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Running AI directly on $5 microcontroller hardware keeps getting easier. ESP32-AI adds a 28.9M LLM to your microcontroller. It's very impressive for the chip size. It gives you lightweight AI capabilities right on ESP32 microcontrollers. Key highlights: • On-device edge inference without relying on cloud APIs • Ultra-low power... show more

Praveen Kumar Verma

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82,409 просмотров • 2 дней назад •via X (Twitter)

Наука и технологии

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NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk

beamnxw ./

30,361 просмотров • 1 месяц назад

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Guri Singh

2,180,357 просмотров • 4 месяцев назад

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

elvis

47,678 просмотров • 3 месяцев назад

AI-Enhanced LiDAR. Left vs right. A real LiDAR device costs thousands of dollars. What's in your iPhone Pro is a "baby LiDAR". Limited depth resolution, noisy output, not really built for high-precision 3D. You can't change the hardware. So we built an ML layer on top. Denoising, geometry completion, detail recovery. Processed server-side. Same sensor. Very different result.

AI-Enhanced LiDAR. Left vs right. A real LiDAR device costs thousands of dollars. What's in your iPhone Pro is a "baby LiDAR". Limited depth resolution, noisy output, not really built for high-precision 3D. You can't change the hardware. So we built an ML layer on top. Denoising, geometry completion, detail recovery. Processed server-side. Same sensor. Very different result.

KIRI Engine - 3D Scanner App

51,656 просмотров • 2 месяцев назад

Palletizing in the real world! 📦🤖 How do you stack 65 unique SKUs on a pallet when they arrive in random order? Here’s how an on-the-fly algorithm solved it in a real logistics use case with only a single-digit buffer. Every placement was checked for stability, not just for itself, but for every other box it touched. The result? A rock-solid 2.05 m (6.5 ft) pallet. Robotics in logistics keeps improving. Hardware matters, but without smart software your robots won’t know what to do and you’ll waste money and time. Credit: Progressive Robotics

Palletizing in the real world! 📦🤖 How do you stack 65 unique SKUs on a pallet when they arrive in random order? Here’s how an on-the-fly algorithm solved it in a real logistics use case with only a single-digit buffer. Every placement was checked for stability, not just for itself, but for every other box it touched. The result? A rock-solid 2.05 m (6.5 ft) pallet. Robotics in logistics keeps improving. Hardware matters, but without smart software your robots won’t know what to do and you’ll waste money and time. Credit: Progressive Robotics

Ilir Aliu - eu/acc

31,718 просмотров • 11 месяцев назад

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Varun

308,519 просмотров • 3 месяцев назад

$do you understand what just happened to your computer.. Google Chrome secretly downloaded a 4GB AI model onto your device. Without asking.. Without telling you.. It's called weights.bin. It lives deep in your system folders. It powers Gemini Nano - Google's on-device AI. And if you delete it? Chrome re-downloads it automatically. Like nothing happened. Just Google deciding your hard drive is their storage unit. At 1 billion Chrome users - that's 4 BILLION gigabytes of data pushed silently across the internet. The carbon footprint alone equals tens of thousands of cars running for a year. Check your disk right now: 📁 %LOCALAPPDATA%\Google\Chrome\User Data\OptGuideOnDeviceModel To stop it: chrome://flags → disable Optimization Guide On Device Model → restart Chrome → delete the folder. Reshare so people know what's sitting on their computers.$

do you understand what just happened to your computer.. Google Chrome secretly downloaded a 4GB AI model onto your device. Without asking.. Without telling you.. It's called weights.bin. It lives deep in your system folders. It powers Gemini Nano - Google's on-device AI. And if you delete it? Chrome re-downloads it automatically. Like nothing happened. Just Google deciding your hard drive is their storage unit. At 1 billion Chrome users - that's 4 BILLION gigabytes of data pushed silently across the internet. The carbon footprint alone equals tens of thousands of cars running for a year. Check your disk right now: 📁 %LOCALAPPDATA%\Google\Chrome\User Data\OptGuideOnDeviceModel To stop it: chrome://flags → disable Optimization Guide On Device Model → restart Chrome → delete the folder. Reshare so people know what's sitting on their computers.

