Ask HN: HN Algolia API returns 400 for queries with points in numericFilters
The HN search help page⁽¹⁾ still documents points>NUMBER as a supported filter, so hopefully this is an unintentional regression.⁽¹⁾ https://hn.algolia.com/api
⁽¹⁾ https://aws.amazon.com/blogs/security/amazon-threat-intellig...
Hand-hinted by Fabrizio: ”More than 9000 characters optimized from 9pt to 48pt to guarantee the best possible readability.”
It is the ultimate “programmer’s font” and well worth the money for something you stare at every day and the next best thing to a bitmap font in today’s “Retina resolution” world.
Accept no imitations.
They are defined in /System/Library/Frameworks/AppKit.framework/Resources/StandardKeyBinding.dict. You can create ~/Library/KeyBindings/DefaultKeyBinding.dict to augment or override those.
Anything built on AppKit that uses the standard text system (NSTextView, NSTextField, and the field editors that back them) supports those bindings “for free”.
For non-native apps, Karabiner Elements⁽¹⁾ is the answer, using a config like this⁽²⁾.
As noted in this paper⁽¹⁾, ”Publicly releasing model weights is an act of irreversible proliferation. Once weights are released, they can never be recalled.”
Do you really want to risk it on your off-the-cuff reasoning that it’s “far-fetched”?
⁽¹⁾ https://www.lesswrong.com/posts/qmQFHCgCyEEjuy5a7/lora-fine-...
A badge of honor these days!
iOS 17.4 was released in March 2024.
Additional updates to 17.5, 17.6 and 17.7 followed.
Any devices released after the iPhone 15 couldn't run iOS 17.
It makes me wonder how many devices exist that are still running "iOS 17.0 to 17.3.1"...
Input classifiers get applied before it reaches the model so somebody hacking an open-weight model would skip this. Streaming classifiers get polled during decoding; hackers delete this check in the sampling loop.
But both are always applied in closed weight models.
Set Llama Guard to 1.0 and nothing is ever unsafe.
Having the weights gives you the exact affordance an unlearning attack requires, without rate limits.
But to me, it underscores the impending cliff of doom from the continued release of open-weight models: there's no cryptographic or architectural way to give someone full weights while withholding the nefarious capabilities those weights encode.
As noted in this paper⁽¹⁾, “publicly releasing weights is an act of irreversible proliferation”.
I’m sure this will be an unpopular opinion on HN, but open weights are the thing that scares me the most about “A.I.”. There is a lot of research in this area⁽²⁾, and I think most of HN is unaware of it or ignores it. Stripping refusals from Kimi K2.5 took under $500 of compute and about 10 hours, taking HarmBench refusals from 100% to 5% while retaining nearly all capability; the resulting model gave detailed chemical-weapons synthesis instructions.
The gate is only as strong as the least-cautious releaser...
⁽¹⁾ https://www.lesswrong.com/posts/qmQFHCgCyEEjuy5a7/lora-fine-...
I mean the triple-em-dash has been around since Unicode 6.1; The Tironian Sign Capital Et wasn't added until Unicode 13.
I went to make the change, and all the sudden it started working with “>” again (not URL encoded) so this appears to have been a transient issue.
The HN search help page⁽¹⁾ still documents points>NUMBER as a supported filter, so hopefully this is an unintentional regression.⁽¹⁾ https://hn.algolia.com/api
Dig more coal -- the PCs are coming