Haiku 5.5 Is 75% Cheaper: What to Do in Production
Haiku 5.5 on Bedrock costs 75% less than Haiku 4.5, with guidance on when subagents pay off and why access rights matter more than token price.
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24 Showing articles 49–72 of 563
Haiku 5.5 at Luna Pricing: What to Do with a $0.10 Token
Haiku 5.5 costs $0.10/$0.50 per million tokens, like GPT-6 Luna. We examine cost, context thresholds, and vendor lock-in.
Learn moren8n for $50: Cheap to Build, Costly to Fix
Learn why n8n workflows take hours to build but weeks to fix, covering input validation, database state, idempotency, and failure alerts.
Learn more722 AI math papers: what to verify before trusting the claims
AI claims to solve 90 of 500 open problems. We explain why verifying results determines a model's value for business.
Learn moreAn Agent Without Memory, Seams, or Logs Remains a Demo
Four AWS case studies cover assistant memory, agentic RAG, voice, and ISO 42005—and what determines an AI agent’s fate in a company.
Learn moreAgents Write Code, but Git, Reviews, and Metrics Lag
GitHub is rebuilding Git for agents, stacking pull requests and report limits, revealing where AI development hits process bottlenecks.
Learn moreThe State of IT in 2026: Three Worlds
Learn what Gregory Orosz saw at OpenAI, Anthropic, Ramp, and Uber, and how leaders can apply it without paying for AI theater.
Learn moren8n 2.43.0: Automation Needs Releases and QA
n8n 2.43.0 and rising workflow QA demand: how to approve changes by diff, not a contractor’s promise.
Learn morePimcore 2026.3.1: five fixes that affect ownership costs
A review of Pimcore 2026.3.1: separate version storage, focal points, and class sanitization, plus catalog benefits and upgrade planning.
Learn moreAgents in Production: Cheaper Than Control
Claude in GovCloud, MCP promotion for agents, and multi-agent evaluation reveal AI’s real production cost.
Learn moreModels Get Cheaper, Your Security Perimeter Remains Yours
Models get cheaper while risks rise: a Stratechery analysis of AI leaks and how an LLM gateway restores data control.
Learn moreA Working n8n Workflow Is Not Yet an Ops Process
Your n8n workflow ran, but procurement cannot fix errors. Learn ops readiness and manage LLM budgets with LiteLLM.
Learn moreWord-Based Addition: A Test Before AI Deployment
An experiment with Qwen3.8 27B shows how to find a model's error threshold in one evening without wasting an AI deployment budget.
Learn moreReflection Beam: Open 501B Model and Choice
Reflection released the open Beam model with 501B parameters, exploring why rankings matter less and how to avoid LLM lock-in.
Learn moreReviewBench and the 520-Character Token: What Breaks the Pipeline
This review examines ReviewBench, 520-character tokens, TLS without X25519, and CodeQL to show where pipelines break and how to measure an AI reviewer.
Learn moreUI Tests Without a UI Flow: Beware the Green Check
MSTest 4.5 and MTP 2.5 run WinUI 3 and UWP tests in the app's UI thread—learn why STA is not enough.
Learn moren8n in Production: Green Status Does Not Prove Results
Why “no errors” in n8n does not mean “it worked”: silent alerts, LLM tests, human approval, and key protection.
Learn moreA Fivefold Token Price Drop Doesn't Pay for AI Projects
SOL at $2/$10 versus Astra at $10/$50: we calculate request costs and explain why a cheaper model alone cannot pay for an AI project.
Learn moren8n: €480 Instead of €80 Due to One Unit Error
The model read the request correctly, but the bill was €480 instead of €80. Learn why AI automation needs a validator in n8n.
Learn moreAI Asks, Rules Decide: AWS Adjudicated Query Pattern
AWS showed the pattern: the model conducts the dialogue, while a rules engine makes the decision. Why this approach suits AI for contracts, 1C, and PIM — a KT.Team analysis.
Learn moreAgent Framework 1.20: Microsoft Adds Agent Controls
Explore what Agent Framework 1.20 means for production AI agents, including flow gates, file isolation, checkpoints, and SQL Server vectors.
Learn moreInside-Out AI: Why Airbnb Starts In-House
Airbnb’s CTO, formerly on the Llama team, started in-house. Learn why building the team before the client saves AI budgets.
Learn moreAPI Contract Monitoring After Release
Learn why integrations fail after release, how contract tests and runtime validation catch issues, and what the n8n security bulletin means.
Learn moreCopilot Learns to Click: When Computer Use Beats APIs
Copilot clicks through desktop apps without APIs. We examine where computer use speeds up legacy automation and where integration is cheaper.
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