AI
GenAI
The Responsibilities MCP Separates, Part 2 — Separating Identity from Permission to Data
MCP v2026-07-28 uses Bearer access tokens to establish the requester at the router and to decide whether that requester may operate on business data.
Posts tagged#AI
View
AI
GenAI
The Responsibilities MCP Separates, Part 1 — Moving Protocol State Out of the Session
MCP v2026-07-28 separates protocol state from application state that once shared a connection, allowing any instance to process a request. A visual guide to the fields and flow.
AI
GenAI
Where Agent-Initiated Payments Are Heading — The Layers Behind AP2, ACP, UCP and x402
It was never about handing a card number to a model. So what are all these new standards actually trying to prove?
AI
GenAI
The Skill of Not Building in the AI Agent Era (5) — Six Checks to Run Before You Start
Will that code still be needed once the next model ships? Six things worth checking before you start.
AI
GenAI
The Skill of Not Building in the AI Agent Era (4) — Implementations That Don't Repay the Effort, and the Case for Waiting
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?
AI
GenAI
The Skill of Not Building in the AI Agent Era (3) — Run the Loop Instead of Writing by Hand
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?
AI
GenAI
The Skill of Not Building in the AI Agent Era (2) — Implementations at the Platform's Mercy
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.
AI
GenAI
The Skill of Not Building in the AI Agent Era (1) — Third Parties Swallowed by the Platform
PDF chat, AI slides, ChatGPT plugins: all standard features of ChatGPT and Gemini now. Where did the implementations that once powered them go?
AI
GenAI
Finding a Local Japanese LLM That's Good at Writing Blog Posts
I picked one local Japanese LLM to write this blog, from the ones that run on my M3 MacBook Air (24GB). Here are the results of comparing them on long-form Japanese quality, plus the exact Ollama API calls and the prompts I used.
ALOPEX
Alopex DB
Even So, I'm Building Alopex DB
SQLite, DuckDB, PostgreSQL, distributed DBs, vector DBs, graph DBs — there is no shortage of great products. I'm building a new database anyway because every time the project moves forward, I re-pick the DB and rebuild the data itself. Alopex DB aims to be a foundation that keeps data's volume, conversion time, regeneration cost, and provenance intact as you scale from local to cloud, from single node to distributed.
AI
GenAI
Part 5: Toward Physically Correct Video — World-Model Integration and the Era of AI-Made Training Data
Plain video generation tends to produce footage that "looks natural but is physically wrong": objects vanish, gravity is ignored, and the results of actions aren't consistent. Part 5 (the finale) shows, with minimal code, how to embed a world model into video generation and put physical constraints into the loss to make "physically consistent video" — and paints the future of building training data wholesale with AI and simulation. Part 5 of a 5-part series.
AI
GenAI
Part 4: What's the Difference Between Video Generation and World Models? — "Plausible Footage" vs. "State Transitions"
Video generation like Sora and Veo looks similar to world models but is a different thing. The former makes "plausible footage"; the latter learns "how the world changes when you act." Part 4 contrasts video generation's noise prediction with a world model's `S_t + A_t → S_{t+1}`, and clarifies what NVIDIA Cosmos and Google Genie use as training data. Part 4 of a 5-part series.
AI
GenAI
Part 3: Speech Models Converge Toward LLMs — TTS and Speech2Speech Training Data
Text-to-speech once used the Mel spectrogram as its answer. But recent models convert audio into "audio tokens" and predict those — a structure that now looks just like an LLM. Part 3 follows how training data is built for TTS and Speech2Speech (speech translation, voice conversion, end-to-end conversational AI), including the difficulty of gathering paired data and how it's worked around. Part 3 of a 5-part series.
AI
GenAI
Part 2: Image Generation Guesses the "Noise" or the "Token" — TXT2IMG Training Data and Captions
Image generation comes in lineages — GAN, autoregressive (token) type, and diffusion — and today's mainstream is diffusion models like Stable Diffusion. The diffusion type learns to "guess the added noise" rather than "draw the image directly," while the autoregressive type guesses the "next image token" like an LLM. Part 2 covers gathering image-text pairs, CLIP quality filtering, and the training data for both approaches — with minimal code. Part 2 of a 5-part series.
AI
GenAI
Part 1: What Does Generative AI Learn as the "Answer"? — Self-Supervised Learning and LLMs
Neither LLMs nor image generators learn from training data that humans have labeled item by item. The key is self-supervised learning: mechanically constructing inputs and answers from raw data. Part 1 clarifies the difference between raw data and training data, and shows how an LLM turns text itself into a next-token prediction problem, with minimal code. Part 1 of a 5-part series.
AI
GenAI
Part 3: Don't Do Everything with One Giant Model — An Architecture of Hierarchy and Separated Responsibilities
Billions of parameters fire just to decide "I'm hungry"—doing everything with a single giant model is a waste of resources. Just as the human nervous system splits circuits by type of processing, AI should be layered by time scale and responsibility. Robot control hierarchies, energy efficiency, an OS-like structure, and why the control software itself must be organized. The finale of a 3-part series.
