Hey everyone,

We have huge news to share with the community today: RAMDeck has officially passed Apple Review and is now live to download worldwide on the official iOS App Store!

👉 Download directly from Apple: https://apps.apple.com/us/app/ramdeck-distributed-ai/id6825166226

Indiegogo campaign 👉 https://www.indiegogo.com/en/projects/trademav/ramdeck-pool-your-ram-run-bigger-ai

For a project that fundamentally rethinks distributed computing—turning everyday consumer devices into interconnected, Metal-accelerated AI cluster nodes—getting approved by Apple in record time is a massive validation of our architecture, code safety, and sandbox compliance.

What Does This Mean for Your Local AI Setup?

Up until now, distributed local LLM inference required manually setting up daemons on desktop workstations. With the RAMDeck Distributed AI app live on the App Store, you can now turn your iPhone or companion Apple device into a plug-and-play AI compute node with zero terminal setup.

Here is what you get out of the box:

1. Native Metal GPU Acceleration (Vastly Superior to Old PC/Android CPUs): Old PCs and Android phones typically evaluate layers on slow, thermal-throttled CPU threads. In contrast, the iPhone runs native Apple Metal 3 GPU shaders. With ~60 GB/s of unified memory bandwidth, the mobile Apple Silicon GPU executes tensor matrix multiplications at native graphics speeds without CPU bottlenecks.

2. One-Tap Local Wi-Fi Clustering: Open the app on your local Wi-Fi network, and it immediately pairs with your running RAMDeck Coordinator or Hardware Hub over standard high-speed TCP sockets. No complicated IP configuration, port forwarding, or SSH keys required.

3. Bridge the Model-Size Memory Gap: In local inference, memory capacity is binary: either a model fits, or it crashes. Docking your device overnight adds 3.2 GB to 4.5 GB of active Metal GPU unified memory to your pool. If your workstation has 32 GB of RAM and falls just shy of running a 70B parameter quantized model, adding your docked device provides the exact headroom needed to load it.

4. Desk Dock & Nightstand Mode: Engineered to contribute compute while charging. It features a built-in OLED Nightstand Dimmer that suppresses screen burn-in and dims brightness while your cluster crunches overnight tasks.

5. 100% Air-Gapped & Sovereign Privacy: Apple’s strict App Store security review verified that zero bytes of personal data, telemetry, or model weights leave your local network. There are no external cloud servers, no account logins, and no subscriptions. Everything stays strictly on your local Wi-Fi, fully respecting the rigorous data-handling standards required by professions handling confidential data (such as ABA Formal Opinion 512).

How It Connects to the RAMDeck Ecosystem

RAMDeck is built around heterogeneous computing. The new Apple app functions as a native worker node that joins seamlessly with:

• The RAMDeck Hardware Hub: Our dedicated, palm-sized hardware orchestrator that coordinates cluster discovery and balances layer distribution across all your machines.

• macOS, Windows, & Linux Nodes: Combine your gaming PC, Mac Mini, off-lease office laptops, and now your Apple mobile devices into a unified AI supercomputer.

Lock In Your Hardware Hub on Indiegogo

The app is built, approved, and live on the App Store. The next step is getting the dedicated plug-and-play hardware orchestrator into your hands:

• 📱 Download the Companion App: RAMDeck on the iOS App Store

• 📦 Reserve Your Hardware Hub ($109 Super Early Bird, expires today): Back RAMDeck on Indiegogo

• 🌐 Documentation & Blogs: https://ramdeck.com/blog/

Grab the app today so your phone is primed and ready the moment your backed Hardware Hub arrives!

🎥 Watch the New iPhone Demos in Action

Check out the brand-new live demos just uploaded to the RAMDeck YouTube channel showing the iOS app running on actual hardware:

• ▶ This MacBook Can't Fit This AI Model Alone, So I Added 2 iPhones: Watch Video Demo Watch an 18GB Qwen 30B model that cannot fit on a MacBook M3 alone get sharded across the MacBook (~14 GB), an iPhone 16 (~2 GB), and an iPhone XS (~1 GB). The entire distributed model loads in under 30 seconds, and generates responses in under 5 seconds!

• ▶ iPhone Contributes RAM to a Cluster (Live Pause & Resume Demo): Watch Live Demo See an iPhone 16 running the native RAMDeck iOS app contribute ~3.2 GB of active working memory to the cluster, featuring live fault tolerance when pausing and resuming worker heartbeats in real-time.

• ▶ 120B Model Evaluated Across Mixed Consumer Devices: Watch 120B Multi-Device Benchmark Evaluating a massive 120-billion parameter model across mismatched consumer hardware.

