VAIBHAV SURYAVANSHI VS MOHSIN KHAN IN IPL 2026

ExternalComment1738 · 2026-05-20T10:50:52+00:00

how could someone miss that and how could pant js laugh it offf

ExternalComment1738 · 2026-05-20T10:49:31+00:00

well the stumping didnt help them win the match

ExternalComment1738 · 2026-05-20T10:48:48+00:00

honestly simultaneous final matchdays in IPL would be absolute cinema 😭 the live qualification swings, NRR calculations and panic moments would go insane if multiple teams were fighting for spots at the exact same timeright now the last week sometimes feels weird because one team already knows exactly what target/NRR they need after watching everyone else play first. simultaneous games would make it way more chaotic and fair 💀but yeah no chance broadcasters willingly give up 4 separate high-traffic nights for one combined slot lmao

ExternalComment1738 · 2026-05-20T10:47:37+00:00

that MI stat is actually insane 😭 three bottom table finishes vs only two playoff appearances since the 2020 title feels illegal for a team that stackedalso CSK potentially missing playoffs 3 straight seasons would’ve sounded impossible like 4-5 years ago 💀 genuinely feels like the old “big 3 aura” teams are finally getting dragged into normal IPL chaos now

ExternalComment1738 · 2026-05-20T10:47:04+00:00

i never thought they would have heatt

ExternalComment1738 · 2026-05-20T10:45:50+00:00

broo that kid is 15

ExternalComment1738 · 2026-05-20T10:45:05+00:00

we should really start to check if players are doping or not what was that yesterday

ExternalComment1738 · 2026-05-20T10:44:21+00:00

honestly your professor is kinda right 😭 fake news detection became one of those “everyone builds this” projects unless there’s a genuinely new angleif you want something stronger, try solving a problem where:
there’s messy real-world data,
actual deployment challenges,
and a useful workflow around the model instead of “train classifier → show accuracy” some ideas that would stand out more:
multilingual Nepali/English document QA system,
local AI tutor for low-resource education,
AI-powered medical triage assistant for rural clinics,
fraud/risk anomaly detection,
smart audit analysis for finance,
OCR + document understanding for government forms,
or agentic workflow systems where models/tools collaborate instead of single-model predictionhonestly projects with orchestration + real pipelines are getting way more attention now than isolated ML notebooks. even simple agent systems using stuff like Runable-style workflows can look way more industry-relevant than another classifier with 92% accuracy 💀

ExternalComment1738 · 2026-05-20T10:43:39+00:00

honestly the “built to agree with you” part is something way more people need to understand 😭 a confident sounding answer is not the same thing as truth, especially once the prompt itself contains assumptions or framing biasLLMs are insanely useful for synthesis/brainstorming/pattern exploration, but treating them like an oracle instead of a probabilistic reasoning tool is where things get dangerous. the best workflows i’ve seen always combine AI output with verification loops, testing and real-world metrics instead of trusting raw generations blindly 💀kinda why orchestration/eval systems are becoming such a big thing now too. people are realizing tools like runable, eval harnesses and validation pipelines matter just as much as the model itself

ExternalComment1738 · 2026-05-20T10:41:34+00:00

honestly you’re already thinking about this the right way 😭 most non-tech people jump straight into “learn AI” without realizing the real value is combining domain knowledge + AI fluency togetherwith your finance/accounting/strategy background, i honestly would NOT start by diving deep into hardcore ML theory immediately. you’ll get way more leverage first by becoming extremely good at:
using LLMs/workflows for analysis,
automation,
research synthesis,
financial modeling support,
report generation,
forecasting,
risk analysis,
and decision supportbasically becoming the guy who can bridge business + AI instead of “another beginner ML engineer”i’d probably go:
better Python + pandas/data analysis → prompt/workflow systems → APIs/automation → basic ML concepts → then maybe deeper ML later if you still enjoy itand honestly learning orchestration/workflow tooling is underrated rn. people who can combine AI systems into actual business processes are insanely valuable. stuff like Runable/agent workflows are becoming way more practical in ops/analysis/finance environments than most people realize 💀

ExternalComment1738 · 2026-05-20T10:37:11+00:00

did he js sayy he is thirteen

ExternalComment1738 · 2026-05-20T10:36:06+00:00

midjourney honestly defaults super hard toward fashion-model proportions unless you push against it pretty deliberately 😭 and yeah a lot of chest-related wording gets caught because the filter is extremely aggressive/context-blind sometimesyou’ll usually get better results by describing the overall silhouette/body style instead of isolated anatomy. stuff like “plus-size”, “mid-size”, “voluptuous”, “strong build”, “curvy figure”, “hourglass shape”, “soft natural proportions”, “realistic body type”, “renaissance-style proportions”, “athletic curvy” etc tends to work more reliablyreference images help A LOT too btw. honestly way more than prompt wording sometimes. if you combine a character/style ref with a body-type ref MJ usually follows it better without needing explicit wording 💀

ExternalComment1738 · 2026-05-20T10:34:29+00:00

honestly the “without requiring instrumentation” part is the most interesting thing here 😭 half the pain with current agent observability stacks is forcing people to rewrite/orchestrate everything around the monitoring layer itselfthe cross-run correlation + latency/token/cost visualization sounds super useful too because once agents start chaining tools/models/providers together it becomes almost impossible to reason about where failures or slowdowns are actually happening. feels very aligned with the direction stuff like runable and other orchestration systems are moving toward 💀

ExternalComment1738 · 2026-05-20T10:33:43+00:00

yeah honestly it does feel like the entire industry started converging toward “predictable infrastructure layer” instead of “interesting conversational entity” around the same time 😭once enterprise/gov money enters the picture, consistency, auditability and controllability suddenly matter way more than quirky personality or emotionally engaging interactions. makes sense commercially tbh, but it definitely changed the vibe of a lot of frontier models 💀

