Claude Mythos finds mathematical flaws in post-quantum and symmetric cryptography
Anthropic's Claude Mythos discovered improved attacks on HAWK and reduced-round AES — the first time frontier models ind
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Show HN: FeyNoBg — automatic background removal model and training library
Feyn Labs open-sources FeyNoBg, a 263M-parameter background removal model that leads on four of eight published benchmar
MAI-Cyber-1-Flash: Microsoft's first dedicated cybersecurity AI model
Microsoft's first dedicated cybersecurity AI model, MAI-Cyber-1-Flash, scores 96% on CyberGym at half the cost of its pr
The New AI Superpowers: Focus and Followthrough
Why execution reliability, not model intelligence, is the defining bottleneck for AI agents — and how architectural patt
RAG Evaluation: Measuring Retrieval Quality in Production
The full production evaluation stack for RAG — retrieval metrics, observability platforms, CI/CD gates, and automated re
Quantization Tradeoffs: How Much Speed Costs You in Accuracy
A practical guide to quantization tradeoffs — INT8 vs INT4 accuracy costs, GGUF vs GPTQ vs AWQ formats, and when to go a
Building Custom MCP Servers: Patterns for Internal Tool Integration
Patterns and best practices for building custom MCP servers that connect LLM agents to proprietary internal tools — sche
Multimodal LLMs for production: when to use image, audio and video capabilities
A practical decision framework for adding visual, audio and video inputs to LLM products — covering costs, latency, accu
Prompt Injection Explained for Business Users
A practical security guide to prompt injection — how attackers hijack AI models, what business users need to know, and t
Building a custom LLM chatbot: end-to-end guide from specification to deployment
A staged guide for teams building their own LLM chatbot: define scope, choose architecture, implement, test, and deploy
LLM-- monitoring-- and-- alerting: tracking-- performance,-- cost,-- and-- reliability-- in-- production
Build-- practical-- alerting-- for-- LLM-- apps-- —-- four-- failure-- modes,-- tiered-- thresholds,-- rolling-- baselin
Multi-model routing and gateway architecture — routing queries to the right model based on cost, latency, and capability
A practical framework for multi-model routing: how to design gateways, routing policies, and fallback chains that optimi
LLMs for software engineering and DevOps: a practical guide
How LLMs fit into code review, test generation, incident response, and DevOps workflows — what works, what doesn't, and
LLM security hardening for production — input validation, output filtering, rate limiting and PII scanning
A practical guide to the four security layers every production LLM application needs: input validation, output filtering
LLM-- cost-- optimisation-- playbook: practical-- techniques-- for-- reducing-- API-- spend
A-- structured-- framework-- for-- diagnosing-- and-- reducing-- LLM-- API-- costs: prompt-- optimisation,-- caching,--
LLM observability stack: logging, tracing, monitoring, and cost tracking
Compare five LLM observability tools—LangSmith, Arize, Helicone, Weights & Biases, and Datadog—with setup guidance and a
MCP implementer's guide: setting up Model Context Protocol servers, tools, and security patterns
A step-by-step tutorial for building MCP servers in Python and Node, exposing tools and resources, connecting to desktop
LLMs for startups vs enterprises: when to build, when to buy
Decision framework for API-based vs self-hosted LLM: when team size, budget, latency and data sensitivity push you towar
Tool use and function calling: a practical implementation guide
A step-by-step guide to implementing LLM function calling in production: defining schemas, handling parallel calls, erro
The model release treadmill: how to avoid rebuilding every month
A practical guide to decoupling your product from provider churn so every model update does not become a rewrite.
Vector databases: when semantic search is enough and when it is not
A practical guide to deciding whether you need a vector database, a search index or something much simpler.
Schema-first AI extraction: making LLMs useful for messy documents
How to extract structured data from unstructured documents using LLMs: schema design, confidence flags, validation, and
Prompt versioning: treating prompts like production code
How to manage prompt changes in teams: version control, eval-linked releases, approval workflows, rollback strategies, a
Tool-use safety: stopping agents from taking dangerous actions
A practical guide to approval gates, least privilege, dry runs and audit logs for AI agents with tools.
