Cool Techs (2022 Q1)

 

Last Q4 and Q1 learnt about Kotlin Android development.

Tech to learn now Q2:

- Hashicorp Nomad

- Github Actions

- Hashicorp Consul

- Hashicorp Vault

- GCP Artifact Repository

- GCP in genreral 

- Next.js 

- GRPC service

- Best Practice create an API

Collection of C++ Libs / Service that is so cool (2021)

https://github.com/scylladb/seastar

https://github.com/spotify/annoy

https://github.com/luceneplusplus/LucenePlusPlus

https://github.com/pytorch/pytorch

https://github.com/vectorizedio/redpanda

https://github.com/google/filament

https://github.com/IntelRealSense/librealsense

Learning (again)

 - C++ to solve UVA problem (step by step)

- Learn C++ library Seastar (used by Redpanda and Scylladb)

- Learn Kotlin to create Android UI

So many things to learn

Library I'm Interested to Learn

 

Interesting C++ library

1. Google Filament (https://github.com/google/filament)

2. PyTorch (https://github.com/pytorch/pytorch)

3. JTS (https://github.com/locationtech/jts)

4. FAISS (https://github.com/facebookresearch/faiss)

5. Open 3D (https://github.com/intel-isl/Open3D)

6. Librealsense (https://github.com/IntelRealSense/librealsense)

Others (non C++) 

1. TopoJSON (https://github.com/topojson/topojson)

Books to read 

Large-Scale C++ Volume I: Process and Architecture

Re learning Dynamic Programming

 

Solving dynamic programming problems (Cormen et al)

1. Characterize the structure of an optimal solution

2. Recursively define the value of an optimal solution

3. Compute the value of an optimal solution, typically in a bottom up version

4. Construct an optimal solution from computed information 

exhibits optimal substructure, optimal solutions to a problem incorporate optimal solutions to related subproblems, which we may solve independently

A dynamic-programming approach runs in polynomial time when the number of distinct subproblems involved is polynomial in the input size and we can solve each such subproblem in polynomial time

There are usually two equivalent ways to implement a dynamic-programming approach. The first approach is top-down with memoization. The second approach is the bottom-up method.

Installing CUDA

 After installing cuda_11.3.1_465.89_win10.

To integrate with VisualStudio 2019. Copy the files in image 1 to image 2 location. As suggested in this forum. 

Copy it to here:

Then after that can open the CUDA samples and run the examples!

Hi from 2021

 

Hi it seems that I've last posted in this blog six year ago.

Within that time, so many things happening. New interest, new technologies happening. 

I've graduated from Uni as bachelor of science, working as Solutions Engineer in a vendor, as Software Engineer in startup, and my current job  as Software Engineer also in my current occupation.

Working as a software engineer is basically a dream comes true for me. So I'll focus on building my skills and capacity as one. Hopefully I can start to continue sharing my engineering journey here again in this blog!

Regards!