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Gradient goes beyond static content, utilizing idle compute resources from edge devices, personal computers, and local servers to perform real-time processing tasks. The thesis is similar to Pipe’s: operations like AI inference or serverless functions can happen closer to the user, reducing latency. Gradient reported surpassing 20,000 connected nodes... show more

Solana

4,082,734 subscribers

326,691 次观看 • 1 年前 •via X (Twitter)

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Solana1 年前

Delivering content quickly from servers to end users is crucial for apps and services, whether it's static files like images or dynamic data like AI responses. Slow delivery can ruin user experience—think buffering videos or lagging web pages. Solana-based DePIN networks @pipenetwork and @Gradient_HQ incentivize users to contribute excess storage and compute power, respectively. By leveraging a larger network of nodes and tapping into existing infrastructure, they can potentially offer faster delivery, lower costs, and greater reliability.

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Solana1 年前

Pipe Network is a decentralized content delivery network (dCDN). Traditional CDNs cache content (for e.g., images, videos, and webpages) across distributed servers. When users request content, it’s delivered by the closest server, reducing latency compared to an origin server. @pipenetwork takes this design further by incentivizing people to contribute their excess storage and bandwidth. The thesis is simple: more nodes = higher chance a node is closer to the user = faster delivery. The development team behind Pipe recently announced a $10 million raise led by @multicoincap, with plans to launch mainnet in 2025. Learn more from @DavidRhodus' product keynote at Breakpoint:

Solana 的头像

Solana1 年前

What Solana DePIN projects are you excited about? If you’re curious about the broader landscape beyond content delivery DePIN, check out @yashhsm’s report: plus @Syndica_io’s recently released Aug 2024 Solana DePIN Deep Dive

⚡ rift 🐐 🛡💚🌙꧁IP꧂ 的头像

⚡ rift 🐐 🛡💚🌙꧁IP꧂1 年前

Bullish on @Gradient_HQ — The open layer for edge compute on Solana. Set up your #GradientSentry Node and be part of the future. " DePin Is The Future " #GradientNetwork #Solana #DePIN

XRP_Cro 🔥 AI / Gaming / DePIN 的头像

XRP_Cro 🔥 AI / Gaming / DePIN1 年前

💵Earn passive income with Gradient Network - The open layer for edge compute on @Solana ✅ Backed by top tier VCs: @SolanaFndn, @multicoincap, @PanteraCapital and @sequoia Sign up here: 👉 Stay online for 72 hours of uptime to earn #DePIN $GRASS

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MarmZzz1 年前

10% Bonus use this ref link

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Kaorychang1 年前

@Gradient_HQ

Joy Osaretin. (Ø,G) 的头像

Joy Osaretin. (Ø,G)1 年前

@Gradient_HQ What are u waiting for UZOM1Z

crypto mania ❤❤❤ 的头像

crypto mania ❤❤❤1 年前

@Gradient_HQ Use my CODE: BQH3RJ

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artemis || 🌳1 年前

🚀Join me on @Gradient_HQ — The open layer for edge compute on Solana. Set up your #GradientSentry Node and be part of the future. #GradientNetwork

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cromatoforo1 年前

Use code 𝗡𝗦𝗤𝗧𝗜𝟴 for extra awesomeness

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Started with 100 users. Now Gradient runs on 1M+ nodes across 190 countries. is building the open compute layer AI needs — using the devices we already have. This is DePIN on Solana.

Started with 100 users. Now Gradient runs on 1M+ nodes across 190 countries. is building the open compute layer AI needs — using the devices we already have. This is DePIN on Solana.

