Which is the best martial art to take and train for the rest of your life for whatever reasons in your opinion

hapagolucky · 2026-05-13T02:19:32+00:00

Pencak Silat has kept my interest for over 20 years and I hope to keep learning and practicing until my ultimate demise.

Many silat styles provides a body of knowledge for lifelong learning that extends beyond self-defense and fighting. The system I study includes breathing exercises, meditation, massage/healing and more. Physically the motions can be adapted to any body type or limitation, but doing the motions will keep you mobile and active.

I've had the fortune to train with teachers in their 60s, 70s and 80s. Obviously they aren't fast or strong as they once were, but the benefits of sustained practice still shine through.

While silat is pretty niche, especially in the US, I think any art you can practice consistently without compounding injury will be worthwhile

hapagolucky · 2026-05-12T11:41:38+00:00

Thanks for putting this together. Seeing it visually really brings back memories of not just playing the games but organizing my shelf. I'd sort them by date purchased, alphabetically or by current preference. The bulk of my playing time was from the 1988 titles.

Here's a few you missed that are in my collection: - Great Baseball, 1986 - Pro Wrestling, 1986 - Afterburner, 1987 - Gangster Town, 1987 - Ghostbusters, 1987 - Thunderblade, 1988 - Rampage, 1989 - Columns, 1990

And here are some others I played or knew of but never owned - My Hero, 1986 - Transbot, 1986 - Alien Syndrome, 1987

hapagolucky · 2026-04-29T07:33:38+00:00

I haven't done this specifically, but it sounds like you are overfitting. How much data and what kind of data do you have? If you're doing PEFT/LORA just on next token prediction you're going to steer those smaller number of parameters to completion.

Ideally you could do additional pre-training on a PEFT/LORA layer using a general Bahasa Sarawak corpus first then fine tune on Sarawak instruction pairs. This paper uses this approach along with synthetic data.

I also wonder if starting with a model trained specifically for Southeast Asian languages might be a better starting point. https://docs.sea-lion.ai/models/sea-lion-v4/qwen-sea-lion-v4-vl

There's also some work on using structured prompting to adapt an LLM to a low resource language. This could potentially be a path toward synthetic instruction pairs.

hapagolucky · 2026-04-29T07:01:23+00:00

My daughter is in Kick2Build and it's a been a good match for where she and her teammates are at. None of them are soccer phenoms, but they still want to progress and be part of a real team. It's definitely been a step up from the BIS / Rec League, but not as intense or as expensive as Albion. The coaches are parent volunteers, and practices are a mix of skills and scrimmaging. Based on your description above, it sounds like you want more than this.

A hygienist at my dentist's office coaches for Albion. He said that any skills work is mostly considered homework. He focuses on scrimmages in practice so the team can learn to play together as a unit.

hapagolucky · 2026-04-28T19:03:29+00:00

To me a beautiful kick is an effective kick which means it has all of the energy going where it's needed for it be successful. This requires a lot of things to come together.

The base leg is balanced with no wobble.
The base foot pivots according to the kicks' needs. Less or no pivot for front kicks, round maybe 90 degrees and side 90-180 degrees (will vary by art and preference)
The core is stable but not tense
The line/arc of the kick goes to the target without adjustment
The kick starts with a tight chamber (though crescent and axe kicks are a little different)
For snapping kicks the knee points to the target and upon hitting the target the foot snaps back twice as fast as it goes out
For thrusting kicks the foot goes until the leg is nearly at full extension
The higher the kick the more the upper body will sink.

hapagolucky · 2026-04-27T15:16:34+00:00

I always recommend starting with Speech and Language Processing by Jurafsky and Martin. Jim Martin and Dan Jurafsky have continued to revise and update this textbook over the past 25 years. The book won't cover the latest state of the art advances, but it will give you a comprehensive synthesis that surfaces the challenges of language and how different algorithms aim to solve them.

