For people building with AI: new models, local AI, coding tools, pricing and security, explained plainly.
Compare AI coding agent token costs with flat IDE subscriptions like Cursor and Copilot, including real budget data from Uber, DX, and Vantage.
Learn how to review AI-generated pull requests with automated security gates, slopsquatting defenses, and a human checklist built for how AI agents fail.
Build a self-hosted AI router with LiteLLM, open-weight models, and a vector database. Includes AT&T's 56% cost cut, setup steps, and per-seat cost comparison.
Block AI content in search with Google's udm=14 filter, uBlock Origin blocklists, Slop Evader, and AI-filtering engines like Kagi and DuckDuckGo.
Learn how to run several AI coding agents on one repo without merge conflicts, using git worktrees, ownership-based task splits, serial merges, and CODEOWNERS.
Set up a CI gate with secrets scanning, dependency checks for hallucinated packages, and blocking SAST, enforced by branch protection on every pull request.
Build a local AI second brain with markdown notes, Ollama, a local vector store for RAG, and automation. Covers hardware needs, tradeoffs, and maintenance.
Secure employee AI agent usage in four steps by inventorying shadow agents, setting a fast approval policy, brokering MCP access, and monitoring continuously.
Claude Haiku 5.5 vs. local models for coding, compared on API token prices, electricity, and the engineering time each takes to run and maintain for a team.
Can AI solve complex math problems? Top models reached gold-medal scores at the 2025 IMO but still miss basic word problems. Here is where AI math works and where it breaks.
AI decision models vs. LLMs compared on how they work, cost, latency, and benchmark results, with guidance on when to use each for routing and classification.
Compare GPT-6 Astra, Claude Opus 5.5, and open-weight models like DeepSeek V4.1 Flash for internal tools, with pricing, benchmarks, and self-hosting costs.
Compare a $1,000-$2,500 local AI rig with ChatGPT Plus or Claude Pro: break-even in 11-24 months, electricity costs, and open-model quality tradeoffs.
Learn how local vector databases, AST indexes, and semantic tool routers can cut LLM token spend 40-90% by sending only the context each query needs.
Python wrappers hit GIL and memory limits at agent scale. See how GitHub and OpenAI moved to Rust, the benchmarks, the risks, and when a rewrite pays off.
Learn how to evaluate internal AI coding tools using adoption, retention, PR throughput, and developer satisfaction instead of SWE-bench leaderboard scores.
AI agents cost $3,200 to $13,000 a month in production. See how sandboxes, identity, observability, and retries add to token spend, with real pricing.
Learn how to run 671B MoE models like DeepSeek R1 locally with llama.cpp, KTransformers and Unsloth quants, with hardware tiers and tokens per second.
What are the risks of third-party API dependencies? See how CrowdStrike, AWS, and Starlink outages cost billions, plus a due-diligence checklist to reduce risk.
AI code bloat explained: how duplication, falling code reuse, and error-masking show up in GitClear, DORA, and CodeRabbit data, plus metrics to track.
Learn how explainable AI and reverse engineering help you audit AI-generated code, reduce cognitive debt, and meet compliance needs, with data from recent studies.
Compare Portkey, Kong, LiteLLM, Cloudflare, and AWS Bedrock on PII redaction, compliance certifications, and data residency to stop shadow AI data leaks.
Learn how to secure and govern AI agents with a 7-step containment model: inventory, autonomy tiers, least privilege, sandboxing, approval gates, and monitoring.
Compare local AI vs. API costs with real break-even math: GPU amortization, electricity, ops labor and per-token pricing, from 0.3 to 69 months.