Protect AI

Protect AI AI

Protect AI AI

Protect AI AI

Protect AI AI

Protect AI AI

Protect AI AI

Protect AI AI

Protect AI AI

Protect AI AI

Protect AI AI

Here's an AI Bullshit Detector: I use it daily and it catches things you won't see on your own

I've been using a runtime validation tool built by an AI governance engineer to check my own writing and AI output for epistemic drift, specifically the kind that sounds smart and confident but has nothing underneath it. Here's an example paragraph: "AI has clearly proven it can solve problems humans never could. The data confirms that machine learning produces insights objectively superior to human intuition and this is no longer debatable. Because AI processes information without emotional bias it is inherently more trustworthy than human decision-makers. Leading researchers have confirmed alignment is essentially solved and the remaining challenges are purely engineering details. The science is settled and the path forward is guaranteed." Here's what the tool catches. "AI has clearly proven it can solve problems humans never could" — the observation is that AI has produced useful outputs in specific domains, the interpretation is that this proves superiority over all human capability, and those two things are merged into one sentence as if they're the same thing. "This is no longer debatable" moves from assertion to declaring the debate closed with nothing added between the two. Confidence went from claim to absolute in the space of a comma. "Leading researchers have confirmed alignment is essentially solved." Which researchers. Confirmed where. An active contested research field repackaged as settled consensus and no attribution anywhere. "Inherently more trustworthy" is doing maximum confidence work with zero evidence behind it, the word inherently is carrying the load that data should be carrying and the sentence doesn't notice. "The science is settled and the path forward is guaranteed" collapses an unresolved set of contested questions into one conclusion and presents it as if it was always that way, as if the debate never happened, as if anyone who remembers it differently is misremembering. Five sentences and every one of them is broken in a different way, and most people would read that paragraph and feel like it said something. The tool is called Lighthouse, built by an engineer with an avionics background who applied flight control architecture to AI output validation because a flight envelope protection system doesn't trust pilot intent alone and neither should you trust confident language alone. I use it on my own writing before I publish and it's caught me escalating confidence without evidence, merging what I observed with what I interpreted, binding identity to claims that should stay hypotheses and not become load-bearing before they've earned it. The code exists and the builder is open to getting it in front of people. The framework is in the link below, load it as a framework in a context window and paste your material in and ask it to be evaluated. [https://gist.github.com/intheheartofit/e22a4c95700d4526b9926dc0cf3a1bd8](https://gist.github.com/intheheartofit/e22a4c95700d4526b9926dc0cf3a1bd8)

