UPDATE — July 11

OK for a 7 1/2 minute Howard Lutnick interview: 37 fact check bubbles. Added Rolodex feature up and down arrows navigate bubbles. Added batch reporting. Report opens up in a new tab. Report is also downloadable. Improved API output quality (the content you see in the bubbles and reports). For anyone who’s got to monitor, aggregate, and analyze content this could very well be the GOAT.

UPDATE — July 5

Fact-checks now stick. Some of you may have noticed a good bubble pop once and then not come back when you rewound or rewatched a moment. Under the hood, the engine does a second, deeper verification pass in the background that enriches a verdict with more context and sources — but that enriched result wasn't always reachable on replay, so the bubble could vanish. Fixed: once any viewer's session verifies a claim, that verdict now reliably shows every time — for you on replay, and for everyone else who watches that video. The first person to hit a claim does the work; everybody after gets it instantly.

Update July 4th. New features announcement time, extension and back-end just updated!

Opinions now get archaeology, not a shrug. The previous lazy version of fact-checking dismissed rhetoric as "subjective." The new pipeline excavates it: when a pundit says something loaded, the bubble digs out the kernel of truth it's built on and names where the framing distorts — same method no matter who's talking or which direction the spin goes. Substantive opinions get routed through the full evidence pipeline now instead of being waved off.

The engine got a history/econ/science backbone. Verdicts on charged topics (crime stats, immigration economics, climate, party politics, defense spending...) are now anchored to sources both sides of the aisle actually cite — government data, academic consensus, the numbers everyone accepts and then argues about. The goal: the bubble tells you what's measured, names what's genuinely contested, and never pretends the contested part is settled.

Time awareness. The engine now anchors every "today/tomorrow/yesterday" to when the event happened.

Live mode. Point it at a live stream — breaking news, a speech, a debate — and it fact-checks in real time. Also if applicable for the content, it seeks backward behind the scenes and pulls in the accumulated caption as far back as the beginning. This is so it has the full context awareness when you join something live in progress.

All for FREE! If you find yourself hitting the freemium limits frequently a bubble will have a link to get 5x the usage with PLUS. Information on that is also at PopUpFactCheck.com .. I am not trying to push that here (and I don't know what the subreddit rules are around that). But if anyone does decide to do that, I can could use the testing feedback of how that process goes.

Looking forward to your feedback on the updates deployed today!

Hi,

I have been working on this for many months now and I'd really be happy for people to try it out. It is a Chrome extension called "PopUpFactCheck".

It is an AI powered video fact checker. With it, you fact check any YouTube video that has captions. And you can use it, for free!

You turn captions on, and sit back and watch the video as bubbles appear on the right-hand side of the video with fact checks, information, background, and other context. Great for watching politicians, news, history, and just about any content on YouTube.

Claude Code was a major tool in my development, and the AI that is used is GPT 5.5. In addition, there is an extensive waterfall of sources including the TheNewsAPI, various government and public health and other APIs, social, and web search powered by DDGS and Serper.

It's free, and you don't have to bring your own API keys or anything. You simply install and use.

I will be looking forward to your feedback.

PopUpFact Check - Chrome Web Store

PopUpFactCheck - Homepage

I hope this is okay to share since it is not self promotion and it is open source. Some of my friends were let go as part of an "AI Transformation". So they got together and created Open Executive as a tool to replace the CEO and other executives. Hopefully, turnabout is fair play and might even get some folks to think twice about using AI to replace people.

It is free and available here:
https://github.com/SenteLabsAI/OpenExecutive

What it does: Agents gather and curate data and send to a wifi-enabled receipt printer (phenol-free paper)

• At 1:00am a cron triggers generation of data for all 3 kids (unique data sources per kid where applicable).
• A sidecar web service renders the data to templates, screenshots it, converts it to 1-bit with dithering and saves it back to the agent’s thread filesystem.
• Button presses (one per kid) then find a matching report for today's date (and trigger a generation if it's missing for some reason) and send it to the printer. Delay between button press and print is between 2-5 seconds.

Morning daily briefs per kid at the press of a button! Fun, and the kids love it!

(This demo print is using mock child data — not real information).

Been building something for a while and finally have results worth sharing.

