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.