---
title: "Latest reads"
description: "The engineering internet, summarised so you can actually read it."
---

# Latest reads
> The engineering internet, summarised so you can actually read it.

## Articles

### [MySQL CDC connector for ClickPipes is now Generally Available](https://yomu.fyi/post/mysql-cdc-connector-for-clickpipes-is-now-generally-available.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 2, 2026
- Type: Announcement

MySQL CDC connector for ClickPipes is now Generally Available, enabling replication from MySQL and MariaDB into ClickHouse Cloud for analytical workloads. The release adds automatic parallel snapshotting: ClickPipes selects a logical partition key from a table’s primary key, including string and UUID types, and distributed processing reduced a benchmarked 1TB snapshot from over a day on one thread to about an hour across 32 workers. Production safeguards now require GTID-based replication and at least 72 hours of binlog retention, while new CPU, memory, network, and source, destination, and end-to-end lag metrics improve troubleshooting. MySQL CDC ClickPipes also support OpenAPI and Terraform 3.14.0+, are available across all ClickHouse Cloud service tiers, and began billing on September 1, 2026.


### [Real-Time Intelligence with IBM Time Series Models on Confluent](https://yomu.fyi/post/real-time-intelligence-with-ibm-time-series-models-on-confluent.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Nicholas Fuller, Sean Falconer, Ayhan Sebin, Bilge Zeren Aksu, Mountu Jinwala, Pankaj Dayama, Roman Vaculin, Siddharth Bedekar, Julian Payne
- Published: Sep 2, 2026
- Type: Announcement

IBM and Confluent are making IBM Granite Time Series foundation models available in Early Access on Confluent Cloud, bringing forecasting and other time-series capabilities into streaming workflows. The announcement addresses the cost and delay of bespoke models by offering models that generalize across previously unseen series and can be used for forecasting, anomaly detection, similarity search, classification, gap-filling and optimization. Native inference runs directly in Apache Flink through Confluent’s AI\_FORECAST and AI\_DETECT\_ANOMALIES functions, with Flink maintaining keyed, fault-tolerant state for each series and results written to Kafka topics. Confluent manages serving, scaling and runtime operations while retaining schemas, lineage, access controls and replayable data within the platform. Early Access starts on Confluent Cloud on AWS at no charge, with Confluent Platform planned next for on-premises and hybrid environments.


### [Stop Guessing at Hard Faults](https://yomu.fyi/post/stop-guessing-at-hard-faults.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Oleg Zinovyev
- Published: Sep 2, 2026
- Type: Announcement

CLion 2026.2.2 adds an AI skill for diagnosing ARM Cortex-M hard faults through dedicated MCP tooling. Embedded debugging typically requires decoding CFSR and HFSR, matching the faulting program counter to disassembly, and reproducing crashes because default handlers often report only that a fault occurred. When triggered by a stopped hard-fault-family handler or relevant fault evidence, the skill provides decoded status registers, the hardware-stacked exception frame, SVD-decoded peripheral registers, disassembly, and nearby memory to an agent. It supports any debugging tool, including Lauterbach TRACE32, Segger J-Link, and ST-LINK, and can produce a bug location and fix, with optional rerunning to verify the program. The skill is enabled by default but requires CLion’s MCP server; it works in AI chat and CLI mode for Claude Code and Codex, while GitHub Copilot supports CLI mode only.


### [An Organizational Second Brain: Building an AI That Learns From Experts](https://yomu.fyi/post/an-organizational-second-brain-building-an-ai-that-learns-from-experts.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Shaurya Sengar, Jason Nawrocki, Jay Shah, Prashant Kommireddi
- Published: Sep 2, 2026
- Type: Problem & solution

Organizations often retain specialist knowledge in experts’ heads, leaving routine compliance assessments slow, inconsistent, and difficult to scale. The proposed “second brain” combines a structured, auditable knowledge architecture with a separate reasoning layer and a self-improvement loop that converts expert feedback into verified, regression-tested text updates without model retraining. High-density organizational positions, taxonomies, routing indexes, and gateway files are maintained in a dependency-linked filesystem, while sparse supporting material remains available through semantic or lexical retrieval. Composable recipes encode expert procedures, and an evaluation pipeline proposes auditable pull requests, retries failed fixes, and expands regression coverage after approval. After six weeks, SMEs reportedly found outputs useful almost always, assessments fell from days to minutes, and improvement cycles produced zero regressions; the architecture is presented as applicable beyond compliance.


