Making the "AI writes blog" thing into an engineering project later (Part II)

Fri, 03 Apr 2026 21:02:02 +0800

If there are enough tokens, the least effort method is actually quite crude: just feed the model historical articles and let it learn on its own. The problem with this method is that it only suits occasional writing, not continuous work. If you treat blogging as a long-term workflow, relying solely on raw historical articles will quickly go from “simple and direct” to “expensive and messy.”

With these articles, the main thread has shifted. The previous article, AI Writing Blogs: It Eventually Needs to Become an Engineering Process (Part 1): Why a blog-writer is inevitable, discussed automation on the consumption side. This article starts discussing the production side—how to generate style data, how to compress it, and how not to waste tokens; the next article will continue this in AI Writing Blogs: It Eventually Needs to Become an Engineering Process (Part 3): Local Models, Online Models, and Minimax—How They Finally Divide Labor.

The most natural initial thought is to just feed it historical articles.

This path feels too natural. If you want the model to learn your writing style, the most intuitive way is certainly to feed it old articles. It’s best to include all the posts from your history that are most like your own, and let it summarize them itself. For a single task, this approach has no flaws. In fact, many times the results are quite good. If the context is long enough, the model is powerful enough, and there are enough historical articles, the style can indeed be captured. But the problem isn’t “can it write this one article,” the problem is “for the next one, the one after that, do we have to repeat this process?” Feeding a new batch of old articles every time brings several very practical side effects:

The same batch of material repeatedly occupies the context window.
Token overhead grows almost linearly with the number of drafts written.
The model sees more and more noise, causing genuinely useful signals to become diluted.
The drafting action and style maintenance action become completely bound together; neither can be easily reduced. In other words, when tokens are abundant, eating it raw certainly works. But from an engineering perspective, we cannot keep doing that forever.

This is also why the data module and the data generation module must be separated

I later realized that the core idea can be summarized in one sentence: separating the consumption side from the production side.

blog-writer is responsible for the consumption side. It only reads an already published runtime and then writes out the article according to a fixed contract.

Meanwhile, scanning, filtering, scoring, compressing style data, and provider comparison—all of this should be placed in another production pipeline. This is what later became blog-style-suite.

Looking at the git history, this turning point is very clear.

The commit 84a06b5 on April 1, 2026, at 21:47 clearly replaced the original blog-style-maintainer skill with a repository-level CLI tool. This action speaks volumes because once you have scan/build/rebuild, an output directory, and a recovery mechanism, it’s no longer like a simple skill; it’s more like a normal Python project.

By the commit 9e92b8e on April 1, 2026, at 23:05, blog-style-suite was further broken down into modules like scanner.py, builder.py, and compressor.py. By this stage, the thinking process was already highly engineered:

scanner.py is responsible for scanning articles from disk and extracting structured features.
builder.py is responsible for scoring, selecting, caching, and runtime assembly.
compressor.py is responsible for the compression steps that involve the model.

This represents a completely different approach compared to simply writing a super prompt.

Saving Tokens: Not by Magic, But by Preprocessing and Batching

The most valuable part of this entire engineering setup, I think, is the commit bc4b950 from April 2, 2026, at 19:41. That commit was very direct: it reduced AI calls from about 2000 times down to a maximum of 5 times per provider. How was this achieved? It wasn’t by “making the prompt smarter,” but by doing the necessary preprocessing beforehand. The current flow in blog-style-suite is very clear:

The scan stage is purely heuristic, requiring 0 AI calls.
The build stage first performs heuristic scoring, also requiring 0 AI calls.
Then, it performs one batch selection and labeling for each of the four lanes: technical / finance / essay / tooling.
Finally, there is one author style compression step. Counting this up, the cold start requires at most 5 calls. More critically, these 5 calls are not spread across every single article; they are concentrated on high-value summary materials that have already been preprocessed. This is where preprocessing truly saves tokens. It’s not about saving a few words; it’s about changing the process from “calling per article” to “batch calling by stage.” Furthermore, caching has been implemented. In builder.py, there are lane batch fingerprints, provider checkpoint recovery, and contractions like review_pool_per_lane = 12 for local model context. If you change a small amount of data, the entire pipeline doesn’t need to rerun. These kinds of designs might not look flashy, but every single one is highly practical because they solve the problem of “don’t let the same batch of tokens burn twice.”

Essentially, this data structure is compressing the truly useful signal.

Once this is broken down, the data structure will be smooth. I am now more willing to understand it as three layers.

Layer One: `scan.json`

This is the shared raw material. It contains structured signals such as article path, title, date, category, tags, opening paragraph, closing stub, headings, screening results, and lane classification. It is not directly consumed by blog-writer; rather, it is passed to the production side for further processing.

Second Layer: `{provider}.source.json`

This is the provider-level checkpoint. Building upon the shared raw materials, it includes intermediate states such as scoring results, lane selection, fingerprint, and cache status. In other words, it is more like a “semi-finished product during processing,” with an emphasis on being recoverable, reusable, and resumable.

