AIAI London
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DeepSeek Researchers Introduce DeepSeek-V3.2 and DeepSeek-V3.2-Speciale for Long Context Reasoning and Agentic Workloads
How do you get GPT-5-level reasoning on actual long-context, tool-using workloads with out paying the quadratic consideration and GPU value that often makes these methods impractical? DeepSeek analysis introduces DeepSeek-V3.2 and DeepSeek-V3.2-Speciale. They are reasoning-first fashions constructed for brokers and targets top quality reasoning, lengthy context and agent workflows, with open weights and manufacturing APIs….
A Coding Guide to Build Intelligent Multi-Agent Systems with the PEER Pattern
In this tutorial, we explore a powerful multi-agent system built around the PEER pattern: Plan, Execute, Express, and Review. We run the entire workflow in Google Colab/Notebook, integrating agents with specialized roles and leveraging Google’s Gemini 1.5 Flash model via a free API key. As we walk through the system, we observe how each agent…
Microsoft AI Releases Fara-7B: An Efficient Agentic Model for Computer Use
How can we safely let an AI agent deal with actual internet duties like reserving, looking out, and type filling immediately on our personal units with out sending all the things to the cloud? Microsoft Research has launched Fara-7B, a 7 billion parameter agentic small language model designed particularly for laptop use. It is an…
7 LLM Generation Parameters—What They Do and How to Tune Them?
Tuning LLM outputs is essentially a decoding drawback: you form the mannequin’s next-token distribution with a handful of sampling controls—max tokens (caps response size beneath the mannequin’s context restrict), temperature (logit scaling for extra/much less randomness), top-p/nucleus and top-k (truncate the candidate set by chance mass or rank), frequency and presence penalties (discourage repetition or…
