AI articles and tutorials

In-depth explainers and tutorials on LLMs, AI agents, tools and the latest model releases.

• Understanding Jev: A New Paradigm for Python AI Developers — Learn how TypeSafe's Jev (System One) model introduces a specialized classification paradigm that is up to 25x cheaper and faster than Claude Haiku.
• Apodex 1.1 Mini: Scaling 35B Models with FrontierAgent — Learn how Apodex 1.1 mini uses the open-source FrontierAgent harness to achieve frontier-level performance through multi-agent orchestration and smart memory.
• The 2026 Roadmap to Becoming a Top Agentic AI Engineer — Learn the 12 essential skills for Agentic AI Engineering, from system thinking and MCP to state management and production deployment with PydanticAI.
• Building God's Eye View with AI Coding Agents — Learn how God's Eye View uses AI coding agents to create a global tracking system for planes and ships using plain English instead of manual coding.
• JEV by TypeSafe AI: Steerable RAG Reranking at Lower Cost — Learn how JEV from TypeSafe AI enables steerable reranking in RAG pipelines, boosting accuracy from 21% to 54% while slashing costs compared to LLMs.
• Building with Jev: TypeSafe's High-Speed Decision AI — Learn how Jev from TypeSafe replaces token-based LLMs with a probability-based architecture to enable real-time, zero-hallucination business automation.
• Structured Output for Browser LLMs with Transformers.js 4.3 — Learn how Transformers.js 4.3 uses the new structured-output package to enforce JSON Schema and regex constraints on LLM token generation in the browser.
• Inference Engineering: Baseten's Guide to DFlash and KV Compaction — Philip Kiely of Baseten explains the shift toward 'training for inference,' detailing DFlash speculation, TurboQuant trade-offs, and KV cache compaction.
• Superlinked: Scaling a Fleet of Small Open-Source Models — Learn how Superlinked solves the infrastructure bottlenecks of serving multiple small AI models using a pull-based queue and a Rust sidecar architecture.
• Debugging vLLM: How AI21 Fixed Two Silent Jamba Model Bugs — Learn how AI21 engineers used logprob forensics and memory pressure to find two critical bugs in vLLM affecting the hybrid Jamba model architecture.
• Optimizing RMS Norm with FlashNorm and CUDA Streams — Learn how Filip Makraduli uses weight folding and CUDA stream synchronization to speed up RMS Norm in transformers by up to 35% while avoiding race conditions.
• Google Cloud's ScientistTwo: Automating Science via Agent Swarms — Explore how Google's ScientistTwo uses a 24-agent swarm of frozen LLMs to automate the scientific process and drive 'system state optimization.'
• Scaling AI Inference: Why Orchestration is the New Frontier — Meta engineers Nishant Gupta and Naman Ahuja explain why AI inference is moving from simple model serving to complex, distributed orchestration at scale.
• Building Custom AI Agents with the Hermes Agent Harness — Learn how to build personalized AI agents like Tacobot and Lifebot using the Hermes agent harness to automate workflows and track personal productivity.
• How OpenAI Routes LLM Inference at Scale — Learn how OpenAI evolved its inference load balancer from a proportional controller to a global optimizer to minimize latency and protect KV cache locality.
• Cactus Needle 3: High-Speed Function Calling Without an LLM — Learn how Cactus Needle 3 delivers 66ms function calling on a CPU with only 35MB of parameters, bypassing the need for GPUs and JSON parsers.
• Wiki Foundation Model: Solving RAG Bottlenecks with Neural Graphs — Learn how the Wiki Foundation Model (WFM) evolves RAG by replacing sparse knowledge graphs with trainable neural graphs to enable complex multi-hop reasoning.
• From Chatbots to Agents: Exploring Modaal, Claude, and ElevenLabs — Learn how new AI tools like Modaal, Claude Slides, and ElevenLabs Reception are shifting AI from simple chat interfaces to autonomous, agentic actions.
• Jensen Huang and Mark Zuckerberg on AI Safety as Engineering — Explore the shift in AI safety philosophy as Jensen Huang and Mark Zuckerberg redefine safety as a solvable engineering problem rather than a philosophical mystery.
• Measuring AI Agent Success: Clay's Quality, Throughput, and Cost Framework — Learn how Clay evaluates AI agents using a mix of deterministic matching, factual evals, and LLM-as-a-judge to balance quality, speed, and cost.
• How to Use Ollama for Local AI Privacy with ChatGPT UI — Learn how to replace cloud-based AI with local models using Ollama to keep your sensitive data off OpenAI's servers while maintaining a seamless UI.
• Understanding AI Agent Memory: Short-Term vs Long-Term — Learn how AI agents use context windows for short-term memory and external storage for long-term memory to maintain continuity across multiple runs.
• Why TCP is Failing AI Clusters and How Homa Fixes It — Learn why Stanford's John Ousterhout argues that TCP's sender-side congestion control is stalling AI GPUs and how the Homa protocol solves it.
• Union Alpha: The Stealth Model Challenging Frontier AI — Discover how the stealth Union Alpha model uses sparse attention and a lightweight architecture to outperform giants like Claude Opus 5 in coding tasks.
• ScienceBuddy and the Rise of Self-Improving Scientific AI Agents — Learn how PhAI Labs' ScienceBuddy uses recursive self-improvement and GRPO to boost scientific AI accuracy from 42.2% to 73.3% across complex research tasks.
• How Stripe is Building Infrastructure for Agentic Commerce — Explore how AI agents are becoming a new class of customers and why Stripe is developing the payment infrastructure needed to handle agentic transactions.
• Agentic Engineering with Browser Use and GPT-6 Astra — Explore how Magnus, CEO of Browser Use, leverages GPT-6 Astra and the Director framework to scale AI agents and eliminate human operational bottlenecks.
• DeepSeek V4.1 Flash: Engram and Selective Layer Architecture — Explore how DeepSeek V4.1 Flash uses Engram for recurrent memory and CED/CSA2 to slash KV cache and compute costs for faster, efficient inference.
• Controlling AI Agents with LangChain Managed Deep Agents Middleware — Learn how to use LangChain's middleware for Managed Deep Agents to implement PII redaction, custom logging, and policy enforcement for secure AI agents.
• Modular AI Skills: How Vercel and Anthropic are Upgrading Agents — Explore the top 10 AI skills from GitHub, including tools from Vercel and Anthropic that transform LLMs from simple chatbots into specialized agents.

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