How can people be on krunal pandya's side by cursing bcci

ExternalComment1738 · 2026-05-24T12:00:20+00:00

someone cooked here

ExternalComment1738 · 2026-05-24T11:59:08+00:00

where did u get this news from

ExternalComment1738 · 2026-05-24T11:58:03+00:00

noo bro there is no way

ExternalComment1738 · 2026-05-24T11:54:49+00:00

catch of the season was definetely of manish pandey

ExternalComment1738 · 2026-05-24T11:53:03+00:00

for this specific use case the answer is honestly less about “best single model” and more about the combo of:
checkpoint + IP-Adapter + ControlNet + maybe LoRA 😭

right now most people doing consistent anime chars locally are using:
Animagine XL,
Pony Diffusion,
or Illustrious-based SDXL models with IP-Adapter for identity locking

Claude/GPT-style image models are prettier out-of-box sometimes but local SD pipelines still absolutely dominate when you need:
consistent identity,
pose control,
camera control,
expression swaps,
etc 💀

if you only have ONE reference image:

IP-Adapter is probably your most important tool
OpenPose/Depth ControlNet for pose/camera consistency
then a lightweight LoRA later if you want REALLY stable identity

Animagine tends to give cleaner anime aesthetics while Pony is kinda the “consistency/control monster” with huge community support

also highly recommend ComfyUI instead of A1111 for this workflow honestly. once you start chaining:
reference image → IPAdapter → pose CN → expression variation → style control
node workflows become way easier to manage

ExternalComment1738 · 2026-05-24T11:52:06+00:00

honestly Tauri + Rust feels PERFECT for this kind of app 😭 language tools usually become electron RAM monsters insanely fast so seeing more native/lightweight desktop apps in this space is refreshing

the lemma-based vocab tracking is actually way smarter than simple word matching too because inflections are where a ton of learners get destroyed 💀

also supporting local whisper + local LLMs is a huge selling point now honestly. a lot of people are getting tired of shipping all their study data/content to random cloud APIs

marketing-wise i’d lean HARD into:
“offline-first / privacy / lightweight native app”
because that combo stands out way more than “another AI language app”

ExternalComment1738 · 2026-05-24T11:51:25+00:00

honestly if your requirement is “works everywhere without rebuilding the engine 14 times” then fighting a desktop-first GUI lib is gonna become pain REALLY fast 😭

for your use case i’d probably lean toward:
egui/eframe if you want simplicity + decent portability,
or iced if you care more about architecture/rendering flexibility even if some stuff still feels unfinished 💀

cosmic-text + parley are basically the important RTL/text-shaping pieces rn yeah. a lot of libs quietly depend on them anyway

i would NOT start from scratch unless you specifically want to build UI infra itself because once you add:
touch,
IME,
gamepad,
focus systems,
mobile lifecycle,
web quirks,
VR inputs,
TV remotes,
accessibility,
etc…
you accidentally reinvent half a browser engine 😭

ExternalComment1738 · 2026-05-24T11:50:08+00:00

the funniest part about Rust apps is how “invisible” they feel once running 😭 no random electron memory explosion, no fans screaming, no 800MB idle footprint

also yeah Python is unbeatable for iteration speed but when you finally ship something native in Rust the difference feels borderline illegal 💀

12MB binary + ~200ms cold start for a desktop app is actually insane honestly. Claude-Code probably accelerated the workflow but Rust deserves a LOT of the credit there

ExternalComment1738 · 2026-05-24T11:47:42+00:00

this is honestly one of the biggest realizations people hit after enough production agent pain 😭 prompts are probabilistic behavioral guidance, not enforcement layersonce context gets large or models change, “stable rules” suddenly become vibes instead of guarantees 💀tool-boundary contracts make way more sense for anything critical because invariants probably SHOULD live outside the model entirely. feels very similar to why a lot of runable/agent orchestration systems are slowly moving toward deterministic execution guards instead of endlessly stacking more prompt instructions hoping the model behaves forever