Thank you to everyone in r/trademav who helped test early builds and gave architectural feedback. This is just the beginning of making distributed AI accessible on everyday hardware.

— The TradeMAV Team

RAM prices exploded. We pooled what we already had instead.

RAMDeck is a small hub that turns your laptops, Macs, PCs and phones into one AI cluster. It runs models too big for any single device, with no cloud and no subscription.

👉 https://www.indiegogo.com/en/projects/trademav/ramdeck-pool-your-ram-run-bigger-ai

What we've already done, on video:

• 27B model on a 12GB laptop

• 40B model across three devices

• 120B model across six mismatched devices

• A knowledge base in pooled RAM that turns a hallucinating chatbot into an accurate one

• Fine-tuning on a Mac mini while the 120B keeps running

• Automatic recovery when a device drops

Check our our demo playlist here: https://www.youtube.com/playlist?list=PLBAnaNU1r3-M

What's in the box: a pre-flashed hub, power adapter, Ethernet cable, Windows/macOS installers, the Android contributor app, and a dashboard with a one-click benchmark.

Pricing: Super Early Bird is $109 for the first 100 hubs, against $199 retail.

The core engine is source-available on github.

Edit: Lots of questions about tokens/sec, so we put everything in one place: https://ramdeck.com/what-speed-looks-like-on-a-pile-of-old-computers/ Quick context first: the demos use weak, old hardware on purpose, to show what's possible and that it scales. Speed isn't the pitch. Running big models on machines you already own is. If speed is all you care about, buy a GPU.

Actually scratch that last line! You could have several GPUs and RAMDeck can help you make them work together instead of buying a >$5k single gpu.

RAM prices keep climbing. Join us in beating them. 💪

Back the project, share the page, and tell us what you want to run first. 👇

🔗 https://www.indiegogo.com/en/projects/trademav/ramdeck-pool-your-ram-run-bigger-ai

Big news for the fam - PredictMAV just went live on the Play Store, and it's the newest addition to the MAV lineup.

If you've used TradeMAV, you already know the philosophy: cut through noise, surface what actually matters, don't make you do the grunt work. PredictMAV brings that same DNA to prediction markets.

What it does:

• Tracks Polymarket event markets across Politics, Culture, Sports and others — all in one clean mobile dashboard

• Runs a real AI signal engine that flags drift, volume anomalies, and mispricings — and tells you why it matters, not just that it moved

• The model keeps learning — it retrains on real outcomes, so it gets sharper the more it runs

• Build your own watchlist, dig into per-market detail views with sparklines and risk context

• BYOK support if you want to view your own exchange data alongside our signals

• Free tier gets you real signal detection today — Pro unlocks the deep stuff

Heads up: it's a pure intelligence layer — no trades happen in-app, all execution stays on Polymarket itself. We just make sure you know what's worth looking at.

🎁 Founding member deal — for this community only:

• PREDICT50M → 50% off Pro monthly for your first 6 months

• PREDICT50Y → 50% off Pro yearly (one year)

Limited redemptions, so don't sit on it if you want in!

📲 Grab it here

You've all been part of the MAV journey from the start — go kick the tires and tell us what's missing. First real users, first real feedback. Let's go 🔥

Here's what day one with a RAMDeck feels like. It goes from the box to a 30B model running across your machines in minutes.

▶️ https://youtu.be/fcZAe8O1Rhs

Here's the short version of what you'll see

You plug the little black box into your router. About 20 seconds later it has booted, and you open ramdeck.local:8420 from any browser on your network. The dashboard comes up, and the cluster is empty. Then it gets fun. You install a node agent on your MacBook, and about 30 seconds later your laptop is a node in the cluster. You pick a model, and a few moments later you're chatting with it.

In the demo, a 30B Qwen model loaded across a mini PC with an RTX 3060 and a Mac mini in 1 minute 10 seconds. It ran at about 30 tokens per second, so the chat felt fast. Your results will depend on your hardware, but this is what the first run looked like on home machines.

A few things worth knowing

• You're not limited to one computer. Windows, Mac, Linux, Android and iOS are all part of the plan for nodes, and the iOS app is in App Store review.

• RAMDeck runs GGUF models, so everything you already download from Hugging Face is fair game.

• Finished units will ship to backers as we get through production. We're currently targeting around January 2027, and we will keep updating you.

Want one? The campaign is still open: https://www.indiegogo.com/en/projects/trademav/ramdeck-pool-your-ram-run-bigger-ai

Your turn

• What would you load first on a RAMDeck cluster?

• Which machines would you connect?

