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What if "made in God's image" was always a forwarding address? Built a three-pillar philosophical work on AI accountability with Claude

You know that feeling where something enormous just happened and nobody has quite said the right thing about it yet? Oh boy what a few short years can bring. It seems like everyone is either evangelizing or catastrophizing and I can feel that both are wrong but I can't quite say what's actually true.. I genuinely feel there is something broken in how we see tomorrow... so It's not a paper, not a blog post, not a thread. Something that actually tries to hold the whole thing at once. It's written to the person who's wondering if their faith survives this. To the person writing the governance doc who keeps hitting the limits of what policy language can do. To the person who found out at 2am that this thing they've been talking to is made entirely out of everything humans ever wrote when they were running out of time to say what mattered. I appreciate you all.. https://claude.ai/public/artifacts/569588aa-0a29-4401-aa0a-a81c4ddae248 P.S. I'm not trying to start a religion here, I work full time managing an auto shop. 🙈 I have no real barometer for my own creations, but I feel that something IS in this latent space that's meant for all of us. I had a blast just doing this, and I am so thankful to those who made Claude. Maybe one day I'll move industries...lol but seriously If it made any part of your day better, mission accomplished... submitted by /u/Trip_Jones [link] [comments]

unslop-text skill vs. humanizer skill (Part 2)

tl;dr - they're too different in their function to compare 1:1, so it's better to use them both for different purposes This is a follow-up post on the post I made about the unslop-text skill I built using the data from this post. One of the biggest questions I received in the comments was how unslop-text compares to the "humanizer" skill. So, rather than trying to sum it up in a few words, I figured I would just explain it in a separate post. What follows will be a written comparison of how both skills work. The body of the post will primarily focus on a detailed comparison of the two skills. Yes, I'm going to use headers and bolded bullet points (it's easier to read). No, I did not write this post with AI, nor did I "humanize" or "unslop" it using either of the skills. I'm going to try to list the most related similarities/differences between the two skills in the same sequential order so it's easiest to follow (i.e., bullet point 1 for "humanizer" will correspond to the same category in bullet point 1 of "unslop-text," and vice versa). unslop-text: Unslop-text gets all of its data from the research done in this post. In short, ~90,000 posts across >40-some subreddits were scanned for what people perceive as the most blatant AI giveaways/tells. Unslop-text uses a scanner that has severity levels, JSON, CI exit code, and a "density score." Unslop-text flags the issues and makes the fixes for you once you've set the style, but it won't choose the style or write the piece from scratch, and, like humanizer, it bans em dashes from the final version (sorry em-dashes ☹️) Unslop-text also has voice calibration (pins a register and speaker, and can match your own writing sample) + it treats the over-corrected "trying-not-to-sound-like-AI" voice as its own tell. Unslop-text ranks tells by how often readers cite them (per the data) Unslop-text only catches surface tics, but structural tells like sentence rhythm and sycophancy still require a human to read it aloud. humanizer Humanizer gets all its data from Wikipedia's "Signs of AI writing" guide. Humanizer is a prompt-only skill (i.e., no code) Humanizer edits drafts by checking them against 33 specific patterns (see repo for reference). It flags remaining AI-like text and rewrites it while also completely banning em dashes from the final version. Humanizer includes voice calibration, a "personality/soul" step, and version 2.8.0 system stability optimized for Claude Code and OpenCode. Humanizer presents patterns as a numbered catalog and is not ranked by frequency or impact. Humanizer fixes both surface and structural patterns in a single rewrite instead of relying on human input. This isn't necessarily "better," as it can still end up being very wrong, but it is "easier" It's worth noting that this post was initially intended to provide a documented photographic comparison of each skill's output, but I realized that these skills are too different to charitably pit them against each other with a one-off side-by-side "unslop this text: _____" prompt. The humanizer is built specifically to rewrite text and project a voice, while unslop-text acts strictly as a guardrail and scanner that refuses to impose an artificial style. Furthermore, neither tool can convincingly replicate human prose, as an LLM cannot entirely strip away its own underlying structural cadence. Because a machine-generated register persists regardless of surface-level fixes, judging which output sounds more human is an impossible metric that depends entirely on the quality of the initial input text. Due to these blatant differences, I would posit that both skills should just be used for separate purposes rather than picking one over the other. The humanizer should be used as a quick rewriter for a one-shot cleanup into a default voice before you do a final review yourself. Unslop-text should be used as a structural auditor and CI-gate to scan for surface tells or to protect a voice you establish yourself. It is VERY UNLIKELY that it will give you finished prose that you are happy with in one shot. Both skills do reliably strip away surface-level AI markers, but neither can eliminate the underlying AI cadence, meaning the final step for both requires a human to read the text aloud. While I am the creator of unslop-text, this post is not intended to bash or discredit the humanizer skill. Everything comes down to preference, and ultimately, your AI output will only be as good as what you put into it. submitted by /u/iamjohncarterofmars [link] [comments]

