Tommy ✓

@Shaughnessy119 · joined 26 Nov 2013

Early Stage Investor | Founding Partner @Delphi_Ventures | Co-Founder @Delphi_Digital | Host @PodcastDelphi | My Opinions

69 891Followers

4 232Following

15 247Posts total

4MViews on collected posts

Against accounts of the same size

5 posts from the last 90 days, next to the 10K–100K follower range. below its peers on both reach and engagement.

Median views2 084this account2 968median for 10K–100K

Reach, %2.98%this account9.18%median for 10K–100K

Engagement, %0.99%this account1.42%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	2 084	2 968	0.70×
Reach (views ÷ followers)	2.98%	9.18%	0.32×
Engagement rate	0.99%	1.42%	0.69×

Others in this range → Compare with another account → How these benchmarks are built →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

44.4K28 May

45.3K

38.8K

35.2K

32.7K

34.6K

47.1K

32.4K

3.2K29 May

3.2K3 Sep

2.1K

14.4K

2K

1.7K4 Sep

Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.21%28 May

0.28%

0.23%

0.19%

0.35%

0.43%

1.01%

0.34%

1.30%29 May

0.79%3 Sep

0.96%

1.87%

0.99%

1.57%4 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.42%.

What the audience does

Likes41.0%9 493 in total

Reposts6.2%1 435 in total

Replies1.8%407 in total

Quotes0.6%141 in total

Bookmarks50.4%11 658 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Latest posts

