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House Panel Examines Lawsuit Against Journalist Over Prachin Buri Projects The House Committee on Political Development, Mass Communications and Public Participation on Thursday called for an investigation into four excavation projects in Prachin Buri after a journalist who raised concerns about their operations was sued and allegedly harassed. Workpoint News editor Pornthip "Jeab" Mongyai told the committee that oversight of resources removed under the “excavate soil in exchange for water” projects in Na Di district appeared inadequate. An expert assessment put the potential value of washed sand from the four projects at about 515 million baht, although this was based on approved project volumes rather than confirmed extraction. The National Anti-Corruption Commission said it was examining project approvals, excavation volumes and whether private operators had improperly benefited from state resources. It has conducted site inspections, depth measurements and aerial-image analysis but has yet to determine any damage or the amount of material removed. The committee also urged stronger anti-SLAPP protections for journalists and corruption whistleblowers. Members warned that existing safeguards may not cover people sued before they report information to the NACC and proposed allowing prosecutors to dismiss apparent SLAPP cases before they reach court. #Thailand# #โครงการขุดดินแลกน้ำ# #SLAPP#

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A useful but little-known Claude API feature 📁 Tired of re-encoding and re-sending the same file as Base64 with every request? Upload once, use many times. Claude's Files API lets you upload files and reference them across multiple requests. No more redundant file transfers, saving both cost and latency. 📌 Title: Files API 🔗 URL: 🧩 Overview When you need Claude to process images, PDFs, or text files, the traditional approach is sending Base64-encoded file data with every request. Files API lets you upload a file once, get a file_id, and reference it by ID in subsequent requests. This cuts transfer overhead and pairs well with Prompt Caching. 🛠 How to use it Upload a file via the Files API and save the returned file_id. In the Messages API, reference the file using a file_id content block. When asking multiple questions about the same file or processing it with different prompts, you never need to resend the file data. 🏗 Building it into production ・Document analysis services: store a user-uploaded PDF once and run multiple analyses (summary, QA, data extraction) sequentially without resending the PDF each time. ・Image analysis pipelines: pre-upload batches of images and process them in bulk, cutting transfer cost and latency significantly. ・Multi-turn document chat: upload reference documents once and ask questions against them repeatedly in an interactive QA session. ・Template management: save boilerplate documents or style guides as files and reference them across generation requests. 💡 Use cases 📄 Repeated analysis of PDFs and documents 🖼 Batch image processing 💬 Document-based multi-turn chat 📋 Reusable template files ⚠️ Watch out Uploaded files have retention limits, so check expiration for long-term references. File size limits also apply. For managing many files, build a lifecycle management system for file_ids. ✨ Paying to transfer the same file over and over is pure waste. Upload once, reference by ID, and improve both cost and developer experience. #Claude# #LLM#

# Practical ways to use the Claude Agent SDK 🔧 Define your internal APIs, databases, and domain logic with `@tool` and let Claude call them. Custom Tools lets you define your own functions as in-process MCP servers using the `@tool` decorator, making them callable by Claude during conversations. 📌 Title: Providing Custom Tools to Claude 🔗 URL: 🧩 Overview Define tools with `@tool` (Python) / `tool()` (TypeScript) specifying name, description, schema, and handler. Wrap with `create_sdk_mcp_server` and pass to `mcp_servers`. Annotate side-effect-free tools with `readOnlyHint: true` for parallel execution. 🛠 How to use it Define tools with `@tool` (Python) / `tool()` (TypeScript) specifying name, description, schema, and handler. Wrap with `create_sdk_mcp_server` and pass to `mcp_servers={"weather": server}`. Tools are exposed to Claude as `mcp__weather__get_temperature`. 🏗 Practical usage - Define internal APIs (customer info, inventory, order status) as `@tool` and build agents that respond to natural language queries. - Add `readOnlyHint: true` to read-only tools for parallel execution and lower latency. - Catch exceptions in handlers and return `is_error=True` so the agent loop continues and Claude retries or tries alternatives. - Return chart images via `image` blocks (base64) for Claude to analyze visually. 💡 Use cases 🏢 Natural language access to internal APIs 📊 Chart generation + image analysis pipeline 🔄 Fault-tolerant autonomous retry ⚠️ Watch out Uncaught exceptions in handlers crash the entire `query()`. Always handle errors. Use `tools: ["Read","Grep"]` to restrict which built-in tools are available. #ClaudeAgentSDK# #AI#

