AI Tools & Frameworks Directory

Instruction-based image editing models are impressive at following prompts. But when edits involve physical interactions, they often fail to respect real-world laws. In their paper “From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors,” the authors introduce PhysicEdit...

80% of ML projects fail from bad problem framing, not bad models. A 5-step protocol to define the right problem before you write training code. The post Stop Tuning Hyperparameters. Start Tuning Your Problem. appeared first on Towards Data Science.

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Visual intuition with Python The post Graph Coloring You Can See appeared first on Towards Data Science.

Skills are what make OpenClaw more than a local assistant, and these are the most popular ones worth installing today.

This article guide you through an example use case to turn a PRD into a functioning software prototype using Google Antigravity.

What they don't tell you about "dream tech jobs" The post I Quit My $130,000 ML Engineer Job After Learning 4 Lessons appeared first on Towards Data Science.

An AI agent combines a large language model for reasoning, access to tools or APIs for action, memory to retain context and a control loop to decide what happens next.

Autonomous AI agents are easily among the most efficient uses of AI to date. And once you begin to put it to work, OpenClaw shines out as one of the leading enablers of AI automation. If you’ve figured that out by now, here is a list of OpenClaw prompts that will help you do more […] The post 20 Ope...

You've built an AI agent that works well in development.

Master path operations, Pydantic models, dependency injection, and automatic documentation. The post Code Less, Ship Faster: Building APIs with FastAPI appeared first on Towards Data Science.

Build faster Python applications by mastering async programming and learning how to handle I/O bound workloads efficiently with real world examples.

Great LLMs need great data. Discover the pipelines, tools, and RAG architecture shaping the future of AI-ready data engineering

Traditional search engines have historically relied on keyword search.

Do you build GenAI systems and want to deploy them, or do you just want to learn more about FastAPI? Then this is exactly what you were looking for! Just imagine you have lots of PDF reports and want to search for specific answers in them. Either you could spend hours scrolling, or you could […] The...

What if I told you, you often lose your next big role to someone much less credible than you? Unjust, yes, but certainly not untrue. Here is the reality: recruiters, founders, and collaborators don’t discover talent through Kaggle notebooks. They discover it through visibility. Visibility on the wor...

Reducing LLM costs by 30% with validation-aware, multi-tier caching The post Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale appeared first on Towards Data Science.

If you have both unique domain expertise and know how to make it usable to your AI systems, you’ll be hard to beat. The post Context Engineering as Your Competitive Edge appeared first on Towards Data Science.

How reusable, lazy-loaded instructions solve the context bloat problem in AI-assisted development. The post Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel appeared first on Towards Data Science.

A case study on techniques to maximize your clusters The post Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not? appeared first on Towards Data Science.

This article explores five infrastructure patterns that make Docker a powerful foundation for building robust, autonomous AI applications.

You must have faced the never-ending wait of an AI model taking its time to answer your query. To put an end to this wait, the new Mercury 2 reasoning model of Inception Labs is now live. It works a bit differently from others. It employs diffusion to provide quality answers at nearly instant speed....

Start asking what question the explanation should answer. The post Stop Asking if a Model Is Interpretable appeared first on Towards Data Science.

Using large language models (LLMs) — or their outputs, for that matter — for all kinds of machine learning-driven tasks, including predictive ones that were already being solved long before language models emerged, has become something of a trend.