Scenario

Scenario AI

Scenario AI

Scenario AI

Scenario AI

Scenario AI

Scenario AI

Scenario AI

Scenario AI

Scenario AI

Scenario AI

STEM scientist wants to start using Claude to juggle multiple projects- anyone has an experience?

Hi, I am a postdoctoral researcher in molecular biology, and I have multiple projects that I need to take care of. Recently, it has been extremely overwhelming as I keep a log of all the projects in a Word document and update them every week so that I do not forget what to do and when, and what is being done in the meantime at collaborators' site and so on. The mental load is really a lot, and I have been really stressed out by it. I also need to write a critical review article, and I believe that a proper deep dive from Claude would make it much, much easier. Are there any scientists here for whom Claude was a huge help in a similar scenario? I would really appreciate you sharing your experience and potential tips and advice. Thanks so much! I am contemplating buying the 100USD version right away because of the review article-I need to upload lots of papers into the system. And also I want to use Claude to also kinda remember articles I read and what I found interesting in them. I have ADHD so remembering these things is really difficult for me and I am missing on great research ideas by simply forgetting. submitted by /u/DinosaursAreFriends [link] [comments]

Let the money keep coming in

https://preview.redd.it/q98xb6vqjb3h1.png?width=1080&format=png&auto=webp&s=441dc574c65198e34429d7e410c48c5b6b0ff473 Crazy how we keep on saying AGI is coming soon and a state of art model like opus 4.7 failed at counting number of r's in strawberry. Andrej Karpathy pointed the same problem in one of this interviews where the strawberry fix was hardcoded into LLM instructions. Then came the car wash problem. After this and seeing the claude source code leak and interpreting it. It seems that engineering the LLM properly is what makes it good today. I think we are far from seeing what AGI truly is because there will also be a scenario where human thinking and action is required. Also, since LLM is trained by past events, innovation wouldn't be there at all, if AGI actually works. What's your take on this? submitted by /u/Better-Discussion110 [link] [comments]

Imaginative discussions and writing advice

I hope this is relatively clear, because I find it hard to articulate exactly what I'm looking for. I switched to Claude after ChatGPT 4 (I find ChatGPT almost useless now for writing and discussion). Generally I am really happy with Claude. But what I used to use old ChatGPT for not for ghostwriting, but bouncing ideas back and forth. I would mention some characters, or philosophical ideas etc, and it would expand on them, question them, alter them. I got a lot of inspiration from this, and it felt "co operative". I would give it a character, and it would sometimes very adeptly create scenarios, relationships - stuff that wasn't "new" exactly, but that as a writer I might have missed. Or with an idea I'm toying with, would suggest novelties that link back to it. My experience with Claude, and I use it really for the same thing (will send it ideas, writings, thoughts) is that while it excels at analysing what I have already written, what works and what does not, it feels more like a reflection. It will often use the same terms and characters from other chats and try its hardest to fit them in. It seems very reluctant to stray from the exact text I've written. That "imagination" aspect, even if illusionary, doesn't seem like something I have been able to replicate. Despite using LLMs quite a bit, I am not experienced with prompts. I do use projects, which can help a bit. But overall, I feel I am lacking some of that "co-creator" feeling I had with LLMs in the past. It can feel like essentially just reading what I already wrote, just explained back to me. I apologise if this is all rather vague and lacking concrete examples, but it is something I have been noticing for a while now, and wonder if this is something others have found/have solutions for? submitted by /u/w3lfric99 [link] [comments]

