For if you know what you want, and will be content with it, you can be trusted. But if you do not know, your desires are limitless and no one can tell how to deal with you. Nothing satisfies an individual incapable of enjoyment.

― Alan Wilson Watts, The Book: On the Taboo Against Knowing Who You Are

slept till 11 a.m., ate my usual breakfast, and felt so tired that i went back to bed. woke up at 3 something. felt so hungry and fatigued, wanted to stay home for the rest of the day and rot in bed. i've never felt so drained.

eventually had to get up to eat, didn't have lunch so i went to gourmet kitchen to get their $8 for 2 entrees + rice/noodles. felt like i was in hong kong for 30 minutes, sat in the cramped restaurant listening to the workers and customers exchanging words in cantonese. the egg noodles were dry, and fried fish was salty and oily. the long beans were crunchy though. I remembered why i hate eating out now.

things are slow now, but will start to ramp up again. it's in these moments where i have (too much) time to think about my future, about what gives me meaning and happiness, about my motivations and aspirations.

lately i've been letting my emotions take control of my thoughts when i could be doing things instead. I could call and text my family if i miss them, i could talk to friends and ask them about their day, i could go out and make new friends if i feel lonely, i could face my problems and worries directly, and actively solve them, instead of letting it consume me slowly. i've been finding myself lacking connection, when i could just put more effort into joining conversations with friends, even if it's about a topic i find uninteresting, i should just say whatever pops into my head. my tiredness can be fixed by prioritizing sleep and diet. there are solutions to everything. even if they are not clear, and not obvious, there are steps you can take. as long as you keep taking action and moving forward in a positive direction.

i'm turning 23 in a week, and it scares me. it scares me how fast time passes. how uncertain the future can feel at times. how it feels like i'm still stuck in the past, unwilling to let go and move on. it scares me that i'm in SF, a vibrant and beautiful city full of possibilities, and i'm feeling empty and depressed, lacking purpose and connection. that i have so much room to grow, in my faith as a christian, in my communication skills, in staying connected with my family and friends, in dealing with my insecurities, in my technical skills. how do i know i'm living my life right? what is right? how do i know i'm making the right decisions in life? how do i make the fullest of my time and experience being in SF in my early 20s? what mistakes am I making that I'm not even aware of? how do i minimize regret?

i have to turn emotions into action. take more risks. run more experiments. do more things that excites me. stay curious.

got up at 9 and was so tired that i didn't want to go. i told myself i didn't do much for this hackathon anyways. took 8 and then 30 bus, got there at 11 something. ate sun chips and pretzels with two cups of coffee. met up with my friends.

went for finalist judging and the judges were bombarded with too much AI. we could've strategised better for the finalist demo.

waited till 2:45 p.m. and we didn't get any emails for finalist announcements.

saw the 10 finalist demos for the hackathon, there were workout accountability with crypto, watermarking media, DeFI, swapping crypto with ai wearables, playing games with your NFTs, disaster response with blockchain, etc.

there were over 100 slides for the prize announcements.

it was crazy to see 400k dollars distributed to the winners out of 221 projects.

deeptrust.eth won two prizes:

🔒 Best Use of Compute Over Private Data (Decrypting within a Lit Action) ⸺ $3,000 by Lit Protocol

🏆 Innovative Applications of ZK in Deep Learning and the ETH Ecosystem ⸺ $2,000 by Nethermind

the biggest lessons we got out of the hackathon was we need a UI if we wanted to be finalists. and we should've just presented to more sponsors, even if we didn't use their technologies.

personally, i learned that if i took more actions or put more effort into the hackathon, things could've ended up differently. I should've played to my strengths, and focused on building a nice UI, instead of waiting to integrate with another part of the project. i had no confidence in my abilities for this project, and that impacted my contribution.

had conversations about career, relationships, friendships, parents, agency, impact on the world, malaysia, increasing surface area of luck, and also about the future of the project.

got so much swag from the hackathon, spent 30 minutes pasting stickers on my luggage while watching leah.

realized i've forgot most of the biblical stories and i find it hard to devote time to study the bible and grow in my faith. the goal is to read mere christianity before bed everyday, and take some classes from the bible project every sunday.

took photos around palace of fine arts. kept wishing i had a wider lens for larger objects like the palace. you can only do so much with a 23mm f/2 lens that can't zoom, it is still "brutally simple and highly effective" though.

got to the hackathon at 1 p.m., had fried chicken and fries for breakfast, talked a bit more about the idea

decided to go back home to hack on the idea for better internet connection.