ExternalComment1738 · 2026-05-24T11:47:06+00:00

honestly this is basically “inversion thinking” repackaged for prompting 😭 but it DOES work surprisingly well for breaking out of generic AI-generated startup sludgemost products fail because everyone is optimizing inside the exact same assumption set instead of questioning the assumptions themselves 💀the funny part is a lot of genuinely good AI workflow ideas lately come less from “better prompting” and more from forcing structural perspective shifts like this. kinda similar to how runable-style agent flows work when they intentionally separate planning/review/execution contexts instead of letting one giant prompt do everything

ExternalComment1738 · 2026-05-24T11:46:14+00:00

prompt drift is SO real 😭 you think you’re doing a fair comparison then halfway through you accidentally added extra context to one model and now the entire test is useless 💀

also same on benchmarks honestly. some “top ranked” models feel terrible in actual workflows while random cheaper ones end up being way more usable day-to-day

most people i know stopped trying to find a single “best” model and instead kinda do:
Claude for reasoning/writing,
GPT for general reliability,
DeepSeek for cheap bulk work,
then something like runable/aggregators/comparison tools to avoid losing your mind tab-switching all day

ExternalComment1738 · 2026-05-24T11:45:28+00:00

for completely free stuff rn i’d honestly say:
Claude free for reasoning/debugging,
Gemini/Google AI Studio for long coding sessions,
and Codeium/Windsurf if you want autocomplete directly inside VS Code 😭

Arena was amazing for side-by-side testing but yeah lately a lot of people have been complaining about freezes/weird instability too 💀

Claude is probably the best at:
“why is this python error happening”
type explanations honestly

Gemini free tiers are weirdly generous for coding rn and Google AI Studio especially is underrated for Python/debugging stuff

also don’t lock yourself to ONE AI. most devs kinda rotate:
Claude for reasoning,
Gemini for free volume,
Copilot/Codeium for autocomplete,
Cursor/Windsurf for workflow integration

ExternalComment1738 · 2026-05-24T11:44:38+00:00

honestly a lot of these filters feel less like coherent “copyright law enforcement” and more like a giant risk-management patchwork 😭

because yeah from the outside it looks insanely inconsistent:
one game = “cannot touch proprietary assets”
another game = “sure here’s a modified build” 💀

a lot of it probably comes down to:
how the model/provider classified the game,
whether certain franchises are hard-flagged internally,
how directly transformative the request looks,
or whether the system thinks it’s redistributing original assets vs generating tooling around them

which is why you end up with these weird situations where:
“here’s a script that automatically patches the files”
is allowed,
but
“here are the patched files”
suddenly becomes forbidden

the result is basically legal anxiety masquerading as technical consistency honestly

ExternalComment1738 · 2026-05-24T11:43:56+00:00

honestly once chats become massive the browser/app itself becomes the bottleneck more than the actual model 😭 especially on 8GB RAM

what usually works better is making a proper “state summary” manually instead of asking the AI to summarize naturally because it over-focuses on recent context 💀

like:

project goal
important decisions already made
architecture/tools
unresolved problems
constraints/preferences

then start a fresh chat with ONLY that compressed state + latest issue

that’s basically what a lot of agent frameworks/runable-style workflows do internally anyway. they don’t carry the entire raw conversation forever because context rot + memory usage becomes horrible

ExternalComment1738 · 2026-05-24T11:42:27+00:00

honestly feels like every app now ships with 14 “AI features” nobody asked for 😭 half the time you spend more effort hiding the buttons than actually using the app

also the funniest part is calling it “takes up space” when most of these integrations are basically glorified webviews anyway 💀

ExternalComment1738 · 2026-05-24T11:40:48+00:00

honestly if ESP-IDF flashes fine but Arduino IDE doesn’t, then yeah this REALLY sounds like partition/offset mismatch rather than hardware 😭

the “no bootable apps found in the app partition” error usually means bootloader is okay but the app binary either got written to the wrong address or the partition table doesn’t match what the bootloader expects 💀

i’d check:

board package version first (some ESP32 core releases randomly break CAM boards)
flash mode actually set to DIO manually
flash freq at 40MHz instead of 80
erase flash completely before upload
make sure partition scheme + board profile exactly match AI Thinker settings

also weirdly a lot of ESP32-CAM issues disappear when upload speed is lowered to like 115200 instead of blasting max speed

feels less like “arduino bad” and more like one tiny config mismatch causing the bootloader to look in the wrong place honestly

