Category: AI News
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A Developer’s Guide to Systematic Prompting: Mastering Negative Constraints, Structured JSON Outputs, and Multi-Hypothesis Verbalized Sampling
[ad_1] Most developers treat prompting as an afterthought—write something reasonable, observe the output, and iterate if needed. That approach works until reliability becomes critical. As LLMs move into production systems, the difference between a prompt that usually works and one that works consistently becomes an engineering concern. In response, the research community has formalized prompting…
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200,000 MCP servers expose a command execution flaw that Anthropic calls a feature
[ad_1] Anthropic created the Model Context Protocol as the open standard for AI agent-to-tool communication. OpenAI adopted it in March 2025. Google DeepMind followed. Anthropic donated MCP to the Linux Foundation in December 2025. Downloads crossed 150 million. Then four researchers at OX Security found an architectural problem that affects all of them. MCP's STDIO…
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Per-token AI charges come to GitHub Copilot
[ad_1] A token is often described as representing around three-quarters of a word. Thus, giving an LLM a text of 10,000 words to examine would equate to 12,000-13,000 tokens of content. In developer terms, if a body of code which Copilot were to examine (for refactoring or bug-hunting for example), comprised of 10,000 ‘words’ (expressions,…
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A Coding Implementation to Parsing, Analyzing, Visualizing, and Fine-Tuning Agent Reasoning Traces Using the lambda/hermes-agent-reasoning-traces Dataset
[ad_1] In this tutorial, we explore the lambda/hermes-agent-reasoning-traces dataset to understand how agent-based models think, use tools, and generate responses across multi-turn conversations. We start by loading and inspecting the dataset, examining its structure, categories, and conversational format to get a clear idea of the available information. We then build simple parsers to extract key…
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Salesforce launches Agentforce Operations to fix the workflows breaking enterprise AI
[ad_1] Enterprise AI teams are hitting a wall — not because their models can't reason, but because the workflows underneath them were never built for agents. Tasks fail, handoffs break, and the problem compounds as organizations push agents deeper into back-office systems. A new architectural layer is emerging to address it: workflow execution control planes…
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How enterprise AI governance secures profit margins
[ad_1] According to SAP, enterprise AI governance secures profit margins by replacing statistical guesses with deterministic control. Ask a consumer-grade model to count the words in a document, and it will often miss the mark by ten percent. Manos Raptopoulos, Global President of Customer Success Europe, APAC, Middle East & Africa at SAP, observes that…
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A Coding Deep Dive into Agentic UI, Generative UI, State Synchronization, and Interrupt-Driven Approval Flows
[ad_1] In this tutorial, we build the entire Agentic UI stack from the ground up using plain Python, without relying on external frameworks to abstract away the core ideas. We implement the AG-UI event stream to make agent behavior observable in real time, and we bring in A2UI as a declarative layer that allows interfaces…
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Why OpenAI's 'goblin' problem matters — and how you can release the goblins on your own
[ad_1] AI is more than a technology — it's magic. Don't believe me? Why, then, is one of the leading companies in the space, OpenAI, publishing entire official, corporate blog posts about goblins? To understand, we first have to go back to earlier this week, on Monday, April 27, 2026, when a developer under the…
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How EMEA CIOs can jumpstart AI rollouts
[ad_1] Getting stalled enterprise AI rollouts in the EMEA region moving again will require CIOs to aggressively audit their systems. Over the past 18 months, AI deployments across Europe advanced far beyond initial testing. Companies poured capital into large language models and machine learning, expecting heavy operational upgrades. IDC research reveals that boards are slowing…
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Step by Step Guide to Build a Complete PII Detection and Redaction Pipeline with OpenAI Privacy Filter
[ad_1] In this tutorial, we build a complete, production-style pipeline for detecting and redacting personally identifiable information using the OpenAI Privacy Filter. We begin by setting up the environment and loading a token classification model that identifies multiple categories of sensitive data, including names, emails, phone numbers, addresses, and secrets. We then design helper functions…