Too much world knowledge is trapped in presentation media (video, html. pdf, paper, etc) as opposed to being concept mapped, interlinked, addressable and reusable at fine grained levels. Defeats bridge between #AI and human cognition.

CVE remediation is a dependency-graph problem. Scanning attempts to infer from built artifacts. Flox/Nix derive artifacts runtimes from the causal dependency graph. Remediation = dbms lookup declarative edit: identify artifacts/envs, pin a replacement ref, promote. Read more in the comments!

Security and compliance work often comes down to certifying system state by reconstructing from scattered clues: scans, SBOMs, build records, tickets, cloud inventory, Kubernetes data. That is not a diligence problem. It is a delivery-system problem. Secure software by construction means the runtime environment carries provenance, traceability, and auditability from build to production. This changes the basis of proof, eliminating guesswork with versioned, reproducible, auditable environments across the SDLC. Read more about it here: buff.ly/5uhP9hm

Shared surface memory, collaborative memory validation, audit log for mutation traceability, OpenRouter embedding opt-in, /no_think for Qwen3, and strict fact match guards.\n\nAll on GitHub: github.com/AxDSan/mnemosyne

We created an agentic AI model pipeline utilizing a fleet of our new TT-QuietBoxes and Tenstorrent Galaxies to download random models from @huggingface to port to our hardware, compile, and test for accuracy. After thousands and thousands of models, the model pass rate has been holding steady at 90%. Run anything.

The Open-Source MCP Memory Server Your AI Agent Is Missing. AI agents forget everything between sessions. Hindsight gives them persistent, structured memory via MCP. One Docker, Inc command to run the full stack locally. Connect any MCP-compatible client. Three core operations: retain (store), recall (search), reflect (reason) — plus mental models that auto-update as memories grow. Know more: linkedin.com/pulse/open-sour…

We provide a guide for app developers on moving away from the Play Integrity API to the standard Android hardware attestation API to permit GrapheneOS at grapheneos.org/articles/atte…. It can also be used to permit other operating systems. We plan to update and overhaul our guide soon.

🔍LLMs4OL: Large Language Models for Ontology Learning Challenge Three tasks and variant ontologies are being explored for ontology learning using LLMs in two evaluation phases: 1️⃣Few-shot testing 2️⃣Zero-shot testing sites.google.com/view/llms4o… #ISWC2024 #LLMs4OL #OntologyLearning

Zig inference engine for AMD GPUs github.com/zolotukhin/zinc

You can now run frontier AI models where not even the gpu provider can see your data. 15 models with hardware-enforced privacy (TEE) on Chutes. No other open-source inference provider offers this. Here's the full lineup and why it matters ↓

Today we're thrilled to announce the launch of our newest offering: Determinate Secure Packages. A drop-in replacement for Nixpkgs, it offers secure, signed, auditable Nix packages for the enterprise, including CVE monitoring and SLA-backed remediation, per-release SBOMs, optional FIPS-compliant builds, packages cached in FlakeHub Cache, cryptographic signing, and more. Link in thread 🔗🧵👇

The Nix moment is upon us determinate.systems/blog/the…

Native Swift inference server for MLX models github.com/SharpAI/SwiftLM

8. SkillX: Automatically Constructing Skill Knowledge Bases for Agents 🔑 Keywords: SkillX, Hierarchical Skill Design, Iterative Skills Refinement, Exploratory Skills Expansion, Generalizable Agent Learning 💡 Category: Natural Language Processing 🌟 Research Objective: - To develop SkillX, an automated framework to create reusable skill libraries for LLM agents, improving generalization and efficiency across different environments. 🛠️ Research Methods: - Utilized a fully automated pipeline involving Multi-Level Skills Design, Iterative Skills Refinement, and Exploratory Skills Expansion to construct a skill knowledge base. 💬 Research Conclusions: - SkillX enhanced task success and execution efficiency when integrated with weaker base agents, demonstrating the importance of structured, hierarchical experience representations for generalizable learning. 👉 Paper link: huggingface.co/papers/2604.0…

Team's latest paper, "HyperMem: Hypergraph Memory for Long-Term Conversations," has been accepted at ACL 2026. HyperMem introduces a hypergraph-based hierarchical memory architecture that captures high-order associations across extended dialogues, moving beyond pairwise relations to unify scattered content into coherent memory units. On the LoCoMo benchmark, it achieves state-of-the-art performance with 92.73% LLM-as-a-judge accuracy. Long-term memory is core to what we are building at EverMind. This work reflects our commitment to advancing the foundations of how AI systems remember, reason, and maintain meaningful interactions over time. Read the full paper: arxiv.org/abs/2604.08256