DEV Community: Farhan Munir

✅ Milestone Completed: Prometheus Data Format Integration

Farhan Munir — Sun, 19 Apr 2026 07:36:58 +0000

✅ Milestone Completed: Prometheus Data Format Integration

This sprint focused on making telemetry output fully Prometheus-compatible while preserving our existing collector pipeline for CPU, memory, and disk I/O.

What I implemented

Added Prometheus text exposition format (v0.0.4) output
Included # HELP and # TYPE metadata
Supported gauge and counter metric families
Added label-based dimensions (CPU modes, disk devices)
Kept output deterministic and scrape-ready for a /metrics style endpoint

Metrics now covered

CPU usage and per-mode CPU time distribution
Virtual memory: used, available, total
Swap memory: used, total
Disk I/O bytes: read/write (aggregate + per-device)
Disk I/O operations: read/write (aggregate + per-device)

Validation results

Output verified against Prometheus exposition rules
No syntax violations found
Metrics are parseable and scrape-ready

Prior milestone context (OpenMetrics)

Before this, I shipped OpenMetrics-aligned output for the same core Linux host metrics with:

# HELP, # TYPE, # UNIT metadata
# EOF termination
Valid CPU, memory, and disk payload output

Next improvements

Evaluate ratio-based CPU values (0–1) vs percentage
Extend optional unit handling for stronger tooling interoperability
Continue OpenMetrics alignment where needed

Repo: https://github.com/ronin1770/heka-insights-agent

Milestone 2: Standardizing Telemetry Output with JSON, Prometheus, and OpenMetrics

Farhan Munir — Thu, 16 Apr 2026 15:25:23 +0000

Milestone 2: Standardizing Telemetry Output with JSON, Prometheus, and OpenMetrics

In this milestone, we are focusing on one thing only: data format standardization.

The Heka Insights Agent already collects CPU, memory, and disk telemetry.

Now the goal is to emit the same logical metrics in three standard output formats:

JSON
Prometheus text exposition
OpenMetrics text format

Why This Milestone Matters

If an agent has no clear format strategy, every downstream integration becomes custom work.

That slows down adoption and increases maintenance cost.

By standardizing format early, we get:

stable contracts for integrations
easier validation and testing
portability across observability stacks
clearer boundaries between collection and export

Milestone Scope (Only Data Format)

This milestone does not include transports, retry logic, or backend adapters.

It only covers how telemetry is represented and serialized.

Included:

canonical internal metric model
naming/type/unit rules
serializers for json, prometheus, openmetrics
deterministic output behavior
contract tests with golden files

Out of scope:

Datadog/New Relic senders
batching/compression/persistence
new collector domains

Canonical Metric Contract

Every metric will be representable through one shared contract:

name (string)
description (string)
type (gauge or counter)
unit (e.g. bytes, seconds, percent, count)
value (number)
labels (map of string to string; empty allowed)
timestamp_unix_ms (optional integer)

This contract is the core design decision in Milestone 2.

Serializers consume this model and render format-specific output without changing metric meaning.

Naming and Semantics Rules

To keep the output stable and machine-friendly:

metric names are lowercase snake_case
all names are prefixed with heka_
counters end in _total
unit suffixes are explicit (_bytes, _seconds, _percent)
label keys are lowercase snake_case
metric identity must stay consistent across formats

Current Metric Mapping

Initial canonical mapping includes:

heka_cpu_usage_percent (gauge)
heka_cpu_time_percent (gauge with mode=<field>)
heka_memory_virtual_used_bytes (gauge)
heka_memory_virtual_available_bytes (gauge)
heka_memory_virtual_total_bytes (gauge)
heka_memory_swap_used_bytes (gauge)
heka_memory_swap_total_bytes (gauge)
heka_disk_read_bytes_total (counter)
heka_disk_write_bytes_total (counter)
heka_disk_reads_total (counter)
heka_disk_writes_total (counter)

Format-Specific Requirements

JSON

UTF-8 JSON object
includes schema_version (starting at v1)
includes generated_at (RFC3339 UTC)
includes top-level metrics array

