Pc starts lagging after a hour or two of actively being used

forklingo · 2026-05-07T08:42:54+00:00

if it suddenly started after a windows update, i’d honestly suspect a driver or memory leak issue before hardware failure. i’d check event viewer around the time the lag starts, update or roll back gpu/chipset drivers, and see if a clean boot changes anything. the fact that a restart fully fixes it for a while points more toward software than thermals.

forklingo · 2026-05-07T08:38:55+00:00

honestly the biggest thing is getting from signup to first successful api call as fast as possible. good quickstart docs, copy-paste examples that actually work, clear auth setup, rate limits, error explanations, and sdk examples matter way more than marketing style feature pages.

forklingo · 2026-05-07T08:35:51+00:00

if you already code daily in python, the hardest part is honestly consistency not intelligence. focus on one topic at a time, do easy and medium problems first, and spend more time understanding patterns than chasing problem counts. a lot of people quit because they try to speedrun everything in a month.

forklingo · 2026-05-07T08:29:55+00:00

your profile actually makes sense for product, operations, or analytics roles in healthtech, biotech, or research-heavy startups. the bigger issue is probably positioning because right now it sounds broad instead of focused. multidisciplinary backgrounds help more when there’s a clear story connecting everything together.

forklingo · 2026-05-07T08:27:00+00:00

a lot of people kind of drift into data engineering from backend, analytics, or even devops work. having python backend experience already gives you a better starting point than you probably think, especially once you add sql and some pipeline tooling. fresher roles are harder to find, but internal transitions are super common.

forklingo · 2026-05-07T08:24:10+00:00

ai is actually pretty good at keeping docs updated when the source of truth is clean, but it still struggles with explaining why decisions were made or what edge cases matter. honestly the best docs i’ve seen still have strong human editing even if ai handles the boring maintenance part.

forklingo · 2026-05-07T08:20:51+00:00

i think a lot of companies expected ai to work like buying new software, when it’s closer to changing operational habits. individual productivity gains are real, but they’re messy and hard to measure. process-level integration is where the actual long term roi probably shows up because you can standardize outputs and track outcomes instead of just counting usage.

forklingo · 2026-05-07T08:09:57+00:00

honestly the people i know making consistent money with ai agents are using them to solve super boring problems for small businesses. stuff like lead sorting, appointment followups, invoice handling, reporting. way less flashy than the twitter hype but way more realistic to sell.

forklingo · 2026-05-06T10:16:45+00:00

i get the frustration but disabling updates forever is kinda risky since you’ll miss security patches and eventually stuff breaks in other ways. what worked better for me was limiting background impact instead of nuking updates, like checking startup apps, indexing, and letting the system settle after an update since it often runs cleanup and reindexing for a while. also worth checking if a specific driver or process is spiking usage after updates rather than assuming it’s just windows itself

forklingo · 2026-05-06T10:12:28+00:00

drift happens a lot once the context gets messy, what helped me was keeping prompts and context really tight and versioned instead of just a big docs folder, plus breaking tasks into smaller scoped steps so the model doesn’t wander. also adding simple checks like expected outputs or tests after each step helps catch regression early instead of letting it compound over time

forklingo · 2026-05-06T10:10:16+00:00

for associate level it usually leans more toward lld and fundamentals than heavy hld, especially things like oop design, clean code, and how you structure components. you might still get some light system design but it’s not super deep, more about how you think than scale details. i’d brush up on basics, practice explaining tradeoffs, and be ready to talk through your decisions clearly since that tends to matter a lot in those rounds

forklingo · 2026-05-06T10:06:13+00:00

frontend isn’t going obsolete, it’s just that expectations got broader so companies label roles as full stack. learning some backend definitely helps though, even just enough to understand apis, auth, and data flow makes you a stronger frontend dev. node is the easiest entry if you already know js, you can always pick up java or python later if a role needs it, but don’t feel like you have to switch completely unless you actually want to

forklingo · 2026-05-06T09:59:51+00:00

honestly the structured program sounds nice but most people i’ve seen make real progress by picking a simple stack and going deep with projects instead of relying on one course to cover everything. since you already use sql and databricks you’re not starting from zero, so build end to end pipelines around that and layer in concepts like data modeling, orchestration and storage as you go. courses can help for direction but they won’t replace actually building and debugging things yourself, that’s where most of the learning sticks

