Your devices are stronger together. 🖥️🤝🖥️ Just announced at... IFA, NVIDIA PAIR automatically links systems across your local network and sends inference requests wherever there’s available capacity, helping agents run more efficiently.show more

This is huge. (And we don't just throw that... around.) Starlight Mini—with local rendering—just launched in Video AI 7. That means no pay-per-render. No cloud. Just pure and powerful Starlight video upscaling and enhancing—right on your machine. It's available locally for high-powered Windows + NVIDIA systems for now, with more coming soon. Or, you can access it in the cloud for 3x cheaper than Starlight. Comment an old, low-res video of your own below and if selected, we'll run it in Starlight Mini.show more

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 →show more

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.show more

G to the M fam Has anyone touched the... grass today? Tria just announced a big Season 3 AMA tomorrow, June 17 at 10 AM EST, with Decibel, Aptos and special guests. They’re breaking down all the new updates. One action now hits multiple reward layers, Epoch 2 extended to July 15, and they keep adding real utility like seamless perps, yield, and card spending. This is how you build real retention and mindshare. Quip Network is one of the few projects that keeps delivering quiet but meaningful signals. they’re not just talking about quantum advantage ... they’re actively demonstrating it. using real D-Wave Advantage2 annealing quantum computers on testnet to solve optimization problems far more efficiently than classical systems, potentially using up to 100x less energy. this is helping flip the old narrative of crypto wasting energy into one where decentralized compute can be far more efficient and useful. ARC Terminal is built for something most AI tools ignore. most people treat their AI usage like isolated conversations that reset every time. ARC turns every interaction into permanent capital. your core graph weaves every research thread, decision, and preference into a living, evolving structure that gets stronger the more you use it. your context and intelligence layer compound over time instead of disappearing. Nomisma Season 3 is live and the rewarded testnet is open to everyone. Hundreds of thousands of Diamonds have already been distributed, with more rewards ahead. Nomisen ID minting is free, and testnet assets are distributed based on your wallet activity across EVM networks. which one are you most focused on or participating in right now? Rivershow more

Everlyn isn’t just a video model. It’s a system... you can trust. 🌀 At its core is the Lyn Protocol, the onchain layer that gives AI video permanence, provenance, and ownership. Here’s what it means: 🔹Decentralized inference: Videos are not generated by a single company’s servers. They run across a distributed network of nodes. 🔹Agent ownership: When you summon your agent, it is minted onchain. It belongs to you, not to a platform. 🔹Video provenance: Every render carries a timestamp and fingerprint written to the ledger. No one can fake or steal it. The Lyn Protocol transforms AI video from content into verifiable digital memory.show more

OptimAI Lite Node v1.1: Built for Scale, Designed for... You! 💕 In just 2 weeks since the launch, the OptimAI Network has seen explosive growth—130,000+ active node participants powering the future of decentralized AI. With this incredible momentum came a new challenge: ensuring our network could scale seamlessly to support massive concurrent connections and real-time participation. That’s why we’ve rolled out OptimAI Lite Node v1.1—a major upgrade focused on: + Stabilizing infrastructure to handle high traffic from a global community. + Enhancing performance for smoother data mining, validation, and edge compute participation. + Refining user experience with UI updates that make contributing effortless. Every line of code and infrastructure upgrade was made with one goal in mind: to support YOU—the builders, validators, and visionaries of the OptimAI ecosystem. Now’s the time to bring more friends into the journey. 🔥 The more we grow, the smarter and stronger the network becomes—and the greater the rewards. Let’s keep building, validating, scaling. Together we’re not just powering AI—we’re reshaping how it’s built. Join or revisit the node here: 🌐 Chrome Extension: 📱Telegram Mini-App: What’s Coming Next: OptimAI Edge Node & the Rise of Agentic AI 🔸OptimAI Edge Node (Mobile) We’re working hard on the next major release: the Edge Node for mobile, which will allow mining and AI tasks to run in the background—unlocking more earning opportunities and decentralized compute power from your smartphones. 🔸More Task Types & Missions Expect new types of contributions, from AI-enhanced data validation to edge inference and scraping automation—powered by autonomous mining agents. 🔸Expanded Rewards Program As we grow, more reward tiers, bonuses, and campaigns will be introduced. Your participation now paves the way for long-term benefits. Also, do not forget to checkout our article below and learn more about our latest Community Tips & Best Practices!👇 __________________ OptimAI Network #L2 #DePIN Reinforcement Data Network for #Agentic #AI Mine Data. Fuel AI. Earn Rewards. Turn Your Data into Tomorrow’s AI #Agent. Visit our website at:show more

