#ai-tools
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In the Weights: Measuring Your Digital Presence in AI Models
In the Weights is a new tool that evaluates how well various LLMs recall specific individuals without web search, effectively serving as a modern, AI-centric vanity search.
VibeThinker-3B: High-Performance Reasoning at 3B Parameters
VibeThinker-3B is a compact, open-source reasoning model that achieves performance comparable to massive models on math and coding tasks by using a specialized 'Spectrum-to-Signal' post-training pipeline.
Building End-to-End Forecasting Pipelines with TimeCopilot
TimeCopilot provides a unified interface for forecasting that integrates statistical models, foundation models, anomaly detection, and LLM-driven interpretation into a single workflow.
Building Complex Software with Long-Running AI Agents
Long-running AI agents can execute multi-day, complex engineering pipelines—such as building an OS or optimizing 3D web scenes—by self-correcting through dependent tasks rather than relying on single-prompt generation.
Google Cloud TechBuilding a One-Click AI Record Summary in Salesforce
Streamline Salesforce workflows by using Einstein Prompt Builder and Screen Flows to create a zero-code AI summary button for complex records.
Optimizing AI Apps with LLM Routing
Stop relying on a single 'best' model. Implementing an LLM router allows you to dynamically match requests to models based on cost, latency, and task complexity, ensuring production stability and efficiency.
Governing AI Agents with Looker and MCP
By using the Model Context Protocol (MCP) to connect AI agents to Looker's semantic layer, developers can replace fragile raw SQL generation with governed, model-aware data interactions.
Scale Your Expertise, Not Your Job Titles
Instead of using AI to perform roles you aren't trained for, use it to encode your unique professional expertise into systems, allowing your specific skills to scale across an entire project.
New Usage Analytics and Spend Controls for ChatGPT Enterprise
OpenAI has introduced granular credit usage analytics and flexible spend controls for ChatGPT Enterprise, allowing administrators to track consumption by user, product, and model while setting tiered budget limits.
GLARE: Natural Language Interfaces for Global Model Explanations
GLARE provides a natural language interface for querying global model explanations, allowing users to interpret complex AI behavior through conversational prompts rather than static visualizations.
Deontic Policies for Runtime Governance of Agentic AI
The paper proposes using deontic logic—a system of formal rules defining obligations, permissions, and prohibitions—to govern the runtime behavior of autonomous AI agents.
Building Reliable AI Code Generation Pipelines with Salesforce CodeGen
To move AI-generated code from prototype to production, implement a multi-stage pipeline that includes automated unit testing, safety sandboxing, and model-based reranking to filter out hallucinated or insecure outputs.
Liquid AI's New 350M Multilingual Retrieval Models
Liquid AI has released LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M, two efficient, bidirectional retrieval models optimized for multilingual search across 11 languages.
The Rise of Agentic Traffic and Microsoft's Model Strategy
Agentic AI bots now dominate web traffic, signaling a shift in how we interact with information. Meanwhile, Microsoft is pivoting to first-party models, prioritizing safety and cost-efficiency for enterprise users.
Architecting Long-Running AI Agents for Multi-Day Workflows
Move beyond stateless chatbots by implementing event-driven dormancy, durable checkpointing, and decoupled evaluation to manage complex, multi-day workflows.
Google Cloud TechSingles Reject AI for Connection, Accept It for Utility
While 47% of U.S. singles hold negative views toward AI in dating, they remain open to using AI tools for profile optimization and conversation starters, provided the human connection remains authentic.
RODS: Improving Multi-Turn Tool-Use Agents via Reward-Driven Synthesis
RODS (Reward-Driven Online Data Synthesis) improves multi-turn tool-use agents by generating high-quality synthetic training data through iterative reward-based filtering, addressing the scarcity of complex, multi-step interaction data.
Decoupling Search from Reasoning in LLM Agents
Native search grounding in LLMs creates rigid, expensive, and opaque agent architectures. Moving to a Decoupled Search Grounding (DSG) layer allows for vendor-agnostic control over retrieval, caching, and cost, while maintaining accuracy.
SciRisk-Bench: Evaluating Safety in AI for Science
SciRisk-Bench is a new benchmark designed to evaluate the safety risks of AI models specifically applied to scientific research, focusing on multi-dimensional risk assessment.
Improving AI Scientist Reliability via Research Harnesses
The paper proposes a 'Research Harness' to externalize synthesis and validation, addressing the reliability issues inherent in autonomous AI research agents.
CEO-Bench: Measuring Long-Term Strategic Reasoning in AI Agents
CEO-Bench is a new evaluation framework designed to test whether AI agents can maintain strategic coherence and decision-making over extended, multi-step business scenarios.
The KV Cache Compression Race: TurboQuant vs OSCAR vs EpiCache
KV cache compression is the new frontier for scaling LLM inference, with TurboQuant, OSCAR, and EpiCache offering distinct strategies to balance memory footprint against model accuracy.
Why Your First Hire in 2026 Should Be a Specialist, Not a Generalist
Generative AI has commoditized generalist skills, making the traditional 'T-shaped' hire a liability. Startups should prioritize deep specialists who can leverage AI to perform at an elite level.
The Shift Toward User-Controlled AI Recommendation Algorithms
Major social platforms are moving from opaque, one-size-fits-all algorithms to user-tunable systems, leveraging LLMs to allow granular control over feed content.
Building AI Agents with Google's Agent Development Kit (ADK)
A practical walkthrough on using Google's Agent Development Kit (ADK) to build autonomous agents that can interact with text-based environments, specifically demonstrated through a retro-inspired adventure game.
How IoC Containers Work: A Deep Dive into NestJS and Spring
Dependency Injection (DI) containers are not magic; they are registry systems that combine object factories, lifecycle managers, and metadata reflection to automate object construction and dependency resolution.
Escaping Provider Lock-in with RubyLLM
Avoid hard-coding provider-specific logic by abstracting your AI layer. RubyLLM allows Rails developers to swap between GPT, Claude, Gemini, and local models without rewriting service objects.
Solving the Physical AI Data Bottleneck
XDOF is building the infrastructure for physical AI by providing the high-fidelity, large-scale training data that robotics models currently lack, moving beyond the limitations of low-quality video data.
Pramaana Labs Uses Formal Verification to Secure Enterprise AI
Pramaana Labs raised $27M to integrate formal verification—using the LEAN programming language—with LLMs to ensure deterministic, error-free outputs in high-stakes fields like tax, law, and drug discovery.
DeepInsight: Evaluating the Physical AI Stack
DeepInsight proposes a unified infrastructure for evaluating AI systems across the entire physical stack, addressing the fragmentation in current performance assessment methodologies.
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