- A pre-print proposes that artificial selection on dogs could reach human-level intelligence within 100 generations (≈600 years) if generation sizes are scaled 100×, but critics argue the quantitative-genetics math ignores truncation selection and heritability.  
- Discussion of plugging large language models (e.g., GPT-3) into OpenCog’s modular “blackboard” architecture—symbolic reasoning, reinforcement learning, sensory and actuator subsystems—to offload math and logic tasks that pure next-token prediction handles poorly.  
- OpenCog maintainers are reportedly doubling-down on symbolic methods rather than integrating LLMs, while some participants still view the modular blackboard approach as the most promising route to standardized, engineerable AGI.  
- Recent review article on machine-learning-driven protein design covers AlphaFold2 and RoseTTAFold advances: https://www.cell.com/trends/biochemical-sciences/fulltext/S0968-0004(22)00308-5.  
- Eureqa-style symbolic regression is revisited as a way to discover compact physical laws; debate on whether such representations can be efficiently mapped to hardware (polynomial cores vs. ReLU-based AI accelerators).  
- Artificial-life simulations are criticized for lacking developmental biology and realistic resource dimensions; participants estimate cellular protein state-space at ~10^(2.7×10^12) versus the naïve 10^500 figure, illustrating the complexity gap.  
- Goodhart-style concerns raised: any quantitative metric for “open-ended evolution” in ALife risks being gamed, yielding technically compliant but biologically uninteresting outcomes.  
- Stripe and nixbuild.net posts on achieving fast, secure CI via remote Nix builds: https://stripe.com/blog/fast-secure-builds-choose-two and https://blog.nixbuild.net/posts/2022-03-16-lightning-fast-ci-with-nixbuild-net.html.  
- Unreal Engine 5.1’s Nanite virtualized geometry and real-time global illumination showcased as near-photorealistic out-of-the-box; debate on why smooth dynamic LOD and impostor generation took so long to become standard.  
- Langchain tooling (https://pypi.org/project/langchain/) noted as automating LLM-driven code generation for delegating calculations to external programs, complementing the symbolic-reasoning discussion.