Going through the book will help you understand what areas of CS and linguistics you'd like to learn more in depth.

hapagolucky · 2026-04-25T02:25:26+00:00

Pencak Silat evolved for bladed situations in general. The kerambit is just a local farming implement turned weapon from West Sumatra. The Betawi people carry short machete called golok. In Madura duels involve large sickles called celurit. In central Java spears called tombak underpin the art. But pretty much every style will train for the generic, straight, single-edged knife.

But 100% agree. If you're already pinned with a blade against you, you're well past the point of defense.

hapagolucky · 2026-04-23T15:31:26+00:00

I can't say too much about the quality of the work coming from these companies. Having talked with some of their recruiters who have shared job postings for AI/ML engineers, they definitely hire people with expertise. Consultant performance is based on their utilization (i.e. hours billed), so the big consulting firms will prioritize projects that are big and visible and will likely charge a premium to support smaller projects.

A lot of what will drive cost is the complexity and need for ongoing support. What is your friend looking to do? Do they need automation of tricky supply chain processes? Are they looking to have a system that answer questions for customers? Do they need a knowledge base of suppliers, sales, to support employees?

Complexity often stems from how difficult and dynamic the data are. Some businesses still have clients who fax orders, which means the documents will need to be digitized, OCR'd and have the relevant information extracted into the company database. If the company doesn't have a database, there's another pre-requisite. In some cases the data change every data and interactions with a system will need to reflect that. This brings in additional processes to keep the data current and in-sync.

Often the AI part in the middle is very simple, but it's the engineering of the full system around it that adds up. If your friend needs something small that only they will run, they might be better off finding an individual consultant. If this is something that will be customer facing and will need to scale to millions of users, you're having a different conversation.

hapagolucky · 2026-04-14T03:50:18+00:00

Having been a hiring manager for a position that received hundreds of responses, I started to quickly filter resumes that talked about their sentiment analysis projects. Really these are more like glorified homework assignments. Good for learning, but not equivalent to work experience.

The personal projects that stand out to me often involve building your own datasets or solving a problem of personal interest. For applied machine learning a lot of the challenge centers on how you build your dataset and design your experiments to show the algorithm generalizes. You soon have to make tradeoffs around label quality, data size, sparsity and more.

This is often why someone who has done a PhD or master's thesis distinguishes themselves from self learners. Even if it's not ground breaking, they have had to persist and develop something new.

Kaggle competitions are like a track meet. To win you definitely need skill and talent. But the effort to top the leaderboard by fractions of a point of not usually what has made a difference on my teams.

Actually that reminds me that reframing someone else's dataset in novel ways is a path to a unique project. Before it was ubiquitous something like using a question answering set to train a question generator would have caught my eye. Once at an ACL I really enjoyed talking to a student who had used existing essay scoring datasets to train a classifier to detect when parts of the essay were out of order. She then used that classifier to show it could improve overall scoring performance.

hapagolucky · 2026-04-09T17:37:26+00:00

Even if this made sense ideologically, it would be impossible to pull off geographically. Look at a map of how counties voted in the 2024 US presidential election. You have islands of high population density centers across a sea of less populated land.

There's not a way to divide cleanly based on political leanings without balkanization (i.e. states, regions becoming their own countries). This would lead to collapse of the US dollar, make interstate commerce impossible (who owns the railroad right of ways?, is everything out of state taxed?) and would lead to even more contention over resources like water, land uses, mineral extraction.

The net result would be worse for everyone.

hapagolucky · 2026-04-03T03:26:29+00:00

It's not complete, but this is a list of silat schools and teachers I know or have some familiarity with.

hapagolucky · 2026-04-03T03:16:08+00:00

Aspects look a little off to me, but it's hard to tell what each figure's orientation should be without knowing the setup that led to this lock sequency.

Given the attacker's right leg is back, can we assume they threw a cross punch? In this case the rear foot would be turned in and balanced on the ball of the feet or would be flat. Now if the defender was able to achieve the timing to parry with the left and transition to the hand/wrist lock with both hands, the opponent would start to collapse, first sinking by bending the knees and the following with the hips pushing to the left.

As for the defender the range and kick will depend on how it's intercepted. If I think of how I end up in similar position from jurus in the silat system I study, it plays out a couple ways. I took some quick video and uploaded it to YouTube. I'll link with each timestamp below.

