Autoblogger: Automating Tutorial Blogs with AI Agents and vibe coding

I built Autoblogger after realizing how tedious it would be to write up a technical tutorial by hand on a project of mine. My recent side project, zsh-tips-agent (an AI-driven shell-tips tool), was a fun experience to write because I basically just debugged the code (the rest was done with a few llm chat interfaces). After all of that not writing, I didn’t want to type out a long blog post about it, so I turned to an agentic approach. And of course, rather than devoting the time to do all that writing for a simple blogging tool, I used the same pattern I had previously to develop autoblogger. As I noted on Reddit, I “did not want to write the whole blog myself, so I…wrote (or rather…LLMs wrote) an agent-based system to generate a blog from a git repo”. The result was a surprisingly coherent draft — the agent “generated this nice blog” — but, as expected, “some things…needed to [be] refined”. In a move that feels a lot like burning my remaining programming creds, I decided that I should write a blog post about autoblogger instead. This is not a "how-to" style tutorial. Instead, this is a high-level overview, about the type of planning you need to do to make an agentic tool like this.

The core of Autoblogger is built on Hugging Face’s smolagents library. Hugging Face describes smolagents as “the simplest framework out there to build powerful agents”. In practice, this means you can orchestrate multi-step AI workflows with very little code. Under the hood, a CodeAgent in smolagents actually executes Python code as part of its planning process. For example, it can run commands like git log or git diff to fetch commit history and file changes, then feed those results to a language model for interpretation. The framework is model-agnostic (you can plug in GPT-4, Claude, an LLaMA, etc.) and tool-agnostic (it even supports integrations with LangChain, web searches, or Hugging Face Spaces). In short, smolagents lets Autoblogger “run powerful agents in a few lines of code”, treating the blog-writing task as a sequence of automated steps.

Figure: Conceptual diagram of an AI agentic workflow. The system perceives data, plans sub-tasks, and acts iteratively to achieve its goal (e.g. writing a blog). Sources: SleekFlow blog on AI agent workflows.

Autoblogger’s pipeline mirrors the ideas in that diagram. An agentic workflow “breaks down complex tasks into smaller, manageable steps”. Concretely, Autoblogger first analyzes the Git repository: it reads commit messages and diffs from zsh-tips-agent’s history. Then, the agent uses the LLM to generate prose for each part of the history. For example, it might ask: “Explain what changed in this commit and why it matters.” The LLM drafts a paragraph (in Markdown) for that commit. Finally, the system assembles those pieces into a narrative post, adding an introduction or summary where needed. This process of assembly was chosen because a blog post, and the file content insertions that we want to do in a blog post, can be quite long. As one source notes, agentic AI “follow[s] an iterative, interactive approach”: the agent observes the code base, decides what to document next, acts by invoking the model to write text, then loops again.

In practice, this meant the blog’s structure naturally followed the project’s development history. I found that the auto-generated outline already made sense: each section corresponded to a set of commits. As I described on Reddit, “the structure of the blog is based on the git history”, which saved a lot of outlining effort. In effect, Autoblogger treated the git timeline as a tutorial outline. With smolagents handling the logic, I could just rely on the higher-level guidance that went into the design (e.g. “write this as a friendly tutorial”), letting the CodeAgent loop through commits.

Generating the code for this project was done in a fairly mundane way. No special extensions were used, or dedicated tools like aider. I simply took my design ideas to the llm, generated the code, briefly reviewed it, applied it in the project, and tested it. There I would interate similarly to how you might with test-driven development, until I had accomplished the goal at that step. A difference in the iterative phase (with any errors) is that I must now pay more attention because the model will more often fail to correctly identify the problem. Sometimes you have to provide solutions, or rewrite the code yourself, especially with regards library versions, but this is amazingly approachable I feel, as a way to spec out a PoC. Models have advanced so much that they can take on a project of this scale (about 1100 lines of code) in what made sense to me to chunk into four git commits, and nearly no hand coding whatsoever.

