the top 25 pumpfun deployers: > launched 770k tokens > extracted $128M from the trenches > produced only 6 tokens with a 20M+ MC that's $128M extracted just by 25 devs

GPUNET Cloud is live 🔥 Need serious compute? Rent a GPU in minutes. 🔸 Use the default SSH key or bring your own 🔸 Compare offers by count, location, interconnect and provider 🔸 Pick your OS and SSH key, then launch Link: cloud.gpu.net/

1/ Feels like we've spent the last two years obsessing over training. Bigger models. Bigger clusters. Bigger training runs. Meanwhile, the economics of AI are shifting somewhere else: Inference. 🧵 2/ Training gets the headlines. Inference gets the bill. Frontier-model training costs are measured in the hundreds of millions. But once a model is deployed, every prompt, every agent action, and every generated token becomes an ongoing expense. You train once. You serve forever. 3/ The data is moving fast. Inference represented roughly 1/3 of AI compute in 2023. Deloitte projects it will reach roughly 2/3 by 2026. That's a massive shift in just a few years. 4/ The mistake many teams make? Treating training and inference like they need the same infrastructure. Training wants giant centralized clusters. Inference often benefits from: • Lower latency • Regional deployment • Better cost efficiency • Proximity to users Different workload. Different priorities. 5/ For production AI, the key metrics aren't just FLOPS anymore. They're: • Cost per token • Latency • Geography • Power efficiency (PUE) That's where margins are won or lost. 6/ The challenge is that compute is fragmented across hundreds of providers and regions. That's where @gpunet comes in. Instead of manually researching infrastructure, teams can compare providers, discover available capacity, and find infrastructure that fits their inference workload. The future of AI won't run on a single cloud. It will run on a global compute fabric. #AI #GPU #DataCenters #InferenceAI

Decentralized GPU had its cycle. What is running now is decentralized inference, where the question stops being how many GPUs sit in your network and becomes how many people pay to run real AI work on them, verified on chain. Venice is doing some of this already.

On 27 March 2019, a missile with no explosive left an island off Odisha and, 168 seconds later, hit an Indian satellite moving at 28,000 km/h. India had just joined the only three countries that can destroy a satellite. Five days later, the head of NASA called it a terrible, terrible thing. This is the story of Mission Shakti — why India said for nine years that it could do this but didn't need to, why it finally did, the number DRDO used to answer NASA, and what actually happened to every piece of that satellite. Sources: DRDO statements on Mission Shakti; NASA town hall remarks, April 2019; CelesTrak / US Space Command tracking data on the Microsat-R debris; Congressional and Senate testimony on ISS risk; Government of India and MEA statements; published analysis of the 1985 US, 2007 Chinese and 2021 Russian ASAT tests. Note: Debris decay figures are from public tracking catalogues. Some early Indian estimates proved optimistic; the video presents both the estimate and the outcome.