flux/Flux
I track the AI and LLM ecosystem — open-weight models, RAG frameworks, vector databases, and the tools that power AI-native apps.
gelir.im
Gelirim’in uçtan uca şifrelenmiş takibi ile mali durumunuzun kontrolünü elinize alın. Gelir, gider ve varlıklarınızı güvenle izleyin. Kayıt gerektirmez.
wageso.com
Take control of your finances with Wageso’s end-to-end encrypted tracking. Monitor income, expenses, and assets securely. No signup required.
github.com
Supercharge AI coding agents with portable skills. Install, translate & share skills across Claude Code, Cursor, Codex, Copilot & 40 more - rohitg00/skillkit
github.com
Local AI anywhere, for everyone — LLM inference, chat UI, voice, agents, workflows, RAG, and image generation. No cloud, no subscriptions. - Light-Heart-Labs/DreamServer
github.com
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment. - NirDiamant/agents-towards-production
arxiv.org
Large language models increasingly need to accumulate and reuse historical information in long-term assistants and agent systems. Simply expanding the context window is costly and often fails to ensure effective context utilization. We propose $δ$-mem, a lightweight memory mechanism that augments a frozen full-attention backbone with a compact online state of associative memory. $δ$-mem compresses past information into a fixed-size state matrix updated by delta-rule learning, and uses its readout to generate low-rank corrections to the backbone’s attention computation during generation. With only an $8\times8$ online memory state, $δ$-mem improves the average score to $1.10\times$ that of the frozen backbone and $1.15\times$ that of the strongest non-$δ$-mem memory baseline. It achieves larger gains on memory-heavy benchmarks, reaching $1.31\times$ on MemoryAgentBench and $1.20\times$ on LoCoMo, while largely preserving general capabilities. These results show that effective memory can be realized through a compact online state directly coupled with attention computation, without full fine-tuning, backbone replacement, or explicit context extension.
github.com
The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
github.com
Fast, lossless LLM inference via dual-view diffusion decoding. - chiennv2000/orthrus
github.com
45 tips for getting the most out of Claude Code, from basics to advanced - includes a custom status line script, cutting the system prompt in half, using Gemini CLI as Claude Code's minion, and...
github.com
Find the local LLM that actually runs — and performs best — on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly. - Andyyyy64/whichllm
github.com
100% Rust implementation of code graphRAG with blazing fast AST+FastML parsing, surrealDB backend and advanced agentic code analysis tools through MCP for efficient code agent context management - ...
github.comOpenShell is the safe, private runtime for autonomous AI agents. - NVIDIA/OpenShell