Saturday, October 17, 2026

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Semtech Analyst Day Review

Semtech raised Q3 guidance and targets $3.2 billion of fiscal 2030 revenue, excluding upside from its AI data-center optical and copper interconnect businesses.

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@StableBread

Fajasy on X

Alpayana, a private Peruvian mining group, asked for 19.9% of Magna Mining $MGMNF (TSX: $NICU) when management was “thinking something probably below 9.9,” per CEO Jason Jessup. On August 28, 2026, Alpayana, chaired by Alejandro Gubbins, bought 62,356,682 shares at CAD$2.25/share for US$99M. The US$99M more than quadrupled Magna’s cash at the time. Alpayana runs six underground mines in Peru and Mexico, most of them bought from other miners, including the Los Quenuales mines from Glencore $GLNCY in December 2022 and Sierra Metals, a copper-zinc miner, for ~US$176M in 2025. While it holds at least 10%, Alpayana can join future equity raises and top up after dilution, and it gets one board nominee or an observer. Jessup says Magna’s pre-deal plan already funded Levack, its flagship restart project, from cash and the cash flow of McCreedy West, its producing mine. So the US$99M mainly spares Magna from “delaying the startup construction at Crean Hill,” its permitted nickel mine, “by 1 or 2 years,” and gives it “a bit of a war chest” for acquisitions in Sudbury, Ontario, where its mines are. He also says Magna “could have five mines in production in 5 years and not have to ever do another financing again.” Neither restart study is out yet (both are due in the first half of October), so the claim rests on capital budgets investors haven’t seen. After the deal, Alpayana holds 19.9%, institutions 47.4%, retail investors 26.9%, and management and directors 5.8%. The institutional stake includes the 16.0% held by Dundee Corporation $DDEJF, a Toronto investment company.
@convequity

Convequity on X

OpenAI’s TPU lesson makes me more cautious on $CBRS. Richard Ho, OpenAI’s hardware chief and a former TPU designer, says one lesson from Google was that “you have to make it programmable.” Models change too quickly to overfit the chip to one architecture. That caught my attention because TPU is already programmable. Groq statically schedules execution in its compiler, while Cerebras makes a different bet on wafer-scale compute and distributed local memory. The question is how much of the performance advantage survives when models change, including MoE routing and memory demands. Ho also wants hardware that can move across inference workloads without locking the fleet into fixed ratios of specialized machines. That matters when buyers are committing years of capex and power. If OpenAI and other large buyers increasingly favor that flexibility, I expect CBRS to remain a specialist over the next few years. The absolute market can still be large enough for Cerebras to build a very good business, but I’m more skeptical that its architecture becomes the mainstream choice. "As long as you’re willing to live with those limitations, they’re good. But if you’re able to make it programmable, then you have a lot of flexibility for adapting to change. And we had to adapt to change. TPU also adapted to different changes in the algorithm, even at that time with CNNs. Clearly now, in the world of LLMs and agents, the mix is changing pretty rapidly. So that was the pitfall we really wanted to avoid: to overdesign and overfit to one particular model. We looked at it in that context." ---[54] The Man Who Built OpenAI's First Chip TechTechPotato

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