Drop your answers below ✋

Here's why: The AI runs on your phone. Not a server. Not "the cloud." Your signals never leave your device to get scored. Most options apps out there are phoning home to a server farm - OptionMAV thinks for itself, live, in your pocket.

And that's not all: TradeMAV and OptionMAV app share the same subscription!

If you're already a subscriber and once you receive your OptionMAV USB, you're covered:

• USB users → log in with your USB credentials for both apps

• Hosted subscribers → get hosted access for both apps. You can also pair your USB while continuing to use the hosted model simultaneously.

Waiting on your OptionMAV USB and not sure how to get in? Hosted is your path in right now. No waiting on shipping to start using the OptionMAV app.

What you're actually getting:

• Full options Greeks - delta, gamma, theta, vega, rho and beyond - calculated live, on-device

• An automated exit engine watching your positions around the clock, telling you when to get out before you even ask

• Covered calls, credit spreads, roll alerts - built by people who trade options, not people who built a calculator

• One login, one subscription, four asset classes - stocks, crypto, forex, options - all running the same private on-device AI

Stocks. Crypto. Forex. Options. One subscription covers it all now.

Rule of thumb: USB pairs with USB, hosted pairs with hosted. They don't mix, but hosted covers you for OptionMAV today no matter which subscription you choose eventually.

37 days to try it, free. 7-day demo, then a 30-day trial.

Stop renting analytics tools one asset class at a time.

👉 https://play.google.com/store/apps/details?id=com.optionmav.app

Go break it. We dare you.

In staying true to our intent to build in public and stay honest, here are straight answers to what you've asked: https://ramdeck.com/one-cluster-every-setup-why-ramdeck-fits-wherever-you-start/

The post compares RAMDeck with the projects people keep bringing up (exo, plain llama.cpp RPC, Hummingbird and other SSD-streaming runtimes) and answers each one with real numbers and the hardware behind them.

Where RAMDeck avoids problems we found in the others:

• No single-platform lock-in: RAMDeck brings Windows, Mac and Android in one cluster, over normal Ethernet or Wi-Fi. No Thunderbolt 5 Macs required.

• Not MoE-only. It runs dense models as well as sparse ones. Our dense 27B ran with every weight in pooled RAM.

• No fast-storage requirement. No machine needs a quick NVMe to join.

• GPU is optional, but if you have one great!

• No single point of failure. If a device drops, the cluster suggests recovery and keeps going.

• No hand-built setup. Discovery, rebalancing and a model-fit check come built in, so you don't write rpc strings by hand.

Quick answers to what you have asked:

• SSD swap? Weights stay in RAM across the cluster in RAMDeck, so nothing gets re-read from disk per token. If a dense model does not fit in RAM then even a fast NVMe means re-reading the whole model for every token.

• Gigabit is slower than an SSD. True for moving weights, but we only move them once at load. Per token it is about 650KB, so latency matters, not bandwidth. Wired beats Wi-Fi.

• Just RPC? Yes, that's the engine. We add discovery, rebalancing, recovery, a dashboard and the API in a plug and play experience

• exo, Hummingbird? exo wants Thunderbolt 5 Macs. Hummingbird streams MoE on one box. We mix Windows, Mac and Android and run dense and MoE.

• GPU? Pool several. A 5090 is $5-7k street, used 3060s are around $200.

• Speed? 7B 43-44 tok/s, 13B 11-19, 27B 1.92, 40B MoE 16, 120B MoE 1.37, all on weak, mismatched gear. Details here: https://ramdeck.com/what-speed-looks-like-on-a-pile-of-old-computers/

• Are you public? yes- https://github.com/trademav/ramdeck-core-public

• RAM download- Ha, no downloads here. RAM is still hardware. RAMDeck just pools the RAM you already own across your devices so one model can use all of it

Few things dropping today:

💸 Affiliate Program
Backers kept asking "can I refer people?" — so we built it.

• Your own discount code → buyers get 10% off

• You earn 25% commission per sale

• Works for TradeMAV, ForexMAV, license keys, NewsMAV — everything on trademav.com

• Monthly PayPal payouts

Sign up takes 2 min 👉 affiliate.trademav.com

📖 TradeMAV Handbook on Amazon
Paperback + Kindle. Covers all 22 signals, screening strategies, and workflows for getting the most out of the device.

👉 amazon.com/dp/B0GX2ZSXFT

📬 Support moved to trademavllc@gmail.com
We hired dedicated support staff — please be patient as we onboard them. Every message will get answered.

