#distributed LLM inference
523 related articles
·3 min
New Gemini Jailbreak Method: Deep Analysis of the Observer and Accomplice Technique
Deep analysis of a Gemini jailbreak technique—the Observer and Accomplice method—examining how it exploits contextual manipulation and reasoning chain inconsistencies to bypass AI safety alignment.
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·4 min
Harness Engineering: The Core Infrastructure for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to go from demo to production.
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·4 min
Harness Engineering: The Core Infrastructure for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to reach production. Based on Scott Moss's workshop.
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·3 min
Deep Dive into Inkling: The First Frontier Open-Source Multimodal LLM from the West
Thinking Machines releases Inkling, an open-source multimodal LLM with near-trillion MoE parameters, 1M token context, Apache 2.0 license. Deep dive into architecture, benchmarks, and pricing.
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·4 min
Laguna S 2.1 Deep Dive: Hands-On Local Deployment of a 118B Parameter Coding Model
In-depth review of Poolside's Laguna S 2.1 open-source coding model: MoE architecture, RL training, DGX Spark local deployment, and real-world agentic coding tests with 8B active parameters.
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·5 min
Running GPT-5.6 Inside Claude Code: Best Practices for Subagent Orchestration and System Prompts
Deep dive into running OpenAI GPT-5.6 inside Claude Code: comparing Codex vs Claude Code on subagent orchestration, workflow design, and system prompt quality, revealing how harness engineering determines model output.
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·3 min
Gemini 3.1 Pro Rate Limiting Exposed: Why Paying Users Can't Actually Use Their Full Quota
Deep analysis of Google Gemini 3.1 Pro's rolling window rate limiting, revealing the massive gap between paper quotas and actual usability for paying subscribers.
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·3 min
Gemini 3.1 Pro Rate Limiting Exposed: Why Paid Users Can Never Use Their Full Quota
Deep analysis of Google Gemini 3.1 Pro's rolling window rate limiting, revealing the gap between paper quotas and real usability for paid AI subscribers.
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·2 min
Gemini Always Crashes on the 16th Turn? Error 1076 Root Cause Analysis and Workarounds
Google's Gemini consistently triggers Error 1076 on the 16th conversation turn, regardless of context size. Analysis points to a session state management defect, with three workarounds provided.
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·3 min
The Real Daily Life of MLE and MLOps Engineers: Key Differences Between Three Roles and Career Paths
An in-depth look at the real daily work of data scientists, MLEs, and MLOps engineers — covering responsibilities, essential tools, and career paths to help you find your direction in AI.
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·3 min
A Beginner's Guide to AI Agents: Two Learning Paths for Developers and Practitioners
How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.
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·3 min
Google Launches Gemini 4 Pre-training: Its Most Ambitious AI Model Training Yet
Google DeepMind announces Gemini 4 pre-training has begun, calling it their most ambitious training yet. A deep dive into its technical direction, compute scale, multimodal breakthroughs, and competitive impact.
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·3 min
The Open-Weights Model Debate: Balancing Safety and Openness
An in-depth analysis of the open-weights model debate: public release brings transparency and innovation, but raises safety and misuse risks. Exploring tiered release, red-teaming, and governance challenges.
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·3 min
The Open-Weights Model Debate: Balancing Safety and Openness
An in-depth analysis of the open-weights model debate: publicly releasing model weights enables transparency and innovation but raises safety risks. Explores tiered release, red-teaming, and the industry dynamics behind open AI governance.
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·3 min
Poolside Laguna Model Launch: Can Silicon Valley's Open-Source Push Catch Up to China's AI?
Poolside launches Laguna open-weight model after 18 months of silence, pitting 118B parameters against Kimi K3's 2.8 trillion. Can Silicon Valley's open-source push close the gap with Chinese AI?
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·5 min
Complete Breakdown of Core Competencies for AI Agent Engineers: From Demo to Production-Grade Systems
Deep breakdown of 4 core AI Agent engineer competencies: business decomposition, multi-Agent architecture, quantitative evaluation, and engineering delivery—bridging the gap from Demo to production.
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·5 min
The Complete Guide to AI Agent Development: A Four-Stage Progressive Roadmap
A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.
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·3 min
Poolside Laguna Model Release: Can Silicon Valley Open Source Catch Up with Chinese AI?
Poolside releases its Laguna open-weight model after 18 months of silence, challenging Moonshot's Kimi K3 with 118B vs 2.8T parameters. Can Silicon Valley close the gap with Chinese AI?
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·3 min
Ollama Prepares for Trillion-Parameter Open-Source Models as the Open-Source Ecosystem Heats Up
Ollama scales up for trillion-parameter open-source models like Kimi K3 and Qwen 3.8. Hugging Face demands $100M from OpenAI, Alibaba Coder goes mobile, and DeepSeek pauses fundraising.
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·3 min
Getting Started with Codex: A Beginner's Guide to AI Agentic Coding Tools
A detailed guide to getting started with Codex and Claude Code AI agentic coding tools, from environment setup to hands-on practice, helping beginners master AI programming assistants.
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