#LLM
125 related articles
Large Language Models Discover New Quantum Circuits, Outperforming State-of-the-Art on Seven of Eight Tasks
Study proposes an LLM evolutionary framework that auto-designs quantum circuits, outperforming state-of-the-art on 7 of 8 tasks while reducing Toffoli gates and qubits, and discovering a novel reversible modular inversion architecture.
AI-Assisted Test Failure Analysis: Treating LLMs as a Diagnostic Layer, Not a Self-Healing Tool
GeekyAnts proposes using LLMs to analyze Playwright test failure artifacts while keeping execution deterministic and AI as a diagnostic classifier. This critical breakdown examines its architecture, redaction risks, hallucination issues, and self-healing test traps, with deployment recommendations.
OpenRouter Alternatives: Choosing Between an AI API Relay and a Self-Hosted Gateway
Compare OpenRouter, self-hosted LiteLLM, and Xinghuo AI Gateway — understand the ops responsibility gap between managed relays and self-hosted gateways, plus reproducible token cost reconciliation and API acceptance steps.
Turning Structured Output Into a Game: Hands-On with Token Arcade
Token Arcade turns structured output and constrained decoding into an interactive game. Through coin-flip and JSON-generation levels, it visually shows how LLMs mask disallowed tokens at each step, helping developers understand the mechanics behind structured output.
Understanding RAG: How to Make AI Answer Questions Using Your Own Data
A deep dive into how RAG (Retrieval-Augmented Generation) works: from document chunking and embedding to semantic retrieval and LLM answer generation, plus common pitfalls and real-world use cases.
Stop Trusting AI Providers Blindly: Two Steps to Close the Model Provenance and Privacy Gaps
Hosted AI APIs have two blind spots: model versions can be silently swapped and prompt destinations can't be verified. This article introduces a local proxy layer with model fingerprinting and prompt redaction to turn blind trust into conditional verification, and analyzes the limits of each approach.
Prompt Engineering in Practice: From Basics to Advanced LLM Prompt Templates
A systematic guide to prompt engineering from basics to advanced: prompt formula, zero-shot and few-shot, chain-of-thought, ReAct, tree of thoughts, meta-prompting, with copyable templates and an anti-pattern comparison table.
Cermin: An AI Engineering Practice for Building a Personal Thought Mirror with LLMs
Developer Arkoes07 built Cermin using a Telegram bot and LLMs to auto-capture and classify personal thoughts. Explore the architecture, kind/topic classification, and model selection decisions.
Top 5 AI Gateways Compared: How to Manage Production LLM Traffic
Deep comparison of Bifrost, Cloudflare, Vercel, LiteLLM, and OpenRouter AI gateways — covering performance, routing, reliability, cost control, and deployment for production LLM traffic management.
Stop Writing Prompts Like Prose: Use the Four-Part Contract to Kill LLM Drift
LLM outputs drifting between runs? The fix isn't better wording — it's structure. Learn the four-part prompt contract (Role, Task, Constraints, Output) with code validation to ship stable, debuggable prompts.
RAG or Fine-Tuning? You're Asking the Wrong Question When Building AI Systems
RAG or fine-tuning? Learn why this is a false dilemma. RAG solves knowledge problems; fine-tuning solves behavior problems. Production AI systems need both.
Open-Weight Model APIs in Practice: Selection Criteria and SDK Integration Guide
Learn the three ways to run open-weight AI models, key API selection criteria (protocol compatibility, precision, pricing), and code examples using OpenAI SDK, Anthropic SDK, and curl.
HMA Multi-Agent Alternation Framework: Breaking the Monolithic Bottleneck in Long-Horizon ML Engineering
HMA (Humanize MLE Agents) uses multi-agent session alternation and context resets to break long-horizon MLE bottlenecks, achieving 78.2% SOTA medal rate on MLE-bench with just 1/4 the time budget.
The Growing Perception Gap Around LLM Capabilities: Why Our Understanding of AI Is Fracturing
LLM capability perceptions are fracturing fast. This article explores why users reach opposite conclusions about the same AI tools — and how to close the gap.
Don't Let LLMs Do Math: AI Judges Language, Code Keeps Score
How ExamReady's developer split LLM judgment from code-based scoring to build a reproducible, testable AI grading system for German B1/B2 exam prep.
Can LLMs Be Data Pipeline Detectives? A Benchmarking Experiment
Developer Olga Braginskaya benchmarks LLMs as data pipeline detectives on Kaggle, testing whether AI can automatically identify root causes of data loss.
Build a Semantic Caching Proxy with FastAPI to Cut LLM Bills by 58%
Learn how to build an OpenAI-compatible semantic caching proxy using FastAPI, Redis vector search, and OpenTelemetry to cut LLM inference costs by up to 58% with per-tenant guardrails.
LLM Response Caching in Practice: Building an Exact-Match Cache for OpenAI-Compatible APIs
How to build a client-side exact-match cache for OpenAI-compatible APIs. Covers cache key design, SQLite & Redis storage, streaming response caching, and request coalescing — with full Python code.
Building Model Fallback Chains: When to Switch LLMs and a Python Implementation Guide
How to build an LLM model fallback chain under an OpenAI-compatible base URL. Covers when to switch models, which errors should trigger fallback vs. raise immediately, core loop code in Python, and testing strategies.
Multi-Agent Apps Hit 429 Rate Limits: A Multi-Provider Failover Deep Dive
A developer building a LangGraph multi-agent debate app kept hitting 429 rate limits on free LLM APIs. Learn how a multi-provider failover chain and state decoupling solved the reliability problem.