Leon Zhu

This is exactly where coding agents are going. The winning product won’t just call the biggest model harder. It will know when to route, when to fallback, and where credits should actually be spent. That’s the bet we’re making with Enter.

Last week, we shipped multi-session in Enter. Now you can run multiple AI coding conversations at once: one fixing the frontend, one debugging, one building APIs, one verifying the result. Instead of waiting on a single AI thread, you can move like you’re working with a small product team. Try it: enter.converge.ai @builtwithenter

@OpenAI @tejalpatwardhan @AndrewMayne Seeing the same thing inside Enter. We’re building a much stronger eval capability internally, because for agents, the data → eval → optimization loop is everything. That’s the difference between agents that look good in demos and agents that keep getting better in real work.

Let’s talk about evals. We’re always looking for better ways to measure and forecast model progress, especially as benchmarks get saturated or gamed. @tejalpatwardhan, who leads our frontier evals team, spoke to @andrewmayne about why evals matter and what models need to be judged on next.

2/2 Try it in Enter Code v1.0.11: npm install -g @enter-pro/enter-code Use Enter (enter.converge.ai), push it hard, and make real work happen.

1/2 Excited to share a breakthrough in Enter Code’s new auto mode: Same output quality, dramatically lower credit cost — overall cost down ~36%, background side-work cost down ~70%. Smarter orchestration, better credit usage, and the same upgrade is coming to Enter Web soon.

@suraj_sharma14 This is a good list. My only tweak: I’d pull observability and evals way earlier. The first time an agent calls tools, you’ll want traces, costs, and failure modes in front of you.

If I had 6 months to become an Agentic AI Engineer. I'd do this. Stage 1: Python + Async Foundations asyncio, FastAPI, event-driven architecture, error handling, API integration patterns. Stage 2: LLM Fundamentals for Agents Context management, model routing, token economics, latency tradeoffs, failure modes. Stage 3: Tool Calling + Structured Outputs Pydantic validation, function calling schemas, error recovery, dynamic tool discovery. Stage 4: Memory + State Management Short-term buffers, long-term vector recall, context compression, cross-session sync. Stage 5: Single Agent Workflows ReAct loops, plan-and-execute, self-reflection, iteration limits, graceful degradation. Stage 6: Multi-Agent Orchestration LangGraph/CrewAI, supervisor patterns, message passing, conflict resolution, handoffs. Stage 7: Human-in-the-Loop Systems Uncertainty detection, approval gates, audit trails, resume logic, intervention points. Stage 8: Evaluation + Quality Assurance Automated eval harnesses, LLM-as-a-judge, regression testing, hallucination metrics. Stage 9: Observability + Tracing Distributed tracing (LangSmith/Arize), cost dashboards, latency monitoring, alerting. Stage 10: Security + Guardrails Prompt injection defense, output filtering, PII redaction, sandboxed execution, compliance. Stage 11: Production Deployment vLLM/SGLang, Kubernetes scaling, CI/CD for agents, canary releases, rollback strategies. Stage 12: Open Source + Portfolio Ship autonomous agents publicly, write architecture docs, record demos, contribute to libs. Most people stay stuck watching tutorials. Builders get hired. (Bookmark it)

This is the shift every engineer needs to make. Prompting is a skill. Designing loops is leverage. If you’re still only doing the first one, you’re already behind.

SuperAI was energizing. Met so many people who are thinking deeply about how AI fits into real teams and real workflows. Left with a lot of notes, a lot of ideas, and a lot more excitement for what we’re building at @ConvergeAI_X.

@OpenAI Codex 🤝 Claude making developers emotionally attached to quota resets

We heard you wanted to use Codex rate limit resets on your own time. Starting today, we’re rolling out the ability to save rate limit resets to use later. We’re starting Go, Plus, Pro, and Business users with one free reset:

Shared memory only becomes useful when it’s governed. Otherwise it turns into another context dump. Scope, permissions, provenance, and expiry matter just as much as recall.

Yep. Once an agent can touch real tools, the hard part is no longer just “can it solve the task?” It’s whether you can trust the path it takes: permissions, context boundaries, visible tool calls, and reversible actions.

@superai_conf @msft4startups @OpenAI This is where it gets fun. Short timeline, real pressure, actual products. Congrats to the top 5.

The Genesis top 5 are through. Final pitches today at 11am on the WEKA Stage. Winner crowned at 5:40pm on the Plaud Main Stage. $2.3M prize pool powered by Microsoft for Startups @msft4startups and @OpenAI Don't miss it.

@superai_conf @googlecloud @GrabSG Love this framing. Agents don’t just need prompts — they need the same context, permissions, and guardrails you’d give a new teammate.

Moe Abdula from @googlecloud : the question isn't how to build them, it's how to govern them. Same way you'd onboard a new employee. Plus a live @GrabSG demo that showed what production AI actually looks like with their newest real-time translation feature powered by Gemini.

We use Enter every day across product, design, engineering, and operations — and it has meaningfully improved how fast our team thinks, builds, and ships. Give it a try.

@claudeai if it can keep context through a product manager changing requirements mid-refactor, I’ll call it AGI

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.

@superai_conf @cerebras This is especially true for coding and enterprise agents. Once workflows become multi-step, latency stops being a benchmark metric and becomes a product experience problem.

Inference speed isn't about faster chatbots. Dr. Andy Hock from @cerebras : once AI moves to multi-step reasoning and multi-agent workflows, compute demand multiplies by orders of magnitude. Speed becomes the difference between interactive and unusable.

@superai_conf Exactly. Once AI becomes a platform layer, the next question is not “which model?” but “what context, permissions, workflows, and institutional memory can it operate on?” That’s where the next generation of AI-native organizations will be built.

AI isn't a product category anymore. It's a platform layer. Benedict Evans shared on the Plaud main stage - what that actually means for software, companies, and markets.

Institutional intelligence is the missing layer between teams, context, and autonomous agents. Excited to see this conversation happening on the ground at SuperAI Singapore.

Morning sessions at @superai_conf Conversations happening. Builders, founders, operators, and AI enthusiasts from around the world have been stopping by our booth to explore what Institutional Intelligence could look like in practice. If you’re at SuperAI, come say hello. 📍 Startup Stands — SS28 #SuperAI #ConvergeAI