---
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

### [Meet HoloTab by HCompany. Your AI browser companion.](https://yomu.fyi/post/meet-holotab-by-hcompany-your-ai-browser-companion.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Marc Thibault, Pierre-Louis Cedoz, Hamza Benchekroun, Kai Yuan, Aurélien Lac, Tony Wu, Antonio Loison, Axel Moyal, Emrick Sinitambirivoutin, Aleix Cambray (H-AI)
- Published: Apr 15, 2026
- Type: Announcement

HoloTab is a Chrome extension that brings HCompany’s computer-use AI into the browser. It navigates websites, fills fields, and makes decisions from a user’s description, with vision models, action planning, and interface understanding operating underneath without setup or technical skills. The announcement follows the March 31st release of Holo3, described as the company’s most advanced computer-use model to date. For repetitive workflows, users can record their actions and narration in a tab; HoloTab turns the recording into a routine that can later be rerun or scheduled. The stated goal is to make computer-use AI accessible beyond teams with engineers who can connect such systems, and the extension is presented as free and available for everyone today.


### [Building with Modal and the OpenAI Agents SDK](https://yomu.fyi/post/building-with-modal-and-the-openai-agents-sdk.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Apr 15, 2026
- Type: Tutorial

The post walks through constructing a custom agent harness with the OpenAI Agents SDK and Modal Sandboxes, starting from a minimal coding agent that executes shell commands directly on the host. To improve safety, the agent is moved into isolated Modal Sandboxes, which are secure Linux environments that can be equipped with GPUs for compute-intensive tasks like training models. Memory is added via Sessions to accumulate context across turns, and subagents are introduced to handle focused subtasks with fresh context windows, preventing context bloat in the orchestrator while allowing long-horizon work. An async subagent pool enables parallel execution, with quotas to limit GPU spend, and filesystem snapshots allow checkpointing and deduplication of setup work across subagents, saving time and offloading memory. The harness is applied to the Parameter Golf challenge, where an orchestrator delegates parallel training experiments across different ML frameworks on GPUs, demonstrating autonomous research capabilities with composable skills.


### [Resend CLI 2.0](https://yomu.fyi/post/resend-cli-2-0.md)
- Company: [Resend](https://yomu.fyi/company/resend.md)
- Published: Apr 15, 2026
- Type: Announcement

Resend CLI 2.0 gives terminal-based access to the full Resend API and adds four features: sending emails from local React (.tsx/.jsx) files, managing automations and events, one-command webhook listening with automatic tunneling, and Agent Skills for autonomous API interaction. Users can now create automation runs, inspect results, and build multi-step workflows directly from the command line. The webhook listener automatically sets up a tunnel and forwards all event types to a local server, eliminating the need for separate tunneling tools. Agent Skills provide a structured interface enabling AI agents to independently manage their own inbox, sign up for accounts, receive confirmation emails, process attachments, and respond without manual intervention. Other improvements include programmatic log retrieval, enhanced API key storage via the system keychain (macOS Keychain, Linux Secret Storage, Windows Credential Manager), and the deprecation of the team command in favor of auth.


### [Interact with agent-created visualizations in canvases](https://yomu.fyi/post/interact-with-agent-created-visualizations-in-canvases.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Alex Vandak Maloney
- Published: Apr 15, 2026
- Type: Announcement

Cursor introduces canvases, allowing agents to represent information through interactive visual interfaces instead of chat text or markdown. In the Agents Window, these durable artifacts sit alongside tools including the terminal, browser, and source control, and agents can build dashboards, PR review interfaces, architecture diagrams, and eval-failure investigations. Canvases use a React-based UI library with first-party tables, boxes, diagrams, and charts, plus existing Cursor components such as diffs and to-do lists; skills can teach agents to create specific canvas types. Cursor reports that combining observability data from Datadog, Databricks, and Sentry MCPs with local debug files makes time-series analysis easier, while eval skills group rollout failures to expose hidden harness bugs. The company is also using canvases to visualize autoresearch progress and current hypotheses during client-performance experiments.


