Burkan

I love creating cool stuff and combining multiple tech into one to re-imagine solutions for problems I experience.

[Burkan @BurkanKale]

Most lack a good foundation or real culture. They never experience the joy of helping others. You expect them to grow, but the sad truth is: some people are already living their full potential.

[Nous Research @NousResearch]

Hermes Agent now has Automation Blueprints, turning cron jobs into clickable, fillable, conversational workflows.

[Burkan @BurkanKale]

If it means our understanding and definition of what work is to change so be it, we have to adapt, learn, create, upgrade ourselves as a race.

[Burkan @BurkanKale]

If so, we might be the only race that has access to technology this advanced as we speak & if we don't advance it as fast as possible, it might be ripped away from us

[Burkan @BurkanKale]

Are we the only race that has access to advanced predictive thinking and generative artificial intelligence technology?

[Burkan @BurkanKale]

and also other races use species like us to somehow test/trials and brute force their way into breakthroughs?

[Burkan @BurkanKale]

Single source of truth for advertisement. David Ogilvy, the Mad Man himself.

[diva @divaagurlxw]

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