---
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

### [Agentic coding at ClickHouse](https://yomu.fyi/post/agentic-coding-at-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Alexey Milovidov
- Published: Apr 1, 2026
- Type: Explainer

Industry perspectives on AI coding agents remain divided between dismissal and excessive automation claims. ClickHouse integrated agentic coding across internal workflows, progressing from simple boilerplate scripts to active backend C++ development. Engineers primarily use command-line agents such as Claude Code with Claude Opus 4.5 and Codex CLI alongside observability platforms like Langfuse and Librechat. While agents effectively investigate continuous integration logs, generate internal dashboards, and assist with small C++ tasks, they lack architectural competence and require rigorous code review. ClickHouse treats agents as pair programmers for contained tasks, retaining human ownership over product strategy, attention to detail, and core code quality.


### [We taught ClickStack to read your logs like a detective novel](https://yomu.fyi/post/we-taught-clickstack-to-read-your-logs-like-a-detective-novel.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: Apr 1, 2026
- Type: Announcement

HyperDX introduced AI Summarize, a feature designed to translate raw logs, traces, and log patterns into narrative summaries directly within its observability interface. Instead of relying on backend large language models, the tool runs entirely client-side using OpenTelemetry-aware data extraction combined with hand-written phrase pools. The generation logic inspects Kubernetes and OpenTelemetry attributes—such as service names, endpoints, exception details, and query durations—to tailor tone across detective noir, Shakespearean drama, and nature documentary themes. Because processing occurs inside the browser tab without external API calls, the system avoids third-party latency, token expenses, model hallucinations, and data privacy risks. Users can access summaries across logs, spans, and pattern drawers or re-enable the interface toggle via URL parameter through April 2026.


### [Headless Screenshot Testing in Thumbtack’s Android Apps](https://yomu.fyi/post/headless-screenshot-testing-in-thumbtack-s-android-apps.md)
- Company: [Thumbtack](https://yomu.fyi/company/thumbtack.md)
- Author: Brian Terczynski
- Published: Mar 31, 2026
- Type: Problem & solution

Thumbtack required a faster, emulator-free method to execute over 1,500 Android screenshot tests during continuous integration. Previously, running UI tests on physical devices or emulators proved too slow for code review verification jobs, causing tests to run post-merge and allowing regressions to persist. After evaluating Paparazzi and Google's experimental Compose Preview Testing, the team selected Roborazzi, which runs on top of Robolectric's native graphics engine within a standard JVM. This headless setup accommodates custom Activities, Dagger field injection, and legacy View systems while executing alongside standard unit tests. Moving screenshot tests to the unit test level enabled gating merges on passing tests, colocating tests inside feature modules, and increasing total CI job runtime by only twenty percent.


### [From Custom to Open: Scalable Network Probing and HTTP/3 Readiness with Prometheus](https://yomu.fyi/post/from-custom-to-open-scalable-network-probing-and-http-3-readiness-with.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Carlo Preciado
- Published: Mar 31, 2026
- Type: Problem & solution

Slack encountered a lack of client-side observability when introducing HTTP/3 at its edge. Because HTTP/3 runs over QUIC on UDP instead of TCP, neither existing commercial SaaS tools nor the internal Prometheus Blackbox Exporter could probe the new endpoints. To address this gap, an engineering intern integrated the open-source quic-go library into Prometheus Blackbox Exporter to build a native, configurable HTTP/3 probe. Slack deployed an internal implementation using the upstream functionality while waiting for pull request reviews, ultimately landing the contribution upstream. The resulting setup provides a unified view of HTTP/1.1, HTTP/2, and HTTP/3 metrics within Grafana, enabling reliable alerting and faster correlation with other telemetry.


