I have a bachelor’s in CS. Looking for a job!
find me anywhere in linktr.ee/papetoast
papetoast
Karma: 1,021
I relate to this. (I’m also not a native English user! and I feel we are similar in the other points too.)
For me, a mix of the following made me slightly better at writing: reading Babble, reading about importance of deliberate practice, writing about unimportant things (high stakes writing would give me way too much pressure at the beginning that procrastination immediately kicks in), and sometimes being confident enough that I think even writing a poor version of the thing I wanted to communicate is a net positive to the person I’m talking to or society in general (compared to predictably procrastinating if I try to improve the writing)
For reaching out to people, i’m not too sure which kind of reaching out do you mean.
I pattern matched your option 2 to speculative execution, with agents you can branch off whenever the agent reach an uncertainty on the research goal.
https://metr.org/notes/2026-03-19-org-uplift-game/
Speculative execution: To prevent serial bottlenecks (see next section), researchers may use two forms of speculative execution: starting lots of long experiments they’re not sure the project needs, and guessing results of experiments and feedback (see Tom Cunningham’s “Bottlenecks can be loosened with agents” section)
...
Once you can use agents to automate large parts of work it feels like you’ll now be bottlenecked on the non-automated parts. But in fact the non-automated parts can often be predicted, and this loosens the bottleneck.
Imagine every report has the following:
Agent’s best-guess about what comments you’d get from Beth, Hjalmar, Ajeya.
Agent’s best-guess about survey results, if you launched the survey.
Agent’s best-guess about benchmark results.
Agent’s best-guess about how this will be received on Twitter.
In addition you could click through to see why the agent guessed each. I feel these would meaningfully loosen bottlenecks, I could iterate until the information I received from the world (human feedback, data, surveys) was maximally informative, and only then send out for review.
I don’t, but here are other people’s thoughts.
Midjourney Medical | Hacker News
jmhmd: Some initial thoughts as a practicing radiologist:
(…)
They show the reconstructed images as though they are a low resolution CT, and promise that quality will improve as they iterate. This is cool, but ultrasound is not CT. Ultrasound cannot image the lungs, as they are filled with air. You cannot find bone lesions, as the sound waves do not penetrate the cortex. You cannot image many structures in the abdomen if they are surrounded by gas-filled bowel. The brain is encased in bone, so you might get some penetration but it will be very limited. Even with theoretically perfect AI reconstruction, these scans will not be true “full body” in that there will be structures that are not reliably imaged. Imagine paying for weekly full body scans for years, everything looks fine, then its the lung cancer surrounded by air and invisible to ultrasound that kills you (that’s why we use CT for lung screening!)
The images they show are very cool, and do appear to show the correct structures. I realize this is early, but fuzzy shapes of organs is very, very far from medically useful. The whole point of screening is to identify problems early, often by definition, small. This technology looks like it will be best for seeing large, superficial (close to the skin) structures, whereas for effective screening, you want the opposite—small, deep structures.
(…)
Many people mistakenly believe that early diagnosis is the final boss in medicine, that if only we could find every cancer early we could prevent all those deaths. There are, in fact, many, many other hurdles and bottlenecks. Many chronic, expensive diseases do not have clear imaging manifestations. The claim that “it’s completely possible that with enough early imaging in the future, the world could avoid 30% of all deaths and 50% of all healthcare costs”, I think, to any practicing physician, would sound completely divorced from reality.
IshKebab: I used to work in ultrasound, and full body scans with the body underwater is definitely feasible and probably a good idea.
Also there’s absolutely no way that it will be as good as MRI. In general ultrasound imaging is shit. The main reasons it is used are because it is very cheap and completely harmless. The actual images you get are mostly just speckle.
https://www.astralcodexten.com/p/preliminary-thoughts-on-the-midjourney + his twitter thread
I think the narrative among the SF AI crowd has escaped its basis in the medical facts, so I want to throw a bit of cold water on it. I’m a psychiatrist, which is about as far as you can get from radiology while still being a doctor, so this is speculation only, and you can ignore it if you find an actual radiologist or ultrasonographer with opinions. Still, my take is that this scanner isn’t useful for most current serious medical applications. It could potentially be used to pioneer a new class of low-risk screening applications, but it’s unclear whether these are good, and depends a lot on what other future technology gets invented in parallel.
Why can’t this immediately replace existing medical image modalities like normal ultrasound, CT, or MRI?
Ultrasound is great, but it can’t penetrate bone or air. Many things doctors want to look at involve bone or air in some way.
Couldn’t this technology enable new, non-specific-diagnostic uses for healthy people?
why don’t people get yearly whole-body MRI screenings? Some people do—companies like this provide them, and some rich people who can pay $2,000 out of pocket consume them. But the medical consensus currently recommends against them because they’re more likely to produce dangerous false positives than helpful true positives, and studies have failed to demonstrate benefit.
Couldn’t this technology become more useful in the future?
Yes. I think the best way to think of this is as a bet that future technology develops in a way that allows new possibilities for diagnostic ultrasound—or, even better, an attempt to gather the training data / interest / investment that will make this happen.