Prometheus

Prometheus text exposition format (0.0.4)
include # HELP and # TYPE lines
deterministic label ordering
no OpenMetrics-only directives

OpenMetrics

OpenMetrics text format
include # HELP, # TYPE, and # UNIT when known
terminate payload with # EOF
metric names and labels remain aligned with Prometheus mode

Configuration Contract

One selector controls serialization:

OUTPUT_FORMAT=json|prometheus|openmetrics

default: json
invalid values: fail fast with a clear startup error

Acceptance Criteria

Milestone 2 is done when:

same logical metric set is emitted in all three formats
names/types/units are consistent
Prometheus and OpenMetrics outputs validate
JSON includes schema metadata and metrics array
output order is deterministic
golden-file tests exist for each format

GitHub Milestone Breakdown

Work is tracked through:

M2-1 canonical metric model
M2-2 collector-to-canonical mapping
M2-3 JSON serializer
M2-4 Prometheus serializer
M2-5 OpenMetrics serializer
M2-6 output format config + validation
M2-7 fixture/contract tests
M2-8 docs update

Repo

Heka Insights Agent Update: Architecture + Configuration Docs Now Reflect Runtime Behavior

Farhan Munir — Thu, 16 Apr 2026 05:37:17 +0000

Build Update (April 16, 2026)

This week I focused on documentation quality and operational clarity for heka-insights-agent.

The goal was simple: make docs match the code exactly, so contributors and operators can reason about behavior without reading every module first.

What I updated

Rewrote docs/architecture.md from scratch
Rewrote docs/configuration.md from scratch
Expanded README.md with project context, setup, and environment guidance

Architecture documentation improvements

docs/architecture.md now documents:

the actual runtime topology and control loop in src/main.py
collector boundaries and behavior (CPUCollector, MemoryCollector, DiskCollector)
logging subsystem behavior in src/logger/config.py
current payload shapes emitted by collectors
known gaps (no sender layer yet, no tests yet, no schema versioning yet)
practical extension points for next phases

This gives a real “as-implemented” architecture baseline instead of aspirational text.

Configuration documentation improvements

docs/configuration.md now includes exact behavior for the two active runtime settings:

LOG_LOCATION
CPU_POLL_INTERVAL_SECONDS

It also documents:

source/precedence rules
defaults and validation behavior
failure modes
local setup and production recommendations

Important behavior clarified

Current config loading is split across two env files:

root .env is used for LOG_LOCATION
src/.env is used for CPU_POLL_INTERVAL_SECONDS

That split is now explicitly documented to reduce startup/debug confusion.

Why this matters

For an agent project, docs are part of reliability.

Operators need to know what can fail at startup, where config is read from, and what telemetry shape to expect downstream.

This update makes onboarding and future refactors safer.

Next steps

add transport/sender layer (backend adapters)
add collector-focused tests
consolidate config loading into a single source
define schema/versioning strategy for emitted payloads

Repo: https://github.com/ronin1770/heka-insights-agent

Reel Quick - Added Docker Support

Farhan Munir — Wed, 15 Apr 2026 14:17:25 +0000

Date: 2026-04-15

Project: Reel Quick (FastAPI + Next.js + ARQ + Mongo + Redis + optional GPU workers)

Context

We containerized the stack and tried to run it in production mode with Docker Compose.

Initial startup failed for both frontend build and backend runtime.

Issues Found

Docker Compose path mismatches:
Wrong env_file paths (docker/env/*.env expected but files were in docker/).
Wrong nginx config mount path (./docker/nginx/nginx.conf while actual file was docker/nginx.conf).
Build context was incorrect for a compose file located inside docker/.
Frontend TypeScript build failure:
location field type mismatch in frontend/app/create_video/page.tsx.
Value inferred as string | undefined but state expects string | null.
Backend container crash on startup:
ModuleNotFoundError: No module named 'db'.
backend/main.py used non-package imports like from db import ....

