thomasikzelf

The takeaway might be that historic data of market data might often be enough to make a reasonable prediction. Only external data that nobody else has (used) can make your prediction better then the market.

Making a prediction with same accuracy as the market: easy Making a prediction with more accuracy then the market: very hard

Comment · 3 months ago

Cool app! I also had some ideas a while ago that I think the current breed of aim trainers are still missing.

In-game practice like the Range, DM, and Team DM still feels like the gold standard to me. I generally only use aim trainers when they offer a noticeably higher return per minute, rather than just teaching me how to get good at aim trainer specific mechanics.

Here are a couple of things I think an aim trainer should have:

- Dynamic difficulty: I feel it works best when trying to keep accuracy relatively high, but fluctuating the target accuracy threshold over time and adjusting difficulty dynamically to match it. That keeps you challenged without breaking form.

- Dynamic scenarios: Instead of picking scenarios manually, I think a trainer should ideally detect what you suck at and launch those directly.

- Habit coaching: It would be awesome if a coach flagged bad habits in real time—like clicking too fast (low accuracy), doing a slow tracking movement instead of a sharp flick (mircoflicks), or using tracking when click timing is needed.

- Biomechanics: Finding the exact "perfect" sensitivity feels less important to me than ensuring your arm and wrist are working together properly, altough this might depend on your aiming style.

- Simplicity: A lot of trainers overwhelm you with numbers and menus. I image a simple "click here to start and follow feedback" workflow with nothing else for the user to learn. Seeing a bunch of numbers is less important to me then just getting better (maybe with a final score).

Valorant-specific focus: Trainers often feel too generic for tactical shooters like Valorant, where angle holding, target reading, and micro-corrections dominate.

I built a small aim trainer to experiment with these ideas myself: https://mousecontrol-thomaswelter-fceff2dbe456668c6bedef32db... (the aim trainer is very basic, only made for myself, fixed sens etc).

- In this tool, difficulty scales dynamically while playing (the parameter in parentheses changes on the fly):

- Angle Hold (enemy speed increases): Practicing holding angles against multiple targets. Holding angles is crucial in Valorant (just look at how Primmie plays).

- Follow (standstill window decreases): Forces active mouse correction and reading the target before shooting. I tend to click too fast, so this forces me to slow down and micro-correct.

- Jiggle (jiggle speed increases): Targets perform tight jiggles. I find players who spam small jiggles really hard to hit, so this directly targets that weakness.

- Micro Flicks (snap distance increases): Micro flicks are easy enough to train in the Range, so I mostly use this mode when I am away from my gaming PC.

- Static (target size decreases): Standard static clicking, but with dynamic scaling as targets shrink.

- Strafe (movement speed increases): Moving targets with limited hit windows. When watching pro DM VODs, most kills are on stationary targets, but hitting moving players is still vital and tricky to isolate in-game.

I am still not very good at Valorant but the aim train ideas are interesting. Let me know what you think!

Comment · 4 months ago

I have visited factories for work and my experience is the same. There is so much stuff that could easily be automated but is not because it is too expansive for too little value to make a custom one off machine. The big high volume things will be automated but these machines will have 90% success rate and lot's of stuff that needs to be done by hand. You can search for factory tours on youtube to get an idea. Here are two videos, an Amazon warehouse and a Tesla factory. the big heavy stuff is automated but lot's of work is still done by hand. https://www.youtube.com/shorts/-R6cBkza17k https://www.youtube.com/watch?v=45slYC99uUg

Comment · 4 months ago

It looks like one stole the design from the other, I don't know which one, haha.

Comment · 4 months ago

Nice, I will be following your posts! I just bought a robot arm myself, the seeed studio B601DM (€1500 6+1 axis), it works great and is open source hardware as well and a bit more solid then the so101. I also opted to not use ros, I don't want to give up control by putting another framework in between. Is your plan to see whats possible right now or do you also have ideas on how to improve sota?

Comment · 5 months ago

firefox android does not support CSS animation-timeline, and firefox desktop needs layout.css.scroll-driven-animations.enabled. This probably should not be used for any critical features.

Comment · 5 months ago

You can make some really cool stuff with css scroll animations. I used SVG paths with a scroll animated dash offset to draw an image while scrolling. Zero javascript, it feels so smooth. https://thomaswelter.nl (the background)

Comment · 7 months ago

The declarative vs imperative example is strange here. Why is the imperative example so convoluted? This is what one could write in js:

  badge.textContent = count > 99? '99+' : count
  badge.classList.toggle('show', count > 0)
  paper.classList.toggle('show', count > 0)
  fire.classList.toggle('show', count > 99)

The declarative example also misses the 99+ case. I don't think this example describes the difference between imperative and declarative well.