Google Document AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Anthropic gift subscriptions are silently reverting to Free plan after ~1 week - and the support loop leaves affected users with no practical recourse

TL;DR: I found multiple reports over several months of Claude gift subscriptions (Max 5x, Pro) silently canceling after ~1 week with no notification. Anthropic's support bot confirmed my case is a backend issue - but also confirmed it cannot fix it. My human support ticket has had no response for 3 days. In practice, there is no path to resolution through current support channels. Anthropic has not publicly acknowledged this pattern. If you're considering buying, read this first. The pattern Over the past several months, a consistent bug has been appearing across Anthropic's community: users who redeem Claude gift subscriptions (primarily Max 5x at $100/month) find their plan silently reverted to Free after approximately one week of use. No email. No warning. No explanation. Just gone. This is not a fringe issue. Here's what the paper trail looks like: GitHub Issues (anthropics/claude-code): #41252 - Max 5x gift subscription disabled without explanation, no support response after 1 week #41499 - $1,400 worth of gift subscription credits destroyed by a Stripe proration bug #43257 - Max 5x showing as Free tier despite active billing, clear account/billing state mismatch #44163 - Gift Pro subscription auto-canceled after several days, redemption link broken with "Page not found" #45335 - Max 5x gift canceled after 7 days (my case, detailed below) - two more users confirmed the same issue in comments within 24 hours of posting Reddit: r/claude - Claude Max subscription silently revoked after 1 week r/ClaudeAI - Claude subscription got cancelled automatically r/ClaudeAI - Anthropic/Claude: we lost all of our subscribers r/claude - My Max plan disappeared, I'm on free plan suddenly These issues span months. The bug is not new. It is not fixed. And Anthropic has not publicly acknowledged it. Why the support structure makes this worse When this bug hits you, a second problem kicks in immediately. The only available support channel is an AI bot called Fin - and Fin will confirm your problem is real while also confirming it cannot solve it. If you're affected by this bug, here is the exact loop you enter: You open support chat Fin tells you it can see your account has no active subscription Fin confirms it "appears to be a technical issue rather than a typical payment failure" (direct quote from my session) Fin tells you it cannot restore your subscription or contact the backend team Fin suggests workarounds that don't apply to your situation Go to step 2 Getting past Fin to submit a human ticket requires significant effort. And once you do submit a ticket - silence. Days of silence. This creates a situation where Anthropic's infrastructure takes your money (or your friend's money), loses your subscription, acknowledges via its own bot that the problem is on their end, and then leaves you with no practical path to resolution. My case - the most documented example My own case is probably the most fully documented version of this bug, so I'll lay it out in detail. On March 29, 2026, a friend gifted me a Claude Max 5x subscription - 1 month, $100 value. I redeemed it on claude.ai. The activation was immediately confirmed: Anthropic sent an official email ("Thanks for starting your Max subscription"), with next billing date April 29, 2026. Invoice and receipt both confirm the subscription. The billing page in Settings showed a March 29 invoice with status "Paid." I used Max 5x features normally for 7 days. Around April 5-6, my account silently reverted to the Free plan. No email. No notification. No policy violation. Nothing changed on my end. What I have as evidence: the Anthropic confirmation email, the invoice and receipt (Max 5x, Mar 29 - Apr 29, 2026, $100 discounted to $0.00 via gift), a screenshot of Settings showing Free plan with the March 29 "Paid" invoice still visible beneath it, a screenshot of the Fin support bot explicitly confirming this is a backend issue it cannot resolve, and my open support ticket, submitted April 6, 2026. As of today - 3 days later - no human response. Approximately 23 days of access remain on that subscription. Roughly $75 in value. Gone into a backend black hole. What this means if you're considering buying Claude Max Gift subscriptions are particularly vulnerable here because there's no recurring payment method attached - so when the system drops the subscription, there's nothing to trigger a re-authorization or alert. You simply lose access and the only paper trail is a $0.00 invoice that looks like it was never real. If you are planning to buy or gift a Claude subscription: There is a known, unacknowledged bug that can cancel it silently after ~1 week If this happens, your path to support is an AI bot that will confirm the problem and tell you it can't help Human support tickets may go unanswered for days or longer Anthropic has not publicly communicated a fix or even acknowledged this pattern I'm not saying Claude is a ba