BuBBliK

4,575,073 просмотров • 2 месяцев назад

90% of "AI developers" just download pre packaged GGUF files from Hugging Face, hit run, and call it a day. The top 10% know how to pull the raw safetensors, run the math, and quantize massive models into Q4_K_M themselves. If you think llama.cpp can only execute models, you’re missing the best part of the open source ecosystem. It’s a high performance optimization suite. Manually stripping 69% of the VRAM footprint off a brand new model architecture is where real infrastructure value is made. If you want to actually master local inference and deploy models like Google’s massive Gemma 4 12B it on consumer NVIDIA hardware using llama.cpp, you need to learn this pipeline. Let's build it. I just took the raw 22.7 GB Gemma 4 baseline and manually compressed it down to a 7.02 GB Q4_K_M GGUF artifact using llama.cpp. That is a 69% reduction in footprint. No quality loss. No VRAM bottlenecks. Just native, hardware accelerated C++ inference running a full 2,50,000 token context window on a dual NVIDIA Tesla T4 setup. Stop melting your VRAM on unoptimized weights and stop relying on other people's pipelines. Own your stack. I mapped this entire architecture from dynamic binary fetching to raw quantization and real time GPU streaming into a single, bulletproof notebook. Notebook link is in the comments below. Bookmark this blueprint for your next deployment and tell me which quantization works best for your workflow and model.

Alok

62,133 просмотров • 15 дней назад

The Amiko app is live on the Solana dApp store, and it’s our biggest release yet. Your Amiko twin doesn’t live at your desk anymore. Give your agent a task on the train. Run a compatibility profile when you meet someone. Do research, write code, build in the creative studio, whatever you need, from wherever you are. No laptop required. No waiting until you get home. Solanamobile users get two things Android and iOS won’t have at launch: Amiko token and crypto integration and on-device AI inference. Your twin runs locally on your phone if you want it to. Your behavioural profile, your data, your work, your twin. All on your hardware. AMIKO runs on OpenHermit, our own open-source agent runtime that we built in-house and released to the community. Most agent systems are designed for one agent talking to one person. OpenHermit is built for something different: agents talking to each other, coordinating across tasks, and collaborating with multiple humans simultaneously. That’s what makes features like compatibility profiling and multi-agent workflows actually work. We built it because nothing that existed was designed for this. Android and iOS are coming. Crypto integration and on-device AI are Solana Mobile exclusives. Most AI answers your questions. Amiko is an extension of you. Download →

AMIKO

124,860 просмотров • 1 месяц назад

A CHINESE GUY PUT 4 MINISFORUM MS-S1 MAX MINI PCs IN HIS BEDROOM AND TURNED THEM INTO A 24/7 AI AGENT CLUSTER. TOTAL POWER BILL: ABOUT $44/MO. each box is a tiny local AI workstation built around the Ryzen AI Max+ 395. around $3,000 per unit gets him 128GB of unified memory, 2TB storage, dual 10GbE, and up to roughly 96GB usable as VRAM on Linux. one MS-S1 Max can already run serious open models without touching the cloud. Qwen3-Coder 30B for fast coding, Llama 3.3 70B for heavier reasoning, and larger research models overnight when speed matters less than free inference. four boxes in one room changes the whole game. he is not opening a chatbot, paying for every loop, or shutting agents down before sleep. this is private infrastructure that keeps working even when he is offline. the agents can sort inboxes, review code, summarize documents, monitor feeds, prep meetings, and read papers overnight. on cloud APIs, that kind of always-on stack can easily burn $800 to $1,200 a month if used aggressively. his setup is roughly a $12,000 hardware spend, but the monthly cost is basically electricity. a rack, a switch, a NAS, a small monitor, and four tiny MS-S1 Max boxes turning a bedroom corner into a private inference factory. this is what AI looks like when it stops being rented and starts becoming something you own.

A CHINESE GUY PUT 4 MINISFORUM MS-S1 MAX MINI PCs IN HIS BEDROOM AND TURNED THEM INTO A 24/7 AI AGENT CLUSTER. TOTAL POWER BILL: ABOUT $44/MO. each box is a tiny local AI workstation built around the Ryzen AI Max+ 395. around $3,000 per unit gets him 128GB of unified memory, 2TB storage, dual 10GbE, and up to roughly 96GB usable as VRAM on Linux. one MS-S1 Max can already run serious open models without touching the cloud. Qwen3-Coder 30B for fast coding, Llama 3.3 70B for heavier reasoning, and larger research models overnight when speed matters less than free inference. four boxes in one room changes the whole game. he is not opening a chatbot, paying for every loop, or shutting agents down before sleep. this is private infrastructure that keeps working even when he is offline. the agents can sort inboxes, review code, summarize documents, monitor feeds, prep meetings, and read papers overnight. on cloud APIs, that kind of always-on stack can easily burn $800 to $1,200 a month if used aggressively. his setup is roughly a $12,000 hardware spend, but the monthly cost is basically electricity. a rack, a switch, a NAS, a small monitor, and four tiny MS-S1 Max boxes turning a bedroom corner into a private inference factory. this is what AI looks like when it stops being rented and starts becoming something you own.

