Tags: AI

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.

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.

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.

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.

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.

When launch clawdbot on the cloud, you're handing over cognitive data to the vendor. There's a reason why people buy 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.

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!

The bottleneck for AI agents isn't in database engines but in upper-layer integration. Muscle memory, associative memory, and trial-and-error courage will be key.

Your ability to ask questions—and your taste in what to ask—determines your position in the AI era. When answers become commodities, good questions become the new wealth. We are living in the moment this prophecy comes true.

Context window economics, the polyglot persistence problem, and the triumph of zero-glue architecture make PostgreSQL the database king of the AI era.

Who will be revolutionized first - OLTP or OLAP? Integration vs specialization, how to choose? Where will DBAs go in the AI era? Feng's views from the HOW 2025 conference roundtable, organized and published.

The database for the AI era has been settled. Capital markets are making intensive moves on PostgreSQL targets, with PG having become the default database for the AI era.

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.