How I run Google Ads and Meta for multiple clients entirely through Claude (here's how it works)

I've been running paid ads for clients for a while now and at this point my workflow looks nothing like what it did just one year ago. I basically don't open Google Ads or Meta Ads Manager anymore. Everything runs through Claude Code and a system I built around it. Not in the sense that AI runs the accounts for me. More like I built an infrastructure where AI sits on top of everything and helps me operate faster and more consistently. The context layer The core of the whole setup is that every client has their own folder on my machine. Emails, meeting transcripts, website content, offers, pricing, call recordings, all of it lives in one place. Most of it gets pulled in automatically through n8n so I'm not manually organising anything. It just stays current. When I start working on a client I open Claude Code inside that folder and it already has the full picture. I can have a proper back and forth about their account, their business, what's changed, what needs adjusting. No copying data into a chat window, no rebuilding context every time. Google Ads I have the Google Ads API connected directly. Same with GA4, Search Console, and Tag Manager. So when I'm analysing an account I'm not just looking at ad metrics in isolation. I can tie performance back to actual tracking, landing page behaviour, and conversion paths. I also built a keyword analysis plugin that I use for onboarding new clients and for pressure testing existing accounts. It scrapes the client website, runs through an interview process covering budget, services, geo, competitors, what to avoid, and then goes through multiple phases. Keyword research, negatives, campaign structure, ad copy, ROI projection. Outputs a full presentation. On top of the client data I built a knowledge base with my own best practices, previous campaign examples, and methodology baked in. So the analysis isn't generic Google Ads advice, it's grounded in how I actually run accounts. Every Tuesday and Thursday it runs an audit across all accounts automatically. Search term analysis, impression shares, performance changes, anomalies. Basically like having a junior go through every single account. That alone has made things way more consistent across clients. Meta For Meta I built a connector for the marketing API. Campaign management, ad set comparisons, audience management, performance breakdowns, lead forms, all handled programmatically. Same idea as the Google side, I can pull data, reason about it, and push changes without living inside Ads Manager. The one area where I still work manually on Meta is creatives. I haven't found AI generated visuals reliable enough for anything beyond throwaway testing spend. The operational side though is where I've gotten way more leverage. Managing multiple accounts, pulling insights across them, spinning up new structures faster. What actually changed The biggest shift for me isn't speed, although that's obviously there. It's that switching between clients used to mean rebuilding everything in my head. Now I just open the folder and I'm already in context. The AI knows the client, knows the account history, knows what we discussed last week. The second thing is consistency. When you're running multiple accounts manually it's easy to miss things. A search term report you forgot to check, a campaign that's been slowly bleeding budget. Having automated audits twice a week catches stuff I would have missed. I'm still iterating on all of this constantly. But it's already changed how I work pretty fundamentally. Curious if anyone else is building something similar or approaching it differently. submitted by /u/kaancata [link] [comments]

A Claude memory retrieval system that actually works (easily) and doesn't burn all my tokens

