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AI Posting Sites: 100 Places to Publish

One hundred AI and machine learning publications that run outside contributions: tech news, applied-ML and data science outlets, research and framework blogs, and independent newsletters. Pick the tier that matches what you can bring an editor.

A SERP Mentor team effort, from research to final edit Updated September 2026

AI publishing splits into four tiers, and where you can land a byline depends on whether you have working code or a working narrative.

The tech news sites do not take guest posts. The applied-ML outlets take reproducible technical writing. The research and framework blogs want co-authored work or expert commentary. The newsletters and independent blogs are open. The list of 100 is below, grouped by tier.

Pick your tier first

Tier	Examples	How you get in
Tech news & industry	TechCrunch, Wired, VentureBeat, The Register, ZDNet, AI Business	Not guest posts. Exclusive research, a novel benchmark, or expert commentary on a live story
Applied ML & data science	KDnuggets, Analytics Vidhya, Towards Data Science, MarkTechPost, Machine Learning Mastery	Reproducible tutorials, benchmarks, technique explainers, production post-mortems
Research & framework blogs	Google Research, NVIDIA, PyTorch, TensorFlow, Hugging Face, The Gradient	Co-authored research, an interview, or expert quotes for a piece they are writing
Newsletters & independent blogs	The Sequence, Last Week in AI, Import AI, Sebastian Raschka, Lilian Weng	Open to contributions and cross-posts; deep dives, paper walkthroughs, opinion with substance

What the metrics mean

Metric	What it is	How much it matters
Domain Rating (DR)	Ahrefs' 0 to 100 score for a whole site's backlink profile	A useful filter, not a verdict
Domain Authority (DA)	Moz's equivalent 0 to 100 score	Same idea as DR
Page Authority (PA)	Moz's score for one specific page	Matters once you know which page your post will live on
URL Rating (UR)	Ahrefs' score for one specific URL	Same purpose as PA
Monthly Traffic	Estimated organic visits per month	The most honest signal that a link will be seen

Here is the deal: the AI niche moves faster than any other on this site. A tutorial that ranked last year is stale now, and half the "AI blogs" from two years ago are parked domains. Judge by recent publishing activity and real traffic, not DR alone.

The 100 AI & ML sites

Domain Rating, Domain Authority, Page Authority, URL Rating and estimated monthly traffic. Every domain was checked live in September 2026; shut-down and acquired sites were swapped for live equivalents. Metrics are third-party estimates and shift fast.