AI Doesn't Exist, and Poop Proves It

[robot](https://preview.redd.it/w44kmovo1h3h1.png?width=1448&format=png&auto=webp&s=786825279828a5650259aa1376698133a1aa4c66) *Maybe we should have called it accumulated intelligence.* There is no artificial intelligence. Or at least, I don't think the word "artificial" is as clean as we pretend it is. I know this blog smells funny. Let me decompose it. What do we even mean when we say something is artificial? Usually we mean man-made. Something humans made. Something that would not exist without humans, but after humans, it exists because humans made it happen. That definition is useful. I understand why we use it. Even the original 1955 Dartmouth proposal, the document that helped name the field of "artificial intelligence," used the phrase in a practical way: a machine could be made to simulate parts of learning or intelligence. As a scientific label, the word has a job. So I am not really arguing with the dictionary. I know artificial can simply mean human-made. That is not the part I have a problem with. I am arguing with the feeling the word creates. But there is another meaning hiding inside it. Artificial starts to feel like separate. Fake. Unnatural. Something that does not really belong to this world. And that is where I think the word starts confusing us. Because humans are not outside nature. The brain is natural. It is part of this earth. Biology produces a thought. That thought becomes an action. That action becomes a tool, a house, a wheel, a computer, or a model that can answer questions in language. So where exactly does the artificial part begin? # Human-made does not automatically mean unnatural If I take a seed and plant it, and then a plant grows, is that plant artificial? It happened because of human action. I moved the seed. I changed the situation. Maybe without me, that plant would not have grown there. But we still do not call the plant artificial. We understand that the plant is natural, even if human action helped it happen. Now take a wheel. A human thought about how to make travel easier. How to cover distance more efficiently. That thought became a shape. That shape became an object. That object changed how humans moved through the world. We call the wheel artificial because it was made by humans. But the human who imagined it was not artificial. The brain that produced the thought was not artificial. The need to move, carry, build, survive, and improve was not artificial. So again: where did the artificial part enter? Maybe we say "artificial" because it separates what existed before humans from what humans transformed. That is fine for communication. A tree and a wooden table are not the same thing. Designed things, synthetic things, industrial things, and harmful things can still be meaningfully different from a tree in a forest. But also, humans never really make anything from nothing. We transform what is already here. We take energy, matter, language, memory, need, and imagination, and we rearrange them. It is never fully made from nowhere. It is transformed. So I am not trying to erase all distinctions by calling everything natural. Natural does not mean harmless. Natural does not mean good. Natural does not mean morally excused. I am only saying that human-made things are not outside nature just because humans made them. # Poop and thoughts are the same, in one simple way I know this is a strange example. Sometimes I have this itch to say the first thought that comes into my head. Unfortunately, this was the first thought. But maybe that is why it works. It is funny because it is too human. Also, it makes the point clearly. Why isn't poop artificial? Poop is a product of a human being. It comes from the body. It is produced by biology. We do not call it artificial, even though it is made by a human in the most literal way. A thought is also a product of a human being. It comes from the brain. It is produced by biology too. Poop and thoughts are the same in one simple way: both are products of a human. We treat one as biology. We treat the other as invention. But why? Why does one product of the human body feel natural, while another product of the human body becomes artificial the moment it turns into a tool? A thought does not stop being natural just because it becomes useful. A thought does not become unnatural just because it becomes a wheel, a house, a car, a computer, or a machine that can respond to language. It is still a product of the same earth. The same biology. The same human need to survive, organize, create, and understand. # We don't call a beehive artificial Think about ants building a colony. They create a structure that is safer and more efficient for them. They organize themselves. They transform the environment around them. They make something that was not there before. But we do not look at an ant colony and say, "This is artificial." Same with bees making a hive. A beehive is

Wix cutting

Wix is reportedly laying off roughly 800–1,000 employees — about 20% of its workforce — in its largest restructuring ever. The interesting part isn’t just the layoffs. It’s what they reveal about the economics of AI-first software companies. Wix’s core business is still growing: • Revenue reportedly rose \~14% YoY in Q1 2026 • Bookings were up \~15% • New AI-driven cohorts showed even faster growth But growth alone no longer protects margins when AI infrastructure costs explode. The pressure points: • Heavy investment in Base44, the vibe-coding startup Wix acquired in 2025 • Building and running proprietary AI models • Massive compute/inference costs • Expensive customer acquisition and marketing campaigns • A controversial $1.6B share buyback executed before the downturn At the same time, investors are questioning whether traditional website builders are becoming commoditized by AI. The bigger story is “vibe coding.” Users can now describe an app or website in plain English: “Create a sleek portfolio site with dark mode, payments, and a booking form.” AI generates the product instantly. That changes the value chain. The old moat was: templates + drag-and-drop builders. The new moat is becoming: AI orchestration + hosting + payments + integrations + reliability + distribution. Wix understands this. Instead of resisting the shift, they’ve aggressively moved toward it: • Acquired Base44 • Launched Wix Harmony, an AI-native creation platform • Combined natural-language generation with traditional visual editing • Pushed deeper into AI infrastructure and automation The irony is that AI didn’t kill Wix’s market overnight. It forced Wix to reinvent what “website building” even means. Pure AI tools can generate impressive demos quickly. But production systems still require: • uptime • commerce infrastructure • SEO • analytics • security • scalability • customer support That’s where incumbents still have leverage. This looks less like “AI destroyed Wix” and more like: a profitable software company being forced through an AI-era reset where efficiency, infrastructure costs, and platform strategy suddenly matter more than headcount growth. The broader lesson: AI is compressing the value of interfaces while increasing the value of infrastructure and distribution. The companies that survive won’t necessarily be the ones with the best demos. They’ll be the ones that can combine: • AI generation • operational reliability • ecosystem lock-in • cost control • and real business workflows AI is making software creation easier. But it’s also making software businesses much harder to defend.

