โCreation is the distillation of intent into form. Intent is usually inseparably embedded into the form of the artifact. A human iteratively (sometimes painstakingly) shapes and reshapes their creation until it sufficiently matches whatโs in their mindโs eye.โ
beautifully said
@theskory vim mode it's a bit bugged:
- works pretty well for navigating tool calls
- it's bad in the final output (which is the most important), the whole output is considered as a single block so you cant navigate it properly with j and k
For the rest best TUI available rn
@skcd42 vim mode it's a bit bugged:
- works pretty well for navigating tool calls
- it's bad in the final output (which is the most important part), the whole output is considered as a single block so you cant navigate it properly with j and k
I built RoboRank: "It's like Leetcode for roboticists"
- On Leetcode you learn how to reverse a doubly linked list
- RoboRank teaches you to debug control systems, implement filters, and use powerful tools like @rerundotio to debug simulations. All scored on a global leaderboard
Security things from the last few days:
- CopyFail (linux pwn'd)
- CopyFail 2/Dirty Frag
- 13 advisories in Next.js
- Over 70 CVEs addressed in MacOS 26.5
- ~50 CVEs addressed in iOS 26.5
- YellowKey (Windows Bitlocker pwn'd entirely)
- GreenPlasma (Windows privilege escalation)
- CVE-2026-21510 and CVE-2026-21513 confirmed to be used by Russia for Windows RCE
- CVE-2026-32202 separately confirmed to be used by Russia for sensitive document access
- Mini-Shai Hulud (over 300 JS and Python packages compromised via GitHub Action cache poisoning)
- Google confirms they have identified AI-powered exploitation of zero days in an unidentified "open-source, web-based system administration too"
- Canvas (popular LMS used in most schools) pwn'd entirely
- PAN-OS (palo alto networks) pwn'd with a 9.3 severity CVE-2026-0300
Are you scared yet?
"You have infinite LINE budget (the AI will generate as much code as you want). But you have the same finite complexity budget as always."
https://t.co/C2mTWGVo1M
this is fascinating, they train an encoder/decoder but use LLM matching the target model's shape for each part, so the latent space is just plain language and they can detect reward hacking, unwanted behavior and more
could even see it being used as an eval to quantify how smart a model is, i love this