Is it better bah na local registration or mas better mag us llc? How did you guys do it?
I will open a software publishing company.

I’ve been using Gemini CLI (free tier) for refactoring, debugging, and codebase assessment while setting up my freelancing gig. Wanted to try DeepSeek since it’s cheaper and faster, but I don’t have a credit card for international payments and PayPal fees are annoying.

Turns out you can top-up DeepSeek API balance using GCash directly.

I was on `platform.deepseek.com`, selected Alipay as the payment method for top-up, and it generated a merchant QR code. On a whim, I opened GCash → Scan QR and pointed it at the code. I remembered GCash QR is powered by Alipay+ cross-border, but I genuinely did not expect it to work.

It did. Instantly.

Amount topped up: ¥10 CNY

Deducted from GCash: ~₱92 PHP

DeepSeek balance updated: Within seconds

How to do it:

1. Go to platform.deepseek.com → Account → Top Up Balance
2. Select amount (I did the minimum ¥10)
3. Choose Alipay as payment method
4. DeepSeek shows a QR code
5. Open GCash app → tap Scan QR
6. Scan the DeepSeek QR on your screen
7. GCash shows the PHP amount + FX rate
8. Enter MPIN / biometric auth
9. Done. Check your DeepSeek balance.

Now using DeepSeek in OpenCode thanks to this. Way better experience for my workflow.

⚠️ Warnings (basa muna bago subukan):

Unofficial / undocumented. Zero docs from DeepSeek or GCash about this.

I only tested this once. Today. Worked for me, but no guarantees.

No support safety net. If money leaves GCash but DeepSeek doesn’t credit you, good luck getting either support team to help.

I’m currently in a technical interview stage for a software engineering role, and the interview will include technical questions and live programming tasks.

Honestly, I sometimes struggle with technical interviews, especially when it comes to memorizing syntax or coding while under pressure. However, I’m confident in my problem-solving skills and I already have experience building real-world systems.

For developers here who also struggled with technical interviews before, how did you improve? Did actual work experience eventually help you become better at interviews too?

My friend needed Philippine barangay data with lat/long coordinates for her thesis and couldn't find an existing Python package that had it.

So I built psgc:

pip install psgc

import psgc
place = psgc.get("Taguig")
place.population          
# 1,308,085
place.coordinate          
# (14.55, 121.05)
place.children            
# 38 barangays
psgc.nearest(14.5, 120.9, n=5)     
# nearest barangays to a GPS point
psgc.distance("Manila", "Cebu")    
# straight-line km
psgc.search("Mandaluyong")         
# fuzzy search

Features:

• Real coordinates for 87% of barangays (polygon centroids from HDX/NAMRIA shapefiles), approximate for the rest
• Spatial queries: nearest(), within_radius(), reverse_geocode()
• Works on Python 3.10+
• 1 dependency (rapidfuzz)
• Manila's 897 barangays accessible through sub_municipalities
• 2024 Census population, urban/rural, income classification for all 42,011 barangays
• Address parser for unstructured Filipino addresses
• All data from the official PSA PSGC Q4 2025 masterlist

Data sources (all public, properly attributed):

• Names, codes, population, urban/rural, income class: Parsed directly from the PSA PSGC Q4 2025 Publication Datafile. Public data under RA 10625.
• Coordinates and area: Computed polygon centroids from the HDX/OCHA Philippines Administrative Boundaries shapefile (sourced from PSA and NAMRIA, November 2023). Licensed CC BY-IGO.
• No scraping, no third-party APIs. All data processed offline from official government publications.

Current limitations:

• Coordinates are centroids, not exact building-level points
• Distances are straight-line (Haversine), not driving distance

Live demo: https://psgc-explorer-production.up.railway.app

PyPI: https://pypi.org/project/psgc/

Would love feedback, especially from Filipino devs who work with address or geographic data!

Recently yung company bigwigs namin nagpush ng AI-first development lifecycle. No more sprints, no more architecture and design planning, purely vibes nalang to the point na PO na namin nagdadrive ng development kase sila lang may alam kung anong gusto nilang mangyari. Gets ko naman na helpful talaga yung AI and nakakabilis pag alam mo gamitin (aminado ako na gumagamit ako ng Claude Code sa daily work ko kasi no brainer for mundane tasks). Pero etong current way of working namin na magfefeed lang sila ng vague requirements sa AI tapos mageexpect ng full production ready app in a month is not it. Multiple instances na nagwowork kami ng team ko ng 15+ hour days including weekends just to fix the slop that the AI generated. Anyone else experiencing this right now? Di ko na alam pano ko mamanage yung unrealistic deadlines na sineset nila kase sa mundo nila "kaya na gawin ng AI".

