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The Python Ecosystem That Changed AI Development
How one open-source ecosystem made state-of-the-art AI accessible The post The Python Ecosystem That Changed AI Development appeared first on Towards Data Science.
How to Organize All of Your Coding Agent Tasks
Optimise how you interact with your coding agents The post How to Organize All of Your Coding Agent Tasks appeared first on Towards Data Science.
How to Build a Context Layer and a Company Brain
What it actually takes to turn a company's scattered knowledge into something an LLM can reliably use — and why the demo is 5% of the work. The post How to Build a Context Layer and a Company Brain appeared first on Towards Data Science.
A Simplified View of the Jacobian Conjecture
The full conjecture is stated over abstract fields, but the counterexample is a concrete 3D function that we can explain and visualize using familiar geometric ideas and a little algebra. The post A Simplified View of the Jacobian Conjecture appeared first on Towards Data Science.
How to Decode the Temperature Parameter in LLMs
How statistical physics explains the transition from deterministic predictions to generative AI. The post How to Decode the Temperature Parameter in LLMs appeared first on Towards Data Science.
Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels
arXiv:2607.26121v1 Announce Type: new Abstract: Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical or operational harm, task completion alone does not establish trustworthiness. We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably under environmental and system variation while maintaining risk within acceptable bounds. We term this objective sustained safe success. Its supporting mechanisms are organized into four interdependent layers. The model layer generates task-competent action proposals with calibrated uncertainty and explicit safety preferences. The system layer realizes authorized actions dependably through integrated sensing, computation, control, hardware safeguards, fault containment, and fallback. The evidence layer substantiates bounded claims through evaluation, verification, validation, tra
Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning
arXiv:2607.26059v1 Announce Type: new Abstract: We report a striking phenomenon: deep reinforcement learning agents trained with frozen, randomly initialized CNN feature extractors spontaneously develop extremely sparse fully-connected representations, without any sparsity-inducing objective. In the first fully-connected layer (FC1, $3{,}136 \to 64$), agents compress task-relevant information through as few as 1-3 neurons out of 64 for deterministic Pong (5-11 for stochastic Pong), while trainable CNNs activate 55-64 neurons under matched conditions. We establish four principal findings. First, FC1 sparsity scales with task complexity: 1-11 for Pong, 19-26 for Breakout, and $\sim$42 for Space Invaders. Width-scaling confirms this reflects task structure rather than a fixed capacity fraction. Second, within-game scaling emerges: three identical Pong seeds produce 5, 7, and 11 active neurons. The 5-neuron seed plateaus at $+14$ reward, while the others reach expert performance ($+18.4$,
Sim2Win: A Team-Agnostic, Event-Based Pre-Match Outcome Prediction and Tactical Profiling System for Football
arXiv:2607.26061v1 Announce Type: new Abstract: Pre-match tactical decision-making in professional football relies heavily on subjective expert analysis and identity-based scouting systems that cannot generalize to unseen teams. This paper presents Sim2Win, a team-agnostic, event-based pre-match tactical recommendation framework that reframes match outcome prediction as a tactical decision-support problem. Using StatsBomb open event data from eleven competitions spanning 178 teams and 1,411 team-match records, Sim2Win constructs five-match rolling tactical profiles, engineers four interpretable tactical feature ratios, clusters team behaviors into eight playstyles via K-Means, and trains thirteen classifiers to estimate win, draw, and loss probabilities from tactical matchup representations. The system operates without team names or identity features, enabling generalization to teams never seen during training. A rigorous Leave-One-Competition-Out (LOCO) evaluation demonstrates that Si
Embodied Agents Take Control: Minimal-Interface Zero-Shot Agents Rival Industrial-Scale Policies in Vision-and-Language Navigation
arXiv:2607.26148v1 Announce Type: new Abstract: Autonomous embodied agents must sustain a long decision-making loop that involves perceiving, acting, verifying, and self-correcting over many steps. Current systems sustain this loop through task-specific workflows or embodied policies. We study a third form, agentic embodied control, in which a general-purpose agent holds the loop itself. Using zero-shot navigation as a controlled testbed, we evaluate three software-engineering agent harnesses given only a monocular RGB camera and discrete actions. Under this strictly minimal condition, replicated default-effort configurations reach 70.7$\pm$3.5% success (opus-5, mean over three runs), and fable-5 reaches 78% at maximum effort. When a trained waypoint tool is exposed alongside primitives as an optional capability, the hybrid fable-5 agent reaches 76.7$\pm$0.6% at default effort, using half the environment steps and less than one quarter of the wall time of the maximum-effort primitive r
Prompt Engineering Is Solved—Prompt Management Isn’t
Prompt engineering helps you write better prompts—but it doesn’t help you change them safely. This article explores a common production failure where a simple variable rename breaks every live call, and introduces a lightweight static analysis tool that treats prompts like contracts, catching breaking changes before they ship. The post Prompt Engineering Is Solved—Prompt Management Isn’t appeared first on Towards Data Science.
Why Your Best Predictive Model Gives the Wrong Treatment Effect
Why prediction-driven variable selection misses confounders and how Bayesian Adjustment for Confounding attempts to fix it. The post Why Your Best Predictive Model Gives the Wrong Treatment Effect appeared first on Towards Data Science.
How to Create Custom Skills in Claude: A Step-by-Step Guide
Claude can review data, check code, write reports, and prepare presentations, but teams still end up repeating the same structure, validation rules, company standards, and final-check instructions in every conversation. That repetition wastes time and often leads to inconsistent results. Custom Skills solve this by packaging reusable instructions, workflows, templates, scripts, examples, and reference files [ ] The post How to Create Custom Skills in Claude: A Step-by-Step Guide appeared first on Analytics Vidhya.