Knockout Earnings & Valuation Dislocation: The Quant Case for Seagate ($STX) Short-term price weakness and pre-midterm seasonality across the AI ecosystem often obscure fundamental strength. Seagate Technology ($STX) reported blowout quarterly results, powered by data center demand which triggered strong quantitative factor grades across Growth and Profitability. The Key Valuation Metrics: - PRO Quant Portfolio Holding: Added to the PRO Quant Portfolio (PQP) in mid-September as a high-conviction, under-the-radar AI hardware play. - Knockout Top-Line Acceleration: Quarterly revenue surged ~60% YoY, with data center infrastructure accounting for roughly 80% of total sales. - Stellar Forward EPS Growth: Features a 92% forward EPS growth rate (385% above the sector) and a 75% 3-to-5 year EPS CAGR. - Growth-Adjusted Value: Trades at an ~80% discount to the sector on a price-to-earnings growth basis and a 35% discount on GAAP P/E. - A+ Profitability Engine: Generates a 26% net income margin (331% above sector) and over $3B in operating cash flow alongside an announced forward dividend. Emma Johnston's Take: "When you look at the current opportunity compared to those growth and profitability metrics, this pullback is a pretty enticing opportunity... With factor grades like this and some of that fear receding... we could potentially see the stock take off." #Investing# #StockMarket# #Seagate# #STX# #AI# #DataCenters# #QuantInvesting# #ProQuant# #SeekingAlpha#

It's only been a day since 0.7.1, but oMLX 0.7.2 is out with changes worth shipping now. This release brings faster DeepSeek V4.1 on every oQ build, Lightning MTP in distributed inference, and Qwen3.8-Flash-Next GGUFs on Splash 1.4.0. Faster DeepSeek V4.1 on oQ2e to oQ4e - oQ3e with Lightning MTP + CED: decode 49.4 -> 68.2 tok/s (+38%), prefill 700 -> 949 tok/s (+36%). Every quant from oQ2e to oQ4e gets faster, with similar prefill gains. - Prefill holds around 900 tok/s all the way to a 128K prompt. - Measured on M3 Ultra 512GB. I'll run more tests on M5 Ultra 256GB. Lightning MTP in Distributed Inference - DeepSeek V4.1 and GLM-5.3 run Lightning MTP across Macs with both PP and TP. - With TP on two M3 Ultras, both models decode more than 50% faster, even over a TCP ring. I'll share exact numbers after more tests over RDMA. - A pipeline stall is fixed, so DeepSeek V4.1 across two Macs went from about 1 tok/s to about 28 tok/s. More Models on Splash - Qwen3.8-Flash-Next GGUFs run on Splash 1.4.0, and oMLX offers the matching DFlash2 draft. - Split GGUFs download and show up as one model, and mlx-community OptiQ builds now start. Other Improvements - Better prompt cache reuse in Claude Code sessions, on both the built-in engines and Splash. - Reasoning models answer Claude Code's auto-mode classifier before it times out. - DFlash works with ParoQuant Qwen models. - SpecPrefill no longer disturbs other requests decoding next to it, and Gemma 3 gets the right positions in sparse prefill. - Keyboard access to the web UI menus and better contrast in the dark theme. Thanks to everyone who sent PRs and tested 0.7.1 so quickly!