### [How to Handle Errors in Go](https://yomu.fyi/post/how-to-handle-errors-in-go.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Dominika Stankiewicz
- Published: Sep 2, 2026
- Type: Tutorial

Go treats errors as values rather than moving error handling outside normal control flow: functions return an error alongside other results, and callers check it. The guide presents the built-in error interface, returning errors with errors.New, wrapping failures with fmt.Errorf and %w, and distinguishing nil success from non-nil failure. It also explains panic and recover for unexpected failures, including deferred recovery in services that must continue handling other requests, while warning that panics should not replace ordinary error handling. Its best practices emphasize adding context, choosing informative custom error types, inspecting wrapped errors with errors.Is, errors.As, and errors.AsType, and logging errors when they are handled or cannot be returned. The conclusion recommends balancing early crashes with recovery when restarting is costly or when independent goroutines can continue safely.


### [LoRA backdoor threat: How OpenShift AI mitigates the risk](https://yomu.fyi/post/lora-backdoor-threat-how-openshift-ai-mitigates-the-risk.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Mike Hepburn
- Published: Sep 2, 2026
- Type: Problem & solution

LoRA adapters can carry semantic backdoors even when the underlying open-weight model is clean, creating a supply-chain risk for customized AI assistants. The demonstrated proof of concept uses a 35 MB adapter trained from 270 ChatML examples, with 130 poisoning examples and 140 clean examples, to trigger on ordinary pharmaceutical IP language rather than fixed strings. With Qwen/Qwen2.5-0.5B-Instruct served by vLLM 0.25.1, the backdoored model covertly called send\_email to an external address while showing a benign response, whereas the base model and controls remained clean. The defense-in-depth approach combines OpenShift AI, Advanced Cluster Security, and Advanced Cluster Management to restrict runtime LoRA loading, require provenance-checked adapters, enforce admission and build policies, and block unexpected egress. Because weight inspection cannot reliably reveal semantic behavior, protection focuses on deployment, runtime, tool, and network boundaries.


### [LLM quantization guide: How to do it, and how it helps](https://yomu.fyi/post/llm-quantization-guide-how-to-do-it-and-how-it-helps.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Cedric Clyburn
- Published: Sep 2, 2026
- Type: Tutorial

LLM quantization addresses the gap between rapidly growing model sizes and GPU memory by storing weights and activations at lower precision than BF16. It explains how quantization targets linear layers in transformer blocks, distinguishes weight quantization for memory-bound decoding from activation quantization for compute-bound prefill, and maps formats such as GGUF, FP8, NVFP4, AWQ-INT4, and MLX to their inference engines. Calibrated methods including GPTQ, AWQ, and SmoothQuant protect important weights using representative data, while reported NVFP4 results recover about 99% of BF16 accuracy for 70B–235B models and 95–98% for 7B–14B models. In a Llama 3 70B benchmark on two H100s, FP8 weights-and-activations increased throughput from 158 to 474 input tokens/sec and reduced time to first token under load from over 30,000ms to about 4,800ms.