Appendix: Highlights From The Comments On Twitter
I mentioned this on Twitter and got some great responses.
The responses from real radiologists were universally negative. Here are some examples: (screenshots are annoying to embed please go to the blog)
The trivial reason is that due to the limitations of physics ultrasound will always be less capable at resolving anatomy than MRI or x-ray-based methods that we already have
Reinforcement learning towards broadly and persistently beneficial models
Can public chat data predict real-world AI misalignments?
I think you forgot to link the post: https://isene.org/2026/05/Audience-of-One.html
Links #3: 2026/06 Part 1
A trick for mentally calculating squares of two digit numbers (via bilibili):
Basically, choose
Example:
For 26, the closest multiple of 10 is 30, so
This algorithm can be extended recursively for squares of n digit numbers, though it is seems less useful.
There are more details in Table 5 Decision Thresholds that I didnt quote. Basically 80%+ is rejected.
NeurIPS 2026 is using Pangram to reject LLM writing
This year, the NeurIPS 2026 Position Paper Track made the decision to require that all papers be substantially human-written, with AI used for only copy-editing or similar peripheral changes to the main text.
To assess if authors were largely abiding by this policy, we partnered with Pangram
178 submissions (18.4% of all submissions) will be desk rejected
123 submissions (12.7%) will be requested to provide evidence of substantial human engagement or risk a desk reject.
Conference
# Papers
Pangram AI Score
≥ 50%
≥ 90%
= 100%
NeurIPS PPT 2025
536
28.5%
11.9%
8.2%
NeurIPS PPT 2026
971
70.5%
42.7%
28.2%
NeurIPS D&B 2025
996
5.6%
0.8%
0.4%
NeurIPS E&D 2026
996
43.7%
9.3%
2.1%
FAccT 2022
159
0.0%
0.0%
0.0%
FAccT 2025
204
1.0%
1.0%
0.0%
A null effect on pain relief from acupuncture in a pre-registered, improperly double-blinded study (via National Geographic via Facebook)
I didn’t read the paper beyond AI summary, I read the national geographic article in full (which is misleading according to claude)
Selected AI Summary (Full Transcript)
Short version: the underlying paper is methodologically honest and reasonably well-run, but its evidence for acupuncture having a specific (beyond-placebo) effect is weak. The National Geographic article substantially oversells it — it leads with a fragile secondary finding and quietly drops the fact that the trial’s primary outcome was null.
The pre-registered primary outcome was vulvar pain (Average Pain Intensity) at end of treatment, real needles vs. sham. Result: no difference. Effect size 0.06, 95% CI −0.36 to 0.24, p=0.70. Secondary outcomes (dyspareunia, sexual function) also null. Response rates were essentially identical: 58% acupuncture vs. 57% placebo. So the confirmatory test the trial was built around failed.
The only “win” came from Aim 2, a secondary duration analysis: among people who responded, the placebo group relapsed to baseline pain faster (hazard ratio 2.72, 95% CI 1.13–6.54, p=0.017). That single number is the source of the article’s “12 weeks vs. 4 weeks” headline.
Blinding failed in the active arm — and this is the deepest issue. The whole point of the design is to strip out expectation. Bang’s index showed the placebo group was properly blinded (~0, non-significant), but the penetrating-needle group was not: acupuncturists guessed correctly far above chance (index 0.43–0.58, p<0.001) and so did the real-needle patients (0.34–0.35, p<0.01). So in a trial designed to isolate specific effect from placebo, practitioners and patients in the active arm often knew it was real. Differential expectation conveyed nonverbally could produce exactly the durability gap they saw — with zero physiological mechanism. The authors acknowledge this. It’s the single best alternative explanation for the only positive result.
I don’t really want to write this quick take, but omitting this negative result would filter evidences beyond my comfort.
For the record, I didn’t downvote you. I don’t live in the US and don’t find it immediately worthwhile to understand. I won’t verify the prompt’s truthfulness, but the prompt is biased even if it is all true facts, just by the way it demonstrates the user’s position on the matter. Biased in the sense that it will predictably cause AIs to lean towards one position more than the other.
After 2020 Honor is no longer directly nor indirectly controlled by Huawei, per wiki and this content farm post that I googled and this random wiki in Chinese
https://claude.ai/share/8940c08e-c01a-4c41-af9d-6eb77c0c6cbd
though, asking AI with such a biased prompt is a bad idea, so I refuse to read the output beyond a skim nor write about my reaction after reading. It also feels disrespectful that you didn’t even offer your opinion and demands the reader’s opinion.
wow Snopes’ journalism is great
As a man, I find it difficult to be consciously sympathetic of the strength difference. The force that I use to turn the faucet knob to a comfortable, definitely closed position actually requires an uncomfortable amount of force to open for my grandma. Would be interesting if someone made bottles and knobs that are 2x harder to open than usual...
bump
what face is the narcissism face? I couldn’t google anything that looks trustworthy