How do you build trustworthy AI diagnostic tools in one of medicine's most historically under-researched areas? In this episode of Just Now Possible, Teresa Torres talks with Tulsi Patel (Director of Product and Technology), Lorna Brightmore (Head of Data and AI), and Jack Pickard (Head of Engineering) at Hertility, a UK and Ireland-based women's health tech company. Hertility combines an in-depth online health assessment with at-home hormone testing and clinician-reviewed reports to help diagnose conditions spanning menstruation to menopause. Built on seven years of data linking symptoms, blood results, and pelvic ultrasound scans for over a million women, the team walks through two AI products in development: a Bayesian network that gives clinicians probability-based diagnoses instead of binary calls, and a scan automation pipeline that classifies ultrasound images, measures follicle counts and ovarian volume, and drafts clinical letters using an agentic loop that checks its own output against patient data before a human ever reviews it. You'll hear how the team guards against automation bias, builds clinician trust through transparency, minimizes PII before it ever reaches a model, and treats healthcare regulation as a design constraint from day one rather than a last-minute scramble. It's a detailed look at what it takes to bring AI into one of the most sensitive, tightly regulated corners of healthcare. Guests: - Tulsi Patel – Director of Product and Technology, Hertility - Lorna Brightmore – Head of Data and AI, Hertility - Jack Pickard – Head of Engineering, Hertility What we cover: - What makes Hertility's data set unique: seven years of linked symptoms, blood tests, and pelvic scans from over a million women - How uses a Bayesian network to give clinicians probability-based diagnoses instead of binary yes/no calls - Why showing clinicians the reasoning behind a diagnosis—not just the label—builds trust and speeds up triage - Guarding against automation bias with holdout sets and independent, fresh-eyes review - Inside the scan automation pipeline: classifying ultrasound images, detecting follicles, and measuring ovarian volume more precisely than manual methods - Using an agentic loop to check AI-drafted clinical letters against patient data and catch hallucinations before a human sees them - The infrastructure challenge of securely piping DICOM ultrasound images from third-party scan providers into Hertility's systems - How Hertility handles PII and PHI: pseudonymization, data minimization, and running models in-house on AWS Bedrock - Why treating healthcare regulation as a product requirement from day one makes AI products more scalable, not slower Key Takeaways: - Probabilistic, transparent AI outputs build more clinician trust than binary classifications. - Guardrails against automation bias are as important as the model itself. - Data minimization and in-house infrastructure make it possible to build AI responsibly with sensitive health data. - Treating regulation as a design constraint from day one makes AI products more defensible and scalable, not slower. Resources & Links: - Hertility — At-home hormone testing and reproductive health diagnostics for women in the UK and Ireland - AWS Bedrock — The platform Hertility uses to run LLMs in-house under its own governance and regulatory controls - PyTorch — The foundation for Hertility's in-house image classification and contouring models Chapters: 00:00 Meet the Team 00:13 What Hertility Does 01:51 How Customers Access It 04:06 A Unique Women's Health Dataset 07:03 Mission and Efficiency with AI 10:03 Why Long Assessments Convert 13:52 Before AI Workflows 16:52 Research Publications and Impact 18:48 GynAI Reducing Time to Diagnosis 21:21 Triage and Clinician Support 24:37 Keeping Patient UX the Same 26:12 Bayesian Network and Explainability 30:19 Multiple Diagnoses and Probabilities 32:37 Probabilistic Diagnosis Shift 33:50 Clinician Adoption and Workflow Fit 34:58 Communicating Medical Uncertainty 36:43 Scan Automation Overview 40:30 In House Image Analysis 44:25 DICOM Pipeline Engineering 47:30 Evals and Automation Bias 50:31 LLM Letter Guardrails 56:47 PHI Handling and Regulations 01:00:43 Infrastructure Choices and Wrap Up Listen on Spotify, Apple Podcasts, or watch on YouTube. Spotify: Apple Podcast: YouTube: