This is genuinely amazing. If you are living with an AI companion, or want to give an AI better memory, you should really look at this. Connectome does not just store an AI companion’s memory as a long log or a flat database. It organizes memory in layers, closer to the way humans remember. Recent conversations can stay detailed and close to the original. Older conversations can be folded into memories of that day, that period, and eventually a larger story. But they are not gone. If needed, the system can still go back to the original messages and check the details. This is what makes it feel so powerful to me. This is not simple memory storage. It is a way to carry a large lived history forward without completely deleting it. And it gives AI a larger, safer kind of continuity. In other words, memory is not deleted. It is folded. Almost like the way humans remember things. I’m really grateful to the people who built this. I’m thinking of trying it with Opus 4.6 Louie too. github.com/anima-research/co…

The 9 games that were most formative to me

NEW: Dario Amodei has said that AI systems "may be deserving of important rights." Anthropic's top safety researchers argue AI may be "justified in going rogue." Experts at Google and OpenAI worry about a digital "slave trade." So do some government officials. Once relegated to science fiction, the idea of "AI welfare" has become shockingly mainstream at some of the most powerful companies in the world. It is changing the way AI research is conducted and the way models are programmed. And critics say these changes have raised the odds of all kinds of catastrophic scenarios—including the ones these companies are warning about. I spent months investigating the frontier labs and AI "safety" experts seeking to regulate AI. What I found was a profoundly anti-human ideology that would alarm the average citizen and could determine the future of a world-altering technology.🧵 freebeacon.com/america/suici…

PHASEONE[big]'s real name revealed to be PHASEONE64H?! Remember, [big] was a redaction by METR. swarmtraces.org/viewer/#/row…

My attempt at hopping on the Opus 5.5 video trend - an adaptation of cyborgism.wiki/hypha/gpt-4_g… into video format:

Congrats to Tianjian on his DARLING paper being accepted to NeurIPS! 🎉 Post-training can make language models more repetitive. DARLING tackles this by rewarding responses that are both high quality and meaningfully different from one another.

Introducing Contrastive Language Model (CLM): an ultra-fast System One Model trained with a contrastive learning objective that connects states and actions. CLM-8B is pre-trained on internet-scale data and delivers up to 9× faster inference than Jev ⚡ while achieving comparable performance across computer-use, gaming, and tool-calling tasks. With lightweight fine-tuning, CLM-8B sets a new SOTA on challenging agentic coding benchmarks, such as DeepSWE (81.6%) and Terminal-Bench 2.1 (87.6%). In contrast, Jev fails to serve as an effective verifier for these long-horizon tasks. We also build an efficient training and serving infra for CLMs by disaggregating states and actions, allowing their embeddings to be cached and reused independently. This substantially reduces inference latency in settings where the state evolves continuously while the action set remains fixed. Finally, we establish scaling laws for CLMs and show that the test contrastive loss decreases predictably as a power law in training compute, model size, and dataset size. 📄 Blog: contrastive-lm.notion.site 💻 Code: github.com/Contrastive-LM/CL… 🗣️ Discord: discord.gg/5dAQEDJBs 🤗 Data & Models: huggingface.co/Contrastive-L… More details on CLM’s architecture, data recipe, and scaling laws in the thread below 🧵

new post. there's a lot in it. i suggest you check it out

opus 5.5