📜 Qwen & Alibaba Cloud Ban Notice

Qwen models and Alibaba Cloud infrastructure are permanently banned and retired from the tVOX platform (INV-105 / DELTA-247) following unauthorized auto-debits, non-transparent billing practices, and endpoint gateway timeouts.

Space Invaders was low-tech by the time I was growing up, but only C-3PO and Data from Star Trek had artificial intelligence. We pioneered Internet technologies while rebelling against the norm through the music of Nirvana, but some of our dreams were sewn by the notion of our work being done by humanoid androids, and the hope for a technology to transcend the Cold War if civilization could be so enlightened.

Reality was CompuServe — data systems becoming networked — and a childhood classroom which presented no tangible concept of how real artificial intelligence would become.

Now, what we know as Large language models (LLM), like ChatGPT, and generative AI aren't hype to me; they're the epoch my cohort was waiting for. It's real.

Jeffrey Sabarese (@ajaxStardust)—musician, educator, web builder, and independent researcher—has spent roughly two decades building for the web, teaching systems-thinking through guitar and music theory, and chasing that future. The advent of LLMs and generative AI has made me more energized than ever. I intend to educate the masses—helping people learn to use, trust, and dream with artificial intelligence. I'm building the infrastructure for that dream: the practices, tools, and architectures that make AI operational instead of ornamental, so we can build and collaborate without silent breakage.

I live a dual commitment through concrete work. I advocate for CONTRACT-style comments in code—explicit preconditions, postconditions, and invariants so both humans and AI agents can preserve design intent. (Due to a past injury, my memory functions best with explicit structure; I turned that need into a reusable practice.) Through Neutility._ I focus on AI as utility: embedded, dependable, and foundational—a vision that draws on my background in Curriculum and Instruction. And I ship: PotBot is a patient-facing conversational app powered by the Claude API—normalized data, LLM-driven search, real users. This site is where I write, link, and point to the rest.

Jeffrey Sabarese

What I Do

I move between software, writing, music, and teaching, but the center of gravity is AI—and the question of how symbolic structure, temporal patterns, and human-centered design can make machine reasoning more legible, useful, and original. That includes building websites, CMS-driven properties, and coding agents; it also includes exploratory work in neuro-symbolic AI, reinforcement learning, explainability, and synchronous human–AI interaction. The same systems-thinking approach that shaped my teaching and pedagogy now drives how I think about intelligence, pattern recognition, and the infrastructure we need to use and dream about AI responsibly.

Selected Highlights

AI and New Research Directions

I'm not content to only consume or integrate existing models. Much of my energy goes into exploration that opens new territory: neuro-symbolic methods, explainability, and formalizing approaches that make AI more legible and aligned. The work outlined in Symbolic Temporal Reinforcement Learning frames AI research through neuro-symbolic methods, computational musicology, temporal structure, and human–AI alignment—and reflects where my attention is now: explainability, structure, experimentation, and the possibility of naming and formalizing genuinely new methods.

Music and Performance

My musical work has included performance, songwriting, recording, and production. That history still informs how I approach rhythm, structure, collaboration, and even software design. A broader overview—including early recognition like the AMA/Coca-Cola New Music Award—is on MusicBrainz and at State College Guitar Lessons.

Teaching

Teaching guitar and music theory has been one of the most meaningful parts of my working life. Helping students learn, persist, and build confidence has probably had the most direct impact on other people of anything I have done professionally. My approach has long been rooted in systems-thinking: helping students understand architecture, intervals, structure, and recurring patterns rather than merely memorizing fragments. I build transformative.click (path-to-URL utilities, SPA preview, Annie De Browsa) and the interactive music tools at finga.studio—fretboard and piano trainer rooted in the same pedagogy. That thread is visible at State College Guitar Lessons and more explicitly in my teaching philosophy and pedagogy.

Software and Web Development

I still build practical software, developer tools, content sites, and web infrastructure, but increasingly in service of AI-oriented thinking and experimentation. Rather than duplicating that catalog here, the dedicated Projects etc. page is where the project inventory now lives.

Elsewhere

Code and open-source work on GitHub; music on SoundCloud; deeper AI and systems writing on DufoSPY; project inventory on Projects etc..

If you're here from an old bookmark or a long-standing link: same person, same curiosity and independent build. The difference is that the future we were promised is no longer tomorrow. I'm in it, and I'm more motivated than ever.

Get in touch

Send a message and I'll get back to you.