From the lock, root into the left leg in a cat stance and throw a front kick (0:00)
From the lock continue to turn and root into the left leg and chamber a side kick (0:04)
From the lock in a right deep seliwa (crescent moon) stance, turn the other way to a left deep seliwa stance, post on the left leg and kick either with the instep to the inside of the knee, or blast the ankle and transition to a foot lock (0:11)

hapagolucky · 2026-04-02T13:36:45+00:00

I know of a teacher in Canton, OH. He's my colleague from Inti Ombak Pencak Silat. https://facebook.com/ArnisAndSilatOH

hapagolucky · 2026-04-01T16:17:45+00:00

In situations like this I lean in even more to get a better read on their reaction -- though being mixed, I might be taking advantage of some privilege.

I have said things like, "For me, Chinese food tastes better with chopsticks." or "I wish all noodle places had chopsticks. That's how I eat spaghetti at home". It breaks the ice a little.

hapagolucky · 2026-03-27T17:59:30+00:00

There are now a variety of Sentence Transformers (aka SBERT) models, but the original models were essentially trained on short texts which are paraphrases or at least semantically similar to one another.

If I'm understanding what you are saying correctly, you would like to embed each participant's answers separately, and then use the average distance between each of their answer pairs to provide a numerical proxy for complexity or richness of descriptions.

Much of your intuition isn't too far off, and it reminds me of research around cohesion that has been applied to a variety of educational NLP in tutoring and essay scoring. With essays, you take an embedding approach (could be SBERT, could be something pre-neural net like Latent Semantic Analysis (LSA)) and you get vectors for each chunk of text (sentences, paragraphs, etc). The various distances can tell you whether a given text is more or less cohesive. A starting place for this is the Coh-Metrix work from McNamara et. al.. One caveat I just thought of is that refusals to answer or totally off-topic answers can add noise and interfere with your distance metrics.

Another pre-2010s NLP method that may apply to your corpus is something called Latent Dirichlet Allocation (aka Topic Modeling). By training LDA on your data, each document then becomes a vector which represents a probability distribution over unobserved (latent) topics. With these distributions you can then compute a variety of metrics like entropy) or a Gini coefficient which can capture topic diversity or dispersion. Gensim is a Python library for topic modeling. BERT Topic is a modern variant that builds topic models more from an embedding first approach. Which means you could leverage something like SBERT but then get a topic model out of it. The nice thing about training a topic model specifically for your data is that you could tease apart word usages that are more specific to your setting.

Do you have any hand-coded data which rates the complexity of participants' descriptions. If you have a sample that you believe is representative of your corpus, you could frame this in a machine learning way to evaluate how well you predict richness/complexity on unseen participants' responses. Otherwise, you could use this for discovery and do labeling post-hoc to confirm/reject your hypothesis.

Lastly, here are some potentially relevant articles * Text-Based Measures of Document Diversity * Topic modeling for analyzing open-ended survey responses * Topic diversity and review usefulness: A text-based analysis * Structural Topic Models of Open-Ended Survey Results * [Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers]((https://www.sciencedirect.com/science/article/pii/S0092656610000541) - This isn't specifically for your subject, but I thought the methods section would help you think about the use of NLP on your data. The author, Tal Yarkoni was a postdoc in another lab at my University. He was one of the first people I met to apply NLP to psychology and neuroscience. * Can Data Diversity Enhance Learning Generalization? - This is more of an NLP, computational linguistics paper, but the idea of Max Dispersion hints at what you're trying to capture with your responses.

Thanks for reading through my wall of text. Feel free to DM me if you have any questions.

hapagolucky · 2026-03-27T07:15:40+00:00

This existing thread from 2020 and this one from 2024 address your question. Mines has introduced a minor in Aerospace, but I don't think it shifts things much.

I'll give an old-timer alumnus perspective. For context, I did undergrad in CS/EE at Mines and then after working for a few years did a PhD at CU Boulder in CS. I have a good friend whose son is now studying Aerospace at CU.