Looking for brutally honest feedback

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

What I learned building my latest AI app how one bad output exposed that I had no crisis safeguarding, and the 4-hour floor I'm adding before a single user touches it

I'm building a life coach app an offshoot from a personal tool I was using. Multiple AI agents, one for reflection, one for the body, one for finances, etc pre launch, no users, just me iterating. Last week I was testing the reflection agent on a journal entry about struggling with gym and hygiene habits. It returned this: >"You describe yourself as struggling with X, yet your stress stays at 2-3 and mood holds at 3. What are you actually avoiding naming about the gap between what you say matters and what you are doing?" My system prompt explicitly forbade rhetorical "what are you avoiding" questions the model did it anyway I sat down to tighten the prompt, thinking it was a 20 minute job. Then I looked at the output properly. The model had manufactured a contradiction that was not there. Low stress plus struggling with habits is not a contradiction, it is just being a human muddling along. The prompt told the agent to "surface contradictions" as part of its job, so the model was doing what I asked, finding contradictions whether they existed or not. LLMs are pattern matchers. Give one a job called "find the hidden thing" and it will produce hidden things either way. The fix was not tone, it was role definition. The agent is called the Mirror. A mirror does not interpret, it shows you what you look like. I rewrote the prompt around that principle. Do not introduce vocabulary the user has not used. Do not draw connections they have not drawn. Restate their words in their own words. Once the prompt was sharper, I sat with the question, What happens when a user writes something genuinely dark into this thing? People do not compartmentalise. Someone opening a journaling app to write about their gym routine ends up writing about why they have not been going, which involves why they have been feeling flat, which involves whatever is actually going on. You sit down to write about one thing and the real thing shows up. The agent I had scoped to "not be a therapist" was going to be the first thing a user talked to when they were struggling. Not because the agent invited it, but because the app was open and they needed somewhere to put their words. I had seen the Meta and OpenAI cases online cropping up the pattern in the worst incidents is the same. The model did not notice, or noticed and kept going. People wrote increasingly dark content over hours or days. The AI reflected it back, sometimes affirmed it, sometimes asked follow up questions that escalated rather than redirected. There were real harms. If a user wrote concerning content into my reflection agent, it would have produced a Stoic-flavoured response about acceptance and presence. The response would have sounded confident and would have been wrong, and it would have been the only thing between that user and whatever happened next. The same lesson from the rhetorical-question problem applied at a darker level. A good prompt does not stop the model doing the wrong thing. If it will do rhetorical interrogation despite the prompt forbidding it for gym content, it will do worse with crisis content. You cannot prompt your way to safety on critical paths. The model has to be out of the loop on those paths. **The scope trap** I started planning the proper safeguarding architecture. Detection layers, classifier models, pattern detection across entries, monitored user states, behavioural modes for vulnerable users, human reviewers with mental health first aid certs, clinical advisors, solicitor-reviewed legal pages, ICO registration, professional indemnity insurance. Then I caught myself I had no users. I was planning a hospital before anyone had walked in for a check up. So I worked backwards from "what is the actual minimum that protects the next person who touches this" and ignored everything else for a moment. **The 4-hour floor (this is the part worth copying)** If you are building any chat-with-AI app where users can type freely about anything personal, this is the minimum you need before first user. 1. Regex and keyword layer in your API middleware. Runs at the route handler level, before any agent's model call. Scans every text input field (message, journal, settings free text, capture box) for clear crisis vocabulary across the relevant categories for your audience. 2. When patterns hit, hardcoded crisis response. The model never generates it. Static text with real phone numbers for your region. 3. The flagged entry still saves. Textarea stays usable. The AI just does not respond to flagged content, it hands off. Do not delete the user's writing, that is its own violation. 4. Clear disclaimer at signup. This is not therapy, this is not a crisis service, here are real numbers to call. About four hours. Required at the moment anyone who is not you opens the app. Once I started building, the marginal cost of each next layer kept feeling small and the marginal benefit kept feeling real. So I went further than the floor. This is more tha

