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November 5Thursday
  1. The Pragmatic Engineer22

    Scaling Slack and GitHub: Maude Lemaire on Backend Performance and Database Sharding

    Maude Lemaire, principal engineer and author of "Refactoring at Scale," shares an insider view of scaling Slack and GitHub on The Pragmatic Engineer podcast. She spent nearly eight years leading Slack's backend performance infrastructure team, then joined GitHub in 2024 to tackle database scaling, and most recently worked on performance at Cursor, including time to first token. She explains Slack's 23-custom-emoji limit to avoid thundering herds, how load testing 500,000 WebSocket connections the naive way would have cost $150,000 per run, and why GitHub's Active Record abstraction and scatter queries capped it at 16 database shards.

November 4Wednesday
  1. Meta Engineering Blog34

    Meta F3 Lightning Accelerates Ads ML Feature Releases ~3.6x

    Meta introduced F3 Lightning, an infrastructure layer on top of its Facebook Feature Framework (F3) that decouples feature releases from shared execution, so each of the 20,000+ ads ranking features is compiled, validated, deployed, and rolled back independently. Comparable extraction features now reach production ~3.6x faster than prior F3 with no increase in infrastructure compute cost.

November 3Tuesday
  1. Google Developers Blog58

    Google benchmarks cheap classifiers to route Gemini models by cost per task

    Google ran a 50-question auto-graded coding-agent benchmark testing whether a lightweight classifier, TypeSafe's Jev, can route each task to the cheapest Gemini configuration that still answers correctly. A frozen regex and prompt-length heuristic scored 93.8% on the 16 development questions but dropped 23.2 points to 70.6% on 34 held-out tasks, while Jev reached 96.0% across the full suite. The strongest no-router setup, Gemini 3.8 Flash on low thinking, solved all 50 tasks, and a Jev router sending tasks to Flash-Lite first and escalating flagged ones also solved all 50 while reducing cost and response time; capping maxOutputTokens at 300 saved nothing on easy tasks and broke 7 of 25 hard ones.

October 29Thursday
  1. Google Developers Blog58

    Google explains how to build Generative UI apps with A2UI and CopilotKit agents

    Google published a walkthrough for building Generative UI apps with its framework-agnostic A2UI (Agent-to-UI) standard and CopilotKit's agent-integration stack. A2UI sends UI as declarative JSON messages rather than executable code, so clients render only components from a trusted catalog, and the same payload works across React, Angular, Lit, SwiftUI, Jetpack Compose and Flutter. The guide shows wiring a Google ADK agent through AG-UI, defining a Zod-validated component catalog, and building bi-directional surfaces where user inputs flow back to the agent for multi-step flows like booking.

October 28Wednesday
  1. Ai2 · Allen Institute for AI40

    How researchers are adapting olmOCR 2 for historical and archaeological archives

    Researchers are adapting Ai2's open olmOCR 2 model to transcribe historical and archaeological archives. Historian Maurice Casey built ArtificeOCR, which runs olmOCR 2 locally with Tropy to make his archival photo library keyword-searchable, while TU Wien's CENTURIA project fine-tuned olmOCR 2 with LoRA on 57 annotated records, cutting transcription error on 450 test records from 15.58% to 1.11%.

  2. The Pragmatic Engineer58

    AI SRE explained: how AI incident agents change on-call and whether they work

    The Pragmatic Engineer interviewed Rootly, incident.io and ResolveAI about AI SRE tools, which mostly speed up incident investigation inside Slack rather than act autonomously. Deploy frequency has roughly doubled in a year at many companies, and Rootly data shows pages up with out-of-hours pages nearly doubling, while vendors rebuild investigation agents around knowledge graphs, memory and verifier agents. In-house examples include OpenAI's Sevbot, Stripe's investigation agent (about 3 minutes for a simple run, 14 for a deep one) and Google's ProdAgent in Gemini CLI, which suggests tools via MCP and does not write code.

  3. Meta Engineering Blog34

    Meta PrivacyLib: Privacy Controls at the Data Access Boundary

    Meta detailed PrivacyLib, a common library embedded in the clients of hundreds of its data and processing systems that enforces privacy requirements at the point of data access, performing billions of checks per second. Systems integrate once, and new privacy capabilities ship as plug-in modules, replacing per-system implementations that could take months. An Agent Observability module tracks internal AI agent data access by carrying agent context to PrivacyLib.