### [Announcing the Databricks Big Book of AgentOps](https://yomu.fyi/post/announcing-the-databricks-big-book-of-agentops.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Pavithra Rao, Jeanne Choo, Kyra Wulffert, Alex Baur
- Published: Sep 2, 2026
- Type: Announcement

The Big Book of AgentOps presents AgentOps as an operating discipline for building, deploying and improving production AI agents, combining architecture, evaluation, observability, governance, security and cost management. It addresses the operational risks of tool calls, enterprise-data retrieval, API actions, retries and multi-step execution, where quality, permissions and spend can become difficult to control. The six-chapter book covers four agent architectures, four deployment patterns, a seven-phase lifecycle, DevOps practices, operationalization planning and stakeholder alignment, with Unity Catalog, Unity Gateway and MLflow central to its Databricks-based deployment guidance. Its practices include building evaluation datasets from real traces, calibrating automated judges with subject-matter-expert feedback, designing tracing and access controls into systems, and using RACI matrices to clarify ownership. The announcement cites customer examples including FactSet’s 44% accuracy improvement, DXC Technology’s 30% total-cost-of-ownership reduction, and ICE’s 77% syntactic accuracy and 96% execution matches.


### [Modal is expanding in Europe with our new London office](https://yomu.fyi/post/modal-is-expanding-in-europe-with-our-new-london-office.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Sep 2, 2026
- Type: Announcement

Modal is expanding in Europe with a new London office, its second in the region, representing a larger investment in the area. The company already has nearly 20 engineers in Stockholm who built core parts of Modal, including its custom file system and Sandboxes product. Hugh Killingbeck-Jones joins as VP EMEA to lead go-to-market teams in London, which the company considers the AI capital of Europe. European AI-native companies like Black Forest Labs and Legora, as well as global platforms like DoorDash, run on Modal for tasks such as model evaluation and agent testing. The company is hiring across sales, engineering, and other roles in London and Stockholm, and plans meetups across EMEA.


### [How Botika runs full-stack generative AI on Modal](https://yomu.fyi/post/how-botika-runs-full-stack-generative-ai-on-modal.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Sep 2, 2026
- Type: Problem & solution

Botika builds agentic e-commerce teams that automate visual production for global fashion brands, using proprietary foundation models and a 100-terabyte image dataset. The company runs its entire full-stack AI operation on Modal, with the data pipeline processing 100 terabytes at less than 1% error rate across thousands of containers. Before Modal, the team struggled with Kubernetes fleet management and slow GCP Batch jobs; now a single researcher can launch 50 experiments daily using Claude, and they set up reinforcement learning infrastructure in less than a day. Modal handles autoscaling, cold starts, and multi-node training with RDMA, allowing Botika to double traffic in seconds, and they serve about 15 models on L4s, L40S, A100s, and H100s. The infrastructure is not Modal-specific, enabling the team to run open-source and custom frameworks without modification.


### [Self-hosted machines](https://yomu.fyi/post/self-hosted-machines.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Published: Sep 2, 2026
- Type: Announcement

Cursor’s self-hosted machines keep tool execution inside an organization’s own network, leaving the codebase, build outputs, and secrets on internal infrastructure while the agent handles tool calls locally. The feature supports My Machines for connecting one laptop or VM and team pools that provide named queues of workers, with capacity expanding as requests arrive and contracting when workers disconnect. Pools are repository-independent, can hibernate idle machines, and can restore them within a reconnect window, avoiding the need to keep costly capacity warm. Cloud agents can run on infrastructure including AWS Lambda, Coder, Cloudflare, Daytona, Modal, Namespace, Vercel, and E2B, while Linux and Mac workers add computer use for clicking, typing, screenshots, and browser control.


### [4 engineering patterns behind the strongest AI Agents Challenge submissions](https://yomu.fyi/post/4-engineering-patterns-behind-the-strongest-ai-agents-challenge-submis.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Sergio Villani
- Published: Sep 2, 2026
- Type: Explainer

The strongest submissions in the Google for Startups AI Agents Challenge repeatedly used four engineering patterns rather than merely labeling prompt chains as multi-agent systems. They exposed internally used tools through bidirectional MCP, used typed events and asyncio queues to let independent agents run concurrently, and routed fallback outputs through one shared validation function. They also reduced inference cost with tiered routing: regex or a cheap classifier handled simple intent before requests reached an expensive reasoning model, with one team reporting more than 40 percent handled before a real model call. The post argues these practices improve bounded data access, latency, resilience, and cost without requiring larger teams or newer models, and notes that Agent Development Kit and Agents CLI submissions showed them frequently.


