Memory. I use Supermemory for this. Before, Pipa loaded context files and knew to update them. A memory tool adds teammate-like recall: goals, preferences, latest business state, and small details that should carry across runs. Good memory tools also know how to supersede and delete memories, which matters once the agent has more autonomy. - Source: dev.to / about 1 month ago

We wire everything up with Vision Agents as the voice agent framework, Stream for WebRTC audio and video, OpenAI Realtime for speech in and speech out, Anam so the agent shows up as a face on the video, and Supermemory so answers come from search over your uploaded documents instead of guesswork. The code stays small and most of the behavior lives in one registered function that asks the memory store for relevant... - Source: dev.to / 2 months ago

My friends and I are working on https://supermemory.ai, an AI second brain to help you remember content from saved webpages and notes. - Source: Hacker News / almost 2 years ago

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