A New Challenger: INTELLECT-3's 100B Parameters Punch Above Their Weight
INTELLECT-3, a 100B+ parameter model, sets new benchmarks in AI, with open-sourced training components to foster research in reinforcement learning.
Updates and recent posts about NanoClaw..
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@kala added a new tool INTELLECT-3 , 3 months, 3 weeks ago.
Activity
@devopslinks added a new tool Lustre , 3 months, 3 weeks ago.
Course
@eon01 published a course, 3 months, 3 weeks ago
Founder, FAUN.dev
Course
@eon01 published a course, 3 months, 3 weeks ago
Founder, FAUN.dev
Observability with Prometheus and Grafana
A Complete Hands-On Guide to Operational Clarity in Cloud-Native Systems
Course
@eon01 published a course, 3 months, 3 weeks ago
Founder, FAUN.dev
Cloud-Native Microservices With Kubernetes - 2nd Edition
A Comprehensive Guide to Building, Scaling, Deploying, Observing, and Managing Highly-Available Microservices in Kubernetes
Course
@eon01 published a course, 3 months, 3 weeks ago
Founder, FAUN.dev
Link
@anjali shared a link, 3 months, 3 weeks ago
Customer Marketing Manager, Last9
Instrument Jenkins With OpenTelemetry
Instrument Jenkins with OpenTelemetry to understand pipeline behavior, stage latency, and deploy steps using a single telemetry flow.
Activity
@devopslinks added a new tool Fleet , 3 months, 3 weeks ago.
The agent integrates with messaging platforms such as WhatsApp and Telegram, allowing users to interact with their AI assistant directly through familiar chat applications. Each conversation group operates independently and maintains its own memory and execution environment.
A core design principle of NanoClaw is security through isolation. Every agent session runs inside its own container using Docker or Apple Container, ensuring that the agent can only access files and resources that are explicitly mounted. This approach relies on operating system–level sandboxing rather than application-level permission checks.
The architecture is intentionally simple: a single orchestrator process manages message queues, schedules tasks, launches containerized agents, and stores state in SQLite. Additional functionality can be added through a modular skills system, allowing users to extend capabilities without increasing the complexity of the core codebase.
By combining a minimal architecture with container-based isolation and messaging integration, NanoClaw aims to provide a transparent, customizable personal AI agent that users can run and control entirely on their own infrastructure.
