Noah MacCallum shares his enthusiasm for GPT-4.1, declaring it his new daily driver. He notes that his team has spent hundreds of hours experimenting to optimize the model's performance and offers to share key learnings to help others get the most out of it from day one.
A primary takeaway highlighted in the post is GPT-4.1's strength in agentic workflows, suggesting the model excels when used in autonomous, multi-step task execution scenarios. The post appears to be part of a longer thread with an accompanying image, likely containing more detailed tips and best practices for working with the model.
Andrew Ng discusses tool use as a key design pattern in AI agentic workflows. He explains that tool use involves giving an LLM access to functions it can request to call for purposes such as gathering information, taking actions, or manipulating data. Common examples include LLM-based systems that can perform web searches or execute code. The post is part of Ng's broader series on agentic AI design patterns, highlighting how tool use enables AI agents to extend their capabilities beyond pure text generation by interacting with external systems and APIs.
Andrew Ng highlights the emergence of a new role gaining traction in Silicon Valley: the AI Forward Deployed Engineer (FDE). This position involves engineers being embedded directly within client organizations to help customize AI solutions, including building and tuning agentic workflows tailored to each client's specific needs. The role reflects the growing demand for hands-on AI implementation expertise as companies increasingly adopt agent-based AI systems but require specialized support to adapt them to their unique operational contexts.
A developer working in a corporate environment using GitHub Copilot asks the Reddit community what AI coding tools are worth exploring in 2026, noting that Copilot's agent mode with model switching (e.g., Claude Opus) already handles full end-to-end tasks including frontend/backend implementation, MCP server connections, and multi-step orchestration. The discussion centers on whether tools like Cursor, Claude Code, Codex, and Gemini CLI offer meaningful advantages beyond what Copilot now provides.
Commenters draw important distinctions between inline completion and agentic task-level tools. Several users highlight Claude Code as the standout for complex refactors and multi-file navigation, citing its 80.8% SWE-bench score and features like skills, hooks, and subagents. Cursor receives praise for its sub-agent orchestration and unlimited Auto mode at $20/month. For corporate environments prioritizing privacy, open-source BYOK tools like Aider and OpenCode are recommended, allowing teams to bring their own API keys without routing data through third-party infrastructure.
A recurring theme is the emergence of planning and tooling workflows — tools like GSD (Get Shit Done) and BMAD that layer structured planning on top of CLI agents, enabling a discuss-plan-execute-verify loop. Multiple users emphasize that the real productivity gains come not from better autocomplete but from terminal-based agents that can edit files, run tests, and iterate autonomously, with some developers reporting they no longer use traditional IDEs at all.
Andrew Ng highlights an important shift in how large language models (LLMs) are being developed and optimized. While LLMs have traditionally been tuned to answer human questions — a paradigm popularized by ChatGPT's breakout success — there is now a growing trend toward optimizing models to integrate into agentic workflows.
Ng argues that this transition will deliver a huge boost to agentic performance, suggesting that the next frontier in AI isn't just better question-answering but enabling models to operate autonomously within multi-step, goal-directed processes. This aligns with the broader industry movement toward AI agents that can plan, reason, and execute complex tasks rather than simply respond to prompts.