A curated collection of development tools, libraries, services, and learning resources I use, like, or want to explore.
- DBA → Databases, Data Storage, Catalogs & Lineage, Secrets Management
- Integrations → Ingestion (ETL/ELT), APIs, Webscraping, Automation
- Analytics / Reporting → Visualization, Notebooks, Statistics & Data Manipulation, Geospatial & Enrichment, Machine Learning
Curated reading lists I maintain on Medium:
Regular rotation — a mix of AI, security, and Python:
| Podcast | Cadence | Length | Focus |
|---|---|---|---|
| The AI Daily Brief | Daily | ~15 min | AI news |
| NVIDIA AI Podcast | Weekly | ~30 min | AI applications |
| Cybersecurity Headlines | Daily | ~5 min | Security news |
| Python Bytes | Weekly | ~30 min | Python ecosystem |
| Darknet Diaries | Monthly | 1–2 hr | Hacking / attack stories |
- Modern
- FaunaDB
- SingleStore
- Apache Druid — competitor to SingleStore
- RethinkDB — live DB
- Firebase
- DynamoDB
- Decentralized cloud storage
- Storj — interesting for future encrypted messaging use cases
- Data reading/writing (GCP, Azure, AWS)
- Flatfile — data onboarding platform
- Fivetran — cloud data integration
- Matillion — cloud data integration
- Apache Gobblin — open-source distributed data integration
- Singer — open-source standard for data movement scripts
- Meltano — open-source ELT for DataOps
- Airbyte — open-source data integration
- Stitch — simple, extensible cloud ETL (Talend)
- Hevo — no-code data pipeline
- Apache Hop — open-source data integration
- Meroxa — real-time data ingestion
- Portable — cloud-hosted ELT
- Others: Talend, StreamSets, Alooma (Google), Xplenty, Striim, Panoply, Stambia, HVR
- dbt — transformations
- Apache Beam
- Pachyderm
- Dataframe.ai
- WHALE — search-like data tool
- Superset
- Streamlit
- Hex
- Deepnote
- Polynote (Netflix)
- best-of-jupyter — collection
- Summary statistics
- Sidetable — pandas sidecar
- Test selection
- Dates
- Time series
- General
- Pingouin — stats
- Repeated Measures (Rizopoulos)
- Handbook of Parametric and Nonparametric Statistical Procedures, 5th Edition
- Data checks / schema / types
- Libraries & toolkits
- Kepler.gl — geocoding / visualization
- MovingPandas
- GeoPandas
- geopy
- geog
- python-geospatial — collection
- 22 Python libraries for geospatial data analysis
- Clustering geospatial data — example
- Using SingleStore as a geospatial DB
- Geographic enrichment by ZIP code
- ZIP / ZCTA / judicial crosswalk for ACS
- ZIP-to-FIPS crosswalk (HUD) — select
ZIP-COUNT - Census Reporter
- Census API — demographics, economics, families, housing, social, health insurance, poverty/SNAP
- Historical data
- Visual Crossing — historical weather
- Covid Act Now API — historical COVID by state / FIP
- Health & disparities
- Food environments
- Food retail stores
- Regulated agencies
- TTB — wine, alcohol, fuel, guns, etc.
- DC liquor licenses
- API directories
- Public API search
- listt.xyz
- m3o.com
- Nylas — communication-focused
- Payments
- Security
- TypingDNA — typing biometric
- Scheduling
- Speech / NLP
- Food
- News
- Signatures
- Airflow
- See also: Open-source Airflow alternatives
- ngrok — quick localhost tunneling
- TensorFlow (Google)
- PyTorch (Meta)
- spaCy
- NER annotation example
- Language translation
- Science journals
- DALL·E 2 (PyTorch) — text-to-image
- Awesome Text-to-Image (free)
- AugLy (Meta) — image augmentation
- FaceSynthetics (Microsoft) — synthetic faces
- face_recognition
- Norfair — real-time tracking
- DeepSORT walkthrough
- Digital cloning
- Microsoft Recommenders — best practices
- Surprise
- Collaborative
- Competitions
- Notebooks
- Libraries
- Hugging Face
- BlobCity Cloud
- Homemade ML
- Model Zoo
- Pretrained model collections: Audio, NLP, CV
- Awesome production ML
- awesome-jupyter
- best-of-jupyter
- DL Colab notebooks — fakes / audio / video / pose
- Starter notebooks
- awesome-notebooks — GoogleSheets, Airtable, Sendgrid, Slack, etc.
