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How Netflix Built Its LLM Serving Stack (18 minute read)
Netflix details how it deployed and operated LLM inference within its existing production infrastructure. The article covers engine selection, model packaging, API design, deployment strategy, output constraints, and trade-offs that emerged under real workloads.
Jun 12 | Blog
I Tried to Build a Context Layer for My Agent in a Weekend. Reader, I Did Not Build a Context Layer for My Agent in a Weekend.
A "simple" weekend project turns into real infrastructure, and why agent context deserves a boring, reliable foundation.
SponsoredJul 20 | Tech
scroll-world (GitHub Repo)
scroll-world is a skill that turns any brand into a scrollable 3D world. It works with Claude Code, Codex, and any SKILL.md-compatible agent. The skill builds immersive, control-scrubbed 'fly through the world' landing pages for any industry or brand. A video example is available in the repository.
Jul 20 | Dev
A Practical Guide to Reducing Token Spend (10 minute read)
A "swamp workflow" is a method of optimizing AI agents by structuring complex tasks into deterministic code, reducing resource consumption while preserving functionality. Implementing a swamp workflow decreased token usage by 8 times and cut execution time in half for a code review task compared to traditional skills, which inefficiently used numerous tokens and runtime.
Jul 20 | Founders
You're doing "Activation" wrong (14 minute read)
Your activation rate can improve while the company gets worse. AI can polish your ads and onboarding enough to bring in more signups, while the share of those customers still paying two months later falls. Most activation dashboards will show the first number and miss the second. A better funnel starts when someone first interacts with the product, then follows them through setup, aha, activation, and habit. Also track how much you spent for every account that survives month two. If signup conversion rises while month-two survival falls, you did not fix onboarding. You got better at enrolling the wrong people.