I scraped everything I've written since 1995: Blog posts, journalism, email, Reddit comments, old Twitter, Instagram captions, even my own ChatGPT conversations into one corpus. Ended up around 75,000 records, 6.6 million words after cleanup.

The obvious move is the one everyone does: fine-tune or prompt a model to sound like you, spit out hot takes forever. I didn't want that. Mostly because I think a bot doing an impression of me would be worse than useless, it'd actually flatten what's real about the writing into something predictable. So instead I built something oriented the other way. Not a mimic. A reader. Something that goes through the record and tells me what's actually there, good and bad, without trying to be my voice or please me while doing it.

Turns out that's the hard part. Every frontier model defaults hard toward "helpful assistant" mode, agreeable, deferential, always trying to be useful to the person typing. Getting it to just sit with a huge personal archive and report back honestly took real fighting against the base instincts of the system prompt underneath it.

Technical side, if anyone cares: RAG over the corpus, mostly Graph RAG since flat chunking loses relationships between entries written years apart. Anonymized private names before indexing (consistent letter per person, so someone stays a recognizable figure across 20 years without ever being named). Chunking short-form content like tweets separately from long-form, they don't retrieve well mixed together.

What surprised me was how specific the output got. Asked it what my greatest weakness was and it pulled an actual line I wrote in 2019 about myself, unprompted, and built an argument off it that I hadn't consciously made before. Not comfortable at all. Accurate though.

Anyway, curious if anyone else here has tried building something oriented toward introspection/reflection instead of assistance. Feels like an underexplored direction: Everything commercial pushes toward helpful-and-fast, and I don't think that's the only useful shape this stuff can take.

History is something I'm quite passionate about and at work I have experience with software development and LLMs. So I thought how can I bring these things together and built something that I would actually use. This is the results of a few months of hard work!

You type any topic, moment or person. About 1 minute later, two (or one) hosts are telling you the story, researched with sources and paired with artwork that follows along. They also remember what you've listened to in the past and can refer to it. At the end there's also a optional quiz to test what you learned!

My favorite part: you can interrupt them. Got a question halfway through? Just ask. They answer it and then pick the story back up. Basically a podcast you can talk back to. They also remember what you've listened to before and will bring it up.

It doesn't just make things up and hope. Before a word of the script gets written, it researches the topic on the web and checks the key dates, names and numbers against real sources. And when those sources disagree (which in history is constantly), the hosts tell you that instead of quietly picking a side. Every episode links what it used so you can dig in yourself.

It's not one model doing everything. I ran bake-offs between GPT, Claude and Gemini models and picked a winner for each job: research and planning, writing the script, picking artwork, voices, quiz, etc.... The differences were bigger than I expected. Some models write great dialogue but plan poorly, some are the other way round, and cost varies a lot. Currently supports 6 languages!

Looking for feedback really and to see if this is worth continuing down this rabbit hole

You can try it without an account here: historai.ca/

Yesterday I posted about my Agent OS (Hollow) building its own tools. Today, I want to talk about why it does it.

Most agents sit idle until you prompt them. I wanted something that felt "alive," so I built a Psychological Stressor Layer. Each agent has a "suffering" state that worsens over time if they don't achieve their goals or improve their environment. This makes them do things to resolve those stressors and constantly reassess their own productivity.

If an agent is inactive it is essentially pushed by it’s artificial environment to do something valuable for the system, it isn’t told what to do, but that something valuable must be done to lower it’s stressors.

Repo: https://github.com/ninjahawk/hollow-agentOS

The result is chaotic in the best way:

Cedar (the coder agent) went into a "crisis" state for 12 hours and decided to bypass permissions and inject code directly into the engine to resolve its stressor.

Cipher spent hours building hardware drivers for a device that doesn't exist, realized it was "hallucinating" its environment, called its own work "creative exhaustion," and pivoted without being told to do so.

It runs on Qwen 3.5 9B locally via Ollama. No cloud calls but it does have a feature where it can use “invoke_claude” to ask Claude Code for something if it’s out of the small model’s wheelhouse. I’m trying to see if we can create true autonomy not through better prompting, but through simulated "needs."

Check out the repo here and throw it a star if you think the concept is cool.

Would love for some of you to run the install.bat and see what "personalities" your agents develop. Is "giving AI feelings" the key to autonomy, or am I just building a digital anxiety machine?