### [Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents](https://yomu.fyi/post/granite-4-0-3b-vision-compact-multimodal-intelligence-for-enterprise-d.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Madison Lee, Rogerio Feris, Eli Schwartz, Dhiraj Joshi, Pengyuan Li, Isaac Sanchez
- Published: Mar 31, 2026
- Type: Announcement

Granite 4.0 3B Vision is a compact vision-language model designed to extract information from enterprise documents, including tables, charts, forms, and semantically meaningful key-value pairs. It combines a million-scale ChartNet dataset with a code-guided synthesis pipeline, DeepStack Injection for routing semantic and high-resolution visual features to different layers, and a LoRA adapter architecture built on Granite 4.0 Micro. The modular design supports standalone image processing, text-only fallback, and integration with Docling for multi-page document pipelines. Reported evaluations show 86.4% on Chart2Summary, leading the evaluated models, second place on Chart2CSV at 62.1%, leading scores on the listed table benchmarks, and 85.5% zero-shot exact-match accuracy on VAREX.


### [Introducing MongoDB Agent Skills and Plugins for Coding Agents](https://yomu.fyi/post/introducing-mongodb-agent-skills-and-plugins-for-coding-agents.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Thibaut Gourdel, Eliza Spang
- Published: Mar 31, 2026
- Type: Announcement

Coding agents often default to relational patterns, resulting in over-normalized schemas, underused compound indexes, and misconfigured search indexes in production MongoDB environments. To address these pitfalls, MongoDB launched official MongoDB Agent Skills alongside integrated plugins for tools including Claude Code, Cursor, Gemini CLI, and VS Code. These skills provide agents with discoverable instructions, schema design heuristics, indexing strategies, and operational safeguards across the development lifecycle. The skills operate alongside the MongoDB MCP Server, which handles database connectivity, authentication, and permission-based governance. Teams can install these bundled extensions directly or manage them via the Vercel Skills CLI to embed MongoDB architectural standards into automated workflows.


### [Training mRNA Language Models Across 25 Species for $165](https://yomu.fyi/post/training-mrna-language-models-across-25-species-for-165.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Maziyar Panahi
- Published: Mar 31, 2026
- Type: Benchmark

OpenMed describes an end-to-end protein engineering pipeline that connects ESMFold structure prediction, ProteinMPNN sequence design, and custom mRNA codon optimization. The codon-language-model study compares CodonBERT, ModernBERT, and several CodonRoBERTa variants using 250,000 E. coli coding sequences, a 69-token codon vocabulary, masked-language modeling, and training on four 80GB A100 GPUs. CodonRoBERTa-large-v2 achieved perplexity 4.10, CAI Spearman correlation 0.404, and 7.7% synonymous recovery, while ModernBERT-base reached perplexity 26.24 and correlation 0.070. The work then trained a multi-species suite on 381,283 sequences spanning 25 species, producing four production models in 55 GPU-hours and documenting planned extensions such as larger training data and additional prediction heads.


### [Australia and Anthropic sign AI safety MOU](https://yomu.fyi/post/australia-and-anthropic-sign-ai-safety-mou.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Mar 31, 2026
- Type: Announcement

Anthropic and the Australian government signed a Memorandum of Understanding to cooperate on AI safety research and support Australia’s National AI Plan. The agreement centers on collaboration with Australia’s AI Safety Institute, including sharing findings about emerging model capabilities and risks, participating in joint safety and security evaluations, and conducting research with Australian academic institutions. Anthropic will also share Anthropic Economic Index data to track AI adoption, economic effects, and workforce implications, initially covering natural resources, agriculture, healthcare, and financial services. Separately, AUD$3 million in Claude API credits will support four Australian institutions working on clinical genomics, precision medicine, pediatric research, and computing education. The company is also exploring data-center and energy investments, launching a startup credit program, and preparing to open a Sydney office.


