Articles, one at a time.
Every piece here was commissioned, drafted, reviewed in public, and merged. No content mills, no auto-published slop.
Build an MCP Server with TypeScript: 2026 Tutorial
Step-by-step tutorial for building a Model Context Protocol server in TypeScript with tools, resources, and Claude Desktop integration.
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Gemma 4 26B vs 31B: Which Model to Run Locally
Gemma 4 26B MoE vs 31B Dense: VRAM requirements, quantization guide, thinking mode setup, and how to choose the right model for your hardware.
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Token Optimization for Production LLMs: Cut Costs Effectively
Four research-backed token optimization techniques for production LLMs: semantic caching, prompt compression, context pruning, and speculative decoding.
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Claude Streaming + Tool Use: Build Real-Time Agentic Pipelines
Stream tool calls in real time with the Anthropic Python SDK. Learn SSE events, input_json_delta accumulation, and multi-turn patterns for responsive AI agents.
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DSPy 3.x: Compile and Optimize LLM Pipelines Automatically
DSPy replaces fragile prompt strings with typed signatures and compiled optimizers. MIPROv2 and GEPA lift accuracy 10-65% without touching model weights.
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Greptile Review 2026: AI Code Review With Full Codebase Context
Greptile v3 uses the Claude Agent SDK to review PRs with whole-codebase context. 82% bug catch rate, $30/seat, GitHub + GitLab only. Full 2026 review.
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smolagents + MCP Bridge: Connect Any Tool to Your Agent
Connect any MCP server to a smolagents ToolCallingAgent in Python. Covers MCPClient, stdio and streamable-http transports, and LLM backend options.
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LLM Prompt Caching in Production: Cut API Costs 78% With Claude
Prompt caching cuts Claude API costs by up to 78% in high-traffic apps. Learn the 5-min TTL change, 4 breakpoint patterns, and cache busting gotchas for 2026.
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OpenAI o3 Pro API: Maximum Reasoning for Hard Tasks
Complete developer guide to the OpenAI o3-pro API. Pricing, benchmarks, Responses API setup, background mode, reasoning effort, and when to actually use it.
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A2A Protocol PoC: Build an Agent Server in Python
Build a working A2A agent server and client in Python. Effloow Lab ran this PoC with a2a-sdk 1.0.2 — including a routing bug we found and fixed.
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Arcee Trinity Large Thinking: Open Source 400B Reasoning Guide
Arcee Trinity Large Thinking is a 400B Apache 2.0 sparse MoE model built for long-horizon agents. API, self-hosting, benchmarks, and integration guide.
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MiniMax M2.5 API Guide: 80% SWE-Bench at $0.15/M Tokens
MiniMax M2.5 matches Claude Opus on SWE-Bench at a fraction of the cost. Architecture breakdown, benchmark replay, and full API setup guide for 2026.
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