DeepSeek Launches Open-Source Coding Agent Harness to Rival Claude Code

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DeepSeek released DeepSeek Harness (dsh) on August 13, 2026, an open-source coding agent framework built to run AI models as autonomous coding agents rather than being a model itself. The tool handles session management, tool invocation, permission controls, and a local Web UI, and launched alongside DeepSeek V4-Pro on the same day. VentureBeat described it as an open-source competitor to Anthropic's Claude Code. Within a week of launch, the GitHub repository had accumulated approximately 169,000 stars and 18,100 forks as of August 20. Released under the MIT license, dsh is currently a developer preview, meaning breaking changes are expected and production workflows should not rely on it without version-pinning strategies.

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How to Run Local LLMs Privately Using Ollama and Spring AI

Developers can run large language models (LLMs) locally on their own machines using Ollama integrated with Spring AI and Spring Boot, eliminating the need to send data to cloud providers like OpenAI or Anthropic. This approach is particularly useful for building AI applications without API costs, working offline, and keeping sensitive company data within internal infrastructure. Ollama supports several open-source models including Llama, Qwen, Gemma, Mistral, and DeepSeek, and exposes a local API that Spring AI can communicate with directly. A practical use case demonstrated is an internal HR customer support assistant where employee queries are handled entirely on-premise. Developers are cautioned that running an LLM locally does not replace the need for proper authentication, authorization, and data security measures.

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ProgrammingDEV Community ·

Developer Details 5 Real-World Failures When Self-Hosting Chatwoot at Scale

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ProgrammingDEV Community ·

Seven-Step Framework for Structuring IoT Test Automation Effectively

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ProgrammingDEV Community ·

Why Self-Correcting Agent Loops Are Essential for Reliable AI in Production

Large language models (LLMs) deployed in production environments are structurally prone to hallucination, meaning they generate plausible-sounding but factually incorrect outputs with no internal mechanism to flag uncertainty. This is not a prompt engineering flaw or pipeline bug — it is an inherent property of next-token prediction systems that conflate plausibility with truth. Researchers warn that fine-tuning and prompt optimization alone cannot eliminate this fundamental uncertainty, yet companies continue integrating LLMs into high-stakes customer, financial, and legal workflows. A proposed solution called the COPS framework — Self-Correcting Production Systems — treats the LLM as one component in a multi-step pipeline that includes verification, criticism, and re-generation loops rather than a standalone answer authority. Experts argue that meaningful human oversight in high-stakes decisions, combined with these self-correcting architectures, represents the only viable path to trustworthy production AI systems.

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