Root Causes

Relative paths in compose were not aligned with actual file layout.
Optional API response property (file_location?) was used directly inside state update.
Backend entrypoint (uvicorn backend.main:app) requires package-safe imports (backend.*).

Fixes Applied

Docker and Compose

Updated docker/docker-compose.yml:
- build.context changed from . to ...
- env_file paths corrected to backend.env and mongo.env.
- nginx bind mount fixed to ./nginx.conf.
Updated docker/backend.env:
- Added:
- UPLOAD_FILES_LOCATION=/app/video_files
- INPUT_FILES_LOCATION=/app/video_files
Added repo-root .dockerignore (Docker uses ignore file from build context root).
Synced docker/dockerignore entries.
Updated docker/README-docker-prod.md run commands.

Frontend

Fixed type narrowing in frontend/app/create_video/page.tsx:
- Captured file_location into uploadedLocation.
- Guarded before setFiles(...).
- Used guaranteed string value in state update.

Backend

Converted backend imports to package imports in backend/main.py:
- from db import ... -> from backend.db import ...
- Similar conversion for logger, models, objects, workers.
Updated backend/objects/sound_prompt_preset.py import to backend.objects....

Commands Used for Deploy

# Stop all running containers (host-wide)
docker ps -q | xargs -r docker stop

# Start Reel Quick with GPU workers
cd /home/farhan/reel-quick/docker
docker compose --profile gpu up -d --build

# Verify
docker compose ps
docker compose logs -f api --tail=200

Validation Checklist

docker compose ps shows api, frontend, nginx, mongo, redis, workers as running.
api logs no longer show ModuleNotFoundError: No module named 'db'.
Frontend image builds successfully (npm run build passes in container build stage).
Upload endpoint works (POST /uploads).
Workers/control panel endpoints return expected data.

Key Takeaways

Keep compose file paths consistent with its directory and build context.
Use package-qualified imports for Python app modules in containerized runtimes.
Narrow optional API fields before state updates in strict TypeScript projects.
Add .dockerignore at the actual build context root to avoid bloated builds.

Build Log: Implementing Full Text Overlay Feature in Reel Quick (with Accurate Live Preview)

Farhan Munir — Fri, 10 Apr 2026 05:46:36 +0000

Build Log: Implementing Full Text Overlay Feature in Reel Quick (with Accurate Live Preview)

In this build, I implemented the complete text overlay workflow in Reel Quick: from UI controls to background processing, plus a preview system that better matches final output.

Repo: https://github.com/ronin1770/reel-quick

Why this feature mattered

The earlier flow allowed adding overlay text, but styling control was limited and preview confidence was low.

Users needed to:

control text appearance (size, color)
control placement (top/center/bottom)
preview changes instantly before processing
avoid trial-and-error renders

The goal was to make text overlays practical for real reel production, not just a placeholder UI.

What the feature now includes

1. Overlay content + timing

Users can create overlays with:

text
start time
end time

2. Style controls

Added styling inputs in the dialog:

Font size range: 40–200
Text color:
- HTML5 color picker (input type="color")
- HEX input (synced with picker)
Position selector:
- top
- center
- bottom

3. Live preview (client-side only)

The overlay updates instantly while editing:

no backend call
no queue call
no video reprocessing

This lets users iterate quickly before clicking process.

4. Dialog UX improvements

The modal was redesigned to be usable at production scale:

controls on the left
preview on the right
scrollable modal for smaller viewports
action buttons always reachable

The key technical challenge: preview/output mismatch

A big issue was that selected preview size didn’t visually match rendered output.

Root cause

Frontend preview text used raw CSS px in a display container, while backend renders text on actual output video resolution.

Same number, different render context.

Fix strategy

I changed preview scaling logic to account for real video dimensions:

Read intrinsic source dimensions from video metadata (videoWidth, videoHeight)
Measure actual preview frame dimensions in the modal
Compute scale factor:
- scale = min(previewWidth/sourceWidth, previewHeight/sourceHeight)
Render preview text as:
- previewFontSize = selectedFontSize * scale
Preserve source aspect ratio in preview container
Mirror vertical placement behavior (top/center/bottom with scaled edge padding)

Result: preview size and placement now feel much closer to final rendered video.

