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At an Oct. 12 H1 2026 results briefing, Hebang Bio said H2 production and sales of phosphate ore are operating normally and it currently sees no risk of large swings in selling prices or gross margins. The company said glyphosate sector supply/demand

2026-10-12

At an Oct. 12 H1 2026 results briefing, Hebang Bio said H2 production and sales of phosphate ore are operating normally and it currently sees no risk of large swings in selling prices or gross margins. The company said glyphosate sector supply/demand and prices are driven by multiple factors; both of its glyphosate production lines are running at full capacity. Hebang is prioritizing inventory reduction and cash generation this year and will adjust shipment volumes based on market forecasts. El Niño and other climate phenomena add uncertainty to global agricultural activity and herbicide demand.

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2026-10-12

Iran foreign ministry spokesman BAGHAEI said Tehran is holding ongoing consultations with regional states and is willing to assist in peacefully resolving disputes. He warned sustained tensions among Muslim countries create openings for the Israeli r

Iran foreign ministry spokesman BAGHAEI said Tehran is holding ongoing consultations with regional states and is willing to assist in peacefully resolving disputes. He warned sustained tensions among Muslim countries create openings for the Israeli regime and said US and Israeli actions have intensified the regional crisis. BAGHAEI expressed concern over developments on the Arabian Peninsula and said the people of Yemen should not bear pressure, sanctions or a blockade, calling Yemen’s decade‑long plight unjustifiable.
2026-10-12

In his Interconnects newsletter, AI researcher Nathan Lambert wrote that many aspects of the AI training and inference stack are highly verifiable. Training metrics, such as tokens per GPU per second, essentially represent training speed. Inference m

In his Interconnects newsletter, AI researcher Nathan Lambert wrote that many aspects of the AI training and inference stack are highly verifiable. Training metrics, such as tokens per GPU per second, essentially represent training speed. Inference metrics, such as tokens per prompt and FLOPs per token, ultimately reflect the cost per answer. Both are highly optimizable, especially with mature sub-problems and architectural tradeoffs to leverage. I anticipate that within a few years, AI agents will help optimize the entire process end-to-end, bringing our inference capabilities close to the underlying computing power limits of accelerators like GPUs. In recent years, companies have already seen significant gains in inference efficiency—for example, after releasing a model at a certain price, they can save an additional 10–30% in service costs. The entire stack will continue to compound, and I expect the effective cost of model intelligence to decrease almost exponentially in the coming years (possibly faster than recent trends). These low-hanging fruits of efficiency can be harvested within a few years. In the longer term, the co-design of accelerators and models has an even longer timeline, potentially bringing several orders of magnitude more efficiency improvements on flexible GPU platforms. During this period of rapid efficiency improvements, the flexibility of GPUs will be beneficial because the architectural exploration space is crucial and a major lever for automated research. In my view, it is reasonable to predict that pre-training research (at least in terms of architecture and data selection for current models) will be automated within 2–3 years.
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