Solana

971,644 次观看 • 1 年前

Meet Gradient — turning everyday devices into a decentralized global network that powers faster streaming, content delivery, and compute 🛜

Meet Gradient — turning everyday devices into a decentralized global network that powers faster streaming, content delivery, and compute 🛜

Solana

229,448 次观看 • 1 年前

Blockchains should be accessible, fast, and frictionless. Hear from the co-founders of Orca 🌊, Yutaro Mori & Ori | cofounder of @orca_so, about how Solana’s low latency and high throughput enables them to focus on building their product 🐋 #OnlyPossibleOnSolana

Blockchains should be accessible, fast, and frictionless. Hear from the co-founders of Orca 🌊, Yutaro Mori & Ori | cofounder of @orca_so, about how Solana’s low latency and high throughput enables them to focus on building their product 🐋 #OnlyPossibleOnSolana

Solana

72,803 次观看 • 2 年前

SoloHost Emerges to Improve Pi's Private AI & Distributed Computing Future On Pi2Day 2026, Pi Network unveiled SoloHost in its Beta phase on Desktop, giving developers an open, permissionless way to publish self-hosted apps and Node utilities. Pioneers can now install local AI agents on their own computers. What's more, all data and processing stay on-device for maximum privacy, without cloud sharing. With 420,000+ Pi Nodes, SoloHost also unlocks distributed computing. Nodes can soon contribute spare power to AI tasks and earn Pi, while developers can easily reach millions of Pioneers. By removing complex setup barriers, SoloHost transforms Pi Nodes from blockchain infrastructure into practical tools for privacy-first AI and real compute utility.

SoloHost Emerges to Improve Pi's Private AI & Distributed Computing Future On Pi2Day 2026, Pi Network unveiled SoloHost in its Beta phase on Desktop, giving developers an open, permissionless way to publish self-hosted apps and Node utilities. Pioneers can now install local AI agents on their own computers. What's more, all data and processing stay on-device for maximum privacy, without cloud sharing. With 420,000+ Pi Nodes, SoloHost also unlocks distributed computing. Nodes can soon contribute spare power to AI tasks and earn Pi, while developers can easily reach millions of Pioneers. By removing complex setup barriers, SoloHost transforms Pi Nodes from blockchain infrastructure into practical tools for privacy-first AI and real compute utility.

BSCN

31,857 次观看 • 2 个月前

Variational Autoencoder by hand ✍️ ~ 11 steps walkthrough below A VAE learns the structure of your data, the mean and variance of its hidden features, and then generates new data from that structure. A GAN only learns to fool a discriminator. It can make convincing fakes without ever knowing what the data is really made of. That is the difference, and it is the whole reason VAEs matter. In 2024 ICLR gave its first ever Test of Time Award to the VAE paper, "Auto-Encoding Variational Bayes" by Diederik Kingma and Max Welling, ten years on. How does it work? Goal: encode three inputs into a distribution, sample from it, decode it back, and read every loss gradient off the page. = 1. Given = Three training examples X1, X2, X3, copied to the bottom as their own targets. Reconstructing your own input is what puts the "auto", meaning self, in autoencoder. = 2. Encoder, layer 1 = Let us multiply the inputs by weights and biases, then apply ReLU, crossing out every negative. = 3. Mean and standard deviation = We multiply the features by two more weight sets. The first predicts the means μ of the latent distributions, the second their standard deviations σ. = 4. A random offset = Let us sample ε from a standard normal, mean 0 and variance 1, and multiply it by σ. This is a random step away from the mean, scaled by how uncertain each feature is. = 5. Mean plus offset = We add the offset back onto μ, and these become the decoder's inputs. Keeping the randomness out in ε is the reparameterization trick: it lets gradients flow straight through the sampling. = 6. Decoder, layer 1 = Let us multiply by weights and biases and apply ReLU again. Here -4 is crossed out. = 7. Decoder, layer 2 = We multiply once more. The output Y is the decoder's attempt to rebuild X from the sampled distribution. = 8. Gradient for the mean = Let us push μ toward 0. A lot of math, the SGVB estimator, collapses the KL gradient to simply μ itself. = 9. Gradient for the standard deviation = We want σ to approach 1. = 10. And its formula = That same math simplifies the gradient to σ minus 1/σ. = 11. Reconstruction gradient = We want the reconstruction Y to match the input X. Mean squared error simplifies its gradient to Y minus X. Takeaway: the two gradients you just calculated each sit at the heart of a modern method, so one VAE teaches you both. The KL divergence is the penalty RLHF like GRPO uses to keep a fine-tuned model from drifting off its base. The reconstruction loss, plain mean squared error, is exactly what trains a diffusion model to denoise. Draw one VAE by hand and you have quietly learned the core of both. 💾 Save this post!

