Speed matters. Not speed in sprint or linear dashboards. Not speed in story points. - Source: dev.to / about 2 months ago

Model Context Protocol, for context, is the emerging standard for letting AI agents pull live data from external systems without custom integration code. Freshworks has implemented it as a native layer in Freddy AI, which means agents can now reach into Notion, ClickUp, Linear, Workday, Rippling, and the rest of the enterprise stack — not through brittle webhooks or bespoke connectors, but through a standardized... - Source: dev.to / about 2 months ago

Issue trackers: GitHub Issues, Linear, or Jira work well because technical debt records live in the same tool as feature work. This makes them easier to pull into sprint planning and keeps the debt backlog visible alongside the feature backlog. The main risk is that debt issues get buried under feature issues without careful labeling and triage discipline. - Source: dev.to / 2 months ago

Linear and similar tools can track velocity metrics per area of the codebase over time, making the before/after comparison straightforward to document. - Source: dev.to / 2 months ago

Most engineers fail salary negotiations because they use vague statements like "I work hard" or "I’m a good teammate" instead of quantified, verifiable impact. After 15 years of negotiating offers, I’ve found that engineers who tie their ask to concrete business outcomes land 30% higher offers than those who don’t. For example, instead of saying "I improved the API", say "I reduced API p99 latency by 400ms, which... - Source: dev.to / 2 months ago

The stack runs on Qdrant for vector storage, Ollama for local embeddings, and optional Neo4j for a knowledge graph that I added later. I also set it up to route different operations to the best LLM for each task. It provides eleven tools for your Claude Code instance to manage long-term memory operations, and your memories data never leaves your machine. - Source: dev.to / 5 months ago

Qdrant: Open-source vector database optimized for hybrid search and easy integration with ML workflows. - Source: dev.to / 8 months ago

Yes, Java SDKs are critical. But you don't need to rebuild entire orchestration engines just to write agents in Java. The ecosystem already has platforms solving the hard problems: memory (Zep, Mem0, LangMem), tools (specialized platforms), vectors (Pinecone, Weaviate, Qdrant), observability (LangSmith, Helicone, Langfuse). Integrate, don't rebuild. - Source: dev.to / 9 months ago

James Allsopp adds, "LangChain or LlamaIndex for managing LLM workflows, especially if you're adding vector search or documents." These tools handle multi-step processes, essential for complex apps. - Source: dev.to / 11 months ago

📦 Qdrant for fast vector search and retrieval. - Source: dev.to / 11 months ago