Been using @WisprFlow for almost one month on my desktop and the experience has been awesome. It has reduced the friction between my thinking and typing speeds. Give it a shot and you'll be addicted to it. If you wanna try, here's the link: wisprflow.ai/r?SHREYAS1524

Hey people, is there any scope for written content these days? It seems that audio-visual content has taken over our digital lives completely. Are there enough growth opportunities on the written content side?

An important clarification.

Looks like @Microsoft is vibe-coding their updates.

Anthropic, you need to slow down dude. The way you're releasing models is insane. At least keep them months apart, so that we can at least recover from the ruckus that the previous one has caused.

As an AI Engineer. Please learn >Harness engineering, not just prompt engineering >Context engineering, not just long prompts >Prompt caching vs. semantic caching tradeoffs >KV cache management, eviction, reuse, and memory pressure at scale >Prefill vs. decode latency and why they optimize differently >Continuous batching, paged attention, and throughput optimization >Speculative decoding vs. quantization vs. distillation tradeoffs >INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality >Structured output failures, schema validation, repair loops, and fallback chains >Function calling reliability, tool contracts, argument validation, and idempotency >Agent guardrails, loop budgets, tool budgets, and termination conditions >Model routing, graceful fallback logic, and degraded-mode UX >RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness >Retrieval evals: recall, precision, grounding, attribution, and citation quality >Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals >LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift >Cost attribution per feature, workflow, tenant, and user journey not just per model >Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries >Multi-tenant isolation, cache safety, and cross-user context contamination prevention >Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool >Latency, quality, cost, and reliability tradeoffs across the full inference stack >Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions

The more you're involved in the placement process of your college, the less you trust on-campus placements. Contradict me if I'm wrong.

Experienced ML engineers out there, what's your one piece of advice to someone who is starting ML from scratch?

Yes, started it lately after realising that to reach somewhere, I have to fucking start from somewhere.

How can I explain the weight of this scene to someone who has never used cloud models???

Does anyone know a student-discounted AI API platform which can handle high amounts of requests (around 300 RPM) and is discounted for students? Would really help

When you run models locally, you realise the amount of computation which actually goes in to give out simple results.

Finally reached Amateur rank in maths on @MatiksHQ. matiks.org/profile/rhys._.7

Why @MatiksHQ why?

It's great hearing the achievements of indigenous space-tech companies. Kudos to @GalaxEye for their success in their Drishti mission and the successful launch of the world's first OptoSAR satellite.

Looks like @rapidobikeapp people have taken their strategy to the next level with the new feature.