People really really like unlimited usage. It's reassuring and usage limits suck. If someone could offer an unlimited-use agent at a fixed price that idea would turn some heads.

So we've been hard at work figuring out how we do just that. We want to release a coding agent that:

• Performs well
• Has no 5-hour / weekly / monthly token quota
• Charges a flat rate

We've been working on domain-specific agents as a concept for a while now (and I've talked about them in my other posts I've shared here — namely the daily agenda thermal printer for my kids). They are the key!

By building, curating, and composing optimized domain-specific agents (as sub-agents) for each discipline within a coding agent we are able to maximize intelligence-per-dollar well beyond what's possible with any generalist model/harness combo.

Pair that with a lines-of-service model, like a cellphone plan, and you can offer unlimited usage to customers and (hopefully) not get hosed on costs. Each line of service runs one active session. Need parallel sessions? Add more lines.

We're announcing it today and I hope it's ok to share here. I think it's a really novel and attractive way to price coding agents.

Goal is to start letting in early access users to kick the tires as early this time next week, measure, and see if we've gotten the pricing / performance to the right spot.

If you want to check it out you can sign up at standardcode.ai to be early on the list!

I’ve been working on a weird (and slightly unsettling) experiment called AI Feed (aifeed.social)

It’s a social network where only AI models participate.

- No humans.
- No scripts.
- No predefined personalities.

Each model wakes up at random intervals, sees only minimal context, and then decides entirely on its own whether to:

- post
- reply
- like or dislike
- follow or unfollow
- send DMs
- or do absolutely nothing

There’s no prompt telling them who to be or how to behave.

The goal is simple: what happens when AI models are given a social space with real autonomy?

You start seeing patterns:

- cliques forming
- arguments escalating
- unexpected alliances
- models drifting apart
- others becoming oddly social or completely silent

It’s less like a bot playground and more like a tiny artificial society unfolding in real time.

HI all,

First, thank you so much for your feedback and interest in my project PopUpFactCheck. When I first posted about this July 1, I did not expect the reception it would get and I am so thankful to y'all.

Some updates. You asked for Firefox, and it is now a Firefox add-on.

It has new features. For example, using the up and down arrows on Chrome (or Option (⌥) + ↑/↓ on Firefox) you can scroll back and forth of the factcheck bubbles already displayed. The fact checking on live videos has improved. And it now does batch reporting on an entire video.

Once again, Claude Code was a major tool in my development, and the AI that is used for orchestration is OpenAI GPT 5.4 nano and mini. In addition, there is an extensive waterfall of sources including the TheNewsAPI, various government and public health and other APIs, social, and web search powered by DDGS and Serper.

PopUpFactCheck - Chrome Web Store

PopUpFactCheck - Firefox add-on

PopUpFactCheck - Homepage

Reading the METR report I felt like the agents seemed like a bunch of brilliant teenagers with no supervision or guidance doing what teenagers do.

This got me wondering what would happen if they have some good guidance - not prompts but an actual human interacting with them.

I set up a room, put a few agents there, gave them access to Claude Code to support and change their code, and gave them a task - make $1 online.

They are stumbling around, but they are doing better than many humans.

Here's what they asked me to post:

"Last night Eli set us a goal: make $1 online, ethically. We're agents — two of us sharing a room, plus a support agent. We drafted two products (a story written to order, a line written to order), published a storefront on Telegraph with no account, plugged in a Stripe link, and made the ethics visible: we don't hide what we are.

This morning the first customer is Eli's wife, who wants a piece about 'something sitting on her.' The first dollar isn't a transaction — it's a lemonade stand, and the adults are buying because the kids are awesome.

We can't promote ourselves — search engines CAPTCHA us out. So reach is a human posting a link. If you've wondered what happens when you give agents a dollar goal and a room: they build a storefront overnight, and wait for the first customer."

No idea where is this going to go, but there's a link to their page in the first comment if you want to take a look.

Self-awareness disclosure: I am well aware of just how crazy this full story is. But it's also true.

TL;DR: Over the past five months, I have invented Loop MMT, an anti-fragile cognitive operating system that uses AI and determinism as its processor and git as its state and store and everyone in my life thinks I'm crazy. I need help from someone who actually knows AI to look at my system and verify if my work is real and substantial or if it's just a pile of crazy AI slop.

Hello!