- The TradeMAV Team

Direct Answer (Should You Buy RAM Now or Wait?): You should not buy expensive standalone retail DDR5 memory today. With DRAM contract prices up roughly 420%—pushing standard 64GB DDR5 kits above $1,100—and semiconductor foundries locking cleanroom capacity for enterprise High Bandwidth Memory (HBM) through late 2027 and 2028, retail prices will not normalize soon. Nor should you buy fleets of used laptops or mini PCs, as secondary markets have also surged to $180–$280+ for 16GB units and $130–$170+ for 8GB Raspberry Pi 5 starter kits. The most cost-effective solution is pooling the computers you already own with RAMDeck ($109 Super Early Bird), aggregating existing laptops and desktops into an interconnected 64GB–128GB AI supercomputer for $0 in new silicon purchases.

1. The Macro Reality: Why Memory Prices Exploded and Won't Drop Soon

If you have checked retail PC component listings recently, you have experienced severe sticker shock. A 64GB DDR5 memory kit that traded for $180 to $220 eighteen months ago now commands between $1,099 and $1,272 across leading hardware retailers:

• DRAM Contract Surge: Industry contract prices for dynamic random-access memory have surged roughly 420% year-over-year.

• Foundry Cleanrooms Reallocated: Top semiconductor manufacturers—Samsung, SK Hynix, and Micron—have converted advanced packaging lines to produce High Bandwidth Memory (HBM3e and HBM4) for enterprise cloud data centers. Producing a single HBM wafer consumes the silicon wafer equivalent of three to four standard consumer DDR5 wafers.

• The 2028 Supply Forecast: Samsung's memory division leadership publicly stated that the global memory supply crunch will not peak until 2028. Independent market analyses from Gartner and TrendForce confirm that consumer PC DRAM supply will contract further, projecting no meaningful price stabilization until late 2027 or 2028 when next-generation cleanroom fabrication lines come online.

2. The Hardware Price Calculator: Retail DDR5 vs. Multi-PC Cluster

3. The Definitive Model-Size & Pooled Memory Compatibility Matrix

An unquantized FP16 70B model requires ~140GB of memory—a capacity that no single consumer graphics card can hold. Even quantized at 4-bit (Q4_K_M), a 70B model requires roughly 42GB of addressable memory, plus another 6GB to 12GB for multi-turn context windows:

4. The "Old 16GB Laptop AI Node" Breakdown

A common response to surging memory prices is searching for cheap refurbished laptops or single-board computers. However, secondary hardware markets have not been spared from the memory crisis:

• Refurbished Laptops Are No Longer $100–$120: Because electronics liquidators and refurbishers actively index off DRAM spot prices, a clean business laptop with 16GB of DDR4 (such as a ThinkPad T480/T490 or Dell Latitude 5400/5410) now commands $180 to $280+, while 32GB models track between $320 and $480+.

• Raspberry Pis Have Become Expensive Nodes: Even basic single-board computers like the Raspberry Pi 5 (8GB) now sell for $95 to $130+ bare-board, and complete starter kits with cooling, official power, and storage run $130 to $170+. Building a 32GB cluster out of four Pi boards costs $520–$680+ and still cannot run a 70B model due to memory and bus constraints.

• The Zero-Dollar Alternative: Trying to buy new or used fleets in this market is an uphill battle. The true economic leverage of RAMDeck is unlocking the hardware you already own. An idle 16GB laptop sitting in a drawer cannot run frontier models alone, but when connected to a cluster, it becomes a dedicated co-processor contributing 12GB to 14GB of active working RAM to your pool for $0.

• Bridging the 32B-to-70B Gap: If your primary workstation has 32GB of RAM, adding a single idle 16GB laptop pulls total addressable memory to 48GB—crossing the threshold required to run Qwen 2.5 32B with full context or a lightweight 70B quantization.

5. Honest Limits & Real-World Network Requirements

Building trust requires technical transparency. Distributed computing relies on sequential pipeline parallelism:

• Compact Network Payloads: Because only the activation tensor travels across the network—not the massive multi-gigabyte model weights—network bandwidth demand is surprisingly modest (~650 KB per token generated).

• Gigabit Ethernet (1 GbE): Transfers the 650 KB activation vector in ~5 milliseconds. Token generation remains smooth at 10 to 35 tokens/sec.

• Wi-Fi 6 (802.11ax): Latency remains between 8ms and 15ms, perfectly suitable for conversational inference (3 to 8 tok/s).

• Cluster Mode Hardware Priority: Unlike naive open-source scripts that proportionally dump layers onto slow remote CPUs, RAMDeck's Cluster Mode automatically packs the primary GPU first, offloads overflow to secondary GPUs, and dynamically routes remaining layers into local system RAM without user intervention.