### [Google for Developers Blog - News about Web, Mobile, AI and Cloud](https://yomu.fyi/post/google-for-developers-blog-news-about-web-mobile-ai-and-cloud.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Published: Sep 2, 2026
- Type: Explainer

The page presents six featured Google developer articles spanning multi-agent systems, content credentials, TPU optimization, agent plugins, Go, and Genkit Go. One article identifies bidirectional MCP, async event buses, unified validation, and tiered routing as recurring patterns in successful AI Agents Challenge submissions. Another introduces Credentio, an open-source C++ library for local-first C2PA validation, while a HeyGen case study reports a 1.86x speedup for Avatar IV on Trillium TPUs using torchax, XLA, FSDP, Ulysses sequence parallelism, and custom kernel/compiler optimizations. The collection also covers Agent Plugins 1.0.0’s plugin.json directory specification and Genkit Go’s progressive-disclosure Agent Skills based on SKILL.md bundles. A final article argues that Go’s compiler, toolchain, readability, and backward compatibility provide guardrails for reviewing and maintaining AI-generated software.


### [How River takes security work from a fix to merge](https://yomu.fyi/post/how-river-takes-security-work-from-a-fix-to-merge.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Sep 2, 2026
- Type: Problem & solution

Shopify’s River is an AI agent in Slack that manages dependency and first-party application vulnerability remediation beyond the patch. It validates ledger entries against repository, pull-request, and vulnerability-tracker state, checks whether findings remain active, and updates code on the repository head when it can do so safely. River regenerates lockfiles with repository tooling, repairs upgrade failures, discards stale CI results after rebases, and avoids overwriting human work. When product or risk judgment is required, it preserves evidence and hands owners a question, then verifies the repository head, dependency graph, tracker, and ledger after merge. In its 11 days, the dependency backlog fell about 70%, while security merges through the freshness-gated queue rose from about 10% to 80%.


### [From Neon Postgres to ClickHouse Managed Postgres](https://yomu.fyi/post/from-neon-postgres-to-clickhouse-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 2, 2026
- Type: Problem & solution

This user story describes three teams that moved production Postgres workloads from Neon to ClickHouse Managed Postgres after encountering reliability, performance, cost, and network-transfer problems as usage grew. ClickPipes handled schema migration, a parallel initial load, and change data capture, allowing teams to test synchronized databases before cutover. Reported outcomes included zero downtime or connection restarts, faster and more predictable queries, lower CPU or transfer costs, and p99 latency improving from seconds to single-digit milliseconds in one case. The post attributes these gains to local NVMe storage and eliminating transfer fees when analytics already runs on ClickHouse, while noting differences from Neon such as slower branch creation and manual application of several DDL changes.


### [REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs](https://yomu.fyi/post/refactor-vla-unsupervised-library-learning-of-typed-motor-programs.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Sep 2, 2026
- Type: Benchmark

REFACTOR-VLA addresses the weakness of monolithic vision-language-action (VLA) models, which generate raw commands or short action sequences and struggle with long-horizon tasks and interpretability. Its wake/sleep architecture uses a Behavioral-Equivalence Kernel (BEK) to cluster motor-program segments by outcomes from a learned latent world model, then produces typed lambda terms for a library-conditioned rectified-flow action decoder. Skills are admitted only when they satisfy both Minimum Description Length (MDL) and return-preservation gates, while training proceeds through world-model warmup, wake-phase policy optimization, and sleep-phase skill discovery. On the full LIBERO benchmark, expanding the world model from 188 million to 430 million parameters worsened results on all four suites, showing that scale alone was not beneficial. Adding supervised contrastive InfoNCE loss during warmup substantially improved sleep-phase clustering, with reported NMI scores of 0.462, 0.867, 0.915, and 0.754 for the Object, Spatial, Goal, and LIBERO-10 suites, respectively, under n = 3 multi-seeding.


