We found 36 YouTube channels similar to Code Brew Labs
Both target audiences interested in AI-enabled app and SaaS ideas (high search overlap on ai app development and ai-based SaaS ideas), with substantial content alignment around micro SaaS concepts (82%), indicating they share the same niche but differ in content style.
Shared emphasis on micro SaaS ideas and app development (scoring 61% search overlap and 84% content alignment), meaning they attract similar developers interested in healthcare/fitness app themes, though Dennis leans into healthcare and fitness specifics.
Strong alignment on micro SaaS ideas and ai-based SaaS ideas (71% match with 68% search, 77% content), indicating overlapping audience seeking startup and SaaS concept inspiration, with content spanning broader startup stories.
Similar focus on micro SaaS ideas and app development (71% match; search 47%, content 88%), suggesting overlapping interest in building SaaS offerings, fintech/healthcare themes present but content tilts toward broader app development services.
Both engage with ai-based SaaS ideas and AI solutions for startups (61% match; search 40%, content 75%), indicating shared audience of startup founders exploring AI-powered SaaS concepts, though YC’s content is more accelerator/mentorship oriented.
Shares interest in tech development concepts via micro SaaS and AI ideas (60% match; search 40%, content 73%), showing overlapping audience around tech startups, but Blocktunix emphasizes blockchain/web3 in contrast to Code Brew Labs.
30 additional channels that were close
| Rank | Relevance | Channel | Similarity | Subscribers | SERP | Appearances |
|---|---|---|---|---|---|---|
| #7 | 60% | Idea Usher | 86% | 2.5K | 21% | 5 |
| #8 | 59% | Dan Martell ▶ | 80% | 2.4M | 27% | 7 |
| #9 | 57% | Luuk Alleman | 81% | 10.4K | 21% | 4 |
| #10 | 57% | ProCoders: Web & Apps development ▶ | 75% | 1.3K | 29% | 3 |
| #11 | 56% | IBM Technology ▶ | 71% | 1.6M | 34% | 4 |
| #12 | 56% | Greg Isenberg | 74% | 567K | 29% | 5 |
| #13 | 55% | Liam Ottley | 83% | 746K | 14% | 3 |
| #14 | 55% | Digital Transformation with Eric Kimberling ▶ | 74% | 549K | 25% | 5 |
| #15 | 53% | The Wall Street Journal ▶ | 68% | 6.6M | 31% | 2 |
| #16 | 52% | Simplilearn ▶ | 75% | 6.1M | 18% | 5 |
| #17 | 52% | PixelCrayons | 72% | 1.5K | 21% | 5 |
| #18 | 51% | TEDx Talks ▶ | 65% | 44.2M | 30% | 6 |
| #19 | 51% | Codesistency | 70% | 142K | 23% | 2 |
| #20 | 51% | JavaScript Mastery | 76% | 1.2M | 13% | 3 |
| #21 | 50% | GreatStack | 75% | 1.2M | 13% | 2 |
| #22 | 49% | Appy Pie | 74% | 109K | 13% | 3 |
| #23 | 49% | Service Design Show | 70% | 20.7K | 17% | 1 |
| #24 | 49% | Alex Chen | 69% | 3.6K | 19% | 3 |
| #25 | 48% | Columbia Business School | 70% | 107K | 14% | 1 |
| #26 | 47% | Simply Explained | 68% | 488K | 16% | 2 |
| #27 | 47% | Dave Burji | 66% | 12.8K | 19% | 2 |
| #28 | 47% | Travis Marziani | 69% | 471K | 14% | 1 |
| #29 | 47% | TechPrep | 68% | 74.9K | 15% | 2 |
| #30 | 47% | Dino Cajic | 70% | 1.0K | 12% | 1 |
| #31 | 47% | Philipp Lackner | 68% | 249K | 14% | 1 |
| #32 | 45% | ART PROGRAMS | 68% | 8.1K | 12% | 1 |
| #33 | 45% | Third Stage Consulting Group | 67% | 6.1K | 12% | 3 |
| #34 | 44% | LeanVlog | 63% | 52K | 15% | 2 |
| #35 | 44% | Colin Darke | 63% | 30.6K | 14% | 1 |
| #36 | 43% | Enable | 62% | 1.1K | 14% | 1 |
Mikey No Code — 89% match, 109K subscribers; Dennis Babych — 75% match, 61.6K subscribers; Starter Story — 73% match, 786K subscribers. All three focus on entrepreneurship, app development, and business ideas, sharing overlap in tech-focused, how-to content and case studies similar to Code Brew Labs.
Code Brew Labs creates content in the app development and tech business space, with video titles about building apps (fitness app, dating app), AI-powered ideas, and micro SaaS. The channel averages around 6.2K views per video, with recent uploads ranging from about 2.2K to 15.9K views; typical uploads appear to occur multiple times per week.
We analyze Code Brew Labs's recent videos, generate topic-relevant search queries, check YouTube search results, and compare the meaning of each channel's content to measure similarity. The result is a ranked list sorted by SERP overlap, semantic similarity, and search appearances.
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