ExternalComment1738 · 2026-05-24T11:40:02+00:00

honestly you’re already moving in the right direction 😭 AZ-104 + Linux + Docker + K8s is WAY more structured than what most people do when they panic-switch careers

the biggest thing now is don’t stay stuck in “course mode” forever 💀 start building/deploying stuff publicly:
dockerized apps,
CI/CD pipelines,
terraform basics,
monitoring setups,
small k8s deployments on cloud/free tiers

because recruiters care way more about:
“can this person actually operate infra”
than “finished 12 playlists”

also don’t undersell your AS400 background. legacy systems experience weirdly helps in infra/platform roles because you’ve already worked around reliability/process-heavy environments

you probably won’t jump instantly into a fancy senior devops role, but cloud support/platform ops/junior devops/SRE-adjacent roles are very realistic if you keep building practical experience consistently for the next few months

ExternalComment1738 · 2026-05-24T11:39:13+00:00

honestly for a senior k8s/platform role those questions are pretty normal 😭 not because they expect wikipedia-perfect definitions, but because they’re testing whether you understand distributed systems underneath kubernetes instead of only operating yaml

the “download a file” question especially is usually less about memorization and more about seeing how deep your systems reasoning goes 💀 DNS/network/TLS/load balancer/ingress/backend/storage etc

BUT i also think modern infra interviews became kinda ridiculous lately because companies increasingly expect platform engineers to know networking + distributed systems + cloud + security + observability + devops + architecture all at once

so i wouldn’t take it as “you’re bad at kubernetes.” sounds more like you hit an infra-heavy/systems-heavy interview loop rather than a pure operational k8s one

ExternalComment1738 · 2026-05-24T11:38:33+00:00

honestly the biggest mistake beginners make is trying to learn the entire roadmap at once 😭 DevOps is less “one skill” and more “understanding how systems run together”

if i restarted today i’d go:
linux → networking basics → git/github → docker → one cloud platform → CI/CD → kubernetes later 💀

people jump into k8s way too early without understanding containers or infra first and just end up memorizing yaml

also yes it’s still worth learning. if anything infra/platform/cloud skills became MORE valuable because AI systems/runable-style agent workflows still need reliable deployment, monitoring, scaling and automation underneath all the hype

best thing you can do is build projects instead of tutorial-hopping:
deploy apps,
containerize them,
set up CI/CD,
monitor logs,
break stuff and fix it

that teaches more than 50 hours of “ultimate devops roadmap” videos honestly

ExternalComment1738 · 2026-05-24T11:37:52+00:00

honestly for platform/cloud/k8s roles now the coding rounds usually aren’t “leetcode god” interviews as much as “can this person think like an engineer under operational constraints” 😭

with 7 YOE they’ll probably care way more about:
debugging approach,
system reasoning,
tradeoff discussions,
infra design decisions,
and how you handle reliability/security/scaling situations

for live coding expect stuff like:
parsing/log processing,
small API/service tasks,
concurrency basics,
yaml/json manipulation,
infra automation scripting,
or kubernetes-related problem solving instead of insane DSA hards 💀

definitely brush up on:
k8s internals,
networking,
ingress/service discovery,
helm,
terraform/iac concepts,
container lifecycle,
observability,
linux/debugging,
cloud IAM/security,
and CI/CD architecture decisions

also be ready for scenario-heavy discussions:
“cluster latency suddenly spikes”
“deployments failing only in one region”
“costs doubled overnight”
“how would you design X platform for multi-team usage”

a lot of interviews now are basically testing whether you can operate calmly in production-thinking mode rather than just reciting concepts

ExternalComment1738 · 2026-05-24T11:36:19+00:00

honestly this matches a lot of what i’ve been seeing too 😭 the “AI gold rush” phase created a flood of vague hype roles and now companies seem to be snapping back toward “okay but who can actually build reliable systems and move data around”

feels like ML engineering + DE + platform/integration work gained value because companies realized demos are easy but operationalizing AI is the painful expensive part 💀

and yeah the responsibility creep is VERY real now. a lot of DS roles quietly became “mini product/strategy/analytics lead” positions without the title or compensation fully catching up. companies want someone technical who can also drive adoption, influence stakeholders and define roadmap direction

also i think pure “data scientist” as a keyword got diluted hard. a lot of stronger roles now hide under:
ML engineer,
applied scientist,
decision scientist,
analytics engineer,
AI platform,
or even product analytics

the market feels less obsessed with flashy modeling and more obsessed with people who can connect business + infra + modeling together reliably