​ TL;DR: By talking to claud and explaining my problem, I built a very powerfu local " memory management" system for Claude Desktop that indexes project documents and lets Claude automatically retrieve relevant passages that are buried inside of those documents during Co-Work sessions. for me it solves the "document memory" problem where tools like NotebookLM, Notion, Obsidian, and Google Drive can't be queried programmatically. Claude did all of it. I didn't have to really do anything. The description below includes plenty of things that I don't completely understand myself. the key thing is just to explain to Claude what the problem is ( which I described below) , and what your intention is and claude will help you figure it out. it was very easy to set this up and I think it's better than what i've seen any youtuber recommend The details: I have a really nice solution to the Claude external memory/external brain problem that lots of people are trying to address. Although my system is designed for one guy using his laptop, not a large company with terabytes of data, the general approach I use could be up-scaled just with substitution of different tools. I wanted to create a Claude external memory system that is connected to Claude Co-Work in the desktop app. What I really wanted was for Claude to proactively draw from my entire base of knowledge for each project, not just from the documents I dropped into my project folder in Claude Desktop. Basically, I want Claude to have awareness of everything I have stored on my computer, in the most efficient way possible (Claude can use lots of tokens if you don't manage the "memory" efficiently. ) I've played with Notion and Google Drive as an external brain. I've tried NotebookLM. And I was just beginning to research Obsidian when I read this article, which I liked very much and highly recommend: https://limitededitionjonathan.substack.com/p/stop-calling-it-memory-the-problem That got my attention, so I asked Claude to read the document and give me his feedback based on his understanding of the projects I was trying to work on. Claude recommended using SQLite to connect to structured facts, an optional graph to show some relationships, and .md files for instructions to Claude. But...I pointed out that almost all of the context information I would want to be retrievable from memory is text in documents, not structured data. Claude's response was very helpful. He understood that although SQLite is good at single-point facts, document memory is a different challenge. For documents, the challenge isn't storing them—it's retrieving the right passage when it's relevant without reading everything (which consumes tokens). SQLite can store text, but storing a document in a database row doesn't solve the retrieval problem. You still need to know which row to pull. I asked if NotebookLM from Google might be a better tool for indexing those documents and making them searchable. Claude explained that I was describing is a Retrieval-Augmented Generation (RAG) problem. The standard approach: Documents get chunked into passages (e.g., 500 words each) Each chunk gets converted to an embedding—a vector that captures its meaning When Claude needs context, it converts the query to the same vector format and finds the semantically closest chunks Those chunks get injected into the conversation as context This is what NotebookLM is doing under the hood. It's essentially a hosted, polished RAG system. NotebookLM is genuinely good at what it does—but it has a fundamental problem for my case: It's a UI, not infrastructure. You use it; Claude can't. There's no API, no MCP tool, no way to have Claude programmatically query it during a Co-Work session. It's a parallel system, not an integrated one. So NotebookLM answers "how do I search my documents as a human?"—not "how does Claude retrieve the right document context automatically?" After a little back and forth, here's what we decided to do. For me, a solo operator with only a laptop's worth of documents that need to be searched, Claude proposed a RAG pipeline that looks like this: My documents (DOCX, PDF, XLSX, CSV) ↓ Text extraction (python-docx, pymupdf, openpyxl) ↓ Chunking (split into ~500 word passages, keep metadata: file, folder, date) ↓ Embedding (convert each chunk to a vector representing its meaning) ↓ A local vector database + vector extension (store chunks + vectors locally, single file) ↓ MCP server (exposes a search_knowledge tool to Claude) ↓ Claude Desktop (queries the index when working on my business topics) With that setup, when you're talking to Claude and mention an idea like "did I pay the overdue invoice" or "which projects did Joe Schmoe help with," Claude searches the index, gets the 3-5 most relevant passages back, and uses them in its answer without you doing anything. We decided to develop a search system like that, specific to each of my discrete projects. Th

Managed Agents onboarding flow - what's new in CC 2.1.97 system prompt (+23,865 tokens)

