Practical reporting on AI systems, tools, and engineering execution.
A Ruby/Rails framework for production-grade LLM pipelines with cost tracking, safe tools, and observable retry logic — filling a gap in Ruby's AI tooling stack.
VeriEvolve creates executable evaluators from task specs, turning fitness signals into a self‑improving loop for tool‑using agents.
Researchers introduce LIP, a method to bypass text/image bottlenecks by training lightweight connectors that map specialist model outputs directly into LLM internal states, tested on protein and molecule specialists with Gemma 3 and Kimi K2.6.
GRAM groups tasks by subspace alignment before merging, reducing interference when combining locally fine-tuned models.
An unofficial dsh plugin that surfaces Command Code models with live quota, cost estimates, and multi-account rotation — built for teams already running DeepSeek Harness.
RamaLama wraps AI model serving in OCI containers, auto-selecting GPU-optimized images so developers can treat models like container images using familiar Podman or Docker commands.
AWS engineers replace guesswork with live measurements of Q_max and T_total to reliably scale text-to-speech endpoints on SageMaker without hidden costs or SLO breaches.
Rescale used AWS AI-DLC to build a test-failure triage agent in five days. We compare the AI-execute-human-decide model against traditional dev for speed, control, and production readiness.
Plank provides a Ratatui TUI, REPL, and headless mode for shell, file, and web tasks, with DeepSeek, Qwen, and Gemma 4 inference behind a Mac-first distribution model.
A curated, community‑maintained directory of ready‑to‑use agent prompts that can be copied into Grok Bot, Rakazo, or any agent and instantly turned into scheduled bots.
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