Developer insights, AI tool comparisons, and practical guides for building with AI.
A working guide to the 2026 LLM evaluation stack: Ragas for RAG metrics, LightEval and OpenCompass for benchmarks, DeepEval in CI, and tracing for online eval.
A working comparison of FLUX.1 Kontext, Qwen-Image-Edit, Step1X-Edit, MagicQuill and SUPIR on license, VRAM, instruction fidelity, and 4-bit speed.
A working comparison of LiteLLM, Portkey, and Bifrost on routing, caching, budgets, observability, and real latency overhead in production.
How llm-d, AIBrix, NVIDIA Dynamo, GPUStack, OpenLLM and Xinference actually differ on Kubernetes, and when a single vLLM box still beats a cluster.
A working guide to real-time voice agent stacks: latency budgets, turn detection, interruption handling, transport choices, and what each layer actually costs.
A working guide to real-time detection and segmentation in 2026: RF-DETR, YOLO26, SAM 3, open-vocabulary models, SAHI tiling, and the license traps.
A benchmarked comparison of MinerU, Docling, Marker 2, Surya, PDF-Extract-Kit and Zerox for RAG ingestion, covering layout models, OCR fallbacks, GPU cost and the AGPL traps.
A working developer's comparison of Kokoro, Zonos, Kyutai TTS, F5-TTS, Piper, Chatterbox and the rest: cloning quality, latency, CPU cost, and license traps.
A component-level guide to gsplat, VGGT, DUSt3R, CoTracker and Depth Pro: capture specs, VRAM, export formats, and the licenses that block shipping.
A verified 2026 comparison of terminal-native coding agents on model flexibility, sandboxing, MCP support, cost control, and what their licenses really permit.
How the leading open-weight image-to-3D models compare on mesh quality, PBR textures, VRAM, speed, and the licenses that decide what you can ship.
Whisper is no longer the default: how Parakeet, SenseVoice, Kimi-Audio, Ultravox and Moshi compare on accuracy, speed, streaming and hardware.
Constrained generation used to be a library you bolted on. It is becoming a feature of the inference engine. Why that matters for agent reliability.
I built the same simple agent task in three frameworks back to back. Here is what each one feels like in practice and where each one fits.
Memory is the most overhyped feature in agents, and also the one most teams botch. Here is what Letta and Mem0 actually do and when you actually need them.
Two leading open source paths to running OpenAI Whisper. One is a CPU-friendly C/C++ port, the other rides CTranslate2 and a GPU. Which one fits your workload?
There are now half a dozen viable agent frameworks, and they all claim the same things. This guide cuts through the noise by matching frameworks to actual use cases.
Step-by-step on installing F5-TTS, prepping a clean reference clip, running CLI and Gradio inference, and a candid comparison to Coqui TTS and XTTS-v2.
A survey of where open source video generation actually is. Open-Sora's DiT approach, AnimateDiff's motion modules over Stable Diffusion, and StreamDiffusion for the real-time adjacent case.
A first-person take on putting LiteLLM in front of OpenAI, Anthropic, and a local Ollama instance, with routing rules, fallbacks, and observability. Plus when not to bother.
Aphrodite Engine forks vLLM and adds the long tail of quantization formats and samplers that the community-quantized model world actually uses. Here is what it does well and where vLLM still wins.
A practical walkthrough for standing up Open WebUI on your own box, plugging Ollama in for local models, and rotating to remote backends per chat through a unified proxy.
The 'RAG is dead' meme misses what is actually happening. Hybrid retrieval, late-interaction models, agentic retrieval, and contextual chunking are quietly reshaping the field.
A practical, end-to-end fine-tuning walkthrough with Unsloth: dataset prep, LoRA config, 4-bit quantization, training, and exporting to GGUF for llama.cpp.
A qualitative comparison of four popular open-source vector databases across architecture, hybrid search, scaling, SDKs, and license.
An end-to-end blueprint for a fully self-hosted RAG system using Ollama for inference, Qdrant for the vector store, and AnythingLLM for ingestion and chat.
Open-source coding agents now do far more than complete the next token. We compare Cline, Roo Code, Continue, and Aider, and what makes an agent different from an assistant.
A direct comparison of ComfyUI and SwarmUI: ComfyUI is the node-graph engine power users love, SwarmUI wraps it in a friendlier interface. Who each is for, what extensions look like, and the deployment story.
DSPy reframes prompts as code that can be compiled and optimized. Here is what that actually means, why it has gotten popular, and where it sits next to structured-output libraries like Outlines and Guidance.
A practical setup walkthrough for serving a Qwen3 variant locally with vLLM on a single 24GB consumer GPU, with notes on which sizes fit, quantization choices, useful CLI flags, and the OpenAI-compatible endpoint.
A survey of where the major open-source LLM inference engines stand: vLLM, llama.cpp, Aphrodite, SGLang, LMDeploy, and LightLLM. Where each one fits, what hardware it targets, and how they compare on quantization and structured output.
After a long stretch with Cursor, I moved my daily AI pair programming work to Aider. Here is what the terminal-first, git-aware, model-agnostic workflow looks like, and what I gave up to get there.
A comprehensive look at where AI dev tools stand today - what works, what does not, and what is next.
Using AI as your pair programmer works - if you know how to work with it. Here are 10 tips.
You do not need to pay for AI dev tools. These free options are legitimately good.
A step-by-step walkthrough of building and shipping a dev tool using AI coding assistants.
Building mobile apps with AI assistance - from React Native to Flutter to native Swift/Kotlin.
VS Code plugins are not enough anymore. AI-native editors are taking over for a reason.
Model Context Protocol is connecting AI to everything. Here is how MCP servers work and why they matter.
AI tools that help you debug faster - from error explanation to root cause analysis.
If you want AI code assistance without sending code to the cloud, these self-hosted options work.
Getting good output from AI code generators requires technique. Here is what works.
Python-specific AI tools for code generation, debugging, testing, and package management.
A framework for cutting through the hype and picking AI tools that actually help.
AI security tools that find vulnerabilities before attackers do. Here are the ones worth using.
A practical comparison of Claude and ChatGPT for day-to-day development tasks.
AI-powered testing tools promise to write your tests for you. We checked which ones deliver.
When you are a team of 1-5 developers, these are the AI tools that give you the most leverage.
AI agents are the next wave. Here is a practical guide to building agent-based workflows.
Stop dreading docs. These AI tools make writing technical documentation actually bearable.
Two AI-native code editors going head-to-head. Which one should you actually switch to?
How AI is making DevOps less soul-crushing - from incident response to IaC generation.
AI tools that write SQL, optimize queries, visualize schemas, and make database work less painful.
From v0 to Bolt to AI Figma plugins - frontend development is getting a massive AI upgrade.
Forget GUI apps - these AI terminal tools integrate right into your shell workflow.
The best free and open-source AI tools for developers that deserve more attention.
From generating components to writing hooks to catching bugs - AI tools that speed up React work.
AI tools purpose-built for the JS/TS ecosystem - from type generation to bundle analysis.
From generating OpenAPI specs to automated endpoint testing, these AI tools speed up API work.
AI code review tools are everywhere now. Here is how to actually set them up and get value from them.
We put Cursor and Copilot head-to-head across speed, accuracy, and real-world coding tasks.
A head-to-head comparison of the top AI coding tools that are actually worth using in 2026.