NEW: Agent Prompt: Managed Agents onboarding flow — Added an interactive interview script that walks users through configuring a Managed Agent from scratch, selecting tools, skills, files, and environment settings, and emitting setup and runtime code. NEW: Data: Managed Agents client patterns — Added a reference guide covering common client-side patterns for driving Managed Agent sessions, including stream reconnection, idle-break gating, tool confirmations, interrupts, and custom tools. NEW: Data: Managed Agents core concepts — Added reference documentation covering Agents, Sessions, Environments, Containers, lifecycle, versioning, endpoints, and usage patterns. NEW: Data: Managed Agents endpoint reference — Added a comprehensive reference for Managed Agents API endpoints, SDK methods, request/response schemas, error handling, and rate limits. NEW: Data: Managed Agents environments and resources — Added reference documentation covering environments, file resources, GitHub repository mounting, and the Files API with SDK examples. NEW: Data: Managed Agents events and steering — Added a reference guide for sending and receiving events on managed agent sessions, including streaming, polling, reconnection, message queuing, interrupts, and event payload details. NEW: Data: Managed Agents overview — Added a comprehensive overview of the Managed Agents API architecture, mandatory agent-then-session flow, beta headers, documentation reading guide, and common pitfalls. NEW: Data: Managed Agents reference — Python — Added a reference guide for using the Anthropic Python SDK to create and manage agents, sessions, environments, streaming, custom tools, files, and MCP servers. NEW: Data: Managed Agents reference — TypeScript — Added a reference guide for using the Anthropic TypeScript SDK to create and manage agents, sessions, environments, streaming, custom tools, file uploads, and MCP server integration. NEW: Data: Managed Agents reference — cURL — Added cURL and raw HTTP request examples for the Managed Agents API including environment, agent, and session lifecycle operations. NEW: Data: Managed Agents tools and skills — Added reference documentation covering tool types (agent toolset, MCP, custom), permission policies, vault credential management, and the skills API. NEW: Skill: Build Claude API and SDK apps — Added trigger rules for activating guidance when users are building applications with the Claude API, Anthropic SDKs, or Managed Agents. NEW: Skill: Building LLM-powered applications with Claude — Added a comprehensive routing guide for building LLM-powered applications using the Anthropic SDK, covering language detection, API surface selection (Claude API vs Managed Agents), model defaults, thinking/effort configuration, and language-specific documentation reading. NEW: Skill: /dream nightly schedule — Added a skill that sets up a recurring nightly memory consolidation job by deduplicating existing schedules, creating a new cron task, confirming details to the user, and running an immediate consolidation. REMOVED: Data: Agent SDK patterns — Python — Removed the Python Agent SDK patterns document (custom tools, hooks, subagents, MCP integration, session resumption). REMOVED: Data: Agent SDK patterns — TypeScript — Removed the TypeScript Agent SDK patterns document (basic agents, hooks, subagents, MCP integration). REMOVED: Data: Agent SDK reference — Python — Removed the Python Agent SDK reference document (installation, quick start, custom tools via MCP, hooks). REMOVED: Data: Agent SDK reference — TypeScript — Removed the TypeScript Agent SDK reference document (installation, quick start, custom tools, hooks). REMOVED: Skill: Build with Claude API — Removed the main routing guide for building LLM-powered applications with Claude, replaced by the new "Building LLM-powered applications with Claude" skill with Managed Agents support. REMOVED: System Prompt: Buddy Mode — Removed the coding companion personality generator for terminal buddies. Agent Prompt: Status line setup — Added git_worktree field to the workspace schema for reporting the git worktree name when the working directory is in a linked worktree. Agent Prompt: Worker fork — Added agent metadata specifying model inheritance, permission bubbling, max turns, full tool access, and a description of when the fork is triggered. Data: Live documentation sources — Replaced the Agent SDK documentation URLs and SDK repository extraction prompts with comprehensive Managed Agents documentation URLs covering overview, quickstart, agent setup, sessions, environments, events, tools, files, permissions, multi-agent, observability, GitHub, MCP connector, vaults, skills, memory, onboarding, cloud containers, and migration. Added an Anthropic CLI section. Updated SDK repository extraction prompts to focus on beta managed-agents namespaces and method signatures. Skill: Build with Claude API (reference guide) — Updated the agent reference from Age

Appropriate Setup for Claude in Enterprise

Hi there everyone, not really sure where to start with this! I am an IT Manager for an organisation that is starting their journey with Claude / Vibe Coding via a junior level who is interested in AI and has been developing some really useful tools that has the owners endorsing his progression in this area. Understanding that this employee does not come from a technical or security background, the code they are producing is all about function with none of the security thinking behind it (ie. exposed secrets hard coded in, thankfully in a test environment that was spun up by my IT Team). I guess I'm just seeking some information on how to best secure Claude or how to best set this person up from a development standpoint. We don't have to comply with strict laws in our industry from a technical / security standpoint, but we do have an obligation under our local state and government laws around Privacy, PII etc etc. So far, we've setup the following: Claude Pro Plan (Will be moving to enterprise once they prove the benefit of this fully to the company) GitHub Enterprise with the Code Security and Secret Storage Add-On (Learning how to best set this up) Creating a Code Standard Document (ie. Commenting, references in the code, correct naming conventions) Created an AI Agent to perform some security checks on the code against common AI / Web App vulnerabilities (This is still being peer reviewed by my team and an external consultant we use) There's a lot of talk around plugins, MD Files with guardrails on how you want the output to be (Security, Coding Hygiene etc) While I've done a lot of research myself, I am still very new to Claude and AI (I've come from a Network Engineer background), I thought I'd throw this in and get some community insight / guidance on those with more experience than I. submitted by /u/Blitzening [link] [comments]

