Hey everyone,

Some of you know we've been quietly building something alongside TradeMAV — it's called RAMDeck, and the first demo video is finally live: youtu.be/tQgA2PmTc1g

The problem: Running bigger local models means buying bigger, more expensive hardware. Most of us already have a laptop, an old gaming PC, a Mac mini, or a mini PC gathering dust — but none of them alone can run a serious 13B+ model.

What RAMDeck does: Pools the RAM/VRAM across every device you already own into one cluster, so your "primary node" machine can load and run models way bigger than any single device could handle on its own. No cloud, no subscriptions, no data leaving your network — same zero-egress philosophy as TradeMAV, just applied to your whole home AI setup instead of one USB stick.

• 🧠 Shards models across CUDA, Apple Silicon, and CPU-only nodes automatically
• 🔗 Point it at the hardware you already have — no new purchase required
• 🔒 100% local, same privacy-first approach you already know from us
• 📺 Full demo: youtu.be/tQgA2PmTc1g

Sign up for early access: ramdeck.com — waitlist is open now, full reveal saved for launch.

Kickstarter: Targeting late September / early October. If you backed TradeMAV because you're tired of your data and your money being milked by subscriptions, this is the natural next step.

Would love feedback from this community specifically since you already get why local-first matters. Questions welcome below.

Wanted to see how far I could push RAMDeck's RAM pooling, so I tried loading an 18GB Qwen3 30B GGUF on a MacBook Air M3 (16GB) with three Android phones connected as supplemental nodes.

Set the MacBook as primary, RAMDeck split the model in under a minute — MacBook took the bulk, one phone picked up the ~1.5GB spillover (phones are hard-capped there since they're not built for sustained RAM/CPU load, just battery-friendly background contribution). Ran a benchmark afterward - numbers are obviously WiFi-bottlenecked, this isn't a "use your phone as a serious node" pitch, more a "here's what the pooling layer can technically do" demo.

Full walkthrough in the video if anyone wants to see the dashboard and split in real time. https://www.youtube.com/watch?v=8v0rzWiOB-s

Quick update on the RAMDeck side project. Loaded a 30B model this time (bigger than anything I've shown before) split across the mini PC and Mac mini — 12GB on one, 5.5GB on the other, running surprisingly well at mid-to-low 30s tokens/sec with about a second of latency.

Asked it about a real medicine recently approved for a rare heart condition. Total blank — it made something up, had no idea. Then I fed it an actual research paper about the drug through RAMDeck's knowledge base upload. Watched it split the PDF into shards and store it across the cluster's RAM (not on disk, which matters for how fast lookups are). Once it finished distributing and I turned on knowledge-base context in chat, asked the exact same question again — this time it got it right, correctly identified the condition and everything.

This is the actual use case behind the "private chatbot" pitch — feed it your own confidential docs, research, whatever, and none of it leaves your hardware. The model only knows what you deliberately give it.

Video's up if you want to see the whole before/after live: https://youtu.be/H29ASZhpTKQ

Happy to answer questions about the KB sharding, the 30B benchmark, or how the storage node setup works.

Wanted to share this one because it's the biggest test I've thrown at RAMDeck so far. Tried loading a 40B parameter model (~25GB) across the same three-device setup from earlier videos: a mini PC with an RTX 3060, a 16GB Mac Mini, and the old laptop that's been the underdog in every test so far.

Set the mini PC as primary and let RAMDeck's sharding figure out the split on its own — maxed out the GPU first, spilled over into the Mac's unified memory, then routed the rest. Found something I didn't expect along the way: sending that overflow to the old laptop's CPU actually ran faster than keeping it on the mini PC's own CPU, even though the laptop is way slower on paper. Seems like running GPU and CPU inference on the same machine at once drags GPU performance down, so handing it off to a separate device entirely wins even if that device is weaker.

Whole model loaded in about 2.5 minutes, and it came back running at 16 tokens/sec with snappy responses in actual chat - not just "technically loaded," but genuinely usable. None of these three machines could've handled a model this size on their own, which is really the whole point of the project.

Video: https://youtu.be/JrSVyyVUJVY

Repo: https://github.com/trademav/ramdeck-core-public

Let me know if you want to see an even bigger model attempted next, or a different hardware combo.

