Kryptolytix ⓚ

True Agentic workflows leverage multiple modals to work towards creating something great Imagine if it was trained, tailored and guided specifically for creating trading strategies, with access to backtesting and creating reports to circle back to self improve Imagine no longer LabAssistant is here. More soon Myquantumvault.com

The takeaway from Fable 5 being BANNED by the government: GET GOOD AT LOCAL MODELS SO YOU HAVE 100% CONTROL. My entire weekend was going to be building my craziest ideas with Fable 5. That's now cancelled. So instead of building with Fable this weekend, I've decided I'll go deep on local models: 1. Start with the runtime. Download Ollama or LM Studio first. This is the thing that actually runs models on your machine. 2. Match the model to your hardware. A model's size is measured in billions of parameters (7B, 32B, 70B). Bigger is smarter but needs more memory. Rule of thumb: a 7B model runs on almost any laptop, a 32B needs a good Mac with 32GB+ RAM, a 70B needs serious hardware like a DGX Spark or a maxed-out Mac Studio. 3. Know which model for which job. Qwen 3 is the best all-around choice for most tasks. DeepSeek for reasoning and coding. Gemma 4 when you need something tiny that runs on a phone. Llama when you want the biggest community and the most fine-tunes. 4. Quantization. You can shrink a model to run on weaker hardware with barely any quality loss. Look for versions labeled Q4 or Q5. This is how a model that "needs" a server runs on your laptop. Learning this one concept changes everything. 5. Connect it to your agent. Point Hermes or your agent stack at a local model. 6. Context window is your real constraint locally. Cloud models give you huge context for free. Local models make you pay for it in memory. A bigger context window eats RAM fast. Keep your sessions tight and your prompts lean or your machine chokes. 7. Learn to give local models tools. A smaller local model with web search, file access, and code execution beats a giant model with none. The capability gap closes fast when you wire up the right tools. The model is the engine but the tools are the wheels. 8. Fine-tuning is more accessible than you think. You don't need this on day one, but know it exists. You can take an open model and train it on your own data so it gets good at your specific domain. I'll probably do a breakdown at some point on this @startupideaspod if people are into it. The lesson from this ban is basically don't build your entire workflow on something that can disappear with a single letter. Own part of your stack. Local models are insurance. It reminds me when people realized they don't own social media accounts. And then you saw people build email lists etc. I remember running a startup and my biggest traffic source was organic FB. All of a sudden, algo changed, and I lost 99% of my traffic. Same sorta moment (but bigger) for AI. This is a wake up call.

The AI Strategy Creator is live in QuantumLab! Describe a trading idea in plain English and it drafts a full strategy, checks it compiles, and runs an independent review to flag risks. No coding required. Every generated strategy has stop loss, partial TPs, breakeven, and ATR trail built in by default. Save it, backtest across tickers and timeframes, generate an insights report, then hit Improve. The AI reads the backtest data and refines the strategy based on what actually worked. Idea to optimized strategy without writing a line of code. myquantumvault.com/quantumlab

We now fully support the @solanamobile Seeker and have an app pushing to the @solana dApp Store! Connect to SeedVault, @Jupiter Wallet, @phantom mobile or your fav wallet using the Mobile Wallet Adapter from the browser too! No need to use the wallet browser at all. Solana @perps on the move, backtest a strategy, deploy, trade from anywhere.

Results Of A Surprise Stress Test - @pacifica_fi Yesterday I decided to put Pacifica through a little surprise stress test. I didn’t tell the team what I was doing. But as I continue shifting more of my volume there, it has become important for me to know whether the venue can actually handle size. solana:Dfh5DzRgSvvCFDoYc2ciTkMrbDfRKybA4SoFbPmApump getting crimed yesterday was the perfect opportunity. I built an aggressive scaled short throughout the pump. On top of that, I placed large limit orders directly on the book around mid. Then I slammed large market orders as well. At one point, I was roughly 2/3 of open interest. That was the stress test. What I wanted to see was simple: could I cause a price dislocation on this venue, or were Pacifica’s market makers and arbitrage flows strong enough to absorb the pressure and keep the market functioning? I was watching the spread, mark price, spot/perp alignment, depth reloads, order matching, liquidity response, and whether the book would get jumpy or unstable once size hit it. To my surprise, everything ran smoothly. Arbitrage appeared to do its job quickly, and the market handled the pressure far better than I expected. I’ve done similar stress tests on other exchanges in the past to see whether they could handle my size. Many of them failed. Pacifica didn’t. Ironically, because the solana:Dfh5DzRgSvvCFDoYc2ciTkMrbDfRKybA4SoFbPmApump move played out almost exactly how I expected, I actually regret not playing it for maximum profit. Because I exited in a similar manner to how I entered. I did make five figures on the trade, but the test became more important to me than the PnL. I walked away from yesterday with more confidence in Pacifica than I had going in. 🫡 From the depths of the Pacifica Ocean — The White Whale 🐋

Today we pushed our new facelift! New landing page and colour scheme throughout. Dive in to myquantumvault.com and check it out!