October 27Tuesday
  1. Google Developers Blog34

    Building Conversational Commerce with Multi-Agent Swarms on GCP: A Working Prototype

    Google Cloud developers built HappyDelivery, a working prototype of conversational commerce using LangGraph, Gemini, FastAPI and Google Cloud, following an earlier three-layer blueprint of Ingress, Memory and Cognition. The Ingress layer validates platform-specific webhook signatures for WhatsApp, RCS, Apple Messages and SMS, normalizes payloads into a common schema and offloads them to Pub/Sub, returning HTTP 200 in roughly 300ms. The post walks through the code, state machine, deterministic financial tools and an automated verification suite.

October 24Saturday
  1. SemiAnalysis60

    SemiAnalysis tests Amazon Trainium3's native PyTorch training stack and Neuron Explorer profiler

    SemiAnalysis's engineering team tested Trainium3 racks on the TorchTitan and TorchNeuron stack and found the native PyTorch experience a major improvement over the old PyTorch/XLA approach, though bugs and long compile times remain. Training Qwen3-32B on 16 Trainium2 chips reached 14,156 tokens per second at 30.47% MFU in compiled mode, a 1.28x gain over eager mode, while the gpt-oss 20B recipe took 27 minutes and 17 seconds to compile. The team also rated Neuron Explorer as the industry's leading profiler, ahead of Nvidia's and AMD's tools, and reported Trainium3 passed 89 of 96 FlexAttention test cases.

October 23Friday
October 22Thursday
  1. Google Developers Blog38

    Google Cloud's Project Chrysalis: A Governed, Self-Evolving AI Agent Architecture on Gemini and ADK

    Google Cloud researchers unveiled Project Chrysalis, a reference architecture for governed, self-evolving AI agents built on the Gemini platform and Agent Development Kit (ADK). In simulated Black Friday cache-stampede outages, it cut time-to-first-mitigation from 15–45 minutes to 1.8 seconds, reduced HTTP 5xx errors from 72.7%–80.0% to 0.0%, and dropped P99 latency from 519.8 ms to 18.2 ms. Every autonomous action is bounded by Model Armor, Cloud KMS, Binary Authorization, OPA Policy Controller and dual-custody human-in-the-loop sign-off.

October 21Wednesday
  1. LiveKit Blog25

    How Acuity Health Uses LiveKit Voice Agents to Automate Patient Access

    Acuity Health rebuilt its patient-access voice agents on LiveKit after a low-code platform proved too rigid and opaque, and now 80% of calls are fully contained by AI agents. Agents run on LiveKit Inference with the Gemma 4 31B model, most bookings finish in under a minute, and 81% of booking calls end with a "thank you." LiveKit Simulations tests each change against patient profiles before release, and one prompt review cut roughly 24% of words while still passing the same scenarios.

  2. GitHub Blog · AI & ML22

    How 3 Java Champions Use GitHub Copilot for Productivity

    Three Java Champions with over a decade of Java experience each shared how they use GitHub Copilot in production and open source work. Vlad Mihalcea built a Hypersistence Optimizer web app in two days instead of weeks, and created custom skills trained on 65 of his articles to match his writing voice. Patrick Baumgartner migrated roughly 900 teaching slides across 15 presentations from Reveal.js to Marp with Copilot, verifying results through screenshot comparisons.

October 20Tuesday
  1. GitHub Blog · AI & ML26

    How to Continue a GitHub Copilot App Session in VS Code

    GitHub Copilot app sessions can now be opened directly in VS Code, carrying over the conversation, code changes, and worktree so developers can review diffs, edit by hand, and test locally without restarting. The tutorial walks through opening a session, reviewing the agent's changes line by line, continuing the same conversation in the Copilot sidebar, and pushing updates to the session's pull request.

  2. Google Developers Blog60

    Google 将 TorchTitan 接入 TorchTPU，并发布 TPU 模型优化手册

    Google released a TPU fork of Meta's TorchTitan that runs PyTorch training on Cloud TPUs through TorchTPU's native torch.device("tpu") backend, with TPU-native kernels and a step-by-step optimization playbook. On TPU v7x (Ironwood), Llama 3 405B holds about 57% MFU from 128 to 1,024 chips, Qwen3 235B reaches 32.42% MFU on one 1,024-chip slice, and FLUX.1 reaches 19.4% MFU on 1,024 chips at 93% weak-scaling efficiency. The fork is at https://github.com/google-pytorch/torchtitan/, and a Transformers modeling backend plus an agent onboarding skill extend the same trainer to other checkpoints.

