Can I promote my AI tool on r/MachineLearning?

No. r/MachineLearning permits research writeups, papers and genuine technical breakdowns, and removes product announcements that carry no research depth. There is no promotional thread to route into and no phrasing that makes a launch acceptable. If you have real research — an eval methodology, a training result, a negative finding — that is a post, and it should be publishable with your product name absent.

Where can I post my AI SaaS on Reddit?

r/AI_Agents is the most tolerant serious option: promotion goes behind the showcase flair, and it wants build notes before showcases. r/LocalLLaMA will take you if something in your work is genuinely open. r/ChatGPTPro, r/ClaudeAI, r/OpenAI and r/artificial are all strict and want technique or model insight rather than products, and r/MachineLearning bans promotion outright.

Why do Reddit users hate AI wrapper posts?

Because the readers in these subs can build a wrapper in an afternoon, so a post whose only content is that a model was called does not tell them anything they do not have. The reaction is to the claim, not the product. Posts that show the parts a wrapper does not have — the eval, the retrieval design, the cost work, the failure classes — do not get the same treatment.

Can I post my Claude or GPT-powered app in r/ClaudeAI or r/OpenAI?

Not as an app. Both communities are about the labs and their own products, so an application built on the API is a third-party mention there and is removed unless it carries model-behaviour insight. What does work is posting the finding itself: a reproducible failure mode, a prompting technique, a behaviour that changed after a model update. Your product stays out of the submission.

How do I get feedback on an AI agent from Reddit?

Bring an eval and ask a narrow question. "Our retrieval misses 94 of 118 failures, here is the chunking strategy, what would you try next" gets engineers replying, because it is a problem they recognise and can answer. "Would you use this" gets nothing, since nobody can evaluate an agent from a description and the ask reads as a pitch in question form.

How to promote an AI tool on Reddit without getting banned

Every AI community on Reddit is absorbing more launches per day than it has readers with patience for them. The result is a trained reflex: a post opening with "I built an AI tool that" is scrolled past before the second sentence, and in several of these subs it is removed before enough people see it to scroll past.

The hostility is not to AI products. It is to the wrapper pattern — a system prompt, an API key and a landing page, presented as though the model's capability were the author's. The people reading in r/LocalLLaMA and r/ChatGPTPro build that on a Saturday afternoon. They can tell, and unlike most audiences they will ask the question in public, in the thread, with a follow-up.

Which also describes the way through. This is the most technically literate promotional audience on Reddit. Publish the eval set, the failure classes, the cost per run and the model you rejected, and the same community that deletes launch posts will read two thousand words about yours and argue with it constructively.

Where AI tools can actually be promoted

Each of these is a real community with its own rule. The rule is the part that matters — a good post in the wrong thread is still a removed post.

r/AI_AgentsConditional
The most promotion-tolerant serious AI community, and the one where people are actively looking for agent tooling rather than being interrupted by it.
The rule here: Self-promotion belongs behind the showcase flair, and a fresh account posting a launch with no comment history is removed as spam regardless of how good the product is. Post build notes — architecture, models, framework — before you ever post a showcase.
r/LocalLLaMAConditional
Builders of self-hosted and open-weight systems, with an unusually high tolerance for long technical posts and the deepest benchmarking instinct on the site.
The rule here: Something in the post has to be open — weights, a repo, a reproducible benchmark. A closed commercial pitch with no open component is removed no matter how well it performs.
r/ChatGPTProStrict
Power users who will pay for tools that save them work, which makes it tempting, and who will take your prompt apart line by line, which makes it dangerous.
The rule here: Prompt-engineering technique is the accepted content. Anything that cannot hold up under that critique gets downvoted out of sight before a moderator reaches it, so post the technique and let the product follow.
r/ClaudeAIStrict
Concentrated audience of people building on the model you probably build on, and the sub where a genuine behaviour finding travels furthest.
The rule here: The post needs a Claude-specific insight — a prompting technique, a reproducible behaviour. An app that happens to call Claude is not Claude content and is removed as promotion.
r/OpenAIStrict
Enormous reach, and the place people go when a model changes under them. Useful for model-behaviour posts, not for your product.
The rule here: The sub is for OpenAI's own products, not for third-party apps built on the API. A product mention with no model-behaviour insight attached to it is off-topic by definition.
r/MachineLearningNo promo
Listed so you do not try. Research and engineering only, and the audience most likely to be sent your launch by someone who wants to see it fail.
The rule here: No promotion at all. Papers, research writeups and genuine technical breakdowns are the entire permitted set, and a product announcement with no research depth is removed without a warning.

