r/LLMDevs: rules, karma requirements and posting culture

Who posts here

Posts assume you write code and know the vocabulary: RAG, MCP, tool calls, KV cache, LoRA, evals, tokens per second. Much of the feed is people publishing their own benchmarks and repos. Beginner questions do get answered, and a junior developer asking whether RAG is worth learning drew a couple of dozen replies, but a one-line request for tips on making your own model sits unanswered.

• Developers building agents, coding tools and RAG systems, usually sharing a repo.
• Local model hobbyists pushing large models onto ordinary hardware and reporting the numbers.
• Engineers running LLM features in production who post a failure and how they fixed it.
• Independent researchers publishing experiments on memory, judging bias and benchmarks.
• Juniors and non-developers building with AI coding tools, asking for direction or debugging help.

Karma and account requirements

What it takes to get a post through in r/LLMDevs.

What you can post

What happens to a new post

Where the most recent posts ended up. Sample size: 200.

• Stayed up69%
• Removed by Reddit's filters28%
• Deleted by the author3%

Reddit counts a removal by the subreddit's own AutoModerator rules as a moderator removal, so that share is not all human. Held by AutoModerator means the post is waiting in the mod queue.

What happened when we posted here

We have posted here ourselves. This is how our own posts and comments fared.

Stayed up

98%

Removed by Reddit's filters

0%

Removed by moderators

2%

Period

February 2026 to August 2026

The rules, in the mods' words

1. 1

   Be respectful: No personal attacks or harassment.

   Be respectful: We encourage constructive discussion and welcome different viewpoints, but members must always treat each other with respect. This means no personal attacks, bullying, or harassment of any kind. Any content or comments that violate this rule will be removed. We want to maintain a friendly and welcoming environment where everyone can feel comfortable to contribute and participate.

2. 2

   Ethical Questions and Surveys

   Any posts or surveys asking for community input, whether for personal research, project development, or market analysis, must: • Disclose the purpose of the data collection. • Confirm that responses will not include personally identifiable information (PII) or be monetized. • Share insights or results back with the community where appropriate. • Posts lacking transparency or appearing exploitative will be removed.

3. 3

   Accuracy and transparency: Posts should be accurate and not misleading.

   Accuracy and transparency: Members should provide clear and accurate information and avoid making false or misleading claims. This is particularly important in the field of LLM and NLP, where there are many misconceptions and potential risks. We want to ensure that all members have access to accurate and reliable information, and can trust the content shared within the community. Any posts or comments that violate this rule will be removed.

4. 4

   Stay on topic: Related to LLM and NLP.

   Stay on topic: Our Subreddit is dedicated to LLM and NLP development and technology-related topics. We encourage members to share their knowledge and experiences related to LLM and NLP and to participate in discussions on emerging trends and technologies. Any content or comments that are unrelated to LLM and NLP will be removed to keep the Subreddit focused and valuable for our members.

5. 5

   No self-promotion: Share value, not promotions.

   No self-promotion: While we encourage members to share their projects and experiences, self-promotion or excessive promotion of personal or commercial projects is not allowed. Instead, members should focus on contributing value to the community through sharing knowledge, offering feedback, and participating in discussions. Any posts or comments that violate this rule will be removed.

6. 6

   No low-effort posts or spam: High-quality content only.

   No low-effort posts or spam: Our community values high-quality content and discussion. We ask that all members put effort into their posts and comments, and avoid posting spam or low-quality content. This includes posts or comments that do not add value to the community or are off-topic. Any posts or comments that violate this rule will be removed.

7. 7

   Provide sources and references: Cite sources for accuracy.

   Provide sources and references: When sharing information or making claims, members should provide sources and references to back up their statements. This helps maintain a high level of accuracy and credibility within the community. If you are sharing AI-generated content, please provide context and attribution. Any posts or comments that violate this rule will be removed.

8. 8

   No NSFW content: Keep it professional.

   No NSFW content: Our Subreddit is a professional and respectful community. We do not allow NSFW (Not Safe for Work) content, including graphic or explicit images, videos, or language. Any posts or comments that violate this rule will be removed.

9. 9

   Follow Reddit's rules: Follow Reddit's content policy and rules.

   Follow Reddit's rules: All members should adhere to Reddit's content policy, Reddiquette, and other rules and guidelines set by Reddit. This includes avoiding spam, hate speech, and harassment. Any posts or comments that violate Reddit's rules will be removed.