Variational Autoencoder by hand ✍️ ~ 11 steps walkthrough below A VAE learns the structure of your data, the mean and variance of its hidden features, and then generates new data from that structure. A GAN only learns to fool a discriminator. It can make convincing fakes without ever knowing what the data is really made of. That is the difference, and it is the whole reason VAEs matter. In 2024 ICLR gave its first ever Test of Time Award to the VAE paper, "Auto-Encoding Variational Bayes" by Diederik Kingma and Max Welling, ten years on. How does it work? Goal: encode three inputs into a distribution, sample from it, decode it back, and read every loss gradient off the page. = 1. Given = Three training examples X1, X2, X3, copied to the bottom as their own targets. Reconstructing your own input is what puts the "auto", meaning self, in autoencoder. = 2. Encoder, layer 1 = Let us multiply the inputs by weights and biases, then apply ReLU, crossing out every negative. = 3. Mean and standard deviation = We multiply the features by two more weight sets. The first predicts the means μ of the latent distributions, the second their standard deviations σ. = 4. A random offset = Let us sample ε from a standard normal, mean 0 and variance 1, and multiply it by σ. This is a random step away from the mean, scaled by how uncertain each feature is. = 5. Mean plus offset = We add the offset back onto μ, and these become the decoder's inputs. Keeping the randomness out in ε is the reparameterization trick: it lets gradients flow straight through the sampling. = 6. Decoder, layer 1 = Let us multiply by weights and biases and apply ReLU again. Here -4 is crossed out. = 7. Decoder, layer 2 = We multiply once more. The output Y is the decoder's attempt to rebuild X from the sampled distribution. = 8. Gradient for the mean = Let us push μ toward 0. A lot of math, the SGVB estimator, collapses the KL gradient to simply μ itself. = 9. Gradient for the standard deviation = We want σ to approach 1. = 10. And its formula = That same math simplifies the gradient to σ minus 1/σ. = 11. Reconstruction gradient = We want the reconstruction Y to match the input X. Mean squared error simplifies its gradient to Y minus X. Takeaway: the two gradients you just calculated each sit at the heart of a modern method, so one VAE teaches you both. The KL divergence is the penalty RLHF like GRPO uses to keep a fine-tuned model from drifting off its base. The reconstruction loss, plain mean squared error, is exactly what trains a diffusion model to denoise. Draw one VAE by hand and you have quietly learned the core of both. 💾 Save this post!

Tom Yeh

17,011 次观看 • 1 个月前

The Gemini Solana Credit Card is here. Earn SOL back on every fiat dollar you spend — and automatically stake your rewards. The physical card is 16 grams of matte metal shimmering with Solana's gradient, available as a limited edition. 👇

The Gemini Solana Credit Card is here. Earn SOL back on every fiat dollar you spend — and automatically stake your rewards. The physical card is 16 grams of matte metal shimmering with Solana's gradient, available as a limited edition. 👇

Solana

458,433 次观看 • 10 个月前

🌟 Another Epic Sneak Peek: OptimAI Core Node’s Edge AI Computing Power! We’re excited to continue unveiling the upcoming OptimAI Core Node features! This time, we’re introducing Edge AI Computing, a game-changing feature that will make your devices even more powerful in contributing to the OptimAI Network. 🔥 What is Edge AI Computing? With the OptimAI Core Node, your device’s idle computing resources (CPU/GPU) and storage will be put to work, powering critical AI computing tasks, including: 🔸Edge Inference: Running real-time AI predictions directly on your device without sending data to centralized servers. 🔸Hot LLM Models: Accelerating large language models for high-speed NLP tasks. 🔸Generative AI Models and More: Contributing to the training and inference of cutting-edge generative AI, like text-to-image models, deep learning, and more. More Contributions, Bigger Rewards Await! This Edge AI Computing feature allows us to leverage the full potential of decentralized computing power, reducing latency, increasing efficiency, and making AI more accessible. The more you contribute, the bigger the rewards!🔥 Stay in the Loop—Start Today with OptimAI Lite Node! While the OptimAI Core Node is still in the works, you don’t have to wait to join the action. The more you contribute, the greater the rewards! Let’s build a stronger, smarter, and more decentralized AI network together. Stay tuned for more exciting updates! 🔸Extension Node: 🔸Telegram Node: 🔸Register at: #DePIN