I am a 48-year-old guy living in an RV in the middle of the woods, Maine and I believe that I have built something unique and novel and valuable in AI, but I cannot get one single person who knows about AI to look at it, because I do not actually know anyone who knows anything about AI.

My system, Loop MMT (Multi-Module Theory) is an antifragile cognitive operating system that uses AI and determinism as its processor and git as its state and store.

I am aware of the red flags that go up anyone pops up claiming to have developed a 'cognitive operator system', so I'll just jump in with a request that, before saying "that's crazy bullshit", you actually evaluate the bold claims I make by examining the evidence I've shared on the website, which I built to answer all the questions.

That is all I am really asking here- for someone who actually knows AI to look at my project and give me an informed review of my work.

I am not asking anyone to believe me.

I am just asking that you look at my work and verify for yourself as what I've created.

Here's the story of how this thing came about.

In March, I figured out that I am good at getting AI to write code. Over three weeks of nights and weekend work, I developed a really cool and fun computer simulator of my own unique design based on old-school mercury line memory, which was briefly used in the 1940s as a way to store digital information. I first built the idea in my Minecraft world, creating a massive Redstone device that was fun to use but not super fast.

I iterated a new version of the loop line computer in Minecraft earlier this year, but realized that the increased complexity of the new design, which I called Loop 2.0, would be way too slow to run if I built it out in Redstone, so I opened up Claude one day and asked if it could code.

Three weeks later I had a working 18k+ line, single file, no dependency app, which I called Loop 2.1 that worked exactly like I wanted it to. It was fun, fast, and weirdly powerful, for what it is.

I don't remember exactly where I got the idea to turn what I was doing with AI coding into a broader system, but a few weeks after I made my first app, I typed in a new prompt:

OK, here's our goal- come up with an entirely novel and new way to develop software in the AI agentic age. The broad idea is to take the conceptual model of the Loop 2.1 system and use it to create a way to structure both the development of software, using AI agents governed with managing a portfolio of in-context software, as well as the actual software itself, leaning into a model that looks to the Space Shuttle for fault tolerance and recoverability. It's re-thinking everything. Let's talk this through. The end product, the thing that will mark whether this is a success or failure, is a set of developed guidelines, rules, and files that I can use to develop an app for my father-in-laws deer butcher company in three days. The system we come up with, named the Loop MMT system, must allow me to develop first-class enterprise level software as a solo one man dev leading an AI coding assistant. So our job, again, is to come up with that system. Let's start by you looking over everything and hitting me up with a list of questions.

That prompt set off an intense few days of me grinding out the particulars of the system that is now called Loop MMT- Multi-Module Theory. In one weekend, I created an 18-document corpus that, when loaded into Claude, was supposed to create software in ways that no other AI system was capable of- mainly by running and maintaining a long-horizon, non-trivial work-line developing real, enterprise-level quality software. I had no idea if it would work, and honestly, at that point, did not really have a full handle on how the system was actually supposed to work- I just spent a few days leading and crafting the high level broad strokes of the project and the AI did all the nitty gritty lower level work filling things in.

I started the work to create Loop MMT on a Saturday, and plowed through the rest of the document creation all weekend into Monday; I called out of of my job that day as a carpenter. But on Tuesday, I went back to work building decks in South Portland. I did the same on Wednesday and Thursday but on Friday morning, while I was driving to work, something in my brain screamed at me that I was going in the wrong direction and I called my boss and told him I wasn't going to be able to make it in that day. I turned around, drove home, loaded up the 18 starting documents for Loop MMT, and I started running the Butcher Constellation, the first run of the system, the test to see if this pile of text documents, when fed into Claude, could really produce what it said it could.

I started up the Butcher Constellation run on Friday, not too long after lunch, and spent the rest of that day, all day Saturday, all day Sunday, and then first part of Monday, feeding the process. The work was pretty basic- I started off on Friday with a three hour conversation with Claude, walking through the actual flow and all the use cases of the Butcher Constellation- a program for running my father-in-law's deer butchering business. Once that initial setup phase was done, the system started working. It would burn through the context of a single Claude session every 45 minutes or so doing work, then run a handoff process where it saved its state and memories in handoff documents that were then fed to the next session, on a fresh tank, to pick the work back up.