Layer Three: `{provider}.runtime.json` and `published.runtime.json`

This is what the consumption side truly cares about—the finished product. It retains:

author_style
lanes
samples
writer_guide In essence, it compresses a large collection of historical articles into one ready-to-consume runtime style asset. The published.runtime.json in particular is crucial for the publishing stage. The blog-writer only reads this file and does not need to scan content/post, nor does it need to care about the complete images of all providers in the suite directory. Once this boundary is established, the consumption side becomes much lighter. The writing model no longer sees a pile of raw old articles, but rather a pre-processed, high-density signal.

Not Everything Should Be Left to the Model

I’m increasingly feeling that the most correct judgment in this entire engineering process isn’t “add more models,” but rather “don’t throw tasks that shouldn’t be done by a model onto it.” Things like these are much better handled by local rules first:

Frontmatter parsing
Extracting introductory paragraphs
Headings extraction
Determining author/repost/model attribution
Detecting blockquote ratios
Hard rule filtering for things like , embedded prompts, and body length. Having the model do these tasks isn’t impossible, but it’s wasteful. What models are better suited for are parts that involve ambiguity or trade-offs. For example, which few articles in a lane best represent the current sentiment, or extracting author style tags from high-scoring articles. Therefore, what makes blog-style-suite truly valuable isn’t just “saving tokens,” but rather its re-division of labor among humans, rules, and models—assigning each party the tasks they are best suited for.

Preprocessing isn’t about saving a few tokens; it’s about making the act of writing sustainable.

For the conclusion in the second article, I want to make it more direct.

When you have plenty of tokens, reading historical articles raw is fine. In fact, if you only write one or two pieces, it might even be less mentally taxing.

But as soon as you want to turn this into a long-term workflow, preprocessing becomes non-negotiable. Because without preprocessing, the writing model has to re-read old materials every time, and style maintenance and article generation are always mixed together.

The significance of blog-style-suite is to untangle this mess.

It’s not about making the system look complex, nor is it just for another project name; it’s so that blog-writer can remain lightweight, stable, and focused on “only the action of writing.”

Having reached this point, the next question naturally follows.

Since the production side has been separated, what model should bear this cost? Local models, online models, or Minimax—where should each one stand in the workflow? I’ll save this for the next article: AI Writing Blogs: How It Eventually Has to Become Engineering (Part 3): The Division of Labor Between Local Models, Online Models, and Minimax.

References

Repository Commit: 84a06b5dc743f2e9bc6e788d53496a1261bc63ae
Repository Commit: 9e92b8e6a15d03e6392aff7f3b2dcb0992fe5043
Repository Commit: bc4b950cbb13e37d1fdb16a9d23325cfefa6f90e
Repository File: scripts/blog-style-suite/README.md
Repository File: scripts/blog-style-suite/style_pipeline/scanner.py
Repository File: scripts/blog-style-suite/style_pipeline/builder.py
Repository File: scripts/blog-style-suite/style_pipeline/compressor.py
Effective Runtime: .agents/data/blog-writing/published.runtime.json

Writing Notes

Original Prompt

$blog-writer This content is quite extensive, so I've split it into a series of articles: Last year, many drafts were written using large models. Back then, the process was to create an outline or a list of questions myself, and then have the AI generate the draft, copy the content into a local md document, fill in header information, tag information, and publish the article; recently, I used Codex a lot and found that its web search capability is very strong. So, could I write a skill to automate these tasks? This led to the first draft of the skill blog-writer. I also thought about having the AI learn my previous writing style, which caused blog-writer to consume a lot of tokens when running. Subsequently, I optimized blog-writer in several versions, splitting out the data module and the data generation module. The original data generation module was still an independent skill. As I continued writing, I realized that it would be better as a Python project, which led to blog-style-suite. Then, I found that training on style data also consumes a lot of tokens, so I wanted to use a local large model and connected to a local LLM. I then thought about comparing the differences between the local LLM and the online version, so I integrated minimax; the evolution history of blog-style-suite and blog-writer can be analyzed from the git commit history. Additionally, based on the code for local blog-writer and blog-style-suite, I can discuss the design ideas, how token saving was achieved, and how the data structure was designed—the core design concepts. If tokens are abundant, it can consume entire historical articles; preprocessing can save a lot of tokens.

Writing Outline Summary

This article shifts the focus from the act of writing drafts to data engineering, with the core answer being “why modularization is necessary.”
The introduction directly acknowledges that “using raw historical articles works,” which makes the subsequent arguments for splitting more convincing.
It elaborates on the three structural layers: scan.json, source.json, and runtime.json, avoiding vague architectural discussions.
bc4b950 is placed in the middle as a turning point because “reducing from about 2000 times to 5 times” best illustrates the value of preprocessing.
The conclusion re-separates the consumption side and the production side, setting the stage for model division in the third article.

Token on Uncle Xiang's Notebook