🌟 Another Epic Sneak Peek: OptimAI Core Node’s Edge AI Computing Power! We’re excited to continue unveiling the upcoming OptimAI Core Node features! This time, we’re introducing Edge AI Computing, a game-changing feature that will make your devices even more powerful in contributing to the OptimAI Network. 🔥 What is Edge AI Computing? With the OptimAI Core Node, your device’s idle computing resources (CPU/GPU) and storage will be put to work, powering critical AI computing tasks, including: 🔸Edge Inference: Running real-time AI predictions directly on your device without sending data to centralized servers. 🔸Hot LLM Models: Accelerating large language models for high-speed NLP tasks. 🔸Generative AI Models and More: Contributing to the training and inference of cutting-edge generative AI, like text-to-image models, deep learning, and more. More Contributions, Bigger Rewards Await! This Edge AI Computing feature allows us to leverage the full potential of decentralized computing power, reducing latency, increasing efficiency, and making AI more accessible. The more you contribute, the bigger the rewards!🔥 Stay in the Loop—Start Today with OptimAI Lite Node! While the OptimAI Core Node is still in the works, you don’t have to wait to join the action. The more you contribute, the greater the rewards! Let’s build a stronger, smarter, and more decentralized AI network together. Stay tuned for more exciting updates! 🔸Extension Node: 🔸Telegram Node: 🔸Register at: #DePIN

OptimAI Network

44,382 次观看 • 1 年前

Backpropagation by hand ✍️ ~ 11 steps walkthrough below Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time. So I drew and calculated one entirely by hand. Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias. = 1. Given = A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0]. = 2. Backprop gradient cells = Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first. = 3. Layer 3 softmax = We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired. = 4. Layer 3 weights and biases = Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together. = 5. Layer 2 activations = We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward. = 6. Layer 2 ReLU = Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else. = 7. Layer 2 weights and biases = We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up. = 8. Layer 1 activations = Let us multiply dL/dz2 by W2. = 9. Layer 1 ReLU = We apply the same gate again, now on a1. = 10. Layer 1 weights and biases = Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient. = 11. Update = We subtract, and the network has learned. In practice a learning rate scales this step. The gradients: dL/dz3 = [0.5, -0.5, 0] dL/da1 = [1, -2, 2, -1] dL/dz1 = [0, -2, 2, -1] The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning. 💾 Save this post!

Backpropagation by hand ✍️ ~ 11 steps walkthrough below Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time. So I drew and calculated one entirely by hand. Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias. = 1. Given = A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0]. = 2. Backprop gradient cells = Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first. = 3. Layer 3 softmax = We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired. = 4. Layer 3 weights and biases = Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together. = 5. Layer 2 activations = We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward. = 6. Layer 2 ReLU = Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else. = 7. Layer 2 weights and biases = We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up. = 8. Layer 1 activations = Let us multiply dL/dz2 by W2. = 9. Layer 1 ReLU = We apply the same gate again, now on a1. = 10. Layer 1 weights and biases = Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient. = 11. Update = We subtract, and the network has learned. In practice a learning rate scales this step. The gradients: dL/dz3 = [0.5, -0.5, 0] dL/da1 = [1, -2, 2, -1] dL/dz1 = [0, -2, 2, -1] The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning. 💾 Save this post!