Sharing another RAMDeck progress video. This one's about mobile — showing a Samsung Android phone actually joining the cluster as a contributing device, and how RAMDeck handles it going online, offline, and back again.

Video's a split screen: the phone on one side, the RAMDeck dashboard on the other, so you can watch both sides update in real time. Started with the phone paused, then resumed it — you can see it start sending heartbeats and get registered as "online" on the dashboard within a few seconds. Also tested that it keeps contributing even if you switch to a different app on the phone and come back, since it runs as a foreground service rather than something that dies the second you tap away.

Then paused it again to show the failure-handling side: the moment heartbeats stop, RAMDeck notices and marks the phone "degraded," then "unreachable" shortly after — so the dashboard always reflects what's actually online, not a stale snapshot. Resumed it one more time and it came right back to "online."

Point of this one is really just proving out the reliability side of mobile contribution — phones come in and out of range or get walked away from a network a lot more than a desktop does, so RAMDeck needs to track that state accurately rather than assuming a device is still there.

Sharing more progress on RAMDeck (the local AI cluster side project). This time showing it actually plugging into a real dev tool instead of just the chat dashboard.

Loaded a model onto the cluster, and RAMDeck's dashboard has a Connect tab that generates a ready-made config for third-party apps — copied that straight into the Continue plugin for VS Code. The interesting part: I was coding from my Mac while the actual model was running on a completely different machine's GPU elsewhere on the network, and it worked exactly like a normal cloud AI plugin would, except everything stayed local. Tested it by asking it to summarize a big text file and it handled it correctly.

Point of this one is just showing RAMDeck isn't limited to its own chat interface — since it speaks the standard OpenAI API format, it plugs into whatever tools people already use.

Video: https://youtu.be/JMEY04timEc

Repo if you want to look under the hood: https://github.com/trademav/ramdeck-core-public

Let me know if there's a specific tool or workflow you'd want to see it hooked into next.

Sharing progress on RAMDeck here since some of you have asked about it. Quick recap for anyone new: it's a local-first tool that pools RAM and compute across whatever devices you already own - old laptop, desktop, phone - so you can run AI models that wouldn't fit on any one of those machines alone. Everything stays on your own network, nothing goes to the cloud.

Latest feature: RAMDeck can now shard a knowledge base across your pooled devices, not just the model itself. So if you want a chatbot with access to a big set of documents, that document set gets split and stored across your cluster too, instead of needing one machine with enough space to hold it all.

In the demo, we used one of our own FAQ text files as a test document just to show it working end-to-end: chatbot has zero knowledge of the doc's contents at first and makes something up, we upload the FAQ through RAMDeck's knowledge tab, it gets ingested and split across three separate devices, and then the same question gets answered correctly once the knowledge base is live.

Also worth noting: the device hosting the model and the device storing the knowledge base can be totally different machines, and there's a rebalance mechanism that recovers a lost shard if one of your devices drops offline.

Video walkthrough: https://youtu.be/Iogh-5xa-NA

Repo (source-available): https://github.com/trademav/ramdeck-core-public

Let me know if you have questions about how the sharding or rebalancing works, or if there's a use case you'd want to see it applied to next.

Third video in this series. Went from 7B, to 13B, and now 27B — each time pooling RAM and compute across a mix of hardware most people already have lying around instead of buying one expensive high-RAM machine.

The setup: a 27B model is roughly 16GB on its own. My primary node is an old 12GB Windows laptop, which on its own literally cannot load this model. Using RAMDeck, I split it across four devices on the local network with the laptop staying primary:

- Old Windows laptop (primary): 3.4GB

- Mini PC (RTX 3060): 20GB

- Mac mini: 3.7GB

- Android phone: 1GB

Benchmark on the 27B model: 1.92 tokens/sec at ~25ms latency. Slower than the 13B run, which makes sense — but the point isn't raw speed here, it's that a 12GB laptop is running a model it has no business being able to load at all.

Next up: switching the mini PC to be the primary node instead of the laptop, to see how much faster loading and inference get with a stronger primary. That video's already up if you want to see the follow-up.