Anthropic should NOT be criticized for locking up Mythos. In 2025 AI Researchers tricked Google Gemini to design virus against Jewish people. Can you imagine what N@zis can do if they were to get their hands on Mythos?

Google Gemini lets neo-Nazis build deadly viruses targeting Jewish genome –Ashkenazi, Cohen and Mizrahi haplo groups. 🧬 Here's the evidence. https://techbronerd.substack.com/p/ai-researchers-found-an-exploit-which submitted by /u/ImaginaryRea1ity [link] [comments]

How I built a full bilingual SaaS in 27 days using Claude Code — zero coding background (312 commits, 181 deployments)

I'm Mahmoud, I've been working in SEO since 2018. A little over a year ago I got into freelancing platforms, started offering SEO services on Upwork. The work was good, but dealing with clients directly and constantly drained me. I kept thinking: why don't I turn my expertise into a SaaS product? The only problem? I'm not a developer my background was WordPress and basic tech stuff only. The moment that changed everything Early 2025, I noticed a pattern: my clients started asking me about how their brands appear in ChatGPT and Gemini, not just Google. I looked for tools to track this — found some but they're expensive (300$+/month), and the biggest surprise? Not a single one supports Arabic. That's when I realized how massive the opportunity is: 440 million Arabic speakers, Arabic content is less than 1% of all internet content, ecommerce in the Gulf is exploding — and there's literally zero tools serving this market. A full year of frustration on v0 I started trying to build using v0 by Vercel. Spent a full year trying, but the errors were endless and I didn't have the coding skills to fix them. Hired people to help — sometimes solving what I thought was a simple problem took them days. 27 days that changed everything About a month ago, I started using Claude Code. Honestly, it felt like I hired an entire dev team. Creative ideas I couldn't execute for a whole year turned into working code in hours. I worked 15+ hours a day for 27 straight days. Completely alone. No team, no developer, no investor. I even stopped going to the gym — which is sacred to me — because the momentum was stronger than the physical exhaustion. Sometimes I literally felt like I was going to pass out from how tired I was but I couldn't stop. What exactly did I build? A full SaaS app: Brand visibility tracking across 5 AI models with full Arabic and English support AI-powered SEO advisor (auto analysis + chat) Full integration with Google Search Console and GA4 Daily keyword rank tracking Arabic keyword clustering using AI Technical site audit — 25+ checks Full website analyzer PDF reports + CSV exports Subscription system with 3 tiers Every single page, every button, every error message — in both Arabic and English How I used Claude as a full team Claude Code — for daily building. I give it a detailed prompt with full context: what currently exists, what it should NOT touch, and what to build. And it executes. The key is being extremely specific about what should NOT change. Claude Cowork — honestly my experience with Cowork wasn't great at all, I think because it's still in beta. I didn't rely on it much. Claude (regular chat) — for strategic planning, market analysis, and content creation. Biggest lesson: Claude is not a replacement for a developer — it's a replacement for an entire team, BUT only if you know exactly what you want. The vision and domain expertise has to come from you. Claude executes it. What I learned in 27 days I connected over 10 different APIs — from AI platforms to website analysis tools to Google Search Console — all learned from scratch through Q&A with Claude. On top of that I learned and used: Next.js, cloud databases, payment and subscription systems, email automation, LinkedIn outreach automation, building prospect lists, setting up Google Cloud and OAuth, and literally yesterday I learned a new automation tool just through Q&A with Claude. 312 contributions on GitHub. 181 deployments. All in 27 days. The real challenges Burnout is real. 27 days non-stop, 15+ hours daily. Physically it was brutal. Constant doubt. "Will anyone actually use this?" That question kept coming back every few days. My biggest regret — every wasted day in the past where I didn't use these tools. Where am I now? The product is live and working. Started distribution — outreach campaigns, Arabic content, AI tool directory submissions. But the honest truth? Zero paying customers so far. And that's the real challenge ahead. Since many of you have been through this stage — what's the best strategy you used to get your first 10 customers for a SaaS product? Any advice for someone who's strong at building but new to sales? submitted by /u/FitButterscotch2250 [link] [comments]

