Omar Alonso
5K posts
Omar Alonso
@elunca
Data gaucho. Information retrieval, timelines, knowledge graphs, crowdsourcing, human computation. Author: https://t.co/iirVQx4GTt
SF Bay Area, CA Beigetreten Nisan 2009
331 Folgt1.3K Follower
Predicting the future is very very hard. Keep working on fundamentals.
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Omar Alonso retweetet
If you are a software engineer "experiencing some degree of mental health crisis", now hear this, because I've been coding for 50 years since the days of punched cards and I have a salutary kick in your ass to deliver.
Get over yourself. Every previous "programming is obsolete" panic has been a bust, and this one's going to be too.
The fundamental problem of mismatch between the intentions in human minds and the specifications that a computer can interpret hasn't gone away just because now you can do a lot of your programming in natural language to an LLM.
Systems are still complicated. This shit is still difficult. The need for people who specialize in bridging that gap isn't going to go away.
As usual, the answer is: upskill yourself and adapt. If a crusty old fart like me can do it, you can too.
Tom Dale@tomdale
I don't know why this week became the tipping point, but nearly every software engineer I've talked to is experiencing some degree of mental health crisis.
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Omar Alonso retweetet
TempWeb deadline extended to January 14th. Please consider submitting your research. More details temporalweb.net @TheWebConf
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Omar Alonso retweetet
Just published my annual predictions update, tracking from Jan 1st 2018, with new commentary and new ten year predictions. It is long. rodneybrooks.com/predictions-sc…
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a key lesson from this is that looking at the data should be the first thing you do, not a last resort after you try to debug some surprising low scores. it really amazes me how many people neglect to do this very obvious thing, and how unintuitive this advice is to them.
Lei Yang@diyerxx
Got burned by an Apple ICLR paper — it was withdrawn after my Public Comment. So here’s what happened. Earlier this month, a colleague shared an Apple paper on arXiv with me — it was also under review for ICLR 2026. The benchmark they proposed was perfectly aligned with a project we’re working on. I got excited after reading it. I immediately stopped my current tasks and started adapting our model to their benchmark. Pulled a whole weekend crunch session to finish the integration… only to find our model scoring absurdly low. I was really frustrated. I spent days debugging, checking everything — maybe I used it wrong, maybe there was a hidden bug. During this process, I actually found a critical bug in their official code: * When querying the VLM, it only passed in the image path string, not the image content itself. The most ridiculous part? After I fixed their bug, the model's scores got even lower! The results were so counterintuitive that I felt forced to do deeper validation. After multiple checks, the conclusion held: fixing the bug actually made the scores worse. At this point I decided to manually inspect the data. I sampled the first 20 questions our model got wrong, and I was shocked: * 6 out of 20 had clear GT errors. * The pattern suggested the “ground truth” was model-generated with extremely poor quality control, leading to tons of hallucinations. * Based on this quick sample, the GT error rate could be as high as 30%. I reported the data quality issue in a GitHub issue. After 6 days, the authors replied briefly and then immediately closed the issue. That annoyed me — I’d already wasted a ton of time, and I didn’t want others in the community to fall into the same trap — so I pushed back. Only then did they reopen the GitHub issue. Then I went back and checked the examples displayed in the paper itself. Even there, I found at least three clear GT errors. It’s hard to believe the authors were unaware of how bad the dataset quality was, especially when the paper claims all samples were reviewed by annotators. Yet even the examples printed in the paper contain blatant hallucinations and mistakes. When the ICLR reviews came out, I checked the five reviews for this paper. Not a single reviewer noticed the GT quality issues or the hallucinations in the paper's examples. So I started preparing a more detailed GT error analysis and wrote a Public Comment on OpenReview to inform the reviewers and the community about the data quality problems. The next day — the authors withdrew the paper and took down the GitHub repo. Fortunately, ICLR is an open conference with Public Comment. If this had been a closed-review venue, this kind of shoddy work would have been much harder to expose. So here’s a small call to the community: For any paper involving model-assisted dataset construction, reviewers should spend a few minutes checking a few samples manually. We need to prevent irresponsible work from slipping through and misleading everyone. Looking back, I should have suspected the dataset earlier based on two red flags: * The paper’s experiments claimed that GPT-5 has been surpassed by a bunch of small open-source models. * The original code, with a ridiculous bug, produced higher scores than the bug-fixed version. But because it was a paper from Big Tech, I subconsciously trusted the integrity and quality, which prevented me from spotting the problem sooner. This whole experience drained a lot of my time, energy, and emotion — especially because accusing others of bad data requires extra caution. I’m sharing this in hopes that the ML community remains vigilant and pushes back against this kind of sloppy, low-quality, and irresponsible behavior before it misleads people and wastes collective effort. #ICLR #ICLR2026 #NeurIPS #CVPR #openreview #MachineLearning #LLM #VLM
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Omar Alonso retweetet
🚨 ACM The Web Conference 2026 deadlines are just around the corner! Don’t miss your chance to submit your work!
📅 Research/Industry: Abs Sept 30, Papers Oct 7
📅 Short Papers: Abs Nov 10, Papers Nov 17
📍 Dubai · Apr 13–17, 2026
👉 www2026.thewebconf.org/important-date…
#TheWebConf #CFP
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Special Issue o Temporal Web is now available dl.acm.org/doi/10.1145/37… (@TheOfficialACM @TWebACM )
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@digital_phreak @oasis Note the Man City reference near Noel's amplifiers
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@oasis Anyone know who the guy with a scarf and jacket on that literally just stood behind Noel the entire concert but did nothing
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Pink Floyd released A Momentary Lapse Of Reason, September 7, 1987. Favorite track?
Signs of Life
Learning to Fly
The Dogs of War
One Slip
On the Turning Away
Yet Another Movie
Round and Around
A New Machine, Part 1
Terminal Frost
A New Machine, Part 2
Sorrow
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The Web Conference 2026 (WWW) website is up and running www2026.thewebconf.org/index.html
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