API + backend integration

Good news: backend already supported style fields, so no backend API redesign was needed.

The frontend sends per-overlay payload with:

style.font_size
style.text_color
position.preset

Then it follows the existing pipeline:

Save overlays POST /videos/{video_id}/text-overlays
Enqueue processing POST /enqueue/text-overlay
ARQ worker picks job and runs MoviePy text overlay composition

Data flow (end to end)

User opens text overlay dialog
Configures text + timing + style + position
Verifies in live preview
Saves overlay config
Enqueues job
Worker validates and renders final video
Processed text-overlay video becomes available for download

Validation and guardrails

Font size is clamped to configured range
HEX color is normalized/validated
Overlay timing is validated before processing
Position options are constrained to supported presets
Preview updates remain instant and local

What changed from earlier behavior

Old frontend default style used a very small fixed size
New feature provides interactive style control + accurate preview scaling
Modal UX is now horizontal and production-friendly

What’s next

Potential follow-ups:

font family selection
stroke/shadow controls in UI
drag-and-drop custom placement
multiple overlay tracks/timeline editing

If you want, I can also generate:

a shorter Dev.to teaser version
an accompanying screenshot checklist for the post
a “before vs after” section with technical diff notes

Build Log: Shipping a Lean Python Telemetry Agent (CPU, Memory, Disk)

Farhan Munir — Wed, 08 Apr 2026 09:29:51 +0000

Build Log (April 8, 2026)

Today I implemented the first production-ready telemetry collectors for heka-insights-agent and wired them into the main polling loop.

What I built

Added an optimized CPUCollector in src/collectors/cpu.py
Added a MemoryCollector in src/collectors/memory.py
Added a DiskCollector in src/collectors/disk.py
Wired all collectors into src/main.py with a shared loop
Added environment-based poll interval support via CPU_POLL_INTERVAL_SECONDS
Added python-dotenv in requirements.txt

CPU collector design

I built CPU collection around psutil.cpu_times(...) snapshots and delta math (single source), instead of calling both cpu_percent and cpu_times_percent per cycle.

Key design points:

No thread offloading (to_thread) for this workload
First cycle is warm-up by design
Supports basic and detailed output modes
Optional per-core output
Uses MonotonicTicker to keep fixed cadence without drift

Memory collector design

Memory collection is intentionally lightweight:

One call each to psutil.virtual_memory() and psutil.swap_memory()
basic mode returns compact key fields
detailed mode returns full psutil fields
Raw byte values are preserved (server-side compute handles transformations)

Disk collector design

For disk, I chose cumulative I/O counters (not rates) because central compute is done server-side.

Uses psutil.disk_io_counters(perdisk=True)
Returns aggregate and per-disk counters
Filters to physical devices only
Excludes partitions from per-disk payload
Added device-name cache with periodic refresh to reduce repeated filtering overhead

Main loop wiring

src/main.py now runs:

CPU collector
Memory collector
Disk collector

All on the same interval, with separate log lines per collector.

Poll interval is loaded from .env via:

CPU_POLL_INTERVAL_SECONDS

Invalid values fall back safely to default 5.0s.

Profiling notes

I profiled a 120-second run and reviewed both process stats and cProfile output.

Key findings:

Agent CPU cost is very low (near-idle for this polling interval)
Max RSS is about 15 MB
Runtime is dominated by intentional sleep (expected)
Collector costs are small; disk collection is the heaviest of the three

What changed after profiling

Based on profile output, I optimized disk collection further:

Added cached physical-device list to avoid filtering every cycle
Kept output shape unchanged (disk_io + disk_io_perdisk)

Current status

The agent now has a clean baseline telemetry pipeline with low overhead and clear extension points for transport/shipping.