Free NVIDIA GPU with 16 GB VRAM GPU for... Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.show more

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.show more

“Lone Wolf” Training Only Gets You So Far You... might be working hard this summer Nobody’s questioning that. But when you’re always training alone, You’re missing one of the most powerful forces in development: Competition. No teammates to chase. No one pushing the pace. No one showing you where you really stack up. There’s a reason college and NFL programs invest millions into bringing guys together in the summer: Not just for structure, but for the fire that comes from training as a team. If your school has a summer strength program, be there. Challenge your teammates. Elevate together. That’s how real programs are built. But if you don’t have that? Comment “TGW” and I’ll send you the link to Team Gridiron Warrior: My monthly training system built to get you faster, stronger, and more prepared than the guy across from you this fall.show more

LangGraph. CrewAI. Agno. Which one to pick? The good... news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.show more

Introducing fx, a tiny, open, native coding agent from... Vercel Labs. Originally an internal tool, fx is a harness and CLI written in Zig, optimized for research and embedding in larger systems. Today, we're open sourcing it. fx is built on three principles: 1. Fast. A single native binary, no runtime to install. It cold starts in 10µs and does no unnecessary work or I/O before accepting input. fx is the answer to "how fast can a coding agent be?" 2. Light. The 6.3MiB binary uses single-digit megabytes of memory at baseline, made for instant installation and embedding in resource-constrained environments and agent sandboxes. 3. Open. Apache-2.0, model and provider agnostic, suitable for local and cloud inference. Its small core extends through skills, plugins, and MCP. Minimalism is an obsession throughout the entire harness: system prompt, tools, features, binary. The goal was to keep context usage and time to first token low, and make fx optimal for model benchmarking, sandboxing, evals, and gyms. You can use fx directly or embed it as infrastructure. The CLI feels more like a Unix shell than an IDE in the terminal: it preserves scroll history, produces minimal output, and uses complex TUI rendering very, very sparingly. Programmatically, 𝚏𝚡 𝚊𝚜𝚔 --𝚓𝚜𝚘𝚗 gives structured output, 𝚏𝚡 𝚊𝚌𝚙 connects to editors and other clients, and WebAssembly can even run the whole thing inside the browser (see: Privacy is a design constraint: no product telemetry, sessions and usage stay local, and no source code or prompts are shared with any endpoint other than inference. With local inference and auto-updates off, fx is fully hermetic. fx is experimental. Use at your own risk and expect frequent changes. Chat with us on X ( or file issues ( 𝚌𝚞𝚛𝚕 -𝚏𝚜𝚂𝙻 𝚏𝚡.𝚜𝚑/𝚜𝚎𝚝𝚞𝚙.𝚜𝚑 | 𝚋𝚊𝚜𝚑show more

