for physics-based simulation. Targeting €1B revenue in 2026; new 10MW inference DC announced. Hugging Face Launched an app store for the Reachy Mini robot. ~10,000 units shipped. Also reported a malicious repo masquerading as an OpenAI release that accumulated 244K downloads before takedown — relevant for anyone pinning models from HF in production. My take as someone building on top of these APIs: The 3x Opus Fast Mode price cut and Qwen 3.7 Max's pricing + autonomous duration are the real signal this week. The cost floor on premium-tier inference is dropping faster than most app-layer products have repriced for. Anyone running multi-step agent workflows needs to recompute unit economics this week — either pass through the savings or reinvest the margin. The other pattern worth noting: OpenAI and Anthropic are both pushing into Excel/M365 surfaces. Distribution is becoming the next battleground, not raw model capability. If you're building a productivity SaaS, the giants are now inside the same surface as you. submitted by /u/ksraj1001 [link] [comments]