Tom Yeh

960,130 次观看 • 1 个月前

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

Sami Kassab

35,706 次观看 • 7 个月前

Sub-agents in (latent) space! We’ve been working on a side project. As far as I know, this is the first massively multiplayer, completely LLM-driven game. Come play Gradient Bang with us. See if you can catch me on the leaderboard. This whole thing started because I wanted to explore a bunch of things I’m currently obsessed with, in an application of non-trivial size, that felt both new and old at the same time. So … a retro-style space trading game built entirely around interacting with and managing multiple LLMs. Factorio, but instead of clicking, you cajole your ship AI into tasking other AIs to do things for you. Some of the things we’ve been thinking about as we hack on Gradient Bang: - Sub-agent orchestration - Partial context sharing between multiple LLM inference loops - Managing very long contexts, and episodic memory across user sessions - World events and large volumes of structured data input as part of human/agent conversations - Dynamic user interfaces, driven/created on the fly by LLMs - And, of course, voice as primary input If you’ve been building coding harnesses, or writing Open Claw agents, or doing pretty much anything that pushes the boundaries of AI-native development these days, you’re probably thinking about these things too! This is all built with Pipecat AI, the back end is Supabase, the React front end is deployed to Vercel, and all the code is open source.

Sub-agents in (latent) space! We’ve been working on a side project. As far as I know, this is the first massively multiplayer, completely LLM-driven game. Come play Gradient Bang with us. See if you can catch me on the leaderboard. This whole thing started because I wanted to explore a bunch of things I’m currently obsessed with, in an application of non-trivial size, that felt both new and old at the same time. So … a retro-style space trading game built entirely around interacting with and managing multiple LLMs. Factorio, but instead of clicking, you cajole your ship AI into tasking other AIs to do things for you. Some of the things we’ve been thinking about as we hack on Gradient Bang: - Sub-agent orchestration - Partial context sharing between multiple LLM inference loops - Managing very long contexts, and episodic memory across user sessions - World events and large volumes of structured data input as part of human/agent conversations - Dynamic user interfaces, driven/created on the fly by LLMs - And, of course, voice as primary input If you’ve been building coding harnesses, or writing Open Claw agents, or doing pretty much anything that pushes the boundaries of AI-native development these days, you’re probably thinking about these things too! This is all built with Pipecat AI, the back end is Supabase, the React front end is deployed to Vercel, and all the code is open source.

kwindla

455,905 次观看 • 4 个月前

CEO of phygitals explains why they chose to build on Solana "I think one thing Solana does really well and they're hard to beat at is both foundation and ecosystem support from builders and founders. They have the Superteam initiative, which is a great initiative where you can get tons of resources and learnings and free resources to build. And then the general community and collector community within Solana is very vibrant. Our whole go to market was pretty much utilizing ungraded/cheap Pokémon cards that we acquired while we didn't have much funding or profits. We gave these out to top Solana NFT communities and through the liveliness in the ecosystem, Phygitals was created. The initial user base was all Solana NFT collectors or traders and in that sense they have such a large user base, which makes it incredibly easy to build on."

CEO of phygitals explains why they chose to build on Solana "I think one thing Solana does really well and they're hard to beat at is both foundation and ecosystem support from builders and founders. They have the Superteam initiative, which is a great initiative where you can get tons of resources and learnings and free resources to build. And then the general community and collector community within Solana is very vibrant. Our whole go to market was pretty much utilizing ungraded/cheap Pokémon cards that we acquired while we didn't have much funding or profits. We gave these out to top Solana NFT communities and through the liveliness in the ecosystem, Phygitals was created. The initial user base was all Solana NFT collectors or traders and in that sense they have such a large user base, which makes it incredibly easy to build on."

Solana

48,231 次观看 • 4 个月前

Pipe (Pipe Network) is a decentralized content delivery network (dCDN) that accelerates delivery by leveraging users’ excess storage & bandwidth. More nodes = faster delivery. More from David Rhodus 🇺🇸 at Breakpoint '24

Pipe (Pipe Network) is a decentralized content delivery network (dCDN) that accelerates delivery by leveraging users’ excess storage & bandwidth. More nodes = faster delivery. More from David Rhodus 🇺🇸 at Breakpoint '24

Solana

78,975 次观看 • 1 年前

BREAKING: Stephen from Metaplex announces first public launch of the Genesis SDK, a way for anyone to build their own fair launch platform or access token data

BREAKING: Stephen from Metaplex announces first public launch of the Genesis SDK, a way for anyone to build their own fair launch platform or access token data

Solana

31,515 次观看 • 8 个月前

Why We Ship: raposa. From surviving one of the biggest rugs to winning the People's Award at Breakpoint. SOK built Raposa to give back to the ecosystem, not extract from it.

