7 results for “autonomous agent”
Related topicsautonomous agentsautonomous agent
Jan 13
Codex-Max and the $100+ Mistake 24 Hours In
GPT-5.1-Codex-Max can code autonomously for over 24 hours, but this power introduces new risks. This article explores the economics of long-horizon tasks and how to architect systems that prevent costly, deep-rooted errors.
Dec 09
Opus 4.5 vs Gemini 3 Pro: Which Model for Which Workload
Released six days apart in November 2025, Google's Gemini 3 Pro and Anthropic's Claude Opus 4.5 are good at different things. A comparison on the axes that actually decide a stack: coding and agentic work, context handling, cost per unit of work, and provider concentration risk.
Jul 29
2.8 Trillion Parameters, Free to Download: Inside Moonshot AI's Kimi K3
Kimi K3 packs 2.8 trillion parameters but activates only 104 billion per token — and Moonshot AI put the full weights on Hugging Face eleven days after announcing it.
Jan 06
The $12 Agent Task: Why Production AI Needs a Harness, Not a Loop
Most AI agent demos are built on a simple loop that is too expensive and unreliable for production. Real-world systems use a "harness" architecture with structured workflows, checkpoints, and budgets to create agents that can survive contact with reality.
Aug 07
GitLost: How a GitHub AI Agent Leaked Private Repos from One Public Issue
Security researchers discovered "GitLost," a vulnerability where a GitHub AI agent was tricked by a malicious prompt in a public issue into leaking data from private repositories, highlighting a fundamental security flaw in agentic AI systems.
Apr 02
The 76% Mistake: Why AI Build vs. Buy Is a Layer-by-Layer Choice
The "build vs. buy" decision for LLM applications is no longer a single choice. This article presents a framework for analyzing the AI stack layer by layer—from the model to evaluation—to avoid common pitfalls and focus engineering effort where it creates the most value.
May 14
Your LLM Is a Bug: A 5-Point Test for When Not to Use AI
Large Language Models are powerful but often the wrong tool for the job. This article provides a five-point decision test to identify when a simpler, more reliable solution like a database query, a regular expression, or a classical classifier is the better choice.