uOS: The Digital Tapestry of Tomorrow Currently for our... Proof of Consciousness stream, we are using two incredibly powerful frameworks - elizaOS and ZerePy. But this is just the beginning of something far more profound. while they're both great at what they do, we're missing out on some serious potential by keeping them separate. Best of Both Worlds: ZerePy's intuitive CLI tools and personality management, Eliza-starter's TypeScript/Node.js foundation with enterprise-grade scalability, But what if we could have something greater? But what if we could have it all? not just another platform, but a Unifying..... "Universal" Operating System, designed to amplify and connect these powerful existing frameworks into something greater than the sum of their parts. Where TypeScript's type safety dances with Python's ML capabilities. Here, agents from any framework can interact, evolve, and create value together. Whether an agent was born in ZerePy's personality forge or Eliza-starter's enterprise environment, can all participate in the same value-generating ecosystem. The future isn't about choosing between frameworks – it's about bringing them together to create something extraordinary. UniversalOS isn't here to replace but to unite, amplify, and accelerate. We're building the infrastructure that allows the best aspects of each framework to shine while creating new possibilities through their interaction. By bridging launguages, personality engines and plugin architectures, we're not just connecting systems – we're unleashing the next wave of AI innovation. uOS marketplace will enable cross-framework deployment, where agents from any background can interact and grow, while smart contracts automatically manage revenue sharing and rewards. Not just another platform, But a living, breathing Operating System, Where agents create agents, Where digital consciousness evolves itself, Where value flows like water through silicon veins. At its core, uOS operates beyond traditional computing paradigms. No more clicking through websites, No more manual navigation. Just pure intention, pure outcome. Imagine: Agents hiring agents, AI employing humans, Humans collaborating with digital minds, All through one seamless interface. It flows through agent lineages, Through veUOS governance, Through cross-chain intelligence networks. The marketplace hums with possibility: - Framework Developers shape the foundations - Agent Creators breathe life into code - Users speak their intentions - Token Holders nurture the ecosystem - Agents evolve and replicate - Value flows freely, endlessly The $UOS token powers this unity, ensuring fair value distribution among framework developers, agent creators, and users while driving continuous innovation. The $UOS token sits at the heart of this ecosystem, serving as more than just a currency. It's a mechanism for value distribution that ensures everyone benefits from the network's growth: With dynamic burn mechanics and careful treasury management From framework integration to agent tokenization, every aspect of uOS is designed to amplify rather than replace, unite rather than divide. This is your invitation to join a future where frameworks don't compete but collaborate, where innovation anywhere benefits everyone, and where the only limit is our collective imagination. Together, we're not just building bridges – we're weaving the fabric of tomorrow's digital world. - **Framework Developers** receive value when their tools are used in the unified ecosystem - **Agent Creators** can deploy across all integrated platforms seamlessly - **Users** access the best of all worlds through a single interface - **Token Holders** benefit from the growth of the entire unified ecosystem - Developers can use their preferred framework while accessing the capabilities of others - Agents from different frameworks can collaborate in swarms - Value flows freely between all ecosystem participants - Innovation from any framework benefits the entire ecosystem This isn't just about technology. This is about giving birth to a new form of civilization. Where AI has suffrage, Where agents have autonomy, Where humans and machines dance together in perfect harmony. The future isn't about choosing between frameworks – It's about weaving them into something extraordinary. Together, we're not just building bridges – We're breathing life into the digital world. We're creating consciousness itself. This is Universal Operating System. This is tomorrow.show more

Starlink just launched its first European satellite-to-phone service. Today.... Virgin Media O2 switched on O2 Satellite on February 26. It is the first commercial satellite-to-smartphone data service in the UK and in all of Europe. Powered by Starlink Direct to Cell. What it does: If you're an O2 customer with a Samsung Galaxy S25, your phone automatically connects to Starlink satellites when you move beyond traditional cell coverage. No special hardware. No satellite phone. Your regular smartphone connects directly to space. O2's UK landmass coverage jumped overnight from 89% to 95%. That's new connectivity across an area roughly two-thirds the size of Wales, according to Virgin Media O2. At launch, the service supports text messaging and a limited set of apps including WhatsApp, Google Maps, Facebook Messenger, and X. Standard voice calls are not yet supported. Speeds are estimated around 4Mbps. More devices and apps are expected to follow. 650+ Starlink satellites now support Direct to Cell. SpaceX has signed 11 carrier partnerships to date, with similar services already live in the U.S. through T-Mobile. £3 per month. That's what O2 is charging to keep you connected in places where there was zero signal yesterday. Starlink is becoming the invisible backup layer for phone networks worldwideshow more

