Meet mcpd: requirements.txt for agentic systems
mcpd is to agents what requirements.txt is to applications: a single config to declare, pin, and run the tools your agents need, consistently across local, CI, and production.
Davide Eynard
Dad of 3564020356.org and two amazing kids. Interested in open applications of ML on federated systems. Trustworthy AI at moz://a.ai. Genetically a teacher, forever a student. At: @mala@fosstodon.org
Wasm-agents: AI agents running in your browser
One of the main barriers to a wider adoption of open-source agents is the dependency on extra tools and frameworks that need to be installed before the agents can be run. In this post, we show how to write agents as HTML files, which can just be opened and run in a browser.
Smarter Prompts for Better Responses: Exploring Prompt Optimization and Interpretability for LLMs
Generative AI models are highly sensitive to input phrasing. Even small changes to a prompt or switching between models can lead to different results. Adding to the complexity, LLMs often act as black-boxes, making it difficult to understand how specific prompts influence their behavior.
Build Your Own Timeline Algorithm: A Blueprint
Timeline algorithms should be useful for people, not for companies. Their quality should not be evaluated in terms of how much time people spend on a platform, but rather in terms of how well they serve their users’ purposes.
Benchmarking DeepSeek R1 Models using Lumigator: A Practical Evaluation for Zero-Shot Clinical Summarization
New state-of-the-art models emerge every few weeks, making it hard to keep up, especially when testing and integrating them. In reality, many available models may already meet our needs. The key question isn’t “Which model is the best?” but rather, “What’s the smallest model that gets the job done?”
Evaluating DeepSeek V3 with Lumigator
A typical user may be building a summarization application for their domain and wondering: “Do I need to go for a model as big as DeepSeek, or can I get away with a smaller model?”. This takes us to the key elements: Metrics, Models, and Datasets.
Taming randomness in ML models with hypothesis testing and marimo
The behavior of ML models is often affected by randomness at different levels, from the initialization of model parameters to the dataset split into training and evaluation. Thus, predictions made by a model (including the answers an LLM gives to your questions) are potentially different every time you run it.
Local LLM-as-judge evaluation with lm-buddy, Prometheus and llamafile
In the bustling AI news cycle, where new models are unveiled at every turn, cost and evaluation don’t come up as frequently but are crucial to both developers and businesses in their use of AI systems. It is well known that LLMs are extremely costly to pre-train; but closed