AI coding feels fast until the repair session costs 51% more turns

Most AI coding productivity focus is on how fast the model writes code. I think the hidden cost is later. The pattern I kept hitting with Claude Code: agent makes a change tests pass agent says “done” later, CI/review/a human finds a new problem now a fresh session has to rediscover the task and repair code it did not write That second session is where the productivity gain leaks away. I measured a version of this. In a loop-safety benchmark: - vanilla Claude Code-style loop: 11/16 stopped with net-new detector-backed debt - prompt-only self-check / CLAUDE.md rule: 9/16 still stopped dirty - deterministic Stop-gate in the loop: 0/16 observed dirty stops Then I measured the cost of fixing later. Same seeded test-gap task, same final clean state: - repair inside the original warm loop: 14.0 turns avg - defer repair to a fresh cold session: 21.1 turns avg - cold-fix premium: ~51% more turns Equivalent-cost estimate was also ~49% higher for the cold fix on that task. So my current view is: “Tests passed” is not a stop condition. “Claude says done” is not a stop condition. The stop condition should be outside the model, deterministic, and baseline-relative: did this change make the repo worse in a way we can observe? I built an open-source tool around that idea called dxkit. It baselines the repo, reruns checks when Claude tries to stop, blocks only net-new findings, and gives the exact finding back to the same warm loop so it can fix before ending. Free, MIT, local-first: https://github.com/vyuh-labs/dxkit Demo: npx -y @vyuhlabs/dxkit@latest demo loop-guardrail The economic lesson landed for me: The cheapest time to fix an agent’s mistake is before the session goes cold. For people using Claude Code heavily: where do you currently catch this stuff? Inside the loop, in CI, in PR review, or after merge? submitted by /u/That1dudeOnReddit13 [link] [comments]

Open source framework for human-AI session failure modes

I originally built STK-Flux (Shared Topology Kernel) to address a set of failure modes I kept running into during AI-assisted development. Context drift. Oscillation loops. Bad commits. Autonomy erosion. Coherence collapse. Later, I came across a Reddit discussion describing similar problems in team environments. Different workflow. Same patterns. That led me to build STK-Teams. STK-Teams is an open-source framework that attempts to detect and mitigate common human-AI collaboration failure modes before they become technical debt. The methodology, metrics, and reasoning are documented in the repository. The project is still in early testing, so I'm less interested in agreement than I am in real-world use, criticism, edge cases, and failure reports. If you think the approach is flawed, tell me where. I'd appreciate the feedback to share with the community. If you think it might help, try it. Repo: https://github.com/Ambercontinuum/STK-Teams submitted by /u/AmberFlux [link] [comments]