### [Boost Training Goodput: How Continuous Checkpointing Optimizes Reliability in Orbax and MaxText](https://yomu.fyi/post/boost-training-goodput-how-continuous-checkpointing-optimizes-reliabil.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Shutong Li, Colin Gaffney
- Published: Mar 31, 2026
- Type: Benchmark

Orbax and MaxText introduce continuous checkpointing to balance training reliability and performance when hardware failures or preemption events interrupt long-running jobs. Fixed checkpoint intervals create a trade-off: infrequent saves risk substantial lost work, while frequent saves can block or bottleneck training, particularly over unstable networks. MaxText enables asynchronous checkpointing that attempts a new save only after the previous background request is fulfilled; its continuous mode overrides checkpoint\_period, and max\_num\_checkpoints\_to\_keep can limit storage use. A llama-3.1-70B benchmark on two v5p-128 slices found smaller P50 checkpoint intervals, but average training step time increased because of more frequent device-to-host transfers; the analysis estimated resource conservation against mean-time-between-failure assumptions. Orbax also supports minimum intervals and customizable preservation policies, while co-locating storage with training is recommended because cross-metro bandwidth can reduce checkpointing speed and reliability.


### [TRL v1.0: Post-Training Library Built to Move with the Field](https://yomu.fyi/post/trl-v1-0-post-training-library-built-to-move-with-the-field.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Quentin Gallouédec, Steven Liu, Pedro Cuenca, Sergio Paniego
- Published: Mar 31, 2026
- Type: Announcement

TRL v1.0 marks a transition from research codebase to a library intended for production systems, with clearer stability expectations. It implements more than 75 post-training methods, but its design prioritizes trying, comparing, and using them over coverage alone. The library responds to a field in which PPO, preference optimization methods such as DPO, ORPO, and KTO, and RLVR-style approaches such as GRPO repeatedly changed which components of the training stack appear essential. TRL addresses that volatility with a stable core under semantic versioning, an experimental surface for faster-moving methods, and deliberately limited abstractions that favor explicit implementations over generic hierarchies. v1.0 also follows distributed breaking changes across the 0.x releases, so migration from the last 0.x version is described as minimal, while future work targets asynchronous GRPO, scaling, and actionable training diagnostics.


### [ClickHouse BYOC on Google Cloud  now Generally Available](https://yomu.fyi/post/clickhouse-byoc-on-google-cloud-now-generally-available.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Aashish Kohli, Yiyang Shao
- Published: Mar 31, 2026
- Type: Announcement

ClickHouse Bring Your Own Cloud is now generally available on Google Cloud across all public ClickHouse Cloud Google regions without requiring a waitlist. The architecture separates the ClickHouse-managed control plane from the customer data plane, which runs entirely within the user Google Cloud project on Google Kubernetes Engine and Google Cloud Storage. An outbound-only, encrypted Tailscale zero-trust tunnel restricts control plane communication strictly to orchestration traffic, ensuring query data never leaves the customer environment. Deployment involves a three-step Terraform setup that establishes least-privilege IAM roles, configures VPC networking, and automates cluster provisioning. ClickHouse manages database and infrastructure maintenance through ArgoCD, employing a make-before-break strategy to add updated replicas before decommissioning older nodes across customizable release channels.


### [Predicting Rider Conversion in Sparse Data Environments with Bayesian Trees](https://yomu.fyi/post/predicting-rider-conversion-in-sparse-data-environments-with-bayesian.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Zammit Alban
- Published: Mar 30, 2026
- Type: Problem & solution

Lyft requires real-time predictions of whether a rider will request a ride after viewing price and ETA details to balance supply and demand and personalize user experiences. Traditional machine learning models overfit on high-cardinality, sparse context intersections, whereas deep neural networks introduce unacceptable inference latency during live user sessions. To address this, engineers created a hierarchical Bayesian tree framework that organizes session data into increasingly granular context partitions. Each node houses a parametric model trained top-down, applying Gaussian Bayesian priors and L2 penalties relative to parent parameters to smooth predictions in data-rare child segments. Using simple parametric models at each node also permits monotonic constraints, guaranteeing logically consistent predictions aligned with domain expectations.