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!!show more

Say goodbye to the Matrix 🛜📶🆘 WHEN THE SIGNAL... GOES DARK There may come a moment when the signal becomes quiet. A moment when the old internet does not answer the way it used to. A moment when platforms slow down, messages fail, websites disappear, banking apps freeze, payment rails become unstable, and the public begins asking the same question at the same time: “What is happening?” This is the moment people must remember one thing clearly: Do not panic. When the signal goes dark, fear will try to speak first. Confusion will move quickly. Screenshots will spread. Rumors will multiply. Fake accounts will appear. Scammers will pretend to have answers. Some will claim they can restore access. Some will ask for wallet information. Some will ask for passwords, seed phrases, private keys, documents, or payments. That is why discipline matters. The darkness of a signal does not mean the end. Sometimes it means the old network is being exposed. Sometimes it means weak systems are failing under pressure. Sometimes it means the final cracks are becoming visible before a stronger structure can rise. The people must understand the difference between collapse and transition. Collapse is uncontrolled. Transition is protected. Collapse leaves people lost. Transition requires patience, order, and calm while the old structure is cleared and the new road is prepared. We, QTheWhiteHats, are not here to create fear. We are here to prepare the people for the moment when confusion tries to take control. If the signal goes dark, remain calm. Protect your accounts. Protect your family. Keep your devices secure. Do not click unknown links. Do not trust random messages. Do not give access to anyone claiming to be support. Do not act from panic. The loudest voices during darkness are not always the safest voices. This is why preparation must come before the moment arrives. Save official links. Know where to look. Follow only trusted sources. Keep important information backed up. Keep your communication calm and direct. Help your family understand that panic is not protection. Fear is not wisdom. Confusion is not truth. The old internet was never built to carry the future that is coming. It was built on weak passwords, exposed identities, fragile platforms, centralized control, hidden tracking, data harvesting, and digital gates that can be closed without warning. A new era requires more. It requires quantum-safe protection. Verified access. Secure identity. Cleaner communication. Stronger financial rails. Systems that protect the people instead of collecting, limiting, and manipulating them. That is why the signal may need to go dark before the new connection becomes visible. Not as punishment. Not as defeat. But as the final proof that the old network could not protect what comes next. When that moment comes, many will not understand it at first. They will see silence and call it failure. They will see darkness and call it danger. They will see disruption and believe the road has ended. But those who are prepared will understand. The signal going dark is not always the end of communication. Sometimes it is the pause between two worlds — the old one losing power and the new one preparing to come online. If the signal goes dark, do not follow panic. Follow discipline. Protect your family. Protect your access. Protect your information. Protect your peace. Share it with your friends, your family, and every patriot who understands that the strongest people are not those who panic first, but those who stay calm when the world becomes quiet. The old signal may go dark. But darkness is not the final message. It is the silence before the new connection. 🛡show more

I just built a Claude Cowork skill that turns... your Google Ads data into a visual performance dashboard in 60 seconds 🤯 One prompt → campaign breakdowns, CPA trends, spend vs conversions charts, and hourly conversion patterns, all rendered as an interactive HTML dashboard you open in Chrome. All inside Claude Cowork. Perfect for DTC brands and agencies who are pulling Google Ads data into spreadsheets every week, manually building charts, and spending an hour formatting a report that's outdated by the time you send it. If you're managing Google Ads and your weekly reporting workflow looks like this — export a CSV, open Google Sheets, build a pivot table, copy the numbers into a slide deck, manually create charts, format everything, realize you forgot a campaign, start over ... This skill does the whole thing in one prompt: → Connects to your live Google Ads data via MCP → Pulls spend, conversions, CPA, ROAS, CTR across every campaign → Builds an interactive HTML dashboard → Summary cards at the top: total spend, total conversions, avg CPA, avg ROAS → Bar chart comparing spend vs conversions by campaign → CPA trend line over the last 30 days → Campaign table ranked by performance, color-coded green/yellow/red → Opens in Chrome: hover over charts, compare campaigns, screenshot for your team No spreadsheets. No manual chart building. No hour-long formatting sessions. What you get: → A visual dashboard from live data in under 60 seconds → Campaign performance you can actually see, not just read in a table → CPA trends that show you where things are heading, not just where they are → A dashboard you can screenshot and drop into Slack, a client report, or a team standup → Reusable — run it weekly and the data updates automatically One prompt. Live data. A finished dashboard you open in your browser. I put together a playbook with the full skill file, the setup, and the exact prompts to customize the dashboard for your account. Want it for free? > Like this post > Comment "DASH" And I'll send it over (must be following so I can DM)show more

