How Kun Chen ships 20–40 PRs daily without reviews

#1 📝 Simon Willison

datasette-agent-edit 0.1a0 - Released a base plugin, datasette-agent-edit, implementing core text-editing tools (view, str_replace, insert) to be reused by other Datasette Agent plugins for agentic edits to existing text.

#2 ▶️

How This Ex-Meta L8 Engineer Ships 40 PRs a Day with AI Agents | Kun Chen

Peter Yang

#3 ▶️

Improve Your Agentic AI Trading With a Great Data Pipeline

All About AI

#4 📝 PromptLayer Blog

How to track LLM analytics in PostHog - Log a small, consistent set of LLM events in PostHog (llm_request_started, llm_request_completed, llm_request_failed, llm_output_rated, llm_task_completed) with core properties like trace_id, request_id, prompt_version_id, model, provider, environment, latency_ms, input_tokens, output_tokens, estimated_cost_usd, and status plus product/outcome/eval fields, send events from your backend, and never include raw prompts/outputs—use safe references (prompt_version_id, prompt_hash, document_type) and link to traces for debugging. Example payload in the article shows model gpt-4.1-mini with latency_ms 1840, input_tokens 1284, output_tokens 312, estimated_cost_usd 0.0048, prompt_version_id pv_2026_06_04_003, and trace_id trace_01J7ZP8E9K4VQ2.

#5 𝕏

Madhu Guru warns that enterprises struggle to convert complex workflows into representative evals and build truly agentic harnesses, with most solutions still relying on simplistic tests and basic automation.

#6 𝕏

Guillermo Rauch built the Vercel AI Gateway to recover over 1 trillion tokens monthly with zero markup—adding redundancy, observability, usage APIs, caps and strict zero-data retention enforcement, much like Stripe’s smart retry model.

#7 𝕏

Kevin Yien notes that although chat-driven interfaces are currently the most efficient, he doesn’t believe all UIs will converge into chat windows and instead advocates for “proactive UI” that’s generative yet prompt-free.

#8 📝 Mario Zechner

Modern Engineering Values - The author says he rarely writes code by hand anymore and has shipped or contributed to multiple projects largely AI-written—Vite+ (Rust features, ~90% AI-written), fate 1.0 (100% AI-written), Codiff (100% AI-written), Athena Crisis (70+ bugfixes, 100% AI-written), and Void (100% AI-written, not yet shipped)—because coding agents now produce production-quality code in minutes. He describes a Codex CLI + GPT‑5.5 high workflow (one project per window, create a failing test first, strict guardrails, /review cycles, and Codiff walkthroughs) and argues teams must change processes (e.g., push to main faster) to retain that new velocity.

#9 𝕏

Garry Tan argues that building AI like a Foxconn factory—rigid, boxed agents—stifles real intelligence, and that truly reusable agents must be unboxed so they can flexibly handle unexpected situations beyond fixed code.

#10 𝕏

Garry Tan clarifies Paxel never uploads user code to the cloud and promises to shift more processing on-device as local models improve.

#11 𝕏

Peter Yang highlights how ex-Meta L8 engineer @kunchenguid is now shipping 40+ PRs a day with agents—10× the usual rate—overwhelming code review, QA and merge workflows. He argues teams need streamlined planning, validation, review and merging systems to handle that scale.

#12 𝕏

Guillermo Rauch highlights that while most of their effort is on building failover for widespread downtime, there’s also a long tail of customer edge cases—like API keys getting rate-limited or hitting caps with the main provider.

#13 𝕏

Madhu Guru debunks the myth that AI training data is low-skill grunt work, showing that advancing the model frontier requires expert-curated, domain-specific data for high-value tasks.

#14 📝 PromptLayer Blog

How to choose LLM tracking tools - Choose an LLM tracking tool by defining the jobs it must do—record exact prompt identifiers and versions, model/provider/parameters (e.g., gpt-4.1, claude-3-5-sonnet; OpenAI, Anthropic, Google, AWS Bedrock), trace multi-step agent workflows (classification, retrieval, tool calls, final synthesis), connect production traces to eval datasets and CI, surface cost/latency breakdowns, enforce field-level redaction/PII masking, retention and tenant controls, and provide alerting with clear ownership. Practical examples from the article include tracking prompt version hashes and per-request token/cost counts (a complex agent flow can cost ~$0.80 vs ~$0.02 for a simple chat), using golden datasets and LLM-as-judge in CI, and alerts such as citation accuracy <85% for 30 minutes, p95 latency >12s, payment API errors >3%, or safety violation rate >1%.