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Things I like about Xbox Ecosystem / Hardware Features: • Free Cloud Saves • Achievements • Quick Resume • Free Next Gen Upgrades • Free Back Compat Upgrades • Free Dynamic Backgrounds • Play Anywhere • Gifting • Rewards Integration • Downloading Via App

21,562 Aufrufe • vor 8 Monaten •via X (Twitter)

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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.

Alok

174,987 Aufrufe • vor 13 Tagen

My Tinder date showed up 10 minutes late. She ordered an oat latte. Looked around. Looked at me. "So. What do you do?" "Building trading systems." "Like crypto?" "No. Open-source. Anyone can audit the code." She raised an eyebrow. "So you code for free?" "No. The code makes money." "Sure it does." I didn't argue. I opened my laptop. One wallet I was tracking turned $1,300 into $19,700 in 24 days. Another flipped 232 trades with 82% winrate. One more pulled $5.8M in volume in five weeks. She stopped stirring her coffee. "That's... from a script?" Exactly. Then I showed her the repos. All free. All public. First: 86M+ trades on Polymarket. Every outcome since day one. Free to download. Second: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution. Third: ML + heuristics. I fed 14,000 wallets into Claude. One prompt. 4 minutes. Found 47 traders with 70%+ winrate. Bot mirrors them with 60-second delay. She went quiet for a long time. Then: "I do product marketing at a startup. $82K. I cried in the parking lot last Tuesday." I didn't say anything. She finished her coffee. Looked at me. "How long have you been doing this?" "Eight months." "And you're on Tinder?" I didn't have a good answer. She opened her phone. Unmatched me on Hinge. Then handed it back: "No. Send me the GitHub links instead." Copytrade him: Tinder dates don't ask about the money. They ask why you're on the app. Then they keep the links and leave you unmatched.

Lunar

48,316 Aufrufe • vor 1 Monat

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Rima

24,085 Aufrufe • vor 1 Monat

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4nzn

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CA: 0x172ae9e9b46770a70f479404d76e2f6561507011ef77a247fe3f58e7a5840a0d::manny::MANNY Your smart, hands-free edge tool in the crypto market. This powerful automated bot is designed to buy low and sell high with precision. It scans hundreds of coins in real-time, waiting for the right indicators—trend strength, volume spikes, price momentum, and bullish patterns—before entering a trade. Once in, it manages risk with dynamic stop-loss and take-profit levels, so your capital is always protected. Every trade is backed by a multi-layer confluence strategy, ensuring only high-confidence setups are executed. ✅ Advanced entry logic ✅ Fully automated buy/sell execution ✅ Built-in profit protection and cooldown filters ✅ Real-time alerts (Telegram/Twitter ready) ✅ JSON-based state memory for continuity ✅ Minimal setup, maximum performance ✅ Excludes low-quality coins automatically (e.g., BTC/ETH filters optional) ✅ Plug-and-play friendly — run it locally or integrate it into your system. ✅ Clean, professional trade alerts with price and PnL details ✅ Recovers automatically from connection issues or downtime Whether you’re a pro or just getting started, this bot helps you stay ahead of the market—24/7, emotion-free with pure mathematics. This bot has been in development for the last 6 months. I, Chronos, the developer behind it, have been testing for a while for the best configuration for a trading bot. I believe I have something good going on here. The bot automatically posts all the trades via IFTTT and X integration to its X account. Everything is automated. So how can people rent it, and how will it bring value to the project? Soon, the bot can be rented out via a cloud server. A customer must buy 30 USD worth of Memecoin_MANNY token (CA:0x172ae9e9b46770a70f479404d76e2f6561507011ef77a247fe3f58e7a5840a0d::manny::MANNY). After buying it and depositing it into a special wallet, he will be granted access to the bot. . The bot runs only on the backend — users interact with it via an interface (web app, Telegram bot, or API). A web dashboard and Telegram bot interface will be created. This lets users Start/stop their bot session See trade logs or results. Connect their API keys securely. Get alerts and updates The idea of all this is to offer a service but also bring value to the project. More bots will be developed. This is only the beginning. Cheers Chronos #python #memecoin_manny #spot #trading #bitcoin #eth #Binance #bybit #memecoin #VALHALLA

Ex Machina

24,488 Aufrufe • vor 1 Jahr

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.

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308,089 Aufrufe • vor 3 Monaten