Fauzi Noorsyabani

Google Student Ambassador | Information System Student | Gym
Documenting my self-growth Journey

[Fauzi Noorsyabani @fauzinss]

as always, aku suka claude,

[Claude @claudeai]

We've doubled usage limits in Claude Cowork for the next month.

Delegate bigger, more complex tasks to Claude.

[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

[Fauzi Noorsyabani @fauzinss]

Someday that “when ya” will become “alhamdulillah ya Allah, finally I got all the good things”.

[femiiiiii. @femiiiszn]

adulting really humbles you. i finally understand why some people go quiet, stop posting, or just disappear from social media.

[aditya @adxtyahq]

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.

[Fauzi Noorsyabani @fauzinss]

@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).

[Fauzi Noorsyabani @fauzinss]

capcut premium semingguan dong #zonauangㅤ #zonajajan

[Aadit Sheth @aaditsh]

Andrej Karpathy shares a 3-step blueprint on how to master anything

[Codez @0xCodez]

https://t.co/Iu8L1r89Mp

[Asteri @Asteri_eth]

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

[Fauzi Noorsyabani @fauzinss]

@charger67W thank you mas, bermanfaat sekali informasinya🙏

[Fauzi Noorsyabani @fauzinss]

build strength so difficulty no longer controls u

[Rama @dntyk]

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.

[Fauzi Noorsyabani @fauzinss]

Sebaik apapun kita, orang yang gak suka sama kita itu bakal tetep ada.

reminder for me.

[Fauzi Noorsyabani @fauzinss]

@sandhikagalih @tibudiyanto 👏👏

[Fauzi Noorsyabani @fauzinss]

Tiba-tiba di DM diajak collab sama CEO and Founder startup dari SG. Alhamdulillah yaAlloh semoga semakin tambah banyak $$$$$.

[Fauzi Noorsyabani @fauzinss]

@karpathy 🫡🫡🫡

[Andrej Karpathy @karpathy]

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.

[Fauzi Noorsyabani @fauzinss]

@cinnamo0ns_ nitip dulu ya, mau bikin jga :))

[Fauzi Noorsyabani @fauzinss]

My 10K in this month, Alhamdulillah