The evidence-led AI website manifesto: how theLLMs will review claims
How theLLMs reviews claims, sources content, dates evidence, and handles uncertainty. A public editorial standard for tr
Safe prompt templates: reducing brittle instructions and hidden assumptions
Why prompts fail silently, how to treat prompts as tested product assets, and the versioning, testing and acceptance cri
Refusals and over-refusals: testing whether safety blocks useful work
A plain-English guide to distinguishing sensible safety boundaries from over-refusal that breaks legitimate use cases.
Red teaming an LLM feature: a practical first-week checklist
A practical first-week checklist for finding failure modes in a new LLM feature — with concrete test items, sample promp
PII handling for LLM apps: minimisation before redaction
A practical guide to reducing personal-data exposure in AI features by minimising what you send before you try to redact
Model drift without training: why API behavior changes over time
Why hosted LLMs change their outputs even without a version bump, and how pinned models, eval regression sets, and chang
Rerankers explained: the quiet quality layer in RAG systems
How rerankers improve retrieval precision with a worked example, model names, latency numbers, and a decision framework
LLM-- observability-- basics: traces,-- prompts,-- evals-- and-- feedback-- loops
A-- framework-- for-- monitoring-- LLM-- applications-- in-- production: what-- to-- trace,-- which-- metrics-- matter,-
Jailbreaks-- vs-- product-- safety: what-- operators-- can-- realistically-- control
A-- practical-- guide-- to-- separating-- model-level-- safety-- from-- app-level-- permissions,-- tool-- boundaries-- a
Latency-- in-- LLM-- apps: first-- token,-- total-- time-- and-- user-- experience
A-- plain-English-- guide-- to-- why-- AI-- features-- feel-- slow,-- what-- to-- measure,-- and-- how-- to-- separate--
AI agents vs workflows: a plain-English difference for teams
A decision guide for operators who need to know when deterministic automation is enough and when real a
Access control for RAG: why retrieval permissions matter before generation
Retrieval permissions must be enforced before generation, not after. Learn where teams get document-level access control
AI coding agents: what to measure before trusting them
A practical measurement framework for evaluating AI coding agents on real engineering work — review burde
Building a minimum viable RAG system without overengineering
A staged approach to building your first RAG system: ingest, retrieve, answer, cite, evaluate, monitor. Start simple, me
AI output monitoring: what to log, sample and review
A practical guide to balancing observability with sensitive-data retention risk in production LLM systems.
AI incident response: what to do when a model gives harmful or wrong advice
A practical guide to adapting incident response for prompts, outputs, evals, rollbacks and customer-facing AI failures.
Building an internal AI policy bot: safe pattern or risky shortcut?
A practical architecture checklist for building AI chatbots over internal policies: citation requirements, permission mo
MCP explained: tools, resources, prompts and the current hype gap
A clear guide to what Model Context Protocol is, what it is not, and why the marketing sometimes runs ahead of the wirin
Citation quality in AI answers: source-grounded does not mean source-faithful
A practical guide to testing whether cited sources actually support the generated claim, not just whether the answer loo
Eval gaming: when models optimise for the test rather than the task
A guide to spotting benchmark overfitting and test-specific behaviour before it turns into product disappointment.
Inference vs training vs fine-tuning: three terms operators confuse
A plain-English guide to the three phases of model work, what each one changes, and what the difference means for budget
Eval CI for AI apps: testing prompts before every release
A guide to adding automated LLM evaluation to your CI pipeline — so prompt and model changes are tested before they reac
Chunking documents for RAG: size, overlap and metadata choices
A practical guide to breaking source material into retrievable pieces without wrecking meaning or search quality.
Chat history is not memory: how LLM apps remember users
A plain-English guide to the difference between chat history, profile memory, stored app data and training data, with pr
Data leakage in LLM apps: logs, prompts, files and vendor retention
A practical guide to where LLM data leaks happen, what to minimise before sending data, and what retention settings to c
Editorial rule
Every guide needs a stopping point
- Define the job, input, output, assumptions, and failure modes.
- Show the checks that prove the workflow worked, or say what remains unproved.
- Link back to Cache for concepts instead of re-explaining everything on every page.
- Keep tool/provider claims dated. LLM tooling ages like milk in a warm server rack.