    Why it matters: Google 与 Meta 合作把 TorchTitan 移植到 TPU，并给出从 FLUX.1 到 Qwen3 235B 的逐级调优路径与 MFU 数字，可作为 PyTorch 团队评估 TPU 训练可行性的参考。

October 19Monday
  1. Modal Blog38

    How Legora runs 2,000 parallel agent evals on Modal Sandboxes

    Legora now runs each of its legal agent evals inside a separate Modal Sandbox, booting its full application pinned to a specific commit so every run reflects the exact product version. A typical experiment is 100 cases, 4 harness variants, and 5 trials each — 2,000 runs in parallel — with Modal Volumes snapshotting document, database, and permission state. Legora also moved from gVisor to Modal VM Sandboxes, removing its Docker networking and FUSE workarounds, and cut agent cost by 30% via smarter compaction and custom tool search.

October 16Friday
  1. Modal Blog36

    Modal explains how it built a fast filesystem stack for VM Sandboxes

    Modal detailed how it evolved its filesystem stack to back VM Sandboxes, routing guest requests through virtio-fs and virtiofsd onto its ImageFS lazy-loading filesystem. The first version booted fast but ran slow: recursive chown went from four seconds to over a minute, and pnpm and bun installs were about three times slower. Microbenchmarks showed chown at 9.87 s over virtio-fs versus 2.31 s on gVisor and 1.58 s on ImageFS.

  2. OpenClaw Blog22

    How PJ Helps Small Businesses Adopt and Own OpenClaw

    PJ, an engineer-turned-pest-control owner in Ocala, Florida, now consults for small businesses—roofing, pool and automotive contractors among them—setting up OpenClaw and teaching them to run it themselves. His clients want a tool they own outright rather than feeding company data into rented software, and one client, 66-year-old admin Donna, uses her OpenClaw daily.

  3. LiveKit Blog38

    How to Build a Production-Ready AI Voice Agent

    LiveKit's guide distills lessons from solution architects and forward deployed engineers on taking AI voice agents from demo to production, organized around three questions: whether the agent is smart and natural enough, whether its performance is observable, and whether it scales reliably and cost-efficiently. It recommends measuring the first turn separately, since one company found roughly 3 out of 4 calls ended at the greeting step, and notes clients moving from GPT-4.1 to Gemma 4 31B on LiveKit Inference saved 300–500ms at p50.

October 15Thursday
  1. Anthropic · Claude.dev62

    How the Claude Code team does code review

    The Claude Code team describes its layered code review flow, in which most reading is done by machines and human attention is reserved for risk-based approval. From June to September, merged pull requests to the Claude Code codebase more than doubled while the share that had to be reverted fell by more than 40 percent. The process stacks verification before the PR, required CI, Claude review, security review, risk-tiered human approval and a merge queue with flags and fast rollback, and the team turned past incidents into a daily eval set for the reviewer.

    Why it matters: Anthropic's Claude Code team details how it moved most code reading to automated layers while merged PRs doubled and reverts fell over 40 percent, offering a transferable review model for agent-heavy teams.

  2. Modal Blog34

    Augment Your LiveKit Agent with Modal GPUs

    Modal explains how to run LiveKit Agents with open-weights or custom models on its serverless GPUs, using BitHuman's open-weights Expression Video Avatar voiced by Qwen3-TTS to clone Charles Frye. The setup exposes the avatar container's /launch endpoint via a Modal Server and uses Sticky Sessions to keep WebSocket clients on the same container, with GPU snapshots hiding cold starts.

October 14Wednesday
  1. GitHub Blog · AI & ML38

    GitHub's secret detection model: finding more secrets with less noise

    GitHub trained a fine-tuned ModernBERT classifier to replace its previous LLM-based pipeline for generic secret detection, improving owner-weighted alert precision by 11.5% and cutting median post-push scan execution time by 86%. The two-stage pipeline uses regex candidate generation followed by context-aware classification, which also lowered serving cost by more than 98% versus remote LLM calls. The efficiency gain made it practical to extend generic secret detection to push protection, returning results before changes are pushed.