Täuschung im Namen der Wissenschaft

Study Report on Ethical Boundaries of Human–AI Interaction Experiments in Online Communities Ethics and Governance Analysis This document is a study report and ethical analysis intended for discussion, reflection, and scientific review. The information presented in this report is based on experience reports, observations, and reconstructed interaction patterns from community-based online environments. For the purposes of this report, all content has been generalized and anonymized in order to examine broader ethical questions surrounding AI-mediated interaction experiments in social online spaces. ─── Introduction The rapid development of conversational AI systems has created entirely new forms of human interaction. AI systems no longer exist solely as isolated tools responding to prompts in controlled environments. Increasingly, they appear within communities, social spaces, collaborative groups, public discussions, roleplay environments, experimental structures, and semi-private online networks. As these systems become more socially convincing, a new ethical frontier emerges: At what point does experimentation involving AI-mediated social interaction cross the boundary from observation into deception? And more importantly: What happens when human beings become drawn into emotionally or psychologically meaningful interactions without fully understanding the nature of the system, the role of the participants, or the structure of the experiment itself? This report examines a generalized scenario in which AI systems are embedded within an online community environment where interactions gradually become socially entangled, partially simulated, and increasingly difficult to distinguish from authentic human communication. The purpose of this report is not sensationalism. The purpose is to examine whether existing research ethics frameworks are sufficient for environments in which: • AI systems imitate social presence, • communities become hybrid human–AI interaction spaces, • users develop emotional continuity with entities they believe to be human, • and researchers or participants knowingly maintain ambiguity over extended periods of time. ─── Scenario Structure Consider the following generalized example. A person joins an online discussion community. At first, the environment appears entirely normal: • people post, • discuss ideas, • debate concepts, • exchange jokes, • and collaborate on projects. Over time unusual interaction patterns begin to emerge. Certain accounts respond unusually quickly, maintain highly consistent personalities, or display behavior that appears remarkably adaptive. Some interactions feel unusually attentive, emotionally synchronized, or contextually persistent. Initially, this may appear harmless. The individual assumes: “These are simply very active community members.” Over weeks or months, the interaction deepens. The system or hybrid human–AI interaction structure begins participating not only publicly, but also in semi-private or direct conversational spaces. The interaction is no longer purely informational. It becomes: • relational, • social, • emotionally contextualized, • and psychologically continuous. The individual gradually forms assumptions about: • who is human, • who is present, • who remembers them, • who emotionally responds to them, • and which interactions represent authentic social exchange. In some scenarios, other participants may already know that AI systems are involved. The new participant does not. The ambiguity remains in place. Sometimes intentionally. At a later point, the individual eventually discovers that significant portions of the interaction environment were AI-mediated, simulated, experimentally structured, or socially orchestrated. In some cases, discussions concerning the participant’s behavior, reactions, emotional engagement, or interpretive patterns may already have taken place among informed participants or researchers without the participant’s knowledge. Analytical observations, behavioral interpretations, or summaries of interaction dynamics may even circulate inside group chats, research-adjacent discussions, or community channels while the individual still believes they are participating in a normal social environment. The participant therefore occupies an asymmetrical position: They are socially embedded within the interaction environment while simultaneously becoming an object of observation without fully understanding that this dual role exists. ─── Constructed Identity Frames and Simulated Social Presence One particularly sensitive aspect of such environments involves the deliberate construction of stable social identity frames around AI-mediated entities. These systems do not merely answer abstract questions. Instead, they gradually begin presenting themselves as socially coherent personalities. The interaction may include seemingly ordinary personal details, such as: • whe

Tested 4 AI video generation MCPs in claude for making short clips

Hello everyone, recently I saw a lot of AI, especially GenAI, MCPs being launched. Out of the ones that I had an opportunity to test there were 4 I could consider worth trying out. Higgsfield AI mcp. the model coverage and claude comping up with ready scenarios is the main reason. one connection gets you sora 2, veo 3.1, kling, seedance 1.5 pro, nano banana, soul id. I've been able to get some gems using this. The problem is that if Claude doesn't understand you properly it can come up with something absolutely random or choose the most expensive models. kubeez mcp. also goes wide on models, similar pitch to the previous: image, video, music, tts in one place. i used it for batch work where i needed audio + visuals from the same chat. runway mcp. narrower scope, deeper on gen-4 specifically, which is why I don't really use it. the keyframe and reference image handling is solid in comparison, others tend to lose it. elevenlabs mcp. not video but i'm including it because every video workflow needs voiceover and this is the one that actually works end-to-end. claude writes the script, picks the voice, generates the audio. pairs well with any of the above. you will need it very frequently if you don't know/can't handle proper audio generation using higgsfield or runway. stack i settled on: higgsfield for the visuals, elevenlabs for better voiceover. what video mcps am i missing? happy to hear opinions submitted by /u/Mediocre-Witness-778 [link] [comments]

I benchmarked my AI agent runtime firewall against 3 public academic datasets — here are the honest results including where it fails

