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Hands-on guides to LLMs, agents, prompt engineering, and the AI tools I run every day for real work, not demos.

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Open Source Vector Databases: Qdrant vs Milvus vs Weaviate

Open Source Vector Databases: Qdrant vs Milvus vs Weaviate

Five open source vector databases are worth a shortlist in 2026. Qdrant is Rust-based and wins on single-node latency and filtered ANN. Milvus 2.5 is the billion-scale pick with disk and GPU indexes. Weaviate bundles hybrid search and generative modules. Chroma is the simplest Python option for prototypes and agent memory. pgvector 0.8 is the smart bet when Postgres already runs your data. LanceDB earns a mention for multimodal, read-heavy work on S3. The right pick depends on where your data sits, how big the index gets, and whether you want strict p95 latency or built-in RAG glue.

A lightning-bolt-shaped racing vehicle speeds across a landscape of terminal windows while small subagents fan out and a rocket waits on a launchpad.

Gemini 3.5 Flash: 76% on Terminal-Bench, 4x Faster Output

Google released Gemini 3.5 Flash on May 19, 2026. The fast, lower-cost tier scored 76.2% on Terminal-Bench 2.1 and, by Google’s own measure, generates output about 4 times faster than other frontier models. Flash is available today across the Gemini app, Search, and the API. Gemini 3.5 Pro is confirmed for next month.

Key Takeaways

  • Gemini 3.5 Flash launched on May 19, 2026 and is free to use in the Gemini app and Google Search.
  • It scored 76.2% on Terminal-Bench 2.1, a test of finishing real terminal tasks end to end.
  • Google says Flash produces output about 4 times faster than rival frontier models.
  • The model is built for agents that run long, multi-step jobs and call tools.
  • Gemini 3.5 Pro, the larger sibling, is confirmed for next month.

What is Gemini 3.5 Flash?

Gemini 3.5 Flash is Google’s new fast, lower-cost tier of the Gemini 3.5 family. It was announced and made generally available on May 19, 2026, according to the Google announcement post . The “Flash” name has always meant a model tuned for speed and price.

Two identical metal engine blocks on a workbench, the second one fed by funnels of code fragments and tuned by a robotic precision arm.

Cursor Composer 2.5 vs Composer 2: What Actually Changed

Cursor Composer 2.5 is an incremental upgrade over Composer 2, not a new model. Both run on Moonshot’s open-source Kimi K2.5 checkpoint, so the entire difference is training. Composer 2.5 learned from 25x more synthetic coding tasks plus targeted reinforcement learning. Standard pricing holds at $0.50 per million input tokens.

Key Takeaways

  • Composer 2.5 and Composer 2 share the same open-source base model, so only the training changed.
  • Cursor trained Composer 2.5 on 25 times more synthetic coding tasks than the older version.
  • The standard model costs $0.50 per million input tokens and $2.50 per million output tokens.
  • A faster variant exists for $3.00 input and $15.00 output per million tokens.
  • Cursor is now building a much larger coding model from scratch with 10x more compute.

What is Cursor Composer 2.5?

Composer 2.5 is Cursor’s in-house coding model and the direct successor to Composer 2. It runs inside the Cursor editor, which slots into a crowded field of AI coding tools . The model is built for sustained work, not just quick one-shot answers.

Blender MCP: Control Blender With Claude AI Through Natural Language

Blender MCP: Control Blender With Claude AI Through Natural Language

Siddhartha Ahuja’s Blender MCP is the open-source project that puts Claude at the Blender keyboard. A Model Context Protocol server talks to a Blender add-on over a TCP socket on port 9876. From there, Claude can build shapes, paint materials, read the scene, pull free assets from Poly Haven , make meshes through Hyper3D Rodin , import Sketchfab models, and run any Python inside Blender. The repo has 19,694 stars, an MIT license, and sits at version 1.5.5. Similar add-ons exist for Unreal, Godot, Maya, and Figma. This one has the biggest crowd and the deepest tool list by far.

Claude Agent SDK: Build Custom AI Agents Without Reinventing the Orchestration Layer

Claude Agent SDK: Build Custom AI Agents Without Reinventing the Orchestration Layer

The Claude Agent SDK is the Claude Code engine stripped down to a library. Same agent loop, same built-in tools, same context handling, but you call it from your own Python or TypeScript code instead of the CLI. If you’ve used Claude Code to read files, run shell commands, search codebases, and edit code, the SDK points that same machinery at any problem you want. No human needs to sit in the loop.

Claude Code for Data Analysis: Process 500K Rows Without Writing Code

Claude Code for Data Analysis: Process 500K Rows Without Writing Code

Yes, you can point Claude Code at a 541,909-row retail dataset and walk away with a six-sheet Excel workbook, professional charts, and a parameterized report script, without opening a Python file or debugging a single line of code. The complete workflow takes roughly 15 to 20 minutes from raw data to finished output.

The goal is real delegation. Claude handles setup, cleaning, math, and charts. You focus on the right questions to ask.

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Most Popular

What X and Reddit Users Are Saying about Claude Opus 4.7

What X and Reddit Users Are Saying about Claude Opus 4.7

How power users on X and Reddit reacted to Claude Opus 4.7: praise for agentic coding, token burn concerns, and teams' practical prompting habits.

Gemma 4 vs Qwen 3.5 vs Llama 4: Which Open Model Should You Actually Use? (2026)

Gemma 4 vs Qwen 3.5 vs Llama 4: Which Open Model Should You Actually Use? (2026)

Gemma 4, Qwen 3.5, and Llama 4 compared on benchmarks, licensing, speed, and hardware so you can pick the right open model fast.

Qwen3.6-35B-A3B: Alibaba's Open-Weight Coding MoE

Qwen3.6-35B-A3B: Alibaba's Open-Weight Coding MoE

Alibaba's sparse Mixture-of-Experts: 35B total parameters, 3B active per token. Q4 quantization runs on MacBook Pro M5, matches Claude Sonnet performance.

MiniMax M2.7: Model That Almost Matches Claude Opus 4.6

MiniMax M2.7: Model That Almost Matches Claude Opus 4.6

MiniMax M2.7 review: 230B Mixture-of-Experts reasoning model with strong benchmarks, self-hosting options, and a tenth the cost of Claude Opus 4.6.

Running Gemma 4 26B MoE on 8GB VRAM: Three Strategies That Work

Running Gemma 4 26B MoE on 8GB VRAM: Three Strategies That Work

Run Google Gemma 4 26B MoE with sparse activation on budget 8GB GPUs using aggressive quantization, GPU-CPU layer offloading, and tensor parallelism techniques.

AI Coding Agents Are Insider Threats: Prompt Injection, MCP Exploits, and Supply Chain Attacks

AI Coding Agents Are Insider Threats: Prompt Injection, MCP Exploits, and Supply Chain Attacks

Study of 78 coding agents including Claude Code, Copilot, Cursor: all vulnerable to prompt injection attacks succeeding 85% of the time with adaptive vectors.

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