Next planned work:

Add payload shipping to backend endpoint
Add bounded retry/backoff
Add collector-focused tests

Repo URL

ronin1770 / heka-insights-agent

A lightweight agent for collecting essential Linux system telemetry and shipping it to a configurable backend.

heka-insights-agent

A lightweight agent for collecting essential Linux system telemetry and shipping it to a configurable backend.

Test

View on GitHub

Build Log: End-to-End Text Overlay Workflow for Video Processing

Farhan Munir — Thu, 02 Apr 2026 16:04:23 +0000

Build Log: End-to-End Text Overlay Workflow for Video Processing

Today I completed the core text overlay workflow across the frontend, backend, and worker pipeline. The main goal was to make text overlays behave like a real production feature instead of a partial UI action. That meant saving overlays, enqueueing processing jobs, running worker execution, tracking status, and making sure users can download the final processed file when rendering is complete.

What changed

The biggest update was implementing the full text overlay processing lifecycle from the UI down to the background worker.

A user can now create overlays for a video, save them through the API, enqueue a rendering job, and return to the videos page while processing continues in the background. Once the job is finished, the videos listing reflects that state and download actions point to the processed overlay output instead of the original file.

I also finalized one important behavior change in overlay saving. Instead of merging new overlays with existing ones, each save call now replaces the stored overlays for that video. This prevents stale overlay entries from lingering and causing recurring overlap conflicts.

Backend work completed

On the backend, I added the API and queue flow required to support the feature end to end.

The following routes are now in place:

POST /videos/{video_id}/text-overlays
POST /enqueue/text-overlay
GET /text-overlay-jobs
GET /videos/{video_id}/text-overlays/download

The save endpoint now stores the submitted overlays for a given video. The enqueue endpoint pushes a text overlay processing job into the queue and returns quickly, so the frontend does not need to wait on worker readiness before moving forward.

To support job visibility, I introduced a text_overlay_jobs collection for tracking processing records. This collection is now used for queued, pending, finished, and failure state tracking. I also added the required database initialization and an index on text_overlay_jobs.video_id so lookup remains efficient.

On the worker side, I added process_text_overlay_job and wired a dedicated text_overlay_worker into the queue system using TEXT_OVERLAY_QUEUE_NAME. That worker is also registered in the control panel worker setup so it is managed consistently with the rest of the background processing stack.

Another important backend fix was changing overlay persistence behavior from merge to replace. Earlier behavior allowed stale overlays to remain in the record, which could trigger overlap validation conflicts even after the user thought they had corrected the layout. Replacing overlays on save solved that problem cleanly.

Frontend updates

On the frontend, the CreateTextOverlayPage is now connected to the real processing flow.

The Done / Process Video action now performs the expected sequence:

Save overlays
Enqueue the text overlay job
Redirect the user back to /videos

I also added a loading and disabled state to prevent duplicate submissions while the request is in progress. This gives the action more predictable UX and reduces accidental repeat clicks.

Error handling was improved as well. Instead of showing vague failures, the frontend now parses backend validation messages more clearly, including field paths where possible. That makes it much easier to debug malformed overlay payloads during testing.

Because some UI controls are still not finalized, I added temporary default values for fields that are not yet exposed in the interface. Current defaults are:

position: top/center
style: font size 16, weight normal

These defaults let the feature move forward without blocking on the final design of overlay controls.

Videos page behavior

I also updated the /videos listing page so the new workflow feels integrated instead of isolated.

The Video ID column was removed to simplify the table and give more room to user-facing actions.

The Text Overlay column now reflects job state more meaningfully. If the overlay job for a video is finished, the action shows Done. Otherwise, it shows Create.

Download behavior was also updated. When an overlay job is finished successfully, the output and error download actions now point to the processed overlay video instead of the original file. If overlay processing has not completed, downloads continue to use the regular video file.

That small change is important because it makes the final output feel connected to the workflow the user just triggered.

Why the replace-on-save decision mattered

One of the more important logic decisions today was changing overlay saving from merge semantics to replace semantics.

Merging sounds convenient at first, but in practice it created stale state problems. Old overlays could remain attached to the video even after the user thought they were working from a clean set. That caused overlap validation issues to reappear and made the experience feel inconsistent.

