Hello developers! I'm Charlie. I've independently developed three AI tools: TransMonkey, Imgkits, and TeachAny. I'll be sharing my architecture, automation stack, and the real-world challenges I encountered while using these products.
1.Architecture for a solo developer Frontend
- I use Next.js, React, and Tailwind.
- I choose them for SEO, reuse, and fast themes. I use MDX for docs and demos.
Backend
- I run serverless functions for burst tasks like translate, generate, and export.
- I run a job queue and workers for long tasks like OCR, media, and large translations.
- I store assets and previews in object storage behind a CDN.
Data and documents
- I use Postgres for jobs, quotas, and audit logs.
- I parse document layout into blocks, lines, and tables to keep structure.
- I use small embeddings only when retrieval helps, like glossaries and terms.
AI and ML pipelines
- I do OCR. I build a layout graph. I translate by segment to keep order.
- I use LLMs for text. I use vision models with masks and seeds for control.
- I add guardrails after generation. I map font fallback. I apply RTL/LTR rules. I add safety filters.
Observability and reliability
- I send logs and traces to one place. I add per-job metrics.
- I track outcome metrics that users feel: table cell parity, subtitle drift, brand color delta, plan readability score.
- I use feature flags and staged rollouts.
Security and privacy
- I keep data for a short time by default.
- I provide delete endpoints. I collect minimal PII. I show clear controls.
2.Automation stack that helps me ship fast DevOps
- I use CI/CD with PR previews.
- I run tests for layout parity, subtitle timing, color deltas, and rubric checks.
- I use Infra as Code for repeat setups.
- I schedule cleanup jobs for previews and temp keys.
Workflow automation
- I orchestrate with queues. I retry with backoff. I send failed jobs to a dead-letter queue.
- I show early previews and honest progress bars.
- I send webhooks when tasks finish.
3.Problems I met that match my products, and how I fixed them
Let me first briefly introduce my three artificial intelligence tool websites.:“
TransMonkey: AI translation for 130+ languages that preserves original layout across PDFs, Word, Excel, images, video, and audio.
Imgkits: AI creative studio that turns prompts into high‑quality images/videos with strong control and built‑in editing.
TeachAny: AI teaching assistant that adapts materials to classroom context and diverse student needs.”
A.TransMonkey: translation with layout intact and real-world files Real problems:
- Many PDFs have mixed fonts. Some fonts miss glyphs after translation. Text shows as blocks.
- Many tables break after translation. Cell counts change. Merged cells split. Borders shift.
- Many files mix RTL and LTR. RTL in tables flips order. Numbers wrap wrong.
- Long SRT/VTT files drift after translation. Line length changes, so timings slip.
- Scans are low quality. Skew and noise cause wrong reading order.
Fixes:
- I run OCR with skew fix and denoise. I detect blocks, lines, and tables. I keep reading order.
- I build a layout graph for pages, headers, paragraphs, tables, and captions.
- I translate by region. I keep tokens per cell. I preserve cell count.
- I map font fallback per script. I test glyph coverage before render. I pick safe fonts automatically.
- I apply RTL/LTR rules per region. I keep number direction and punctuation rules.
- I retime subtitles after translation. I cap line length. I keep gaps. I check overlap and drift.
- I run post checks: page parity, table cell parity, caption timing, and font coverage.
B. Imgkits: on-brand image and video generation with stable framing
Real problems:
- Prompts change framing. Faces move. Key objects crop.
- Brand colors drift across batches. One variant is too dark. Another is too bright.
- Small text becomes blurry at social sizes. Upscaling breaks strokes.
- Background removal leaves halos on hair and glass.
- Users want 3–6 good options fast, not 100 random images.
Fixes:
- I use masks to lock composition. I set control points for subject and horizon lines.
- I fix seed and guidance per brand preset. I vary only one parameter per batch.
- I measure color delta to brand palette. I reject out-of-range shots. I adjust with LUTs.
- I render at target size or exact scale factors. I use vector text or SDF text layers when possible.
- I use matting models with hair detail. I add edge-aware feather and color decontaminate.
- I produce 3–6 controlled variants per job. I label differences. I let users pick fast.
C. TeachAny: lesson plans for mixed-ability classes that are practical
Real problems:
- One plan does not fit all levels. A single reading level loses many students.
- Many outputs are too long. Teachers have 40 minutes and one device per group.
- Some activities are not checkable. Teachers need quick checks for understanding.
- Accommodations are vague. Teachers need concrete supports they can use now.
- Standards mapping is weak. Plans miss the target objective.
Fixes:
- I collect standards and clear objectives first. I lock time and device limits.
- I define learner levels. I add supports like sentence frames, visuals, and adjustable text.
- I generate multi-level tasks for the same goal. I include an exit ticket or quick check.
- I keep outputs short. I add steps, timings, and materials. I make it editable.
- I add teacher notes with why this helps. I show alignment to the standard.
Conclusion
I independently developed three AI tool websites. I resolved issues such as missing layouts, brand drift, and one-size-fits-all approaches. I used a simple stack, clear checks, and robust security measures. Now, the products I deliver are trustworthy. I will continue to leverage user feedback to improve these products.
Top comments (3)
Really cool to see how you’ve approached scaling AI products solo! The way you used queues, retries, and feature flags feels like something most teams overlook until it’s too late.
Nice work Charlie
Thank you for the compliment.Olivia John
Comments welcome