- Reduced deployment time by **66%** by [implementing ability](https://github.com/apache/incubator-kie-kogito-operator/commit/175a6356c5474f2360ccb8ae835e0b9b2d653cf1) to
using only command-line (**Kubernetes/GoLang** used for this and three below).
- Implemented ability for Kubernetes operator to fetch data
from a deployed service and update config with data to
deprecate reliance on startup script.
- Added startup probes to handle starting legacy application containers that require additional startup time.
- Refactored probes to [have default values](https://github.com/apache/incubator-kie-kogito-operator/commit/af4977af228ec8648be28779259d4552246b656f) assigned based on
deployed YAML while also fixing reconciliation issues.
- Rewrote the **Jenkins** nightly pipeline to run [in a GitHub
using a trigger keyword to test all submitted PR's.
- Took initiative to write [documentation](https://github.com/apache/incubator-kie-kogito-operator/blob/1534c03d1d26bec08a16608a775782bf8b305de9/docs/GUIDE_FOR_KOGITO_DEVS.md) on how to get started with the project to onboard new
developers and mentored the incoming intern.
- **Reduced deployment time by 66%** by implementing a
[solution](https://github.com/apache/incubator-kie-kogito-operator/commit/175a6356c5474f2360ccb8ae835e0b9b2d653cf1) for deploying locally-compiled binaries onto
Kubernetes/OpenShift via command-line, **cutting average
deployment times from 45 minutes to 15 minutes**.
(**Kubernetes/GoLang** used for this and three below).
- **Eliminated 80% of manual configuration errors** by enabling
the Kubernetes operator to automatically fetch data from
deployed services and update configurations, **deprecating
legacy startup scripts and reducing overall startup time
by 40%**.
- **Improved application stability** by introducing startup
probes for legacy applications with longer boot times,
**resulting in a 50% reduction in startup-related failures
and downtime during production launches**.
- **Enhanced system reliability** by refactoring probes to
[assign default values](https://github.com/apache/incubator-kie-kogito-operator/commit/af4977af228ec8648be28779259d4552246b656f) dynamically based on deployed YAML
files and fixing reconciliation issues, **increasing probe
accuracy by 30%** and preventing misconfigurations.
- **Increased CI pipeline efficiency** by rewriting the
**Jenkins (Groovy)** [nightly pipeline](https://github.com/apache/incubator-kie-kogito-pipelines/commit/4c83f1aecdea2c1ba2796b79839a90d4083dce88) to run in a GitHub PR
environment, allowing for automated testing of all
team-submitted PRs prior to merging, **reducing manual
intervention by 60%**.
- **Demonstrated leadership and collaboration** by actively
contributing to **Agile** sprint planning in a 12-member team,
driving improvement in sprint velocity through
optimized task delegation and idea generation.
- **Increased project reproducibility** by taking initiative to
write a [reusable GitHub parameters file](https://github.com/apache/incubator-kie-kogito-pipelines/commit/4c83f1aecdea2c1ba2796b79839a90d4083dce88#diff-7d2c018dafbccec859077d19bf1ade53ec9c7649f235528ce89f5632b109f7e6) for the pipeline,
**enabling 100% reusability** and ensuring consistent pipeline
setups across different environments.
- **Streamlined developer onboarding** by authoring
comprehensive [project documentation](https://github.com/apache/incubator-kie-kogito-operator/blob/1534c03d1d26bec08a16608a775782bf8b305de9/docs/GUIDE_FOR_KOGITO_DEVS.md) and mentoring an
incoming intern, **reducing onboarding time by 50%** and
enhancing new team members' productivity within their
- Web app to give rarity rankings to NFT's within minutes of their metadata being revealed and check which are listed (based on rarity and price filters) on the OpenSea marketplace using their API.
- Reverse engineered the ranking algorithm to match the
leading rarity ranking site's rankings ([scraped](https://github.com/Kevin-Mok/rarity-surf/blob/django/rarity_check/project/scrape.py) using
Selenium) with a **discrepancy of <0.25%**.
- Used app to frontrun purchases of **top 5%** rarity NFT's
against competing buyers.
- Wrote **Django (Python)** [backend](https://github.com/Kevin-Mok/rarity-surf) to fetch metadata from IPFS, store rarity rankings in PostgreSQL and serve rarity data using GraphQL.
- Wrote **React** [frontend](https://github.com/Kevin-Mok/rarity-surf-frontend) with hooks to dynamically load rarity data. Styled with Tailwind.
- **Developed a full-stack web application** to generate rarity
rankings for NFT's integrated with OpenSea's API,
enabling users to **quickly identify rare NFT's** and check
their listing status, **improving market research efficiency by 80%**.
- **Reverse engineered a proprietary ranking algorithm** to
mirror the leading rarity ranking site’s results,
**achieving 99.75% accuracy** by
utilizing data scraping techniques [with Selenium](https://github.com/Kevin-Mok/rarity-surf/blob/django/rarity_check/project/scrape.py),
increasing the platform's trustworthiness among users.
- **Optimized purchasing strategy** by leveraging the app to
frontrun competitors in purchasing top 0.5% rarity NFTs,
**boosting acquisition success rate by 90%** and allowing
users to gain a competitive edge in the marketplace.
- **Architected a robust Django (Python) [backend](https://github.com/Kevin-Mok/rarity-surf)** to fetch and process
NFT metadata from IPFS, store rarity rankings in
**PostgreSQL**, and serve the data via GraphQL API, **ensuring low-latency access and scaling to handle 2,000+ concurrent requests**.
- **Developed a dynamic React [frontend](https://github.com/Kevin-Mok/rarity-surf-frontend)** using hooks to load
rarity data in real-time, styled with Tailwind for
mobile responsiveness, **improving user experience
