Shopify Abandons React Native for Swift & Kotlin
Shopify announces its decision to abandon React Native in favor of native mobile development using Swift for iOS and Kotlin for Android to improve app
Master Coding with practical tips, prompt engineering techniques, and productivity hacks.
77 articles
This is the largest section on Promptsicle: using AI tools, models, and agents to write, review, and ship code. Topics range from local model setups and quantization to coding agents, benchmarks, and the developer-workflow questions that come with them.
Articles are grounded in primary sources, project repositories, release notes, official docs, or the original write-up, so readers can dig into the implementation rather than take a summary at face value.
Shopify announces its decision to abandon React Native in favor of native mobile development using Swift for iOS and Kotlin for Android to improve app
Developers in 2026 strategically combine multiple AI models to leverage specialized strengths, optimize costs, and build more robust applications through
Explores design patterns and architectural approaches for building AI agents that maintain state, execute tasks continuously, and operate autonomously over
Explores how Scikit-LLM leverages large language model embedding spaces to enhance text classification tasks through dimensionality reduction and feature
This guide explores methods and techniques for interpreting how large language model-based text classification systems make decisions, including attention
This guide explains how to update the llm-anthropic plugin to work with version 1.0.0 of the Anthropic SDK, covering breaking changes and migration steps for
SmolVM is a hardware-based virtual machine sandbox designed to safely execute untrusted code by providing isolated environments with strict resource controls
Explores how to use Bun's WebView API for lightweight web scraping, enabling developers to extract data from websites efficiently using a minimal JavaScript
Eval-Driven Development for Production AI Agents emphasizes building reliable AI systems by creating comprehensive evaluation frameworks before deployment,
Explores ten real-world scenarios for choosing embedding models, covering use cases from semantic search and recommendation systems to multilingual
alchemy-utils is a Python library that simplifies cross-database operations by providing unified utilities and helpers for working with multiple database
This article explores the challenges and best practices for transitioning machine learning models from experimental Jupyter notebooks to robust, scalable
Meta launches Muse Code, a new AI-powered coding assistant that directly competes with Cursor by offering advanced code completion, chat-based programming
Speculative decoding accelerates large language model text generation by using a smaller draft model to predict tokens that a larger model then verifies in
This comprehensive guide explores FinOps implementation strategies for Microsoft Fabric, covering cost optimization, monitoring, governance frameworks, and
Explores innovative approaches to designing artificial intelligence systems specifically optimized for embodied robots operating in physical environments
AI Agent Repository Management Without Cloning enables developers to interact with and manage code repositories through AI agents without requiring local
Learn how developers can efficiently debug ray tracing applications using NVIDIA OptiX Toolkit's comprehensive debugging features, profiling tools, and
SGLang demonstrates superior performance compared to Hugging Face Text Generation Inference in recent benchmark tests, showing faster processing speeds and
Exploring how combining multiple affordable AI models can outperform a single premium model through ensemble techniques, offering cost-effective solutions for
Prompt caching reduces LLM API costs by up to 90% by storing and reusing repeated context across multiple requests, eliminating redundant processing and
The Fraud Ops Escalation Agent with Snowflake CoWork streamlines fraud investigation workflows by integrating real-time data analysis, automated case
This guide explores using large language models as automated judges to evaluate AI model outputs on Azure, covering implementation patterns, best practices,
A developer shares how they dramatically reduced AI image generation expenses from dollars to cents per image through optimization techniques and strategic
This guide explains how to run 70-billion parameter language models on local hardware, covering system requirements, optimization techniques, and practical
RedTensor Engine announces a 200x performance boost through advanced optimization techniques, promising to dramatically accelerate machine learning workloads
This article explains how asynchronous HTTP requests work, covering the event loop, callbacks, promises, and async/await patterns in modern web development.
Streaming text-to-speech technology significantly reduces latency in AI voice applications by converting text to audio in real-time chunks rather than waiting
A look at the Model Context Protocol authorization spec: OAuth 2.1 roles, token validation, scopes, and the discovery flow between clients and servers.
How long-running AI agents manage memory through compaction, note-taking, and sub-agents, based on Anthropic's context engineering guidance.
Abliteration uncensors language models by finding the refusal direction in the residual stream and orthogonalizing weights against it, without retraining.
GitHub's official hardening guidance explains how Actions workflows can expose secrets and how least privilege and pinned actions reduce the risk.
The BIRD-SQL benchmark measures how well large language models turn natural language questions into SQL across messy, real-world databases.
How the Hugging Face Transformers library speeds up LLM inference with static kv-cache, speculative decoding, FlashAttention-2, and quantization
How the batch, ubatch, and parallel options in llama.cpp's llama-server control concurrent request handling and where to read throughput metrics.
How vLLM uses quantization, paged attention, and continuous batching to run large language model inference across a wide range of GPU hardware.