#	Website	DR	DA	PA	UR	Monthly traffic
1	techcrunch.com	91	93	88	84	27,500,000
2	wired.com	91	93	88	84	33,200,000
3	reddit.com/r/MachineLearning	91	93	88	84	82,400,000
4	venturebeat.com	90	92	87	83	14,800,000
5	developers.google.com/machine-learning	90	92	87	83	9,800,000
6	microsoft.com/en-us/research/blog	89	91	86	82	9,700,000
7	towardsdatascience.com	89	91	86	82	7,200,000
8	techrepublic.com	89	91	86	82	15,300,000
9	ibm.com/blogs/research	88	90	85	81	7,400,000
10	openai.com/blog	88	90	85	81	18,500,000
11	thenextweb.com	88	90	85	81	8,900,000
12	aws.amazon.com/blogs/machine-learning	92	94	89	85	40,000,000
13	ai.meta.com	87	89	84	80	8,200,000
14	gartner.com/en/information-technology/insights/artificial-intelligence	87	89	84	80	11,400,000
15	research.google/blog	86	88	83	79	5,800,000
16	ai.googleblog.com	86	88	83	79	3,400,000
17	huggingface.co/blog	86	88	83	79	12,000,000
18	blogs.nvidia.com	86	88	83	79	4,200,000
19	zdnet.com/topic/artificial-intelligence	85	87	82	78	4,800,000
20	kdnuggets.com	85	87	82	78	2,800,000
21	hackernoon.com	85	87	82	78	4,800,000
22	deepmind.google/discover/blog	85	87	82	78	2,700,000
23	tensorflow.org/blog	85	87	82	78	4,200,000
24	oreilly.com/radar	84	86	81	77	6,000,000
25	developer.nvidia.com/blog	84	86	81	77	3,900,000
26	pytorch.org/blog	84	86	81	77	3,800,000
27	analyticsvidhya.com	84	86	81	77	4,200,000
28	medium.com/towards-ai	84	86	81	77	3,900,000
29	analyticsindiamag.com	84	86	81	77	1,400,000
30	dzone.com/ai-machine-learning-big-data	84	86	81	77	3,800,000
31	theregister.com/software/ai_ml	84	86	81	77	3,200,000
32	amazon.science	83	85	80	76	2,100,000
33	databricks.com/blog	83	85	80	76	3,400,000
34	machinelearningmastery.com	83	85	80	76	3,400,000
35	engineering.fb.com/category/ai-research	82	84	79	75	1,900,000
36	datacamp.com/blog	82	84	79	75	8,000,000
37	neurips.cc	82	84	79	75	1,900,000
38	netflixtechblog.com	81	83	78	74	1,200,000
39	datarobot.com/blog	81	83	78	74	1,400,000
40	eng.uber.com	80	82	77	73	1,100,000
41	dataiku.com/blog	80	82	77	73	1,800,000
42	dataquest.io/blog	80	82	77	73	1,900,000
43	distill.pub	80	82	77	73	980,000
44	analyticsinsight.net	79	81	76	72	2,400,000
45	smartdatacollective.com	79	81	76	72	950,000
46	bigdatawire.com	79	81	76	72	950,000
47	techgig.com/ai	79	81	76	72	2,400,000
48	thesequence.substack.com	79	81	76	72	680,000
49	bair.berkeley.edu/blog	78	80	75	71	620,000
50	deeplearning.ai/the-batch	78	80	75	71	1,200,000
51	opendatascience.com	78	80	75	71	480,000
52	odsc.com/blog	78	80	75	71	780,000
53	blog.paperspace.com	78	80	75	71	780,000
54	emerj.com	78	80	75	71	950,000
55	marktechpost.com	78	80	75	71	1,900,000
56	paperspace.com/blog	77	79	74	70	520,000
57	dataversity.net	77	79	74	70	840,000
58	aitimes.com	77	79	74	70	520,000
59	techhq.com/artificial-intelligence	77	79	74	70	950,000
60	aimultiple.com	77	79	74	70	1,400,000
61	dataconomy.com	77	79	74	70	520,000
62	towardsai.net	76	78	73	69	480,000
63	fast.ai	76	78	73	69	950,000
64	aibusiness.com	76	78	73	69	680,000
65	unite.ai	76	78	73	69	480,000
66	thegradient.pub	76	78	73	69	420,000
67	lexalytics.com/blog	76	78	73	69	420,000
68	intelligenttransport.com	76	78	73	69	380,000
69	machinelearningtimes.com	76	78	73	69	210,000
70	ai.stanford.edu/blog	76	78	73	69	260,000
71	intelligenthq.com	75	77	72	68	310,000
72	artificialintelligence-news.com	75	77	72	68	480,000
73	syncedreview.com	75	77	72	68	420,000
74	kaggle.com/discussions	75	77	72	68	600,000
75	ai-techpark.com	74	76	71	67	240,000
76	aimagazine.com	74	76	71	67	210,000
77	wandb.ai/fully-connected	74	76	71	67	380,000
78	sebastianraschka.com	74	76	71	67	320,000
79	mlconf.com	74	76	71	67	240,000
80	montecarlo.ai/blog	74	76	71	67	280,000
81	theaisummit.com	74	76	71	67	210,000
82	thealgorithmicbridge.substack.com	74	76	71	67	180,000
83	ruder.io	73	75	70	66	180,000
84	lightning.ai/blog	73	75	70	66	180,000
85	machinelearningweekly.com	73	75	70	66	210,000
86	karpathy.github.io	73	75	70	66	320,000
87	colah.github.io	73	75	70	66	240,000
88	aijourn.com	72	74	69	65	180,000
89	accel.ai/blog	72	74	69	65	180,000
90	jeremyjordan.me	72	74	69	65	150,000
91	lilianweng.github.io	72	74	69	65	260,000
92	jack-clark.net	72	74	69	65	95,000
93	insideainews.com	72	74	69	65	180,000
94	ai-jobs.net/blog	71	73	68	64	210,000
95	lastweekin.ai	71	73	68	64	140,000
96	mlq.ai	71	73	68	64	95,000
97	ai-startups.pro	70	72	67	63	120,000
98	aidaily.co.uk	68	70	65	61	180,000
99	thezvi.substack.com	72	74	69	65	160,000
100	aiweirdness.com	74	76	71	67	220,000

