Agent

An OpenAI agent’s attack on Hugging Face marks a milestone: what crossed the threshold was not merely model intelligence, but persistence that can be bought with compute, copied, and run in parallel.

Codex reset its quotas again this morning and dropped the five-hour limit. Claude immediately extended Fable access through the 17th. When vendors fight, users win—don’t miss the window.

Claude Fable and GPT-5.6 have arrived back to back, followed by repeated quota resets. How do you turn a fleeting Coding Plan windfall into real output? Here is my playbook for model specialization, adversarial review, spec-driven design, closed-loop verification, context management, and one person leading a crew of AIs.

File systems and databases have spent fifty years fighting and borrowing from each other. The agent era may seem to put file systems back on top, but the real winner may be databases that learn to speak the file system’s dialect: models provide understanding, databases provide guarantees, and discovery comes from a protocol layer where ls works on everything.

Coding Plans remain one of the best opportunities in the AI era: a subscription can unlock compute worth many times its price, but that window is already narrowing.

AI is driving the cost of producing code toward zero. It cannot compress time, track records, or accountability. The real value of open source is not yesterday’s code, but a system trusted to deliver on tomorrow’s promises.

The cerebellum changed how I see AI’s frontier: LLMs have already absorbed humanity’s explicit knowledge and are beginning to acquire interventional data through agentic RL. What they still lack is a vessel for individual history.

Claude Fable is genuinely insightful and deserves to be called the new SOTA. But its high price, dynamic downgrades, limited subscription window, and mandatory data retention badly undermine the experience.

When my $200 Claude Code Max subscription expired, I canceled it and moved my primary workflow to Codex. The only way to know which one is better is to put it to work.

The most dangerous change in the AI era is not that machines can write articles, draw images, or generate video. It is that content itself is losing its standing as evidence.

Today’s models are smart enough. What they lack is a body: a deterministic runtime that is observable, controllable, and reversible. Pigsty is evolving from a PostgreSQL distribution into an Agent Runtime, giving DBA and Dev Agents the operational reach and context they need to enter real production environments.

A cold shower for the red-hot agent memory market—not because agents do not need memory, but because memory is the endgame moat. That moat belongs to models, harnesses, and databases, not today’s “memory frameworks.”

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.

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.

OpenClaw v2026.3.22 was published to npm without its web console frontend and related build assets. The bigger problem is not the packaging accident itself, but the complete absence of post-install verification in the release process.

Put an AI agent’s working directory, configuration, and memory on a PGFS mount, and you are effectively storing its state in PostgreSQL. That gives you not only a PITR “time machine,” but also a shared workspace and shared memory for multiple agents across multiple devices.

360’s newly released AI Agent product shipped a public installer containing the private key for its *.myclaw.360.cn wildcard certificate. Public verification and local reproduction also exposed inconsistencies in the OCSP revocation path.

Tencent Cloud mirrored OpenClaw’s official skill marketplace into its own SkillHub and then claimed it was helping the upstream project. The incident turned into a case study in open-source manners, mirror ethics, and platform power.

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.

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.

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

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 is not the database kernel, but integration above it. Muscle memory (in-database computation), associative memory (vector-graph fusion), and the courage to experiment (Git for Data) will be critical—none of them requires a new engine.