Heisenberg's Random Stat: The $SPY has now made 7 straight monthly higher lows so far. This is the longest of such streak since December 2016-January 2018. 2017 was such an unique year in where the $SPY never had a single red month on a total return basis and the $VIX saw sub 10's! Rare. Interesting.

The entire hype factor of a “big name” launch is the randomization, Where nobody really expects it and the speculation fuels price. When you declare something, it’s priced in everything and more. That LAPTOP dogshit will be the worst P&D, arguably ever. Highly recommend ignoring it entirely and not paying even 1% attention when it comes to trading it. Don’t even open the chart. ~ Dr. Axius.

Terminal games, reimagined over P2P. 🕹️ Meet RandomSpacer — an arcade space-shooter built entirely in the terminal for the Aleph Hackathon (Pear Track). Inspired by Asteroids and Space Invaders, it packs real-time multiplayer, rankings, and zero central servers. 🎥 Watch the demo: 🔗 Project details:

UNIQURE $QURE Q3’26 EARNINGS HIGHLIGHTS 🔹 Revenue: $5.2M (Est. $6.2M) 🔴; +41% YoY 🔹 EPS: -$0.88 (Est. -$0.92) 🟢 Announcements: 🔹 FDA Priority Review: BLA for ifezuntirgene inilparvovec; PDUFA target action date May 1, 2027 🔹 Confirmatory Study: approximately 200 patients; 2:1 randomization; U.S. screening by end of 2026 🔹 Safety: manageable profile across 51 total treated patients Other Q3 Metrics: 🔹 Cash & Investments: $768.4M; runway into 2029 🔹 R&D Expenses: $35.5M 🔹 SG&A Expenses: $23.9M Comments: 🔸 “We strongly believe AMT-130 has the potential to deliver meaningful benefit to HD patients. With our BLA under Priority Review and preparations for the confirmatory study underway, we are committed to bringing this therapy to patients and families who continue to live with this devastating disease.” 🔸 “As we continue to build our commercial capabilities and prepare for a potential launch, we are energized by what lies ahead, and our financial strength provides us the runway to deliver on our plans,”

Did you know that when @axisrobotics delivers data to a robotics company, it does not hand over a pile of recordings. It delivers a Package. Each package is measured across four dimensions. → Scenario sets the environment, such as a kitchen countertop or industrial sorting. → Atomic Skills covers primitives like grasp, place, push, and rotate, and continuous skill chains carry more training value than isolated motions. → In task Randomization multiplies diversity across layouts and assets. For example, 40 layouts times 4 asset variants gives 160 instances. → Total Trajectories sets the volume, with packages ranging from 10,000 to 1,000,000. Every one of those trajectories comes from a person operating a robot in a browser.

TL;DR: Pinecone released VQ-bench, a benchmark that treats today's zoo of vector quantization methods as combinations of a small set of shared primitives, making it possible to compare them fairly under the same conditions. Title: VQ-bench: a Composable Vector Quantization Framework URL: Points 🧩 Defines composable primitives like Center, Normalize, PCA, and RandomRotate that any quantizer can be built from 🔗 Existing methods like E-RaBitQ reduce to just 4 chained primitives: Center, Normalize, Random Rotation, Angular Cast 📊 Benchmarks 14 quantizers on 5 VIBE datasets using Reconstruction MSE, Recall@10, and encode time 🥇 PQ and OPQ consistently achieve the lowest Reconstruction MSE ⚡ EDEN encodes far faster than PQ, OPQ, and E-RaBitQ while keeping recall competitive 🛠️ Adding a new quantizer often takes just a few lines of code, and a new primitive automatically composes with every existing one Putting fragmented quantization methods on the same evaluation footing should make it much easier to pick the right one for your use case. #VectorSearch# #VectorDB#

A malicious image uploaded to OpenAI’s public forum became a path into its internal code repository. Hacktron reports that researchers chained an image-processing vulnerability with a sign-in flaw, compromised employee ChatGPT accounts, and used a connected Codex account to open a harmless internal pull request. They say they avoided reading internal code. OpenAI confirmed its fix roughly 14 hours after the report. My interpretation: AI can reduce the work required to exploit a weakness, while connected agents can increase what a compromised account can reach. The model progression is revealing. Hacktron says Opus 4.8 struggled to produce a reliable exploit with memory randomization enabled. Opus 5 helped overcome that obstacle. Skilled human guidance remained important. This is one team’s experience, not a controlled comparison. But it illustrates why security assumptions need revisiting: Your application can become easier to attack even when you change nothing. A vulnerability previously considered expensive to exploit may become practical as the tools improve. There is also a lesson about containment. Discourse’s remediation included patched software and restrictions on image-processing access to files and networks. Updating the decoder addresses known defects. Restricting its permissions helps contain the next one. Connected agents deserve the same scrutiny. Once an attacker controls an account, ordinary requests can become unauthorized actions. The agent can understand the task correctly and execute it successfully using somebody else’s permissions. Model alignment cannot establish who is behind a compromised session. For teams deploying agents, I would add one test: assume a connected account has been compromised, then measure what the attacker can actually do. Which resources become reachable? Which actions require independent approval? Does revoking the session terminate delegated access? Every connector expands what an assistant can do for you. Its security review should establish what someone controlling that assistant could do as you.