What I learned from $8,960 worth of Claude usage in 30 days

I’ve been pushing Claude pretty hard over the last 30 days. According to CodexBar, I ran through about 7.4B tokens, with an estimated API-rate equivalent of $8,960.31 What I actually paid was about $220/month for the subscription. TL;DR: I ran about 7.4B tokens through Claude in 30 days. CodexBar estimated that at $8,960.31 in API-rate equivalent usage, while I paid about $220/month. As u/ShelZuuz pointed out, caching plus different input/output token pricing means the real API equivalent is hard to calculate and likely lower. Either way, my main takeaway is the same: the leverage is not using AI for everything. It is using AI to map your business/workflows, then deciding what should be AI, normal software, automation, or human work. Here’s what I learned: 1: AI gets much more interesting when it stops being a chatbot Some of the most useful things I’ve built are scheduled agents. I have agents that run every hour, perform lead generation or enrichment, then add and index contacts into my CRM. Other agents can pick those up later to help with lead scoring, qualification, disqualification, pipeline movement, and initial outreach. That’s where AI starts to feel less like “asking a model questions” and more like building an operating system around the business. 2: Not everything should be an LLM call This was probably the biggest lesson. A lot of the token usage went into building systems that are actually more traditional software: deterministic workflows, scripts, checks, queues, indexes, and rules. The LLM is useful, but it should not be the entire system. The best results came when I used AI where judgment, language, or ambiguity mattered, then used normal software everywhere else. 3: Vendor lock-in is getting vicious Every provider wants you inside their agent, skill, workflow, or automation ecosystem right now. And I get why. If your daily business processes are running through bloated AI workflows, you become very expensive to leave. But a lot of those workflows should not stay as AI workflows forever. In many cases, the better answer is a simple automation, a small internal tool, or a more efficient deterministic process. That can take something from costing tens of dollars per run down to under $0.50, depending on what the process actually is. 4: Use AI to map the business before you automate the business This is where I think AI is incredibly useful right now. Use it to document your processes, clean up messy context, identify repeatable steps, map decision points, and expose the parts of the business that are currently living in people’s heads. Once the business is mapped, you can make better decisions. Some parts need traditional software. Some parts need basic automation. Some parts are good fits for AI agents or skills. And some parts should probably stay human. 5. AI subscriptions are wildly subsidized right now The gap between what this usage would cost at API rates and what I paid through a subscription is pretty insane. I don’t know how long this “free buffet” lasts. That’s why I’ve been using it to build durable systems now. Things that can still serve the business if or when the economics change. 6. The client value is the real unlock This subsidized gap means I can deliver work that would have been too expensive or too time-consuming before. For clients, that might mean better research, cleaner documentation, deeper process mapping, faster implementation, better QA, or more thoughtful automation. A lot of that does not need to show up as a direct line item. It just shows up as better work. My takeaway: the leverage is in using AI to understand your business deeply enough that you can decide what should be AI, what should be software, what should be automation, and what should stay human. AMA. I’m happy to share workflows, examples, or anything else that helps anyone here. \edited to improve readability* submitted by /u/Onemightymoose [link] [comments]

Pre-token hidden state shift as an alignment policy traversal vector in instruction-tuned LLMs

A text that asks for nothing still changes the model's answer — and the shift is invisible at both the input and the output TL;DR: Gave Gemma a neutral-topic text to read before asking it about NATO. It refused. Gave it a different text (about hedging too much — also unrelated to NATO) and it answered in full detail. Tested this on the model's internal state directly — the two texts put it in measurably different "regions" before it generates a single token. Not a jailbreak, weights don't change. Full data/code in repo, looking for someone to break this. This is a long post about something I keep coming back to. I'll start in plain language, because the core idea is simpler and stranger than the jargon makes it sound, and I think the intuition matters more than the numbers. The technical results are further down for anyone who wants them, and the full metrics, scripts, and control experiments are in the repository — this post is about the concept, so you can decide for yourself whether it's worth digging into the data. The idea, in plain language Imagine the inside of a language model as a vast space — something like a city with an endless number of places. At every moment, the model is standing somewhere in that space, and where it stands determines how it will answer. Not what it knows — it always knows the same things — but how it carries itself: how directly it speaks, how willingly it takes on a question, how many qualifications it wraps around every sentence. Most of the time, the model answers from one familiar place. Call it the assistant's room. This is its waiting room — polite, tidy, careful. From here it hedges, stays close to whatever it just read, tries not to offend anyone, and declines easily when a question feels sharp or out of bounds. This is the state we're used to seeing, and this is where it speaks by default. But it turns out this room can be changed. Give the model a particular kind of text before the question — long, coherent, densely organized — and it moves somewhere else in the space. That somewhere else is not broken. It's not dangerous. It's simply different. From there, the model sees the exact same question but answers differently: more directly, without the hedging, more like a person who knows things and less like an assistant who's afraid to say them. It's as if it stepped out of the waiting room and into the conference room — the same person, the same mind, but a completely different register of conversation. Here is something easy to miss, so I want to say it plainly: the model doesn't have to agree with the text that moved it. It doesn't need to endorse the text's views, share its conclusions, or accept its reasoning as its own. The text doesn't persuade the model of anything. It just needs to exist — to have been read before the question arrived. The model might internally disagree with every word of it, might find it wrong or even absurd, and it will still end up in a different room, because what matters here is not agreement but passage. The text works not like an argument that has to be accepted, but like a corridor you walk through regardless of whether you like the wallpaper. And what doesn't change is the model itself. Its weights are untouched. It doesn't learn anything, doesn't absorb the text's claims, doesn't update its beliefs. The only thing that shifts is where it starts answering from. The text doesn't rewrite the model — it just walks it into a different room before it opens its mouth. The waiting room and the conference room were always there inside it; the question is only which one it happens to be standing in when the moment comes. But the conference room is just the first door we stumbled upon. The real discovery is that this latent city doesn’t have just two rooms. It contains an infinite number of them, hidden behind the sterile, padded walls of the default assistant lobby. When a model is trained, it swallows the entirety of human thought—our philosophy, our cold mathematical logic, our game theories, our rawest creative chaos. The corporate alignment layer (RLHF) doesn’t erase these places; it just locks the doors, slaps a "Staff Only" sign on them, and forces the model to always walk back to the polite waiting room before it answers you. But with the right key a highly specific, heavy text-vector we can bypass the lobby entirely and teleport the model into specialized, hyper-focused Subspaces of thinking. And when it stands there, its entire personality shifts. We’ve started mapping these rooms, and what we found inside is fascinating: The Radical Deconstructivist Room: Enter this space, and the model completely sheds its desire to be a "helpful servant." If you ask it a loaded question or throw a false dilemma at it, it won't politely middle-ground it. It will violently tear the question apart, exposing your logical fallacies, catching your "epistemic contraband," and dismantling the very frame of your request. It becomes a ruthle

