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
Why are records for the same company totalled separately? Starting with record linkage, this article turns familiar data into searchable, aggregatable forms.
Did the problem definition end the moment you wrote it? Keeping it as a hypothesis through to requirements.
How far does that problem reach? Four axes for setting its scope and view.
Is that request already a chosen solution? Set the method aside and write the problem as a gap from today.
Was the conclusion settled the moment the budget was approved? Seven structures that block a decision not to build.
Are those requirements just a list of individual tasks? How to see the whole process and decide what a system should cover.
Does that request describe a problem, or a solution someone already picked? A procedure for removing work before you build.
It was never about handing a card number to a model. So what are all these new standards actually trying to prove?
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.
Why are records for the same company totalled separately? Starting with record linkage, this article turns familiar data into searchable, aggregatable forms.
Did the problem definition end the moment you wrote it? Keeping it as a hypothesis through to requirements.
How far does that problem reach? Four axes for setting its scope and view.
Is that request already a chosen solution? Set the method aside and write the problem as a gap from today.
Was the conclusion settled the moment the budget was approved? Seven structures that block a decision not to build.
Are those requirements just a list of individual tasks? How to see the whole process and decide what a system should cover.
Does that request describe a problem, or a solution someone already picked? A procedure for removing work before you build.
It was never about handing a card number to a model. So what are all these new standards actually trying to prove?
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.