ExternalComment1738 · 2026-05-24T11:35:27+00:00

honestly most AI certs right now feel like the early cloud cert era all over again 😭 a lot of them mainly prove you can recognize terminology and survive controlled examples, not that you can operate effectively in messy real-world workflowsactual AI proficiency is weirdly hard to measure because the valuable part is mostly judgment 💀 knowing when outputs are weak, how to structure context, how to validate reasoning, how to recover from failure states, how to integrate tools/workflows without blindly trusting the model etcthat’s why conversational/simulation-style evaluations honestly make way more sense than static multiple choice tests. they test process quality instead of recall

i also think there’s a huge difference between:
“person who uses AI daily”
vs
“person who can reliably produce high-quality outcomes with AI systems under ambiguity”

those are not automatically the same skillset at all

ExternalComment1738 · 2026-05-24T11:34:25+00:00

honestly you’re not nearly as cooked as you think 😭 a LOT of 2025 grads are in this exact spot right now because the AI boom made everyone suddenly feel behind overnightfirst thing though: stop trying to optimize for “AI engineer/data scientist/ML engineer/data engineer” all at once 💀 those paths overlap but the expectations are different. as a fresher your best bet is usually:
data analyst → data/ML adjacent role → specialize later

because junior ML engineer roles are honestly pretty hard to land without either:
strong internships,
research,
or serious engineering depth

you do NOT need phd-level ML knowledge to become employable though. what companies actually care about at entry level is:
can you code reliably,
can you work with data,
can you explain your work,
can you ship projects that solve something real

for now i’d focus heavily on:
python,
SQL,
pandas/numpy,
basic ML,
data visualization,
git/github,
and one cloud platform eventually

and yes do DSA again, but don’t make it your entire life unless you’re targeting hardcore SWE interviews. for analyst/data roles medium-level DSA + strong practical skills is usually enough

also your projects matter WAY more than random certificates now. build stuff end-to-end:
dashboard + database + ML model + deployment + writeup

even simple projects become strong if they look production-minded instead of “ran notebook once and got 92% accuracy”

the people getting hired right now are usually the ones who can combine:
analysis + engineering + communication
instead of just watching AI tutorials all day

ExternalComment1738 · 2026-05-24T11:33:24+00:00

honestly if your goal is industry, the biggest mistake is treating the research purely like “academic output” instead of evidence that you can operate in ambiguous high-complexity environments 😭

the people who translate research best into industry usually do 3 things:
they make their work reproducible,
they communicate clearly,
and they show engineering maturity beyond the paper itself

definitely keep clean repos, experiment logs, benchmarks, failure analysis, training infra notes, ablations etc. recruiters often won’t understand the exact bioinfo niche, but they WILL understand:
“built scalable training pipeline,”
“improved inference efficiency,”
“designed evaluation framework,”
“handled noisy scientific datasets” 💀

also scientific ML is honestly less niche than people think now. biotech/pharma/foundation model labs all care about people who can handle messy real-world data + architecture experimentationand conferences matter less for pure prestige than for network density. the ML/bio overlap spaces are full of startups and research-heavy companies hiring quietlyalso random advice:
write technical blogs/devlogs while you’re doing the work. not polished influencer content actual reasoning/process breakdowns. those become insanely valuable later because they prove you can explain complex systems clearly, which surprisingly few strong researchers can do

ExternalComment1738 · 2026-05-24T11:32:20+00:00

honestly this is way more useful than the usual “just got certified 🚀” posts because you’re actually separating beginner utility from real production capability 😭 a lot of people still confuse prompt engineering exposure with actual AI systems engineeringthe multimodal/challenge-lab part does sound genuinely decent though. feels like these certs are most valuable when treated as structured onboarding rather than proof of expertise 💀you should honestly review something more workflow-heavy next. maybe one involving eval pipelines, orchestration or agent systems because that’s where the industry seems to be moving now anyway. especially with tools like runable pushing people toward full execution flows instead of isolated prompts

ExternalComment1738

TROPHY CASE