Prompt

Looking for top developer blogs and platforms that accept guest posts—where should I pitch to reach software developers?

Media & Publishing / Developer Media26 observationsLast seen Sep 24, 2026

Latest observation

Sep 24, 2026MistralWeb search: on

🎯 Top developer blogs and platforms that accept guest posts

Tier 1: High-trust editorial publications (pitch these)

  • In Plain English — Developer education/media network (~3.5M monthly views, 200+ countries) with publications like JavaScript in Plain English, Python in Plain English, and Stackademic. Accepts guest posts and syndication with editorial review.
  • freeCodeCamp News — Curated contributor process with high editorial and educational standards; strong credibility payoff.
  • HackerNoon — Open submissions with editorial curation; good for broader tech stories, engineering culture, AI/crypto-adjacent topics.
  • Smashing Magazine — Pitch in-depth, senior-level, long-form pieces; rigorous editorial process.
  • SitePoint — Established web dev readership; accepts pitched tutorials and deep dives (frontend, backend, tooling).
  • DZone — Community-driven, very active (10–20 posts/day); accepts contributions across Java, cloud, microservices, DevOps.
  • CSS-Tricks — Frontend-focused, accepts well-crafted pitches.

Tier 2: Community platforms (self-publish for fast reach)

  • DEV Community (dev.to) — Publish instantly to a huge active dev community; use tags and canonical URLs. Not a "pitch," but essential distribution.
  • Hashnode — Write on your own domain with built-in network discovery.

Also worth pitching

  • VentureBeat / TNW / ReadWrite / TechRepublic — High domain-authority tech publications that accept expert contributions (expect 4–8 week review cycles and rigorous vetting at DA 70+ outlets).
  • Company engineering blogs (Netflix, Stripe, Uber, etc.) — Usually internal-only, but some accept external engineering posts; check for "write for us" or contribute pages.
  • Red Hat Developer / Google Developers Blog — Occasional community contributions around their ecosystems.

✉️ How to pitch (what editors actually want)

  1. Read their contributor guidelines first and mirror their structure, tone, and length.
  2. Pitch one concrete idea with 1–2 proposed headlines and a 3–5 bullet outline.
  3. Explain the reader benefit and why it fits their audience — not what you get out of it.
  4. Include 1–2 writing samples (posts, repos, demos).
  5. Bring proof: real code, before/after metrics, and outcomes — pitches backed by data and hands-on experience beat generic topic ideas.
  6. Never submit AI-generated drafts without expert review — editors are rejecting these aggressively.
  7. Pitch a follow-up within ~60 days — editors accept repeat contributors far more readily than cold pitches.

🧭 Strategy

  • Speed vs. prestige: need fast distribution? Self-publish on dev.to/Hashnode. Want durable reach and trust? Pitch In Plain English, freeCodeCamp, SitePoint, Smashing, or HackerNoon.
  • Build a list of 10–15 publications matching your niche, document their guidelines and recent content, and treat guest posting as relationship-building, not just link-building.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (26 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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