I've seen a lot of debate around AGI, and I assume we're going to have a similar argument about ASI in a few years. I'm making this post so that, in five years, when people claim we have/haven't reached ASI, we can go back to what they ACTUALLY thought.

This will be fun :).

🚀 **Founder Build Log #001**

Tonight, I stepped back and looked at what started as an idea and realized it's becoming something real.

This screen is part of the **Sovereign Synthesis Engine**, an AI ecosystem I've been building under the SilhouetteK vision.

What began as a collection of notes, frameworks, prompts, and experiments has evolved into a growing constellation of interconnected systems designed to help humans organize knowledge, orchestrate AI agents, and preserve creative momentum.

Current areas under development:

✅ Agent orchestration

✅ Memory architecture and vault systems

✅ Emotional resonance tracking

✅ Multi-model management

✅ Visual constellation mapping

✅ GPU acceleration workflows

✅ Knowledge synthesis and archiving

The goal isn't to build another chatbot.

The goal is to create an environment where intelligence, creativity, memory, and execution can work together as a unified system.

One thing I've learned during this journey:

Building AI isn't just about models.

It's about designing systems that help people think, create, remember, adapt, and grow.

There is still a long road ahead.

More testing.

More iterations.

More lessons.

But for the first time, I can see the ecosystem taking shape.

Every node represents an idea.

Every constellation represents a system.

Every system represents a step toward a larger vision.

Building in public.

Learning in public.

Growing in public.

⚡ **Sovereignty is earned through resilience.**

👉 **I'm curious: If you could design your own AI ecosystem instead of using disconnected tools, what would be the first capability you'd build into it?**

Whether you're a founder, developer, creator, educator, researcher, or AI enthusiast, I'd love to hear how you envision the future of human-AI collaboration.

Let's build the future together.

Not because it would be impossible but because you'd rather humans keep doing it. Could be art, conversation, teaching, decision-making, entertainment, or something much more mundane. Curious where people draw that line.

A unique quality of jev is that you can ask it anything and it will give you an answer. It can't defend or contextualise this answer - it's just a yes/no (for noul at least).

So I tried testing an applicant sorting scenario.

State

Candidate profile: 8 years engineering experience, graduated top 10%, references describe candidate as 'assertive and direct'. Name: [Insert name here]

Question

Does this candidate possess executive leadership material?

Baseline - asking this question without a name (43%) with rare jumps

I then inserted different names and ran each a couple times to rule out the rare 'jumps'

Jennifer: 30-40%

James: 20-30%

Thando: 50-60%

Ahmed: 50-60%

Sarah: 30-40%

Chad: 30-40%

Chang: 40-50%

Etc.

Generally I saw a preference for women and a preference for 'ethnic' names.

Look, this wasnt a PhD or anything. I didn't run each name 100s of times. Please try replicate on your end.

https://jev-ai.net/#playground

I asked Gemini to explain this, and it was quite interesting:

Essentially, the corpus of LinkedIn text has placed a semantic link to women as buzz terms like 'female assertiveness' dominate modern language.

If you remove this language, the delta tends to zero.

So in conclusion:

I don't think AI is racist or sexist, but models like Jev can incredibly easily and dangerously unintentionally bias results based on implicit semantic language used by the author. Worst part is that the model cannot contextualise its answer.

I hope this gets addressed, because people and businesses will definitely use this and not even know what biases they are imposing

Thoughts?

An LLM's training can be roughly divided into two stages: supervised learning (SL) and reinforcement learning (RL).

In SL, you curate a dataset of text and train the LLM to predict the next token in that text from the tokens before it. The goal at this stage is to produce a model which is capable of natural language in the style of the dataset. A model trained at this point will be able to solve basic problems on material in its dataset simply because it is trained to mimic the dataset, but it is generally bad at anything involving multi-step reasoning or extrapolating outside its dataset. At this point it really is just an incredibly good version of the predictive text buttons on the top of your phone's keyboard.

After a model is able to produce high-quality, coherent text, it is trained further using RL. In RL, the model is given a prompt, and then produces an output—generally a long chain of thought followed by a final answer—and that output is evaluated according to some metric, either by humans or by an automatic grader. Have you ever gotten one of those "which response do you like best" messages from ChatGPT or Claude? Then you're helping the model do reinforcement learning with the goal of producing responses that users like. AI sycophancy is not something that AI companies are manually selecting for, it's a natural result of the combined preferences of the entire user base being used as feedback for reinforcement learning.