spent most of the day learning about smart contracts, getting confused by phala docs, and figuring out how to write lit actions

dinner was roasted duck curry from Osha Thai BBQ. will attempt to cook it one day.

took in so much information today, both in the AI and in crypto world. this was the kind of idea that requires intuitive understanding of transformers, honored to be teamed with J for this hack. to think that all this won't happen if i never replied to a twitter post about a malaysian independence party. i should post more stuff on the internet. i've been getting contracts and meeting requests for my hackathon projects lately, the things we do today have returns in the future. especially the things that we do out of interest and curiosity, not by obligation or envy.

everyone was at their limits at 2 a.m., had to figure out a way to hack everything together, recorded demo at 3 a.m., ai generated documentation, submitted at 3:30, slept at 4.

5 bucks oat milk latte to keep me functioning for the day. DSA class is just 7 classes with 3 homeworks, 1 final exam, and one final project. thinking of using cuGraph by nvidia for a graph analysis project. i can also see myself learning something useful for leetcode interviews in the upcoming months. i should take notes and start creating a system for acing lc.

distributed computing was just pyspark. why not just use ray for all distributed stuff? is spark and ray essentially the same thing? one is just packaged into a product?

day 1 of eth global sf hackathon. so new to the web3 world. talked to a bunch of companies, got swags, ideated with claude by dumping the entire list of sponsors into it, did this until 2 something, took a waymo home at 3am, slept at 4.

Talent is insignificant. I know a lot of talented ruins. Beyond talent lie all the usual words: discipline, love, luck, but, most of all, endurance.

– James Baldwin

felt so dead upon waking up, body was sore down to my bones, had to literally drag myself to class.

intro to ml class is the entire section. i was looking over the entire class, thinking about how, in a year, everyone in this room will be at different companies, different states, maybe even countries, we might pass by on the streets, but i'll probably never see them again in my life. the friend circle was already been solidified, everyone else will just be acquaintances, the type of friends where you have small talk in the elevator about classes and exams, but nothing further.

been trying to work on my rag project over my blogs. decided on qdrant as vector db and cohere r for chat, embedding, and ranking. the hard part will be integrating with next.js and hosting the backend somewhere. but the QnA should be straightforward. getting blocked by tiredness and context switching between different possible solutions. the hardest part of starting a project is defining the MVP. what is the simplest solution that works, and using what? stop trying to use the "best" thing, just go with what works best for a proof of concept, then optimize after.

went to jtown with S to buy stuff from daiso, and also a gift for H. there's so many little trinkets here, got distracted by minor life improving items, like a protector for my iphone camera and a hook to put my bag instead of putting it on the floor on the living room, and 2.25 bold unsweetened green tea by ito en just to taste the boldness.

ended up at the Kimochi shop and bought a tiny oil painting by a local sf artist, internally debated with myself whether it was a good deal. never bought art before in real life, so was very unsure of myself. i have a major case of scarcity mindset and i'm allergic to spending money for anything other than necessities and food.

recommended by the japanese lady to buy iichiko shochu. today i learned about shochu, which is distilled instead of fermented like sake, and has higher alcohol content, and can be made from from not just rice, but also barley and sweet potato.

bund shanghai restaurant had really good food. xiaolongbao, pan fried buns, 东坡肉, rice cakes, shansi leng mian (eel cold noodles), crab roe tofu, and other dishes i can't remember the names

came home and started feeling intense confusion, missed home and people, had out of body experiences, loss of identity and meaning and purpose, spiralling, afraid, frustrated, lost, uncontrollably crying, sentimental, melancholic, all emotions jumbled up together that i couldn't snap out of. watching howls' moving castle helped bring me back.

talked to mom and it's the same topics about my sister and her faith, about life and relationships, about church. i should be more attentive to her, i wish my family could be in one place. i hope all of us can be in sf soon.

felt very unaccomplished and procrastinated sleep until 3 a.m. probably going to regret it tomorrow.

meta practicum delayed so today was more deep learning and building a project to do RAG on my blog posts. lots to think about for the RAG system, how to chunk, how to enhance the query, how to evaluate the response, how to do hybrid search, which embedding model, which vector database, which LLM for the response, how to speed up the query, how to structure the response so that it references blog posts, and most importantly, how to package all this into a backend for my blog to use.

watched nocturnal animals and that movie was really dark. it's the kind of movie where you're left just with the pain of the characters. you don't know what to do with it. there's no resolution or takeaway, just bitterness and sorrow. the highway scene is so traumatic.

