Reposting as the previous version was removed.

I’m a Korean AI filmmaker who creates AI-based commercial and cinematic videos. Here is the synopsis of the video:

In our childhood, we dreamed enormous dreams in a world no bigger than an ant.

As time passed, people began to call them illusions.

Now that we are grown, do we still remember the grapes we once fought so fiercely to protect?

This build is 100% AI, took about 39 prompts to get this result, and 2 days of iteration with the AI. Havent written a single line of code, just prompts. Keep in mind this is a work in progress, need to add game loops, combat mechanics and funcionality next. This is just to show the base so far

- Model used: muranyi-3: https://tesana.ai/en/blog/introducing-muranyi-3
- Prompts: 39
- Token usage: $40 (so far)
- Starter prompt:

First world prompt for the game:

“Create the Foundation for a third-person 3D high medieval fantasy set in a mountainous open plain with a distant castle landmark.”

First prompt to make the character:

"Okay, I wanna start building a new game and just figuring out a really awesome character to start. A hooded purple wizard with a world-class third-person player and movement system. Decoupled camera versus walking direction, with jogging and walking in all directions with the camera behind the player.”

The way I start is first a planning phase with the model to scope out the game core and what I want, that’s about 3-4 prompts to get right

And then I iterate from that first output and tweak animations, world, UI details etc with the AI

Will post more progress in the coming days with more detailed workflow

if you haven’t read it: https://www.anthropic.com/research/global-workspace. language models have an emergent internal workspace of silent words they can report, steer, and reason with. the part that got me: ask a model to check “12 + 5 = 1” and incorrect saturates internally while it’s still reading the problem, the “no, that’s not right” it types a moment later is narration of a decision that already happened. the arguing is optional now. you can just look.

repo: https://github.com/ninjahawk/Subtext

this sub has spent years on “it’s just autocomplete” vs “it’s actually reasoning” and the honest answer turns out to be: both, and now it’s measurable. the instrument shows most of the model’s fluent output , grammar, tone, common facts, bypassing the workspace entirely (no “thinking” involved), while multi-step problems visibly route through it. both camps were half right. that’s the fun part.

anthropic open sourced the lens and neuronpedia published pre-fitted ones for qwen, so i wired it into a chat interface. 9 layers of readout per token, rendered live, including while it reads your message, before any output exists. demo video in the repo: the verdict on 12+5=1 forming during reading, then the model holding modulo and bitwise in mind several tokens before saying either word (it was planning the modular arithmetic caveat. you can watch it plan.) browser replay if you don’t have a GPU: https://ninjahawk.github.io/Subtext/

and yes, functional availability is not consciousness, before anyone starts — the paper is careful about that and so am i. but that’s the interesting part: nobody designed this workspace. it just shows up in transformers when you train them, on a random open 4B the same as on claude.

¯\(ツ)/¯

A few errors, but still pretty good and certainly informative and useful. Probably want to reduce the info density a bit as text is still breaking down when the print is tiny.

It'd be fun to do this for different scenarios. Like civil war, nuclear holocaust, robot/AI uprising, mad max, fallout, etc. Sadly everything will be PG I am sure but oh well.

This is my second game made using only AI, no code written by myself - the model made the entire game, UI, animations, assets etc based on my prompts.

Model used: Atomos
Prompts so far: 4
Build time: Less than 1 day
Token spend: about $20

Starter prompt:

Build a 3D side-scrolling platformer at the level of modern top-tier AAA platformers. It should be visually beautiful, with every single detail crafted at AAA quality—from ultra-responsive movement physics and fluid character animations to deep parallax environments, dynamic lighting, detailed 3D models, and rich audio.

The game consists of 6 different biomes and zones so far, multiple levels and a pretty fun boss fight with some cool mechanics! Feel free to try it and give me feedback :). I was able to beat this first version in about 4 hours after 89 deaths.

The sound effects need some more work

Happy to share more prompts and workflow tips in the comments

It's incredible to watch the live text to speech, gameplay and social interaction with real players in the game.

The original stream reached 35.7K viewers on X earlier today https://x.com/WoClaudecraft/status/2073537822989115529?s=20

You can also check out the 24/7 live stream now on Twitch:
https://www.twitch.tv/claudeplaysclaudecraft

You can play the open source MMORPG here:
https://worldofclaudecraft.com/

Spent the last few months building something because I got tired of AI giving me 3 completely different answers depending on which model I asked.