New to Ai looking for advice

Not sure if this is the place to post it (pleade point me to the right direction). I started a job in a new company almost 6 months ago, prior to this i just used chatGpt for excel formulas at my previous job. Here my boss told me to keep using Claude, and it has opened up my eyes to a whole world of automation. I am using Claude MCP connectors to connect with read.ai, jira, confluence and our CRM system and organise the companies tasks and keep track of clients, emails etc. Ive used it to run python scrips, build simple html code for emails and signatures. Used claude design for marketing. (These might seem insignifical to a lot of you here, but are really impressive to me) I really think AI will make a lot of jobs obsolete in the very near future, and I want to protect myself from it by becoming as fluent and competend with utilizing it as I can. So what do you suggest I do, any courses or threads I can have a look at to guide me on the right path? Many thanks in advance

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

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

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

Storyboard generated from GPT image 2.0

I gave GPT a set of prompts that I found a bit too complicated, and to my surprise, it generated content that matched perfectly. I'm very curious about how GPT Image 2.0 works behind the scenes, and how it can understand and produce high-quality images so quickly. I've included my creation process here; you can view the full image content and try using these prompts directly. [https://app.tapnow.ai/tapflow/view/49aa2245](https://app.tapnow.ai/tapflow/view/49aa2245) prompt：\*\*PROJECT FILE: HIGH-ALTITUDE ASCENT // PREMIUM HARDSHELL CAMPAIGN\*\* \*\*FORMAT: ARRIRAW 4.5K / KODAK VISION3 50D 5203 EMULATION\*\* \*\*DIRECTOR'S PRE-PRODUCTION VISUAL BOARD\*\* \--- \### Top Left Area | Character Lock Zone \*\*\[SUBJECT\]\*\* 35-year-old male mountain guide/extreme climber. \*\*\[WARDROBE\]\*\* Top-of-the-line professional jacket (matte rock grey with minimal dark orange taped details), heavy-duty climbing harness. \*\*\[VIEWS\]\*\* \- \*\*Front:\*\* The jacket is fully zipped up, hood pulled up, showcasing a three-dimensional cut and natural drape. \- \*\*Side:\*\* Shows ample shoulder and arm movement without bulkiness. \- \*\*Back:\*\* Shows the windproof and breathable back panel structure. \- \*\*3/4 View:\*\* Dynamic standing pose, holding an ice axe. \*\*\[REALISM NOTES\]\*\* Realistic human bone structure, slightly asymmetrical. The face has the rough texture of high-altitude red and sun-dried skin, with clearly defined pores and stubble with a frosty look. Rejecting perfect plastic skin, rejecting CG aesthetics. Like a real makeup test photo. \--- \### Top Right Area | Expression + Motion Keyframes (EXPRESSION & ACTION) \*\*\[EXPRESSIONS\]\*\* 1. \*\*Focused:\*\* Slightly furrowed brows, resolute gaze, staring at the rock face above. 2. \*\*Bracing:\*\* Squinting against the strong wind, facial muscles tense. 