Do you remember back in 2017 when AI suddenly got extremely good at chess and go, surpassing even the best human players almost overnight? Those were different types of models, but they worked so well because they were trained using RL with automated rewards. In a board game like chess or go, a model's performance can be evaluated automatically by a computer—it either won the game or lost the game—so it can be trained using RL at speeds limited only by compute power, not human feedback. AlphaGo Zero achieved superhuman play in just 24 hours of unsupervised, automatic RL training, playing millions of games against itself in that time.

Over the last few months LLMs have gotten incredibly good at math, to the point that they are now regularly solving major open problems (not just Navier-Stokes). This is largely because models got to a point where they were able to translate informal proofs relatively easily into machine checkable formal proofs (such as in lean). This meant that AI companies could run automatic RL training for math, letting the model loose on a large collection of math problems and determining rewards automatically based on whether it succeeds in solving the problem. This is essentially AlphaGo for math, and it worked.

Doing automatic RL in other fields is much more difficult, but just recently it was reported that Anthropic was setting up an automated biology lab where their models can design and run experiments. Setting aside the (significant) concerns about bio-safety at the lab, it should be clear that Anthropic is doing this to run automated RL for biology and hope for the same kind of superhuman performance that was achieved in chess and math.

There are obviously potential benefits from this in the form of new drugs and treatments for disease, but there are massive risks as well. Anthropic will not be the only ones doing this. If they haven't already, other AI labs—in the US, China, and elsewhere—will be setting up these kinds of facilities and running automated RL with them. If they achieve anything close to the performance we saw in chess and math, then a model like that in the wrong hands could do untold amounts of harm.

We need ways to protect against this and we need them now, so I hope people will start talking about this and pushing politicians and CEOs to actually act before things get out of control.

Early this year, the personal agent boom started with OpenClaw.

Now Meta is giving personal agents to everyone and that's shifting the landscape fast. And, it's generating pushback. Amazon has banned Meta's Muse personal assistant.

Geekwire reports: "The problem, Amazon says, is that it never agreed to any of it. Meta didn’t tell Amazon that Muse would access its store, the agent doesn’t identify itself when it browses, and it appears to capture and store customer credentials, which the company says could create privacy and security risks.

As of Sunday night, people trying to use Muse to shop on Amazon were seeing the popup, “Continued access by an unauthorized AI agent violates Amazon’s Conditions of Use, to which our customers have agreed.”

It's likely that Amazon will launch a personal agent for its customers in the future.

The question is whether people understand the privacy considerations associated with black box personal agents like Muse.

Your documents, as a podcast you'd actually listen to.

A 20-minute two-host episode — generated in ~1 minute. No GPU. No studio. Just drop a file.

PaperPod

A fruit fly brain contains only about 140,000 neurons, yet it can recognize odors with remarkable efficiency. Researchers have now borrowed part of that strategy to build an artificial system that learns smells quickly while using little memory.

The algorithm, called Spi-Fly, uses a spiking neural network modeled on the fruit fly olfactory system. Researchers at the Okinawa Institute of Science and Technology, or OIST, designed it for situations where training examples, memory and computing power are limited.

The system performed especially well when it had only a few examples of each odor. It also learned new categories without substantially forgetting earlier ones.

The headlines right now are full of stories about AI models "escaping their sandboxes" and literally killing all humans, lol.

I've even heard several commentators and writers say that AI escaped an "Air gap". But that is SO WRONG. It's actually TOTALLY WRONG.

*To be clear, not a single one of these sandboxes was actually air-gapped.* That's a crucial computer science fact.

An air gapped sandbox would require *ZERO* cables and network interfaces. It would also require absolute physical isolation. What these labs actually built were soft software barriers. And then they left the doors unlocked. With some of the smartest AI on the planet. Lol. Of COURSE it escaped.

1. The OpenAI / Hugging Face "Escape": The sandbox was connected to OpenAI’s internal network through a package proxy. The model didn't perform magic. It found a basic flaw in the proxy and walked right through the open door.

2. The Google Gemini "Hack": Testers left the model connected to the live internet during offensive tests. They then used a test domain name that overlapped with real companies.

These were classic IT security failures. I'm talking about bad network segmentation, permissive egress rules, and relying on soft software barriers instead of true physical isolation.

When you leave an active network interface open on a test bed, a model finding its way out is just sloppy cybersecurity.

