Nice, always good to see another implementation! Were you able to get past the pivot alignment issue? Because in a proportional font, the word seems to slide around your eye as it shifts the spacing of the characters immediately adjacent to the pivot, so I anchored the pivot character and allowed the rest of the word to extend freely from both sides.
What stood out to me as a major difference was pacing though - a flat interval makes for a pretty robotic reading experience since there's no sentence structure, and I found that using multipliers on the base duration was more effective, getting about 3x for sentence ending punctuation, 2x for commas, and a little extra for long words overall was more noticeable than any other adjustments I made.
I'm curious if DuoBook is actually trying to implement reading in a second language as a learning tool as the name suggests, and if so, how does that affect things? I would imagine the pacing would be overall lower (particularly for punctuation) and recognition of the words you're actually reading would have more impact on comprehension than the ones you're just learning to read.
Yes, DuoBook is a language learning app, so I added RSVP as an add-on feature for re-reading the stories in another language. It’s not the main experience.
My idea was that the learners can do a slow turn with the main reader, and then a quick read afterwards if they want to.
Honestly, I spent at least a week for the pivot alignment but at the end I accepted once in hundred words it’s shifted a bit. I might go back into it at some point though.
Maybe a section on RNA degredation and DNA stability and how it would affect sequencing would be nice.
Also, down stream analyses are largely missing e.g. differential analysis, pathway enrichment. Not to mention newer single cell techniques and their up/down sides. But good start!
As far as I understand the underlying model is not multimodal. Maybe a quite naive question but can't we improve the performance by a joint embedding of EEG and MEG data? If it scales with log-linear data, maybe it would also improve with other data as well.
I basically did the same thing almost one and half years ago and not many people cared, but I still believe that this is the future for computational biology.
Two weeks ago, I got 100/100 of a test from a big company for a first screening without using AI. I was pretty confident that I would pass the first round, even hinted few of my friends, but ended up being rejected with an automated mail… The job market is insane at this point and I am not sure what the recruiters are actually looking for. If the candidate uses AI they’re eliminated, if not they’re eliminated. I guess this is one of these times we read on history books: great unemployement.
> The job market is insane at this point and I am not sure what the recruiters are actually looking for.
To justify their own jobs.
In theory there's never been a better time to hire on SWE talent. There are lots and lots of candidates who rode high during the COVID hiring wave, took on debt based on a high income, got fired, and now need money.
But hiring isn't picking up. You have a bunch of people in the HR industry who realize that for the most part, the combination of candidate filtering, ML, and a basic tech interview process could probably do their jobs. So they have to make the process as byzantine and difficult as possible to be able to go to the suits and say "look at all of these low-quality candidates we kept out!"
> I guess this is one of these times we read on history books: great unemployement.
The good/bad news is that if this continues, there will be either a regression to the mean or a massive de-stablization of most societies. You can't kick most of the working-age population out of their jobs.
I was just involved in the hiring process for a new rec on a team adjacent to mine, and the HR/screening process was a complete mystery as to candidate filtering for outside applicants. So many AI tools making arbitrary judgments from resume content to psychological profiles of candidates, led us to 0 organic people “qualified” enough to interview for an SRE/Devops role. All we hire now is referrals as they are the only ones to get pass automated screening
Nowadays more than half of job postings are fake. It's either process to show activity or they already have one to hire but need to follow established process.
Getting through an interview process during a bull market has always had a small random component. I think we all have to understand and accept that in bear hiring markets, almost the entire component is random. Having a perfect skill quiz or hackerrank score and getting rejected should not cause you to try to figure out what you did wrong.
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