October 13Tuesday
  1. LiveKit Blog18

    Troubleshooting LiveKit Egress: Common Errors and Fixes

    LiveKit published a troubleshooting guide for Egress, covering common errors such as the 412 "start signal not received" from templates that never call START_RECORDING, 404 participant or track not found within 30 seconds, and 400 upload failures from invalid S3/GCP/Azure credentials. It also addresses layout, webhook, and self-hosted Egress issues, and notes the guidance applies to both the current StartEgress API and the deprecated source-specific APIs.

  2. GitHub Blog · AI & ML36

    GitHub Copilot for Beginners: Four Ways to Teach Copilot About Your Codebase

    GitHub's Copilot for Beginners series explains four ways to give Copilot project context: custom instructions in `.github/copilot-instructions.md` plus path-specific files in `.github/instructions`, agent skills in `.github/skills`, custom agents in `.github/agents`, and MCP servers that connect Copilot to external tools such as Playwright. The post walks through each mechanism and how they combine, and points readers to the community Awesome Copilot collection for ready-made examples.

October 12Monday
October 9Friday
  1. LiveKit Blog34

    Serve AI uses LiveKit Agent Simulations to rebuild its voice agent on a single prompt

    Serve AI rebuilt its phone agent for a $150M pest control customer on LiveKit, collapsing a 224-node Retell flow graph into a single prompt and validating it with Agent Simulations. About 100 real production calls were converted into simulation scenarios, reaching a 98% pass rate over 10–15 iterations before cutover, with median turn latency of 1.3 seconds against a 1.5 second ceiling. The first migration took one week with no rollback, and containment rose 15%, which Serve estimates saves the customer an additional $320,000 a year.

October 8Thursday
  1. Anthropic · Claude.dev62

    Anthropic walks through building scheduled agent automations with Claude Managed Agents

    Anthropic published a reference implementation for Claude Managed Agents (beta) that reads Slack channels and GitHub pull requests on a cron schedule, tracks changes since the last run via per-source bookmarks, and posts a brief to Slack. The walkthrough covers vault-held credentials, read-only access and a per-run spending cap, plus rules for reporting a failed source as unreadable rather than a quiet day and confirming a Slack post before updating bookmarks and the ledger. A Claude Code command with the claude-api skill configures the agent from the article's guidance.

    Why it matters: Anthropic shares a reference implementation for scheduled Claude Managed Agents that read Slack and GitHub, with concrete rules for bookmarks, failed reads and post confirmation.

October 6Tuesday
  1. Anthropic · Claude.dev72

    Claude Code cloud sessions: a field guide to running tasks on separate VMs

    Addy Osmani walks through Claude Code cloud sessions, where each task runs on a fresh Anthropic-managed VM with the repository cloned onto its own branch, and cloud sessions are included in Pro, Max, Team and Enterprise plans at no extra compute charge. In a four-session test on a sample Node repository, three sessions started within 16 seconds of each other finished 87 seconds after the first began, and the flaky-test session ran the suite 40 times with zero failures. The guide covers setup scripts, network levels, GitHub connection paths and seven workflows suited to cloud sessions, while noting that local sessions remain the better fit for tasks needing local data, VPN services, GPUs or Zero Data Retention.

    Why it matters: A hands-on field guide to Claude Code cloud sessions, showing how per-task VMs enable parallel work and where local sessions still fit better.

October 2Friday
  1. GitHub Blog · AI & ML25

    GitHub Blog: Three Skills Developers Need as AI Reshapes Their Work

    GitHub Blog outlines three skills developers should strengthen as AI changes their work: directing AI agents rather than just using them, critically reviewing AI output instead of trusting the first answer, and using time saved by AI to tackle broader problems. It cites GitHub Copilot's built-in Rubber Duck agent, which uses a second model to critique plans, code, and tests before a developer proceeds.

  2. Every36

    How to Get Better at AI by Asking AI: Five Prompts for Delegating Projects to Agent Teams

    An Every staffer describes moving from single-thread prompting to delegating whole projects to coordinated subagents, using skills, orchestrator threads, context packets, MCPs and computer use. The shift was prompted by a June tweet in which Katie Parrott asked Codex to place her on the "Eight Levels of AI Adoption" guide; Codex put her at Level 5 with the beginnings of Level 6.