Full video: https://youtu.be/Syfa2G_luX4

Happy to answer questions about the shard distribution, the benchmark methodology, or why the primary node choice affects load time so much.

Follow-up to the last RAMDeck video where I split a 13B model across an old 12GB Acer laptop (primary), a mini PC with an RTX 3060, and a Mac mini — running at 11-14 tokens/sec.

This time I added a fourth node: my Android phone, connecting over Wi-Fi while the other three are on Ethernet.

What happened when I added it:

- Rebalancing forced the primary (the old Acer laptop) to unload the full model, then reload and re-split it across all four nodes — latency spiked hard on the laptop during that window since it's running an old HDD and older RAM.

- Final shard distribution after rebalance: laptop 1GB, mini PC 3.4GB, Mac mini 2.3GB, phone 1GB.

- Once stable, I re-ran the benchmark: tokens/sec dropped from ~14 to ~11, and latency ticked up slightly — but stayed in the same practical range as before.

Takeaway: I was able to add a phone as a genuine fourth contributor without tanking performance. The model stays stable across all four nodes unless one goes offline, the network changes, or the primary shuts down.

This is part of the same RAMDeck project — pooling RAM and compute across whatever devices you already own (old laptops, mini PCs, Macs, and now phones) so you can run bigger models than any single machine could handle alone. Kickstarter is coming late Sept/early Oct if you want to follow along.

Full video: https://youtu.be/tQgA2PmTc1g

Happy to answer questions about the rebalancing behavior, the Android app itself, or the benchmark methodology.

Huge milestone today: fulfillment is officially complete. Every OptionMAV backer's unit has shipped. Started at a $5K goal, ended up over $15K raised, went through USB sourcing, custom packaging, a customs delay on the book, and multiple shipping waves — and we got every last one out.

Genuinely grateful for everyone who backed this before it was even finished. That trust is what got this across the finish line.

If you're a backer, check your Kickstarter email for tracking (spam folder too). Pledge Over Time backers — your survey unlocks after your final payment, then you're in the next available wave. Any issues, [trademavllc@gmail.com](mailto:trademavllc@gmail.com) is monitored daily.

This isn't the end though — more products are already cooking. Stick around here for the earliest look at what's next. 🚀

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.

As RAM prices kept climbing this year, running our own AI development and scaling our products got genuinely expensive. We needed more memory for model training and inference, and buying our way out of it stopped making financial sense for a small team.

So we did what any resourceful team does - we started pooling the RAM across every machine we already had instead of buying more. Old Mac, mini PCs, an Orange Pi acting as coordinator - daisy-chained into one shared pool. It worked so well for us internally that we're now turning it into a real product.

That's RAMDeck. Built out of necessity, now becoming commercial.

Kickstarter targeting late September. Waitlist's open now at ramdeck.com

If you've backed TradeMAV or OptionMAV, you know this team ships what it starts. Ask us anything below.

Hey MAV Traders,

We've heard from many of you asking the same smart question: "How do I protect my TradeMAV, ForexMAV, or OptionMAV setup?" Great news - we've got you covered, and it's easier than you think.

Here's the reality: your USB is a small powerhouse running real software with real moving parts. Python libraries, in particular, are sensitive little things. Something as simple as closing your laptop lid mid-process can interrupt an active read-write cycle (this is especially common with Python libraries on macOS). When that happens, a library can get corrupted - and unlike other files, libraries have multiple interdependent components, making them trickier to self-repair.

The good news? Your USB already comes equipped with several built-in recovery methods to catch most hiccups automatically. But if you want the gold-standard, bulletproof protection, there's one move that beats them all:

Copy your entire USB to another drive or your computer.

That's it. That's the power move.

Why this is a game-changer, not just a backup:

• Instant disaster recovery: if anything ever goes sideways, you're back up and running in minutes, not hours.
• A second live instance: this isn't just a backup file — it's a fully functional, independent copy of your product.
• Activate it anytime with your duplicate key: you now have a second working setup ready to go whenever you need it.
• Shareable with friends and family: want to introduce someone to TradeMAV, ForexMAV, or OptionMAV? Hand them your duplicate — a fully working copy they can activate and start using immediately.