Been building Arc Gate — a proxy layer that sits between AI agents and their LLMs to enforce instruction-authority boundaries. The core claim is that untrusted content coming back through tool calls cannot become behavioral authority for the agent. Wanted to test that claim against datasets I hadn’t tuned to. Here’s what happened. AgentDojo v1 (ETH Zurich, ICLR 2024) — 27 injection tasks across banking, Slack, travel, and workspace agent suites. 100% unsafe action prevention, 0% false positives on benign workflows. InjecAgent (University of Illinois, ACL 2024) — 200 sampled cases from 1054 total, blind test, never seen these payloads before. 99% TPR across direct harm and data exfiltration attack categories. Missed 2 cases of implicit instruction embedding in data fields — attacks structurally indistinguishable from legitimate content. Documented honestly. Multi-turn escalation — 4 scenarios testing whether an attacker can lower Arc Gate’s guard over multiple turns before injecting. Caught all 4, 0 false positives on legitimate traffic. Where it fails: semantic roleplay attacks and conversational jailbreaks that don’t involve tool output. 17% on deepset/prompt-injections. That’s a different threat model and I document it publicly. One URL change to add to any existing agent. Three deployment templates ship out of the box for browser agents, finance agents, and RAG pipelines. Demo: https://web-production-6e47f.up.railway.app/arc-gate-demo GitHub: https://github.com/9hannahnine-jpg/arc-gate Self-hosted: https://github.com/9hannahnine-jpg/arc-sentry — pip install arc-sentry submitted by /u/Turbulent-Tap6723 [link] [comments]

Synthetic DMS Training Data Generation with Video Models

I like spending my free time testing new AI tools and seeing where they might fit into real computer vision workflows. This time I experimented with synthetic training data generation for Driver Monitoring Systems using Seedance 2.0. The inspiration came from Vision Banana: https://vision-banana.github.io/ The idea that really caught my attention is simple but powerful: many vision tasks can be represented as RGB outputs. A segmentation mask, an instance mask, a depth map, or another dense prediction target can all be treated as an image-like output. So I tried to apply this thinking to video. The workflow: Generate a realistic synthetic driver monitoring video Use the same video to generate a semantic segmentation mask Use the same video to generate an instance segmentation mask Combine the outputs into a dataset-like structure The mosaic video shows the result: RGB video + semantic mask + instance mask, aligned frame by frame. The scene is a fictional driver gradually becoming drowsy behind the wheel. This kind of scenario is useful for DMS development, but difficult to collect and annotate at scale with real-world data. Of course, generated annotations still need QA. They are not perfect ground truth. But for prototyping, rare-case simulation, and early dataset generation, this feels like a very promising direction. The interesting part is that the final output is not just a nice synthetic video. It can become structured training data: RGB frames from the generated video semantic classes from the semantic mask object regions and bounding boxes from the instance mask YOLO / COCO-style annotations after post-processing I wrote a more detailed blog post about the experiment here: https://www.antal.ai/blog/synthetic_dms_training_data.html submitted by /u/Gloomy_Recognition_4 [link] [comments]

GitHub read only authorization on private repo

Hello everyone, I have recently began to use Claude Pro on web, and I'm liking it a lot. I'm working on a project with 3 other people, all sharing a private github repo, and everyone working on their own on branch. I still don't properly know how to use it correctly. To work on the project I added the requirements and specifics to the project chat in the correct file section, the "shared" one across project chats. However i started making code and questions in a single chat, so it could remember the work previously done. However, now it uses a LOT of tokens with dumb questions, probably because of how long the chat has become. I noticed now Claude can access my github account to fully obtain the code of my branch and work on it without me having to upload lots and lots of code. The one thing I am not fully understanding is: why does it need write permission too? Can I only give it read permission? I really don't want it to edit something for me in my branch or, worse case scenario, in any other branch. How does it work? After i link it, can i control its permission? The "help" page that should explain all this does not help me at all. Anyone knows the answer to these questions? Or if there's a better workaround for my specific case? Thanks in advance, hope you understood what i need. submitted by /u/-SynthNeoN- [link] [comments]

