BethanyG

@vithikaonreddit Hi just DMed!

@thisisbethany_5 Hey would love to connect :)

Looking for experienced Reddit marketers to collab with :)

@JAYMHARX DMed

@thisisbethany_5 I’m available

To all motion designers: I got a lot of DMs yesterday, and it's honestly been a bit overwhelming. It would be great if you could post your portfolio/sample work along with your rates directly in this thread. I'm sure I'm not the only one looking for motion designers, and this way other people in the community can discover your work as well. 🙌

To all motion designers: I got a lot of DMs yesterday, and it's honestly been a bit overwhelming. It would be great if you could post your portfolio/sample work along with your rates directly in this thread. I'm sure I'm not the only one looking for motion designers, and this way other people in the community can discover your work as well. 🙌

@ToonsZippy94576 Great! How much do you charge for this video?

@thisisbethany_5 I’m interested in this. I can help with motion design and product launch videos. Let me know the details and I’d be happy to discuss further here example

Looking for a solo motion designer to help create a product launch video for an upcoming release. Long term collaboration~

@EstaArtss how much do you charge for this one? Thanks!

Just delivered a new motion design explainer for a brand. Client’s loving it what do you guys think? 👀 #MotionDesign #ExplainerVideo #BrandStory #CreativeEdit #DesignCommunity

@Elarathron it would be great if you can share :)

@thisisbethany_5 intersted i'd love to learn more about the project see if we're a good fit happy to share my previous work.

@AHMEDHATATA_X May I get a pricing for those? Thanks!

@thisisbethany_5 Hi Bethany, I’m an experienced motion designer. Check out my work 👇 x.com/ahmedhatata_x?…

Looking for a solo motion designer to help create a product launch video for an upcoming release. Long term collaboration :)

@mokshhuu DMed

Fable 5 is really good on creating motions graphics i just made these frames in just 20 prompts

i love watching model companies race each other. waiting for terminals to finish running. splitting tasks because context gets too long. accepting hallucinations as a fact of life. a lot of what feels normal today will look outdated in a few years. the best part of AI progress is watching entire categories of problems disappear.

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with @TheoTabah from @meetLCA on @startupideaspod. This is the stuff we normally keep internal but all the sauce is yours. @TheoTabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes youtube.com/watch?v=LztPaN… Watch

@SamPeterToT @aakashgupta this is very true

@aakashgupta One lesson I've taken from AI this year: Speed is no longer a growth hack. It's becoming a business model.

Anthropic is about to ship its fourth frontier model since February. OpenAI has shipped one in that window. Google's next flagship keeps slipping. That cadence gap just vaulted Anthropic past OpenAI to the highest valuation of any pure-AI company. Read the release log. Opus 4.6 on February 5. Opus 4.7 on April 16. Opus 4.8 twelve days ago, beating GPT-5.5 and Gemini 3.1 Pro across almost every benchmark. Mythos reportedly goes public tomorrow. A new frontier model roughly every six weeks, all year. Now everyone else. OpenAI's last flagship step was GPT-5.5 in April. Google promised Gemini 3.5 Pro for June and keeps pushing it back, with Sundar asking the I/O crowd on stage for one more month. Shipping speed is the moat. Any single model lead is temporary. Every lab passes every other lab eventually. The company that puts out a new best-in-class model every six weeks never has to be ahead on a given Tuesday. It just keeps landing punches while the others reload. That compounds into the numbers. Run rate went from roughly $10B at the end of 2025 to $47B by late May, and Anthropic expects its first profitable quarter this Q2 while OpenAI keeps burning. So the valuation followed the cadence. $60B in March 2025. $380B in the fall. $965B at the Series H, past OpenAI's $852B. And it explains the move that surprised everyone: Anthropic filed its S-1 on June 1 and beat OpenAI to the SEC by days. You only sprint to the public markets first when you believe your numbers win in daylight. The cadence is the bet. Tomorrow's release is just the next line in the log.

@gauravsbuilding Hi, I love your product and have tried it twice. Two of the videos got 0 plays, and two others have fewer than 100 views. For all of them, I manually posted them to the platform myself. Is this expected?

Today, you can automate your entire marketing and hit $10k MRR without even trying... Just watch me:

@Jason I’m in!

We started an AI founder twitter group... reply with "I'm in" if you're a founder and want to be added

Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will eventually understand how the world works. Yann LeCun said that was stupid. He argued that generative AI is fundamentally inefficient. When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details. It memorizes patterns instead of learning the actual physics of reality. He proposed a different path: JEPA (Joint-Embedding Predictive Architecture). Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space." But for years, JEPA had a fatal flaw. It suffered from "representation collapse." Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical. It learned nothing. To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads. Until today. Researchers just dropped a paper called "LeWorldModel" (LeWM). They completely solved the collapse problem. They replaced the complex engineering hacks with a single, elegant mathematical regularizer. It forces the AI's internal "thoughts" into a perfect Gaussian distribution. The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions. The results completely rewrite the economics of AI. LeWM didn't need a massive, centralized supercomputer. It has just 15 million parameters. It trains on a single, standard GPU in a few hours. Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events. We spent billions trying to force massive server farms to memorize the internet. Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

This PetClaw just replaced my $2k/mo assistant for $30 If you're still doing everything manually, you're wasting time Set up a workflow once, and it handles everything for you How to build your own AI assistant workflow 🧵⬇️

@vitaliidodonov @ViralityEngine lets connect!

Dear algo, Please show this tweet to founders, builders and creators who are struggling to grow their 𝕏 account.