Why We Ship: raposa. From surviving one of the biggest rugs to winning the People's Award at Breakpoint. SOK built Raposa to give back to the ecosystem, not extract from it.

Solana

73,476 次观看 • 4 个月前

Today Sam Asante and I are releasing Typeahead 2.0 on Product Hunt. Sam and I started this company because we think local AI software is going to become a much bigger part of how people use their computers. Typeahead is the first product from that thesis. The first version made the idea clear. AI autocomplete should work in the apps where writing already happens. Since then, we have been shipping toward the product that idea deserves. The app feels faster and more natural. Suggestions are easier to control. The experience is more native to the Mac. It's getting better at staying out of the way until the exact moment it can help. That matters because writing is personal. The best AI writing help keeps you in the sentence. You stay in flow and keep your voice. The machine helps without taking over. Typeahead works across your Mac, runs locally, works offline, and is built to feel like software you own. We’re live on Product Hunt today.

Today Sam Asante and I are releasing Typeahead 2.0 on Product Hunt. Sam and I started this company because we think local AI software is going to become a much bigger part of how people use their computers. Typeahead is the first product from that thesis. The first version made the idea clear. AI autocomplete should work in the apps where writing already happens. Since then, we have been shipping toward the product that idea deserves. The app feels faster and more natural. Suggestions are easier to control. The experience is more native to the Mac. It's getting better at staying out of the way until the exact moment it can help. That matters because writing is personal. The best AI writing help keeps you in the sentence. You stay in flow and keep your voice. The machine helps without taking over. Typeahead works across your Mac, runs locally, works offline, and is built to feel like software you own. We’re live on Product Hunt today.

Hiten Shah

28,113 次观看 • 1 个月前

BREAKING NEWS: For the first time, Solana Breakpoint 2026 will be hosted in London 🇬🇧 November 15-17 at Olympia FOR 24 HOURS ONLY, we're offering subsidized tickets to Solana's premier event in 2026:

BREAKING NEWS: For the first time, Solana Breakpoint 2026 will be hosted in London 🇬🇧 November 15-17 at Olympia FOR 24 HOURS ONLY, we're offering subsidized tickets to Solana's premier event in 2026:

Solana

932,507 次观看 • 8 个月前

Orbit AI Satellite Successfully Achieve World’s First Orbital AI Deployment and Launching Digital AI Sovereignty Decentralized Orbital AI Network Orbit AI Orbit AI🛰️ today announced that the first satellite, “OAI Genesis-1,” has successfully launched and entered Low Earth Orbit (LEO). Amidst fierce competition from tech giants (e.g., Starlink Starlink Elon Musk , Google AI Project Suncatcher) in space AI computing, this launch signifies Orbit AI’s position as the first to achieve real-world AI deployment, formally inaugurating its "Orbit AI Cloud Platform." Genesis-1 is equipped with NVIDIA NVIDIA AI Compute Cores, running a 2.6B parameter AI model for real-time analysis of infrared remote sensing data in space. By processing data on orbit, Genesis-1 drastically reduces critical information retrieval time (e.g., disaster alerts, maritime monitoring) from hours to mere seconds, while cutting transmission bandwidth costs by over 90%. Furthermore, Orbit AI has partnered with from energy company Powerbank (NASDAQ: SUUN) ( utilizing infinite solar power to achieve carbon-neutral computing and projecting a reduction in overall energy operational costs by 60%. Following its triumph at the BNB Chain Hackathon ( Orbit AI protocol is committed to creating an ultimate censorship-resistant deployment environment: Developers can deploy AI models, privacy applications, financial algorithms, and even blockchain nodes on the satellite network. This ensures that code and data operate in a physically isolated, neutral environment beyond the jurisdiction of major nations, guaranteeing extreme digital sovereignty and service resilience. Orbit AI will also leverage the RWA (Real World Assets) mechanism to allow community users to purchase satellite NFT shares, becoming co-owners of this space infrastructure and sharing in its compute revenues, thus building a community-owned orbital AI economy.