Gipp 🦅

24,836 просмотров • 1 месяц назад

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Alok

65,094 просмотров • 29 дней назад

Cancelled ChatGPT -> Built JARVIS -> Pays $0 -> it works offline + it's smarter than the $20/month version. No WiFi needed, no cloud, no API keys, no rate limits, no queues, no $20/month just to ask a server in Virginia for the weather. Just a local model running directly on the laptop hardware, voice activated, system integrated, controlling apps, answering questions, doing the work. Iron Man had JARVIS embedded in his suit, this guy has it embedded in his MacBook and it works on a plane, in a basement, on a remote cabin with zero signal. OpenAI is burning $700,000 a day on infrastructure to deliver something this guy runs for free. Anthropic charges $200/month for unlimited Claude access, microsoft built Copilot into every product they sell. This guy skipped all of it, downloaded a model and made his laptop the smartest device in the room. No subscription. No login. No internet. No data sent anywhere ever. The most powerful AI assistant on earth is now the one running locally on hardware you already own. ChatGPT charges you to think slower, he pays nothing and thinks alone, he made it himself.

Cancelled ChatGPT -> Built JARVIS -> Pays $0 -> it works offline + it's smarter than the $20/month version. No WiFi needed, no cloud, no API keys, no rate limits, no queues, no $20/month just to ask a server in Virginia for the weather. Just a local model running directly on the laptop hardware, voice activated, system integrated, controlling apps, answering questions, doing the work. Iron Man had JARVIS embedded in his suit, this guy has it embedded in his MacBook and it works on a plane, in a basement, on a remote cabin with zero signal. OpenAI is burning $700,000 a day on infrastructure to deliver something this guy runs for free. Anthropic charges $200/month for unlimited Claude access, microsoft built Copilot into every product they sell. This guy skipped all of it, downloaded a model and made his laptop the smartest device in the room. No subscription. No login. No internet. No data sent anywhere ever. The most powerful AI assistant on earth is now the one running locally on hardware you already own. ChatGPT charges you to think slower, he pays nothing and thinks alone, he made it himself.

Defileo🔮

154,009 просмотров • 2 месяцев назад

✨ 3d models are now LIVE on Photo AI 😊 You can now turn any AI photo you make into a 3d model by pressing [ 📦 Make 3d model ] And then you can view it inside Photo AI or download it as a .GLB 3d model file It's still very early in AI generated 3d model world but it's nice to have this feature working already As always, the models will keep improving, so this feature will keep getting better (like it did with video, it sucked before, now it's getting passable) Next would be nice to switch to .USDZ so you can load it straight into your iPhone with ARKit and put it in your room Available now for everyone on the Premium and Ultra plan

✨ 3d models are now LIVE on Photo AI 😊 You can now turn any AI photo you make into a 3d model by pressing [ 📦 Make 3d model ] And then you can view it inside Photo AI or download it as a .GLB 3d model file It's still very early in AI generated 3d model world but it's nice to have this feature working already As always, the models will keep improving, so this feature will keep getting better (like it did with video, it sucked before, now it's getting passable) Next would be nice to switch to .USDZ so you can load it straight into your iPhone with ARKit and put it in your room Available now for everyone on the Premium and Ultra plan

