Machine Learning TabFM: Zero-Shot Table Predictions That Beat XGBoost
Run Google's TabFM on real tabular data. No tuning, no feature engineering, one forward pass.
How-to content for builders, indie hackers, and AI engineers. Less theory, more shipped code.
Machine Learning Run Google's TabFM on real tabular data. No tuning, no feature engineering, one forward pass.
Machine Learning Ship a taskset, swap any harness, and turn compacted rollouts into real RL training samples.
Tutorials Use GPT-5.6's Responses API so the model writes JavaScript to run your tools in one call.
Tutorials Use Anthropic's compact-2026-01-12 beta so long agentic loops survive past the 200K context window.
Tutorials Meituan's 1.6T open MoE topped OpenRouter as 'Owl Alpha.' Call it in Python with the OpenAI SDK.
Tutorials Install Nous Research's self-improving agent, run it on a local model, and build a reusable skill.
Tutorials Load a whole repo into Gemini 3.5 Pro's 2M context, query it without RAG, and cache to cut cost.
Tutorials Use GPT-5.6 Sol's new max reasoning effort and ultra subagents via the Responses API.
Tutorials A runnable Python governor that caps LLM spend per user and auto-downgrades models.
Tutorials Stream Gemini's thought summaries live, control reasoning effort, and track thinking-token cost.
Tutorials Cut MCP agent context up to 99% by exposing tools as a code API the model calls in code.
Tutorials Port your MCP server to the stateless 2026-07-28 spec using the explicit-handle pattern.