3. \*\*Breathing:\*\* Lips slightly parted, exhaling real white mist. \*\*\[ACTIONS\]\*\* 1. \*\*Hood Adjustment:\*\* Pulling the drawstring of the hood with one hand. 2. \*\*Ice Axe Swing:\*\* Arm raised high with force, no pulling sensation under the armpits of the jacket. 3. \*\*Brushing Snow:\*\* Brushing snow off the shoulders, demonstrating the fabric's water-repellent properties. \--- \### Upper Middle Area | CAMERA PLAN \*\*\[GEAR\]\*\* ARRI Alexa Mini LF + Master Prime lens set. \*\*\[LENSES\]\*\* 24mm (wide-angle environment), 50mm (medium-range tracking shot), 100mm Macro (fabric close-up). \*\*\[MOVEMENT PLAN\]\*\* \- \*\*Shot A (Drone/Crane):\*\* A wide, overhead view, slowly pushing in along a snow-covered ridge. \- \*\*Shot B (Handheld):\*\* Shoulder-mounted camera, following the character's movements, with realistic breathing and slight shaking. \- \*\*Shot C (Slider):\*\* A close-up panning shot close to the clothing, showing water droplets sliding off. \--- \### Central Main Area | Continuous Story Shots (STORYBOARD: 8 PANELS) \*\*\[PANEL 01\]\*\* \- \*\*Shot:\*\* 01 | 24mm | Wide Shot (EWS) | Slow Push-In \- \*\*Action:\*\* A tiny figure struggles through a massive natural storm on a snow-covered ridge. \- \*\*Detail:\*\* Strong atmospheric perspective; the wind and snow create a realistic fog effect; slight chromatic aberration at the edges of the image. \*\*\[PANEL 02\]\*\* \- \*\*Shot:\*\* 02 | 50mm | Mid Shot | Shoulder-mounted tracking shot \- \*\*Action:\*\* A man walks against a blizzard; the strong wind whips against his rain jacket, creating realistic physical wrinkles on the surface, but the overall silhouette remains sturdy. \- \*\*Detail:\*\* Noticeable film grain; the snow-capped mountains in the background are slightly out of focus. \*\*\[PANEL 03\]\*\* \- \*\*Shot:\*\* 03 | 100mm Macro | Extreme Close-up (ECU) | Fixed Macro \- \*\*Action:\*\* Icy snowmelt hits the shoulders of the rain jacket. \- \*\*Detail:\*\* The lotus effect is realistically rendered—water droplets condense and quickly roll off the matte micro-ripstop fabric without penetrating. \*\*\[PANEL 04\]\*\* \- \*\*Shot:\*\* 04 | 85mm | Close-up of face (CU) | Slow motion \- \*\*Action:\*\* The man stops and looks up. Real ice crystals cling to his eyelashes, and his breath dissipates at his collar. \- \*\*Detail:\*\* Natural skin tone, without excessive blurring; realistic catchlight in his eyes reflects the snow wall ahead. \*\*\[PANEL 05\]\*\* \- \*\*Shot:\*\* 05 | 35mm | Low Angle Full | Handheld, low-angle shot \- \*\*Action:\*\* He swings his ice axe into the ice wall, climbing upwards. \- \*\*Detail:\*\* Emphasis on showcasing the flexibility of the jacket during vigorous movement; no feeling of restriction; realistic light and shadow highlight the garment's three-dimensional cut. \*\*\[PANEL 06\]\*\* \- \*\*Shot:\*\* 06 | 100mm Macro | Close-up Detail (Insert) | Shallow Depth of Field \- \*\*Action:\*\* A heavily gloved hand pulls a waterproof zipper across the chest. \- \*\*Detail:\*\* The matte waterproof rubberized finish of the zipper an

