I shared this note earlier today with the entire team at Opendoor.
Today we began to say goodbye to our colleagues in India as we wind down our India operations.
Our customers are in America, and that's where our operational work belongs.
Law firms are about to discover that AI audit logs are the most dangerous document they’ve ever created.
The clickstream proves supervision. It also proves how long you spent supervising. A 30-second approval of a complex contract review looks like competent oversight in your mind and looks like negligence under oath.
The firms treating AI logging as a compliance checkbox are building the evidentiary record that will define their malpractice exposure for the next decade. The ones that understand this are designing their human-in-the-loop workflows with the deposition transcript in mind, not just the bar’s model rules.
Logging is not optional. But what you log, how long you spend on each step, and what your approval workflow looks like on the record matters as much as whether you logged at all.
/teach is live
Learn anything, from rubik's cube to vocal harmonies to software fundamentals.
npx skills add mattpocock/skills --skill teach
Best skill I've ever built, video coming soon
https://t.co/GAv1jBFwsX
NEW: malware developers added nuclear & biological weapons text to to their spyware.
Goal? To trigger LLM safety refusals... so that their spyware wouldn't be analyzed by an AI security scanner.
Cleanest practical example I can think of for why over-indexing on first order safety alignment is risky.
When closed (and open) models ship with aggressive refusals, they will be sprinkled with second-order blindspots that attackers will discover...and exploit.
We are only in the earliest days of attackers leveraging these features, and it wouldn't surprise me if users systems that need to handle complex cybersecurity issues demand that models be less safety-blunted.
In the weeds: @SocketSecurity's post also shows why intention matters in how you design a malware analysis pipeline to avoid prompt manipulation.
H/T to colleagues that shared this with me https://t.co/f3Aj9TYxU4
As frontier models (e.g. Fable 5) continue to push the task horizon of knowledge work automation, it becomes ever more important for humans to be able to audit decisions back to the source context.
It is extremely easy for agents to cite an entire document or document page, but much harder for them to trace back to the exact numbers/words/figures within a page.
Today we've launched granular bounding boxes within LlamaParse, which allows you to obtain visual citations of every single word in the document. This allows human users to audit exact words and figures - not just general document regions or entire pages!
Come check it out: https://t.co/TqP6OT5U5O
Design is full of codewords. Knowing them changes what you can ask for, and what you can get back, whether you're working with devs, or an AI.
“tint this neutral color”, “fix this widow”, “nudge it to the optical center”
I wrote them down: https://t.co/aFyd5avj9o
Eski telefonumu Raspberry Pi’a bağlayıp, fotoğraflı twitlerimi Instagram’a atan bir bota çevirdim.
Şu an telefonla yapılan her şeyi taklit edebiliyor.
Twitter API yi ücretli yapan Elon’a selam olsun.
PSA: If you used Claude Fable-5 today with memory turned on you just violated all your NDAs. Anthropic requires a 30 day retention policy including human review, and the memory feature (on by default) searches past chats for context, so sensitive historical chats get pulled in.
Gosh I love the OSINT community. This project throws every plane flying overhead onto your ceiling in near real time – decoded from a cheap radio, w/ live stars and the ISS behind it. Falling asleep under a live map of the sky. h/t @CameronPaczek
Welp, that happened faster than I predicted. Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet's history. https://t.co/2zX5bHdhsa
Judges are rightfully outraged by counsel citing fictitious cases. We are officers of the court. We don’t cite non-existent cases; we twist real ones into non-existent meanings.