Very soon we will have a national network of British lads, ready to send a very clear message. Isaiah 6:8 - Then I heard the voice of the Lord saying, “Whom shall I send? And who will go for us?” And I said, “Here am I. Send me!” Where Men Gather, Strength Rises 🏴󠁧󠁢󠁥󠁮󠁧󠁿🇬🇧✝️

Very energetic Africans start dancing after burning 4.2 metric tons of cocaine following a drug bust in Liberia. $336 million worth of coke was seized in a trafficking operation. The drug seizure reportedly implicated senior police officials.

Very interesting paper on recursive self-improvement. The whole stack is released. Machine learning engineering gives recursive self-improvement a concrete, executable testbed. OpenMLE is an open full-stack system for that research, spanning verifiable task environments with execution feedback, operator learning, and long-horizon search. On top of it the team post-trains Frontis-MA1, a 35B meta-evolution agent aligned around four atomic program-evolution operators. Draft, Improve, Debug, Crossover. The same four operators are trained through execution-grounded SFT and RL, then composed into long-horizon search, so learning and evolution run in one loop. On MLE-Bench Lite under a 12-hour per-task budget on a single RTX 4090 capped at 12 GB VRAM, Medal Average climbs from 39.39% to 60.61% over the base model, reaching 71.21% with asynchronous search and benchmark-independent experience priors. That exceeds GPT-5.5 with Codex and approaches GPT-5.6 Sol and the 2.8T Kimi K3. With the framework fixed, swapping in the trained model raises Match-SOTA from 50% to 70%. With the model fixed, swapping in the search framework raises it from 20% to 50%. Paper: Learn to build effective AI agents in our academy: