Services
Delivery models and flagship work
Alopex Family
Asopitech Labo
NiminoDesktop
A lightweight WebView desktop foundation in Nim
NimculusEditor
A GPU-native editor in Nim and Metal
PoieraPlatform
A control plane concept for AI agents, carrying compact contracts through authorization, deployment, and verification
TheatoraBackend
A composable backend construction kit concept for swapping providers, runtimes, and topology
RhyzoraFramework
A concept for building CLI, TUI, Web, desktop, and MCP interfaces from declarative contracts
Labo overview
What the R&D lab is working on
Enterprise plans
The five-part series that began with 'How Not to Let AI Take Your Career' has been compiled into a single book. It maps how beginners, intermediates, and leaders can turn AI from an answer machine into a partner for building, teaching, and multiplying experience. Available on Leanpub and Buy Me a Coffee (English) and Zenn (Japanese).
Will that code still be needed once the next model ships? Six things worth checking before you start.
Homegrown long-term memory, a workflow DSL, a model router. Each one works, and each is genuinely interesting to build. But what if three months of waiting made them unnecessary?
Boris Cherny, who built Claude Code, says he hasn't hand-written a line of code in eight months. Is the craft of polishing prompts giving way to the craft of designing loops?
The code runs and the tests pass. Only the value of using it has vanished. A look at this failure mode as a stranded asset rather than technical debt.
PDF chat, AI slides, ChatGPT plugins: all standard features of ChatGPT and Gemini now. Where did the implementations that once powered them go?
In the US, an "IT company" means Microsoft or NVIDIA; in Japan, it means NTT Data or Fujitsu. The same words point to different things. One side mass-produces products for the world; the other supports each customer's bespoke operations. That structural gap has split revenue, talent, and competitiveness. How does generative AI reshape it? A look from Japan's weaknesses and strengths.
Coding agents are strong at logic and tests. But they can't tell what a piece of data means, who owns it, how fresh it flows, or which copy is authoritative. It follows from the fact that today's AI has no embodiment: the people who rise in value are those who can design the meaning, quality, lineage, and responsibility of data. A look at the trend with the latest data-engineering discussion.
The five-part series that began with 'How Not to Let AI Take Your Career' has been compiled into a single book. It maps how beginners, intermediates, and leaders can turn AI from an answer machine into a partner for building, teaching, and multiplying experience. Available on Leanpub and Buy Me a Coffee (English) and Zenn (Japanese).
Will that code still be needed once the next model ships? Six things worth checking before you start.
Homegrown long-term memory, a workflow DSL, a model router. Each one works, and each is genuinely interesting to build. But what if three months of waiting made them unnecessary?
Boris Cherny, who built Claude Code, says he hasn't hand-written a line of code in eight months. Is the craft of polishing prompts giving way to the craft of designing loops?
The code runs and the tests pass. Only the value of using it has vanished. A look at this failure mode as a stranded asset rather than technical debt.
PDF chat, AI slides, ChatGPT plugins: all standard features of ChatGPT and Gemini now. Where did the implementations that once powered them go?
In the US, an "IT company" means Microsoft or NVIDIA; in Japan, it means NTT Data or Fujitsu. The same words point to different things. One side mass-produces products for the world; the other supports each customer's bespoke operations. That structural gap has split revenue, talent, and competitiveness. How does generative AI reshape it? A look from Japan's weaknesses and strengths.
Coding agents are strong at logic and tests. But they can't tell what a piece of data means, who owns it, how fresh it flows, or which copy is authoritative. It follows from the fact that today's AI has no embodiment: the people who rise in value are those who can design the meaning, quality, lineage, and responsibility of data. A look at the trend with the latest data-engineering discussion.