- Homemade ML
- LazyProgrammer ML examples
- Susan Li — ML with Python
- TensorFlow examples
- PyTorch examples
- AWS SageMaker examples
- Pulumi
- Terraform tooling
- Infracost
- Brainboard — auto-generate Terraform
- Checkov — config error scanning
- Ansible
- My own (based on Pulumi + AWS)
- OpenStack
- CloudStack (Apache)
- OpenNebula
- Helm
- Knative
- Kubeflow
- Crossplane
- Chaos engineering
- Oracle — Always Free
- Vercel — has always-free tier
- Heroku
- Scout Suite
- Doppler
- Vault
- Metasploit (Rapid7)
- Metasploitable3 — vulnerable VM
- Onion Browser
- Python-based
- Non-Python
Next.js
- Enterprise: nextlessjs.com
- Free (same author): Next-js-Boilerplate
Codebase Generators
- Divjoy — pick backend / frontend / deployment
Paid
- Reactapp — $19 lifetime
- SaaS Rock — $149 lifetime
- Serverless.page — $199 lifetime
- Bedrock — $396 per project
- Nextless — $699 per project
- Rocketapp
- Gravity — docs
- ShipSaaS
Free
My evaluation notes (Free options)
- Nextacular
- Billing: Stripe
- Documentation: limited / WIP — docs
- Deployment: Vercel (auto SSL)
- Databases: relational only (SQL / PostgreSQL / Aurora)
- Pros: multi-domain; relational DB; teams + workspaces; Stripe; Tailwind; email handling
- SaaS Starter Kit
- Billing: Stripe
- Documentation: good / mostly fleshed out — docs
- Deployment: on your own
- Databases: relational (Postgres) + non-relational (MongoDB)
- Pros: ML example built in; onboarding; Docker; Stripe; AWS APIs
- Backend
- Supabase
- Parse
- Appwrite
- Nhost
- Hasura
- PocketBase
- Frontend
- AppGyver
- Fonts / styles
- UX examples
- Docusaurus
- Docz
- API documentation
- Meilisearch
- Typesense
- Deephaven
- Elasticsearch overview
- UUIDs
- Linux cheatsheets
- Airflow
- Calendly
- Blogs
- Chubby Developer
- Medium
- Podcasts
- ByteByteGo — system design archive
- Software architecture patterns (5 min read)
- SSH tunneling explained
- Stanford ML systems (CS329S)
- Intro to ML with scikit-learn (Data School)
- Visual intro to ML (R2D3)
- Fun ML example — diapers and beer
- Non-technical guide to SHAP / Explainable AI
- ML cheatsheet
- Approaching (Almost) Any ML Problem — Kaggle
- MIT 15.003 — Data Science Tools
- Interpretable ML book
- Resource of resources
- Kirill Eremenko (Udemy)
- SQL Fiddle — playground for queries
- SQL Bolt — interactive tutorial for beginners
- Select Star SQL — interactive tutorial
- SQL Murder Mystery — intermediate/advanced
- SQL Indexing for Devs
- SQL Zoo
- SQL Tutorial for Data Analysis
- Other
- Databases & SQL for DS (IBM, Coursera)
- Learn SQL Basics for DS (Coursera)
- SQL Cookbook — O'Reilly
- SQL 57 Practice Problems — Sylvia Vasilik
- SQL for Data Analytics — Packt
- Official / standard
- Official Python tutorial
- Pandas Tutor
- milaan9 — Jupyter learning notebooks —
02_python_datatypes,04_python_dictionaries
- Stanford
- Harvard
- UCSF
- ECU
- Duke
- Emertxe
- Bootcamps & books
- Powerful Python bootcamp
- 2022 Complete Python Bootcamp (Udemy)
- Complete Python Developer (Udemy)
- Python Crash Course (2nd ed.) — Eric Matthews
- Python Cookbook — O'Reilly
- Elements of Programming Interviews in Python — Adnan & Amit
- Intro to Statistics (Udacity)
- Intro to Inferential Statistics (Udacity)
- Statistics & Probability (Khan Academy)
- Statistics in Plain English — Timothy C. Urdan
- Head First Statistics — Dawn Griffiths
- ISLR (Introduction to Statistical Learning)
- ESLR (Elements of Statistical Learning)
- Ashington — Medium blog