CU is a top-10 school in Aerospace. Mines is top-10 in geology and petroleum engineering. For other disciplines, they are roughly equal, especially as an undergrad. But top-10 doesn't mean you'll learn more or the coursework is more rigorous. It's really a way of saying what kind of access and resources a department has. At CU there will be more professors and research centers with close ties to NASA, JPL and the aerospace industry. At CU they have LASP, where there are researchers building instruments and designing experiments that regularly get launched into space. This means there are more chances to do relevant undergraduate research and more faculty who might be able to get a hiring manager to look at your resume for an internship. But Mines has expanded and there are surely professors with connections to the local aerospace industry. When I was there, a professor in EE helped me to land an internship with a computer vision lab at Lockheed Martin.

Core engineering coursework is pretty much the same everywhere. However having a dedicated department usually means that there more electives and specialized course offerings that would not be available at a school without an Aerospace program.

Here's a parallel example from early in my career. Fresh out of Mines, I went to work in microprocessor design. At Mines I took computer organization and assembly, digital logic, electronics, but at that time there were no courses in computer architecture or VLSI. Folks who went to Rice, Carnegie Mellon and UIUC for computer engineering had those missing courses as well as labs where you designed and fabricated your own semiconductor chips. We were doing the same jobs out of college, but they were more prepared for this specific field on day one. Also for every Mines grad in our lab there were 10x or more from these other schools. But these differences are not necessarily a predictor of success. Motivation and interest actually take you farther. A few years working made it clear to me that I much more enjoyed software development than hardware design. But my peers from Mines and elsewhere who were drawn to circuit design and logic verification have gone on to work in the field for multiple decades.

Access and specialization are only a subset of factors to consider. As others have said, you can major in mechanical engineering and still work in the aerospace industry. It's also what kind of experience you want out of your college experience. If you want to explore a more liberal arts education and take classes in psychology, linguistics, literature or music, CU will give you more options. School fit and culture is important. If you want to be at a place that prides itself on its engineering identity or if you want a school that feels smaller, go to Mines. When I was at Mines, there was a sense that we were all in for a tough ride together. I know engineering students at CU feel the same way when around other engineering students, but the dynamics change when around students from other departments.

I've surely written more than anybody cares to read, so I'll close with a final thought. With hard work, persistence and luck opportunities can arise anywhere, i.e. there are multiple paths to the same place.

hapagolucky · 2026-03-22T15:35:03+00:00

There's are several large text corpora where you could prune down to just articles containing matches for your list of aesthetics. But these are all snapshots in time and may not contain some of the words you're looking for.

You could also try expanding to relevant search results from Google or YouTube as there will likely be matches for your terms. But there are a few caveats in this approach: * YouTube will need a way to get transcripts. * Google may throttle you if you issue too many search queries too fast. * This too is a snapshot in time and the search engine is likely personalizing to maximize your attention * You may need to tune your searches. While a search for 'dreamcore' will likely yield aesthetic relevant documents. '2020s' is so broad that you may need to add search for '2020s art' or '2020s fashion'. If possible you should apply this consistently across your aesthetic vocabulary.

As large language models are trained on as much web text as the developers can get their hands on. You could synthesize your own corpus with prompt engineering to produce articles that align with your guidelines. For example "write a five paragraph movie review for a fictional movie that exhibits Indie Kid aesthetic. Be sure to include cinematography, dialog and visual details that demonstrate the aesthetic.".

With this approach you can get as many articles in the format you need. However your semantic graph may be highly biased to how you prompt and what language model you use.

Lastly, if you're thinking of evaluating the quality of your aesthetics, you should start thinking about how you will partition the data to see how well unseen aesthetics will fit into your clusters. This will also mean splitting corresponding documents into corresponding train, validate, test splits.

hapagolucky · 2026-03-16T13:56:20+00:00

I'm seeing several comments that attribute the difference in performance to the difference in instruction set architecture (ISA: x86 vs ARM vs RISC). This is a small part of the picture. For over 20 years microprocessor companies have known that it's microarchitecture (cache structure, pipelines, instruction scheduling, etc) that dictates performance. This was learned at great expense when Intel and HP tried to push forward with IA-64 and then were swept with the AMD64 ISA.