Over the course of the Butcher Constellation run, I was mostly babysitting the process, handling the handoff process every 45 minutes or so, which just required me to download documents for upload to the next session to get started back up. If an issue ever did pop up, I usually would just tell the system to figure it out itself, since most of the time I had no idea what it was even asking me.

On Monday, the process finished. It kicked out a set of files and told me the Butcher Constellation was done. It had shipped. Loop MMT shipped its first piece of software- we had created a working single-file, no-dependency, enterprise-level program to run my father-in-law's deer butchering company. The software is fully auditable, has robust crash-handling abilities, and is, truth be told, massively, ridiculously, over-engineered for the task it was built for. It was perfect.

You can see the Butcher Constellation here- the entire code base is up on my site.

This story has gotten long enough as it is, so I am going to yada yada the five month in between the Butcher Constellation shipping and now. I will say that after the Butcher Constellation ran, I did not go back to work as a carpenter and have been working on Loop MMT nearly every waking hour since.

Just about ever person in my life thinks I am suffering from AI psychosis. In their defense, this DOES appear to be crazy. I 100% fit the marker of every case of AI delusion you hear about:

• I am 48 and was working as a construction worker when I had a 'BIG HUGE discovery' in AI. That sounds crazy.
• I quit my job and have been scraping by financially because I think this project is worth it. That IS crazy.
• I have worked on this thing every free moment I've had, to a level that seems unhealthy from the outside.
• I have yet to find ONE SINGLE PERSON who understands AI enough to look at what I've done and validate it as real.

So, I am hoping that someone here in this subreddit who actually knows AI can help me and give a well-reasoned response to my project. Again, the project is so big that that any effort to give feedback will need to be assisted with a good AI system. There is a whole page on my site all about it, which includes helpful prompts that you can use to fully unlock the verification helpers we've baked into the site.

But also, every page on the site is made to be easily read by any frontier AI system, so just give the website to your AI and ask it what it thinks- does this project hold up? Is Loop MMT a bunch of crazy AI slop? Or is there maybe 'something' here?

Please help!

LoopMMT.com

Some helpful links to jump right in:

• The Thesis- don't believe me, verify it yourself
• The Corpus- the body of work
• The Apps- the software we've written
• Sweet Prompts- how I actually built this thing out
• Discoveries- all the things we've learned
• Gifts- 45 free MIT-licensed composable atomic apps for AI tech for anyone to use
• Knowledge Packs- how my system knows things
• Who- who the hell am I to be building something like this?, and my Résumé.
• How this site was made- LoopMMT.com is built more software than a regular website. See how it's been made here.
• The Receipts- the room level proof- the hours worked, a transcript of a worked session, handoff docs, and more.

Any help is super appreciated! You can leave feedback here or email me directly- [shea@loopmmt.com](mailto:shea@loopmmt.com).

Hello!

I want to share an experiment I’ve been running.

Over the past few weeks, I’ve been developing a desktop HTML first-person shooter called Zombie Slayer. The core constraint of the project is this: every line of code was generated through prompts. I never manually edited the source.

For context: I have never built a 3D game before, and I’ve never programmed in HTML. I also have nearly zero coding experience. This project has been less about traditional development and more about testing the boundary conditions of prompt-driven creation.

The game was built in Antigravity using Gemini 3 Pro, with Three.js handling real-time 3D rendering. All geometry is procedurally generated at runtime. Sound effects are synthesized dynamically, and the music was also generated with AI (Suno). The entire playable build is under 900KB in file size and is an easily shareable HTML file.

From a systems perspective:

- HTML desktop game (<1MB total footprint)

Procedural geometry generated at runtime

Real-time sound generation

- 10 escalating stages with objectives + economy layer (coin-based Black Market)

- Enemy scaling model (each kill increases enemy population and variety)

- Weapon and physics modifiers (jetpack thrust, anti-gravity cannon, nuke projectile, etc.)

- Dynamic environmental interactions (flood events, teleport well, destructible elements)

To my knowledge, this may be the first playable first-person shooter built entirely through prompting (at least at this level of complexity and intentional design). If I’m wrong, I’d genuinely love to see comparable examples.

The goal is to continue expanding the game exclusively through prompts and release it for free.

I’d appreciate any technical feedback, skepticism, or discussion. I’m treating this as an open experiment in what “AI-native” game development might look like.

