R3/R4 project deep dives. Metrics only from the Aug 2026 resume. Full chapter: study book 07. Job 10454435 still none. Labels: Resume-derived IE-asked.
GiftedBooks GitHub currently describes AegisAI — resume wins. StratifyLabs GitHub is default Next.js. InstaRecon is not a project pitch. ConvBI as a brand name is the Ylogx SQL RAG intern work — do not add Warpflow / valAgent as Aug 2026 resume bullets unless they found the repo; then label GitHub in the first sentence.
Problem (who hurt) → architecture (boxes you shipped) → one hard part → one resume metric. Stop. Let them dive. “Walk me through your resume” is still IQVIA plus one number from Ylogx or Horizon.
Analysts faced more than two hundred websites. One scraper misses families of sites. I architected a LangGraph: planner, parallel Firecrawl / Bing / DuckDuckGo / Playwright, ranker on quality, recency, and agreement, then a writer. A failed scrape does not restart the run because state is checkpointed. Two high-ranked pages that contradict each other is the hard part — first-hit retrieval looks confident. I rank by agreement and refuse a span-less sentence. LangSmith shows empty scrapes still feeding the writer. Result: 200+ sites ranked. No extra percentage on the resume.
10x sites: more ranker, not more Playwright on every URL. Why not one Google API? Coverage gaps and rate limits.
Two-hundred-page BRDs. Stuffing the PDF invents citations. Vector-only misses clause IDs. I built Azure AI Search hybrid plus semantic, GraphDB hops, LangGraph adaptive retrieval, LangSmith evals, generated test cases. Lexical for R-141, semantic for policy language, graph for dependencies, cite or abstain. Hard part is test cases from the wrong section. A generated case with no overlapping span is a fail in LangSmith. I did not use Qdrant.
Why GraphDB? Requirements depend on each other; cosine does not hop. What would you do differently? Freeze an eval slice of BRD sections to expected tests before adding a fourth search tool.
Non-technical users could not pull analysis without an analyst. I shipped NestJS / Postgres / FastAPI, a LangChain SQL-RAG chatbot, three-tier RLS and RBAC in the database, Redis on the bot path, CloudFront, ECS, Docker, Route 53, ALB. SQL runs as the user role. App-only WHERE org_id fails on a JOIN. Result: +65% analysis productivity, −35% bot DB latency, 40% faster reports, 99.9% uptime, sub-210 ms, 30 KPI dashboards +60% ops.
NL → Redis (tier-scoped) → SQL RAG → Postgres as user role (RLS) Dashboards: API + cache, no LLM on the sub-210 ms path
10x users: cache and connection pooling and RLS tests, not “we shard” — I did not shard. Why NestJS? Modular DI for role modules. Java Live Code is a separate sentence.
Semi-autonomous Mars rover, ROS2, date fixed, dirt is not Gazebo. I shipped GStreamer 60 FPS, ZED 2 at 2M+ points/s into RViz/Gazebo, costmap fusion. Hard part: stale occupancy — operator feed looks healthy while the planner lies. We downsampled for viz, kept density for the local costmap. 17th / 80+, +40% obstacle detection, −55% collision risk. Gstreamer-UDP on GitHub is a supporting webcam artifact, not a second product. This is not Argus YOLO.
Industrial cameras, PPE and attendance. 73% mAP is not a number I will page a floor on. 15k+ images, YOLOv9 to 89% mAP, 24 FPS, Postgres event logs, 20+ cameras after the gate. Violations −50%, compliance 2×. Video stays off the database. I will not invent Haar cascades or a train/test split the resume does not name. argus-stream-api-server README 404 — no invented routes.
Why not two-stage Detectron? 24 FPS across 20+ feeds. Why not an LLM on the frame? Fluency is not mAP.
VR labs plus RAG on the student’s PDF. Doubts hours → 3–10 minutes. Sub-300 ms, 99.5% uptime, +35% reading, +50% engagement, 2.5× comprehension. PYQ topic ranker is analytics, not an LLM guessing the syllabus. Resume only — not the AegisAI README.
CV SaaS: 3D lab, browser inference, marketplace 50+ models, URDF/WebGL, Gemini RAG bots in the scene. ML iteration −30%. Think Big is reusable experiment, not a TAM I will invent. GitHub README is default Next.js — resume wins.
CUSAT CSE 8.42. IEDC Tech Team — raise the floor, no mentee count. Eight events; CodeRecet 1st; MLH Best Project; Magnathon 2.0 runner-up. Rapid ship is Bias for Action backup, not a fake architecture.