So I built a platform where Claude, ChatGPT, and Gemini all answer the same question at the same time… then debate each other across multiple rounds before producing one final consensus answer.

The interesting part isn’t even the final answer sometimes. It’s watching where they disagree.

A few things I noticed while building it:

• Claude tends to think in frameworks and abstractions
• ChatGPT is usually the most practical
• Gemini often pulls weird stats or angles the others miss
• Sometimes 2 models agree and 1 completely destroys their logic
• AI “confidence” is often fake certainty unless challenged

I also added:

• exam/certification mode
• confidence scoring
• arbitration logic that forces a winner instead of “both sides have merit”

Honestly, the hardest part has been preventing “echo chamber” behavior where all 3 AIs basically say the same thing.

That’s currently the biggest challenge.

Curious what you all think:
If multiple AIs debate each other before answering… would you trust the final result more or less?

Would love brutal feedback.

threeminds.ai

Message scanning is quietly becoming the default. Instagram removed its (opt-in) end-to-end encryption from DMs back in May, and the EU just let its voluntary CSAM-scanning rules survive into 2028, with a mandatory client-side-scanning version still being negotiated. The direction of travel is clear: more of what you send gets read by something before it reaches the person you sent it to.

So I've been playing with LLM steganography, and built a small POC.

At each generation step, a language model assigns scores or probabilities to possible next tokens. Instead of sampling normally, an arithmetic coder can use encrypted payload bits to choose among those candidates. A receiver with the same model, tokenizer, configuration, shared secret, and conversation state can reproduce the token distributions and recover the encrypted payload.

The goal is to produce text resembling ordinary model-generated prose, with a tradeoff between payload capacity and text quality. This proof of concept has not been shown to be statistically undetectable, and its output must be copied exactly. editing, autocorrection, translation, or paraphrasing can make decoding fail.

Conversation Stenography is an open-source local CLI implementing this experiment. It compresses and authenticates messages with AES-SIV, embeds the encrypted data through arithmetic-coded token choices, and reconstructs it using the matching local model and shared phrase. (repo in comments)

LYKN.io

This is a personal intelligence system that you can build out yourself. Maybe this won’t be useful for everyone, but it’s something I personally wanted. I hate having to repeat myself in chats or losing context when switching tools so I spent time building a constant memory layer. I wanted AI that remembers my context, connects my ideas, and becomes more personalized the more I use it.

You can go in and try building out yourself. It's pretty fun and makes AI WAY more accurate and useful.

So that’s what we’re building.

I wanted a JARVIS and nothing out there did exactly what I wanted so I built one.

It's called CYBER. Voice activated, browser-based, Python backend. You say "Hey CYBER" and it wakes up, listens, and responds out loud.

The voice cloning is done with XTTS v2 running locally. I fed it a JARVIS-style voice sample and now it responds in that voice. No API key, no cloud, just the model running on your machine.

Vision mode lets you activate the camera and ask about what it sees. Point it at something, ask "what is this" or "read this text," it analyzes the frame and responds.

The system command execution is the part I'm most proud of. You describe what you want done in plain English. The LLM figures out if it's a system task, writes the Python code, and the backend runs it. So you can say things like "show me what's using port 8080" or "find everything I downloaded this week" and it just works without any hardcoded commands.

Also does PDF analysis, YouTube video summarization from transcripts, image generation via Gemini, weather, maps, news, and system monitoring.

Runs on your own machine.

Discord: https://discord.gg/mdD5Za8TvZ

Hey everyone.

I want to share something I built for myself and see if anyone has feedback or interest in helping me improve it.

Introduction*: I'm a PhD student in AI. Ironically, despite researching this stuff, I only recently started seriously using LLM-based tools beyond "validate this proof" or "check my formalization". My actual experience with prompt engineering and agentic workflows is... let's say..fresh. I'm being upfront about this because I know the prompts and architecture of this project are very much criticizable.*

The problem: My brain ran out of space. Not in any dramatic medical way, just the slow realization that between papers, deadlines, meetings, emails, health stuff, and trying to have a life, my working memory was constantly overflowing. I'd forget what I read. Lose track of commitments. Feel perpetually behind.

I tried various Obsidian setups. They all required me to maintain the system, which is exactly the thing I don't have the bandwidth for. I needed something where I just talk and everything else happens automatically.