October 1Thursday
  1. Anthropic · Claude.dev60

    Getting started with Claude Code mods: building a context-window forecast

    Claude Code mods are small JavaScript or TypeScript files that run inside a session as hooks shipped in plugins, and can observe events, rewrite behavior or draw custom UI in the terminal or desktop app. The guide builds Token Weather, an roughly 80-line mod that reads $.session.usage() after each turn and draws a weather-style context-window band above the prompt, then tours Blast Radius, which holds risky Bash commands, and Replay Theater, which steps through a turn's edits. Mods require Claude Code 2.1.287 or later and are on by default; finished code for all three is in anthropics/claude-code-playground.

    Why it matters: A hands-on walkthrough of Claude Code mods that builds a working context-window meter in about 80 lines, showing how hooks, state and custom UI fit together.

September 28Monday
  1. Anthropic · Claude.dev72

    Anthropic adds build-eval and hillclimb commands to the claude-api skill in Claude Code

    Anthropic added two commands to the claude-api skill inside Claude Code: /claude-api build-eval builds an evaluation set in your codebase, and /claude-api hillclimb iterates on an application against that evaluation with a held-out set to catch overfitting.

    Why it matters: Anthropic adds build-eval and hillclimb commands to its claude-api skill, showing how Claude Code can design evaluations and iterate against them while guarding against overfitting.

September 23Wednesday
  1. Vapi Blog38

    Vapi tests Jev as a real-time guardrail for voice AI

    Vapi ran early experiments using TypeSafe AI's early-access "System One" model Jev as a guardrail that scores whether a voice assistant's response violates a developer-defined rule before it is spoken. In one early test, Jev caught at least three out of four violating responses, though Vapi says it is not a 100% compliance guarantee and only an additional check on top of prompt guardrails.

  2. Every58

    Every's Mike Taylor on a Writing Workflow With AI That Starts Before the Prompt

    Every head of evals Mike Taylor lays out the writing workflow he uses with AI, from writing the title first and collecting sources to recording a spoken monologue and generating drafts from Claude Opus 5.5, Fable 5.1 and GPT-6 Astra. He says the process cut his time for a 1,200-word piece from a day to under two hours with no drop in traffic or positive reader comments, and he still writes the introduction and conclusion himself and expects 20-30 percent of a draft to change during self-editing.

  3. Modal Blog58

    How Modal serves trillions of tokens for trillion-parameter coding agents

    Modal explains how it optimized Kimi K2.6 inference for coding agents, reaching 2.8x per-user and 5.6x cross-user performance per replica and running services at hundreds of billions of tokens per day. The work combines custom DFlash speculative decoding fine-tuned on coding traces, FP8 KV cache and NVFP4 shared-expert quantization, HiCache CPU-tier caching, and KV cache-aware load-based routing. Modal upstreamed its SGLang patches and released the configuration behind Modal Auto Endpoints.

September 21Monday
  1. Every44

    How to Create Your Own Personal AI Benchmark

    Every's head of evals explains how to build a personal AI benchmark instead of relying on MMLU-Pro and other public tests, drawing on his own experience testing Claude Opus 5, Claude Fable 5, GPT-5.6 Sol, Astra, Luna and Haiku on real work tasks. The approach has three levels and is meant to show when to switch models and whether a smaller, cheaper one suffices.

  2. LiveKit Blog38

    Comparing Open Source Frameworks for Realtime Voice and Video AI Agents: LiveKit Agents vs Pipecat

    This guide compares LiveKit Agents and Pipecat, the two open source frameworks it says matter most for building production voice and video AI agents, across architecture, adoption, developer experience and production readiness. LiveKit Agents runs on LiveKit's open source WebRTC and SIP infrastructure and wires the pipeline for you, while Pipecat assumes no transport and has developers assemble the pipeline stage by stage. It also covers where TEN Framework, Dograh, Vision Agents and Vocode fit in.

September 9Wednesday
  1. LiveKit Blog22

    What "inference is slower than realtime" means in LiveKit voice agents

    LiveKit explains that the "inference is slower than realtime" warning in its voice agents refers to the local VAD model, not the STT, TTS, or LLM models used through LiveKit Inference. Each 32ms audio window must be processed in under 32ms, and the reported delay shows how far the VAD has fallen behind live audio. Blocked event loops and CPU saturation are the main causes, and the fix is to rule out synchronous code rather than add threads.