piero @sharped_net

Student of Computer Science GitHub.com/PieroCastillo Lima, Peru Joined January 2017

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piero @sharped_net

5 hours ago

ELEMENTOS FINITOS GO BRRRRRR (esta terriblemente programado y falta optimizar mucho, pero para speedrun en menos de 1 semana esta gozu)

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Ese tipejo hace quedar mal a mi universidad, es arrogante, sus "proyectos" son vibecodeados, no sabe de lo que habla, en la misma uni es un hazmerreir con susposts; es un farsante que nunca debieron darle poder y al igual que Chau solo se aprovecha del nombre de la UNI

AnaCeci @TuVozMiPoder

23 hours ago

Puedes tener MIT, becas y 200 proyectos en el CV. Pero ningún título te da lo que aparentemente te faltó aprender en casa: respeto, principios y decencia. El currículum "impresiona" ; la conducta retrata. @jairahora27

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Vivek Galatage @vivekgalatage

a day ago

The one guide you need alongside your programming assignments - all the time. agner.org/optimize/optim…

Vivek Galatage @vivekgalatage

2 days ago

Talking about performance, this Cppcon talk by Marcos Slomp is quite insightful delving into the Tracy profiler. Working with Chromium and its built-in tooling - with the ability to capture performance traces in production - has spoilt me, in a good way. youtu.be/ghXk3Bk5F2U

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80 LEVEL @80Level

3 days ago

Researcher Rafael Padilla explains how Hierarchical Progressive Rendering uses scene changes and light transport to prioritize the pixels that matter most for path tracing. "Path tracing can produce incredibly high-quality images, but when you make a small change to a scene, the renderer often has to effectively start over. I became interested in whether we could make the renderer smarter about where it spends its time. That is what led to HiPR, or Hierarchical Progressive Rendering" Full interview: 80.lv/articles/how-h…

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DANGER @danger_dannn

2 days ago

University AU. #gachiakuta #riyo #gachiakutafanart

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piero @sharped_net

2 days ago

CAD-FEM SPEEDRUN

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Carles Sáez @csaez_math

4 days ago

In case anyone still doubt it, this is the confimation that everytthing in Tristan's statement is true. Sad day for mathematics

OpenAI @OpenAI

4 days ago

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem

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piero @sharped_net

2 weeks ago

no lo culpo la vrd, sería más chevere programar web si hubiera tecnologías hechas para este desde un primer momento, y no el parche-tras-parche que existe ahora, todo un frankenstein

Bhavya @Bhavyaztwt

2 weeks ago

POV: you hated JavaScript so much you decided to rebuild the internet in Rust

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Xor @XorDev

2 weeks ago

One year ago, I published "Dot Noise" which is a faster alternative to simplex noise. Try using it!

Xor @XorDev

a year ago

"Dot Noise". This is a super lightweight alternative to 3D Simplex Noise, which in many cases is overkill shadertoy.com/view/wfsyRX

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piero @sharped_net

2 weeks ago

@Stringcodes typescript

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piero @sharped_net

2 weeks ago

@SebAaltonen vulkan++

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piero @sharped_net

2 weeks ago

@jakubtomsu_ github repo?

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灰狐 @huihoo

2 weeks ago

一个有着超过 30 年 C++ 开发经验的程序员：Julian Storer 开发了三个优秀的开源项目和产品： Tracktion DAW, JUCE C++ Framework, Cmajor DSP language 对音频、音乐、DSP、DAW、C++ 感兴趣的朋友可关注 github.com/julianstorer

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jendrik illner @jendrikillner

2 weeks ago

Graphics Programming weekly - Issue 452 - August 23rd, 2026 jendrikillner.com/post/graphics-…

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Vlad Erium 🇯🇵 @ssh4net

2 weeks ago

FlashNormal: Detailed Surface Normal Estimation from Flash and No-Flash Images Ruiyang Chen, Feiran Li, Heng Guo, Zhanyu Ma arxiv.org/abs/2608.25360 Abstract: High-quality surface normal estimation is preferred for detailed surface shape recovery and image editing. Existing single image-based methods, though being a practical setup, often struggle to recover fine surface details and are sensitive to inherent shape-reflectance ambiguity. While photometric stereo achieves high-fidelity surface normal estimation from images under varying lights, its applicability is strictly limited by requiring a multi-illumination capture setup. To this end, we propose FlashNormal, a diffusion-based surface normal estimator from flash/no-flash image pairs. While retaining high practicability on modern smartphones, our proposal takes advantage of flash-induced shading variations, and leverages curvature-guided detail enhancement strategy, improving surface detail recovery and mitigating shape-reflectance ambiguity effectively. To evaluate our proposed method, we further present EvalFlash, the first real-world flash/no-flash evaluation dataset containing 20 objects aligned with ground-truth surface normals for quantitative benchmarking. Extensive experiments demonstrate the effectiveness of FlashNormal over state-of-the-art single image-based methods and show a significant out-performance over flash/no-flash-based normal estimation method on EvalFlash.