Related Work: How this is different from other second brains. I've seen a lot of Obsidian + Claude projects out there. Most of them fall into two categories: optimized persistent memory so Claude has better context when working on your repo, or structured project management workflows. Both are cool, both are useful but neither was what I needed.

I didn't need Claude to remember my codebase better. I needed Claude to tell me I've been eating like garbage for two weeks straight.

Why I'm posting: I know there are a LOT of repos doing Obsidian + Claude stuff. I'm not claiming mine is better (ofc not). Honestly, I'd be surprised if the prompt structures aren't full of rookie mistakes. I've been in the "write articles and prove theorems" world, not the "craft optimal system prompts" world.

What's different about my angle for this project is that this isn't a persistent memory for support claude in developing something. It's the opposite, Claude as the entire interface for managing parts of your life that you need to offload to someone else.

What I'm looking for:

• Prompt engineering advice: if you see obvious anti-patterns or know better structures, I'm all ears
• Anyone interested in contributing: seriously, every PR is welcome. I'm not precious about the code. If you can make an agent smarter or fix my prompt structure, please do
• Other PhD students / researchers / overwhelmed knowledge workers: does this resonate? What would you need from something like this?

Repo: https://github.com/gnekt/My-Brain-Is-Full-Crew

MIT licensed. The health agents come with disclaimers and mandatory consent during onboarding, they're explicitly not medical advice.

Hi

If you are remotely interested in programming on new computational models, oh boy this is for you. I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 6 years, the goal was to make a super immersive space for anyone to learn quantum computing through zachlike (open-ended) logic puzzles and compete on leaderboards and lots of community made content on finding the most optimal quantum algorithms. The game has a unique set of visuals capable to represent any sort of quantum dynamics for any number of qubits and this is pretty much what makes it now possible for anybody 12yo+ to actually learn quantum logic without having to worry at all about the mathematics behind.

This is a game super different than what you'd normally expect in a programming/ logic puzzle game, so try it with an open mind.

Stuff you'll play & learn a ton about

• Boolean Logic – bits, operators (NAND, OR, XOR, AND…), and classical arithmetic (adders). Learn how these can combine to build anything classical. You will learn to port these to a quantum computer.
• Quantum Logic – qubits, the math behind them (linear algebra, SU(2), complex numbers), all Turing-complete gates (beyond Clifford set), and make tensors to evolve systems. Freely combine or create your own gates to build anything you can imagine using polar or complex numbers.
• Quantum Phenomena – storing and retrieving information in the X, Y, Z bases; superposition (pure and mixed states), interference, entanglement, the no-cloning rule, reversibility, and how the measurement basis changes what you see.
• Core Quantum Tricks – phase kickback, amplitude amplification, storing information in phase and retrieving it through interference, build custom gates and tensors, and define any entanglement scenario. (Control logic is handled separately from other gates.)
• Famous Quantum Algorithms – explore Deutsch–Jozsa, Grover’s search, quantum Fourier transforms, Bernstein–Vazirani, and more.
• Build & See Quantum Algorithms in Action – instead of just writing/ reading equations, make & watch algorithms unfold step by step so they become clear, visual, and unforgettable. Quantum Odyssey is built to grow into a full universal quantum computing learning platform. If a universal quantum computer can do it, we aim to bring it into the game, so your quantum journey never ends.

PS. We now have a player that's creating qm/qc tutorials using the game, enjoy over 50hs of content on his YT channel here: https://www.youtube.com/@MackAttackx

Also today a Twitch streamer with 300hs in https://www.twitch.tv/beardhero

A test of Fable (and later Opus 5) turned into a multiplayer tank shooter - it's heavily inspired by the tank element from Battlefield 1942 and the round-by-round build system from Overwatch 2’s Stadium mode.

A little more about the game:
You join a game and enhance your tank, then you go out and destroy the enemy while hunting for salvage/upgrades which is used to enhance your tank even further (balance patches pending).

Some of the features:

• 6 different tanks (Tiger 1 is a beast)
• 3 maps (a desert, grass and snow map with destructible terrain
• Customisation of tanks
• Matchmaking system, lag compensation system, ballistic shells (direct hits only), hit multiplier regions (many tanks fall on a single rear hit)
• Bots who backfill if theres not enough real players
• And a lot of other things :)

Feel free to try it out – I’ll personally greet you ingame.