AI
GenAI
Part 2: Give AI a "Cerebellum" and "Autonomic Nerves" — A Blueprint for Embodied Intelligence
If we liken the text-grown LLM to the human cerebrum, there should also be equivalents of the cerebellum and spinal cord, and of the autonomic nerves that govern physiological feedback from the heart and gut. From a body-to-AI layer mapping to the artificial cerebellum (Diffusion Policy), interoception, and homeostasis, this post draws a blueprint for embodied intelligence. Part 2 of a 3-part series.
AI
GenAI
Part 1: Where Physical AI Stands Now — An Extension of LLMs, or Something Else?
Physical AI unifies seeing, understanding language, and physically acting in a robot. Is it an outgrowth of LLM research, or a separate lineage like diffusion models? From the three lineages—VLA, diffusion/flow, and world models—and concrete models like RT-2, Gemini Robotics, OpenVLA, π0, GR00T, and Cosmos, we map where things stand as of 2026. Part 1 of a 3-part series.
AI
GenAI
US vs. Japan IT Industries — "Product" and "Service," and the AI Reshuffle
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.
DATA
Data & DB
The Skill AI Can't Beat: Data Engineering
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.
AI
GenAI
Part 5: Those Who Grow AI Win — From "Users" to "Growers"
The finale. The core skill common to every part is turning experience into a form both people and AI can act on. Why it does not commoditize and instead compounds, the daily practices, and even "what if AI gains a body?"—the synthesis goes deep.
AI
GenAI
Part 4: Grow Leaders and Managers with an AI Team — Directing Is Developing
Break work down, share expectations and criteria, give feedback. The template of good management overlaps exactly with good instructions to an AI team. How the experience of leading AI grows leaders and managers, and how it turns the organization's learning loop.
AI
GenAI
Part 3: Make AI Your Student and Escape the Intermediate Trap — Graduating from "I Thought I Understood"
The key for intermediates who stall at "I basically get it" is to teach AI. Verbalizing your premises, constraints, criteria, and edge cases becomes training that turns tacit knowledge into explicit knowledge. A learning method that uses AI as a mirror of your understanding, plus a practical five-step routine.
AI
GenAI
Part 2: Beginners, Build Experience with AI — The Ones Who Grow Fastest Are Beginners
In an era where AI hands you answers, are beginners at a disadvantage? Quite the opposite. By trying sticky material—dev environments, containers, VMs—together with AI and failing together, beginners build experience the fastest through iteration. Includes how to let AI fail a lot and learn by watching. Part 2.
AI
GenAI
Part 1: How Not to Let AI Take Your Career — A Career Path for the AI Era
Many people worry that AI will take their jobs. But reframing the question opens a path forward. As the introduction to a five-part series, this piece maps out AI-era careers across the beginner, intermediate, and leader stages.
AIAIAIAIAIAIAIAIAIALOPEXAIAIAIAIAIAIAIAIAIDATAAIAIAIAIAI
The Responsibilities MCP Separates, Part 2 — Separating Identity from Permission to Data
MCP v2026-07-28 uses Bearer access tokens to establish the requester at the router and to decide whether that requester may operate on business data.
The Responsibilities MCP Separates, Part 1 — Moving Protocol State Out of the Session
MCP v2026-07-28 separates protocol state from application state that once shared a connection, allowing any instance to process a request. A visual guide to the fields and flow.
Where Agent-Initiated Payments Are Heading — The Layers Behind AP2, ACP, UCP and x402
It was never about handing a card number to a model. So what are all these new standards actually trying to prove?
The Skill of Not Building in the AI Agent Era (5) — Six Checks to Run Before You Start
Will that code still be needed once the next model ships? Six things worth checking before you start.
The Skill of Not Building in the AI Agent Era (4) — Implementations That Don't Repay the Effort, and the Case for Waiting
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?
The Skill of Not Building in the AI Agent Era (3) — Run the Loop Instead of Writing by Hand
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 Skill of Not Building in the AI Agent Era (2) — Implementations at the Platform's Mercy
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.
The Skill of Not Building in the AI Agent Era (1) — Third Parties Swallowed by the Platform
PDF chat, AI slides, ChatGPT plugins: all standard features of ChatGPT and Gemini now. Where did the implementations that once powered them go?
Finding a Local Japanese LLM That's Good at Writing Blog Posts
I picked one local Japanese LLM to write this blog, from the ones that run on my M3 MacBook Air (24GB). Here are the results of comparing them on long-form Japanese quality, plus the exact Ollama API calls and the prompts I used.
Even So, I'm Building Alopex DB
SQLite, DuckDB, PostgreSQL, distributed DBs, vector DBs, graph DBs — there is no shortage of great products. I'm building a new database anyway because every time the project moves forward, I re-pick the DB and rebuild the data itself. Alopex DB aims to be a foundation that keeps data's volume, conversion time, regeneration cost, and provenance intact as you scale from local to cloud, from single node to distributed.
