Tag
10 posts
On 11 November AWS shipped AWS Emulator: a local copy of its APIs, free, no account, public domain, 438 service models. I ran it on a Mac the next day: healthy in 0.42 s, DynamoDB calls in 2.5 ms, AWS's own errors, a Lambda that actually ran. Why it changes the inner loop, and the maths for a ten-person startup.
Most of an agent's LLM calls are not writing anything. They are decisions: which tool, which skill, which parameters, made thousands of times a day by a model paid in seconds and tokens. pankhllm sits where your app already calls an LLM, learns those decisions from its own traffic, and starts making them in 0.2 ms on a CPU with a 262 KB model. What it is not sure about still goes to your LLM. On the same 14 questions: a 12B planner 1,743 ms, Laya 49 ms, pankhllm's own model 4 ms, all 14 correct. Here is what it is, what it is not, and where it stops.
Enterprise teams are ditching traditional SaaS dashboards for autonomous AI agents that monitor, decide, and act. Here's what's driving the shift and what it means for software builders.
AI agents don't just write code — they review it. Autonomous code review catches bugs, security flaws, and design issues that human reviewers miss. Here's how it works.
The single most important capability that turned language models into agents wasn't better reasoning — it was tool use. Here's the technical story of how function calling changed everything.
Traditional RAG retrieves documents and stuffs them into context. Agentic RAG plans queries, evaluates results, and iterates until it finds the right answer.
Building a demo agent is easy. Shipping one that handles edge cases, recovers from failures, and earns user trust is hard. Here are the lessons learned.
Not all AI agents are created equal. A practical comparison of Claude, GPT-4, and Gemini on real software engineering tasks — coding, debugging, and system design.
Single agents are powerful. Teams of specialized agents working together are transformative. Here's how multi-agent architectures are reshaping complex problem-solving.
The shift from AI-as-tool to AI-as-agent represents the biggest paradigm change since the internet. Here's how we got here and where it's heading.