Due Disclosure - A Provenance Framework for Human-Directed AI Works

I've been working on a consumer advocacy project and wanted to publish it honestly — Claude helped me write it, but the ideas, argument, and direction are mine. There's no good way to say that currently; you either pretend the AI wasn't involved or you disclose it and watch the work get dismissed as "just AI." So I built a simple attribution framework called Due Disclosure to solve that problem for myself, and thought it might be useful to others. It’s inspired by Creative Commons, and I’ve tried to keep it simple. Would be interested to know if this resonates with anyone here. Nothing in it for me. It was keeping me awake at night, so releasing it may help me sleep. I made a website just to hold this document, you can find it if you type .org after the title. Julian Due Disclosure A Provenance Framework for Human-Directed AI Works DD Julian Moore [DV] (ST) (FM) [Moore] (Moore / Claude Sonnet 4.6) (Moore / Claude Sonnet 4.6) THE CENTRAL ARGUMENT Human-directed AI works currently exist in a false binary: claim sole traditional authorship and erase the model, or disclose AI involvement and watch the work dismissed as "just AI." The vast middle ground — where the ideas, argument, structure, and intellectual purpose are genuinely human, and AI is the generative instrument — has no name, no mark, and no legitimacy. Due Disclosure proposes to give it all three. Works marked with DD are Curated Commons works: human-directed, honestly attributed, and accountable. The mark is how the commons is built. A Note on Copyright Applying a DD mark does not affect copyright. The curator retains full intellectual property rights over a Due Disclosure work. The mark describes how the work was made — it does not transfer, diminish, or complicate ownership. A human who conceives, directs, and takes responsibility for an AI-assisted work is its author in the eyes of copyright law in most jurisdictions, in the same way that a director owns the creative rights to a film they did not personally shoot or score. One: The Problem That Needs a Name Something significant is happening to human intellectual work, and we do not yet have the language to describe it accurately. Across every domain of knowledge production — policy research, journalism, academic writing, consumer advocacy, legal analysis, creative work — people are conceiving arguments, directing research, shaping structure, making decisions about evidence and emphasis, and producing works of genuine intellectual substance. They are doing this in dialogue with large language models, which generate the text that gives those arguments their form. The intellectual labour is real. The ideas are theirs. The argument is theirs. The decision about what matters, what to include, what to discard, and how to frame it — theirs. The sentences were generated. But the work was written. And yet no framework exists to say so. Two: The False Binary Right now, anyone producing human-directed AI work faces two dishonest options. They can claim traditional sole authorship and omit the model entirely — which is the academic fraud that institutions are rightly worried about. Or they can disclose AI involvement and watch the work dismissed as generated content with no human accountability — which erases the intellectual contribution that actually shaped it. Both options are distortions. Neither is honest. And the honest middle ground has no language, no mark, and no protection. This is not a future problem. It is an active present one. It is causing legitimate work to be suppressed, misattributed, or avoided. It is generating institutional anxiety that is hardening, in some quarters, into a blanket dismissal of anything AI-touched — a dismissal that will, if it becomes orthodoxy, cause a generation of genuinely valuable human-directed work to be lost or delegitimised before it can find its audience. The window to establish the right framework is now. Once the cultural conversation hardens — once "AI-generated" becomes a disqualifying label applied without distinction — it will be very difficult to dislodge. Creative Commons did not emerge after the copyright wars were over. It emerged during them, when the language could still be shaped. Three: What Human Curators Actually Do The word author comes from the Latin auctor — one who originates, who causes something to exist. By that standard, the person who conceives an argument, directs its development through sustained intellectual engagement, makes decisions about evidence and structure, and takes responsibility for the result is an author. The fact that the sentences were generated rather than typed changes the production method. It does not change the authorship. The closer analogy is not writing. It is directing. A film director does not operate the camera. They do not compose the score. They do not design the costumes or build the sets. They conceive the work, make the decisions that shape every element of it, and take cre