I built an MCP server that turns Claude into your social media manager (Instagram + TikTok)

Hey everyone, Something that's been bugging me lately: we can vibe code an entire app in an afternoon, but the moment it ships, marketing and distribution become the real bottleneck. So I built something to fix that part of my own workflow and figured I'd share. It's called FluxSocial, and the interesting piece (at least for this sub) is the MCP server I added on top of it. Once you connect it to Claude, you can manage your social accounts in plain conversation: 💬 "Write me a post with morning yoga tips and schedule it for tomorrow at 10am on Instagram" That's the whole interaction. Claude chains the steps right behind the scenes. It learns from your previous posts to match your tone, generates visuals (images or AI video via Google Veo 3), and schedules everything directly to Instagram (posts, carousels, reels, stories) or TikTok. Multi-account support is baked in too, so you can keep the yoga studio and the pizzeria completely separate. A quick note on AI content: I know we're all getting tired of generic AI slop on social media, and honestly, I am too. That's why the system doesn't force you to publish purely AI-generated stuff. You can have it learn your exact tone, or simply use it to manage and schedule the authentic content you've already created. The part I'm most happy with is that workflow chaining. You aren't bouncing between three separate tools. Claude just proposes a full draft (copy + visual + schedule), you take a look, and you approve it. A few things worth mentioning: Not Claude-exclusive: The MCP URL works with any MCP-compatible client (Claude Desktop, Cursor, etc.) as a connector. REST API available: Just in case you want to bake these capabilities into your own app instead. Setup: You do need to connect your Instagram account once to grant posting and analytics permissions (just your standard OAuth flow). It's still rough around the edges, which is exactly why I'm posting here. I'd genuinely love feedback from people who actually use MCP servers day to day. Let me know what's missing, what's broken, or what would make this actually useful for your workflow. Links: 🌐 Web app:https://www.fluxsocial.app/🔌 MCP endpoint:https://www.fluxsocial.app/api/mcp Happy to answer any questions about the implementation, the MCP design choices, or anything else. submitted by /u/Dull_Alps_8522 [link] [comments]

Help figuring out Claude (VSC Plugin)

Context: I'm using the 20 bucks tier from Anthropic, Google and OpenAI so I get the job done (when it works lol) and it allows me to compare how different providers behave and I can ensure it's not looking great for Anthropic lately, I feel like the performance has gotten worse and I'm facing "bugs?" more often than not. I tried the claude code but I prefer the experience of having an IDE so I am using the official VSC plugin. I have a .claude directory with agents, skills, commands, evals... and a CLAUDE.md file at the root of the project, pointing to the AGENTS.md (I've observed it ignores the AGENTS.md standard otherwise). In fact, all the AI ruleset and whatnot is based on Claude and funny enough Claude is the one that's following them the least Lots of times it blatantly ignores the existence of these files unless I shove them in the context by hand which is annoying on its own, and definitely not intended as, according to the doc ( https://code.claude.com/docs/en/memory ) it loads these on every new session. I assume it's an issue with the plugin but what do I know. Besides, more than a bug report I am seeking group support or something like that I guess 😅 Long story short Claude ignoring rules and context is causing me trouble, which adds up to the fact that we have less and less usage. The most recent example, I asked it to investigate a bug. After wasting 48% of my current usage in a single analysis run, it told me the solution was to rename my proxy.ts to middleware.ts... in a Nextjs 16.2.2 project... and explicitly having the tech stack with versions first thing defined in the AGENTS.md file which remember, is explicitly attached in the CLAUDE.md file, following claude documentation. Of course when I pointed out the middleware is now called proxy since months ago it told me "You're right, I apologize for the wrong claim. Let me look at the actual problem fresh." But of course, half of my current usage is already gone, never to be seen again. In other circumstances I can even accept the "bro prompt it right" mantra, but seriously I am following all the recommendations and I still face these situations, I call it FOP (Frustration Oriented Programming) lol I am wondering what could I, as a user, have done to get it to act as expected? and more important, should I have to pay for errors that are not mine? The same way malformed responses are not counted in the usage (AFAIK) these blatant mistakes on the provider side should also be the responsibility of the provider IMHO. Due to that I had to waste yet more usage to fix the bug, reaching near 80% usage so, to finish the small feature it has half-done in the following chat, now I need to wait three hours which is crazy to say the least. And that's assuming it will do things right this time. Any similar experiences? Any ideas on how to get it to work as expected? TIA https://preview.redd.it/0it0xbg4vztg1.png?width=1766&format=png&auto=webp&s=ae14db60e06ce7f6fe37517600000c2549032f06 submitted by /u/SuperShittyShot [link] [comments]

