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OKCurated by Oleksandr Kuzmenko, AI Product Engineer·Updated July 18, 2026·Sources cited on every story

Recent highlights

This week was all about agent tooling: Claude Code shipped background agents and a native PR review flow inside the terminal, while the MCP protocol v1.2 added resource streaming and granular authorization. Together they make production agents noticeably safer and easier to run.

On the research side, the strongest signal is that structured retrieval consistently beats “long context” on real-world tasks — reinforcing the move toward economical architecture, with prompt caching cutting real API bills by 70%+. Alongside, the practical guides keep coming: from building your own code-review agent to an honest take on when fine-tuning actually pays off.

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  • 1Claude Code26
  • 2Claude15
  • 3Codex11
  • 4Cursor10
  • 5Model Context Protocol6
  • 6Gemini5
  • 7ChatGPT4
  • 8Docker4

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Stories found: 2

Agents & MCPMastodon · Jul 13, 2026 2 min read

Analyzing Claude Code Token Overhead and Caching Costs Against OpenCode

A systematic analysis of developer agents reveals that Claude Code injects around 33,000 tokens of system prompts, tool schemas, and reminders before reading your query, compared to OpenCode's minimal 7,000 tokens. This overhead escalates with system instructions, custom AGENTS.md files, and active Model Context Protocol servers, heavily impacting prompt-caching efficiency.

Why it matters

A systematic analysis of developer agents reveals that Claude Code injects around 33,000 tokens of system prompts, tool schemas, and reminders before reading your query, compared to OpenCode's minimal 7,000 tokens. This overhead escalates with system instructions, custom AGENTS.md files, and active Model Context Protocol servers, heavily impacting prompt-caching efficiency.

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Local LLMsMarkTechPost · Jul 3, 2026 2 min read

Interfaze Open-Sources Multilingual Speech-to-Text Model Powered by Parallel Diffusion

Interfaze has open-sourced `diffusion-gemma-asr-small`, a multilingual speech-to-text model built on Google's DiffusionGemma-26B. It transcribes 6 languages in parallel using a tiny 42M-parameter adapter, processing entire transcripts bidirectionally.

Why it matters

Interfaze has open-sourced `diffusion-gemma-asr-small`, a multilingual speech-to-text model built on Google's DiffusionGemma-26B. It transcribes 6 languages in parallel using a tiny 42M-parameter adapter, processing entire transcripts bidirectionally.

Open full story

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