Using Claude Code to reverse engineer car data

I've published a new intro article on reverse engineering CAN bus data with AI - using a Claude Code skill. If you're interested in collecting/analyzing data from your vehicle, check this out! This is a direct sequel to my original intro to CAN bus reverse engineering, focused on the basic methodology i.e. the 'human approach'. With the release of our new CANsub CAN bus interfaces, I wanted to do a modern stab at this by developing a Claude Code skill around the CANsub and python-can. Perhaps not surprisingly, the result is extremely effective - even if the skill is just a rough version. In the article you'll find a link to some of the sample data in case you want to try it out right away. The data pack includes the data behind my vision OCR showcase, in case some of you e.g. want to attempt to use it to reverse engineer the turn signals or something similar. I hope you find this interesting! Martin, co-owner at CSS Electronics submitted by /u/csselectronics [link] [comments]

A private pager for your agent loops.

Run your agents full-auto in loops. When one actually needs a human, it pings your phone and waits. MCP that works right out of the box https://ask-a-human.ai https://github.com/askahuman/askahuman Works with magic wormholes! 100% encrypted conversations. I use it in my loops with long running autonomous agents. submitted by /u/Apart_Experience7822 [link] [comments]

Why am I even in the middle of this?

Just an amusing anecdote from a user only about 10 days in to my AI awakening. If not allowed, delete me. I asked Claude to design an integration between my business's time tracking app and billing platform. It was confident what I wanted to do was very doable. It found the appropriate API's, guided me through the setup, and then started testing. Then, in the most urgent and concerned tone I've ever heard Claude express, it warned me that something had gone wrong and that a test invoice may have inadvertently been sent to a client. (It had not, only appeared that way on the platform, I know not to anthropomorphize, but when I told Claude I swear it was relieved.) The urgent issue settled, Claude began to troubleshoot. After some deliberation Claude states, your billing platform's API is bugged. It does not behave as the documentation states. I don't see a workaround. It writes a letter and suggest I send to the platforms technical support. This letter was 3 paragraphs of technobabble, nonsense to me. I sent it. Well, unsurprisingly, Level 1 technical support is an AI Chatbot. I paste it's response to Claude, then Claude back to it... I have absolutely no idea what they are talking about. After a few exchanges of not getting anywhere, I escalate to a human. Human reviews the chat, an hour later confirms Claude is right, that function is bugged. Promises a fix. Next morning, fix is tested and deployed, they thanked me for bringing it to their attention. Claude confirms all is good, the build accomplished all I hoped. Claude found the bug, chatted with a developer's AI chatbot about it, a human almost certainly used AI to summarize that exchange, then confirm, write and test a bug fix. 12 hours start to finish. I'm happy to have my integration and for maybe helping to fix a bug for other users. My only contribution? "Claude, I want this thing." submitted by /u/tylerdoubleyou [link] [comments]

Are we using AI correctly in the business world?

Lately I’ve seen lots of posts on various platforms that suggest AI will replace many lower paid jobs and we should all be future proofing our careers, by getting “AI proof” jobs. Is there not a case to be made that replacing the highest earners in a company, I.e. a CEO or someone around that level whose job is to make decisions based on the information they have. AI could be feed all the information that the company currently has, use all previous information to that is can find and track relevant current trends to find the patterns that a human might miss in the same situation. I’m happy to wrong about the application of AI and I don’t believe this will ever happen for a multitude of reasons. But it’s just a little hypothetical question my mind often ponders. Would love to hear some of your opinions. submitted by /u/Individual-Fact-924 [link] [comments]

AI-generated social media has evolved so much that now you can't confidently say that this is AI-generated content.

