FRIDA Decisions: when a judge does not need generation
FRIDA Decisions scores 0.893 on razvilka at one-fortieth the size of an LLM competitor. Where it can replace LLMs and how much it saves.
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24 Showing articles 25–48 of 545
Haiku 5.5 Delivers More at Luna 6 Pricing: Who Should Switch Models?
According to an AINews review, Haiku 5.5 outperforms Luna 6 at the same price. How to choose by cost of outcome without rewriting integrations.
Learn moren8n in Production: Automation Without Alerts — Silent Failure
11 iFLYTEK nodes speed up automation building in n8n, while three alert nodes prevent silent failures. What to deploy first.
Learn moreAn Agent in Production: Boring Commits Matter More Than Demos
A review of Microsoft Agent Framework dotnet-1.24.0 and choosing between Document Intelligence and Content Understanding: where AI projects lose results.
Learn moreThe laptop agent is not working: what a cloud harness provides
Why a coding agent on a laptop does not scale and what the cloud requires: state, sandboxing, access, and limits. A guide for IT leaders.
Learn moreAn agent writes one in every three PRs: how to protect secrets
An agent writes every third GitHub PR. How a secret detector, sandbox, and LLM gateway prevent leaks before commit.
Learn moreHaiku 5.5 Is 75% Cheaper: What to Do With Agents in Production
Haiku 5.5 on Bedrock costs 75% less than Haiku 4.5. Where subagents pay off and why access rights matter more than token price.
Learn moreHaiku 5.5 at Luna's Price: What to Do With a $0.10 Token
Haiku 5.5 costs $0.10/$0.50 per million tokens, like GPT-6 Luna. We calculate the economics, context thresholds, and how to avoid vendor lock-in.
Learn moren8n for $50: Cheap to Build, Expensive to Fix
Why an n8n workflow takes hours to build but weeks to fix: 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 a log 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 the code, while Git, code reviews, and metrics have become the bottleneck
GitHub is rebuilding Git for agents, stacking pull requests and report limits, revealing where AI development hits process bottlenecks.
Learn moreThe state of the IT industry in 2026: three worlds moving at different speeds
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 depends on releases and acceptance testing
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
Pimcore 2026.3.1 review: separate version storage, focal point, and class sanitization. Benefits for catalogs and update 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 are getting cheaper, but the 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.
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