Rules change and moderators enforce their own reading of them. Check the sidebar before you post, every time, and see the full directory of 104 communities for the rest.

What this audience actually upvotes

An eval, with its size and its failure classes. Not "accurate" — 340 cases, this is the baseline it beats, these are the four kinds of input where it is wrong. This audience runs benchmarks recreationally, so an unfalsifiable accuracy claim reads as a lie to them, and publishing where the thing fails buys more credibility than a higher number would.

Cost per run, in currency. Token spend per call and the actual monthly bill at your volume is the most-asked question under every agent post and it is almost never in the post. Answering it before anyone asks marks you as someone who has run this in production rather than demoed it.

The model you rejected and why. Naming the cheap model that turned out to be good enough, or the frontier one that was not worth three times the cost, is a comparison nobody can publish except someone who ran both. It is also the paragraph most likely to be quoted back at you months later, which is the whole point of posting.

The two ratios that decide whether you get removed

Enforcement on Reddit is account-level, not post-level. A moderator opens your profile after one post trips their attention and skims what fraction of the account is about its product. That is why a reasonable post attached to a promotional profile still goes.

9:1 — comments to posts. Reddit's self-promotion guidance implies about nine contributions that have nothing to do with your product for every one that does. It comes from Reddit's 2014 moderator clarification and its current small-business guide restates it as “be a user first”.

3:1 — value posts to promotional posts. Among your submissions, at most one in four should promote anything. Clearing 9:1 while every post you make is a plug still reads as a marketing account.

The mention-deletion test. Delete your product name from the post and reread it. If nothing of value is left, it was an advertisement with context attached and it will be removed. If it still teaches something, the mention is a footnote on a real contribution.

New accounts have a further problem: most large subreddits silently filter posts from accounts under a karma or age threshold, so the post looks live to you and is invisible to everyone else. Check yours with the karma and warmup calculator before you post at all.

What gets AI tools removed

Opening with "I built an AI tool that..."
These communities see dozens a day and have developed a reflex against the phrase itself. It signals that the model is the interesting part of the post, which in an AI subreddit is the least interesting part available. In r/AI_Agents a launch from an account with no history is removed as spam before the content is even weighed.
Instead: Open with the problem and the measured result. A good test: if the post would still be interesting had a person done the work by hand, it is a post. If the only news is that a model was involved, it is not.
Posting a closed hosted product in r/LocalLLaMA
The sub exists for self-hosted and open-weight work. A closed commercial SaaS pitch is the precise thing it is not for, and that removal is fast and unsympathetic. It is also the single most common misread in this niche, because the sub looks like a general LLM community and is not one.
Instead: Open one real component — an eval set, your prompt suite, a small fine-tune, a benchmark harness — and make that the post. If nothing about your work can be opened, this is not your community and no framing fixes that.
Promoting a GPT or Claude app in r/OpenAI or r/ClaudeAI because that is the model it uses
Those communities discuss the labs and their own products. Building on the API makes you a third party there, not a member, and both subs remove product mentions that carry no model-behaviour insight. Founders make this mistake constantly because the audience fit looks perfect from outside.
Instead: Post the model finding you hit while building — a failure mode, a prompt that stopped working after an update, a behaviour you can reproduce. The insight is the submission and the product never appears in it.
Claiming accuracy with no eval behind it
In an audience that benchmarks for entertainment, "95% accurate" without the set size, the baseline and the failure classes is read as a fabrication, and the thread becomes a cross-examination you will lose in public. That transcript then outranks everything else your product name is attached to.
Instead: State the eval set size, what you compared against, and the class of input where it breaks. A published 79% with its failure modes is believed; an unsupported 95% is not.

