AI · LLM · agents · practice
Deep dives into AI tools for real-world development
Case studies, guides and honest reviews: which AI tools actually work in production, and which are hype. No fluff, with examples from practice.
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LLMs, agents, Claude Code and tools — what I tried, what stuck in my workflow, and why.
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Everything fresh: news, write-ups and notes — in order
Today’s roundup: a new local computer from Perplexity, a threat forecast for enterprise software, and fresh approaches to training and evaluating AI agents.
Today’s roundup includes an important update for Python on RISC-V, reproducibility issues with LLM benchmarks, and practical ways to connect models to complex databases.
Today’s focus: weak spots in the memory and security of AI agents, speeding up work with MCP tools, and new discoveries for running models locally.
Today’s roundup covers Harvard instructors’ AI avatars, a practical analysis of agentic system security testing, and several notable experiments with local models.
Today’s practical examples of how AI helps debug Linux and develop products, as well as where agents create new risks. The roundup also includes tools for local use.
Today: how agents can mistake a tool's error for a fact, why AI search returns different answers, and how to gather context more efficiently. Plus several practical projects from the community.
In today's roundup: tools for safer development, new approaches to evaluating AI agents, and hands-on experience from an autonomous product team.
Today: why agents need real limits on their actions rather than just good prompts, and how reasoning is becoming part of the API contract. Plus practical finds from the community and some unusual applications of AI.
In today's roundup: a Microsoft 365 Copilot compromise, video and podcast generation from Reddit posts, and several tools for local AI agents. Plus pieces on security and on training developers in the age of AI.
OGX offers a single API surface for agent applications and lets you change models, vector databases and inference backends without rewriting code. But along with the freedom of choice the developer takes on responsibility for their own server, for compatibility and for operations.
AI tool deep dives — no fluff
I write about LLMs, agents and Claude Code: what actually works in production, and what's hype. Short, to the point, with examples.