tried turmeric ginger latte with oat milk. not sure if i liked it, i will stick to normal coffee. going through nvidia's deep learning course, fun to learn about CNNs and data augmentation. the lectures are short and notebooks don't go too deep, but it does serve its purpose for getting started.

been printing an AI paper everyday at the library. hoping to absorb everything in this field like a sponge, and start training models with PyTorch, and get into distributed training lingo.

but also at the same time reading and writing more, i don't want to be 100% on just AI, i want my creative side to have the chance to see the light. these past few days have been like the calm before the storm, going to be one heck of a semester again. i hope this time, i focus more on understanding the why, and being more curious in class, and giving 100% in the lectures, build more, put in 200% for meta, and remember to have fun in the process. i'm living in the best city for AI, and i have nothing to complain. life is full of possibilities now.

a career cold start algorithm i found online

The first step is to find someone on the team and ask for 30 minutes with them.

In that meeting you have a simple agenda:

• For the first 25 minutes: ask them to tell you everything they think you should know. Take copious notes. Only stop them to ask about things you don't understand. Always stop them to ask about things you don't understand.
• For the next 3 minutes: ask about the biggest challenges the team has right now.
• In the final 2 minutes: ask who else you should talk to. Write down every name they give you. Repeat the above process for every name you're given. Don't stop until there are no new names.

the first 25 mins gives you a framework to integrate new information more quickly. it will index on areas that are under active discussion, signals about the problems the team is currently facing, and adopt the company's lingo to work smoothly with them.

the second gives you a cheat sheet on how to impress teams with early positive impact. some things will take time to fix, and you should internalize these challenges, i.e. "our infra isn't scaling". but there are a surprising amount that you can easily help with, focus on these because they are things the team often neglect.

the third one gives you a valuable map of influence in the org. the more often names show up and the context in which they show up tends to be a different map of the organization that the one in the org chart

for all these, the greatest value isn't in the answers – it's in the asking. taking the meetings and listening shows the proper respect for the team. demonstrating mutual respect builds the trust required to make progress.

Louise Banks: If you could see your whole life from start to finish, would you change things? Ian Donnelly: Maybe I'd say what I felt more often. I-I don't know.

– Arrival

did a bunch of ML reading today, was literally walking to the bus stop while reading NLP with transformers, revised edition. learned more of the bigger picture for what led to transformers.

biweekly costco grocery run, bought beef chuck roast, $16 mixed nut butter, salt and pepper, frozen berries, bull ground meat

continued nvidia's deep learning course while watching fast.ai lectures concurrently. everything is slowly connecting together. it's a struggle to balance the amount of math to dive into, and code to write. the theory and the application. i want to know just enough to give me intuition, and only dive deeper if necessary, otherwise i want to build stuff. i want to train models to do something cool. i want to start making GPUs go brr.

watched arrival, it's such a good movie. their portrayal of extraterrestrial life forms. the concept of language as the foundation of civilization, the glue that holds people together. about time and memory.

watched weights & biases rag in production lectures. idea is to implement rag over my blog posts and have it reference specific posts and answer within <5 seconds. will call it askBen and will be my resume project.

worked on papertrail, have email and attachment processing and calendar integration done. har dto figure out what the mvp looks like. also difficult to not fall prey to premature optimization. i have to remind myself that the code that i (AI) write will be replaced in the next iteration, so the goal is to just get things to work.

so many things to learn. so many projects. i want more free days like this to pursue my own curiosity. come thursday, and i'm going to be swamped with assignments and exams again.

fast.ai lecture 3 & 5

• a derivative = a function that tells you, if you increase the input, does it increase or decrease the output, and by how much
• what is the mathematical function we are finding parameters for? we can create an infinitely flexible function from rectified linear units (ReLU). just create as many ReLU as you want, and you can create any squiggly line. everything from here is tweaks to make it faster and make it need less data
• a relu is torch.clip(y, 0.) anything smaller than 0 is 0
• you just need requires_grad() and backward() to get gradients, access with .grad attribute
• reproducibility (setting manual seed) is not useful when you want to understand data is how much it varies, and how your model behaves under different variations of data.
• broadcasting benefits: code is more concise, all happened in optimized C code (in GPU, it's optimized CUDA code)
• rules of broadcasting: as long as last axis matches
  • tensor([1.,2.,3.]) * tensor(2.0)

nvidia intro to deep learning

• what are tensors?
  • vector = 1d array
  • matrix = 2d array
  • tensor = n-d array
• ex: pixles on a screen is a 3d tensor with width, height, and color channel
• smaller batch sizes i.e. 32/64 for are efficient for model training
• nn.Flatten() expects a batch of data
• nn.Linear(input_size, neurons), number of neurons is what captures the statistical complexity in a dataset
• next(model.parameters()).device - to check which device model params are on
• torch.compile(model) for faster performance

“I forgot that every little action of the common day makes or unmakes character,” Oscar Wilde wrote in De Profundis

went into a rabbit hole into nvidia software.