Teloxide is a Rust framework for building Telegram bots, with declarative message pipelines, typed commands, and finite-state dialogues.
Claude Code hooks run shell commands at lifecycle points and can deterministically block tool calls like risky edits or shell commands.
Claude Code uses CLAUDE.md files and auto memory to carry project context across sessions, with files written by developers and notes written by Claude.
An open-source Discord bot runs Claude Code from chat channels, with sandboxing, role-based access, MCP management, and Git branch organization.
How untrusted input like issue titles can inject shell commands into GitHub Actions workflows, and the environment variable pattern that prevents it.
How developers trace and debug LangChain agents with LangSmith using environment variables and the SDK, with traces viewed in the UI or API.
A FiftyOne plugin runs PyTesseract optical character recognition on dataset images locally and stores the results as labels.
Google DeepMind released Gemma Scope, an open suite of sparse autoencoders that help researchers study the inner workings of Gemma 2 models.
THUDM's GLM-4-9B-Chat has been quantized to GGUF format, letting the bilingual model run locally through llama.cpp across several size options.
How GPU memory, precision, and quantization determine which open-source language models can actually run on a given machine.
The ik_llama.cpp fork adds a split mode graph that brings tensor parallelism for MoE models across multiple CUDA GPUs.
ktop is a Rust terminal monitor that tracks CPU, GPU, memory, network, and temperature for hybrid LLM workloads in a single view.
The llama.cpp web UI can proxy Model Context Protocol servers so a local model discovers and calls external tools through HTTP transports.
llama-swap is a Go proxy that loads and switches local AI models on demand through an OpenAI and Anthropic compatible API.
Kokoro-82M runs text-to-speech fully in the browser through Transformers.js, with WebGPU, WASM, and CPU backends and no server calls.
An open-source Python tool uses a local Ollama model or OpenAI to automatically categorize and label Gmail messages through the Gmail API.
Apple's mlx-lm package fine-tunes large language models on Apple silicon using LoRA and QLoRA, with support for several open model families.
Structured JSON output lets AI models feed downstream systems directly through schema-enforced function calls instead of simulating user interactions.
NVIDIA Model Optimizer is an open-source library that compresses deep learning models with quantization, pruning, and distillation to speed up inference.
How LiteLLM translates OpenAI Chat Completions requests into Anthropic's Claude format, covering tools, streaming, and proxy routing.
MLX-VLM runs vision-language and omni models, including Gemma multimodal variants, on Apple Silicon Macs for local image, audio, and video tasks.
How CUTLASS keeps CUDA binary size down by filtering kernel instantiations and limiting target architectures during compilation.
How the Petals project pools consumer GPUs in a peer-to-peer swarm so people can run very large language models at home together.
How llama.cpp and GGUF quantization make it possible to run language models on consumer laptops without dedicated server hardware.
An ONNX build of Qwen2-VL-2B-Instruct runs vision-language inference in the browser through Transformers.js, no server required.
How Ollama runs open language models locally on a Mac, exposing a local REST API for building AI agents without a cloud connection.
Community llama.cpp benchmarks show AMD Strix Halo running local language models on its integrated GPU with 128GB of shared memory.
How AMD's open-source XDNA driver enables inference on Ryzen AI NPU hardware under Linux, including kernel and OS requirements.
Qdrant is an open-source vector similarity search engine written in Rust, supporting dense and sparse vectors, hybrid search, and on-device deployment.
Qwen3-VL from the Qwen team handles hours-long video with second-level indexing and timestamp-grounded event localization for natural language video search.
How Railway's command-line tools let developers deploy applications by bringing their code and letting the platform handle the rest
How the python-statemachine library models states and transitions, and why that structure suits MCP servers managing multi-step workflows.
How Claude Code loads CLAUDE.md memory files up and down the directory tree, and how to scope instructions for monorepos.
SWE-rebench is an automated pipeline and dataset that continuously collects real GitHub tasks to evaluate software engineering agents.
A research paper introduces self-debugging, a method that teaches language models to find and fix their own code errors without human feedback.
How git worktrees let developers keep multiple branches checked out at once, and how terminal tools like lazygit expose this from the command line.
The BEIR benchmark found that classic BM25 keyword search is a robust baseline that many neural embedding models fail to beat on unfamiliar data.
How Unsloth fits gpt-oss-20b QLoRA fine-tuning into 14GB of VRAM, well within consumer 24GB cards, and the MXFP4 hurdles it had to solve.
Unsloth reports embedding model fine-tuning roughly 1.8 to 3.3x faster than other Flash Attention 2 setups, with about 20 percent less memory.
Unsloth says its gpt-oss fine-tuning uses over 50% less VRAM, letting the 20B Mixture-of-Experts model train on under 14GB.
Unsloth's gradient checkpointing algorithm fits much longer context windows on one GPU by offloading activations to system RAM.