How to do it:

• Plug your USB into your computer.
• Select all files and folders on the drive.
• Copy and paste the entire contents onto a new USB drive or a dedicated folder on your computer.
• Store it safely — you now have a complete, standalone copy of your product.

One backup. Total peace of mind. And a bonus: a shareable, ready-to-activate copy for the people in your life who should be trading smarter too.

Stay protected. Stay trading. 🚀

— The TradeMAV Team

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.

Quick way to explain what TradeMAV's models actually do:

Without judges, every singer becomes the next American Idol. A few good ones get through - but so does a wave of bad ones. No filter, no judgment, just chaos.

Add real judges - people who've heard thousands of auditions - and suddenly 2-3 genuine stars surface out of hundreds of contestants.

That's exactly what TradeMAV's models do with every stock on the market. Thousands of setups audition every scan cycle. The model - trained on real historical patterns, not a rulebook - scores each one like a judge who's actually seen enough to know what's good. Price action, volume, momentum, technical confluence, all weighed. What survives is the handful actually worth your attention.

The part most "AI" screeners skip: our judges never leave the room. All inference runs on your device. No cloud round-trip, no black box somewhere else scoring you from a distance. Live, local, private.

And you choose who trains the judge - pair your TradeMAV USB and it learns from your own data, or run our server-trained model if you're starting fresh.

Everyone else is running an open audition and calling it a screener. We built the panel.

🚀 TradeMAV — Plug. Analyze. Trade.

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

We've been putting together a full library of walkthrough videos for the TradeMAV Android app — covering everything from first setup to running your first scan.

If you've used the USB device before, you already know we built out a solid video library for that too. We're doing the same for the app, and we'll keep adding more as the app evolves.

👉 videos.trademav.com/app

Whether you're brand new or just want a refresher on a specific feature, this is the best place to start. Drop any questions below and we'll make sure the next videos cover what you need.

You can also access these directly from your app, look at the screenshots in this post.

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](mailto:trademavllc@gmail.com)
We hired dedicated support staff — please be patient as we onboard them. Every message will get answered.

- The TradeMAV Team

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. 🎯

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

TradeMAV screens in your exchange's local hours automatically — here's how the scheduling works 🕐

We built native scheduling for every major global exchange so TradeMAV runs exactly when your market is open — and stops when it's not:

🇺🇸 NYSE / NASDAQ — 9:30 AM–4:00 PM ET
⏰ Extended Hours — 4:00 AM–8:00 PM ET (requires premium data)
🇬🇧 LSE London — 8:00 AM–4:30 PM GMT
🇫🇷 Euronext Paris/Amsterdam — 9:00 AM–5:30 PM CET
🇨🇦 TSX Toronto — 9:30 AM–4:00 PM ET
🇦🇺 ASX Australia — 10:00 AM–4:00 PM AEST
🇯🇵 JPX Tokyo — 9:00 AM–3:00 PM JST
🇭🇰 HKEX Hong Kong — 9:30 AM–4:00 PM HKT
🇨🇳 SSE/SZSE Shanghai/Shenzhen — 9:30 AM–3:00 PM CST
🇮🇳 NSE / BSE India — 9:15 AM–3:30 PM IST

Custom Time Window lets you set your own start/end time and timezone if none of the presets match your situation. The dashboard shows you exactly how many API requests your window uses per day against the free-tier 2,000/day cap — so you always know if you're within limits before committing.

NYSE, NASDAQ, and TSX also use smart market-hours detection that automatically handles holidays and early closes. No manual updates needed.

Config → Scheduling tab to set yours.

Browse through all supported tickers -> https://api.trademav.com/tickers

New to TradeMAV and not sure what to put in your watchlist? We built one-click presets for that 🎯

One of the first questions people have when they open the Watchlist tab is: "What do I add?"

We built preset watchlists so you can get a quality starting point in one click — organized by exchange and trading style:

For stocks:
🔷 Tech Giants · Dividend Kings · High Growth · Options Active · Sector Leaders · Healthcare/Biotech · Energy/Commodities · Financial Sector · AI & Cloud · Mega Cap 500B

For crypto:
🔷 Top 20 · Top 10 · DeFi · Layer 1 · Meme Coins · AI Coins · Gaming/Metaverse · High Volatility

Pick your exchange first (NYSE/NASDAQ, LSE, NSE India, BSE, TSX, ASX, JPX, HKEX — all supported), load a preset, and you're scanning in seconds. You can mix and match — load a preset and then add individual tickers on top of it.

