Claudia Ng outlines how to run the open-source Hermes agent for free by pointing it at NVIDIA’s free API tier. She walks through installing the Hermes desktop app, configuring the NVIDIA API key and model (switching from GLM-5.1 to DeepSeek V4 Flash), and enabling web search tools, while noting latency and rate-limit issues on the free tier.

• Local Mac models couldn't hit the 64K-token context window Hermes needs for tool loops, forcing a switch to NVIDIA's free API tier.
• Setup was just a few terminal commands: install the desktop app, run `hermes model` to point at NVIDIA (49 models available), and `hermes tools enable web` for search.
• GLM-5.1 burned through the free tier fast due to timeouts and heavy reasoning overhead, so switching to DeepSeek V4 Flash gave faster responses with shorter reasoning traces.
• Every tool call adds a round trip and grows the prompt, so free-tier sessions can stall once rate limits hit.

Set up the Tansu broker using SQLite on an AWS t3.micro instance, taking advantage of the AWS Free Tier. The guide covers installation, configuration, and performance testing, demonstrating how to launch a low-cost Kafka-compatible broker for early-stage projects.

• Tansu with embedded SQLite runs a full Kafka-compatible broker on a free t3.micro (1GiB RAM), storing everything in a single tansu.db file for trivial backup/restore.
• Despite minimal hardware (~10MB/s EBS baseline throughput), the setup delivers usable, measurable throughput and latency, proving Kafka-compatible workloads are feasible on free-tier resources.
• Running below the t3 baseline CPU utilization accumulates burst credits, letting the broker handle demand spikes without extra cost.
• Tansu itself is lightweight on memory, leaving most of the 1GiB instance free for other system needs.