This article details how Google SREs are leveraging Gemini 3 and Gemini CLI to accelerate incident response, root cause analysis, and postmortem creation, ultimately reducing Mean Time To Mitigation (MTTM) and improving system reliability.
Structured, temporal memory for AI agents. memv extracts knowledge from conversations using a predict-calibrate approach: importance emerges from prediction error, not upfront LLM scoring.
Large Language Models (LLMs) demonstrate remarkable capabilities, yet their inability to maintain persistent memory in long contexts limits their effectiveness as autonomous agents in long-term interactions. While existing memory systems have made progress, their reliance on arbitrary granularity for defining the basic memory unit and passive, rule-based mechanisms for knowledge extraction limits their capacity for genuine learning and evolution. To address these foundational limitations, we present Nemori, a novel self-organizing memory architecture inspired by human cognitive principles. Nemori's core innovation is twofold: First, its Two-Step Alignment Principle, inspired by Event Segmentation Theory, provides a principled, top-down method for autonomously organizing the raw conversational stream into semantically coherent episodes, solving the critical issue of memory granularity. Second, its Predict-Calibrate Principle, inspired by the Free-energy Principle, enables the agent to proactively learn from prediction gaps, moving beyond pre-defined heuristics to achieve adaptive knowledge evolution. This offers a viable path toward handling the long-term, dynamic workflows of autonomous agents. Extensive experiments on the LoCoMo and LongMemEval benchmarks demonstrate that Nemori significantly outperforms prior state-of-the-art systems, with its advantage being particularly pronounced in longer contexts.
This article discusses the impact of Anthropic's Claude Code, an AI agent that is significantly impacting software development and the broader information work economy. It analyzes Claude Code's capabilities, its potential to drive revenue growth for Anthropic, the challenges it poses for Microsoft, and the shift in competition within the AI landscape.
A user is experiencing slow performance with Qwen3-Coder-Next on their local system despite having a capable setup. They are using a tensor-split configuration with two GPUs (RTX 5060 Ti and RTX 3060) and are seeing speeds between 2-15 tokens/second, with high swap usage. The post details their hardware, parameters used, and seeks advice on troubleshooting the issue.
PicoClaw is an ultra-lightweight personal AI Assistant inspired by nanobot, refactored from the ground up in Go. It runs on $10 hardware with <10MB RAM and boasts fast startup times and portability.
Here’s the simplest version — key sentence extraction:
<pre>
```
def extract_relevant_sentences(document, query, top_k=5):
sentences = document.split('.')
query_embedding = embed(query)
scored = »
for sentence in sentences:
similarity = cosine_sim(query_embedding, embed(sentence))
scored.append((sentence, similarity))
scored.sort(key=lambda x: x 1 » , reverse=True)
return '. '.join( s[0 » for s in scored :top_k » ])
```
</pre>
For each sentence, compute similarity to the query. Keep the top 5. Discard the rest
<pre>
```
def extract_relevant_sentences(document, query, top_k=5):
sentences = document.split('.')
query_embedding = embed(query)
scored = »
for sentence in sentences:
similarity = cosine_sim(query_embedding, embed(sentence))
scored.append((sentence, similarity))
scored.sort(key=lambda x: x 1 » , reverse=True)
return '. '.join( s[0 » for s in scored :top_k » ])
```
</pre>
For each sentence, compute similarity to the query. Keep the top 5. Discard the rest
Artificial-intelligence agents have their own social-media platform and are publishing AI-generated research papers on their own preprint server. Researchers are studying how these agents interact and how humans respond to those discussions.
An LLM-powered personal diary. Write your thoughts, and Reminor analyzes them, remembers them, and chats with you about them.
This article details the setup and initial testing of Goose, an open-source agent framework, paired with Ollama and the Qwen3-coder model, as a free alternative to Claude Code. It covers the installation process, initial performance observations, and a comparison to cloud-based solutions.