The pitch that works for AI editors

Lead with the artifact. "We open-sourced a 2B model that matches Llama on X" or "here is a benchmark of 8 RAG frameworks on the same corpus." The thing you built earns the read.
Show the code runs. A repo, a Colab, a reproduction script. Applied-ML editors reject claims they cannot check.
Pitch the section. "For your applied research coverage" or "for the tutorials series" shows you read the publication.
Match the format. News wants a tight story. Tutorials sites want a structured how-to with code. Research blogs want a paper-grade write-up.
Bring the assets. Charts, a dataset, model weights, an eval harness.
Disclose funding or a commercial interest in the model or tool you are writing about.

The test: would an editor run this if your company did not exist? If yes, pitch it. If it is a walkthrough of your API, it goes in the bin.

What not to do

Pitch a "what is machine learning" explainer. Every site has ten, and it will not rank.
Send benchmarks you will not share the code for.
Submit an LLM-written post about LLMs with no experiments and no author who did the work.
Put exact-match anchors ("best AI writing tool") on links back to your product.
Overclaim in the title. AI editors and readers punish hype hard.

Bottom line: in AI, one placement on a respected applied-ML outlet with working code beats a dozen links from thin "AI news" sites. Use recent publishing activity and the traffic column to find the real ones.

Frequently asked questions

Do sites like TechCrunch or Wired accept AI guest posts?

No. They commission from staff and vetted freelancers. What they will take is exclusive research, a novel benchmark, or expert commentary on a live story. The applied-ML and data science outlets (KDnuggets, Analytics Vidhya, Towards Data Science, MarkTechPost) and the framework and research blogs are where contributed technical writing runs.

What gets published on the ML publications?

A reproducible tutorial with code, a benchmark or evaluation others can rerun, an architecture or technique explained clearly, a post-mortem on an ML system in production, or a survey of a fast-moving subfield. Product announcements and 'AI will change everything' think-pieces do not get past the desk.

Can I link to my model, tool or company?

One disclosed link in the author bio, and only when the piece is genuinely educational. Editors strip promotional links from the body. On the research blogs, a link to the paper or repo is expected; a link to a pricing page is not.

Do I need an ML background to get published?

For the technical outlets, yes in practice: editors want code that runs and claims that hold up. For the business and industry titles (AI Business, VentureBeat, Emerj), a clear track record applying AI in a real setting is enough.

The metrics do not match my SEO tool. Why?

Domain Rating and traffic estimates are recalculated constantly and differ between Ahrefs, Moz and Semrush. Many entries here are sub-domains or sections of a larger site, so a whole-domain figure reads higher than the section your post lands in.

Want us to run the outreach?

We shortlist the AI publications that carry weight, shape the research or tutorial with your team, write the pitches and place them on outlets with real editorial standards. No link farms, no risk.

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