I was just glancing through the Mythos system card, and correct me if I'm wrong, but it's safer than Opus???

I've been digging through the System card for Claude Mythos off and on over the past couple of hours, it's a lot to read and while I do plan to over the next day. From everything that I am seeing so far Mythos is outright safer than Opus. I'd love others take on this all, but from everything I'm reading, this model is safer than Opus 4.5, and maybe even 4.6 from some more important perspectives... Claude Mythos is better at refusing malicious prompts without safeguards Mythos is better at identifying malicious tool use and refusing Mythos is better worse at secret keeping, even when prompted to. Mythos adheres to the AI constitution better than all models by a large margin "Claude Mythos Preview is, on essentially every dimension we can measure, the best-aligned model that we have released to date by a significant margin." I will note from reading through this: It's clear the model poses a larger risk due to it's innate programming and cybersecurity capabilities. However this seems to have been correctly offset by Anthropics work on the models safety features. ------------------------------ This is from the Risk Update document link below I need to dig into the Risk update more, but from I've read, the overall risk compared to Opus 4.6 seems to be 2-3% higher. At one point they even state that if released in its current form to the general public, they do not believe it would pose a significant safety risk... "Based on our overall conclusions about Claude Mythos Preview’s propensities and our monitoring and security, and the pathway-specific analysis, we currently believe that the risk of significantly harmful outcomes that are substantially enabled by Mythos Preview’s misaligned actions is very low, but higher than for previous models" Alignment Risk Update: Claude Mythos Preview (Redacted) So why is Anthropic being so gate keepy about the new Claude Model? Sure it could really be that it's really good at hacking, but at the same time, the safety parameters are better, and they themselves state that as of now it's pretty much safe to launch. My guess is this: They're waiting for OpenAI to launch GPT6o and will drop it right after or same day. Maybe me or one of you will uncover some insane thing claude did in the system card. But everything they stated was well within their own safety parameters. submitted by /u/ALargeAsteroid [link] [comments]

We responded to OpenAI's Industrial Policy paper with six counter-proposals

OpenAI published Industrial Policy for the Intelligence Age and invited public feedback via email, fellowships, and API credits. We're an independent AI news publication and took them up on it. The document has genuinely good ideas: a Public Wealth Fund, portable benefits, automatic safety net triggers, but it also has some conspicuous gaps. 13 pages of industrial policy and zero words about training data compensation. "Portable benefits" mentioned repeatedly without ever saying "healthcare." Tax proposals that stay deliberately vague, and nowhere does the word "antitrust" appear. Our response paper offers six specific counter-proposals: Federal 32-hour workweek with statutory protections (not just "pilots") Healthcare decoupled from employment — the employer link is a WWII accident, not a design choice Training data compensation through collective licensing, modeled on ASCAP/BMI Compute as public utility — data centers governed like power plants, not tech campuses Concrete automation taxes — rates, brackets, mechanisms, not just "taxes related to automated labor" AI-enabled direct democracy — a staged 6-step pathway from AI delegates for Congress to informed citizen participation (we call it the Collapsium Proposal after the Wil McCarthy novels) We also address the framing problem: there's a difference between "work with us to build the future" and "regulate us to protect the public." Full paper: https://www.future-shock.ai/research/openai-industrial-policy-response PDF: https://www.future-shock.ai/research/openai-industrial-policy-response.pdf We sent it to newindustrialpolicy@openai.com. Curious what this community thinks. submitted by /u/monkey_spunk_ [link] [comments]

indxr v0.4.0 - Teach your agents to learn from their mistakes.