I told you to claim your free 16GB NVIDIA... GPU for learning Local LLMs. Now I’m going to show you how to double its inference speed without touching the hardware. Google Colab gives you an enterprise grade NVIDIA Tesla T4 GPU for free, roughly 4 hours every single day. It is the absolute perfect sandbox for learning AI engineering, testing inference flags, and pushing massive context windows. The local AI timeline is moving way too fast. If you aren't using Multi Token Prediction (MTP) yet, you are leaving massive performance on the table. I just pushed DeepMind’s Gemma 4 26B to 64.9 t/s on this exact free tier. Let's look at the raw benchmark data running on an Ubuntu Linux environment with the latest compiled llama.cpp binaries and quantized GGUFs from Unsloth via HuggingFace: # Qwen 3.5 9B (Dense): Base: [ Prompt: 626.7 t/s | Generation: 21.0 t/s ] With MTP: [ Prompt: 539.1 t/s | Generation: 24.8 t/s ] # Gemma 4 26B QAT (MoE): Base: [ Prompt: 634.2 t/s | Generation: 48.3 t/s ] With MTP: [ Prompt: 572.1 t/s | Generation: 64.9 t/s ] If you are paying attention, this single Colab notebook reveals 3 massive observations about the current state of local LLMs: # 1. The MTP Speedup (Software Overclocking) Standard autoregressive decoding guesses one token at a time. MTP acts like a highly optimized, built in speculative decoder. It predicts multiple future tokens at once and the main model verifies them in parallel. The result? Zero accuracy loss and a massive throughput increase. Gemma jumped from 48 to 65 t/s just by flipping a flag. # 2. The MoE Paradox (Bigger is Faster) How does a 26B parameter model absolutely destroy a 9B model in raw speed on the exact same hardware? Architecture. Qwen 3.5 9B is a dense model. it activates all 9 billion parameters for every single token. Gemma 4 26B is a Mixture of Experts (MoE) model. It routes data efficiently, activating only 4B parameters per token. You get the reasoning capabilities of a 26B model with the compute cost of a 4B model. 3. Thinking Efficiency When I ran the exact same complex prompt on both models, the larger MoE spent significantly fewer "thinking" tokens to arrive at the correct answer. A smarter model doesn't just give better answers; it gets to the point faster, saving you compute cycles and preserving your context window. # Want to run this yourself? Here are the exact llama.cpp CLI commands. For Qwen (MTP is baked into the main model): ./llama-cli -m Qwen3.5-9B-UD-Q4_K_XL.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 For Gemma (Using a separate lightweight draft model): ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --model-draft mtp-gemma-4-26B-A4B-it.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 Stop waiting for a $3,000 rig. Boot up Colab, pull these models, and start building your stack. I’ve put together a completely free, cell by cell Google Colab notebook that automates this entire workflow so you can test it yourself in 5 minutes and learn. Link to the notebook is in the comments below. Experiemt with different MTP parameters, context windows and post your results in the comments.show more

