Cameron R. Wolfe, Ph.D.
@cwolferesearch Creator on X United States AI / LLM 1 to check
Not known. Estimate about $80 for one sponsored post.
Average impressions a post 78,977 Averaged across 12 posts, from X's own API. Read Sep 4, 2026. Latest post 52,943 impressions, Aug 24, 2026
Median impressions 71,041 The typical post. Budget against this one.
followers 41,275 From X's own API. Read Sep 4, 2026.
Last posted 14d ago Posts every 37 days, across 12 recent posts. Read Sep 4, 2026.
Last 12 posts
Views per post, oldest to newest. Average 78,977.
What one sponsored post likely costs
Our estimate, not a quoteabout $80for one sponsored post
41,275 followers × $2 per 1,000 followers Sprout Social, 30 May 2024 ↗
41,275 followers x $2 per 1,000 followers as a rule of thumb (Sprout Social, 30 May 2024). This is our arithmetic on a published benchmark, not a quote from the creator.
| Post | Date | Views |
|---|---|---|
| I just published my complete guide to reinforcement learning for LLMs. It's a single, standalone resource for understand | 52,943 | |
| This is (in my opinion) one of the top-3 most useful books to be written on LLMs. I highly recommend reading / buying it | 70,703 | |
| The gpt-oss models from OpenAI are a synthesis of ideas from prior research. Here are 10 interesting papers that were di | 28,446 | |
| Reward models have transformed LLM research by incorporating human preferences into the training process. Here’s how the | 33,641 | |
| Reinforcement Learning (RL) is quickly becoming the most important skill for AI researchers. Here are the best resources | 114,351 | |
| Many recent frontier LLMs like Grok-3 and DeepSeek-R1 use a Mixture-of-Experts (MoE) architecture. To understand how it | 38,621 | |
| The key idea behind DeepSeek-R1 is scaling up simple / effective RL algorithms to solve verifiable tasks. But, why didn’ | 71,379 | |
| o3 clearly shows that we're looking at this "scaling is dead" narrative in the wrong way. We might be done with scaling | 81,599 | |
| Here is a step-by-step guide for successfully finetuning your own LLM judge on granular / domain-specific evaluation tas | 26,909 | |
| Prompt engineering requires a lot of manual effort. Here are four automatic prompt optimization algorithms that can help | 96,934 | |
| o1 has sparked tons of ideas for applying LLMs to reasoning problems in science and math, but one of the most interestin | 79,334 | |
| I find it so interesting (and smart) that Meta / LLaMA is eliminating the dependence of their models on the HuggingFace | 252,865 |
Who sponsors Cameron R. Wolfe, Ph.D.?
No sponsorships have been documented yet. Nothing on record either way, so ask them directly.
What we checked, in full
20 checks, 15 passed, 1 to review
| ✓ | Metrics came from the platform | Every audience figure here is a value X's own API returned, never estimated or inferred. The price estimate is the one number we work out ourselves, and it is labelled and shows its arithmetic. | not published |
| ✓ | Enough recent posts to judge | 12 recent posts sampled (target 12). | not published |
| ✓ | Average impressions ≥ 20,000 | 78,977 average impressions, median 71,041, across 12 posts. | required |
| ✓ | Typical post also clears 20,000 | Median impressions 71,041 and outlier-trimmed mean 66,795 both clear the floor. A sponsored post should land in that range. | integrity |
| ✓ | Tech / dev / software engineering | 7 distinct dev signals in the bio and post text: engineer, architecture, llm, gpt, prompt engineering, pytorch. | required |
| ✓ | Not a gaming channel | No gaming signal in the text. X publishes no content category, so this is a text judgement. | required |
| ✓ | Not a hardware / gear channel | No hardware/gear pattern. | preference |
| ✓ | Target region (US / UK / EU / AU / NZ / SG) | United States (US), from the creator's self-entered location text. Self-reported by the creator, same as a platform country field. Shown with its confidence rather than treated as independently verified. | required |
| ✓ | Like rate ≥ 0.15% of impressions | 1.19% like rate, 0.024% comment rate, averaged per post. | integrity |
| ✓ | Average is not one viral outlier (X limit 15×) | Top post is 3.6× the median, within the normal spread for X, so the average reflects repeatable performance. | integrity |
| ✓ | Comment volume matches the view counts | Median 18 comments against 696 median likes: a normal ratio. | integrity |
| ✓ | impressions plausible for the followers count | 78,977 average impressions against 41,275 followers (191%). | integrity |
| ✓ | Posted recently | Last post 14 day(s) ago. | preference |
| ✓ | Publishes on a regular schedule | Median 37 day(s) between posts; the 12 sampled posts span 697 days. | preference |
| ! | Sampled posts are recent enough to compare | The 12 sampled posts span 697 days. Impressions counts accumulate over time, so an average across posts this far apart is weighted toward the oldest one and overstates current reach. | integrity |
| – | Same numbers from two endpoints of the same API | No post from this account appeared in both the search index and the profile feed, so there was nothing to cross-check. | not published |
| – | Verified against the platform's own published feed | X publishes no independent public feed to check the API's numbers against. YouTube is the only platform here where that second source exists. | not published |
| ✓ | An independent creator, not a company's own channel | x labels this a "Creator" account. | required |
| – | Same person confirmed on other platforms | No other public profile was linked from this account, so this record covers one platform only. | not published |
| – | Runs paid promotions (platform-declared) | Sponsorships shown for this creator are parsed from the text of their own post descriptions, so they are suspected rather than platform-confirmed. | not published |