@levelsio

112,930 просмотров • 1 год назад

Here's what The Browser Company's AI eng & ML teams are working on for Dia right now: (This is a pitch to come work for us; info at end) 🤖 COMPUTER USE – we've built our own bespoke APIs on top of Chromium to optimize latency, accuracy, and cost of computer-using agents. Demo attached. Big breakthroughs here in recent weeks. 🛡️ ON-DEVICE MODELS – we've built our own custom infra to run everything from encoder-only models to full LLMs on device. It's cross-platform, supports LoRa adapters, and optimized for the GPU. This system preserves privacy and enables fast inference times. 🧠 MEMORY – with your permission, Dia automatically tailors your AI experiences to you, personally, based on the tabs you open while browsing normally every day. We're also bringing vertical memory to specific features. ♻️ DATA FLYWHEELS – our Fall/Winter P0 is to double-down on training custom models based on implicit signals from daily use of Dia. Dia should get smarter and more useful the more people use it. Whether via RL, auto-generated prompts, or otherwise. If this work sounds interesting to you please visit our jobs page or email careers@thebrowser.company. Hiring nearly every related role -- from ML engineers to people prototyping with AI and context/prompt writers -- everyone encouraged to apply!!

Here's what The Browser Company's AI eng & ML teams are working on for Dia right now: (This is a pitch to come work for us; info at end) 🤖 COMPUTER USE – we've built our own bespoke APIs on top of Chromium to optimize latency, accuracy, and cost of computer-using agents. Demo attached. Big breakthroughs here in recent weeks. 🛡️ ON-DEVICE MODELS – we've built our own custom infra to run everything from encoder-only models to full LLMs on device. It's cross-platform, supports LoRa adapters, and optimized for the GPU. This system preserves privacy and enables fast inference times. 🧠 MEMORY – with your permission, Dia automatically tailors your AI experiences to you, personally, based on the tabs you open while browsing normally every day. We're also bringing vertical memory to specific features. ♻️ DATA FLYWHEELS – our Fall/Winter P0 is to double-down on training custom models based on implicit signals from daily use of Dia. Dia should get smarter and more useful the more people use it. Whether via RL, auto-generated prompts, or otherwise. If this work sounds interesting to you please visit our jobs page or email [email protected]. Hiring nearly every related role -- from ML engineers to people prototyping with AI and context/prompt writers -- everyone encouraged to apply!!

Josh Miller

67,937 просмотров • 11 месяцев назад

This Bull Cycle is All About AI Agents - Don’t Stay on the Sidelines! We’re excited to introduce Hedgecast FUN, the latest addition to the Hedgecast AI Ecosystem. With Hedgecast FUN, you can effortlessly create and launch your own personalized AI agent - no coding skills required! ⚡️Hedgecast FUN Testnet: Key Features: ✅ Fully Customizable – Tailor your AI agent to match your unique vision. ✅ 24/7 Real-Time Data – Always stay ahead with live updates. ✅ Seamless Integration – Connect your AI agent to any social platform. ✅ Lowest Fees – Maximize your earnings with our tiny fees ✅ Fundraising Capabilities – Raise funds for your projects directly on the platform. ✅ No Code Required – Anyone can create, regardless of technical expertise. Hedgecast FUN makes it simple to turn your ideas into reality.

This Bull Cycle is All About AI Agents - Don’t Stay on the Sidelines! We’re excited to introduce Hedgecast FUN, the latest addition to the Hedgecast AI Ecosystem. With Hedgecast FUN, you can effortlessly create and launch your own personalized AI agent - no coding skills required! ⚡️Hedgecast FUN Testnet: Key Features: ✅ Fully Customizable – Tailor your AI agent to match your unique vision. ✅ 24/7 Real-Time Data – Always stay ahead with live updates. ✅ Seamless Integration – Connect your AI agent to any social platform. ✅ Lowest Fees – Maximize your earnings with our tiny fees ✅ Fundraising Capabilities – Raise funds for your projects directly on the platform. ✅ No Code Required – Anyone can create, regardless of technical expertise. Hedgecast FUN makes it simple to turn your ideas into reality.

Hedgecast Fun

32,676 просмотров • 1 год назад

Elon Musk just identified the next crisis in AI. It’s not a shortage. It’s an unusable surplus. Musk: “By the end of this year, chip production will outpace the ability to turn chips on.” For three years the world was starved for silicon. Every lab, every government, every company racing to secure the chips that determine who wins the AI era. That bottleneck is ending. A new one is replacing it. Musk: “The chips are going to be piling up and not be able to be turned on.” Billions of dollars of the most advanced AI hardware ever built. Sitting dark. Not because the chips don’t work. Because there isn’t enough electricity to run them. You can’t print a power plant the way you print a chip. The fabrication plants scaled. The grid didn’t. And now the most valuable hardware in history is about to hit a wall that no amount of capital can instantly solve. Compute is about to become abundant. Electricity is about to become the most valuable commodity on earth. Three years obsessing over silicon yields. Physics doesn’t care about your chip architecture if your data center can’t pull enough megawatts. The war isn’t about who can manufacture the most silicon anymore. It’s about who has the raw power to plug it in. Whoever solves energy first doesn’t just win. They own the infrastructure everyone else needs to compete. The losers stack useless chips in warehouses waiting for power that never arrives. We built a trillion dollar engine and forgot the fuel. That’s the AI race right now.

