Why MCP Matters for Scientific Knowledge Graph Queries
A look at how mcp-proto-okn connects natural language to scientific knowledge graph queries and reproducible workflows.
Practical workflows, developer tooling, and applied tutorials.
31 articles
Practical workflows, developer tooling, and applied tutorials.
Hub content is updated incrementally.
A look at how mcp-proto-okn connects natural language to scientific knowledge graph queries and reproducible workflows.
A look at structuring table QA with guided cell navigation and staged inference to improve accuracy and verify evidence paths.
How prompt-guided image compression for VLMs shifts focus from human visual quality to preserving clues needed for tasks.
View LLM agents as runtime-adaptive computation graphs to optimize accuracy, cost, latency, debugging, and control.
Defines skills as executable function code and manages them online via create-run-update-on-fail-save-on-success loops.
Explains why token logprobs differ from natural-language confidence, and how to test multi-candidate prompts with seeds and evals.
Move beyond context/output limits: evaluate LLM code integration with task decomposition, tool parity, and reproducible build/test rubrics.
Model Spec’s chain of command can override custom instructions, causing persona and reasoning drift. Design priorities, exceptions, and fallbacks to improve reproducibility.
Assesses zero-shot MLLMs for video anomaly detection, focusing on false alarms/misses, prompt specificity, 1–3s clips, and PR/F1 evaluation.
A practical look at memory admission control for LLM agents, reducing long-term memory pollution while improving auditability and metrics.
How whitespace, Unicode normalization, and token boundaries can look like reasoning failures, and how to control evaluation setups.
Generative AI recommendations can vary by default. Measure variance via reruns, improve reproducibility with seed and system_fingerprint, and add constraints and checklists.
Tool calls become real actions. JSON validity is not enough—use strict schema checks, allowed_tools, refusal detection, and state-aware gates.
Why conversational AI sycophancy is treated as a quality/alignment risk in official docs and evals, plus practical mitigation prompts.
How to handle relationship-test prompts in AI chats: set refusal boundaries with Safe Complete, document branching rules, and validate via evaluation.
AI coding tool choice depends on not only model quality but also tool calling, agents, and permission design shaping security and team velocity.
PersonaPlex combines text role prompts and audio voice prompts to keep consistent personas in low-latency, full-duplex speech conversations.
A field report from running a community bot: what automation can do, and what still requires human operational control.
LFM2 series enables high-performance local AI on low-memory devices using hybrid architecture and Model Context Protocol.
Explore how the Model Context Protocol (MCP) standardizes data integration for AI agents and resolves data silos in business workflows.
Evaluates the performance of open models like Qwen 2.5 and provides strategies for secure enterprise AI deployment.
Explore strategic workflows using Anthropic's MCP and DeepSeek's CoT to transform AI into proactive coding agents.
Explore how open-source models reduce costs by 90% and secure data sovereignty compared to closed APIs.
A short hands-on review after using clawdbot (moltbot) in a real dev environment. Why I went back to “native” CLI workflows.