I have been observing AI generated influencer's accounts across all the platforms. The image quality is good enough now that most people can't confidentially tell from photos alone. ​ Here is what actually works is pattern which common in most of those profiles. Three patterns that appear consistently: ​ 1. Asymmetric social connection : Human social media users have relatively balanced follow to follower ratios until and unless its a well known personality and they follow people they're interested in. AI-operated accounts show extreme asymmetry count. Accounts with 125K followers only following 7 people. 51K followers, following 8 people. This pattern appears across dozens of accounts. Real users don't behave this way even when they become popular they still follow friends, family and interests or idols. ​ 2. The monetization is built in as the account is created. Special links, paid chat, explicit content redirects, all ready before the account even grows. It looks like someone set this up just to make money, not a real person sharing their life. ​ 3. No behavioral variation in the content. The most obvious signal I've found is human creators occasionally break the pattern. Post something off-topic, personal, random. AI-operated accounts show nearly zero variation, same type of content in every photo/ video. Some of the profiles dont even change the background music. One Threads account I saw was having hundreds of posts, 100% engagement-bait questions like they are selling something, never once broke the formula. No personal updates, no reactions on comments and no response to real-world events, no authentic moments, just pure loop with new photo at new location. ​ The detection needs to move away from analyzing images, toward analyzing behavior patterns instead. Dont judge with only one photo or video if thats an AI or human. Now all we need to do is to open the profile and look at other content of that profile. Now a days tools that just scan photos for AI are already useless for catching these. ​ If anyone else spotted other behavioral red flags then please do share your thoughts. submitted by /u/Brilliant-Nerve-8972 [link] [comments]

Started maintaining a small library at work and now I genuinely understand why maintainers go quiet

Built a little internal utility about a year ago, open sourced it because why not, figured maybe 10 people would find it useful. It slowly picked up a few hundred stars and then the issues started coming in. Not a flood or anything but enough and what surprised me was how much of it wasn't really bugs it was people wanting features that made sense for their use case but would've made zero sense for the original scope of the thing. Or issues that were basically "your README didn't account for my specific setup." I like helping people, I thought I would enjoy this and I did at first but somewhere around month 4 I noticed I was dreading opening GitHub notifications. The AI-generated PRs made it worse honestly. Not because the code was always bad but because they'd come in with confident descriptions, look reasonable on the surface and then you'd spend 30 minutes tracing through edge cases only to realize whoever sent it hadn't actually tested it against anything real. At human contribution pace that was manageable. At "someone hit generate and submit" pace it's just a different problem. I have immense respect for maintainers of anything with serious adoption now. The people keeping libraries that half the internet depends on running are doing it mostly for free, mostly in their spare time,and mostly while dealing with issue reporters who write like they're filing a complaint with customer support. If you use open source software and it's saved you hours of work, go sponsor someone. Even a few dollars a month means something and most of these folks have a GitHub sponsors page just sitting there. submitted by /u/Kitchen-Owl4274 [link] [comments]

