62 articles
Practical guides and news from the fast-moving world of AI.
An agent that can act on your behalf is a new attack surface. Here are the 2026 guardrails that actually work: agent identity, least privilege, and approval gates.
The prompt techniques that still deliver in 2026: few-shot examples, chain-of-thought, reasoning effort, and what to skip for reasoning models.
A context window is not memory. Here are the storage models and context-engineering patterns that let 2026 agents remember without blowing the token budget.
SWE-bench Verified is near saturation and one benchmark no longer tells the story. Here is how to read the 2026 coding leaderboards without getting fooled.
Three ways to customize an LLM, three different problems. Here is when to prompt, when to retrieve, and when to fine-tune.
How to design human-in-the-loop oversight for AI agents in 2026: propose-then-commit, confidence-based routing, and EU AI Act Article 14.
An AI agent is not your user and not a background job, it is a new kind of actor that needs its own identity. Here is how agent auth works in 2026 and why API keys fail.
When an agent fails in production, you need to replay its reasoning. Here is how OpenTelemetry tracing makes agents debuggable.
After years of spend-at-all-costs AI adoption, companies are demanding ROI, capping budgets, and switching to cheaper models.
How accurate AI text detectors really are in 2026, why false positives hurt ESL writers, and why 50+ universities have disabled them.
Your embedding model sets the ceiling on RAG quality. Here is how to read MTEB, weigh cost and latency, and pick the right one for your data in 2026.
The 2026 default for new projects is pgvector if you already run Postgres, Qdrant if you need scale. Here's how to choose without overthinking it.
A 2026 decision framework for RAG vs long context windows: cost, latency, freshness, and access control decide which one fits your system.
AI inference is now the dominant driver of data center electricity growth, and the grid is feeling it. Here are the numbers.
LLM judges now agree with humans ~85% of the time. Here's how to run them at scale without going broke or fooling yourself.
Run Flux and Stable Diffusion locally with ComfyUI in 2026: VRAM needs, model choices, licensing, and a no-API-bill setup path.
The core multi-agent orchestration patterns for 2026, sequential, parallel, supervisor, hierarchical, and human-in-the-loop, and when to use each.
The industry gave up on detecting fakes after the fact and bet on provenance instead. Here is how C2PA, SynthID and watermarking actually work in 2026.
AI moved into the spreadsheet in 2026. Here is what Copilot, Gemini, and Claude each do best in Excel and Sheets, and how to use them without trusting blindly.
Provenance metadata and invisible watermarks now travel with AI media. Here is what C2PA and SynthID can and cannot prove.
Most teams that ship software in 2026 run both. Here's how Claude Code and Cursor differ, what they each cost, and how to build a stack by role.
Open models now match or beat closed frontier ones on key benchmarks. Here is how to pick among DeepSeek, Qwen, GLM, Kimi and Llama by license and use case.
Native multimodal models process text, image, audio, and video together instead of bolting on translators. Here is what changed.
Stop parsing messy model text by hand. Ollama's structured outputs constrain any local model to a JSON schema you define.
How cross-encoder rerankers reorder retrieved results to boost RAG accuracy in 2026, and the two-stage pipeline that production teams use.
Three engines, three jobs. A practical 2026 guide to choosing between Ollama, vLLM, and llama.cpp based on concurrency, hardware, and scale.
AI browsers that click, type, and read your tabs are powerful and dangerous. Here is how the attacks actually work and what to do.
CodeRabbit vs Greptile vs Diamond in 2026: bug-catch rates, false positives, pricing, and which AI code reviewer fits your team.
Five major models now hit 1M tokens and Llama 4 Scout reaches 10M, but advertised size and real recall are not the same thing.
Global data center demand is set to top 1,000 TWh in 2026, and AI sites now pull 100-750 MW each. Here is what that means for the grid and your bill.
4-bit quantization cuts LLM inference cost 60 to 80% and fits a 70B model on one GPU. Here is which format to pick for CPU, GPU serving, and edge in 2026.