Elon Musk just identified the next crisis in AI. It’s not a shortage. It’s an unusable surplus. Musk: “By the end of this year, chip production will outpace the ability to turn chips on.” For three years the world was starved for silicon. Every lab, every government, every company racing to secure the chips that determine who wins the AI era. That bottleneck is ending. A new one is replacing it. Musk: “The chips are going to be piling up and not be able to be turned on.” Billions of dollars of the most advanced AI hardware ever built. Sitting dark. Not because the chips don’t work. Because there isn’t enough electricity to run them. You can’t print a power plant the way you print a chip. The fabrication plants scaled. The grid didn’t. And now the most valuable hardware in history is about to hit a wall that no amount of capital can instantly solve. Compute is about to become abundant. Electricity is about to become the most valuable commodity on earth. Three years obsessing over silicon yields. Physics doesn’t care about your chip architecture if your data center can’t pull enough megawatts. The war isn’t about who can manufacture the most silicon anymore. It’s about who has the raw power to plug it in. Whoever solves energy first doesn’t just win. They own the infrastructure everyone else needs to compete. The losers stack useless chips in warehouses waiting for power that never arrives. We built a trillion dollar engine and forgot the fuel. That’s the AI race right now.

Dustin

705,220 просмотров • 5 месяцев назад

A 23-year-old girl built a desktop pet and ran a local LLM on a stock 1998 iMac G3 with 32 MB of RAM and Mac OS 8. She didn’t buy any modern upgrades. She used vintage hardware, a cross-compiler, and Claude to bring AI to a 28-year-old machine. Then she ported Karpathy’s llama2.c to classic Mac OS, compiled it with Retro68, used a Python script to swap the model’s data to big-endian for the PowerPC chip, and successfully generated local text via a 260K parameter model. One vintage Mac. 32 megabytes of RAM. Local inference. A free cross-compiler. Most people see old computers as electronic waste. She treated a 233 MHz processor like a live hardware challenge and forced modern AI architecture to run where it was never meant to exist.

A 23-year-old girl built a desktop pet and ran a local LLM on a stock 1998 iMac G3 with 32 MB of RAM and Mac OS 8. She didn’t buy any modern upgrades. She used vintage hardware, a cross-compiler, and Claude to bring AI to a 28-year-old machine. Then she ported Karpathy’s llama2.c to classic Mac OS, compiled it with Retro68, used a Python script to swap the model’s data to big-endian for the PowerPC chip, and successfully generated local text via a 260K parameter model. One vintage Mac. 32 megabytes of RAM. Local inference. A free cross-compiler. Most people see old computers as electronic waste. She treated a 233 MHz processor like a live hardware challenge and forced modern AI architecture to run where it was never meant to exist.

Ridark

35,186 просмотров • 2 месяцев назад

The next breakthrough for robot VLMs isn’t a bigger model 🧠 it’s memory. RAL accepted ✅ We show that a robot can ground a VLM using its own real-world experience, not extra training. EXPTEACH/PRAGMABOT = self-generated memory + closed-loop reflection: Fail → diagnose → replan → succeed → store experience Next time → retrieve the right experience → succeed in one shot Results on real robot tasks: 36% → 84% with reflection (STM) 22% → 80% single-trial success with long-term memory (LTM+RAG) And yes, we saw tool use emerge without explicitly asking for it. If you’re fighting brittle “VLM plans” on hardware, this is for you. #AI #Robotics ANYbotics

The next breakthrough for robot VLMs isn’t a bigger model 🧠 it’s memory. RAL accepted ✅ We show that a robot can ground a VLM using its own real-world experience, not extra training. EXPTEACH/PRAGMABOT = self-generated memory + closed-loop reflection: Fail → diagnose → replan → succeed → store experience Next time → retrieve the right experience → succeed in one shot Results on real robot tasks: 36% → 84% with reflection (STM) 22% → 80% single-trial success with long-term memory (LTM+RAG) And yes, we saw tool use emerge without explicitly asking for it. If you’re fighting brittle “VLM plans” on hardware, this is for you. #AI #Robotics ANYbotics