Even so, the agent isn’t perfect. The draft it spits out is more like a first pass than a finished article. In my case, the LLM produced useful descriptions, but they often needed human tweaking for accuracy and tone. This aligns with industry advice: AI content should be a starting point, not the end. For example, an Optimizely blog emphasizes that “what you get from AI is never the end of the process, but only the beginning”. Indeed, after Autoblogger wrote the initial draft, I went through it to fact-check and edit. (LLMs tend to hallucinate—they can invent plausible-sounding details. As MIT Sloan notes, generative AIs sometimes give “fabricated data that appears authentic”, because they mimic patterns rather than verify truth. This is a known pitfall.) I had to correct a few invented code references, failed file retrievals and even a failed section (commit) retrieval, and sharpen or rewrite many details. I also added more of my own voice and examples, since the AI-generated text can sound a bit generic or mechanical. In short, Autoblogger helped draft a full post, but the final polish — ensuring correctness, injecting personality, aligning with the project’s theme — still required me.

In fact, human oversight was crucial. The Optimizely article points out that only a human can inject brand and personality into content. They also remind us that AI “cannot be expected to fact-check” its own output. In keeping with that, I treated the Autoblogger output as a “rough draft” to be refined. I double-checked every statement the agent made about the code. If I hadn’t done that, the risk of propagating a subtle error or hallucination would be high. (For example, generative models are not guaranteed to have the latest library facts, so an outdated reference might sneak in.)

There are other practical limitations worth noting. If I were using OpenAI’s Api, the agent could hit my rate limit. The process would pause until the limit reset, which can be a nuisance. To avoid this, I ended up experimenting with a local LLM setup (via Ollama) so that the agent could keep working off-line without hard caps. Finally, the agent’s writing length is bounded by the model’s context window. To go a bit more into detail, every token the model generates becomes part of its next input. Thus, if a model’s total context window is N tokens, and you feed it I input tokens, at most N – I tokens remain available for output—because once the model emits a token, that token joins the input stream. Generating beyond that forces older tokens out (“eviction”), breaking coherence. For a very detailed project history, this agent might need to chunk the work (as I have done) or else lose earlier content, which can complicate multi-stage blogs.

In summary

Autoblogger shows both the promise and current gaps of AI-assisted documentation. Using smolagents, it let me “turn an LLM into a proactive problem-solver” for blog writing. It automated much of the grunt work – gathering diffs, organizing sections, drafting text – which sped up the process dramatically. Yet it clearly needed me to stay in the loop. The end blog (How to Build zsh-tips-agent: A Step-by-Step Journey) was much easier to produce with Autoblogger, even as it required my final edits for clarity and accuracy.

Looking ahead, agentic AI tools are only going to improve. Better models and larger context windows could reduce errors. More sophisticated tool integrations could help fact-check or link to documentation automatically. But for now, I see this approach as a powerful assistant: it sketches out the content structure and initial prose, while I supervise, adjust, and refine. In the long run, that collaboration—AI drafting, human polishing—is where I expect AI-driven dev documentation to shine.

Sources

Autoblogger is built using Hugging Face’s smolagents framework. Concepts of agentic workflows are discussed in industry articles. The need for human oversight in AI-generated content is well-documented. My own usage anecdotes come from a Reddit discussion about the zsh-tips-agent blog generation.

References

• Hi devs, what are you coding with vibe. : r/programmingmemes https://www.reddit.com/r/programmingmemes/comments/1kvy477/hi_devs_what_are_you_coding_with_vibe/

• smolagents https://huggingface.co/docs/smolagents/en/index

• GitHub - huggingface/smolagents: smolagents: a barebones library for agents that think in code. https://github.com/huggingface/smolagents?via=topaitools

• Learn AI agent workflows: types, benefits, challenges https://sleekflow.io/blog/ai-agent-workflow

• Why AI-generated content still needs the human touch - Optimizely https://www.optimizely.com/insights/blog/why-ai-generated-content-still-needs-the-human-touch/

• When AI Gets It Wrong: Addressing AI Hallucinations and Bias - MIT Sloan Teaching & Learning Technologies https://mitsloanedtech.mit.edu/ai/basics/addressing-ai-hallucinations-and-bias/

• Why AI-generated content still needs the human touch - Optimizely https://www.optimizely.com/insights/blog/why-ai-generated-content-still-needs-the-human-touch/

• Agentic Workflow for LLMs: Boost AI Efficiency and Automation | Medium https://medium.com/@myscale/agentic-workflow-for-llms-boost-ai-efficiency-and-automation-f4839ec78271