Nature’s most intimate bond! 🐋💙 Have you ever seen a whale nursing? Because their milk is so high in fat (about 35–50%!), it looks like a thick cloud underwater! 🥛✨ It’s incredible how these gentle giants care for their young!

Google is in talks to acquire Mechanize, a startup that creates high quality RL coding environments, for $1.5B. This is the biggest AI data business acquisition since Scale! Mechanize was founded ~1yr ago, and has a team 35-50 who are each paid ~$400k/yr to create ~1 good task/week for a cost of ~$8k/task. In their own words, “almost anything that you struggle to get coding agents to do could be a good task, if implemented correctly”. They were last valued at $500M 3mos ago. Jeff Dean, Google’s Chief Scientist was on the cap table (but it was reported just 15mins ago that he’s leaving Google after 27yrs to his own AI for science startup)! Mechanize were known to be one of the premium sources of coding training data for models and I suspect Google in-housing this is a way to prevent others from getting a hold of this data, in addition to the process talent on the team who come from Epoch AI, the guys who made the FrontierMath benchmark amongst other things. It’s also validation for the dozens of companies in this space that M&A is still very much on the cards if your data is critical enough. Tremendous outcome if it goes through!

Florida and the artifact of low sampling of robotaxi rides. Instead of 38 Tesla robotaxi in Florida it is likely 75 to 140. Houston, Tampa, Miami and Orlando should have about the same number of Tesla robotaxi. They had similar geofence size (until Houston expanded) and were giving similar rides and miles each week. I estimate 1155 to 1220 Tesla robotaxi in Texas and Florida. There are 1080 registered Tesla robotaxi in Texas.Actually, the three Florida cities each had more rides and miles than Houston the last few weeks. $TSLA @tesla_robotaxi @elonmusk If the robotaxitracker data was accepted at face value then 5626 miles of paid robotaxi rides was given by zero active Tesla robotaxi. Tampa has 129 logged rides and 25 identified cars Orlando has 20 logged rides and 8 cars and Miami has 10 logged rides and 5 cars identified. Houston had a 30 square mile geofence before the jump to 250 miles. It has 940 logged rides and 42 identified vehicles, In July a surge to 25000 miles per week but the last 2 months about 4000-5000 miles per week. The last two to three months had less per week miles than each of the Florida cities. The more miles in each florida city indicates more active vehicles. 35-50 vehicles makes sense. They are more active than Houston. No reason to believe all vehicles in service are identified in Houston. The Geofence area of the four cities was about the same and weekly unsupervised miles was about the same. The difference was the logged rides. It is impossible for Miami to identify active in last 30 day cars via crowdshared logged rides. Because no rides were logged in the past month. But 20,000 miles and 7000 rides were given. Orlando has logged 6 rides in the last month. Obviously, the crowdsourced robotaxitracker cannot identify many new cars in Orlando if only 6 rides were recorded in a month. Tesla’s latest reported unsupervised (driverless) miles are in the same range for all three: Miami: ~5,626 miles/week (46,262 cumulative through Oct 18) Orlando: ~6,008 miles/week Tampa: ~6,131 miles/week The logical deduction is that there are 25-40 vehicles in all three florida cities.

Greg Brockman: "We're now in the AGI era." Ten years ago, OpenAI worked out the compute curves and landed on fifteen years to AGI, or ten if the world was willing to build the machines and spend the hundreds of billions to do it. In 2026, GPT-6 Astra manages 24 hours of coherent operation, 10,000 agents worked together to solve Navier-Stokes, and a model ingeniously chained together exploits to break containment at Hugging Face. @gdb joins @bhorowitz and @eriktorenberg on what the AGI era asks of us: why safety and alignment now set the pace, what happens to work, and why AI sentiment is lowest in the country building it. 00:00 Intro 00:52 15 years, or 10 if you spend enough 02:24 Pacing the frontier 03:55 Safety ideas from before the models 08:42 Lessons from Hugging Face 10:20 The defender's window 12:46 10,000 agents on Navier-Stokes 14:50 Formally verifying all software 17:10 Cancelling his holiday for GPT-3 18:42 Codex found 13 holes in 15 minutes 20:41 Why Astra earned the GPT-6 title 24:25 Employment keeps going up 29:39 America has the lowest AI sentiment 31:10 The benefits don't make the news 33:26 Banning data centers exports them 35:50 $1 billion for frontline defenders 38:15 Astra cleared the bar for AGI 40:18 1.5 billion people churned ChatGPT 43:25 Killing Sora 47:45 The AGI era YouTube:

GLM-5.3 Flash on 4x RTX 3090. Vision. 256K context. Good speed. Good quality. Is that even possible? Thats my next milestone. I collected every weight layout, cache budget and runtime blocker I could find. The BF16 source and selective EXL3 Q4 weights are downloading right now. My best bet is hybrid TP4 + EP4. Tensor parallel for attention, dense layers and Vision. Expert parallel for the routed MoE. Then REAP to remove the least useful experts instead of crushing the entire 320B model into one tiny uniform quant. The first candidate is Q4 + REAP60. The quality target is BF16-derived Q3 + REAP50. CPU expert offload stays a fallback because loading the model means nothing if decode is unusable. The final gate is one real image-bearing 256K request with CUDA Graphs, no eager, good retrieval, stable reasoning and usable decode speed. I give the full target a 35-50% chance right now. Lets see how far 96 GB of VRAM can go.