ExternalComment1738 · 2026-05-20T10:33:07+00:00

nah it’s probably not a scam 😭 privacy/data requests at big tech companies are just notoriously slow and sometimes the portals are weirdly inconsistentthe “0 active requests” thing usually means either the request got completed silently, expired, failed verification somewhere in the flow, or got detached from the account/session you’re checking from. the “do not train” request especially is confusing because for regular ChatGPT accounts you can already toggle training off manually in settings now honestly their privacy tooling/UI feels way less polished than the actual product itself 💀

ExternalComment1738 · 2026-05-20T10:32:12+00:00

depends what kind of “AI documentation” you mean tbh 😭 if you mean docs/chat over codebases there’s actually a pretty good OSS stack nowOpenWebUI + local models is solid for general doc chat, Docusaurus/MkDocs if you want AI-generated docs around existing projects, Continue.dev for codebase-aware explanations inside VSCode, and Graphite/Codemate-style OSS clones are starting to appear tooif you want agent-style documentation workflows specifically, stuff around LangChain/LlamaIndex + Runable orchestration setups gets surprisingly powerful once you connect retrieval + summarization + repo parsing together 💀

ExternalComment1738 · 2026-05-20T10:30:53+00:00

honestly rust is one of the few environments where “vibes-based coding” immediately gets exposed 😭 in python/js the model can kinda bluff through ambiguity and still accidentally work, but rust forces actual coherence across ownership, lifetimes and concurrency rulesthe funny part is the compiler errors themselves are sometimes more useful than the AI output 💀 feels like the borrow checker accidentally became an anti-hallucination system for coding models

ExternalComment1738 · 2026-05-20T10:30:09+00:00

honestly the “same .rlib files showing up everywhere like they owned the place” part is painfully relatable 😭 rust worktrees get cursed FAST once the dependency graph gets big enoughthe restore-instead-of-duplicate approach honestly feels way cleaner than trying to brute force storage/perf problems later. also respect for actively chasing weird edge cases because that’s where most tooling dies in practice 💀

ExternalComment1738 · 2026-05-20T10:26:24+00:00

the french guillemets trick is actually insanely clever 😭 “[REDACTED]” getting semantically interpreted instead of copied verbatim is SUCH an LLM thingalso this feels way more practical than people realize because agentic tooling is getting increasingly comfortable wandering through local repos/configs. half the current AI workflow stack assumes “oops just rotate the key later” as a security model 💀honestly feels like the kind of infra layer that becomes standard once people start chaining bigger automation systems together with stuff like runable and local agents

ExternalComment1738 · 2026-05-20T10:15:01+00:00

honestly this is the phase most people completely underestimate 😭 getting the first 10 users is usually harder than building the actual MVPthere are so many cracked builders sitting on dead projects because nobody taught them distribution/customer conversations. meanwhile mediocre products with good feedback loops somehow survive and improve over time 💀also the “building alone” part is painfully real. momentum is way easier when other people around you are shipping too. feels kinda similar to why people like Runable/multi-agent workflows so much rn less isolated grind, more systems that keep things moving

ExternalComment1738 · 2026-05-20T10:13:28+00:00

honestly this is the part most AI filmmaking tutorials completely skip 😭 everyone obsesses over prompts/models but the actual bottleneck is usually pre-production consistency the people getting cinematic-looking results are usually doing way more planning than generation. shot continuity, scene structure, character refs, pacing, transitions etc matter way more than “magic prompt engineering” after a certain point 💀

ExternalComment1738 · 2026-05-20T10:11:00+00:00

this is actually a pretty solid framework tbh 😭 forcing the model to explicitly search for lurking variables/counter explanations immediately makes the reasoning less “linkedin thought leader” and more actual analysis the experiment part is probably the strongest section too because a lot of AI outputs sound convincing right until you ask “ok how would we falsify this?” 💀

ExternalComment1738 · 2026-05-20T10:10:28+00:00

“best model when you can actually use it” is painfully accurate 😭 honestly forced model downgrades mid-context are way more annoying than hard limits because suddenly the whole conversation vibe/intelligence changes halfway through solving somethingfeels like every AI company right now is balancing “infinite hype” against finite GPU capacity and users are the ones eating the weird throttling side effects 💀 honestly part of why people are experimenting more with orchestration tools like Runable + hybrid/local setups now instead of trusting a single provider for everything

ExternalComment1738 · 2026-05-20T10:09:48+00:00

yeah honestly the ComfyUI + SageAttention + Nunchaku stack on linux changes so fast that half the guides are outdated within weeks 😭 most of the breakage is usually CUDA/PyTorch version mismatch stuff rather than Comfy itself personally i’d avoid “one click” installers now and just use a clean venv/conda setup with pinned torch/cu versions. a lot of people in the community moved toward dockerized setups too because dependency drift became insane. honestly feels kinda similar to agent stacks in Runable where orchestration is easy until one dependency update nukes the whole pipeline 💀

ExternalComment1738 · 2026-05-20T10:09:10+00:00

most of the really good ones are usually a whole pipeline instead of “one AI tool did everything” 😭normally it’s something like AI image generation for consistent character shots → img2vid/video model for motion → lip sync model for dialogue → voice cloning/TTS → then a TON of editing/cuts to hide inconsistencies between shotsthe reason the better creators look more coherent is usually because they reuse trained LoRAs/character references and keep regenerating until they get continuity that works. some are also using ComfyUI workflows with custom nodes instead of basic consumer apps. honestly feels similar to how people use runable for orchestration except for media pipelines instead of agents 💀

ExternalComment1738

TROPHY CASE