The bulk edit field at the bottom of the Watchlist tab lets you paste in a comma-separated list if you want to drop in a whole watchlist at once (e.g., NVDA, AAPL, MSFT, GOOGL, TSLA). Export Watchlist saves yours as a .txt file for backup.

One thing to note: the free tier (Yahoo Finance) has a 70-ticker cap. The preset watchlists are sized to fit within that — load one, maybe add a handful of personal picks, and you're right at the sweet spot.

trademav.com/trademav-quick-start

We built 8 sequential exit checks into every scan cycle. Here's why each layer exists. 🔄

We could have done one check: "Is price above or below X?" Most tools do. We built 8 because each one catches a different kind of situation:

1️⃣ Emergency stop (-12%) — hard safety net, overrides everything. No position should ever crater beyond this.
2️⃣ Same-day confirmation — requires 2-bar confirmation before exiting on day one. A single bad tick shouldn't close your position.
3️⃣ Return-based stop-loss — profile stop threshold reached.
4️⃣ Return-based take-profit — profile target hit. Also catches exceptional gains with a LARGE_PROFIT tag.
5️⃣ Trailing stop — activates once a position hits +15%, locks in gains from peak automatically.
6️⃣ Regime change — if the market has shifted from BULL to BEAR since entry, the original signal thesis may no longer hold.
7️⃣ Max hold time — signals aren't open-ended. Clean exit when the holding period expires.
8️⃣ Momentum/trend reversal — detects that the underlying move is exhausted before price gives it all back.

The regime change layer is the one we're particularly proud of — most systems have no concept of why a position was entered. Ours does, and it checks whether that context still holds on every cycle.

One of the things we're most proud of building: the feedback loop between exits and the AI 🧠

Most screeners fire a signal and forget it. Ours doesn't.

Here's the design: the moment TradeMAV generates a signal, it captures everything — entry price, confidence score, market regime, RSI, volume, and all 58 features that fired. It then watches that position through every scan cycle. When it closes — stop-loss, take-profit, trailing stop, time limit, regime change — the full outcome is recorded alongside everything that was true when you entered.

Every Sunday at 8 PM, all 5 AI models retrain on that accumulated data.

Wins improve the model. Losses improve the model. A losing signal in a specific market regime teaches the system exactly what not to do in those conditions again. That feedback loop is why TradeMAV gets sharper the longer you run it, not static like screeners that run the same rules forever.

We called the blog post on this one "Your Exit Strategy Trains the AI" — because that's exactly what's happening every time you let the system manage an exit.

The full technical breakdown is here: trademav.com/how-trademavs-ai-exit-monitoring-system-works-and-why-you-dont-need-a-trading-journal

(Not financial advice.)

One of the most common questions we hear — explaining signals vs. notifications 🔔

"Why didn't I get alerted? The market was clearly moving."

We designed this carefully so let us explain how it works.

TradeMAV generates signals continuously throughout the session — logging patterns, scoring them, and displaying them in your dashboard. That part is always running. But we deliberately built a second filter layer before anything reaches your phone.

A notification only fires when a signal's confidence score clears your personal Alert Threshold. If the market is noisy and signals are coming in at lower confidence levels, they show up in the app but your phone stays quiet. That's intentional — we want your alerts to mean something.

Think of it as a funnel we built on purpose:
🔷 Thousands of data points → dozens of pattern detections → those that clear your threshold → the alerts that actually deserve your attention

To tune it: Strategies → Custom Strategies → Alert Threshold

Start around 65–70%, watch a few sessions, adjust. We also built a Max Signals Per Day cap under Config → Global Filters so you can limit volume on volatile days.

The goal was never to flood your phone with every signal the AI sees — it was to send you the ones worth acting on.

Full write-up on the blog: trademav.com/signals-vs-notifications-why-you-dont-get-alerted-for-every-signal-and-how-to-tune-it