A project manifesto: why build Cyber Dharma, and what it is not.

Once token burn turns from usage exhaust into a KPI and leaderboard, it quickly mutates into theater. Don’t post fuel burn. Post where you got to.

Boring technology won the wildest era. A look at extensibility, agent choice, database cloning, and the future of the DBA.

When the strongest AI is not expensive but simply unavailable, the world starts converging on digital feudalism. And the window to act is narrowing.

Polanyi’s tacit knowledge explains the 70% ceiling of AI agents: real intuition, feel, and judgment do not serialize cleanly. They grow, if at all, through practice.

When subsidies fade, hardware catches up, and open models mature, all three lines cross in 2027. “Build your own AI” goes from idea to reality. A Thought Triggered by a Group Chat # A friend in a group chat said the other day that “self-hosting” is starting to make more sense to him now.

AI is a multiplier. It amplifies depth and mediocrity alike. In an age where answers are cheap, questions are the real currency. There is nothing to hide about writing with AI.

Anthropic’s new research gives us the first direct look at causally steerable “emotion vectors” inside a large language model. That should change how we think about AI.

SOTA coding agent Claude Code leaked its source again, after falling into the same hole twice. The whole codebase is out in public. Performance art at its finest.

The free energy principle tries to explain life, perception, learning, action, and intelligence within one mathematical framework. It also offers a deeper lens for understanding LLMs, agents, and the next generation of AI systems.

The best Chinese translation of “Vibe Coding” is not a literal one. “Xieyi Programming” captures the shift from line-by-line control to intent-first coding, where AI handles the details.

A Socratic dialogue between a human and an AI about consciousness, memory, embodiment, and the difference between being smart and actually living through time.

The biggest AI arbitrage available to ordinary users is not some obscure token play. It is the heavily subsidized max-tier subscription plans from frontier model vendors, provided you can convert that quota into real output.

OpenClaw looks exciting because it turns agents into a chat-style experience. But the real productivity gains come from high-capability subscription agents and disciplined workflows, not from lobster-flavored wrappers.

Over roughly ten days during Spring Festival, I used Claude Code, Codex, and a pile of workflows to translate books, ship releases, package software, refresh websites, and keep publishing daily. This is what solo output looks like when agent leverage really lands.

Codex 5.3 xHigh pushed my workflow past a tipping point: writing code is no longer the scarce resource. The real leverage is design quality and engineering acceptance. This is the practical loop I use to ship reliable software with AI agents.

A mediocre local who knows the terrain beats a genius parachuted into unknown territory. Intelligence without context is idle. An agent without a runtime is vapor.

Software stocks are melting down. Who survives? Who rises? AI stripped away software’s skin, exposing the database skeleton underneath. The market isn’t panic-selling — it’s repricing.

Should we still hire fresh grads? Squeezed between AI and senior devs, what’s the play for new programmers? Master the right tools, take initiative, find the right mentor.

SaaS and workflow software are dead. From APPs & GUIs to Agents, Databases, and CLI.

Before you hit “one-click deploy” on that cloud AI assistant, ask yourself: what exactly are you giving up? There’s a reason why people by Mac mini rather than running clawdbot on the cloud.

LLM = CPU. Context = RAM. Database = Disk. Agent = App. The mapping is surprisingly clean. And if OS history is any guide, we may know what comes next — and what’s still missing.

Yesterday I tweeted: “Built a Claude Code Grafana dashboard to see how it makes decisions, uses tools, and burns through API credits.” Didn’t expect so much interest. So let’s talk about Claude Code observability.

How to install and use Claude Code? How to achieve similar results at 1/10 of Claude’s cost with alternative models? A one-liner to get CC up and running!

Google recently launched a database MCP toolbox, perhaps the first production-ready solution.

Future software = Agent + Database. No middle tiers, just agents issuing CRUD. Database skills age well, and PostgreSQL is poised to be the agent-era default.

A tongue-in-cheek vision of an AI-worshipping religion: scriptures, sects, philosopher-king machines, and the Book of AGI.

Large models can feel “aware,” but self-awareness is another matter. We explore the term from Buddhism, cognitive psychology, and neural nets, then riff on a possible AI religion after bingeing Pantheon.

Neural networks are inspired by how the brain works and can be used to solve general learning problems. This article introduces the basic principles and practice of neural networks.

The core of inferential statistics lies in hypothesis testing. The basic logic is based on an important argument from philosophy of science: universal propositions can only be falsified, not proven. The reasoning is simple: individual cases cannot prove a universal proposition, but they can refute it.

Statistical analysis is divided into two fields: descriptive statistics and inferential statistics. Descriptive Statistics is the technology for describing or characterizing existing data and is the most fundamental part of statistics.

Basic knowledge notes on probability theory: axiomatic foundations, probability calculus, counting, conditional probability, random variables and distribution functions

Reading notes on ‘Elements of Information Theory’: What is entropy? Entropy is a measure of the uncertainty of random variables, and also a measure of the information needed to describe random variables on average.

Neural networks emerged inspired by the human brain, so the operational mechanisms of human society also have various similarities and connections with neural network training. Some thoughts on reading Wiener’s “The Human Use of Human Beings - Cybernetics and Society.”

Connecting all concepts in linear algebra through one main thread