Prompt

Where to submit coding tutorials

Media & Publishing / Developer Media4 observationsLast seen Aug 31, 2026

Latest observation

Aug 31, 2026Perplexity

Here are the best places to submit coding tutorials in 2026, grouped by how they work and what they’re best for.


Open publishing platforms (self‑serve, fast)

These let you publish directly; some have light editorial review.

In Plain English (plainenglish.io)

  • Best for: Broad reach across programming topics (JS, Python, AI, cloud, etc.).
  • How it works: Create an account, write or import your tutorial, and submit to relevant sub‑publications.
  • Why use it: Large developer readership, good distribution, and supports canonical/syndication.

DEV Community (dev.to)

  • Best for: Community feedback and visibility.
  • How it works: Free account → write in Markdown → publish.
  • Why use it: Very active dev audience, great for tutorials, how‑tos, and project walkthroughs. Supports canonical URLs if you’re cross‑posting.

Hashnode

  • Best for: Building your own dev blog with community discovery.
  • How it works: Create a blog (custom domain on Pro), publish tutorials, optionally submit to Hashnode publications.
  • Why use it: Good balance of ownership + reach; dev‑native audience.

Stackademic

  • Best for: Educational, learner‑focused coding tutorials.
  • How it works: Publish via their publications on Medium/Stackademic.
  • Why use it: Strong for step‑by‑step tutorials and series aimed at learners.

HackerNoon

  • Best for: Technical deep dives and opinionated tutorials.
  • How it works: Open publishing with editorial oversight.
  • Why use it: Large tech audience; good for more advanced or niche topics.

Medium (and tech publications)

  • Best for: Broad tech audience, including non‑pure‑dev readers.
  • How it works: Write on your profile or submit drafts to publications (e.g., Better Programming, JavaScript In Plain English).
  • Why use it: Good reach and curation; some pubs are very tutorial‑friendly.

More editorial / curated publications

These have higher bars but can give you strong credibility and reach.

freeCodeCamp News

  • Best for: High‑quality, beginner‑friendly tutorials (web, data, AI).
  • Process: Submit via their publication; editorial review; they often request revisions.
  • Why use it: Very high traffic and trust; great for evergreen tutorials.

DZone

  • Best for: DevOps, cloud, architecture, enterprise dev tutorials.
  • Process: Contributor program; editorial review.
  • Why use it: Strong in backend/infra topics; good for advanced content.

InfoQ / The New Stack

  • Best for: Architecture, cloud‑native, advanced engineering tutorials and explainers.
  • Process: More editorial; often commission or accept pitched pieces.

Smashing Magazine / CSS‑Tricks / LogRocket

  • Best for: Frontend, performance, UX‑heavy tutorials.
  • Process: Paid or invited contributions; strong editorial process.
  • Why use it: Prestige in the frontend community; long‑lasting articles.

Niche / topic‑specific options

Depending on your stack:

  • DigitalOcean Community – Hands‑on cloud/devops tutorials.
  • Twilio Blog – Communications, APIs, serverless, dev tutorials.
  • Auth0 / Okta Blog – Identity, security, auth tutorials.
  • MongoDB, Redis, Elastic, Confluent blogs – Database/streaming/data tutorials.
  • IEEE workshops / conferences – For more academic or formal tutorial proposals (e.g., ITW’26, DeepLearningIndaba skill sessions).

These often have “Write for us” or “Become a contributor” pages and are great if your tutorial aligns with their product/tech focus.


Code‑centric and community platforms

Not traditional “articles,” but excellent for tutorial‑style content:

GitHub

  • Best for: Project‑based tutorials, starter repos, and docs.
  • How: Publish a repo with:
    • Clear README as the tutorial.
    • Example code, tests, and deployment instructions.
  • Why: Developers live on GitHub; great for “learn by cloning and running.”

Stack Overflow (and docs‑style Q&A)

  • Best for: Focused, problem‑solving tutorials in Q&A form.
  • How: Write high‑quality answers with code samples and explanations.

YouTube / video platforms

  • Best for: Visual, step‑by‑step coding tutorials.
  • How: Pair with a GitHub repo and blog post.
  • Why: Many devs prefer video walkthroughs; good for complex setups.

How to choose where to submit

  • For maximum reach with minimal friction: In Plain English, DEV, Hashnode, Stackademic.
  • For prestige and long‑term SEO: freeCodeCamp News, Smashing, DZone, InfoQ.
  • For project‑based learning: GitHub repos + a tutorial post on DEV/Hashnode/your blog.
  • For niche stacks: Vendor blogs (MongoDB, Twilio, Auth0, etc.) if your tutorial uses their tech.

A common pattern:

  1. Publish the canonical tutorial on your blog or Hashnode.
  2. Cross‑post to DEV and In Plain English (with canonical tags).
  3. Optionally adapt into a YouTube walkthrough and link everything together.

If you tell me your stack (e.g., React, Node, Python/ML, Go/infra) and level (beginner, intermediate, advanced), I can suggest 3–5 specific targets and how to pitch/submit to each.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (4 observations for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

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