• 5 Posts
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Joined 4 months ago
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Cake day: February 11th, 2026

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  • Man, your writing here all reads like Claude after I’ve given it a list of “AI tells” to avoid in its writing. There’s structural patterns that are pretty easy to see when there are so many samples right next to each other. I strongly suggest not trying to gloss over your use of AI in your projects when posting about them; some people will always hate, but most I think don’t mind AI code as long as it’s been tested properly and doesn’t have any more bugs than you’d find in any other project.

    Problem is, testing encryption properly is difficult, and there’s a lot more to a messenger application than just sending encrypted messages. That’s my criticism: you’re reinventing the wheel for no good reason.

    My best advice is to set up a Matrix server if you really don’t trust Signal, rather than trying to roll your own. Its a lot less work, a lot more secure, and you can modify the source anyway if it doesn’t do what you need.





  • Ahhhh fair play. I have a lot of freedom since I’m paying out of pocket for my own use. I have a pretty beefy rig for running local, but it’s not beefy enough to run deepseek pro and the like 😬 so, I have a bunch of subscriptions to try out a bunch of different models and see what works best in my workflow. I also have a problem with making alts in games, which seems like it rhymes 🤔

    Been pretty impressed with glm5.1 too, before deepseek-v4 came out, but you’d be amazed what even a smaller older coding model can do with the right config and a little proactive context management. I really hope this trend of smaller, better models for local agentic use continues.













  • Misleading headline. What they actually demonstrated is reversing amyloid accumulation and the cognitive deficits in a transgenic mouse whose pathology is essentially just amyloid accumulation. Calling that “reversing Alzheimer’s” treats amyloid buildup and the disease as the same thing, which is exactly the conflation the amyloid hypothesis has been criticised for over the last decade.

    Alzheimer’s in humans is amyloid + tau tangles + neuroinflammation + vascular dysfunction + actual neurodegeneration (entorhinal and hippocampal neurons dying, brain volume measurably dropping on MRI). Tau burden correlates with cognitive decline far better than amyloid does. The IBEC paper addresses one of those layers, the upstream-ish one, in a model that doesn’t reproduce most of the others. Fixing a cause in a young system before damage has accumulated is just not the same operation as fixing an established disease in an old human cortex that’s already lost the cells.

    The human translation data backs this up. Lecanemab clears plaques and slows cognitive decline by about 27% over 18 months. Donanemab clears around 76% of plaques and slows decline by ~35% in early AD. In both trials both arms still declined, treatment just declined a bit more slowly. Northwestern’s Mesulam Institute puts it bluntly: “These medications do not reverse existing disease or stop the progression.” So removing amyloid in a system that already has the full human pathology bends the curve, it doesn’t undo anything.

    What the IBEC team has here is a genuinely interesting result for the cerebrovascular angle, where BBB dysfunction and glymphatic clearance failure are upstream of plaque accumulation rather than a downstream consequence. The LRP1 transport mechanism and the multivalent ligand design are clever and well-grounded. The fair claim is “we improved amyloid clearance and rescued behavioural deficits in an amyloid-overexpressing mouse by targeting BBB transport.” That’s a real contribution. “Reversed Alzheimer’s” sells the mechanism by overstating what it did, and it sets up the same disappointment cycle the field has been through with every other anti-amyloid intervention that worked great in mice.

    Original paper, for anyone wanting the actual data: https://doi.org/10.1038/s41392-025-02426-1