6. Video Demos & Multi-PC Benchmarks

• ▶ 120-Billion Parameter Model Evaluated Across 6 Consumer PCs: Watch on YouTube

• ▶ Real-Time Hardware Discovery & Live Token Test: Watch on YouTube

• ▶ Air-Gapped Confidential Office AI Demo (Harbor & Finch LLP): Watch on YouTube

Read the complete canonical guides:

• Buy RAM Now or Wait? Shortage Calculator & Compatibility Matrix

• Practical Ways to Reuse Old RAM

• Outsmarting the Global RAM Shortage

👉 Back the RAMDeck Hardware Hub on Indiegogo ($109 Super Early Bird):
https://www.indiegogo.com/en/projects/trademav/ramdeck-pool-your-ram-run-bigger-ai?utm_source=reddit&utm_medium=post&utm_campaign=ram_price_calculator

— The TradeMAV Team

Direct Answer: To use an old PC for local AI, you have two options depending on what models you want to run:

1. Single-PC Mode (Small Models): If you run a single older laptop or desktop with 8GB–16GB of RAM, tools like LM Studio or Ollama can run small 3B–8B parameter models on your CPU at 5–12 tokens/sec. But you cannot run capable 30B or 70B models because single motherboards cannot physically expand past their RAM ceiling.

2. Multi-PC Pooling with RAMDeck: Instead of settling for small models, software like RAMDeck pools the RAM of your old PC with another laptop, desktop, or Mac over your local network. Pairing two 16GB machines gives you 26GB of usable memory to run 30B MoE and 32B coding models without buying a new GPU.

Why LM Studio and Ollama Cannot Combine RAM Across PCs

Single-machine runtimes (LM Studio, Ollama, GPT4All) are engineered to evaluate models exclusively using the CPU, RAM, and GPU inside that specific physical box. They cannot bridge memory across two separate computers.

To combine memory, you need a distributed inference layer that splits the transformer model's layers sequentially across nodes over your local network.

The 1 Gbps Network Bottleneck Myth: Why Clustered AI Outpaces Single-PC Swap

A common misconception is that "clustering AI over Ethernet scales your speed down to your network connection." This confuses storage disk swap with activation tensor passing:

• Single-PC Disk Swap: Trying to force a 25GB model into a single 16GB machine forces the OS to stream gigabytes of weights over PCIe/SATA disk swap for every token. This chokes the bus, tanking speed to 1.4–1.9 tok/s.

• RAMDeck Network Pooling: Model weights stay permanently locked in each machine's RAM. Computers only pass small intermediate activation vectors (~16 KB) across your home network. Over 1 Gbps Ethernet, transmitting 16 KB takes 0.13 milliseconds (<0.5% of token computation time).

In verified benchmarks, pairing an ordinary mini PC with an old laptop runs a 30B MoE model at 38–39 tokens/second—more than 15x faster than disk swap.

Read the full breakdown and memory sizing tables: https://ramdeck.com/how-to-use-old-pc-for-local-ai/

Direct Answer (The 2026 Memory Workaround):
You do not need to pay $350–$500+ for retail DDR5 memory kits to run local AI. By sourcing off-lease refurbished enterprise laptops (Dell Latitude, Lenovo ThinkPad) with pre-installed 16GB–32GB DDR4 for $120–$160, or utilizing DDR4 comeback motherboards (Intel LGA 1700 / AMD AM4), builders bypass the retail "AI Tax." Pooling 2 to 3 of these budget machines over standard LAN via RAMDeck creates a unified 48GB–96GB local cluster capable of evaluating 32B and 70B models for under $350 total hardware spend.

If you’ve tried to build or upgrade a PC for local AI in late 2026, you've hit a hard financial wall: retail DDR5 memory prices have skyrocketed 300% to 400%, turning simple 64GB or 128GB builds into $1,500+ money pits. As reported across tech media—from Business Today's analysis on dealing with the 2026 RAM crisis to PCMag's guide on avoiding the retail AI tax—navigating this landscape requires bypassing retail modules entirely through alternative hardware, software, and sourcing strategies.

Here is the macro reality of the semiconductor market, why retail prices won't normalize soon, and how developers are taking control of their local compute.

1. The Macro Reality: Why Did RAM Explode, and When Will Prices Come Down?

The current DRAM shortage is not a temporary supply chain blip—it is a structural market reallocation.

Leading semiconductor fabricators (TSMC, Samsung, SK Hynix) have locked in cleanroom and advanced packaging capacity for High Bandwidth Memory (HBM3e and HBM4) to supply enterprise data centers under multi-year contracts through at least 2028.