Orbit AI Satellite Successfully Achieve World’s First Orbital AI Deployment and Launching Digital AI Sovereignty Decentralized Orbital AI Network Orbit AI Orbit AI🛰️ today announced that the first satellite, “OAI Genesis-1,” has successfully launched and entered Low Earth Orbit (LEO). Amidst fierce competition from tech giants (e.g., Starlink Starlink Elon Musk , Google AI Project Suncatcher) in space AI computing, this launch signifies Orbit AI’s position as the first to achieve real-world AI deployment, formally inaugurating its "Orbit AI Cloud Platform." Genesis-1 is equipped with NVIDIA NVIDIA AI Compute Cores, running a 2.6B parameter AI model for real-time analysis of infrared remote sensing data in space. By processing data on orbit, Genesis-1 drastically reduces critical information retrieval time (e.g., disaster alerts, maritime monitoring) from hours to mere seconds, while cutting transmission bandwidth costs by over 90%. Furthermore, Orbit AI has partnered with from energy company Powerbank (NASDAQ: SUUN) ( utilizing infinite solar power to achieve carbon-neutral computing and projecting a reduction in overall energy operational costs by 60%. Following its triumph at the BNB Chain Hackathon ( Orbit AI protocol is committed to creating an ultimate censorship-resistant deployment environment: Developers can deploy AI models, privacy applications, financial algorithms, and even blockchain nodes on the satellite network. This ensures that code and data operate in a physically isolated, neutral environment beyond the jurisdiction of major nations, guaranteeing extreme digital sovereignty and service resilience. Orbit AI will also leverage the RWA (Real World Assets) mechanism to allow community users to purchase satellite NFT shares, becoming co-owners of this space infrastructure and sharing in its compute revenues, thus building a community-owned orbital AI economy.

Orbit AI🛰️

24,839 次观看 • 8 个月前

Founders: the next breakout game won’t come from a AAA studio. Hear from justin waldron on building at the cutting edge of crypto and AI at Accelerate this May in NYC 🗽

Founders: the next breakout game won’t come from a AAA studio. Hear from justin waldron on building at the cutting edge of crypto and AI at Accelerate this May in NYC 🗽

Solana

286,936 次观看 • 1 年前

“The user experience of the internet is broken. It doesn’t have to be that way. Solana is really the foundation of a new internet.”🔥 Hear from vibhu on how DRiP 💧 is changing the way we own & distribute content on the web. #OnlyPossibleOnSolana

“The user experience of the internet is broken. It doesn’t have to be that way. Solana is really the foundation of a new internet.”🔥 Hear from vibhu on how DRiP 💧 is changing the way we own & distribute content on the web. #OnlyPossibleOnSolana

