How to Build a Powerful LLM Knowledge Base
Use coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science.
O'Reilly AI-ML·
The following article originally appeared on Addy Osmani’s blog site and is being republished here with the author’s permission. Coding agents are extraordinarily good now, and getting better fast. The interesting consequence is that the hard part of engineering moved from writing code to deciding whether to trust it, which makes review the most leveraged […]
Read full articleUse coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science.
Learn about the concept of loops to power your coding agents. The post How to Create Powerful Loops in Claude Code appeared first on Towards Data Science.
Learn how to apply coding agents to verify work in your browser. The post How to Use Claude Code in Your Browser appeared first on Towards Data Science.
The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission. Loop engineering is replacing yourself as the person who prompts the agent. You design the system that does it instead. A loop here can be thought of as a recursive goal where you define a purpose and […]
SWE-Explore: Benchmarking How Coding Agents Explore Repositories
FrontierCode highlights the gap between AI-generated code and professional standards, urging a reevaluation of AI's role in software development. The post Cognition introduces FrontierCode benchmark that exposes AI coding agents’ biggest weakness appeared first on Crypto Briefing.
The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission. A long-running AI agent can keep making progress over hours, days, or weeks. It can do this across many context windows and sandboxes, recover from failure, leave structured artifacts behind, and resume where it left off. For […]
StepFun releases Step 3.7 Flash, a 198B MoE model with native vision, 256k context, and Advisor Mode. The post StepFun Releases Step 3.7 Flash: A 198B MoE Vision-Language Model for Coding Agents and Search Workflows appeared first on MarkTechPost.