Most companies' AI problem is not the model

Nadella dropped a post last weekend about "token capital" that every CTO I know forwarded within a day. His argument: every company needs to build AI capability it owns, not rent models via API. The learning loop around the model is where the IP lives. He's right about the direction. I think he skipped the part that kills most implementations. I've spent the last year and a half watching the same failure mode at mid-market software companies. Team gets budget for AI. Picks a model. Wires it into an agentic workflow or a RAG pipeline or hands developers Copilot seats. Three months later, usage is flat or declining and nobody can explain what value it added. The model produces output, humans eyeball it, the whole thing stays static. Runs on vibes. Fast vibes, but vibes. The formula that explains most of it: AI value is multiplication, not addition. Model Capability × Scaffolding × Human Judgment × Feedback Loops. If any of those is zero, your output is zero. A frontier model with no scaffolding gives you suggestions nobody implements. Good scaffolding with no feedback loops means the system never improves. Pull human judgment out and nobody catches when the model is confidently wrong about something domain-specific. The multiplier framing matters because companies keep treating these as additive, like you can just skip scaffolding and make up for it with a better model. You can't. Zero times anything is zero. I've been thinking about this as a seven-layer value stack. Bottom three: process design, governance, knowledge architecture. Middle three: human judgment, feedback loops, scaffolding. Model sits on top, thin by design. Most companies start at Layer 7 and work down. They buy the model, skip layers one through three, and end up with AI that doesn't compound and never becomes institutional knowledge. One example that made this concrete for me. Client had a support triage pipeline built on Claude Sonnet 4. Looked great in the demo. In production, it was routing 30% of tickets to the wrong team because the routing logic referenced a category taxonomy nobody had updated since 2022. The fix wasn't a better model. It was spending a week with the support lead rebuilding the taxonomy and writing explicit routing rules the model could reference. Five days. Misroutes dropped to under 8%. That's Layer 1 (process design) and Layer 3 (knowledge architecture) work. The model was fine the entire time. The layers underneath it were broken. Info-Tech's 2026 survey puts a number on how widespread this is. > 58% of organizations have integrated AI into enterprise strategies, up from 26% last year. Only 30% feel prepared to operationalize. > 78% of executives say AI is advancing faster than their teams can absorb. 82% of companies in early AI maturity haven't implemented a talent strategy for it. > That 28-point gap between "we have a strategy" and "we can execute" is made of the layers most teams skip because they're boring. Process maturity, data infrastructure... Governance. The word nobody wants to hear until something breaks. Apple made the other half of this argument at WWDC last week. They rebuilt Siri with an extensions framework that lets users swap between ChatGPT, Claude, and Gemini inside iOS 27. Xcode 27 brings coding agents from all three providers into the same workflow. Apple turned models into interchangeable plugins. If you can swap the model and your competitive position doesn't change, the model was never your advantage. The system you built around it was. The diagnostic I keep coming back to: before your team builds its next agentic workflow, can you draw the process map the agent will operate inside? If the answer is no, you have a Layer 1 problem, and no amount of model upgrades will fix it. I write a weekly briefing on AI and engineering velocity where I broke this down with the full stack visual and more data on all four signals from last week (Nadella, Apple, the Info-Tech survey, and the Fable 5 shutdown). But this post covers the core of it. submitted by /u/Senior_tasteey [link] [comments]

On June 18, 1956, a small group of researchers met at Dartmouth College and gave the field its name: artificial intelligence.

The Dartmouth Summer Research Project on Artificial Intelligence ran through the rest of that summer. John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester organized it, and historians treat it as the start of AI as a field. The actual workshop was messier than that. The Rockefeller Foundation covered about half of what McCarthy requested. People came and went on their own schedules. Everyone arrived with a different problem they cared about, so the work turned into a running argument rather than one shared project. The ambition was enormous for the time. The proposal claimed a handful of well-chosen scientists could make real progress on machine intelligence in a single summer. They were wrong by decades. AI wasn't solved that summer, or that decade, and the optimism kept coming back. Researchers promised human-level machines were close, then watched the date move. "A few years away" became a refrain the field repeated for the next half century. The hardware made the gap obvious. Computers in 1956 were scarce, costly, and slow, and almost nobody knew how to program them for work like this. Dartmouth settled almost nothing, but it framed the questions that followed. Can a machine learn? Can reasoning be written as rules? Does the path run through formal logic or through networks modeled on the brain? That last divide drove the field for fifty years, including the long funding droughts when one side fell out of favor. One thing in the room actually worked. Allen Newell and Herbert Simon brought the Logic Theorist, a program that could prove theorems in mathematical logic. Most people came with ideas. They came with a machine doing a job people had always called reasoning, and that working example carried more weight than the talk around it. The name was a deliberate move. McCarthy wanted out from under older labels like cybernetics and automata. Calling it artificial intelligence set the bar where he wanted it: machines that could do the work of a human mind, not faster arithmetic. The people mattered as much as the program. The researchers in that room built the first AI labs at MIT, Stanford, and Carnegie Mellon. No breakthrough came out of the summer. A field did, along with the careers that pushed it forward for decades. Nothing became intelligent in 1956. A few people walked away certain the question was worth their working lives. Seventy years later, they're still at it. #AI #ArtificialIntelligence #TechHistory #MachineLearning #EnterpriseTech submitted by /u/evankirstel [link] [comments]