Solana

184,666 次观看 • 2 年前

A Perfect Pairing! 5G & Edge Computing: Together they enhance responsiveness, capacity and reliability by processing #data closer to where it’s generated! 🌟See🔗 ◀️T-Mobile Business 🔸This is critical for applications such as augmented reality #AR and #customer interaction and especially in contexts where low latency and near-real-time data processing are critical ✅ 🌟 MEC allows data processing closer to the source, reducing latency, improving efficiency, and enhancing resilience, whilst moving computing closer to users. It reduces latency and centralizes data processing. 🔸This is useful for scenarios that demand rapid data action, data #confidentiality or the ability to function without relying on distant #Cloud servers ☁️ Although only about 10% of enterprise data is currently processed outside central servers, this figure is anticipated to grow to 75% by 2025 🌟MEC can help in industries like #manufacturing 🏭#Retail 🛍️#warehousing 🚚#agriculture🌾and #logistics 🚛through faster decision-making, lower costs and more reliable data processing. Fixed and mobile edge applications are emerging, with mobile edge becoming a key area for future growth. 🔸5G's speed, low latency, and #security make it a natural fit with MEC, offering solutions to businesses' needs for faster responses, local data processing and reliable operations ✅ 📈Business Adoption: Though still early, it is anticipated that some 75% of #enterprise data will be processed at the #edge by 2025, driven by advancements in #Smart devices and routers with built-in processing capabilities 📶 💡To find out more, please explore the 🎞️resource below 🔽 See🔗 ⬅️#5G #EdgeComputing #TechNews #TFBPartner BusinessIntelligence @FrRonconi #IoT Jean CAYEUX #RaviVisvesvarayaSharadaPrasad #Telecom #InfoTech Yann Marchand Tony Moroney #DigitalTransformation #Telco Knut Jägersberg Jean-Baptiste Lefevre 💙 #TechForGood 💙 #Sustainability Fati Sule Aurelien Lallemant #IA 🔎 & #RSE 🌎 Lionel Costes #AI Mack ipfconline Greg Valancius Dr. Marcell Vollmer #StaySafe #CES2026 #IoT Dev Khanna Baskaran Ambalavanan Anand Narang #CX Ian Jones Hana Laurent Alaus Xavier Gomez Pinna Pierre - in summer break 😎🤙🏼 Dr. Khulood Almani | د.خلود المانع Eric T. #VR Enrico Molinari #VivaTech2025 Chidambara .ML. Smaksked Skåne AB 🌐

A Perfect Pairing! 5G & Edge Computing: Together they enhance responsiveness, capacity and reliability by processing #data closer to where it’s generated! 🌟See🔗 ◀️T-Mobile Business 🔸This is critical for applications such as augmented reality #AR and #customer interaction and especially in contexts where low latency and near-real-time data processing are critical ✅ 🌟 MEC allows data processing closer to the source, reducing latency, improving efficiency, and enhancing resilience, whilst moving computing closer to users. It reduces latency and centralizes data processing. 🔸This is useful for scenarios that demand rapid data action, data #confidentiality or the ability to function without relying on distant #Cloud servers ☁️ Although only about 10% of enterprise data is currently processed outside central servers, this figure is anticipated to grow to 75% by 2025 🌟MEC can help in industries like #manufacturing 🏭#Retail 🛍️#warehousing 🚚#agriculture🌾and #logistics 🚛through faster decision-making, lower costs and more reliable data processing. Fixed and mobile edge applications are emerging, with mobile edge becoming a key area for future growth. 🔸5G's speed, low latency, and #security make it a natural fit with MEC, offering solutions to businesses' needs for faster responses, local data processing and reliable operations ✅ 📈Business Adoption: Though still early, it is anticipated that some 75% of #enterprise data will be processed at the #edge by 2025, driven by advancements in #Smart devices and routers with built-in processing capabilities 📶 💡To find out more, please explore the 🎞️resource below 🔽 See🔗 ⬅️#5G #EdgeComputing #TechNews #TFBPartner BusinessIntelligence @FrRonconi #IoT Jean CAYEUX #RaviVisvesvarayaSharadaPrasad #Telecom #InfoTech Yann Marchand Tony Moroney #DigitalTransformation #Telco Knut Jägersberg Jean-Baptiste Lefevre 💙 #TechForGood 💙 #Sustainability Fati Sule Aurelien Lallemant #IA 🔎 & #RSE 🌎 Lionel Costes #AI Mack ipfconline Greg Valancius Dr. Marcell Vollmer #StaySafe #CES2026 #IoT Dev Khanna Baskaran Ambalavanan Anand Narang #CX Ian Jones Hana Laurent Alaus Xavier Gomez Pinna Pierre - in summer break 😎🤙🏼 Dr. Khulood Almani | د.خلود المانع Eric T. #VR Enrico Molinari #VivaTech2025 Chidambara .ML. Smaksked Skåne AB 🌐

Sen. Sally Eaves

10,487 次观看 • 1 年前