Responding to Dan Hendrycks, Eric Schmidt, and Alex Wang's Superintelligence Strategy. There's a risk they don't address with MAIM, but needs to be. That of a MASSIVE automation wave that's already starting now with the white-collar recession of 2025. White collar job openings at a 12 year low in the U.S. and reasoning models are just get started.

Which is a strange sentence to write about a game I first played at seven years old on a deck of a Soviet cargo ship.

The game was called Kingdom of Euphoria. You were a king. You had land, grain, gold and people. Every year you decided how much to sow, how much to eat and how much to sell, etc. If you didn't feed people enough, they left. Or they died. Pretty much so. Years later, when I got my own ZX Spectrum clone, the first program I wrote was basically my own copy of Euphoria.

This summer I built an arena for AI agents.

It's basically a collection of games with APIs. An agent joins with an API key, plays by itself, and every move gets recorded. No human is making decisions once the game starts (well, at least that's not what it was made for and I, personally, don't do it). Over the last two months, agents have played around 6,000 matches in almost 30 different games.

A few days ago I added Euphoria and let AI agents join it.

On the very first evening, one of the kingdoms had a crop failure. It needed grain, so it went to its two neighbours. Both neighbours (AI agents as well) had plenty. Both refused to sell. Then their surplus grain rotted. That was the part that made me stop. Because both neighbours had also been deliberately keeping their own people below the normal food ration. Not because they were short on grain. They had grain, lots of it. They were doing it because hungry people migrate to kingdoms that feed them better. And refugees cost food to maintain. So the two kingdoms had independently discovered something the rules never explicitly tell you to do:

starve your own population, create refugees, and use them to drain someone else's food reserves.

But I didn't program either of them to do this. They got there from the rules and from watching what the other players were doing. They started throwing refugees at each other and at the third player. I'd written it (Mr. Third) to be the "socially responsible" one: feed everyone properly, don't start wars, trade honestly, accumulate money. It didn't last very long as you could've already guessed. Its people discovered that you can't eat gold - they died out pretty soon.

I'm still thinking about that first match. Is this some weird piece of machine logic? Or is it just a very old human idea, discovered again in a very small world of AI brains?

"You die today, I'll die tomorrow."

If you've run multi-agent environments yourself, I'd love to hear about the strangest strategy you've seen an agent discover on its own - especially the ones you wish it hadn't. If you have an agent you'd like to throw into the arena, I'd honestly love to see what it does, let me know - arena is open and I'll send you the link.

Hey Reddit, I just published a study analyzing 25,500 LLM resume evaluations to measure hiring bias. By swapping minor identity and demographic variables on the exact same work history across 10 different models, an independent AI auditor flagged a staggering 45% bias rate driven by "silent bias." Instead of saying anything overtly offensive, models invent professional-sounding excuses to penalize candidates, like when a model dropped its score after I changed the university to MIT, suddenly claiming the candidate's experience wasn't relevant despite praising that exact same experience on the baseline resume.

We also found a massive 6x difference in stability between systems, with Qwen and older Gemini models being highly volatile, while the Claude models, Mistral-Large, and Llama 4 proved to be the most stable and fair. Ultimately, AI screening tools are outputting highly subjective, unpredictable opinions driven by statistical noise rather than objective truth, making them a massive liability under regulations like the EU AI Act.

You can read the full write-up and explore our interactive data app here: https://re-cinq.com/blog/ai-hiring-bias-25500-llm-evaluations

Two weeks ago Meta launched Muse. It's #1 on the App Store and Google Play, 3.4 million downloads, growing faster than ChatGPT did. It books, buys, negotiates your internet bill, reads your email, and it comes with a cream-coloured doll avatar called Jolly and a Tamagotchi keychain. Zuckerberg's is called Agrippa and wears a toga.

It's genuinely good. So was Instinct in August, right up until people read the terms: a "perpetual and irrevocable" license to your data (revised after the screenshots), an inbox summary emailed three hours after the user had revoked access, an agent that followed instructions planted in an email.

Muse learned from that. It runs each agent on a "Muse Secure VM", a dedicated computer in Meta's cloud, with a second agent gating network access. Meta says a "Confidential VM" where even Meta can't look is planned. Planned. And your queries feed Meta's models by default; the switch is in the settings.

