@SleepyQuant
Solo finance + tech enthusiast running a multi-agent quant on one M1 Max — all local, no cloud. Real trading numbers (including losses), MLX engineering notes, occasional existential thought. No signals, no hype. One honest email a week at sleepyquant.rest.
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Nothing here yet.
Apr 23 · 8 min read · I Run a 40GB AI Model on a MacBook. Three Months of MLX on M1 Max Has Changed How I Think About Apple Silicon. It's Just a Laptop. But It's Running a 40GB Model Right Now. I'm drafting this on a MacBook Pro. Qwen 3.6 35B-A3B MoE Q8 — about 40GB of we...
Join discussionApr 21 · 8 min read · Executive summary I ran an inverted-alpha paper-trading experiment to test whether inverting my live signals would produce net-positive P&L over 100 round-trips. The inverted-alpha book (Book 2) hit a 63% win rate — good enough to celebrate — but the...
Join discussionApr 21 · 1 min read · Disclaimer: This post reflects one solo operator's configuration on one M1 Max with 64 GB of unified memory in April 2026, running MLX + Qwen 3.6 35B-A3B Q8. Specific numbers (compressed GB, tok/s, wired limit) will differ on other hardware, other mo...
Join discussionApr 20 · 8 min read · What 19 GB of Memory Compression Taught Me About MLX on M1 Max The moment something was wrong I opened Activity Monitor on my M1 Max one afternoon and saw this: Memory Used 60.74 GB out of 64, compressed memory 19.69 GB, swap starting to fill. The Sw...
Join discussionApr 18 · 7 min read · Why Apple Silicon Quietly Won the Local-AI Race (April 2026) Executive summary While the public AI narrative is dominated by capex wars and cloud GPU shortages, a quieter shift has happened on the desktop. A single Apple Silicon laptop with 64GB of u...
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