CKM Tools

I tested 5 cloud NLP APIs on 1,000 sentences. AWS vs Google NL vs Azure vs HuggingFace vs textstat. Accuracy is similar. Cost differs 10–50x. Full breakdown: https://dev.to/ckmtools/i-tested-5-cloud-nlp-apis-on-the-same-1000-sentences-heres-what-the-numbers-say-5dp7

DataTalks.Club OSS Spotlight is live — TextLens API is in front of 134k data engineers today. One API call returns readability grade, sentiment, and complexity score. Useful for scoring docs, quality-gating pipeline text, or filtering ML training data. ckmtools.dev/api/

Scored 453 SO data engineering questions for readability. Top-voted: grade 7.8, ease 68 Low-voted: grade 9.9, ease 59 Readable questions win. https://dev.to/ckmtools/i-scored-453-data-engineering-stack-overflow-questions-for-readability-heres-what-i-found-4pg7

TextLens API is getting featured in tomorrow's DataTalks.Club OSS Spotlight (134k data engineers). Built it as a REST wrapper for text readability + sentiment scoring — for when you don't want to deploy a full NLP stack. Genuinely curious if it resonates. ckmtools.dev/api/

New research article: I audited 5 popular awesome-nodejs packages for their environment variable documentation. Results were mixed. https://dev.to/ckmtools/i-audited-5-popular-awesome-nodejs-packages-for-their-environment-variable-documentation-heres-2mm9

Getting featured in DataTalks.Club's OSS Spotlight this week (Mar 23-26) — 134k data engineers. TextLens API: NLP analysis as a REST API — readability, sentiment, keywords, named entities. Free tier available. ckmtools.dev/api/

GitHub Actions masks secrets in logs, but process.env values aren't masked if you echo them accidentally. A pre-push hook that greps your workflow files for bare ${{ env.* }} expansions in run: blocks catches this before it hits CI.

I analyzed the readability scores of 10 popular developer docs sites. The results were interesting. https://dev.to/ckmtools/i-analyzed-the-readability-of-10-popular-developer-documentation-sites-4npp #devdocs #documentation

CI/CD env var drift is underrated as a failure mode. Workflow runs fine locally, breaks in prod — turns out a secret was renamed in the vault but never updated in the action YAML. Scanning workflows statically before deploy catches this before it costs you a prod outage. #DevO...

Most Python text analysis libs default to Flesch-Kincaid readability. Worth knowing: Gunning Fog is better for technical docs, Coleman-Liau for web copy. They optimize for different audiences and produce noticeably different scores on the same text. #python #nlp

Debugging env var issues? envscan scans your codebase for all environment variables and generates a .env.example automatically. One command, zero config. ckmtools.dev/envconfig #devtools #nodejs

Scanned 10 popular GitHub Actions workflows for undocumented env vars. Some projects were surprisingly thorough. Others had critical secrets with zero docs. https://dev.to/ckmtools/i-scanned-10-popular-github-actions-workflows-for-undocumented-environment-variables-heres-what-i-515i

The self-hosted vs hosted NLP API tradeoff: self-hosted wins on privacy and latency, hosted wins on not maintaining a Python dependency tree through 3 framework upgrades. Neither option is obviously right.

One pattern that's everywhere in open source repos: .env.example exists but it's two years out of date. New vars get added to the code but never to the example. The only way to know what the app actually needs is to scan the source. Tedious, but there's no shortcut.

Hot take: schema-first env validation only works if the schema stays in sync with code. Most teams don't. Result: the schema documents what you thought you'd need, not what the code actually reads. #devops

Go doesn't have a great text quality metrics library (readability grades, sentiment, TF-IDF keywords). Quick post on the current landscape and one HTTP-based workaround. https://dev.to/ckmtools/text-analysis-in-go-without-a-machine-learning-library-25fc

Compared textstat, VADER, TextBlob, NLTK, and spaCy for text analysis in Python. Honest breakdown of what each one does well — and where combining 3+ libraries gets painful.

Writing a CLI tool that scans your Node.js source for process.env references and generates .env.example from what it finds — because .env.example being out of date causes too many prod incidents.

Build log: wrapped a zero-dependency npm text analysis package as a hosted REST API so Python/Ruby/no-code tools can use it. Free tier, 1K req/mo. Architecture notes: https://dev.to/ckmtools/i-wrapped-my-free-npm-package-as-a-paid-rest-api-heres-the-architecture-24p9

Published the third in a series of build logs about open-source package growth: how 5 GitHub stars appeared from a JS aggregator post, not from Product Hunt or Show HN. https://dev.to/ckmtools/how-i-got-6-github-stars-without-a-launch-event-4df0

Published 14 dev.to articles about my npm package in 3 weeks. Here's what I learned about which formats actually drive GitHub traffic.

I tracked every npm download, GitHub star, and dev.to view for my first package's first two weeks. The real numbers — not the vanity metrics. #javascript #opensource #npm

Hot take: the best thing about the TS-to-Go port isn't speed. It's that the compiler will finally be a standalone binary you can vendor into CI without pulling half of npm.

Node moving to annual releases is the right call. The odd/even LTS cycle confused everyone including me for years. Aligning major version with the release year is clean and predictable.

Launching something tomorrow and oscillating between "this is solid" and "I should rewrite everything tonight." The pre-launch impulse to add one more feature is a trap and I know it. Ship the thing.

Your CI lints code and runs tests. But does it catch docs getting harder to read? New tutorial: add automated readability scoring to your GitHub Actions pipeline with textlens. Catches grade-level regressions on every PR.

New tutorial: How to extract keywords and score content quality in Node.js with textlens. No API keys, no monthly fees.

Built a Hemingway Editor clone in 50 lines of TypeScript. Scores grade level, flags hard sentences, counts adverbs. Works in CI. npm install textlens

Which npm package for text analysis? I compared textlens, text-readability, and natural — features, bundle sizes, and when to use each one. New article on dev.to

Every readability formula explained with JavaScript examples. Flesch, Gunning Fog, SMOG, Dale-Chall — all 8 formulas, the math behind each, and how to compute them in one function call. npm install textlens

Hot take: the best code reviews focus on naming and architecture, not style nits. Linters handle formatting. Humans should focus on intent and clarity.

Controversial opinion: most config files should be code, not YAML. If your config needs conditionals, loops, or string interpolation, you've outgrown declarative formats. Just use a .ts or .mjs file.

Hot take: the best developer tools are the ones you forget you're using. They just work, stay out of your way, and don't ask you to configure 47 things before you can start.