Freelancers using Sentry in Jakarta

Freelancers using Sentry in JakartaFreelancers using Sentry in Jakarta

Projects People

Kelvin Desman

Jakarta, Indonesia

Mind of an engineer and the heart of an entrepreneur.

$50k+: Earned

1x: Hired

11: Followers

Mind of an engineer and the heart of an entrepreneur.

LokerDollar.com

Cover image for I didn't believe the TypeScript

I didn't believe the TypeScript 7 speed claims. So I benchmarked it on my own production app — not a toy repo. 1,358 source files. 260K+ lines. The live codebase behind lokerdollar.com (http://lokerdollar.com). One change: tsc → tsgo (TS7, rewritten in Go). Same tsconfig, same tree, --noEmit. Cold type-check: 77.1s → 11.6s. 6.6× faster. −85% wall-clock. Then the part most "10× faster" posts skip — I checked whether it still catches bugs. Seeded 4 classic type errors. Byte-identical diagnostics: same codes, same line and column. The speed isn't bought by missing errors. But I'm not putting it in your CI yet — and here's the honest reason: — It's a release candidate — The native binary drops the JS typescript API, so typescript-eslint type-aware rules, ts-morph, ts-node and ts-jest break — Custom transformers and incremental builds are still maturing My call: run it now as a fast local / pre-PR check, and keep tsc as the authoritative CI gate until 7.0 ships stable. You get a 6.6× tighter local loop with zero risk to the pipeline. The ~$190/year in saved runner minutes is rounding error. The real return is nobody waiting on a turtle. #TypeScript #DevTools #WebDev

Cover image for For every feature I shipped

For every feature I shipped with an AI agent, I shipped more than two fixes. 1,000+ PRs. 84 days. Solo. The throughput was real — but 360 of those PRs were fixes against 150 features. That ratio is the part nobody puts in their recap post. The core problem: the agent is a 20× author, not a 20× reviewer. I had no leverage on verification — just guards built from past failures, useless against anything new. What I'd change: second agent for adversarial review only, and changes small enough that being wrong is cheap. Agentic engineering doesn't remove the hard part. It moves it.

Cover image for Optimize Remote Job Applications for Indonesian Developers w...

Built a RAG job-hunting agent for Indonesian developers applying to remote USD jobs. 28 days of real usage. Here's what I learned. The problem: applying to 50+ remote jobs manually is exhausting. Wrong roles, no resume tailoring, hours wasted per application. The workflow now: One message → agent retrieves relevant jobs → ranks them → generates tailored resume + cover letter → user approves or skips. Stack (still running $0 free tier): · Embeddings: Cloudflare Workers AI qwen3-embedding-0.6b · Vector search: Cloudflare Vectorize, cosine similarity · Database: Cloudflare D1 / SQLite, one batched query · Semantic cache: Cloudflare KV, cosine ≥0.92 threshold · LLM: JSON-mode slot extraction + reply Only variable cost: ~$0.002/turn LLM usage 28-day numbers: · 235 sessions · 329 return visits · 743 agent turns · 357 job cards surfaced → 46 approved applications ~20 approvals per 100 sessions (target was 10–15) Total LLM cost: ~$1–4 Example matches from one natural-language query: Linear — 96/100 · DatAds — 95/100 · Spotify — 100/100 Two decisions I'd make again: · The scorer is deterministic — pure function, 7 weighted components, 0–100. No model drift. Users see exactly why a job scored highly. I didn't trust LLM ranking for something this consequential. · There's a regex fallback behind every LLM extraction. If the model fails or times out, the conversation continues. Users never hit a dead end. What's next: · Tracking downstream outcomes — recruiter replies, interviews Detecting expired/duplicate posts more aggressively Calibrating scoring weights from real approval behavior · The pipeline runs cheaply and generates real approvals. The next challenge is proving it improves actual job outcomes — not just application volume. This is the kind of system I enjoy building: AI-native product on constrained infra, with real users and measurable north-star metrics. If you're working on something similar — hiring pipelines, talent matching, AI agents for job seekers — I'd enjoy comparing notes. And if you're an Indonesian dev hunting remote USD roles, the product is live at lokerdollar.com (http://lokerdollar.com). What would you build differently? 👇 #RAG (https://www.linkedin.com/search/results/all/?keywords=%23rag&origin=HASH_TAG_FROM_FEED) #CloudflareWorkers (https://www.linkedin.com/search/results/all/?keywords=%23cloudflareworkers&origin=HASH_TAG_FROM_FEED) #BuildInPublic (https://www.linkedin.com/search/results/all/?keywords=%23buildinpublic&origin=HASH_TAG_FROM_FEED) #AIEngineering (https://www.linkedin.com/search/results/all/?keywords=%23aiengineering&origin=HASH_TAG_FROM_FEED) #RemoteJobs (https://www.linkedin.com/search/results/all/?keywords=%23remotejobs&origin=HASH_TAG_FROM_FEED) #IndonesianDevelopers (https://www.linkedin.com/search/results/all/?keywords=%23indonesiandevelopers&origin=HASH_TAG_FROM_FEED)

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