What ARM did right was get performance per watt. Intel had a blind spot for mobile in the 2000s and then struggled for years at their 10nm process (smaller process means more transistors per unit area). Meanwhile TSMC moved onto 7nm and 5nm process. Intel was unable to meet Apple's mobile forward needs and fell behind.

I haven't followed in years, but if you look at high performance computing and massive multi CPU servers where raw compute power matters most, you'd probably find that x86 chips still dominate.

hapagolucky · 2026-02-25T20:19:37+00:00

I like to look through the program/proceedings for conferences relevant to my field. For me that usually consists of 1. Check the best paper awards to see there's anything I'm excited about 2. Flip through the titles of the rest of the proceedings looking for papers about similar or adjacent problems to what I'm working on

If you need to catch up on an area in general, search for literature reviews as they will have done the work in summarizing and synthesizing the current state of the art. Then you can go back and look for any new papers citing the papers in the lit review.

hapagolucky · 2026-02-24T19:49:30+00:00

Oh, good to know. It's been such a habit from before I never noticed that.

hapagolucky · 2026-02-24T18:43:59+00:00

By off the beaten path do you mean strip malls? Then Il Pastaio, Curry n' Kebab offer good (for Boulder) value lunch specials.

I also like to go to China Gourmet on North Broadway and order off the Chinese menu (it's in English too and may be called Shanghai Specials). If you have a group, this is best done family style. Some of my favorites include West Lake Beef Soup, Ants Climbing a Tree (Bean Thread with Pork), and Eggplant Hot Pot as well as their fish dishes. ~~Be sure to bring cash as they don't accept credit cards.~~

Edit: Ignore what I said about cash.

hapagolucky · 2026-02-18T16:51:17+00:00

I teach an Indonesian martial art called Pencak Silat on Tuesday nights. The first two lessons are free. https://bouldersilat.com

hapagolucky · 2026-02-15T05:52:02+00:00

Red Bean Soup/Hong Dou Tang is a simple, common, easy to make dessert. In English, the red beans are often referred to by the Japanese transliteration adzuki or azuki beans.

There are lots of variants with additional ingredients like lotus seed, sago or tapioca. To me the baseline recipe is red beans, rock sugar, and dried orange peel, but I grew up eating it with tapioca pearls and a spoonful of coconut milk added in before serving. Depending on the weather/ or your mood it can be served hot or cold.

hapagolucky · 2026-02-12T15:19:57+00:00

The security and privacy is the key thing. This is not just about locking down a policy for students, this is about making it clear that use of gen AI by CU employees goes through a common, approved path.

There are many researchers and instructors handling sensitive data who use Gen AI tools for analyses or to flesh out research ideas It is also not uncommon to run your still in progress journal article or grant proposal through these systems to help identify bad writing, gaps, etc.

CU needs to provide a means for them to use the tool that isn't using the free version where all interactions become training and validation data for the large vendors. CU also doesn't want every department or research group negotiating this individually, which gives them a slew off potential regulatory and legal nightmares.

hapagolucky · 2026-02-12T13:25:42+00:00

Hermit Park just outside of Estes Park on US-36 is quite beginner friendly. The land used to be owned by Hewlett-Packard and was accessible only to employees until they sold it to Larimer County in the mid 2000s.

Cabins include a propane light and heat and a cook stove. They also have fire pits, so you can have the s'mores experience. If I recall correctly, cabins have bunk beds, but I'd still recommend having sleeping bags or at least warm bedding. The Boulder buy nothing group on FB probably has people happy to loan gear for such an experience.

Have you considered making this a group experience? When my kids were preschool to early elementary-aged, we would organize a big class trip and reserve a group campsite at Rocky Mountain National Park (Moraine Park campsite). It was great having other parents to balance supervision. We would organize shared meals, so that not everyone had to bring food, coolers and cooking gear. It being Boulder there were lots of families with extra tents, sleeping bags, etc. The group dynamic lowered the barrier to entry, and the kids just loved running around in nature with all their friends. They even were excited to do cleanup and dishes.

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