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
for anyone asking where to learn this stuff:
• RAG → https://t.co/4bzbUIwV5g
• Agentic RAG → https://t.co/IotOiGmV1Y
• AI Agents → https://t.co/nEeMnVJQbk
• Multi-Agent Systems → https://t.co/pavDPVJEFj
• LangGraph → https://t.co/3miEqqFzF0
• LangGraph (code) → https://t.co/v7kxHZXqba
• MCP → https://t.co/lKawRb4etX
• Memory Systems → https://t.co/LSaT2UaPAS
• Evals → https://t.co/vxChxa1kqQ
• Context Engineering → search "Context Engineering Survey" on arXiv
and please skip the "build an ai agent in 10 minutes" videos
build something, watch it fail, then figure out why.
@whatwordbro setuju! bener banget bang. sekarang gua di semester akhir, dan merasakan hal (ngejalanin kuliah, yg kayaknya bisa lebih baik lagi kalo di ulang ke awal kuliah itu).
Karpathy found a way to reduce token consumption by 90%
The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result
The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again
Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent
Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian
Everything stays on your local machine nothing goes to the cloud
Seorang solo developer sukses MENANG hackathon Anthropic x Forum Ventures. Dia bangun Zenith Chat cuma dalam waktu 8 jam dan dapet hadiah $15 ribu. 💰
Kerennya lagi, dia langsung open-source kan SELURUH setup AI coding-nya. Project-nya dinamain ECC (Everything-CLAUDE-Code)—kasarnya ini kayak "sistem operasi" khusus buat coding berbantuan AI.
Beberapa poin yang bikin project ini gila:
• Ada 11 agen AI siap pakai (buat review kode, planning, testing, dll.)
• Otomatis nyeken celah keamanan yang biasanya gampang kelewat kalau dicek manual.
• Sekali install langsung jalan di Windows/Mac/Linux dan bisa auto detect project setup lo.
• Udah built-in integrasi ke GitHub, Supabase, Vercel, sampai Railway.
• Dilengkapi 12+ skill dan 18+ perintah cepat—bikin banyak task rutin otomatis beres.
Dan semua tools ini dibagikan GRATIS secara open-source, bahkan sekarang udah nembus kisaran 190K stars di GitHub.
Bikin project solo dalam 8 jam, menang $15K, terus kodenya dibagikan gratis ke semua orang. Bener-bener legend.
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.