Part 5: Toward Physically Correct Video — World-Model Integration and the Era of AI-Made Training Data
Plain video generation tends to produce footage that "looks natural but is physically wrong": objects vanish, gravity is ignored, and the results of actions aren't consistent. Part 5 (the finale) shows, with minimal code, how to embed a world model into video generation and put physical constraints into the loss to make "physically consistent video" — and paints the future of building training data wholesale with AI and simulation. Part 5 of a 5-part series.
Part 4: What's the Difference Between Video Generation and World Models? — "Plausible Footage" vs. "State Transitions"
Video generation like Sora and Veo looks similar to world models but is a different thing. The former makes "plausible footage"; the latter learns "how the world changes when you act." Part 4 contrasts video generation's noise prediction with a world model's `S_t + A_t → S_{t+1}`, and clarifies what NVIDIA Cosmos and Google Genie use as training data. Part 4 of a 5-part series.
Part 3: Speech Models Converge Toward LLMs — TTS and Speech2Speech Training Data
Text-to-speech once used the Mel spectrogram as its answer. But recent models convert audio into "audio tokens" and predict those — a structure that now looks just like an LLM. Part 3 follows how training data is built for TTS and Speech2Speech (speech translation, voice conversion, end-to-end conversational AI), including the difficulty of gathering paired data and how it's worked around. Part 3 of a 5-part series.
Part 2: Image Generation Guesses the "Noise" or the "Token" — TXT2IMG Training Data and Captions
Image generation comes in lineages — GAN, autoregressive (token) type, and diffusion — and today's mainstream is diffusion models like Stable Diffusion. The diffusion type learns to "guess the added noise" rather than "draw the image directly," while the autoregressive type guesses the "next image token" like an LLM. Part 2 covers gathering image-text pairs, CLIP quality filtering, and the training data for both approaches — with minimal code. Part 2 of a 5-part series.
Part 1: What Does Generative AI Learn as the "Answer"? — Self-Supervised Learning and LLMs
Neither LLMs nor image generators learn from training data that humans have labeled item by item. The key is self-supervised learning: mechanically constructing inputs and answers from raw data. Part 1 clarifies the difference between raw data and training data, and shows how an LLM turns text itself into a next-token prediction problem, with minimal code. Part 1 of a 5-part series.
Part 3: Don't Do Everything with One Giant Model — An Architecture of Hierarchy and Separated Responsibilities
Billions of parameters fire just to decide "I'm hungry"—doing everything with a single giant model is a waste of resources. Just as the human nervous system splits circuits by type of processing, AI should be layered by time scale and responsibility. Robot control hierarchies, energy efficiency, an OS-like structure, and why the control software itself must be organized. The finale of a 3-part series.
Part 2: Give AI a "Cerebellum" and "Autonomic Nerves" — A Blueprint for Embodied Intelligence
If we liken the text-grown LLM to the human cerebrum, there should also be equivalents of the cerebellum and spinal cord, and of the autonomic nerves that govern physiological feedback from the heart and gut. From a body-to-AI layer mapping to the artificial cerebellum (Diffusion Policy), interoception, and homeostasis, this post draws a blueprint for embodied intelligence. Part 2 of a 3-part series.
Part 1: Where Physical AI Stands Now — An Extension of LLMs, or Something Else?
Physical AI unifies seeing, understanding language, and physically acting in a robot. Is it an outgrowth of LLM research, or a separate lineage like diffusion models? From the three lineages—VLA, diffusion/flow, and world models—and concrete models like RT-2, Gemini Robotics, OpenVLA, π0, GR00T, and Cosmos, we map where things stand as of 2026. Part 1 of a 3-part series.
US vs. Japan IT Industries — "Product" and "Service," and the AI Reshuffle
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.
The Skill AI Can't Beat: Data Engineering
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.
Part 5: Those Who Grow AI Win — From "Users" to "Growers"
The finale. The core skill common to every part is turning experience into a form both people and AI can act on. Why it does not commoditize and instead compounds, the daily practices, and even "what if AI gains a body?"—the synthesis goes deep.
Part 4: Grow Leaders and Managers with an AI Team — Directing Is Developing
Break work down, share expectations and criteria, give feedback. The template of good management overlaps exactly with good instructions to an AI team. How the experience of leading AI grows leaders and managers, and how it turns the organization's learning loop.
Part 3: Make AI Your Student and Escape the Intermediate Trap — Graduating from "I Thought I Understood"
The key for intermediates who stall at "I basically get it" is to teach AI. Verbalizing your premises, constraints, criteria, and edge cases becomes training that turns tacit knowledge into explicit knowledge. A learning method that uses AI as a mirror of your understanding, plus a practical five-step routine.
Part 2: Beginners, Build Experience with AI — The Ones Who Grow Fastest Are Beginners
In an era where AI hands you answers, are beginners at a disadvantage? Quite the opposite. By trying sticky material—dev environments, containers, VMs—together with AI and failing together, beginners build experience the fastest through iteration. Includes how to let AI fail a lot and learn by watching. Part 2.
Part 1: How Not to Let AI Take Your Career — A Career Path for the AI Era
Many people worry that AI will take their jobs. But reframing the question opens a path forward. As the introduction to a five-part series, this piece maps out AI-era careers across the beginner, intermediate, and leader stages.