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Prompt

I’m building a data tool and need media sites with a technical audience

Media publishing / Developer media1 observationLast seen Sep 23, 2026

Latest observation

Sep 23, 2026OpenAI APIWeb search: off

Absolutely — if you’re looking for media sites with a technical audience (useful for content distribution, partnerships, backlinks, paid placements, or audience research), here are strong options by category.

General tech media

  • TechCrunch
  • Wired
  • The Verge (more consumer-tech, but still technical readers)
  • Ars Technica
  • ZDNet
  • InfoWorld
  • Computerworld
  • CNET (broader audience, still tech-heavy)

Developer / software engineering audience

  • Dev.to
  • Hashnode
  • Hacker News (community-driven, very technical)
  • Stack Overflow Blog
  • InfoQ
  • DZone
  • Smashing Magazine (front-end / web dev)
  • CSS-Tricks (front-end, though publishing cadence varies)
  • GitHub Blog
  • Microsoft Dev Blog
  • AWS News Blog
  • Google Cloud Blog
  • Red Hat Blog

Data / AI / ML audience

  • Towards Data Science
  • KDNuggets
  • VentureBeat AI
  • The Batch (DeepLearning.AI)
  • Analytics Vidhya
  • Machine Learning Mastery
  • DataCamp Blog
  • OpenAI Blog
  • NVIDIA Technical Blog

Security / infra / cloud audience

  • Dark Reading
  • The New Stack
  • Cloudflare Blog
  • Akamai Blog
  • Snyk Blog
  • Palo Alto Networks Unit 42
  • CrowdStrike Blog
  • Krebs on Security

Open-source / engineering culture

  • LWN.net
  • Open Source.com
  • Linux.com
  • SourceForge Blog
  • Mozilla Hacks

Ways to use this list for a data tool

If your goal is to build a database of media sites with technical audiences, useful fields include:

  • Site name
  • Primary audience segment
  • Topic coverage
  • Estimated technical depth
  • Submission/guest post options
  • Newsletter availability
  • Social channels
  • Domain authority / traffic
  • RSS/feed availability
  • Paywalled vs open
  • Brand-safe / editorial-only flags

If you want, I can also turn this into:

  1. a CSV-style table,
  2. a tiered list by audience type, or
  3. a larger scrape-ready dataset structure with columns for enrichment.

Brands

TechCrunchWiredThe VergeArs TechnicaZdnetInfoworldComputerworldCnetDev.toHashnodeHacker NewsStack Overflow BlogInfoQDzoneSmashing MagazineCSS-TricksGithub BlogMicrosoft DevblogAws News BlogGoogle Cloud BlogRed Hat BlogTowards Data ScienceKdnuggetsVenturebeatThe BatchDeeplearningAnalytics VidhyaMachine Learning MasteryDatacamp BlogOpenai BlogNvidia Technical BlogDark ReadingThe New StackCloudflare BlogAkamai BlogSnyk BlogPalo Alto Networks Unit 42Crowdstrike BlogKrebs On SecurityLwnOpensourceLinuxSourceforge BlogMozilla Hacks

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation 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 Circuit. 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.

Want this interpreted for your brand?

Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.