AI on ByF (BaiFan) · Daily Deep Think

What Trellis Does, and Whether It Fixes AI Coding Standards

blog@baifan.site (ByF (BaiFan)) — Thu, 30 Apr 2026 14:00:00 +0800

The most annoying thing about coding with AI isn’t that it writes bugs — it’s that it writes them differently every time. Same requirement, three conversations, three implementations. The standards live in your head; the AI doesn’t know them.

Trellis tries to solve this. It’s not yet another AI coding assistant — it’s a framework for injecting project standards into AI: the AI reads your standards before writing code, gets checked automatically after writing, and once the check is done, anything newly learned gets distilled back into the standards. The more you use it, the better the AI knows your project.

Dissecting a PPT Agent: From Research to SVG Output

blog@baifan.site (ByF (BaiFan)) — Tue, 28 Apr 2026 14:00:00 +0800

Conclusion first: this is not another “type a topic → force it into a template → output garbage” AI PPT tool.

It runs a complete expert workflow — requirement research, material retrieval, outline planning, layout planning, visual design — where every step allows manual fine-tuning, or the whole thing can run automatically end to end. In terms of output, pages generated with Gemini 3 Flash already reach commercial delivery quality.

AI API Suffixes: What They Mean and Why They Exist

blog@baifan.site (ByF (BaiFan)) — Tue, 14 Apr 2026 14:00:00 +0800

We often see URLs like these:

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2
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/v1/chat/completions
/v1/messages
/v1/responses

These suffixes are not decoration — they define a complete request and response data structure. Pick the wrong one and your code either errors out or silently loses data.

This article starts at /v1/completions and works its way to /v1/responses, explaining what each suffix stands for, why it was designed that way, and where the common pitfalls are.

The Engineering Reality of Multi-Agent Collaboration: Triggers, Topologies, and Deterministic Merging

blog@baifan.site (ByF (BaiFan)) — Tue, 03 Mar 2026 14:00:00 +0800

Packing multiple probabilistically sampled large language model instances into a single codebase or production pipeline is, fundamentally, constructing a distributed system over an unreliable network using stochastic state machines. The axiomatic foundations of classical distributed computing—deterministic state transitions, reproducible failure modes, Byzantine fault tolerance, and atomic commits—collapse almost entirely when applied to modern generative models.

Tech social media abounds with utopian product demos: one agent searches documentation, another implements business logic, a third writes unit tests, and a fourth conducts code review, while an orchestrator agent glides over the process like an infallible engineering manager. Yet anyone who has pushed such topologies into a 500,000-line monolithic repository or a high-concurrency production environment knows the reality: the system rapidly degenerates into distributed chaos. Unsynchronized modifications cause silent overwrites and split-brain states; non-deterministic rollouts cause cognitive divergence; token consumption explodes in a positive-feedback avalanche; and parent and child processes lock each other in cognitive livelocks.

AI Psychosis: When MTTR Fanaticism Meets Resilience

blog@baifan.site (ByF (BaiFan)) — Tue, 24 Feb 2026 14:00:00 +0800

I have a strong intuition: some companies in the tech industry right now are going through a full-blown AI psychosis, and inside those companies, rational conversation is simply impossible. I’m not talking about strangers — I’m talking about personal friends I deeply respect. That leaves me worried about how this ends.

This fear has a specific source. I lived through the great MTBF (mean time between failures) vs MTTR (mean time to recovery) debate when infrastructure moved from traditional ops to cloud and cloud automation. Now those exact same arguments are resurfacing in a new costume — only the scope has widened from infrastructure to the entire software industry, and possibly to the whole world.