A post that would survive

Written for r/AI_Agents. Adapt the specifics; keep the shape.

Upgrading the model fixed nothing. 80% of our agent failures were retrieval. Numbers from a 340-case eval.

We run an agent that triages support tickets: read the ticket, pull the relevant docs, draft a reply for a human to approve. It was right about two thirds of the time, which is a useless number in a support queue, because at two thirds you still have to read all of them. The reflex was to upgrade the model. We did it twice. Accuracy on our 340-case eval set went 66.2% to 68.5% to 69.1%. Roughly three times the cost per run for three points. So we stopped guessing and hand-labelled every failure. 118 wrong answers: - 94: the correct document was never retrieved at all - 17: the correct document was in context and it answered from a different one - 7: genuine reasoning failures The model was almost never the problem. What actually moved the number: 1. Chunking on headings instead of fixed 800-token windows. Our docs have short sections and fixed windows were cutting answers in half. Worth about 11 points on its own. 2. Retrieving 20 and reranking down to 5, rather than retrieving 5. Another 6. 3. Letting it answer "not in the documentation" and hand off. This lowered measured accuracy and was the change the support team actually noticed, because a confident wrong reply costs more than no reply. We are at 88.7% on the same set now, on the cheaper model, at roughly $0.011 per ticket including the reranker. The thing I would do differently is build the eval set first. We spent six weeks tuning against a feeling. Writing down 340 real tickets with their correct answers took two afternoons and made every decision after it obvious. Our labelling scheme was four categories and one free-text field, which was enough. Happy to write that part up properly if anyone wants it.

Why it survives: It passes the mention-deletion test with room to spare: nothing is named, nothing is linked, and it is still the most useful thing in the subreddit that day. It brings the two things this audience specifically demands — an eval with its size and failure classes, and a cost per run — and it reports that three model upgrades bought almost nothing, which is the opposite of what a vendor would say. It also obeys the r/AI_Agents rule by being build notes rather than a showcase, which is what earns the right to post a showcase later.

Frequently asked questions

Can I promote my AI tool on r/MachineLearning?: No. r/MachineLearning permits research writeups, papers and genuine technical breakdowns, and removes product announcements that carry no research depth. There is no promotional thread to route into and no phrasing that makes a launch acceptable. If you have real research — an eval methodology, a training result, a negative finding — that is a post, and it should be publishable with your product name absent.
Where can I post my AI SaaS on Reddit?: r/AI_Agents is the most tolerant serious option: promotion goes behind the showcase flair, and it wants build notes before showcases. r/LocalLLaMA will take you if something in your work is genuinely open. r/ChatGPTPro, r/ClaudeAI, r/OpenAI and r/artificial are all strict and want technique or model insight rather than products, and r/MachineLearning bans promotion outright.
Why do Reddit users hate AI wrapper posts?: Because the readers in these subs can build a wrapper in an afternoon, so a post whose only content is that a model was called does not tell them anything they do not have. The reaction is to the claim, not the product. Posts that show the parts a wrapper does not have — the eval, the retrieval design, the cost work, the failure classes — do not get the same treatment.
Can I post my Claude or GPT-powered app in r/ClaudeAI or r/OpenAI?: Not as an app. Both communities are about the labs and their own products, so an application built on the API is a third-party mention there and is removed unless it carries model-behaviour insight. What does work is posting the finding itself: a reproducible failure mode, a prompting technique, a behaviour that changed after a model update. Your product stays out of the submission.
How do I get feedback on an AI agent from Reddit?: Bring an eval and ask a narrow question. "Our retrieval misses 94 of 118 failures, here is the chunking strategy, what would you try next" gets engineers replying, because it is a problem they recognise and can answer. "Would you use this" gets nothing, since nobody can evaluate an agent from a description and the ask reads as a pitch in question form.

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Last reviewed: July 2026