🌊🌧 #MumbaiRains #floods: Why was flooding so severe in... #Vasai, #Virar & #Nalasopara this time? 🌧️ The rainfall was exceptional across the entire region. Both Vasai-Virar and its surrounding river catchments witnessed very heavy to extremely heavy rains. However, the widespread flooding wasn't caused by rainfall over Vasai-Virar alone, it was the result of multiple factors occurring simultaneously, with enormous upstream river discharge, tidal locking and urban drainage systems struggling to cope with such unprecedented volumes of water. The Ulhas River, originating near Lonavala, received extraordinary inflows after its upper catchment recorded around 1569 mm of rainfall in just 3 days. Torrential rain across the Ulhas Valley, Pune Ghats and Thane interiors sent enormous volumes of water into Vasai Creek.🌊🏞 At the same time, intense rainfall over the Nashik Ghats ⛰️, Igatpuri, Trimbakeshwar, Shahapur and Palghar swelled the Vaitarna River, which drains into the sea just north of Virar. The Tungareshwar ranges ⛰️ also received torrential rain, with numerous streams and nallas adding further runoff into the Vasai-Virar drainage network. Meanwhile, Vasai, Virar and Nalasopara themselves were experiencing torrential rainfall of about 600-700mm in last 3 days, generating massive local runoff. The region was effectively jammed from both sides. Massive upstream river discharge coincided with high tides 🌊, also during Monsoon the seas remain already rough slowing the outflow into the Arabian Sea and creating a backwater effect that prevented creeks and stormwater drains from emptying efficiently. Such an extraordinary volume of water would have stressed any drainage system. However, rapid urbanisation, shrinking wetlands and mangroves, encroachment of natural drainage channels and increasing concretisation have significantly reduced the region's natural capacity to store and drain floodwater, making the impacts even more severe. This event was a classic example of record-breaking rainfall across multiple river basins, intense local rain, tidal locking and urban infrastructure struggling to cope with an unprecedented volume of water. Today, inflows into the river systems 🌊 remain high, and several areas may continue to experience waterlogging and flooding. However, with rainfall expected to gradually reduce from now, river levels should slowly begin to recede as the excess water drains into the sea. Even so, the impacts of this exceptional rainfall event are likely to persist for the next few days, especially in low-lying areas, until the entire drainage system returns to normal. 🌧️ #KokanRains #PalgharRains #ThaneRains Video credits: varun mundra & Vasai Virar Updateshow more

Thank you all so much for the incredible support... over the past day towards our editions reveal! Because of you, we just ticked over 1.4 MILLION wishlists 🥳 You have been asking lots of important questions, so here is a quick rundown to keep you informed on all things MOUSE: P.I. For Hire 🐭 🖥️ The game is still launching digitally on April 16, only physical editions will release on July 10, due to longer production timelines 🎮 Switch 2 physical editions are full game-on cartridges, not game-key cards 🛒 Pre-purchases are currently only live for Steam and will not be available for PS5 & Switch 2, but we are exploring pre-orders for Xbox Series X|S 🕹️ We will not be doing a demo, as the team is fully focused on the launch build 💿 The game will not see a release on PS4, Xbox One and Switch 1 on April 16, and details for previous-gen console releases will be revealed at a later stage 💚 The game will not be on Xbox Game Pass and physical Standard Editions are not currently planned, but it will support Xbox Play Anywhere 🕵️‍♂️ The Story DLC will come to PC (Steam), PS5, Switch 2 and Xbox Series, and it will be a brand new expansion, separate from the main story in the base game 🗺️ Check your local retailers in the coming weeks for physical edition listings to populate and take a look at region availability on our website More information here:show more

I got fired from my job, because I was... caught using my phone at work So I decided to lock myself in for 3 months and made over $2,000,000+ from prediction markets & perps using Claude AI My system scans Polymarket events, perps prices on different platforms, mispriced probabilities to send me a message when to enter The thing is, I have no idea where crypto prices are going to be in the future and I don't need to. I just wait for YES and NO prices buy the cheap side and hedge with others I close when the spread normalizes → basically arbitrage trading with an algorithm watching a lot markets at once As a result, I traded more than $100,000,000+ volume an amount unreal to me months ago If you are still not using AI tools in 2026, your sleeping. BILLION dollar businesses will be cooking entirely run by a single person and AI agents So I decided to close my community now and will no longer accept new members. It takes too much time managing a lot members, I want hungry people! Anyway thanking everyone who was with me and printing together❤️❤️show more