On-device SLMs like Phi-4-mini and Gemma now run an agentic loop faster, cheaper, and more privately than a cloud giant. Here's when to use them.
Copilot+ PCs advertise 40-80 TOPS NPUs, but can they actually run a local LLM well? Here is what the numbers say in 2026.
The Model Context Protocol's 2026 release makes the core stateless and adds Tasks, MCP Apps, and auth hardening. Here's what changes for builders.
Practical 2026 techniques to cut LLM hallucinations: grounding, structured prompting, verifier models, and self-consistency detection.
AI writes most of the code now. The teams that win treat it like an intern, not an oracle. Here is the workflow that holds up.
Why autonomous agents that run code need isolation, and how microVMs, gVisor, and egress controls keep them caged in 2026.
What the KV cache is, why it eats GPU memory, and how PagedAttention, GQA, and quantization cut waste for cheaper LLM inference in 2026.
Prompt injection is the number-one LLM risk, and agentic systems amplify it. A defense-in-depth playbook for builders shipping AI agents.
Reasoning models trade time and money for accuracy by thinking before they answer. Here is how test-time compute actually works.
Native structured outputs now hit 99.9% schema compliance across the major providers. Here is how they work, and why schema-valid still isn't correct.
AI agents that control a real desktop went production in 2026. Here is how Claude, OpenAI Operator, and Gemini compare on benchmarks, and where each one wins.
How function calling lets LLMs take action in 2026: schema design, parallel tool calls, the ReAct loop, and scaling to large toolsets.
When RAG fails, it is almost always retrieval, not generation. Here are the chunking strategies that actually move the needle in 2026.
A guide to the 2026 AI agent benchmarks that actually signal capability: SWE-bench, GAIA, OSWorld, Tau-bench, and WebArena.
Agents fail silently in ways traditional APM never sees. Here is how tracing, OpenTelemetry GenAI conventions, and the 2026 tooling landscape fit together.
Keyword filters do not stop modern jailbreaks. Here is the 2026 defense-in-depth stack, the attack techniques it counters, and how continuous red teaming closes the loop.
How diffusion language models like LLaDA and Mercury generate text in parallel for huge speedups, and how they differ from GPT-style models.
Native-audio 4K clips, plausible physics, characters consistent across cuts. Here is how the top AI video models actually differ in mid-2026.
How semantic caching answers repeated LLM questions without calling the model, saving input and output tokens in 2026 production stacks.
LoRA vs QLoRA in 2026: how each saves memory, the speed and quality trade-offs, and which to pick for your GPU budget.
Synthetic training data powered the 2026 wave of small strong models, but feed a model its own output blindly and it collapses. Here is how to do it right.
Computer-use agents jumped from 14% to 44% task completion on OSWorld in two years. Here is where they actually work in 2026 and where they still fail.
Midjourney V8, FLUX.2, GPT Image 2, and Nano Banana each win a different job in 2026. Here is which model to reach for and why one tool never wins all.
How GraphRAG uses knowledge graphs to answer multi-hop questions that vector-search RAG cannot, and when the extra cost is worth it.
How knowledge distillation transfers a large LLM's behavior to a small, fast student model in 2026, and when it beats fine-tuning.
The framework wars consolidated to six players in 2026. Here is how LangGraph, CrewAI, AutoGen and the rest differ, and why the framework is the least of it.
Voice AI split into three markets in 2026: plain TTS, speech-to-speech, and full realtime agents. Here is how they differ and why latency decides everything.
Why MoE dominates 2026 LLMs: how active vs total parameters, routing, and top-k experts deliver big-model quality at small-model speed.
Agentic coding tools can quietly bill $500 to $2,000 per engineer a month. Here is where the tokens go and the four levers that cut spend 50 to 70%.
How draft-and-verify speculative decoding speeds up LLM token generation 2-4x in 2026 with no loss in output quality.
Why multi-model AI stacks need a gateway in 2026, and how LiteLLM, Portkey, and Kong handle routing, budgets, failover, and audit logs.