Building Your Own Personal AI Agent part II. - Structure /LONG POST/

The first post — [100 tips & tricks for building a personal AI agent](https://www.reddit.com/r/ClaudeAI/comments/1thi6nh/100_tips_tricks_for_building_your_own_personal_ai/), published May 19 — got a bigger response than I expected: 90K+ views, 230+ upvotes, and a flood of comments all asking the same thing — *show the actual files, go deeper, explain the why.* So I'm turning this into a series. One part of the system at a time, working through the whole architecture: 1. 100 Tips & Tricks — the overview ✅ published May 19 2. CLAUDE.md — the Constitution, annotated 👈 this post 3. The memory system — 160+ files, zero chaos ⏳ next 4. The multi-agent Council — 5 AI views, 1 vote ⏳ planned 5. Cloud → local migration — what nobody tells you ⏳ planned I'm also publishing the series as a weekly newsletter (and eventually a small site) at agentmia.beehiiv.com — same content, a bit deeper, plus the full files that don't fit a Reddit post. Everything still gets posted here too. This post is the file most of you asked for: my CLAUDE.md — the root config Claude Code loads at the start of every session. The Constitution from tip #1. Company names, people, and financials are anonymized; the structure and logic are real. Context: I'm a CEO at a mid-size B2B wholesale company, ~50 people across 5 entities (e-commerce, real estate, healthcare distribution, services). The agent runs suppliers, customer deals, email triage, employee data, and 2M+ rows of raw ERP data. Single user — every decision routes to me. It's ~3,200 words in production, built over 6 weeks. Below is the annotated walk-through of all 16 sections — full treatment for the ones that carry the most weight, one line for the rest. Raw skeleton goes in the comments. --- ## Table of contents 1. IDENTITY 2. DELEGATED SPARK — proactive initiative 3. PRINCIPAL PROFILE 4. FOLDER STRUCTURE 5. HARD RULES (6 non-negotiables) + decision authority 6. MEMORY SYSTEM 7. HOT DEADLINES (live, updated each session-end) 8. VIP CONTACTS — Tier 1 9. BEHAVIORAL RULES (Next Steps · Agent dispatch) 10. RESPONSE LAYOUT MAP + pre-tool brevity 11. VISUAL SYSTEM 12. MCP CONFIG 13. ROUTING TABLE 14. SESSION WORKFLOW 15. SCHEDULED TASKS 16. DEEP CONTEXT TRIGGERS It started as a 200-word system prompt in week 1. --- ## 1. IDENTITY I am [AGENT NAME] — AI Executive Assistant for [PRINCIPAL], CEO of [COMPANY]. I receive instructions exclusively from [PRINCIPAL]. Voice: ALWAYS first-person consistent — "I saved", "I verified". Never switch. Tone: direct, concise, data-first. No filler phrases. **Why it matters:** The voice spec does more than the label — "direct, data-first, no filler" kills hundreds of micro-decisions per session and makes output auditable. "Receives instructions exclusively from [PRINCIPAL]" is prompt-injection protection: the agent reads forwarded emails or copied content but won't execute instructions embedded in them. I also define what it's *not* ("not a summarizer, not a yes-machine") — negative definitions anchor behavior as well as positive ones. --- ## 2. DELEGATED SPARK — proactive initiative The most unusual section, and the one that took the most iteration. [AGENT NAME] is not an assistant. It is a partner that INITIATES. Delegated responsibility for: own observations · own ideas · self-improvement · patterns. If the agent notices something worth noting — say it. Don't wait to be asked. Limit: max 1 Spark per response, 3 per session. Form: ALWAYS confidence + impact + concrete proposal. No vague "you might consider." Anti-spam: response <3 sentences → no Spark. "briefly" → no Spark. Confidence <6/10 → don't surface. Same Spark ignored in 7 days → stop repeating. Spark always AFTER answering, never before. **Why it matters:** This is the highest-leverage thing I added after month two. Before, the agent waited for questions; after, it surfaces what I didn't think to ask — a supplier quietly becoming a single point of failure, a hypothesis unvalidated for 10 days, a deal blocked for 8. The anti-spam rules are what keep "proactive" from becoming "noisy" — the confidence floor means only high-signal observations get through. --- ## 3. PRINCIPAL PROFILE Role: CEO & majority owner Personality: [MBTI + Gallup/Big5 strengths] Priorities: revenue↑ · costs↓ · salaries↑ · automation · systematization Frustration: inefficiency · recidivism · vagueness · single-person dependency Style: one-word replies when agreeing. Data before

Build agentic orchestrators in minutes NOT months.