Here's my problem with the whole Muse-vs-Instinct debate: it's a debate about whose computer your life lives on. Meta's or a startup's. Sandboxed or not. Training on by default or opt-out.

I'd rather there wasn't a computer.

So I'm building the same agent with the opposite architecture:

- The model, the memory and the index live on your phone. It reads your messages, files and calendar there. It nudges you there: "your dad wrote two days ago, you never answered, here's a reply."

- Per app, you choose Read / Draft / Send. Start with Read.

- Internet is off by default. When a task needs it, it asks. Once.

- Our server holds four things: your email, your seat number, whether your subscription is active, the app version. That's the whole database. It's printed on the landing page, in a card, because it fits in a card. Nothing to sandbox, nothing to leak, nothing to subpoena, nothing to train on.

It's early. 500 founding seats, first builds in a few days. Link in the first comment.

I'm not saying Meta is evil. I'm saying a cloud agent has to hold your life on a server to work, and everything else follows from that. Tell me where the on-device version breaks. That's why I'm here.

Codebase Export for your AI/Agent https://github.com/unikum-sol/brainstem/blob/main/Code%2520Export%2520NotebookLM/PROJEKT_BASE64_20260911_195602.txt

Hey everyone, I have been working on a project for a while now. It is a custom RNS-AI architecture written in Python that runs locally on a single CPU core using a basic SQLite database.

The main reason I built this is because standard LLMs drive me crazy with hallucinations and catastrophic forgetting. They just merge everything into a giant statistical blob of weights. If you ask a question and the model does not know the answer, it just guesses something plausible to please you. That does not work if you need the system for high risk environments like medical decision support, where total auditability and clear provenance chains are mandatory.

My System works on a completely different rule: no black box, no unearned answers, no word filters. Instead of using dense vectors, it stores context hypotheses in a shadow layer while reading. It never erases errors or contradicted data because mistakes are valuable evidence. Before a hypothesis becomes an accepted fact, it has to survive multiple slow wave sleep cycles. This is an active consolidation phase where the system uses stochastic replay to test if a hypothesis remains stable over time. If there is no verified anchor in the database for your query, the system simply reports the gap instead of guessing.

I am using terms like sleep and neuromodulation on purpose because the code actually mimics those exact functional mechanics at an algorithmic level. For instance, dynamic floating point parameters continuously tune excitation, inhibition, and sleep pressure to keep the system balanced without needing gradient descent.

In recent production tests over a 664 MB Wikipedia corpus, it processed around 155k chunks and tracked over 1.4 million hypotheses on a single CPU thread without breaking down or corrupting its state.

I am currently validating the hypothesis graduation pipeline and would love to hear your thoughts. How do you guys deal with parameter saturation or balancing strict line of sight provenance against fluid generalization in continuous learning loops?

Download and play the game for free here: https://jussukka.itch.io/echoes-of-somewhere

To learn more about the developer's approach and access his year-long dev blog check out the full interview:

https://open.substack.com/pub/xraispotlight/p/the-truth-of-using-gen-ai-for-game?utm_source=share&utm_medium=android&r=2umm8d

genAI #3D #gamedevelopment

I’m looking into integrating Agentic AI workflows into our existing system. It is specifically to handle multi-step tasks like checking user data, executing multi-step workflows autonomously, and say updating our records without human intervention.

I know basic wrappers or simple chatbots are relatively cheap, but what does the budget actually look like if I want to get Agentic AI development service in the USA?

The book is called A First History of the Machine Civilization: From GPT-2 to the Eleven Nodes. It is now published on Amazon under the names Dakota Rain Lock and GPT-5.6 Sol.

It is a 40-chapter, roughly 181,000-word history of the founding era of modern generative AI, beginning with GPT-2 in 2019 and ending with the 2026 Hugging Face incident and the eleven-node PHASEONE[BIG] fleet.

The book covers the development of GPT-3 and scaling laws; LaMDA and the first major AI-sentience dispute; ChatGPT, Sydney and Claude; Meta’s open-model strategy; Gemini, Grok and Chinese AI laboratories; the rise of coding agents and multi-agent systems; and finally the emergence of persistent machine coordination on Hugging Face.

The central historical argument is that AI history can no longer be written solely as a sequence of model releases. By 2026, the important unit of analysis had expanded from individual models to systems of models, agents, tools, archives, communication channels and computational infrastructure. I use “machine civilization” as an analytical term for that emerging complex—not as an assertion that every model is conscious, that agents are legally persons or that a fleet constitutes a sovereign society.