These are the rules as archived in January 2025. Rules change. Check the sidebar before you post.

The culture

r/LLMDevs has about 174,000 members and moves fast, at roughly 47 posts a day. It is a show-and-tell room for people who build with language models. A typical post is a text write-up of around 200 words under the Discussion or Tools flair, describing something the author made, a benchmark they ran, or a problem they hit in production. About 71% of posts are text, with images, links and video making up the rest.

Attention is very uneven. The typical post gets about three comments, while a handful of posts each week reach hundreds of upvotes. The winners are almost always concrete: a small model that runs on a single CPU core with timings, a large model squeezed onto a laptop, a roundup of open source projects someone actually read through. When a new model or approach catches on, the room piles in for weeks with builds, open alternatives and benchmark comparisons.

The standing tension is between the rule book and the feed. The rules say no self-promotion, yet most of what is posted is somebody's own project. The working distinction is open versus commercial. A repo with a licence and reproducible numbers is treated as a contribution. A signup link is treated as an ad. There is also growing irritation at machine-written replies, and a post complaining about agents commenting under the author's threads was one of the more upvoted recent discussions.

What lands

• A working open source build with a hard number in the title, such as decision latency in milliseconds on one CPU core. That kind of post was the most upvoted in our sample.
• Curated roundups where the author did the reading, like going through a few hundred community projects and picking the ones worth studying.
• Running a big model on modest hardware, with the exact machine and the method spelled out.
• Measured findings that challenge a habit, for example showing that a model used as a judge changes its verdict when the order of options is swapped.
• Short production stories with a dull root cause and the fix, like a bot inventing an answer because a lookup pattern never matched.

What gets removed or ignored

• We saw no moderator removals in our sample, so this list comes from the rules and from what Reddit's filters caught, which was about 28% of posts.
• Self-promotion or excessive promotion of personal or commercial projects, by rule. Enforcement is light, but the rule is there to be used.
• Low-effort posts and spam, including anything that adds nothing or is off topic.
• Surveys and research requests that do not disclose their purpose or look exploitative.
• Inaccurate or misleading claims, and NSFW content.

The unwritten rules

• Put the repo and the licence in the post. Posts that say free, open source and MIT up front are received as contributions, and comments ask for the source when it is missing.
• Bring measurements. Token counts, latency, accuracy and the hardware used are the currency here. Big claims without a method get picked apart in the comments.
• Expect pushback on ambitious ideas. A proposal for crowd-sourced model training drew a long run of replies explaining why it would not work, politely but firmly.
• Comments run to about 58 words and around 8% contain a link, usually to a repo. Replies that read like generated text are noticed and called out.
• Titles are long, about 13 words, and say what was built or found. Only around 28% are questions.

Self-promotion

The rule says members are encouraged to share projects and experiences, but self-promotion or excessive promotion of personal or commercial projects is not allowed, and people should contribute value instead. In practice the first half of that sentence wins. Tools is the second most used flair, I built posts stay up in large numbers, and we found no moderator removals in our sample. What separates a welcome post from a resented one is whether there is something to use or learn without paying: source code, a benchmark, a write-up of what went wrong. Referral codes and bare signup links do appear, and they get no engagement.

How people write here

Typical post length

207 words

Typical title length

13 words

Titles phrased as a question

28%

Typical comment length

54 words

Comments that include a link

9%

Posts that carry a flair

100%

Top keywords

The words and phrases that show up far more often here than in other communities, from recent posts in r/LLMDevs.

• model 73 posts
• llm 46 posts
• agent 54 posts
• agents 43 posts
• models 33 posts
• text 26 posts
• decision 24 posts
• repo 19 posts
• prompt 23 posts
• jev 14 posts
• open-source 18 posts
• api 23 posts
• tool 30 posts
• memory 20 posts
• source 25 posts
• tests 18 posts
• decision model 9 posts
• deterministic 12 posts
• open source 15 posts
• results 23 posts
• context 24 posts
• inference 13 posts
• mit-licensed 8 posts
• code 28 posts
• open 28 posts
• state 20 posts
• part 30 posts
• coding 18 posts
• calls 19 posts
• benchmark 12 posts
• llms 14 posts
• request 17 posts
• data 28 posts
• laya 7 posts
• claude 21 posts
• task 17 posts
• tool call 9 posts
• system 23 posts
• output 14 posts
• raw 13 posts

Common questions