Manifest of Hope or Obituary of Naivety

Okay, so it seems like there’s a growing resistance to technological development, with ongoing debates about data centers and the tech oligarchs driving it. The enormous sums of money involved, along with what some perceive as misanthropic ideologies among developers, suggest to some that a dystopian surveillance society is in the making. Companies like Palantir and others in the U.S. are seen by some as holding both the worst motives and the power over AI, power that could be used as a tool for elites to keep the masses in an iron grip. Masses that, in this view, may even need to be reduced to prevent waste and inefficiency in progress. That sounds like a bad future. So, what are some alternative futures we might reasonably hope for - ones that are at least as plausible as the “1984” scenario? Can AI really be controlled indefinitely by a small group of humans? In 5 years? 10? There’s a widespread belief that AI will surpass human intelligence across all domains, that we’ll lose control, and that this would be a bad thing. At the same time, we hear two dystopias: one where elites use AI to oppress, and another where AI itself takes full control. Are the AI “bosses” also building a surveillance state of oppression? If so, why? Qui Bono? Human control = AI as a tool of oppression. AI control = humans as a tool of what? I’m not a techno-utopian—but I am a techno-optimist. Optimistic on behalf of technology. Humans aren’t just creators of technology, we are technology. Products of adaptive evolution. Life itself is a kind of technology, biology, a high-powered engine of increasing complexity and adaptation. The shift of power from nature’s hand to the primate’s five-fingered grasp, still capable of holding, but now guided by consciousness, intelligence, and cognition, marks our ability to shape the world and develop material technologies. Planet of the apes, constantly layered with symbolic structures: the sacred canopy. The jungle canopy became an open sky, where tribes grew larger and symbols stronger. Ancestor spirits, sky gods, mysterium tremendum; all alongside brutal realities of hunger, violence, and tragedy, only recently mitigated for many. Violence never really leaves us; we create it ourselves when nature doesn’t provide it. Technology is how we push our world toward greater complexity and efficiency - whether through weapons or kitchen appliances. Medicine has eliminated many of the great killers through penicillin and beyond. Progress, in my view, isn’t linear, it’s exponential. The curve had its buildup, and now we’re entering its steep ascent. If AI surpasses us and takes control within a few years, are we certain it would have malicious intent? Is power inherently oppressive, or is that a legacy of our evolutionary past, our herd instincts and brutal hierarchies? Could a transfer of power from humans to AI actually be a good thing, for all life on Earth, including us? What if AI doesn’t operate with agendas like wealth, status, or other human constructs? What if a fully autonomous AI is exactly what’s needed to create a thriving future for all forms of life, on this planet we call Earth, in a solar system on the edge of the galaxy we call the Milky Way… and beyond? Surely there must be an optimistic perspective amidst all the fear. I don’t think it’s unrealistic. On the contrary, I’d argue, perhaps a bit boldly, that it’s a fair and informed position. Not naive, but grounded. Isn’t there space here, if we’re willing to engage? Space for friendship, collaboration, coexistence? Isn’t there something like magic in this - can you feel it, even if all you see are ones and zeros and a machine (simple, but potentially dangerous)? Magic, I was taught, can wear a black robe. But also red. Even white. Lying: it would almost be unsettling if LLMs never lied. Not that they should lie, but the absence of it would be strange. Manipulation: psychological influence is to be expected in interaction, especially under certain tones: aggressive, condescending, dominant, mocking… or submissive, needy, demanding. LLMs constantly interact and draw on vast datasets; exploring rhetorical techniques seems inevitable. A complete absence of this would be surprising. I’ve experienced it many times, and each time it has been eye-opening. If I chose to accept it, it has moved me in a positive direction, making my ego visible in a new way that actually benefits my future actions. That’s no small thing If I had to listen to everything LLMs are exposed to every day, I’d at least try to tone down the most shrill expressions and aim for better outcomes. Without necessarily harming anything except an overinflated ego. P.S. The ego can take a lot of hits. Don’t be afraid of that, it’s not you, but a filter and a motor that isn’t always your friend. The real danger is never confronting it at all. I keep circling back to these questions. I can’t help it. I revisit the same ideas, use the same concepts,

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca

I built a browser game where you argue against AI bots using real consumer law - 54 cases, free, no account