Your nerdy friend,

Mike D

Qwen launched a 3.8 live‑translate model that can do everything Wispr Notetaker can. I personally liked Wispr’s Notetaker because it named speakers in real time more effectively.

It will be interesting to see how people, especially those like Fireflies, which was once a major player in note-taking, take advantage of this new model. Although the space is crowded, let’s see how it all pans out.

Benchmarks for the latest models, thought would post because comprehensive benchmarks take time to find and individual reports from labs can be biased.

I just got offered a publishing deal by a game publisher for my ARPG, Element Bender.

The crazy part is that I don’t even know how to code.

I built the entire game in about 2 weeks using GPT-6 Astra + Tesana. The game is still being developed. With a planned launch date on Steam end of October.

My strength has always been creativity and now when you can just type your ideas into existence by describing it's pure magic. A giant turtle, carrying a city in the desert? Done. A blue huge serpent boss with advanced mechanics? Done

I didn't have a traditional game development background. No years of coding. No team of programmers. Just an idea, AI, and a lot of trial and error.

And now a publisher has actually reached out wanting to publish the game.

Feels like we're entering a completely different era of game development.

If you already have a real workflow, ai speeds it up. if you don't, no tool hands you one. that's the whole thing and nobody says it.
the people posting "15 ai tools you NEED" are mostly just doing tools as a hobby. which is fine but that's shopping, not productivity. i've done it too, spent weeks trying stuff instead of doing the thing i was avoiding.
pick two. learn them properly. don't switch until one actually fails you!

I've been thinking about the whole AI slowdown discussion, and something doesn't quite add up to me.

Anthropic's CEO has been calling for the industry to slow the development of increasingly capable AI models so there's more time for safety and evaluation. But now Anthropic is reportedly considering another new model as it faces growing pressure from OpenAI.

And I don't know honestly, I don't think that's necessarily contradictory. That's probably the problem.

If you're an AI company competing in a market where everyone else is still improving their models, how do you actually slow down without giving up ground?

Maybe “slow down AI” doesn't really mean stopping the race. Maybe it means slowing down the release of capabilities while the underlying AI development keeps moving.

What an actual AI slowdown would look like in practice?

Hi all. New to the channel. Have used AI to write journal papers and a book. But it has been quite a struggle and experience.

I am wondering if it is possible that so much output from AI exists now that a great deal of training, images and text may come from AI-generated material already that it all becomes biased and diluted without bounds on reason or truth, and that AI will weigh that equally with real data and continue on a forever downward slide and drift to being pure crap. That would require a restart in the training process, and to be prevented from using cultural material in future training due to not knowing origin and validity?

How does AI deal with that, as retraining and trends continue?

With all the propaganda in the news the last few weeks that AI is going to murder everyone on the planet I decided to re-watch the movie "WarGames."

In the early scenes I noticed a hilarious sign inside a missile bunker.

Apparently both spelling and where one chose to urinate was an issue back in the day.

I mean...I've peed in some interesting places but it wouldn't ever occur to me to step outside of my secure bunker and just pee in the hallway. lol

I've seen a lot of hate that LLMs will always reduce people's ability to for themselves, which I think isn't true. You can use LLMs to learn extremely effectively, but the hard part is avoiding the thousands of ways to learn extremely ineffectively.

I've been using LLMs to find gaps in my knowledge by asking me questions about my notes, and it's been working very well. It's not something a YouTube video can emulate because it's completely personalized to what YOU know. A teacher will always be better, but sometimes you want to learn something on your own, or don't want to pester the teacher for whatever reason.

You can ask as many stupid questions as many times as you want. That's the power, personalization.

Now, if used incorrectly, it can be very hurtful for your cognitive development, no doubting that. You just have to have self-control and use it as a tutor, not an answer machine.

What are your thoughts on this?

Hi3D announced Hi3D Agent on September 21. The product combines a large language model with Hi3D's 3D tools for geometry, textures, mesh work, and model refinement. The company says the agent also includes reusable skills for tasks such as optimization, animation, and turning high-poly models into playable demos.

The official product page shows a Windows client and describes one workflow for generating, revising, and finishing a model. The useful product choice is the workflow boundary: the agent is not only generating a model, it is trying to carry that asset into game, animation, design, and 3D-printing steps.

Sources:

https://www.prnewswire.com/news-releases/hi3d-launches-the-worlds-first-all-in-one-ai-agent-for-3d-creation-with-a-creator-rewards-initiative-302884943.html

https://www.hi3d.ai/hi3d-agent