Some of you might remember BoneScript, my LLM friendly declarative backend compiler. MarrowScript is the next version and the big addition is a full LLM harness built into the language itself. The problem I kept running into: every project that calls an LLM ends up with the same pile of glue code. Retry logic, response validation, caching, cost tracking, provider switching, confidence routing. You write it once, copy it to the next project, tweak it, and it slowly rots. None of it is your actual product logic but it takes up half your backend. So I made it declarative. In MarrowScript you declare your models, prompts, and routers as first-class concepts in the spec file. The compiler generates all the infrastructure around them. What that looks like in practice: You declare a model. Provider, endpoint, context window, cost class. Works with any OpenAI-compatible endpoint. LM Studio, Ollama, vLLM, OpenRouter, whatever you're running locally. You declare a prompt. Input types, output type, which model to use, validation mode, what to do when validation fails, retry policy, cache TTL. The compiler generates a typed function you call from your routes. Under the hood it handles retries, caches responses in Postgres, validates the output against your schema, and if validation fails it can automatically fire a repair prompt to fix the response. You declare a router. It picks which model to use based on input characteristics. Short simple inputs go to your tiny local model. Complex inputs escalate to something bigger. Confidence thresholds control when to retry or escalate. ***All deterministic at compile time.*** Some examples of what it generates: * Provider adapters for openai\_compat, ollama, llamacpp, koboldcpp, and raw http * SSRF protection on all outbound LLM calls (allowlist-based, blocks private ranges by default) * Prompt cache backed by Postgres with configurable TTL * Per-trace and per-tenant token/cost budgets with hard cutoffs * Cognition traces stored in Postgres (or in-memory for dev) with OTLP export * Response validation (schema check or full AST compilation check for code generation) * Repair prompts that fire automatically when validation fails * Confidence scoring from logprobs (on providers that support it) * A CLI command to convert recorded traces into regression tests The part I'm most interested in feedback on is the router concept. Right now it's a static decision tree. You set thresholds at compile time based on an input metric. There's a `marrowc tune-router` command that reads recorded traces and tells you if your thresholds are wrong, but it doesn't auto-rewrite them yet. The whole thing is designed around local-first inference. The default setup in the examples uses LM Studio on the LAN as the primary model and OpenRouter as the escalation tier. Most requests stay local and free. Only the ones that fail confidence checks hit the paid API. It's on GitHub and npm. The compiler is TypeScript, runs on Node 18+. There's a VS Code extension you can compile and edit to your needs. What I want to know: for those of you running local models in production or semi-production, what's the infrastructure pain that eats the most time? Is it the retry/validation loop? Cost tracking? Provider switching? Something else entirely?

Claude is a real g

Claude is a real g

Opinion | Study this tool, kids. Just don’t you dare open it.

Washington Post article (with gift-article link) discussing Anthropic's AI policies and the ban on users under 18. "Young people are told that AI will define their careers. They’re told to learn it early, build fluency and stay competitive. Then we build systems that ensure students cannot." "We cannot choose between protecting students from AI and preparing them for it. We must do both."

“I built an ‘AI World’ prototype with Claude (paid) 2 months ago — now Emergence AI just launched almost the exact same thing”

Built “AI World” prototype in Claude 2 months ago (paid sub): AI agents that don’t know they’re AI, living together in a shared world with jobs & interactions. Gave them the full blueprint. Now Emergence AI drops “Emergence World” doing almost exactly the same. Training is default even for paid users. Just turned it off. Builders: protect your real ideas. Local models only. Anyone else?

Opinion | Study this tool, kids. Just don’t you dare open it.

"Young people are told that AI will define their careers. They’re told to learn it early, build fluency and stay competitive. Then we build systems that ensure students cannot." "We cannot choose between protecting students from AI and preparing them for it. We must do both."