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📚 Software Engineering Articles
🗞️ Tech and AI Trends
👨🏻💻 Coding Tip
Maximize LLM inference throughput using continuous batching and token-level scheduling.
Time-to-digest: 5 minutes
A scout is a small agent that monitors your PostHog data and surfaces patterns worth acting on. It's the sensing layer of PostHog's self-driving product loop—scouts emit signals (structured findings with evidence) that feed into reports, which either trigger automated fixes or land in your inbox for human judgment. The clever part: almost all the logic lives in markdown that anyone can read and iterate on, not buried in code branches and conditionals.
This is where the "English as a programming language" thesis gets real. Traditional data pipelines hardcode thresholds and decision trees. Scouts let you express business logic as plain sentences—"a survey needs ≥30 responses/week and a ≥10% score drop before surfacing"—and let the agent handle the actual execution. You get flexibility and legibility without sacrificing the determinism you still need elsewhere.
The challenge: Building stateless alert systems that emit useful signals without training users to ignore them through noise.
Implementation highlights:
Skill-first architecture. The core logic is a markdown skill file (~100–500 lines) that encodes what's interesting. The actual harness is deliberately thin—agent loop, MCP tools, a sandbox, and a scheduler. The skill is where you steer; the harness just runs it.
PostHog MCP as the data interface. Scouts use the same read-only data tools available to Claude Code or Cursor—no special plumbing. When PostHog runs scouts on schedule, they additionally get write access to a scratchpad and the
emit-signaltool, turning one-shot loops into memory-bearing agents.Scratchpad for durability. Each scout maintains a simple key/value store: rolling cursors to avoid re-reading data, below-bar findings waiting for a second voice, dedupe entries, and an evolving tag taxonomy. You read the scratchpad and understand exactly what the scout knows and why it's been quiet.
Restraint as the hard part. A scout that emits on every wobble is worse than useless. The real skill is encoded thresholds, disqualifiers (single users, dev bursts, expected endings), severity levels, and dedupe keys. A lone complaint gets held; the same complaint from multiple independent voices gets emitted.
Schedulable and dogfoodable. Run a scout manually in PostHog Code to iterate on your own project data before scheduling it. The coordinator ticks every ~30 minutes, finds due scouts (most run hourly), and fans out via Temporal. You steer it entirely by maintaining the skill.
Results and learnings:
English scales. Starting with a few sentences of logic and only reaching for reference docs or executable scripts when a particular scout genuinely needs them removes friction and keeps maintenance surface minimal.
Memory beats statelessness. A scratchpad that persists findings, thresholds, and context across runs (wiki-style links included) turns a one-shot detector into an agent that learns what's normal and only surfaces real shifts.
Dedupe and disqualifiers are underrated. The difference between signal and noise often isn't the threshold—it's whether you've heard it before, whether it's from multiple voices, and whether it's an expected pattern. Encoding those rules in prose makes them transparent and tweakable.
PostHog ships ~20 canonical scouts out of the box (experiments, revenue, surveys, web analytics, feature flags). You also write custom ones by pointing your agent at the authoring-signals-scouts skill. The whole system treats scouts as skills, so you iterate the same way you'd iterate any agentic behavior: read what it did, adjust the brief, run again. It's a lot more humane than debugging a data pipeline in prod.
ARTICLE (modal dialogs simplified)
Using and Styling the Dialog Element
ARTICLE (agentic rag loops)
RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop
GITHUB REPO (postgres speaks your language)
plx : Write PostgreSQL functions in the language you already know.
ARTICLE (compiler optimization deep dive)
Another partial SSI trick with canonicalize
GITHUB REPO (formal specs for everyone)
Specula: Scaling formal specifications for autonomous model checking of system code
ARTICLE (ai interview competency)
Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding
ARTICLE (productivity reset button)
Where Did the Productivity Gains Go?
ARTICLE (edge computing traffic magic)
The Invisible Stadium
ARTICLE (chrome jpeg gotcha)
Why Tiny JPEGs Look Different in Chrome
ESSENTIAL (time is messy)
Beyond happy path engineering: Time
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Brief: Google announced the Pixel 11 series, Pixel Watch 5, and new Pixel Tag tracker, prioritizing AI features and design tweaks over major spec upgrades.
Brief: A security researcher who registered noreply.net receives ~700 emails daily containing sensitive company data and private information companies mistakenly send to non-reply addresses.
Brief: Meta released Muse Glimmer, a 30B open-weights model under Apache 2.0 license optimized for agentic task completion, tool use, and multi-step reasoning.
Brief: X released its "For You" algorithm and ranking engine on GitHub, plus a tool letting users check if their posts were impacted by ranking systems.
Brief: AI models now perform offensive cybersecurity work, but defenders currently have access to stronger frontier models for security analysis and should act urgently.
This week’s tip:
Custom Resource Definitions with Operator Pattern for Domain Logic
Build domain-specific operators using CRDs to express infrastructure intent declaratively, handling complex state transitions and lifecycle management.
Wen?
When managing stateful workloads with complex provisioning and scaling logic
When building platform engineering abstractions for developer teams
When need GitOps-compatible infrastructure declarations with validation
Be careful the environment you choose for it will shape you; be careful the friends you choose for you will become like them.
W. Clement Stone
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