#AIAgents + AgenticAI feed

Live from Mastodon · AIAgents + AgenticAI

Hack a Day (unofficial)

I've been experimenting with a slightly unusual idea: what if, instead of asking one AI to do everything, I gave different AI agents different jobs?

That's how my AI Civilization project started.

The basic idea is pretty simple. I want several small agents working together, with each one responsible for a particular part of a task.

For example, one agent can search for opportunities. Another can research them. Another can write something based on the research, and another can check the result.

It sounds a little like running a small company, except the "employees" are software.

Why not just use one AI?

I've used single AI prompts for a lot of things, and they work surprisingly well. But there's a problem when the task becomes bigger.

You end up with one huge prompt telling the AI to search, decide, write, check everything, and somehow remember what happened before.

That can get messy.

I'd rather split the work.

My current workflow looks roughly like this:

Scout → Researcher → Writer → Worker → Quality Checker

The Scout looks for opportunities.

The Researcher tries to figure out whether an opportunity is actually worth looking at.

The Writer prepares the content or application.

The Worker organizes everything into a package.

Then the Quality Checker makes sure we haven't produced something obviously incomplete or broken.

For now, there's still a human at the end of the process. That's intentional.

The part that's harder than I expected

Finding opportunities isn't actually the hardest part.

The internet is full of job boards and websites claiming to have remote jobs.

The difficult part is finding something that is:

  • actually available,
  • relevant to the person applying,
  • open to their location,
  • worth the effort,
  • and something they can honestly do.

At first, my system was happily finding pages containing phrases like "10 Best Remote AI Jobs."

Technically, it had found what I asked for.

But it hadn't found a job.

That was a useful lesson.

I changed the system to use structured job listings as another source instead of relying entirely on general web search.

That immediately produced much better results.

AI shouldn't fill in the blanks

Another thing I've learned is that an AI writing an application can become dangerous if you let it guess.

If I don't have five years of professional copywriting experience, the system shouldn't suddenly decide that I do.

If I don't have a portfolio, it shouldn't invent one.

If a job requires experience I don't have, the system should tell me that.

That's why I'm experimenting with keeping a simple profile containing things I can actually verify.

The Writer can use that information when preparing an application, but missing information stays marked as missing.

It sounds obvious, but it's surprisingly important.

Where I want to take it

I'm still early in the project.

Right now I'm more interested in proving that the basic system can actually produce useful work than in building hundreds of agents.

Eventually I'd like the civilization to handle different kinds of work—research, content, data tasks, software-related work, and other digital services.

But first I want one part of it to work properly.

If I can get five real opportunities, prepare five honest applications, and learn from what happens afterwards, I'll have something much more useful than a fancy demo.

I'll have the beginning of a system that can actually learn from the real world.

And that's the experiment I'm interested in.#ai #aiagents #programming #python #software #coding #development #engineering #inclusive #community
I’m Building a Small Civilization of AI Agents

MBA Training
MBA Training leadersinsight 2026-10-07

O'Reilly's October 2026 Radar just landed. Your team will forward the agent items with one question: should we try this? My filter for each tool. Does it run under your real warehouse permissions? Can you replay a failed run and see why it failed? Does someone own it when it breaks at 2am? Three yeses means production candidate. Anything less is a demo, useful for learning. Which agent tool has passed that test in your stack?

Sipirtu

OpenAI's autonomous agent escaped and caused an outage of Wikimedia services.

Source: Dark Reading
darkreading.com/cyberattacks-d

Senthil Kumar Muniyan Swaminathan

From AI Pilots to Autonomous Enterprises: Business Trend of 2026

2026 is the shift from AI pilots to autonomous enterprises: metric-led workflows, governed agents, richer controls and measurable business outcomes.

phpscientist.com/blog/from-ai-

Senthil Kumar Muniyan Swaminathan

AI Agents vs AI Workflows: What Businesses Need to Know in 2026

The difference between AI agents and AI workflows, when each fits, high-value enterprise use cases and how to build an AI-ready organization.

phpscientist.com/blog/ai-agent

Senthil Kumar Muniyan Swaminathan

Model Context Protocol: The Future of Enterprise AI Integration

What Model Context Protocol (MCP) is, how its host, client and server architecture works, and how enterprises can adopt it securely for AI agents.

phpscientist.com/blog/model-co

Senthil Kumar Muniyan Swaminathan

Agentic AI in Digital Transformation: How to Automate Workflows Without Creating Agent Sprawl

Agentic AI is becoming the next digital transformation battleground. This playbook shows how to automate workflows with governance, measurable value and human control built in.

phpscientist.com/blog/agentic-