Replacing overlays on each save call makes the system much more predictable. The saved overlays now reflect exactly what the user submitted in the latest request, which is the safer behavior for this kind of editor flow.

Current workflow

At this point, the text overlay feature supports the following flow:

User opens the text overlay page for a video
User adds or edits overlays
Frontend saves the overlays
Frontend enqueues a processing job
Worker renders the text overlay output in the background
Job status is tracked in text_overlay_jobs
Videos page reflects completion state
Download action serves the overlay-rendered file when available

That means the feature is now functional across storage, processing, status tracking, and output retrieval.

What is still temporary

A few parts are still intentionally temporary or transitional.

The overlay editor is still using fallback defaults for position and style where dedicated UI controls have not been built yet. The current implementation is enough to keep the workflow functional, but the page still needs the final interactive form controls for layout and style management.

The videos page is also currently showing a simplified status signal. This works for now, but it could later become more expressive with states like queued, processing, failed, and finished.

Final result

This was a solid progress day because the feature moved from partial UI scaffolding into a real end-to-end system. The frontend now triggers meaningful backend work, the worker actually processes jobs, job state is persisted, and the final output can be downloaded through the app.

Most importantly, the workflow now behaves in a predictable way. Saving overlays replaces prior state, queueing does not block on hard worker health checks, and completed processing is reflected directly in the videos list.

The next step is to improve the overlay editor UI itself, but the underlying pipeline is now in place.

Build Log: Added the Text Overlay Page UI for Reel Quick

Farhan Munir — Wed, 01 Apr 2026 10:06:27 +0000

Build Log: Added the Text Overlay Page UI for Reel Quick

Today I worked on the frontend for Reel Quick and added the first version of the Text Overlay page.

The goal was to create the page structure and user flow for adding text overlays to a video, without wiring up the full backend save/process flow yet.

What I added

I introduced a new page for creating text overlays:

Route: /create-text-overlay/<video-id>
Added a per-video link from the /videos page so each video now has a Create action for text overlays
Added a scaffolded frontend page that we can expand later into the full editor

UI decisions locked before implementation

Before building, I confirmed a few product decisions:

This phase is design only
Video data should be fetched from the backend API
“Done / Process Video” is UI-only for now
Time display should use mm:ss format like 00:00
Users can add and delete overlays for now
No editing flow yet

That helped keep the implementation focused on layout and interaction, not backend behavior.

What the new page includes

The new Text Overlay page now has:

A header and basic video metadata area
A left panel for video preview
A right panel listing the overlays added so far
A button to open an Add Text Overlay dialog
Delete action for each overlay item

The video preview is designed to load using the backend video API, and completed videos can use the download endpoint for preview.

Add Text Overlay dialog

When the user clicks Add Text Overlay, a dialog opens with three inputs:

Text
Start Time
End Time

The timing controls are implemented as sliders:

Start Time slider: from 0 to video length - 1
End Time slider: from start + 1 to video length

This ensures one important rule stays true:

End time must always be greater than start time.

I also added state logic so the UI enforces that constraint automatically instead of depending only on validation later.

Time formatting

Overlay timings are shown in mm:ss format.

Examples:

00:00
00:12
01:45

That keeps the interface easy to scan and closer to how users expect media timing to look.

Current scope

This is intentionally not connected to the full processing flow yet.

Right now:

the page is focused on structure and interaction
overlays can be added and deleted in the UI
the “Process Video” action is only a placeholder for now

That gives a usable frontend foundation before moving into API integration and processing workflows.

Files touched

Main changes were made in:

CreateTextOverlayPage.tsx
dynamic route page for /create-text-overlay/<video-id>
VideoList.tsx to add the link from the videos table

Environment note

I was not able to run frontend lint/build checks in this environment because npm was not available:

npm: command not found

So this step was completed as a UI implementation pass, but not fully validated through local frontend tooling in the current container.

Why this matters

This is a small but useful step toward turning Reel Quick into a more complete video editing workflow.