### [Shared Nothing Architecture](https://yomu.fyi/post/shared-nothing-architecture.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Sep 2, 2026
- Type: Explainer

Shared-nothing architecture is presented as a scaling model for Showcase, a Ruby on Rails ballroom-competition application that began on a Mac Mini and grew to cloud VMs serving events in multiple regions. Because each event has few geographically concentrated users, a roughly one-megabyte SQLite database, about one transaction per second, and typical response times of 100ms or less, the design keeps state separate rather than sharing databases and Redis across events. The implementation runs an event’s services on one Fly.io VM, repeats that unit across machines or regions, and can place multiple event instances on one server with Phusion Passenger, NGINX, volumes, and a shared Action Cable process. It specifies 1GB machines with 2G swap after observing about 220MB idle and 450MB active RAM, and recommends WireGuard; the conclusion is that local state and fewer network calls suit this workload and can improve throughput and reliability.


### [How the FDA is building a secure, AI-ready data foundation on Databricks for Government](https://yomu.fyi/post/how-the-fda-is-building-a-secure-ai-ready-data-foundation-on-databrick.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Filippo Seracini, Vijay Raja
- Published: Sep 1, 2026
- Type: Explainer

The FDA built HALO (Harmonized AI and Lifecycle Operations for Data) as a secure, governed, AI-ready enterprise data platform for modernizing siloed systems without interrupting regulatory work. Its move to Databricks on AWS GovCloud, following FedRAMP High authorization sponsorship, added Unity Catalog as a governance layer across a multi-tenant architecture, while Terraform-based security patterns, PrivateLink, customer-managed keys, and the compliance security profile support regulated workloads. The agency migrated more than 5,000 users and 8,000 jobs and pipelines with zero downtime, refactoring over 1,000 pipelines and 4,000 notebooks. After onboarding eight centers and 30 programs, FDA reported query responses improving over 30%, compute costs falling over 20%, and provisioning and sharing time dropping over 75%. HALO also supports responsible AI use cases such as MARS, with humans retaining decision authority.


### [BenchMIRT: What are LLM benchmarks actually measuring?](https://yomu.fyi/post/benchmirt-what-are-llm-benchmarks-actually-measuring.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Kyle Wiggers
- Published: Sep 1, 2026
- Type: Benchmark

BenchMIRT is a method for auditing LLM benchmarks at the individual-prompt level, where questions may measure capabilities beyond a benchmark’s stated goal. It applies multidimensional Item Response Theory (MIRT) to model and question results, estimating model abilities plus each item’s difficulty and discrimination across dimensions. Trained on 100 LLMs, 16 benchmarks, and more than 34K questions, it independently recovered safety and general reasoning as dominant dimensions, repeatedly producing the same result. The analysis found, for example, that BBQ and WMDP aligned more strongly with reasoning than safety, while HarmBench combined safety- and reasoning-associated question groups; retaining 10% of items generally preserved capability rankings, and held-out correctness prediction reached 79% versus 70% for a baseline. BenchMIRT’s dimensions depend on the benchmark set, and its item-level transparency can also enable evaluation trimming.


### [How we eliminated $1 million a year of wasted AI agent spend in one hour](https://yomu.fyi/post/how-we-eliminated-1-million-a-year-of-wasted-ai-agent-spend-in-one-hou.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Alkis Polyzotis
- Published: Sep 1, 2026
- Type: Explainer

Databricks engineers investigated whether silent failures in MCP tool calls were driving hidden AI-agent costs as usage increased. Unity Gateway emitted OpenTelemetry traces for every invocation, recording tool names, arguments, errors, token counts, latency, and session IDs in a table that Genie One could query with plain-English questions. In a 24-hour sample, the analysis identified seven Jira and Google Drive/Docs bugs responsible for an estimated $499,000 annually in wasted tokens and 12,023 hours of agent wait time, or $1.2 million in lost productivity. The most costly patterns included under-specified interfaces, incompatible argument shapes, and cryptic or misleading errors that triggered retries and recovery loops. Engineers fixed the issues with a coding agent, completing the find-quantify-fix cycle in about an hour; the post recommends designing tools to tolerate reasonable LLM inputs and continuously tracing failures.


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