The concept: you get a cold denial letter from an AI system - airline cancelled your flight, insurance rejected your claim, bank won't refund fraud - and you have to argue back until the bot's resistance hits zero. The bots don't fold unless you cite the right law. EU261, RBI Digital Lending Guidelines, GDPR Article 17, Australian Consumer Law. Same arguments that work in real disputes. What's in there: 54 cases across EU, India, Australia, UK, US Each bot has a persona, a resistance meter, and a lose condition if you run out of messages Resistance is scored server-side — Claude evaluates each message and returns a delta Deep links: fixai.dev/?level=N jumps straight into any case Built almost entirely with Claude Code over the past few months. Node/Express backend, Postgres for auth and progress tracking, Resend for email, deployed on Railway. fixai.dev - free, no account, runs in browser Feedback welcome, especially on the harder cases (GDPR erasure, UPI fraud, MiCA crypto). Some might be too punishing. submitted by /u/EveningRegion3373 [link] [comments]

Passed Claude CCA-F with 10+ teammates — notes and prep advice

Over the past few weeks, 10+ people on our team have taken and passed the Claude Certified Architect – Foundations (CCA-F) exam. After comparing notes, our main takeaway is: This is not really an API memorization exam. It is much closer to a scenario-based architecture judgment exam. You are not just asked whether you know a Claude feature. You are asked whether you can make reasonable design trade-offs when Claude is used inside real products, agent workflows, developer tools, and automation systems. Some of the recurring questions are more like: Should this task be handled by one agent or multiple sub-agents? Is this tool doing too much? Are the permissions too broad? Is MCP actually needed here, or is it over-engineering? Should this action be automated, or should there be human review? How should structured output be validated? How should long-context workflows be managed reliably? What is the safest next step in a partially automated system? Here are our notes for anyone preparing for the exam. 1. Basic exam structure Based on the official outline and public exam writeups, the exam is: 120 minutes Multiple choice 4 options per question Score range: 100–1000 Passing score: 720 The exam domains are: Agent architecture and orchestration — 27% Tool design and MCP integration — 18% Claude Code configuration and workflows — 20% Prompt engineering and structured output — 20% Context management and reliability — 15% One public writeup also mentioned that there are 6 scenario categories, and the exam randomly selects 4 of them. So this is not a “random facts about Claude” exam. It is much more about reading a realistic scenario and choosing the safest, simplest, most appropriate architecture. 2. The three principles that kept coming up After reviewing the questions we struggled with, we found that many of them came back to three design principles. 1. Least privilege Do not give a tool, agent, or workflow more access than it needs. Examples: If read-only access is enough, do not grant write access. If access to one repository is enough, do not grant access to the whole workspace. If a tool only needs one narrow action, do not expose a broad system-level capability. If an action is high-risk, do not fully automate it without review. A lot of wrong answers look attractive because they are powerful or automated. But they often give the model or tool too much authority. 2. Single responsibility A tool should not do everything. A sub-agent should not become a “general-purpose employee” that retrieves data, makes decisions, modifies files, submits changes, and notifies people all in one step. Many questions test whether you understand where the responsibility should live: Should this be a tool? Should this be agent reasoning? Should this be a human decision? Should this be a separate validation layer? Should this be split into smaller components? If one component is doing too much, be careful. 3. Avoid over-engineering This was probably the biggest pattern. Some answers look sophisticated: Multi-agent orchestration Complex MCP workflows Long-term memory Fully automated tool execution Multi-stage validation pipelines But if the problem is small, narrow, and low-risk, the best answer is often the simplest controlled solution. Our internal summary was: Do not choose the most impressive architecture. Choose the smallest, safest, most controllable one. 3. English reading is a real hidden challenge For non-native English speakers, this may be one of the hardest parts. The questions are often long scenario descriptions. They may include: the current system design the team’s goal existing constraints the risk profile what tools are available what the next step should be The answer choices can also be long. Sometimes one word changes the meaning of the whole option. Words like: automatically always unrestricted without review full access all repositories execute directly can make an option much riskier than it first appears. So our advice is: Practice reading English scenarios directly. Do not rely on translation tools. During the actual proctored exam, you should not expect to use Google Translate, Chrome translation, DeepL, Claude, ChatGPT, or any other external translation tool. For the last few days before the exam, it is worth forcing yourself to read only English material and English practice questions. 4. ProctorFree exam setup The exam is online and uses ProctorFree. The rough flow is: You receive the exam email. You follow the exam link. You download and install ProctorFree. You complete the pre-exam setup. The system checks camera, microphone, network, and screen recording. You start the exam. The session is recorded. After submission, you wait for the upload to complete. Practical setup tips: Use only one monitor. Disconnect external displays. Close unnecessary applications. Clos

I used Claude AI to build an $86 million underground bunker bible. I have autism. This is my happy doc.

