For those who've had a Civil Service interview recently, how did you prepare?

I've been reading through people's experiences of Civil Service recruitment and one thing keeps coming up: knowing the STAR framework and having good examples doesn't necessarily mean you'll perform well in the actual interview.

It made me curious about what people actually do in the weeks between getting an interview invitation and sitting in front of the panel.

Do you prepare a fixed set of examples and try to adapt them to whatever you're asked?

Do you practise answering completely random behaviour questions?

Do you get someone to play the interviewer?

Do you practise strengths questions differently?

Or do you mostly prepare mentally and then go into the interview?

I'm also curious about what happens when the panel asks a follow-up question you weren't expecting. That's the part that seems particularly difficult to practise on your own.

For people who've had both successful and unsuccessful Civil Service interviews, what changed in the way you prepared?

Would be really interested in hearing what people actually did, rather than what the official guidance says candidates should do.

reddit.com
u/strange1807 — 11 days ago

Nepal ma AI ko development kasto cha?

Lately, I’ve been trying to understand Nepal ma AI ko actual scene kasto cha beyond the usual hype around ChatGPT and generative AI.

I see a lot of developers and students using AI tools, people building small projects, and some companies experimenting with AI. Tara, kati ko development actually Nepal mai bhairako cha? Are we mostly using AI technologies developed by others, or are we also building something that could have an impact beyond Nepal?

For example, Nepal ma interesting AI startups, research projects, open-source projects, or companies haru cha bhane, I’d love to know about them.

Also curious about people working in this space:

What do you think is the biggest gap right now?
Talent? Research? Funding? Data? Infrastructure? Or simply lack of opportunities?

Would be really interesting to hear what people here have actually seen or experienced. Maybe there’s a lot happening that I’m simply not aware of.

reddit.com
u/strange1807 — 2 months ago
▲ 5 r/softwarearchitecture+1 crossposts

Building an open-source AI video dubbing tool — feedback on the architecture wanted

Problem: Even AI dubbing today costs $0.5–$2.5/min (ElevenLabs, HeyGen, Rask AI) — adds up fast for dubbing regularly, and still puts real reach out of budget.

What I'm trying to build: A self-hosted, open-source tool where you drop in a video file or a YouTube link, pick a target language, and get back a dubbed video with the new audio timed to match the original. Bring your own OpenAI API key — no subscription, no markup, so cost stays close to raw API price (rough estimate: $0.08–$0.13 for a 5-min video).

Pipeline (the diagram):

Video Downloader — takes a file upload or YouTube URL (auto-fetches existing captions if the YouTube video has them, so transcription can be skipped later)

Audio Extractor — pulls the audio track out of the video

Audio Transcribe — Whisper API for speech-to-text with timestamps (or the captions if already available — cheaper and usually more accurate)

Audio Translate — GPT translates each transcript segment into the target language

Audio Synthesis — TTS generates speech for the translated segments

Audio Alignment & Stitching — stretches/compresses each synthesized clip to fit its original timing window, then stitches into one continuous track

Remux — swaps the new audio track into the original video, final MP4 out

Deliberate scope cuts for v1: single speaker only, no lip-sync, no voice cloning (generic TTS voice for now), original background music/SFX gets replaced rather than preserved, one target language per run, no auth (local self-host only for now — hosted version is a later phase).

Would love feedback on the pipeline shape — anything that looks like it'll bite me later (timing drift, error handling between stages, etc.)?

GitHub: https://github.com/prashantrajbista/voice-dubbing

u/strange1807 — 2 months ago
▲ 4 r/technepal+3 crossposts

I built a tool that turns a prompt into a live, architecture diagram

Been prototyping systems a lot lately and got tired of hand-drawing boxes in draw.io every time. Built a small tool: you type a system description, it streams out a real architecture diagram node-by-node as the LLM generates it (real cloud service icons, not generic shapes)

Still rough — no auth/persistence/multi-user yet, this is a prototyping tool not a product. Happy to share more or open it up if people are interested.

u/strange1807 — 3 months ago

How much time do you actually waste re-keying submissions?

Not in insurance, I'm a developer poking at an idea and would rather hear from people who do the work than guess.

The thing I keep hearing is that a big chunk of the day goes to boring repetitive stuff — typing the same ACORD data into your AMS and then again into carrier portals, wrangling loss runs that every carrier formats differently, chasing renewals in a spreadsheet, cranking out COIs.

Is that real, or overblown? What's the one task in your week that feels like it takes way longer than it should? And has anything you've tried to fix it actually worked?

Not selling anything, no link, no product. Just trying to make sure the problem is real before I build for it. Honest answers welcome, including "this already exists, go use X."

reddit.com
u/strange1807 — 3 months ago

What publicly available recurring data source do you repeatedly search for that still doesn’t exist in clean structured format?

I’m researching gaps in publicly available recurring data that people regularly need for analytics, ML, automation, monitoring, or business workflows.

I’m especially interested in data that is technically public but still difficult to use because it is:

  • trapped in PDFs
  • scattered across websites
  • updated inconsistently
  • available only through dashboards
  • difficult to scrape
  • missing historical archives
  • lacking APIs
  • poorly standardized

Examples could include:

  • government notices
  • procurement/tender data
  • financial filings
  • real-estate listings
  • agriculture pricing
  • shipping/logistics updates
  • business registries
  • market prices
  • legal/regulatory documents
  • municipality/city data
reddit.com
u/strange1807 — 5 months ago

What publicly available recurring data source do you repeatedly search for that still doesn’t exist in clean structured format?

I’m researching gaps in publicly available recurring data that people regularly need for analytics, ML, automation, monitoring, or business workflows.

I’m especially interested in data that is technically public but still difficult to use because it is:

  • trapped in PDFs
  • scattered across websites
  • updated inconsistently
  • available only through dashboards
  • difficult to scrape
  • missing historical archives
  • lacking APIs
  • poorly standardized

Examples could include:

  • government notices
  • procurement/tender data
  • financial filings
  • real-estate listings
  • agriculture pricing
  • shipping/logistics updates
  • business registries
  • market prices
  • legal/regulatory documents
  • municipality/city data
reddit.com
u/strange1807 — 5 months ago

What data you struggle finding?

I was wondering what data tech people in Nepal….are in constant need of and are hard to find. Let’s say for the task of analytics or machine learning..or other task

reddit.com
u/strange1807 — 5 months ago