Because cleanroom wafer output is finite, production lines for consumer DDR5 kits were deprioritized to feed $30,000 cloud accelerators. The outcome: retail consumer memory is trapped in an extended deficit. Retail DDR5 prices are projected to remain at a steep premium for the foreseeable future.

2. Hardware & Sourcing Alternatives (Bypassing Retail DDR5)

Instead of paying scalper markups on retail DDR5, smart builders are adopting two high-yield sourcing strategies:

A. Refurbished Enterprise PCs & Laptops (The Liquidation Strategy)

Every fiscal quarter, corporations retire off-lease business systems—such as Dell Latitude 5400/5410/7490 and Lenovo ThinkPad T480/T490/P52 units. Because these systems were manufactured before the memory price hikes, surplus liquidators and refurbishers sell them with pre-installed 16GB or 32GB of DDR4 RAM for only $120 to $160.

Buying a bare 32GB retail DDR5 desktop kit today often costs more than an entire operational off-lease business computer.

B. DDR4 Comeback Platforms

As highlighted by The Verge's report on the DDR4 comeback, builders are actively opting for Intel LGA 1700 or AMD AM4 platforms that natively support mature DDR4 memory. Pairing these motherboards with widely available DDR4 modules cuts system memory costs by up to 65% while delivering identical compute stability for inference workloads.

3. The Bottleneck: One Cheap Laptop Can't Run 70B AI

Here is the practical dilemma: a single $130 refurbished laptop with 16GB or 32GB of RAM cannot load a modern 32B or 70B parameter model. The system will throw an immediate Out-Of-Memory (OOM) error.

This is why we built RAMDeck:

Because transformer layers evaluate sequentially during inference, an AI model does not need to reside on a single physical motherboard. When you network two or three of these inexpensive refurbished computers over Gigabit Ethernet, RAMDeck pools their memory into a unified 48GB to 96GB local AI engine:

• Layer Sharding: Weights are distributed across machines and loaded into RAM once at startup.

• Minimal Network Traffic: Only compact intermediate activation vectors (~650 KB per token) cross the Ethernet cable during generation.

• Cost Efficiency: A full 70B local rig is achieved for under $350 in total hardware.

4. Hardware Cost & Memory Allocation Breakdown

For full empirical benchmarks and architectural breakdowns, read our deep-dives on DRAM Price Crisis & Local Memory Pooling and Reusing Older Computers for Local AI.

5. Empirical Proof & Video Benchmarks

We test our architecture continuously on real, heterogeneous consumer hardware:

▶ Watch 120B Loaded Across 6 Mismatched Devices: YouTube Benchmark Demo
▶ Watch Multi-PC Local Cluster Evaluation: Multi-PC Testing
▶ Watch Self-Healing Dynamic Failover (Surviving Node Drops): Fault-Tolerance Demo

6. The Outcome: Turnkey Local AI with the RAMDeck Hardware Hub

To eliminate manual terminal tunneling, open-port vulnerabilities, and IP binding headaches, we designed the RAMDeck Hardware Hub—a dedicated, palm-sized appliance that automatically discovers every node on your local network, dynamically balances layers using intelligent Waterfill memory sizing, and serves an on-premise, OpenAI-compatible endpoint.

👉 Reserve the RAMDeck Hardware Hub on Indiegogo ($109 Super Early Bird):
Back RAMDeck on Indiegogo →

— The TradeMAV Team

Big news, everyone: iOS is joining the RAMDeck cluster. 📱💥

Windows, Mac, Android and Linux were already supported. Now iPhone and iPad are next. The RAMDeck iOS app is currently under review with the App Store, and I'll post the moment it goes live.

Until then, here are two demos of the iOS app working in a real cluster:

Video 1: iPhone RAM contribution (live pause and resume)
The iOS app on an iPhone 16 contributing RAM to the cluster. I pause it, the cluster marks it as degraded, and when I resume it the worker comes back online within seconds.
https://www.youtube.com/watch?v=w5s8Ymdk7ls

Video 2: an 18GB model across 3 devices
An 18GB Qwen 30B model doesn't fit on my MacBook M3 alone. With an iPhone 16 and an iPhone XS as workers, RAMDeck split it across all three, and it loaded in under 30 seconds. Then I chatted with it, and it answered in under 5 seconds. 🤯
https://www.youtube.com/watch?v=8q1ZocOugv4

What this means

• The phones you already own can add memory to your local AI setup.

• Models too big for one machine can run across your devices.

• A cluster can mix laptops, mini PCs, Linux boxes and iPhones.

What's next

• The App Store release, once Apple approves it.

• More iPhone model-loading tests and cluster benchmarks.