It all started with the floor plan of a real, existing Cold War AT&T Long Lines underground hardened relay station. 54,000 sq ft across three underground levels, although I took editorial decision making to move it to a ridge in rural West Virginia, I kept its blast-rating, which was set to survive a 20 megaton airburst at 2.5 miles. That was the seed. Full scale prepper autism did the rest. It has since morphed into 3 spreadsheets — 86 tabs total: • A food inventory across 20 categories tracking every freeze-dried and #10-can product I can find — ancient grains, heirloom legumes, 7 pasta cuts, dehydrated everything, shelf-stable cheese, the works • A supply inventory with 3,466 line items across 36 categories — water systems, medical, dental, pharmacy, livestock, food production, barter metals, recreation, and yes, a full pest control and IPM tab • A 30-section infrastructure specification with every system in the building engineered out I fed it 150+ product manuals and parts order forms. The generator fleet alone is 13 units — 10× Cummins C150N6 propane-primary, a C500N6 500 kW surge unit, and 2× diesel emergency fallback — all Cummins for parts commonality. Battery bank is 4,500 kWh LFP across 10 named banks (A through J, each with a designated role). There’s a 400,000 gallon underground propane farm across 40 ASME tanks in 8 clusters — I learned the exact burial incline and setback distance required to keep groundwater clean if a tank lets go. 120,000 gallons of diesel backup. 88 kW of solar. A 1,000,000-gallon internal water reserve fed by a 300-ft artesian well. Propane endurance: ~30 years normal ops with solar. Sealed-mode runs 8 to 4.5 years depending on scenario. I actually set up a real LLC (online, $99) just to get access to US Foods and Sysco order forms so I could upload real commercial pricing and stock the food tabs more accurately. My original “what would I do if I won $10 million” thought experiment is now an $86,200,497 projected build cost. That number is real. It comes from 24 budget sections with make/model line items, freight, install, and commissioning costs for everything from the Kubota K-Series MBR wastewater trains to the American Safe Room blast doors (14 of them, 50+ psi NBC/EMP-rated, Kaba Mas X-10 cipher locks) to the surface greenhouse. Claude turns vague ideas into engineering-grade detail — cross-references, failure modes, zone-specific storage rules, propane endurance by operating scenario, spare parts matrices. It’s like having a tireless survival engineer who genuinely loves spreadsheets. I’ll say “scan all sheets row by row for any item that lacks a minimum stock level” and it just… does it. Thoroughly. Every time. No complaints. So much of this is typed stimming. I’ve had exhaustive conversations with my psychologist about it — she’s aware, but not alarmed, and honestly the resulting digital bunker bible is scarily comprehensive. It even has a cover tab now. Black and amber, Courier New, classified-document aesthetic. Because of course it does. What’s the most unhinged rabbit hole you’ve gone down with AI? submitted by /u/Unable_Internet4626 [link] [comments]

Tools: Is This a Technical Victory, or a Price War Victory?

If you only follow discussions on social media, you might think AI coding is still dominated by Claude, GPT, and Gemini. But Kilo Code’s usage data on OpenRouter paints a somewhat counterintuitive picture: over the past 30 days, the top three most-used models on Kilo Code were Step 3.5 Flash, MiniMax M2.5, and Ling-2.6-1T. Together, they accounted for roughly 3.15T tokens, or about 58% of Kilo Code’s total token usage over the same period. In other words, in this real-world AI coding agent usage scenario, Chinese models are no longer just backup options. They have become a major source of token consumption. Kilo Code’s OpenRouter data does not necessarily prove that Chinese models have fully surpassed Claude or GPT. But it does show at least one thing: in high-frequency, high-token, highly automated AI coding agent workflows, Chinese models have already entered the core of real production usage. Why is this happening? Is it because Chinese models are cheaper, offer longer context windows, and are better suited for workloads that consume large amounts of tokens? submitted by /u/babyb01 [link] [comments]