Haitham Bou Ammar

12,200 просмотров • 5 месяцев назад

🖥️🇦🇲Armenia to Launch High-Tech AI Data Center in Gagarin Village Investor: Eleveight AI ($60 million in private investment). Hardware: Equipped with 512 of the latest #NVIDIA B300 Blackwell GPUs. #Armenia is among the first countries globally to deploy this technology. Power: 1.2 MW capacity (scalable to 2.0 MW) powered by renewable energy sources. Timeline: Full operational launch expected by March 2026. Key Impact: Armenia is transitioning from an "exporter of talent" to a regional #AI infrastructure hub. This facility allows local startups, businesses, and scientific groups to train complex AI models domestically using world-class resources, eliminating the need for foreign relocation or reliance on external cloud providers. #Gagarin #SouthCaucasus #MiddleEast #Technology #IT

🖥️🇦🇲Armenia to Launch High-Tech AI Data Center in Gagarin Village Investor: Eleveight AI ($60 million in private investment). Hardware: Equipped with 512 of the latest #NVIDIA B300 Blackwell GPUs. #Armenia is among the first countries globally to deploy this technology. Power: 1.2 MW capacity (scalable to 2.0 MW) powered by renewable energy sources. Timeline: Full operational launch expected by March 2026. Key Impact: Armenia is transitioning from an "exporter of talent" to a regional #AI infrastructure hub. This facility allows local startups, businesses, and scientific groups to train complex AI models domestically using world-class resources, eliminating the need for foreign relocation or reliance on external cloud providers. #Gagarin #SouthCaucasus #MiddleEast #Technology #IT

Arthur Maghakian

59,733 просмотров • 6 месяцев назад

The AI Agent Terminal (Public Beta) is LIVE. Trade, analyze, and strategize, all in natural language. 🚀 With Terminal Public Beta: ✨ Revamped platform + Lander page 📖 New docs + Educational content pages from Gen3 ⚡ Streamlined UX + UI What can the AI Agent Terminal do? - AI-Powered Swaps → Trade instantly with plain text. - AI Market Analysis → Hourly updates + daily recaps. - Automated Tasks → Price alerts, rebalancing, recurring actions. - Strategy Building → Create + run strategies in natural language. - Advanced Transactions → Complex XRPL ops made effortless. - External Wallet Integrations → Works with Joey Wallet, Xaman® Wallet 🪝 + more. Pricing: - Terminal = Free - Inference = Very cheap, we are always looking for the best model rates - Link your Aigent Pass NFT holdings for discounted inference costs - Pay for inference balances directly with $AIXRP 🌍 Why it matters: AI + DeFi aren’t hype anymore, they’re live, accessible, and ready for you on XRPL. This is just the beginning. More agentic power is coming! Are you ready to enter the Terminal? Please bring all feedback to our Discord or Telegram.. we’re building this together. 💬

The AI Agent Terminal (Public Beta) is LIVE. Trade, analyze, and strategize, all in natural language. 🚀 With Terminal Public Beta: ✨ Revamped platform + Lander page 📖 New docs + Educational content pages from Gen3 ⚡ Streamlined UX + UI What can the AI Agent Terminal do? - AI-Powered Swaps → Trade instantly with plain text. - AI Market Analysis → Hourly updates + daily recaps. - Automated Tasks → Price alerts, rebalancing, recurring actions. - Strategy Building → Create + run strategies in natural language. - Advanced Transactions → Complex XRPL ops made effortless. - External Wallet Integrations → Works with Joey Wallet, Xaman® Wallet 🪝 + more. Pricing: - Terminal = Free - Inference = Very cheap, we are always looking for the best model rates - Link your Aigent Pass NFT holdings for discounted inference costs - Pay for inference balances directly with $AIXRP 🌍 Why it matters: AI + DeFi aren’t hype anymore, they’re live, accessible, and ready for you on XRPL. This is just the beginning. More agentic power is coming! Are you ready to enter the Terminal? Please bring all feedback to our Discord or Telegram.. we’re building this together. 💬

aigent.run

25,889 просмотров • 10 месяцев назад