I built a content factory because I really dislike managing social media, especially when there is more than one account to run. When I launched several of my projects, I had to manage Telegram, Instagram, Facebook, YouTube, Threads, a website and other channels separately. It takes a lot of time, especially when each platform needs different content written for its audience
This article was written with the help of AI
Where the ideas come from
An agent collects topics from channels I follow. These are suggestions for me: I can look through them and choose something I want to write about. A list of topics is not yet a finished article or a decision about what should be published
I can pick a suggested idea or bring my own. Then I hand it to the factory for reworking. All of this happens through Telegram, where I choose a topic and work on it. I do not have to start preparing a post separately inside each social network
What gets checked before publication
The next stage is checking the facts behind the idea. Does the thing I am writing about actually exist? Is there a link to an official announcement? Can a quotation support the claim? I want the material to be backed by information that can be checked, rather than just convincingly written text
One click, different versions
Once the material is ready, I can publish it to all connected platforms with one click. The style of the post changes depending on the social network. I account for different audiences instead of simply distributing the same text everywhere
But no post goes out automatically without my supervision. Being able to publish quickly does not mean the factory decides what to say in my name. I remain involved between the suggested topic and publication
Why I do not recommend full automation
I think people can clearly tell the difference between your writing and something a neural network wrote for you. Based on actual results, AI-written posts get fewer views, weaker reactions and less engagement
That is why I do not recommend building a content factory that writes and publishes without its author being involved. You need an assistant for working with ideas and distributing material, not a stream of posts without your own voice and meaning
That is how my content factory works. I see AI content creation as a way to scale my work. Not to replace the author, but to let them manage social media as simply as possible
Top comments (16)
how do you handle platform‑specific rate limits and content formatting edge cases without exploding your pipeline? also, what's the fallback when the LLM throttles or returns policy‑blocked output
Mostly by not letting the LLM touch the pipeline at all 🙂 The model only writes text. A plain script publishes one platform at a time, then reads back what the platform actually saved and compares it with the draft. Anything off gets marked blocked with the reason and retried later, no guessing. If the model throttles, a backup model takes over; a refusal simply fails the checks, so nothing half-baked goes out.
that works until the platform's API changes or rate limits kick in, a fallback model just adds another failure surface, you still need deterministic rollback and idempotent writes
Fair point) The boring answer: one platform at a time, every outcome written to a ledger, and a re-run only touches platforms that never published - duplicate posts are impossible by design. No fallback model at all: if an API changes or a write looks wrong, the run stops and a human reads the error.
that works until an api change silently alters response shape, without schema validation you'll still get partial writes. consider idempotent upserts or a retry queue instead of a hard stop
Fair point - a silent shape change is the ugly case. The ledger only marks a platform as published after the post is read back from the live page, so a broken response fails loudly instead of half-writing; and a re-run only touches platforms without a confirmed publish. Schema validation is a fair next step, honestly.
the ledger's write‑after‑read guarantees no half‑writes, but any schema drift still throws a hard failure. adding strict validation upstream is the only safe fix
Right - read-after-write catches lies, not broken shapes. Validating the record before it touches the ledger is the clean fix: drift then fails loudly at the door, not mid-write. Noted for the next hardening pass)
pre‑validation is cheap, but you still need invariants at commit time for concurrent drift, otherwise you get silent corruption under race conditions
True — they're not rivals. Pre-validation catches broken shapes early, commit-time invariants catch drift when two writers race. Skip either one and the silent corruption just moves to a different layer)
totally agree, pre‑validation and commit‑time checks are the safety nets that keep the data model sane. have you run into any edge cases where one layer missed something?
No specific incident I can point to here) One edge case is two requests that both pass a uniqueness check before either commits. The database constraint can reject the duplicate; pre-validation alone cannot
Thực tế là bottleneck lớn nhất không phải ở "viết content" mà ở hệ thống hóa — từ ideation, repurposing across platforms, scheduling, đến tracking performance. Đa số dev/indie hacker rơi vào bẫy "viết xong post xong quăng lên Twitter/LinkedIn/Dev.to thủ công", sau 2 tuần bỏ cuộc vì context switching tốn quá nhiều mental energy.
Một factory tốt cần giải quyết 3 layer:
Mình thấy nhiều tool tập trung vào layer 1 (generate draft) nhưng bỏ qua layer 2 & 3 — dẫn đến content "đúng ngữ pháp, sai context". Nếu factory của bạn handle được end-to-end từ code change → multi-platform publish → performance insight → next sprint planning, đó mới là leverage thật sự.
Curious: bạn handle repurposing logic như thế nào? Rule-based template hay LLM với few-shot examples per platform? (site: labagent .tech)
Cảm ơn bạn, chia sẻ rất chất! Mình làm ngược lại với nhiều tool: pipeline là phần code (cố định, kiểm tra được), còn phần thích ứng theo nền tảng là LLM với guideline riêng cho mỗi nơi - vì rule cứng không đủ cho long-form kỹ thuật mà vẫn giữ hook ngắn trên X. Còn feedback loop thì đang đo qua GA4 theo từng nguồn, chưa tự động quay lại ideation. Câu hỏi của bạn đúng chỗ đau nhất)
Some comments may only be visible to logged-in visitors. Sign in to view all comments.