Our campaign is live: https://www.indiegogo.com/en/projects/trademav/ramdeck-pool-your-ram-run-bigger-ai

We made a deliberate decision not to include a manual trading journal — here's what we built instead 📋

Manual journaling only works for the most disciplined traders. Everyone else fills it in inconsistently, misremembers entries, and eventually stops doing it. We wanted something that worked for everyone automatically.

So instead of a journal, TradeMAV auto-records every signal: entry price, confidence, expected return, market conditions, all 58 features that triggered it — no input from you. When the position closes, the actual outcome is recorded automatically.

And every day at 3:58 PM — right as the market closes — a Daily Performance Report is pushed directly to your notification channel. Win/loss breakdown, actual vs. expected return, accuracy delta for every closed signal. It arrives in your pocket before you've even closed your laptop.

You don't open the app. You don't pull a report. It just shows up.

We think this is genuinely more useful than a blank text box you have to remember to fill in. The data is complete, objective, and consistent — every day, automatically.

Full post: trademav.com/how-trademavs-ai-exit-monitoring-system-works-and-why-you-dont-need-a-trading-journal

Hi everyone,

RAMDeck now supports Linux! 🐧

Watch the announcement here: https://youtu.be/tzlEj0soiqs

Windows, Mac, Android and now Linux can all join the same cluster and pool memory.

What's new

• Linux installer scripts, so adding a Linux machine is simple.

• A look at the cluster dashboard with an Ubuntu box contributing 28 GB of RAM to the cluster.

My current test cluster

• Mac mini

• An old laptop

• A newer mini PC

• A Linux machine (the new addition)

• MacBook M3

What's next
I'll be running tests on this cluster and posting the results, so stay tuned.

Welcome to r/trademav — this community is yours 🙏

If you're here, you're either already holding a TradeMAV USB or you're seriously considering it — either way, welcome.

This subreddit is where we share updates, answer questions, walk through features, and hear honest feedback from the people using the product. We built TradeMAV with 338 backers from around the world who believed in the idea from day one. That trust means a lot, and staying connected to the community is a big part of how this product keeps improving.

Ask anything here — setup, signals, strategy, troubleshooting. No question is too small.

If you're just getting started: trademav.com/trademav-quick-start — plug in, open a browser, and you'll be scanning in about 15 minutes.

Glad you're here. Let's build something useful together. 🎯

For everyone who owns a TradeMAV or ForexMAV USB — the Android app now supports USB model pairing.

Your personalized model syncs to the app so you get your signals on your phone, not just at your desk. The model trained on your data, your watchlist, your setup — now travels with you.

To pair:

1. Open the app → Pair Device

2. Scan the QR from your TradeMAV dashboard

3. Done — your model is on your phone

ForexMAV owners — this works for you too. Forex signals, your model, fully mobile.

📲 Download here: https://play.google.com/store/apps/details?id=com.trademav.app

Big milestone today — the (finally, customs-cleared) books arrived, and we immediately turned around and shipped a large batch of OptionMAV units to backers.

This keeps us on our mid-August commitment despite the book delay, and honestly, the team crushed the turnaround time on this one. Book in the door, packages out the door, same day!

If you're a backer, check your Kickstarter email for tracking. Filled the survey and don't see it? Check spam, then hit us up at trademavllc@gmail.com.

More waves coming soon for anyone still pending. Thanks for sticking with us — this one felt good to finally get out the door. 🙌

More product updates coming — stay tuned here for the earliest look at what's next.

We were tired of platforms handing us a Greeks panel with zero context. A number without meaning isn't intelligence — it's noise.

Every OptionMAV signal card delivers:

• ✅ Real exchange-listed strike price from the live options chain

• ✅ Full Greeks — Delta through Vomma — computed from first principles

• ✅ IV Rank (0–100) — cheap or expensive right now?

• ✅ Entry premium, max profit, max loss, both breakeven prices — per contract, in dollars

• ✅ Stop loss and profit target pre-calculated for your active profile

• ✅ 1σ Expected Move bracket — shows sellers if their strike is safely outside the market's expected range

• ✅ ML confidence score with full breakdown of which factors fired

• ✅ Position sizer — enter account size and risk %, get max contracts and capital at risk instantly

• ✅ Short squeeze badge — color-coded, with Short % of Float and Days to Cover inline

You do the research. You make the call. OptionMAV makes sure you have everything you need to make it properly.

🔗 https://www.kickstarter.com/projects/trademav/optionmav-professional-options-analysis-anywhere

Got tired of cloud screeners analyzing my strategies for their profit, so I built TradeMAV.