• nvidia nim: apis to run llm models
• nvidia NeMo
  • curator: scalable data pre processing and curation toolkit for LLMs
  • customizer
  • evaluator
  • retriever
  • guardrails: toolkit for easily adding programmable guardrails to LLM-based conversational systems.
  • framework
• TensorRT-LLM: python api to define llms and build TensorRT engines that contain state-of-the-optimizes inference efficiently on NVIDIA GPUs.
• RAPIDS: GPU accelerated data science/analytics via open source software libraries and APIs.
  • cuGraph: accelerated graph analytics

also did a bit of reading into graph neural networks.

main takeaway is you can create a graph structure from text and images, but it's more useful for heterogeneous structures where the number of neighbors for each node in a graph is variable (as apposed to fixed for text and images).

there are three main predictive tasks for GNNs

(1) graph-level

• ex: molecule as a graph, predict its smell or probability of binding to a receptor
• analogy for image: classify an entire image
• analogy for text: label sentiment of an entire sentence

(2) node-level

• ex: predict identity/role of each node
• analogy for image: image segmentation
• analogy for text: parts-of-speech

(3) edge-level

• ex: image scene understanding, given nodes that represent objects in an image, predict which of these nodes share an edge or what the value of the edge is

the challenges of graphs in ML: representing graphs for neural networks

graphs have 4 types of info

1. nodes
2. edges
3. global-context
4. connectivity (hard part)

first three is straightforward, we create a node matrix N, where each node has an index i that stores the feature for node_i

connectivity is more complicated. first, adjacency matrix are sparse and space inefficient. second, many adjacency matrices can encode the same information, but without a guarantee that they produce the same result (they are not permutation invariant)

solution: represent sparse matrices as adjacency list

they describe connectivity of edge e_k between nodes n_i and n_j as a tuple (i, j) in the kth entry of the list.

conceptual

• A Gentle Introduction to Graph Neural Networks
• Understanding Convolutions on Graphs

applied

• CS224W: Machine Learning with Graphs
• Colab Notebooks and Video Tutorials — pytorch_geometric documentation
• Introduction to Graph Neural Networks with NVIDIA cuGraph-DGL | NVIDIA Technical Blog

found these datasets to work with

• Open Graph Benchmark | A collection of benchmark datasets, data-loaders and evaluators for graph machine learning in PyTorch.
• Yelp Dataset

chinatown stockton street

For a man's ways are before the eyes of the Lord, and he ponders all his paths – Proverbs 5:21 ESV

methodist church chinese service, they're so hospitable and welcoming. they keep saying "so glad to see young people". played ping pong after service. they had sticky rice and che thai.

space X chopsticks capture

italian heritage festival & parade

saw blue angels at fisherman's wharf. they fly down to 18 inches apart from each other. and costs ~120 mil each. the booming sounds they make are both terrifying yet thrilling at the same time.

went for a quick grocery run before the stores closes at 6pm. did a photography walk around chinatown. so impressed by the x100s, the only thing is the pictures that come out of it is dark, and requires lightroom for post-processing. but for a camera that came out 14 years ago, it's working so well.

so many things happening in the world. i'm only experiencing a tiny slice of it.

felt immensely tired today. is this the tiredness from the past week catching up on me? what is considered rest? am i even capable of rest?

A quiet secluded life in the country, with the possibility of being useful to people to whom it is easy to do good, and who are not accustomed to have it done to them; then work which one hopes may be of some use; then rest, nature, books, music, love for one's neighbor — such is my idea of happiness. — Leo Tolstoy

basketball and bbq with IEC friends

talked about giving parents money as a culture in chinese families

napped for 2 hours

woke up feeling confused

final group assignment for regression

staked 0.02 ETH for ETHGlobal hackathon, excited to jump into web3 and see what i can build.

a few good resources

• Ethereum Developer Resources | ethereum.org
• useWeb3.xyz · Learn Web3 development
• Speed Run Ethereum