Building TaboriX: a managed home AI for families, where parents set the rules. 20 years in health tech. Startup builds and AI notes live here.

Grand Forks, ND
Joined April 2009
A builder in one faith community designs a safe, educational playground for their children. Why wouldn’t they open that same playground to families of other faiths who want the same thing: protection, education, and values that last? TaboriX is that playground for every family that wants to guard what matters most. Different faiths. Same desire to raise children well. Stronger together.
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Under thirteen, wonder comes first. Wait and Wonder asks the child to share a guess before the AI starts the assisted work. A child should not outsource the first thought. – at Grand Forks, ND
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AI alignment is bigger than the lab. How do we ensure AI supports human judgment rather than replaces it? And for people of faith, shouldn’t we also ask what God intended for humanity when He created us? If we’re discussing humanity’s future with AI, our understanding of human purpose and God’s design belongs in that conversation, too. I’m opening the discussion. 🧵
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I had the opportunity to discuss AI alignment, human purpose, and faith with @DarrelFrater during his online gathering, First Fruits | Begin Your Workweek With Jesus, on Loopis.live. Here’s a short clip from our conversation. I’m grateful for the opportunity to explore how our faith can inform the conversation about AI and humanity’s future. 🙏 – at Grand Forks, ND
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Glad to see this. Kids' safety by default should be the industry standard. At TaboriX we're taking it one step further: parents set the rules, each family gets its own private AI, and family data is never shared or used for training.
We're sharing progress on ChatGPT for Teens, our ChatGPT experience for people under 18, alongside a preview of College Planner, new study tools, and support for college advisers and teen voices. ChatGPT for Teens applies automatically to accounts identified as belonging to someone under 18, with protections on by default. Coming soon: College Planner brings application requirements, deadlines, tasks and financial-aid steps together for high school students in the U.S. planning to attend a four-year college. We'll keep building these tools, evaluating how they and their protections work in practice, and sharing what we learn. openai.com/index/teens-learn…
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I explore these questions through independent AI alignment research, human agency, relationships, and a faith-informed perspective, along with 12 questions for discussion and progress. Read the article and join the conversation: 🔗 loopis.live/post/xd742dtr7k8…
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A family needs a system of its own. Not a login on someone else's model. Not a picker that a child can switch. The rules follow the person who is signed in. That is the product. Home AI for families. – at Grand Forks, ND
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Thank you @DarrelFrater and Dave Lambert of @RightSideCapVC for today's @TGNVentures Investor Office Hours, and for the chance to learn from you and share about our startups. TaboriX is a private, family-first AI companion for kids. Parents set the rules, and the AI runs on a local box at home or in our secured server room, so family data stays private. taborix.ai
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Daniel Schott retweeted
"Software isn't dead. It's solved. Nobody will hire software engineers by 2028." Reality: • Writing code is being automated • Defining what to build isn't • Debugging production at 3 AM isn't • Owning broken systems isn't • Solving vague business problems isn't AI didn't kill software engineers... it eliminated those whose only skill was typing code. The role shifts from "write code" to "own the outcome." By 2028, will you be the engineer AI replaces or the one managing 10 agents?
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It’s not about the new repo. Anthropic 2.5k/day to 30,000/day code changes. May to September. There’s your inside look at what’s going on in Big Ai.
New on our Engineering blog: Creating in-house source control. In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale. Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: anthropic.com/engineering/re…
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Faith, character, and household rules belong to the mother and the father. The children's agents teach from that nucleus, not from a third party agenda. Link ⬇️
🤖 Made with AI
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🏡 TaborIX | Home AI for Families Inspired by Mount Tabor, we’re building on a timeless idea: a home should be a place of protection, trust, and growth. Bringing AI home to empower families, support parents, and help protect children. Your family. Your values. Your AI.
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Daniel Schott retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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A few Sunday evening reflections. 🧵 There are mornings I open a project I built the day before and can't recall how it works. Yesterday I understood every piece. Today, the details are gone. That's part of being neurodivergent for me. I can go deep on a hard technical problem, then lose the thread overnight. I'm not alone in this. The CDC estimates 15.5 million US adults live with ADHD. For most of my career, this was a ceiling. Big technical work rewards people who carry every detail forward, day after day, and keep pace with a team. That was never how my mind worked. AI changed that. My primary agent, edos, carries the thread for me. It remembers what we built, why we built it, and what comes next. It extends what I'm good at and bridges what I'm not. Some days it feels like working alongside a superintelligence.
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4/ Sources for the numbers in this thread: ADHD in US adults (CDC, 2024): cdc.gov/mmwr/volumes/73/wr/m… Americans' views on AI (Pew Research, 2025): pewresearch.org/science/2025… Jobs created and displaced by 2030 (World Economic Forum, Future of Jobs 2025): weforum.org/press/2025/01/fu… AI safety workforce estimate (Stephen McAleese, 2025): forum.effectivealtruism.org/…
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