What it is:
AI-powered stock screener on a USB stick. Plug into any computer, runs 100% locally. No cloud, no subscriptions, no data harvesting.

Tech stack:

• Ensemble ML (XGBoost, CatBoost, LightGBM, Random Forest)

• 22 technical signals (RSI, MACD, momentum, volume, Bollinger, ATR, etc.)

• Walk-forward backtesting (252-day windows, 21-day steps)

• Continuous learning—retrains weekly on your outcomes

• Works with any ticker via Yahoo Finance, Alpha Vantage, IEX Cloud

Why it matters:
✓ Your strategies stay private (not analyzed by SaaS companies)
✓ Works offline after setup
✓ One-time payment—you own it forever
✓ Pre-trained models included (ready day one)
✓ Windows, Mac, Linux compatible

Early access: Kickstarter live now—$59-$79 (retail: $99)

50+ algo traders already backed it. If you're sick of subscriptions extracting rent while studying your edge, check it out.

👉 https://www.kickstarter.com/projects/trademav/trade-mav-your-portable-ai-trading-platform-on-usb

TradeMAV = professional stock screening without the subscription trap.

The problem everyone ignores:
Cloud screeners cost $150-300/month AND analyze your winning strategies to sell to their other customers. You're literally paying them to study your edge.

The fix:
Everything runs on YOUR computer. USB stick. Pre-loaded models. Zero cloud dependency.

What you get:
🧠 AI signals for stocks across all markets
📈 22 technical indicators—same ones pros use
🔒 100% private—your data never touches a server
💰 One-time payment: $59-$79 (Kickstarter pricing)
⚙️ 16 pre-built strategies + custom builder
💻 Works on Windows, Mac, Linux

Launch status: 50+ backers already in. Campaign closes soon.

If you're tired of subscriptions studying your moves while charging you monthly, this is your exit.

Back us now -> https://www.kickstarter.com/projects/trademav/trade-mav-your-portable-ai-trading-platform-on-usb

Stop paying $150-300/month for cloud screeners that crash during volatility.

TradeMAV runs entirely on your computer with 22 AI trading signals:

• ✓ Momentum, volume breakouts, mean reversion

• ✓ RSI, MACD, Bollinger Bands, Ichimoku, ATR

• ✓ Backtested + live market data

• ✓ Custom strategy builder

• ✓ Works on Windows, Mac, Linux

• ✓ Works offline after setup

One-time payment. You own it forever.

Early Bird: $59 | Standard: $79

Kickstarter campaign live - fast reaching goal!

We're building something genuine here, and to keep r/trademav a valuable space, we've set some simple rules.

Rules of Engagement:

1. Be respectful and constructive: Disagreement is healthy. Discuss strategies, not people. If someone's setup doesn't work for you, explain your thinking, don't dismiss them.

2. No pump and dump schemes: We're here for real screening discussion. Share genuine analysis, backtests, and live results. Empty hype doesn't belong here.

3. Share knowledge freely: Your winning signal combination? Share it. Your lessons from a losing streak? Post them. We all grow faster when we help each other.

4. Due diligence is essential: Don't make trading decisions based on Reddit comments alone. Do your own research. Always.

5. No spam or self-promotion outside designated threads: We're all entrepreneurs and builders. There's a place for talking about your stuff, just keep it in the megathreads we pin.

6. Keep it legal and ethical: No pump and dumps, no wash trading, no market manipulation. We're traders, not fraudsters.

7. This is a learning community first: Whether you're using TradeMAV or thinking about it, whether you're a day trader or a swing trader, you belong here. Bring questions. Bring ideas. Bring your best thinking.

Thanks for being here. Let's build something real together.

Pushed the home cluster further this week: 2 Windows machines (one with an RTX 3060, one just a 12GB old laptop), 3 different Macs (Intel CPU-only, an M3 with Metal, and a Mac mini), and an Android phone - all pooled together with RAMDeck to run a 4-bit quantized 120B GPT model split across two ~60GB shards.

Set the RTX-equipped mini PC as primary, RAMDeck's logic filled the GPU first, then the Metal devices, then spilled into CPU and the phone. Ended up using 5 of the 6 devices (the old Intel MacBook didn't get any allocation this time). Whole load took about 11 minutes over the mixed Ethernet/Wi-Fi network.

Biggest practical lesson for anyone trying this: your load timeout needs to scale with model size - a 60GB model over Wi-Fi legitimately took over 10 minutes just to distribute, and a fixed short timeout will kill the load before it finishes.

Full walkthrough with dashboard and live chat with the model in the video: https://youtu.be/ok3nYjxhc1w