"We have changed our terms of service" - says one of the 10 same emails of a startup you didn't know existed but used its app or site once 15 years ago

Tell me if I am wrong, but I keep coming back to this: For many biologics, the hard part is no longer only making the molecule. It is purifying it, scaling it, and making the right tradeoffs fast enough. Downstream process development is expensive. Every decision pushes against something else: purity, recovery, yield, robustness, time, cost. Chromatography simulation can help, but most tools still feel like they were built for people who already think like modelers. And they cost a lot. Before you can ask practical questions, you often have to deal with model structure, equations, parameters, numerical setup, and a lot of interpretation. But the questions process teams care about are usually much more direct: - How do I improve purity without losing too much? - What happens if I shorten the gradient? - Can I reduce process time without breaking the goal? - ... So I am building open-source, user-friendly chromatography simulation software for process-development teams. The idea is simple: Build the process visually. Define the goal. Let AI help translate that goal into simulation steps you can inspect, edit, and run. I do not want this to be a black box. I do not want to replace scientific judgment. I want to make mechanistic simulation easier to operate for the people who need the answer. Powered by open source tools. Open source, because critical process workflows should not depend entirely on one company’s roadmap. And most importantly: making the manufacturing of better medicine more accessible. I am looking for: 1. downstream process developers willing to test early workflows and collaborate 2. mechanistic modeling people who want to shape the right abstractions 3. sponsors or partners who want open tooling in bioprocess simulation If this is a problem you have felt, I would love to talk.

Has the bioprocessing bottleneck moved downstream? Over the past decades, biopharma has become dramatically better at producing biologics. As one review put it, “mAb titers have been increased a hundredfold from less than 0.1 g/L in the 1990s to 10 g/L in modern fed-batch cultivations” [1]. That’s a huge upstream win. But it also seems to have created a new constraint: we’ve made the “factory” (living cells) more productive, while the “shipping and receiving” side of the process, purification hardware and facility infrastructure, may be struggling to keep up. Upstream feels almost digital in comparison: you can optimize cells to produce more. Downstream runs into physical limits much faster, from filter capacity and flow rates to the sheer buffer volumes and footprint required to purify higher-yield harvests. I’m a software designer, not a bioprocess engineer. But working on complex technical products, I keep seeing the same pattern: when one part of a system improves dramatically, the bottleneck moves. And the highest-leverage work moves with it. So I’m curious: if cost, speed, and access are increasingly being decided downstream, is that where the most impactful innovation is now happening? Or is the reality more nuanced? [1] sciencedirect.com/science/ar… PS: Suez Canal's ChatGPT-generated illustration only for illustrative purpose

this thread about the vesuvius scrolls and how they were read with the help of ml is super interesting.

Happy to have participated in @ethmunich 2023! So much to learn for web3 still. Was great meeting you all!

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I just minted Anon - AceTCG zora.co/collect/pgn:0x5ab38b…

my friends are partying to the sound of brazilian funk in the other room and all i can i think of is LK-99

wow