Instead of jumping straight into processing logic, I wanted to first lock down:

page flow
interaction model
timing constraints
overlay creation experience

That makes the next step clearer: connect the UI to backend save and processing endpoints.

Repo

Project repo: https://github.com/ronin1770/reel-quick

Next step

Next I’ll move from UI scaffolding into actual overlay persistence and processing integration so the page can do more than just model the workflow.

I Added a Queued Video Text Overlay Workflow to Reel Quick

Farhan Munir — Tue, 31 Mar 2026 07:32:18 +0000

Project repo: ronin1770/reel-quick

I recently worked on a backend improvement in Reel Quick that solved a practical workflow problem around text overlays on videos.

Instead of coupling overlay editing directly with processing, I moved the feature to a two-step queued workflow.

What I changed

I introduced two separate backend actions:

POST /videos/{video_id}/text-overlays

Saves or merges text overlay items for a video.
POST /enqueue/text-overlay

Queues processing only when the user is actually ready.

Why I changed it

The old approach risked a few common problems:

processing could start before all overlays were finalized
repeated edits could create duplicate overlay records
there was no clean way to track queued, running, completed, or failed jobs

By separating save from process, the flow became more reliable for users and much easier to reason about on the backend.

Key implementation decisions

A few details mattered here:

multiple overlays can be saved for a single video
duplicate overlay saves are deduped by overlay_id
enqueue accepts only video_id
the backend resolves the input path from the stored video record
processing requires the source video to already be completed
a separate text_overlay_jobs collection tracks async job state

Why this pattern works

I like this design because it reflects how users actually work.

They want to:

add and tweak overlays
save changes while editing
click process only when everything is ready

That sounds simple, but it makes a big difference in reliability.

Repo

Project repo: ronin1770/reel-quick

Final thought

This was a useful reminder that not every feature should be handled in one request-response cycle.

Sometimes the better design is to let users build state first, then explicitly enqueue background processing when they’re done.

I Built an Open Source Tool to Automate Instagram Reels — Looking for Contributors

Farhan Munir — Sun, 29 Mar 2026 06:15:37 +0000

🚀 Why I Started This Project

Creating short-form content (especially Instagram Reels) is still surprisingly manual.

Even with AI tools available, the workflow usually looks like this:

Generate content ideas
Write scripts manually
Edit videos using multiple tools
Stitch everything together
Export and upload

It’s fragmented, time-consuming, and error-prone.

I wanted to simplify this.

💡 What I’m Building: Reel Quick

I started building Reel Quick, an open-source project aimed at automating parts of the Reels creation workflow.

👉 Repo: https://github.com/ronin1770/reel-quick

The goal is simple:

Build a system that helps developers and creators generate and process short-form video content programmatically.

⚙️ What It Does (Current Direction)

The project focuses on:

Automating video generation workflows
Integrating AI for content generation
Simplifying pipelines for short-form media
Making the system extensible for developers

This is not just a tool — it’s meant to be a foundation that others can build on.

🧱 Tech Direction

Current and planned areas include:

Python backend
Video processing (FFmpeg / pipelines)
API-based architecture
AI/LLM integrations for content generation
Automation workflows
🧪 Why Open Source?

Because this problem is bigger than a single implementation.

There are many directions this can go:

AI-generated reels
Automated editing pipelines
Content scheduling systems
Creator tools

Open source allows experimentation and faster iteration.

👥 Looking for Contributors

I’m looking for developers interested in:

Python / backend systems
AI / LLM integrations
Video processing
API design
System architecture

You can contribute by:

Fixing bugs
Suggesting improvements
Building features
Improving documentation
📌 Where to Start

👉 GitHub: https://github.com/ronin1770/reel-quick

Check:

Issues
Roadmap (coming next)
Contribution guidelines
🔥 Final Thoughts

This is an early-stage project, and that’s exactly why it’s a good time to get involved.

If you enjoy building systems, experimenting with AI, or working on automation pipelines — this could be a fun project to collaborate on.

Let’s build something useful together.