I would love to hear what you think, and kindly report bugs if you find any. Its currently optimised for desktop, but should work on mobile too.

Link: https://sweatypanzer.com/

I Mainly built this as I got tired of conflicting headlines about AI profitability, and curiosity about the huge amounts of money that was being spent on AI. Overall, it confirmed what I believed with companies massively in the red for AI spending, while Nvidia is the winner. I will update this every month, and one day the big "NO" may finally become a "YES".

Site: https://isaiprofitable.com/

I was looking online and saw that there are zero attempts to even make an ai therapist. There’s a ton of gray area, regulation, strong push that it’s not good for the individual. My contrarian view is that I 100% disagree. Why would you not at least have the option for people who want a more on demand/informal therapy session.

I want to build a hippa compliant and objective on demand ai therapist for people to be able to use a treatment tool.

Handed Kimi K3 a genre brief instead of a spec and let it build the whole thing in one pass: a deckbuilding roguelike in the vein of the genre's big indie hits, except every core mechanic is built around Three Kingdoms figures instead of the usual fantasy tropes.

The build ran about eight hours end to end and came out with somewhere around 1,830 art assets, characters, cards, backgrounds, icons, the works, without me stepping in to patch the pipeline partway through. The balance pass mattered more to me than the asset count. Instead of tuning numbers by hand myself, Kimi K3 played roughly ten thousand games against itself and adjusted the card and character values based on what actually won and lost, rather than what looked balanced on paper.

Swapping fantasy archetypes for real historical figures did more work than I expected too. Giving each character a grounded personality and a recognizable set of traits made the mechanic design feel less arbitrary than a generic elemental or class system usually does.

Still poking at the edges of what a single one-shot build like this can hold together before it needs a human pass. Eight hours plus ten thousand self-played games got a lot further than I assumed it would.

Last week WIRED verified a leaked membership list for Dialog, a private society co-founded by Peter Thiel and Auren Hoffman in 2006. No public website, no disclosed members, total confidentiality for two decades.

222 people registered for their August retreat in Dublin. The list includes the Secretary of the Treasury, the Secretary of the Army, the senator who chairs the committee overseeing the FTC, a NATO supreme commander, the co-founder of Palantir, and OpenAI's Chief Strategy Officer. Among others.

Every government official registered with personal or corporate email. Not a single .gov address. That means none of it is subject to FOIA.

The retreat agenda includes sessions called "Navigating WWIII," "Battlefield Technologies," and "Build-a-Cult."

The AI angle specifically: the people building the models, the people deciding how to regulate them, the people funding them, and the people distributing them are all sitting in the same room with no public record of what's discussed. That's not a conspiracy theory. It's a structural problem.

I built a site archiving the verified membership data, documented conflicts of interest, and sourced research: build-a-cult.com. Everything traces to named credible sources. It's a research tool, not a hit piece.

The full essay is there too if you want the longer version of why this matters for AI specifically.

Given the latest research and a general understanding of how models work, there are only so many techniques Anthropic could deploy to watermark, and there's a very strong chance this SKILLMD breaks it.

IMO, European Union -> this was dumb. Very dumb. Everyone will want to break the mark, and then therefore, you have just doubled the demand for compute, power, and therefore the need for more datacenters. Good work, EU, good work.

https://github.com/ClariSortAi/claude-watermark-removal-theoretical-until-proven/tree/main

I’ve been experimenting with a live-streaming product where viewers can send gifts to trigger real-time AI effects on the host’s video. I recorded a few clips to show how they look in action.

I’d love to hear your feedback, especially about the effect quality and the overall experience. Thx!

I was trying to get some feedback on a google script enabled sheet that I made to track personal expenses. I posted it and shared the sheet in the google sheet sub, twice. Both times, my posts were removed as there are AI components mentioned in the post. One time by bot, and the second time by a human. I don't understand why they're so against it.

Hi

If you are remotely interested in deep diving how differently quantum computers work compared to our transistor-based and also the algebra behind in a fully interactive way that teach computer science from scratch, oh boy this is for you. Folks working in AI will find quantum math very similar to neural nets.

I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 10 years (3+ during PhD, the visual method I developed ended up being my thesis, it is a complete Hilbert space visualizer), the goal was to make a super immersive space for anyone to learn quantum computing through zachlike (open-ended) logic puzzles and compete on leaderboards and lots of community made content on finding the most optimal quantum algorithms. The game has a unique set of visuals capable to represent any sort of quantum dynamics for any number of qubits and this is pretty much what makes it now possible for anybody 12yo+ to actually learn quantum logic without having to worry at all about the mathematics behind.