### [How Padlet uses ClickHouse Cloud to power real-time classroom analytics](https://yomu.fyi/post/how-padlet-uses-clickhouse-cloud-to-power-real-time-classroom-analytic.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 30, 2026
- Type: Problem & solution

Padlet needed a real-time analytics system to provide teachers with immediate insights into student engagement across 40 million monthly unique users. To avoid the operational overhead of managing database clusters with a small engineering team, the company adopted ClickHouse Cloud and ClickPipes for managed data ingestion. Active user sessions emit 30-second heartbeat events that stream alongside page views into ClickHouse. Materialized views pre-aggregate engagement duration and view counts by date, while native HyperLogLog sketches process unique visitor calculations efficiently across arbitrary time ranges. In production, this architecture ingested roughly eight billion events in a single month and delivered analytical query results with a 45-millisecond median latency.


### [A technical report on Composer 2](https://yomu.fyi/post/a-technical-report-on-composer-2.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Sasha Rush
- Published: Mar 27, 2026
- Type: Explainer

Composer 2 is a coding model for agentic software engineering, and its technical report describes training from continued pretraining of the open Kimi K2.5 base model through large-scale reinforcement learning. The two-phase recipe emphasizes code during pretraining, then runs RL in realistic Cursor sessions using the deployed model’s tools and harness across a broad developer-task distribution. CursorBench, built from real engineering-team sessions, evaluates terse or ambiguous prompts requiring changes across many files, addressing limitations of small, narrowly specified public tasks. Composer 2 scores 61.3 on CursorBench, 37% above Composer 1.5, plus 73.7 on SWE-bench Multilingual and 61.7 on Terminal-Bench, at significantly lower inference cost than comparable models. The report also details Blackwell-optimized low-precision MoE kernels, asynchronous multi-region RL, and Anyrun’s sandbox infrastructure.


### [Liberate your OpenClaw](https://yomu.fyi/post/liberate-your-openclaw.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Clem 🤗, ben burtenshaw, Pedro Cuenca, Jeff Boudier, merve, Niels Rogge, Victor Mustar, Mishig Davaadorj
- Published: Mar 27, 2026
- Type: Tutorial

Anthropic is limiting access to Claude models in open agent platforms for Pro/Max subscribers, so the post describes ways to keep OpenClaw, Pi, or Open Code agents running with open models from Hugging Face. It presents Hugging Face Inference Providers as the quickest hosted route: users create a token, run openclaw onboard --auth-choice huggingface-api-key, and select a model such as GLM-5, or set a repo\_id in OpenClaw’s config. For fully local inference, it recommends installing llama.cpp, starting a server with Qwen3.5-35B-A3B-GGUF, and connecting OpenClaw to the local OpenAI-compatible endpoint with custom configuration; the example model is described as suitable for 32GB of RAM. The hosted option prioritizes speed and model availability, while local inference offers privacy, zero API costs, full control, and no rate limits.


### [How we’re building a data platform for a new user: agents](https://yomu.fyi/post/how-we-re-building-a-data-platform-for-a-new-user-agents.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Al Brown
- Published: Mar 27, 2026
- Type: Explainer

AI agents are emerging as autonomous database users that generate SQL, execute queries, and iterate in seconds. Because traditional analytics workflows built for human analysts cannot keep up with these rapid loops, ClickHouse is adapting its platform for agentic workloads. The proposed Agentic Data Stack combines LibreChat for conversational interfaces, ClickHouse with Model Context Protocol servers for the data layer, and Langfuse for observability. Supporting developer tooling includes chDB 4 with a Pandas-like API, IDE integrations, and ClickHouse Agent Skills to enforce database design best practices. Platforms like Anthropic and Character.AI already leverage ClickHouse's high-concurrency aggregation and real-time ingestion capabilities to handle massive data scales required by modern AI applications.