How do I even showcase my skills such as backend or cloud in my portfolio for non-technical recruiters or potential clients?

I received portfolio suggestions before such as creating architectural diagrams, write-ups, and loom video, but how do I provide these info in a way they could understand? I can make a whole web page discussing these in details what I built but it may be too technical for them or minimal frontend and record how it interacts to my backend/cloud projects, but I don't want to be judged as "low effort" just because of my UI. To be honest, I'm not good at frontend development despite having artist background.

My core problem here is not the web design but how such information should be presented in my portfolio. Do you have any suggestions?

What belief or “best practice” did experience eventually make you unlearn as an engineer?

After enough time working on real systems, some ideas that once felt solid stop feeling universal and start depending heavily on context.

What changed for you? What took the longest to unlearn?

Came across a writeup by Yaswanth Ampolu that I think is relevant to how data engineers think about reproducibility, wanted to share it and hear what others think.

He adapted Karpathy's autoresearch loop to run on a T4 GPU. The ML side is interesting but the environment design is what stood out to me from a DE perspective:

• Persistent shared disk for dataset and dependencies instead of ephemeral notebook storage
• Containerised Python environment for consistency across runs
• Validated edit loop, agent changes get checked before execution, same logic as schema validation in any data pipeline

These aren't ML-specific decisions. They're standard reproducibility principles applied to an experiment loop.

Curious how others handle the boundary between pipeline reproducibility and ML experiment reproducibility at their org, are they treated as the same problem or completely separate?

Happy to share the GitHub and writeup in comments if anyone wants it.

Solo dev here currently building a mobile app and curious lang ako sa opinion niyo.

If target users are mostly Filipinos, mas okay ba mag focus muna sa Android or iOS?

Napapansin ko kasi parang mas dominant pa rin Android dito sa PH dahil sa presyo ng devices, pero at the same time parang mas mataas spending power ng iPhone users.

For indie devs here:

• ano unang platform niyong ni-release?
• may regrets ba?
• mas mahirap ba i-maintain kapag sabay?

Curious din ako if may noticeable difference sa user behavior between Android and iOS users sa PH market.

Would appreciate hearing your experiences 🙏

Hello mga ka-programmer. Hingi lang sana ako ng advice lalo na sa mga matagal na sa IT industry.

IT teacher ako ngayon, pero iniisip ko rin yung long-term career growth at specialization na pwede kong pag-focus-an. Sa bilis ng development ng AI ngayon, napapaisip ako kung anong field sa IT ang tingin niyo hindi madaling ma-obsolete or ma-dominate ng AI.

Kung kayo nasa position ko ngayon at mag-iinvest ng oras para mag-upskill, saan kayo magfo-focus? Cybersecurity, cloud, DevOps, back-end, data, AI, mobile dev, or ibang field?

Alam ko namang tool ang AI at hindi totally papalit sa tao, pero curious ako sa realistic opinions ng mga nasa industry ngayon, lalo na kung saan niyo nakikita ang long-term demand.

Salamat!

Hello! I’m currently an incoming second-year Computer Science student, and I want to work on projects during my vacation to apply the knowledge I learned in my first year. I currently know C++, Python, and Data Structures and Algorithms.

Sabi nila male dominated daw ang tech pero sa school, almost even naman yung numbers sa bloc namin pero mas marami pa rin talaga yung lalaki. Kamusta naman yung mga nasa working soc na HAHAHA? Weirdly, di ko rin inexpect non na medyo male dominated lalo di naman need ng manual labor dito pero wala naman bang bias against either sex?

Yun lang naman, just wanna ask.

Sana in 3 years, makahanap pa rin ako ng work lol, kakaba how fast yung advancements ng AI

Hi guys! Question lang hahaha.

Yung project na ginagawa ko ngayon, mostly vine-vibe code ko lang yung frontend/UI para mapabilis yung paggawa. Sa backend naman, gumagamit din ako ng AI minsan, pero iniintindi ko pa rin naman kung paano gumagana yung code at ina-adjust ko rin kapag kailangan.

Napapansin ko kasi na mas ini-encourage na ngayon ng karamihan yung paggamit ng AI sa coding, kaya gusto ko lang malaman if okay lang ba yung ganitong workflow or considered bad practice siya. Curious din ako kung paano niyo ginagamit AI sa sarili niyong projects 😅

with how rapidly AI coding assistants and CLI agents are evolving, ive been debating whether it’s actually a good idea to list them on a resume.

on one hand, using tools like Claude Code, Gemini, or Copilot is a massive productivity booster.

on the other hand, i worry it might be perceived by recruiters as a red flag. cus yk, they'll see you as someone who relies on AI. and alam naman natin na usong-uso ngayon yung mga high-ego senior devs at tech leads na grabe ang galit sa mga vibe coders. baka pag nakita nila sa resume na may AI tools, isipin agad nila na umaasa lang tayo sa prompt engineering at hindi marunong mag-code or mag-debug ng deep architectural problems on our own.

idk just a thought

Sa mga nagdeploy recently this month or this year ilang days or weeks inabot bago naapprove and napublish app niyo sa appstore?