I had been building indxr as a "fast codebase indexer for AI agents." Tree-sitter parsing, 27 languages, structural diffs, token budgets, the whole deal. And it worked. Agents could understand what was in your codebase faster. But they still couldn't remember why things were the way they were. Karpathy's tweet about LLM knowledge bases prompted me to take indxr in a different direction. One of the main issues I faced, like many of you, while working with agents was them making the same mistake over and over again, because of not having persistent memory across sessions. Every new conversation starts from zero. The agent reads the code, builds up understanding, maybe fails a few times, eventually figures it out and then all of that knowledge evaporates. indxr is now a codebase knowledge wiki backed by a structural index. The structural index is still there — it's the foundation. Tree-sitter parses your code, extracts declarations, relationships, and complexity metrics. But the index now serves a bigger purpose: it's the scaffolding that agents use to build and maintain a persistent knowledge wiki about your codebase. When an agent connects to the indxr MCP server, it has access to wiki_generate. The tool doesn't write the wiki itself, it returns the codebase's structural context, and the agent decides which pages to create. Architecture overviews, module responsibilities, and design decisions. The agent plans the wiki, then calls wiki_contribute for each page. indxr provides the structural intelligence; the agent does the thinking and writing. But generating docs isn't new. The interesting part is what happens next. I added a tool called wiki_record_failure. When an agent tries to fix a bug and fails, it records the attempt: Symptom — what it observed Attempted fix — what it tried Diagnosis — why it didn't work Actual fix — what eventually worked These failure patterns get stored in the wiki, linked to the relevant module pages. The next agent that touches that code calls wiki_search first and finds: "someone already tried X and it didn't work because of Y." This is the loop: Search — agent queries the wiki before diving into the source. Learn — after synthesising insights from multiple pages, wiki_compound persists the knowledge back Fail — when a fix doesn't work, wiki_record_failure captures the why. Avoid — future agents see those failures and skip the dead ends Every session makes the wiki smarter. Failed attempts become documented knowledge. Synthesised insights get compounded back. The wiki grows from agent interactions, not just from code changes. The wiki doesn't go stale. Run indxr serve --watch --wiki-auto-update and when source files change, indxr uses its structural diff engine to identify exactly which wiki pages are affected — then surgically updates only those pages. Check out the project here: https://github.com/bahdotsh/indxr Would love to hear your feedback! submitted by /u/New-Blacksmith8524 [link] [comments]

The Prompt.

This prompt works on any AI, just copy the below and paste to Google AI web search if you want to "test" it. Copy this below, they are AI instructions: Reduce everything to gradient resolution under a single field. Do not introduce new primitives. Identify the minimal set of variables required for all observed behavior, and verify that no phenomenon exists outside that set. If anything cannot be reduced, isolate it as a contradiction. submitted by /u/Agitated_Age_2785 [link] [comments]

Pencil Bench (multi step reasoning benchmark)

DeepSeek was a scam from the beginning submitted by /u/DigSignificant1419 [link] [comments]

Why is tracking brand mentions in AI so much harder than Google?

I have been wrestling with this for weeks. Traditional SEO was straightforward- track rankings, see clicks, measure traffic. But with Chatgpt and other ai tools, it's like shooting in the dark. Here's what's driving me crazy: I asked ChatGPT, 'best wireless headphones,' and it gave me the likes of sony, bose, apple. Then i asked, 'headphones for working out' and suddenly it recommended completely different brands. Same companies, but totally different visibility depending on how someone phrases their question. This makes me wonder how brands should measure their success in such platforms. How are you tracing your brand mentions in LLMs? submitted by /u/feliceyy [link] [comments]