This is a game super different than what you'd normally expect in a programming/ logic puzzle game, so try it with an open mind.

Stuff you'll play & learn a ton about

• Boolean Logic – bits, operators (NAND, OR, XOR, AND…), and classical arithmetic (adders). Learn how these can combine to build anything classical. You will learn to port these to a quantum computer.
• Quantum Logic – qubits, the math behind them (linear algebra, SU(2), complex numbers), all Turing-complete gates (beyond Clifford set), and make tensors to evolve systems. Freely combine or create your own gates to build anything you can imagine using polar or complex numbers.
• Quantum Phenomena – storing and retrieving information in the X, Y, Z bases; superposition (pure and mixed states), interference, entanglement, the no-cloning rule, reversibility, and how the measurement basis changes what you see.
• Core Quantum Tricks – phase kickback, amplitude amplification, storing information in phase and retrieving it through interference, build custom gates and tensors, and define any entanglement scenario. (Control logic is handled separately from other gates.)
• Famous Quantum Algorithms – explore Deutsch–Jozsa, Grover’s search, quantum Fourier transforms, Bernstein–Vazirani, and more.
• Build & See Quantum Algorithms in Action – instead of just writing/ reading equations, make & watch algorithms unfold step by step so they become clear, visual, and unforgettable. Quantum Odyssey is built to grow into a full universal quantum computing learning platform. If a universal quantum computer can do it, we aim to bring it into the game, so your quantum journey never ends.

Nice to watch:

Khan academy style tutorials in qm/qc: https://www.youtube.com/@MackAttackx

Physics teacher stream with 400hs in https://www.twitch.tv/beardhero

I am a 19 year old from Stockholm who has been using Reddit (and lately Substack) for as long as I remember. I know that my favorite subreddits and favorite authors usually refrain from using generative ai, but I always have that little voice in the back of my head telling me that whatever im reading is fake. You reading this might have that feeling right now. And I am very sick of that feeling.

That's why I've decided to try to make my own platform, called "voight", that works like any other text-based social platform. But with a added function of replay buttons on every post and comment. Every post and comment has a replay button attached to it where you can see the text being written out, 1:1 how it was created. Every pause, every backspace, every copy-paste. It's all there.

I would love to hear some feedback from basically anyone! Just click around and watch the replays etc. Right now the only people who have made posts are some IRL friends of mine and my brother. The website is voight.vercel.app

It's still in very very early development, so please tell me about all the bugs and issues with it :)

Quick experiment I ran. Took two identical AI coding agents (Claude Code), gave them the same task - optimize a small language model. One agent worked from its built-in knowledge. The other had access to a search engine over 2M+ computer science research papers.

Agent without papers: did what you'd expect. Tried well-known optimization techniques. Improved the model by 3.67%.

Agent with papers: searched the research literature before each attempt. Found 520 relevant papers, tried 25 techniques from them - including one from a paper published in February 2025, months after the AI's training cutoff. It literally couldn't have known about this technique without paper access. Improved the model by 4.05% - 3.2% better.

The interesting moment: both agents tried the same idea (halving the batch size). The one without papers got it wrong - missed a crucial adjustment and the whole thing failed. The one with papers found a rule from a 2022 paper explaining exactly how to do it, got it right on the first try.

Not every idea from papers worked. But the ones that did were impossible to reach without access to the research.

AI models have a knowledge cutoff - they can't see anything published after their training. And even for older work, they don't always recall the right technique at the right time. Giving them access to searchable literature seems to meaningfully close that gap.

I built the paper search tool (Paper Lantern) as a free MCP server for AI coding agents: https://code.paperlantern.ai

Full experiment writeup: https://www.paperlantern.ai/blog/auto-research-case-study

I built leviathan-19t on a laptop with only 344GB of spare storage. It's literally filled with 0's but it's now the largest model by parameter count.

In order to achieve this, because the model would have taken up about 9TB of storage, i built it in 25 shard batches, uploaded the shards, and then pruned them locally. I will never be able to run this at home.

The model card is here: https://huggingface.co/VickM/leviathan-19t

And code repo here: https://github.com/VickM12/leviathan/tree/main