About Us & Our Offer


About Bedugul Coffee

Tastes somewhere between heaven and earth. Pure Bali Robusta Coffee. Probably the best robusta coffee in the world. Perhaps it is not widely known, but some coffee are blended. Not just arabica with robusta (which is very common), but sometimes a coffee is blended with corn or rice.

What we have is pure robusta coffee from Bali. Hand picked, hand sorted, traditionally roasted and ground by the natives of Bali.

While many people prefer arabica, and many insist that arabica is more superior to robusta, we dare to say that our Bedugul Robusta is not just another robusta, but special robusta with a mild and soft taste, yet bitter, and simply different from any other coffee in the world.

If there is a phrase "don't die before you see Bali", we have a phrase for our coffee, "once you taste it, you'll live forever".

Nevertheless we also supply high grade and best quality arabica from Sumatra (Siborong borong), Aceh (Berg en Dal variety), and Toraja (Sulawesi).

Our brand name is Bedugul Coffee, a beautiful highland in the northern part of Bali, Indonesia.

Fact: the name Bedugul is not as famous as Kintamani for coffee products ... so ... we just (made it up) picked up the name, because we love the place, famous for its iconic temple by the lake Bratan, the Pura Ulun Danu. About our Bali Robusta Coffee ? We didn't made it up, it is truly MADE IN BALI.

If you want to know more about Bedugul, the town, read here.

Contact
Name: Putranto S. a.k.a. Raja Kelana
E-mail: putrantos@gmail.com
Facebook Cause : Bedugul Coffee
Whatsapp: +62 838 7437 3403

Photo Gallery of our coffee plantation in Bali

Perkebunan Kopi



Dengan Hormat,

Yang terhormat Bapak/Ibu/Saudara pengusaha kopi di Indonesia. Kami sedang mengumpulkan informasi / data tentang lokasi-lokasi perkebunan kopi di seluruh Indonesia.

MAKSUD & TUJUAN

1. Tujuan dari angket ini semata-mata untuk membangun suatu database yang nantinya kami harapkan akan sangat bermanfaat dan dibutuhkan oleh para buyers kopi, dari dalam maupun luar negeri. Sehingga mereka tidak perlu mencari informasi tentang kopi ke banyak sumber di internet.

2. Kami ingin mengajak para wisatawan / penggemar kopi berkunjung ke kebun Anda. Untuk itu kami butuh informasi tentang lokasi, contact person, dll.

Informasi tentang kopi disini kami anggap tidak bersifat rahasia, namun demikian kami menjaga privasi Anda, dan tidak akan menyalahgunakan data / informasi pribadi (seperti email pribadi) yang sudah Anda masukkan melalui form ini.

Sebelumnya, kami sampaikan terimakasih sebesar-besarnya atas partisipasi dan kesediaan Anda untuk mengisi formulir dibawah ini.

Salam Sejahtera Selalu !

Hormat Kami,

Admin Preanger Koffie

Apabila ragu atau ada yang ingin ditanyakan, silahkan hubungi kami di +62 838 7437 3403 atau email bedugulcoffee@gmail.com.









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Decoding Google MUM: The T5 Architecture and Multimodal Vector Logic

Google MUM (Multitask Unified Model) fundamentally processes complex queries by abandoning traditional keyword proximity in favor of a Sequence-to-Sequence (Seq2Seq) prediction model. The system operates on the T5 (Text-to-Text Transfer Transformer) architecture, which treats every retrieval task—whether translation, classification, or entity extraction—as a text generation problem. This architectural shift allows Google to solve the "8-query problem" by maintaining state across orthogonal query aspects like visual diagnosis and linguistic context.

T5 Architecture and Sentinel Tokens

The engineering core of MUM differs from previous models like BERT because it utilizes an Encoder-Decoder framework rather than an Encoder-only stack. MUM learns through Span Corruption, a training method where the model masks random sequences of text with Sentinel Tokens and forces the system to generate the missing variables. MUM infers the relationship between "Ducati 916" and "suspension wobble" not by matching string frequency, but by predicting the highest probability completion in a semantic chain. This allows the model to "fill in the blanks" of a user's intent even when explicit keywords are missing from the query string.

Multimodal Vectors and Affinity Propagation

MUM projects images and text into a shared multimodal vector space. The system divides visual inputs into patches using Vision Transformers and maps them to the same high-dimensional coordinates as textual tokens. Affinity Propagation clusters these vectors based on semantic meaning rather than visual similarity. A photo of a broken gear selector resides in the same vector cluster as the technical service manual text describing "shift linkage adjustment." Cross-Modal Retrieval occurs when the system identifies that the visual vector of the user's image overlaps with the textual solution vector in the index.

Zero-Shot Transfer and The Future

Zero-shot transfer enables MUM to answer queries in languages where it received no specific training. The model creates a Cross-Lingual Knowledge Mesh where concepts share vector space regardless of the source language. MUM retrieves answers from Japanese hiking guides to answer English queries about Mt. Fuji because the semantic concept of "permit application" remains constant across linguistic barriers. This mechanism transforms Google from a library index into a computational knowledge engine capable of synthesizing answers from global data.

Read more about Google MUM - https://www.linkedin.com/pulse/how-google-mum-processes-complex-queries-t5-multimodal-leandro-nicor-gqhuc/

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