Maybe in some cases, but not in this case with this agency (SFUSD).

The responses I've received so far from SFUSD (over several years) were each from someone whose whole job is/was dealing with public records requests.

One of the requests that was refused was for a Google Slides deck that was visible to all of SFUSD's ~10000 employees.

I sent a public records request to my local school district on March 9th 2026.

They responded on 19th March 2026 saying they'd respond on in late April.

Since then, every month they've sent an email with some small portion of the records, with a PDF letter saying they'll send a follow up a month later.

I've received 8 of those responses so far, and there's no indication when, if ever, I will have received all the records responsive to my request.

Bicycles are ridden on the road, not the pavement.

Sometimes people ride bicycles on the pavement, but doing so with a long piece of wood would be very dangerous indeed.

I'm skeptical about meta analyses in general because, the times when I've dug into a meta analysis and traced the conclusions back to the original papers, I've always been disappointed, and ended up not being convinced of the conclusions of the meta analysis.

Anyway, I haven't done that exercise for this paper, but I will say:

- Although the authors say they did a "systematic review and meta-analysis of 19 studies", only 12 of the 19 papers are included in the meta analysis.

- Only one of the authors read all the studies they included ("Study quality was assessed by 1 reviewer.")

- That reviewer was Lucy Corrigan, BSc ("Full texts were screened (L.C.) against the same inclusion and exclusion criteria (165 articles)...")

Here is the abstract of one of the 12 studies they included (and marked as 'Good' quality, the highest rating they gave): https://www.tandfonline.com/doi/full/10.1080/19485565.2021.2...

Here is what they found:

  Discrete-time hazard analysis indicates that the log odds of mortality were significantly lower among 18 and 19 year old adults ever diagnosed with ADHD and significantly higher among 46 to 64 year old adults ever diagnosed with ADHD, with a crossover occurring at age 33.

OP says that LLMs were never scored against correctness. But that's an assumption about the optimization process.

This is sort of right:

- pretraining scores on next token, and says nothing about confidence

- RLHF and RLVR scores are binary (1=preferred/correct, 0=worse/incorrect) and the feedback doesn't do anything to push the weights in a direction that improves calibration/confidence

BUT you can ask the model to output its confidence, and then train it to be more correct. RLCR (mentioned in the article I linked above) adds a Brier penalty to the reward, so the model's performance isn't just measured on % correctness. (Presumably TypeSafeAI's RLCD is similar to RLCR, but they haven't published anything about it.)

This eliminates the "always say 30%" loophole OP mentioned. The Brier penalty, (q-c)^2, punishes a forecaster who ignores instance-level information. For example if we have a model that's right 60% of the time, it could use one of these strategies to give its confidence:

  Strategy A, always say 0.6:
  When right (60% of the time): (0.6-1)^2 = 0.16
  When wrong (40% of the time): (0.6-0)^2 = 0.36
  Expected penalty: 0.6×0.16 + 0.4×0.36 = 0.24

  Strategy B: the model can tell its answers apart. Half are 'easy' (right 90% of the time) and half are 'hard' (right 30% of the time), which still averages to 60%. It says 0.9 on easy ones and 0.3 on hard ones:
  Easy: 0.9×(0.1)^2 + 0.1×(0.9)^2 = 0.09
  Hard: 0.3×(0.7)^2 + 0.7×(0.3)^2 = 0.21
  Expected penalty: (0.09+0.21)/2 = 0.15

This seems like a weird statement given that anyone can issue certificates out of the affected CAs.

https://cf.metaaiusercontent.com/q3-revenue-review-7b1e42f0

No one can guess that URL. The q3-revenue-review-7b1e42f0 part is only ever shared with the Meta's server (protected by TLS).

Now imagine the URL is instead:

https://q3-revenue-review-7b1e42f0.cf.metaaiusercontent.com/

The first step for my computer to open that link is to do a DNS query, which immediately shares the entire URL (minus the `https://` and the `/`) with my ISP's DNS server (and anyone sniffing traffic along the path, as DNS queries are usually sent in plain text).

Comments URL: https://news.ycombinator.com/item?id=50155638

Points: 1

# Comments: 1

Install Sill on the Mac, sideload the APK from Releases onto an Android 11+ phone on the same Wi-Fi, tap your Mac in the list that shows on your phone, then look for the 12-digit code in Sill on your Mac, and type it into the Android app.

Then: tap to click, drag to scroll, hold-and-drag to select text or move windows, pinch to zoom the picture on the phone, a Draw toggle for whiteboard apps like tldraw, and a key row with Esc, Tab, Cmd/Ctrl/Option/Shift and arrows. It also works over Tailscale with Sill's Remote Access.

All of the code was written by Claude Code, working from Sill's Swift source and its Python test client; I tested it on a Pixel 9 Pro XL.

Not done yet: drawing the Mac's pointer, a trackpad mode, clipboard sync, and a Play Store listing (I don't have Play developer account, and new individual Play developer accounts can't publish their first app until they've tested an app with a closed test with 12 testers.)

(There's also a separate Android client in draft PR #89 on Sill's repo. This one is a standalone app with an installable APK. I should have checked the issues/PRs before I asked Claude to build this...)

Comments URL: https://news.ycombinator.com/item?id=50123065

Points: 4

# Comments: 2

To me it seems like:

- Jev should be good at telling us 'what is'

- Jev isn't trying to tell us 'what should be'

e.g. if you want your software to assume each human life has the same value, then you should rely on Jev to tell you how many human lives would be lost in each scenario, and then your software can pick. But Jev is not trained on your value system, which is neither objective truth nor something that you can derive from the inputs.

Thank you!

  uv tool install mlx-audio
  mlx_audio.stt.generate \
  --model ibm-granite/granite-speech-5.0-470m-turboctc \
  --audio.wav \
  --output-path transcript