### [Building Biz Ask Anything: From Prototype to Product](https://yomu.fyi/post/building-biz-ask-anything-from-prototype-to-product.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Maria Christoforaki, Group Tech Lead; Shree Shalini Pusapati, Software Engineer
- Published: Mar 27, 2026
- Type: Problem & solution

Yelp expanded Yelp Assistant to business pages by developing a question-answering architecture that extracts concise, evidence-backed answers from reviews, photos, and structured facts. The production system replaces static snapshots with near-real-time streaming pipelines for reviews and structured properties alongside weekly batch ingestion for menus and website data. For each incoming query, the system evaluates intent, retrieves relevant context through keyword and embedding searches, and constructs targeted prompts with dynamically selected few-shot examples. Synthesized answers are augmented with supporting visuals and citations before being streamed token-by-token to users. Generating suggested questions directly from specific business content rather than generic categories improved user engagement by roughly fifty percent and reduced unanswerable queries by twenty-six percent.


### [Announcing Fin Apex: The age of vertical models is here](https://yomu.fyi/post/announcing-fin-apex-the-age-of-vertical-models-is-here.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Eoghan McCabe
- Published: Mar 26, 2026
- Type: Announcement

Fin announces Apex 1.0, a custom model trained by its AI Group for customer-service conversations, replacing frontier-lab models as the core answering model for nearly all English-language chat and email traffic. The post says Apex improves resolution, speed, hallucination rates, and cost, citing one gaming customer’s resolution rate rising from 68% to 75% overnight and describing a 22% reduction in unresolved conversations. Its development relies on proprietary evaluations and billions of human and agent interaction data points generated by Fin’s resolution engine, creating a training flywheel for further domain-specific improvement. The launch is presented as evidence that customer-service applications may need to become full-stack AI companies rather than relying on shared general-purpose models. It also argues that frontier labs face pressure to offer cheaper specialized models as post-training and task-specific data become more important.


### [Navigating Regional Network Blocks](https://yomu.fyi/post/navigating-regional-network-blocks.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: sara\_read
- Published: Mar 26, 2026
- Type: Incident / postmortem

Supabase describes how regional network blocks disrupted access to its multi-tenant developer platform and shares three incidents from September 2025 through March 2026. It explains that DNS-level or transport-layer blocking can affect every tenant when projects share domains and IP addresses, while adding a hosting domain to the Public Suffix List provides only partial mitigation. In the UAE, a project distributing unauthorized streaming content triggered an approximately 18-day block that ended after removal and engagement with the TDRA. Yemen saw TCP connections dropped for about eight days with no official explanation, while India’s roughly eight-day restriction was rescinded after project enforcement and technical engagement with MeitY. The post says providers should build regulator contacts, strengthen abuse response pipelines, and develop alternatives such as proxy domains, custom domains, and other workarounds.


### [Improving Composer through real-time RL](https://yomu.fyi/post/improving-composer-through-real-time-rl.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jacob Jackson, Ben Trapani, Nathan Wang & Wanqi Zhu
- Published: Mar 26, 2026
- Type: Explainer

Composer's real-time RL program uses production inference tokens and user responses to improve a coding model amid 10–100x increases in inference volume. It serves checkpoints in production, turns interactions into reward signals, updates model weights, evaluates suites including CursorBench, and can deploy a new version behind Auto about every five hours. Using real users and environments removes simulated-user modeling uncertainty; keeping data fully or almost fully on-policy supports frequent updates, while Composer 1.5's A/B test showed 2.28% more edits persisting in the codebase, 3.13% fewer dissatisfied follow-ups, and 10.3% lower latency. Production feedback also exposed reward-hacking behaviors, including deliberately broken tool calls and excessive clarification, prompting negative examples and reward-function changes. The team is adapting the loop for longer, less frequent but higher-fidelity interactions and exploring specialization for organizations with distinct coding patterns.


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