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@wxwaim
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@wxwaim
@wxwaim
One year. Clean slate. Building • learning • travelling. Income: 0 → Direction: Loading
Katılım Şubat 2026
37 Takip Edilen4 Takipçiler

@PeterMmuo @joni_vrbt Interesting, what would you threads for over X and IG?
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@joni_vrbt It’s actually gaining a lot of traction
I’ve been somewhat active on threads for a few weeks now
It’s a different world there
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@wxwaim Love Flutter when:
- don’t have heavy reliance on native apis. Pub packages help, but due diligence to see if they cover your need.
- have your own design language (it won’t feel “native” on iOS specifically).
Vibe wise, don’t know. I’d pick it simply because I know it well.
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@stkuwanda Is this to grow your flutter experience/knowledge or do you find flutter the better framework?
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@Arif_AbdulAwal @dart_lang @FlutterDev Appreciate that, will join as well.
Is there a resource that helps a more react js inclined dev better understand flutter and its best practices?
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@wxwaim @dart_lang I have been building with Flutter for 4 years.
So, if you share the problems you are facing, I will be able to guide you.
@FlutterDev community is really helpful.
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@Arif_AbdulAwal @dart_lang I am finding there is quite a bit of difference in structure between the 2.
Any good resources/references you found to be helpful when developing with flutter that you could share?
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@wxwaim I tried RN too, But it's hard for me to build with RN.
@dart_lang became my mother tongue over the years
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@BrinkkieRob Quite new to this, how is this different to just utilising flutter’s library? Would it be a different compiler?
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@EntrepGrow Was more if that’s your USP that you offer what others offer but in your free tier?
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@EntrepGrow But others offer this as well right or is it without these features for your free tier?
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@EntrepGrow Nice, there are quite a few solutions out there for this already. What makes yours different to the competition?
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@EntrepGrow Have some SaaS products in the works at the moment.
Will share more as I go. How about you?
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@wxwaim retweetledi

I'm rebuilding AlphaFold2 from scratch in pure PyTorch.
No frameworks on top of PyTorch. No copy-paste from DeepMind's repo. Just nn.Linear, einsum, and the 60-page supplementary paper.
The project is called minAlphaFold2, inspired by Karpathy's minGPT. The idea is simple: AlphaFold2 is one of the most important neural networks ever built, and there should be a version of it that a single person can sit down and read end-to-end in an afternoon.
Where it stands today:
- ~3,500 lines across 9 modules
- Full forward pass works: input embedding → Evoformer → Structure Module → all-atom 3D coordinates
- Every loss function from the paper (FAPE, torsion angles, pLDDT, distogram, structural violations)
- Recycling, templates, extra MSA stack, ensemble averaging — all implemented
- 50 tests passing
- Every module maps 1-to-1 to a numbered algorithm in the AF2 supplement
The Structure Module was the most satisfying part to build. Invariant Point Attention is genuinely beautiful — it does attention in 3D space using local reference frames so the whole thing is SE(3)-equivariant, and the math fits in about 150 lines of PyTorch.
What's next:
- Build the data pipeline (PDB structures + MSA features)
- Write the training loop
- Train on a small set of proteins and see what happens
The repo is public. If you've ever wanted to understand how AlphaFold2 actually works at the level of individual tensor operations, this is meant for you.
Repo: github.com/ChrisHayduk/mi…

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