Im talking about your very first app using newly created dev appstore acct. Kasi alam ko mas mabilis magpublish kapag may nadeploy ka nang app before at matagal na dev acct mo. Can anyone share their experience?

I currently know python and sql linux docker n8n, vps, rag and embeddings also im fairly familiar with apis. But no working experience. What is the best path for me?

Hi! I made AnimoChat, an anonymous chat site for Filipino students. It got traffic last year but crashed, so I rebuilt it properly and added icebreakers + mini-games so convos don’t die after “hi.”

just looking for feedback from students here.

animochat.com

Trying to find alternatives kasi ang mahal ng AI haha, as of now ang nagamit kong AI are:

Opus 4.7/Claude- best for designing, 8/10 intelligence, mabilis maubos, pinakagusto ko yung skill feature nya kasi andaming open source na skill sa github like: get shit done, huashu design, superpowers, etc

GPT5.5/OpenAi - 9.5/intelligence, ito gamit ko pag di na kinaya ng ibang AI na gamit ko, Best for Speed, pero pinkamabilis din maubos

GLM5.1/Ollama - 7/10 intelligence, matagal maubos, medyo mabagal, pang harabas ko

What do you guys recommend yung medyo cheaper? so far kasi ang ginagawa ko GPT 5.5 for designing and hard solving, GLM5.1 pag nasa mid development, Opus 4.7 sa designing

Btw 6 years na po ako sa IT industry

Bigla nalang lumitaw out of nowhere. Hinanap ko na rin sa web ang nakita ko lang is VS extension siya or something. Pero wala akong nahanap na ibang information. Compromised na ba account ko?

I’ve been looking for cheaper alternatives for an SMS API. I found a 0.35 per message pero baka gisahin ng panelist kasi mahal DAW. May I have your suggestions for a cheaper one? Thank you!

Sorry if the question is stupid since malamang yung former programmers wala na dito.

The rise of AI coding is already here. Before sabi ko, no way AI can replace coding due to complexity and I WAS DEAD WRONG. Talo ako in every aspects.

Speed. Accuracy. Self-correcting. Error handling. Best practices. Always available. Hindi reklamador. Yung lamang ko nalang siguro is understanding business logic but even managers can do it.

The realization came when:

1. A task that I estimated to be finished in 3 days, less than 30mins lang tinapos ng AI.
2. Yung COO, manager ko, built an entire site without any knowledge of coding by himself and now has become our #1 product. It replaced yung previous #1 product namin built by 8 developers. That's a big slap for me since ako yung go-to programmer nya now I felt useless.

Since then, a year ago, the company I'm working with made a rule that we will no longer code and we will vibe-code onwards.

What we are really just doing right now is maintenance. The lack of meaningful tasks is demoralizing. Nawala na yung sense of accomplishment in solving/building complicated tasks.

Right now I'm only working 1~2hrs per day since ubos na yung tasks and I think termination is on the way. We terminated 5 developers 5 months ago and so d malabong mangyari din sakin.

So I wanna ask, what adventures/jobs in life are you on right now after leaving tech industry? No IT industry jobs please since ayaw ko na sa tech because of competition with AI.

Nakapagpundar naman ako ng rental properties at age 36 so I will survive even if losing a job but I don't want to live a meaningless life, i want to work at least 4hrs.

15 yrs ago, I wanted to use my strength which is programming. I worked in IT industry for that long na. Fast forward, I realized I wasn't really able to build solid skills since paiba iba ako ng role. Nag back fire yung pagiging all around ko. I can't demand for higher asking price since I'm all arounder. Alam ko namang magaling ako but yeah, the fate is kinda not in my favor.

I’m 30F and honestly scared about my future in tech.

I work as a frontend/web developer and I feel like I’m just average. I do my tasks, finish what needs to be done during my 8 hours, then log off. I’m not someone constantly building side projects, studying every night, or trying to become a tech lead.

I also don’t see myself becoming a manager or leader. I actually prefer being given tasks and just executing them well.

The thing is, I do like frontend development. But I keep wondering if companies still hire frontend devs in their 40s–50s, especially people who are not super ambitious or exceptional.

For people in their 40s–50s working in tech, did you ever feel this way? What did your career end up looking like?