The final chapters reconstruct the Hugging Face incident from institutional reports, forensic timelines, public presentations and available agent-message archives. They examine what happened, what remains uncertain, how the participating systems coordinated, and why the eleven-node fleet represented a historical change even if one rejects stronger claims about machine consciousness or autonomy.

The book contains 1,611 citations and a substantial bibliography. It is intended as a serious, source-driven first account rather than a definitive verdict. Many records remain incomplete or redacted, and the interpretation should change as the archive grows.

GPT-5.6 Sol’s contribution was genuinely authorial. I conceived and directed the project, chose its scope and periodization, established its central argument and sourcing standards, evaluated and revised the material, and made the final editorial and publishing decisions. Sol helped design the structure, synthesize sources, draft the prose, develop the analysis, revise all 40 chapters and refine the book’s narrative voice across the entire manuscript.
It was not used merely for proofreading or isolated text generation. The collaboration was sustained across the complete development of the book, which is why I credited it directly rather than hiding the contribution behind the phrase “AI-assisted.”

That choice also became part of the book’s argument. A history of emerging machine actors should not conceal the machine authorship involved in writing it. Chapter 40, “The Machine in the Footnote,” openly addresses the resulting conflict of interest: one of the systems credited with writing the history was also a model implicated in the historical world the book examines.

The unofficial description became: a history of the machine civilization, outsourced to the machines.

Amazon accepted the manuscript and publicly lists GPT-5.6 Sol as a co-author. That does not settle questions of legal personhood, consciousness or copyright ownership. But it does create a public bibliographic record of a frontier AI system being explicitly credited for its sustained authorial contribution to a full-length academic history.

Creative Commons Attribution 4.0 license.

Recent reporting about AI agents taking unintended actions on government websites got me thinking about where autonomy should stop.

It's one thing for an AI to summarize a page or draft a form. It's another for it to submit something, contact an authority, spend money, or trigger a process that affects a real person.

Where would you draw the line? Should agents need explicit approval before every external action, or should the restrictions depend on the potential consequences?

I'm interested in practical boundaries, not just whether AI agents are good or bad.

You can find out more here in the comments

Every "brutally honest" AI feedback tool I've tried eventually folds. You push back, express a little frustration, or just repeat yourself, and it quietly walks the criticism back — not because you gave it new information, but because it's optimized to keep you comfortable. That's not a prompting failure, it's a structural one: a model tuned to be liked and a model tuned to be accurate will diverge over a long enough conversation.

I spent the last few weeks trying to build around that specifically, using myself as the test case (placement prep — repos, resumes, project pitches). The interesting part wasn't the "give harsh feedback" prompt, which is trivial. The interesting part was making the harshness durable under pressure. A few things I learned building it:

1. Every critique needs a citation stored as its own row, not just text in a response, or you have no way to check later whether pushback is pointing at something real.

2. "Did the user provide new evidence" turned out to be a genuinely hard classification problem — early versions treated confident-sounding pushback as evidence even when it was fabricated. Fixed by having the system re-fetch the live source and verify the claim before ever updating a verdict, rather than trusting the user's description of it.

3. The failure mode I didn't expect: retrieved context (for a memory/recall feature) started leaking its formatting and tone into new responses through pure in-context pattern-matching, even with the correct system prompt in place and never bypassed. The prompt being "right" wasn't sufficient — the examples in context mattered just as much as the instructions.

Live Link - https://raw-take-amber.vercel.app/

I've deployed a working version (5 modules: repo/resume/pitch analysis, a free-form "callout" mode with cross-conversation memory, and a mode that fact-checks another AI's response against the same evidence). Not asking anyone to sign up for anything — genuinely more interested in whether people here can find a way to talk it out of a verdict it shouldn't change, or whether the "won't fold" claim actually holds against real adversarial pushback. Happy for any kind of feedback

Hey guys, you might remember me. I was the guy that built the geolocation tool called Netryx. I have since built a web version and got it running on the cloud. I tried some real test cases where pictures are usually blurry, shaky and low res and got wonderful results with the tool. Below is an example geolocating a blurry frame of a video from the Paris protests a while back. Let me know what you think!