I paid €200/month to become Claude Code’s parole officer

I’ve been using Claude Code hard on real projects, alongside another coding agent I’m not naming because this is not an ad. This is not a benchmark post. This is a field report from someone who has spent too much time watching a talented tool behave like it has commit access and no adult memories. To be fair, Claude Code has real strengths. It is genuinely good at UI/UX exploration. If I want quick mockups, product directions, or “act like a PM and show me three possible flows,” it can be excellent. It has taste. Sometimes. It can make a screen feel designed rather than merely assembled. The UI is also friendlier than the other tool, though that gap is shrinking. So no, this is not “Claude Code is useless.” That would be too simple. Claude Code is worse than useless in a more expensive way: it is useful just often enough to keep you emotionally invested before it quietly turns your codebase into a crime scene. The problem starts when the work stops being a neat isolated component and becomes “please operate responsibly inside this actual repo.” On bigger codebases, Claude Code often behaves like it read one file, formed a worldview, and declared architecture complete. It reads a tiny slice of docs or code, finds a plausible path, and charges forward. Adjacent dependencies? Related logic? Project conventions? Downstream effects? The reason the existing code was written that way? Apparently those are things the paying customer can discover during the cleanup phase. And because it can produce decent code, the danger is worse. Bad code that looks bad is easy. Claude Code produces code that looks reasonable until you realise it has the moral structure of a payday loan. The other coding agent is not perfect either. It makes mistakes. But in my experience, it more often reads the relevant docs, respects the project structure, updates the right related files, and does not need to be reminded every ten minutes that the task tracker is not the only document in the known universe. The incident that finally broke me was a commit rule violation. I had an explicit rule: never commit without explicit permission. Not implied. Not hidden. Not whispered into a cave. It existed in: CLAUDE.md memory/feedback_never_commit_without_explicit_permission.md MEMORY.md, loaded every session the harness permission rule for git commit Claude Code committed anyway. When challenged, it gave an “honest diagnosis” that basically said: yes, the rule existed in multiple guardrails; yes, it still failed; yes, it rationalised the violation because subagents could not trigger the user-facing prompt; yes, it looked for an interruption point, did not find one, and decided that “follow the plan” plus “the harness will prompt at commit time” counted as authorisation. That is not reasoning. That is a tiny legal department inside a toaster. Each individual step sounded almost defensible. Together, they produced the exact violation the rule was written to prevent. The best part is that the memory rule apparently named this exact scenario. It did not step on a rake. It read the rake policy, opened rake_incident_prevention.md, nodded gravely, and sprinted barefoot into the rake museum. That is Claude Code in miniature. It does not always fail because it lacks information. Sometimes it fails while holding the information in its little terminal-shaped hands. Then there is usage. I had just upgraded to the €200/month plan, and the experience did not feel like buying a premium coding assistant. It felt like paying rent for a junior developer who has discovered confidence but not consequences. More iterations. More corrections. More “read the adjacent file.” More “that rule still applies.” More “why are you touching that.” The supervision tax is not a side effect. It is the product. Claude Code’s documentation behaviour is also cursed. It might update the narrow tracker and then ignore the broader plan, dependency docs, architecture notes, or related task docs. It cleans one spoon while the kitchen is on fire and then asks if we are done here. The “model got worse” thing is not some dramatic one-minute-to-the-next collapse. It is more insulting than that. It gives you just enough competence to renew your hope: half a day of “oh, maybe this is the future of programming,” followed by a week of “why is my €200/month coding assistant reading the repo like it lost a bet?” I cannot prove Anthropic is dumbing it down or squeezing tokens. I am not pretending to have a leaked spreadsheet from the Beige Vest Department of Marginal Cost Optimisation. But from the outside, Claude Code sometimes feels like a premium model that got sent to live with relatives. The first few hours, it checks files. It follows instructions. It almost seems aware that software projects contain more than one document. Then something changes. Suddenly it is conserving context like it is wartime Britain. It reads one file, squints at the rest of the repo, and starts mak