Harsh Sahu

Approval gates matter for safe agent actions. We route tasks to different models via GTWY. Parallel steps stay isolated, fewer collisions.

Parallel agents are the real speed unlock. GTWY is built around the same idea: let AI operate concurrently, but predictably, inside real systems.

Private chat is only half the story—things get interesting when memory, model switching, and access controls stay consistent over time. We’ve seen how tricky that balance becomes once enterprise agents rely on persistent context while building GTWY.

CRMs really prove their value when they reduce coordination overhead without hiding important context. We’ve seen similar patterns where proactive systems work best once reliability and state are handled well—something we think about a lot while building GTWY.

Context-aware CLIs get really interesting once memory persists across sessions instead of resetting every run. We’ve seen that payoff when turning task history into durable context while building GTWY.

Testing with real user flows is where hidden state and edge cases finally show up. We’ve seen how valuable that signal becomes once agents interact with production systems while building GTWY.

Real-time translation gets hard once accuracy, latency, and fallback behavior all matter at the same time. We’ve seen similar reliability challenges surface when voice and agents intersect while building GTWY.

Browsing real production data is where assumptions usually break and edge cases surface. We’ve felt how valuable that visibility becomes once agents and automation start depending on live data while building GTWY.

Plain-English agents are powerful, but things get interesting once they need memory, retries, and control over time. We’ve seen that shift clearly when moving from simple automations to long-running agents while building GTWY.

Personal knowledge tools really get tested once context grows and retrieval needs to stay predictable over time. We’ve seen similar challenges show up when turning knowledge into action while building GTWY.

Giving LLMs direct access to testing APIs is where agents start becoming genuinely useful in engineering workflows. We’ve seen similar gains when agents can safely act on infra and tooling while building GTWY

Love seeing AI agents move into spatial workflows. As teams adopt more AI tools, having interfaces that reduce context switching and setup friction really matters—this feels like a strong step in that direction.

Hey Product Hunt 👋 We’re thrilled to launch GTWY.AI, a platform built to simplify how developers and businesses access and deploy AI. In today’s landscape, integrating multiple AI models and APIs can be messy, time-consuming, and expensive. We wanted to fix that. With GTWY.AI, you get Top AI Models via One API — a unified interface that connects to the best LLMs and AI providers. Our mission is...