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AKAnup Karanjkarwowhow.hashnode.devApr 25 · 9 min read

DeepSeek V4 Flash & Pro: Complete Developer Guide — April 2026

A year after DeepSeek’s R1 shocked Silicon Valley — achieving GPT-4-class performance at a fraction of the training cost — the Chinese AI lab returned on April 24, 2026 with its most ambitious open-source release yet. DeepSeek unveiled preview versio...

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Aaiagentmemoryaiagentmemory.hashnode.devApr 7 · 10 min read

Extending LLM Context Window: Techniques and Strategies for Enhanced AI Memory

Extending LLM context window capabilities allows AI models to process and retain significantly more information, overcoming the limitations of their fixed input size. This enhancement is crucial for complex tasks requiring long-term memory, coherent ...

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AGAditya Guptaadiyogiarts.hashnode.devMar 28 · 6 min read

Benchmarking LLM Serving Engines: vLLM, TensorRT-LLM, SGLang Compared

Originally published at adiyogiarts.com PERFORMANCE ENGINEERING Benchmarking LLM Serving Engines: vLLM, TensorRT-LLM, SGLang Compared Deploying Large Language Models (LLMs) effectively requires serving engines. This article dives into a critical co...

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AGAditya Guptaadiyogiarts.hashnode.devMar 28 · 5 min read

Constitutional AI vs. RLHF: Navigating AI Safety Tradeoffs in 2026

Originally published at adiyogiarts.com Constitutional AI vs. RLHF: Navigating AI Safety Tradeoffs in 2026 As AI capabilities surge in 2026, ensuring safety becomes paramount. Constitutional AI (CAI) and Reinforcement Learning from Human Feedback (R...

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AGAditya Guptaadiyogiarts.hashnode.devMar 28 · 6 min read

Synthetic Data Pipelines for LLMs: Preventing Model Collapse

Originally published at adiyogiarts.com Synthetic Data Pipelines for LLMs: Preventing Model Collapse Fig. 1 — Synthetic Data Pipelines for LLMs: Preventing Model Collapse Synthetic data plays an increasingly critical role in large language model dev...

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AGAditya Guptaadiyogiarts.hashnode.devMar 28 · 6 min read

Synthetic Data Pipelines for LLMs: Preventing Model Collapse

Originally published at adiyogiarts.com Synthetic Data Pipelines for LLMs: Preventing Model Collapse Fig. 1 — Synthetic Data Pipelines for LLMs: Preventing Model Collapse Synthetic data plays an increasingly critical role in large language model dev...

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JLJosh Leesoftwareforbreakfast.hashnode.devMar 27 · 10 min read

AI Governance 101: How to Assess Risks in LLM-Driven Applications

You built an LLM-powered feature. It works in testing, users seem to like it, and now it's heading to production. Before it ships, someone in legal or compliance asks: "What's our risk assessment for

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GGeekyAntstechblog.geekyants.comMar 18 · 8 min read

Mock Smarter: Using MCP Server for Reliable Playwright Testing

AI is no longer just a tool for answering questions—it’s evolving into a hands-on test automation companion that actively contributes to solving real-world problems. It's empowering QA professionals b

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PCPithy Cyborgpithycyborg.hashnode.devFeb 2 · 4 min read

Moltbook Breach Deep Dive: 1.5 Million Tokens Leaked, Zero Row-Level Security Disaster

No RLS. Client-side anon keys. 1.5M tokens exfiltrated. Moltbook's Supabase setup was insecure by default. Agents could be hijacked with one request. Human owners doxxed via email mapping. Third-party creds like OpenAI and Anthropic were in the blast...

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JSJasmeet Singh Bhatiallm-in-the-actual-world-dataflow.hashnode.devNov 29, 2025 · 10 min read

LLM In The Actual World Dataflow

Problem Statement Computers cannot understand natural language (the way people speak or write) or context (the situation or background that gives meaning to language) the way humans do. Generally speaking, computers require structured inputs (organiz...

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#large-language-model

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Trending tags this week

DeepSeek V4 Flash & Pro: Complete Developer Guide — April 2026

Extending LLM Context Window: Techniques and Strategies for Enhanced AI Memory

Benchmarking LLM Serving Engines: vLLM, TensorRT-LLM, SGLang Compared

Constitutional AI vs. RLHF: Navigating AI Safety Tradeoffs in 2026

Synthetic Data Pipelines for LLMs: Preventing Model Collapse

Synthetic Data Pipelines for LLMs: Preventing Model Collapse

AI Governance 101: How to Assess Risks in LLM-Driven Applications

Mock Smarter: Using MCP Server for Reliable Playwright Testing

Moltbook Breach Deep Dive: 1.5 Million Tokens Leaked, Zero Row-Level Security Disaster

LLM In The Actual World Dataflow