What do we think China pays Bernie to campaign to slow down all AI progress in the U.S!? 1.7K views · 22 likes · 0 reposts · 3 replies 04 Sep 2026 If you want to run a local AI model for some things the Hermes desktop app automatically scans all of your hardware for which models you can run Absurdly easy. One click. Then you just talk to Hermes and tell it what you want that local model to run 2K views · 20 likes · 0 reposts · 0 replies 03 Sep 2026 The new easy setup feature is presented automatically upon downloading the app, or you can access it within the Providers section in Settings. https://t.co/L3W7GP1kZe 14.4K views · 238 likes · 12 reposts · 14 replies 03 Sep 2026 Everyone please wait for us venture capitalists to opine on what this means AGI wise 2.1K views · 19 likes · 0 reposts · 1 replies 03 Sep 2026 That looks like AGI to me 3.2K views · 21 likes · 1 reposts · 2 replies 03 Sep 2026 @Shaughnessy119 @Scobleizer And no link to the original post… Here it is. https://t.co/ZJHOatjO0t 3.2K views · 41 likes · 0 reposts · 1 replies 29 May 2023 Also @stephen_wolfram if you're ever interested in a long form podcast to walk through your thoughts, we'd love to host you on @Delphi_Digital's podcast! 32.4K views · 86 likes · 3 reposts · 21 replies 28 May 2023 I think it's incredibly cool that a gigabrain like @stephen_wolfram would open source his thinking on ChatGPT This has been the single best resource I've found so far on learning about @OpenAI's ChatGPT, LLMs and Neural Nets Disc. I def got things wrong https://t.co/79I7YQaVzN 47.1K views · 387 likes · 78 reposts · 9 replies 28 May 2023 I am not an AI researcher but the post made me realize LLM's are nowhere near the AGI or Terminator level AI intelligence some fear Of course it's on the path, but LLM's are probabilistic models focused on continuing sentences. They are really good at it, but not AGI (yet) http 34.6K views · 130 likes · 11 reposts · 4 replies 28 May 2023 So is ChatGPT similar to a human brain? His conclusions: - The neural net architecture may be similar - Training of LLMs way less efficient vs human brain - ChatGPT has no loops to go back and recompute data like humans can which severely limits its computational capability 32.7K views · 95 likes · 12 reposts · 5 replies 28 May 2023 Meaning Space Stephen shares that in ChatGPT, text is represented by an array of numbers in a meaning space. He goes on to describe that the trajectory of what words come next is far from a mathematical or physics like law we can rest our hats on. https://t.co/HK29Hy8LjY 35.2K views · 57 likes · 10 reposts · 1 replies 28 May 2023 Transformers are a breakthrough for LLMs. An analogy is they allow the model to understand the context of words and the relationship between words that are far apart Transformers can read all text at once vs one at a time so are much more efficient and scalable Thanks ChatGPT! 38.8K views · 78 likes · 9 reposts · 1 replies 28 May 2023 An interesting side note for the Crypto audience. Crypto's own @ilblackdragon, the co-founder of @NEARProtocol, is one of the authors on the original Transformers Paper https://t.co/3QqERbv32i First let's recap ChatGPT's process: 45.3K views · 101 likes · 20 reposts · 3 replies 28 May 2023 Onto ChatGPT! ChatGPT+ is a giant Neural Net with 100 Trillion Parameters (GPT3 had 175B) focused on language. That 1,000x the parameters of the brain. Woof. The most important feature is the Transformer https://t.co/D9tpDFhkjO 44.4K views · 76 likes · 8 reposts · 9 replies 28 May 2023 Embeddings Embeddings are laying out words, represented by numbers, to those they are commonly associated with Probabilities are found using vast amounts of text Embeddings give a more natural feel to ChatGPT since words that are commonly associated with each other can be used 49.4K views · 55 likes · 6 reposts · 1 replies 28 May 2023 ChatGPT is often extrapolated as a path to Terminators Stephen counters that the magic of LLMs for writing really isn't that hard. We're not closer to terminators, writing essays just isn't as hard as we think. @stephen_wolfram plz share more on NN's replacing humans (pic 2) h 215.7K views · 198 likes · 40 reposts · 4 replies 28 May 2023 Summing this all up, Stephen shares an image showing the training process for a neural net and how the loss function should decrease over time. If the loss eventually streamlines, yay you have a solid model If it not you can't rely on it and it's time to change the architecture 58.7K views · 56 likes · 5 reposts · 1 replies 28 May 2023 One of the most counterintuitive takeaways that with Neural Nets it's easier to solve more complicated problems than simpler ones. That's good too since I'm dumb and need help with the complicated problems in life. I'll let Stephen take it from here: https://t.co/4aFvfWJsJo 65.3K views · 150 likes · 13 reposts · 3 replies 28 May 2023 Training Neural Nets The goal is to feed a zillion examples, and find weights that reproduce the examples. Everytime an example is used, the weights are adjusted throughout the model. Training is really expensive and computationally intensive. https://t.co/Pmk1XgzG1F 74.7K views · 76 likes · 7 reposts · 1 replies 28 May 2023 Larger networks do better at landing on results. In the below image the goal is to take in a point and recognize it in one of the three regions. I laughed when Stephen said at the boundaries it has trouble "making up its mind". Much human. Unsure results could be dangerous htt 79.8K views · 98 likes · 7 reposts · 1 replies 28 May 2023 Neural Net Explanation - Neurons arranged in layers - Each Neuron has a weight (significance) - ML is first used to find the weights - Neuron evaluates numerical function - Input is fed and neurons at each layer evaluate and feeds results to next layer - End result is reached ht 92.6K views · 87 likes · 9 reposts · 3 replies 28 May 2023 Neutral Nets are similar to a human brain The brain has 100B neurons and are connected to ~1,000 other Neurons A neuron pulses depending on what pulses it gets from other Neurons all with their own connections. This contributes to different weights in the model. Viola! 99.7K views · 92 likes · 9 reposts · 4 replies 28 May 2023 Where to these probabilities come from? ChatGPT is a model that lets people estimate the probabilities which sequences of words should occur. Stephen adds an interesting walk through demonstrating the probability of how often letters occur, and then pairs of letters and beyond 115.2K views · 128 likes · 11 reposts · 1 replies 28 May 2023 The goal of a large language model is to reasonably continue the text it already has ChatGPT's LLM estimates these probabilities Temperature is a parameter that determines how often lower ranked words are used, adding randomness. LLM's are trained on vast amounts of human text 125.7K views · 209 likes · 16 reposts · 3 replies 28 May 2023 Stephen Wolfram of Wolfram Alpha wrote the absolute best post on ChatGPT and Large Language Models. It took me about two hours to read, but significantly increased my understanding of what's going on under the hood of ChatGPT. A few of my favorite takeaways (helps my process) h 2.7M views · 7K likes · 1.1K reposts · 311 replies 28 May 2023

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