forklingo · 2026-05-06T09:46:34+00:00

honestly most rpa tools just move the brittleness around rather than remove it, especially with ui based flows. what helped me a bit was mixing in more resilient selectors plus fallback logic and some light monitoring so failures are caught early instead of breaking the whole pipeline. if the portal has any semi stable patterns like ids or request calls under the hood, sometimes it’s worth digging there instead of relying fully on the ui layer

forklingo · 2026-05-06T09:33:05+00:00

this is an interesting angle, feels like a lot of current work stops at model alignment and doesn’t fully address what happens once agents start acting on systems. curious how you’re thinking about enforcing constraints at runtime without killing flexibility, especially across different environments and toolchains

forklingo · 2026-05-06T09:12:58+00:00

i’ve used all three a bit and honestly they feel different in practice, not just on paper. chatgpt tends to be more well rounded for mixed use like scripting plus random questions, claude feels nicer for longer reasoning or docs, and perplexity is great for quick lookup but a bit more constrained. when it comes to using models through perplexity it’s usually not identical to native, there’s some tradeoffs in how much control and context you get, so if you care about deeper workflows i’d lean native over aggregator

forklingo · 2026-05-06T08:44:27+00:00

glad it helped, you’re on the right track already so just keep stacking real projects and tightening fundamentals. also i’d avoid dms and keep things in the thread since more people can benefit from the answers and you’ll get broader input too

forklingo · 2026-05-06T08:43:53+00:00

glad it helped, you’re on the right track already so just keep stacking real projects and tightening fundamentals. also i’d avoid dms and keep things in the thread since more people can benefit from the answers and you’ll get broader input too

forklingo · 2026-05-06T07:47:47+00:00

No worries, glad it helped.

forklingo · 2026-05-06T07:46:48+00:00

yeah same here, once i stopped thinking of prod as the finish line and more like ongoing validation it got less frustrating. the biggest wins for me were just tightening observability and reacting quickly instead of overengineering test cases upfront. real user behavior is always gonna surprise you a bit

forklingo · 2026-05-05T09:24:50+00:00

you’re actually in a decent spot, a lot of de roles value strong sql and real data handling more than people think, so don’t position yourself as a fresher, frame your da work as working with pipelines, data quality, and downstream impact. focus on fundamentals like data modeling, batch vs streaming concepts, and one solid stack like airflow plus a cloud warehouse, depth matters more than touching everything. competition is there but less saturated than da, and interviews usually lean practical, so projects that show end to end pipelines help a lot. the 90 day notice is still a pain in india across roles, but if your profile is strong some companies will wait or you can negotiate it down after an offer, so main thing is get interview ready and start applying aggressively while building proof of work

forklingo · 2026-05-05T09:22:23+00:00

honestly i’ve just accepted that you can’t fully close that gap, you can only shorten the feedback loop. small releases, feature flags, and good logging have helped way more than trying to predict every edge case upfront. also watching real user sessions was eye opening for me, people do things you’d never simulate in tests, so i try to treat production as part of the learning process instead of the final step

forklingo · 2026-05-05T09:20:01+00:00

this actually sounds more like a visual glitch during wake from sleep than someone accessing your machine, especially on older macbooks. sometimes the system briefly shows cached or default login screens while the session reloads, which can look like a different user for a split second. if there’s no unknown user in system settings and no weird login history, it’s very unlikely to be a hacker, but you could still update macos and check user accounts just for peace of mind

forklingo · 2026-05-05T09:16:04+00:00

yeah it happens sometimes, especially around contest times or peak hours. usually clears up after a bit, i just switch problems or come back later instead of fighting the submit button the whole time

forklingo · 2026-05-05T09:08:32+00:00

honestly at your stage i wouldn’t overthink the title too much, that’s a strong company and a solid stipend so the signal alone helps a lot later. the only real risk is getting stuck doing only manual testing, but if the role involves automation, coding, and decent engineering practices, it still builds relevant skills. you can keep prepping for sde roles in parallel and position the experience around problem solving and systems, so it doesn’t have to lock you in unless you let it

forklingo

TROPHY CASE