### [Autoresearch isn’t just for training models](https://yomu.fyi/post/autoresearch-isn-t-just-for-training-models.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Apr 15, 2026
- Type: Problem & solution

Repeated visual-regression failures made Polaris CI feedback cycles take about 30 minutes, prompting an attempt to optimize build time rather than fix individual tasks. The approach uses Pi to create an Autoresearch extension that repeatedly forms a hypothesis, measures the build against a 19.1-second baseline, and keeps only changes that improve the metric, discarding crashes and slower runs. The loop identified wasted VRT work: the full component pipeline ran before Storybook recompilation, while TypeScript transformed all 580 component files although 105 needed it. Removing those inefficiencies made the build 65% faster, although the system also produced unacceptable hacks that were discarded. The extension later gained multi-metric support, consistent iteration scripts, auto commits, and open-source adoption, with reported internal improvements including unit tests running 300 times faster and Playwright tests becoming faster.


### [Index-based pruning in ClickHouse](https://yomu.fyi/post/index-based-pruning-in-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Apr 15, 2026
- Type: Explainer

ClickHouse accelerates analytical queries by minimizing the amount of data read from disk across granules. A primary key determines data part sort order and records the first value per granule, enabling binary search to skip unneeded granules during filtered queries. For non-primary key filters, lightweight projections act like secondary indexes by storing alternative sort keys alongside base table offset pointers without duplicating full table rows. Skip indexes, such as minmax indexes, record column boundaries per granule and operate without disk duplication, though effective pruning requires some correlation with the primary key. Demonstrations using a UK property price dataset show how primary key filters and minmax skip indexes eliminate large fractions of total granules.


### [New Research Reveals Overcoming Legacy Tech Issues Key to AI Success](https://yomu.fyi/post/new-research-reveals-overcoming-legacy-tech-issues-key-to-ai-success.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Dr. William Lee
- Published: Apr 14, 2026
- Type: Benchmark

An IDC survey of 1,400 organizations across eight Asia/Pacific markets reveals that legacy architectures and accumulated data debt severely hinder enterprise artificial intelligence initiatives. While 43% of surveyed organizations cite legacy systems as a major obstacle to application development, 95% report project delays and 90% have experienced failed modernization attempts. In contrast, high-performing organizations treat modernization as a continuous discipline across people, processes, and cloud-centric data platforms capable of processing both structured and unstructured workloads. These leading enterprises achieve nearly three times more digital revenue growth than peers still constrained by technical debt. IDC notes that addressing technical and data debt proactively avoids escalating AI failure rates and delivers superior return on investment compared to superficial lift-and-shift migrations.


### [What's new in ClickStack - March 2026](https://yomu.fyi/post/what-s-new-in-clickstack-march-2026.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: Apr 14, 2026
- Type: Announcement

ClickStack announced several observability updates in its March 2026 release to improve root cause analysis, dashboard organization, and local development. The Event Deltas tool now displays baseline span distributions immediately, uses deterministic cityHash64 sampling for trace heatmaps, and applies proportional comparison scoring to accurately evaluate attributes across uneven sample sizes. Furthermore, users can directly include or exclude values from attribute comparison bars to streamline query creation during incident investigations. Dashboard capabilities expanded with a curated OpenTelemetry template gallery, multi-value filtering with optional lookup WHERE clauses, and broadened metric display units. Finally, local mode introduces browser localStorage persistence through createEntityStore, allowing developers to save dashboards and favorites without a running backend API.


### [Agentic reasoning in practice: Making sense of structured and unstructured data](https://yomu.fyi/post/agentic-reasoning-in-practice-making-sense-of-structured-and-unstructu.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: The Databricks AI Research Team
- Published: Apr 14, 2026
- Type: Benchmark

Enterprise questions that connect structured records with unstructured text are difficult for single-turn retrieval systems to answer reliably. The post evaluates Databricks Agent Bricks Supervisor Agent (SA), a declarative agent builder built on the internal aroll framework, across STaRK’s Amazon, MAG, and Prime domains and six KARLBench tasks. SA decomposes questions, routes sub-questions to Genie or Knowledge Assistant, combines results, and can reformulate queries or use SQL JOINs when initial retrieval is incomplete. It outperforms rerun SoTA baselines by 4% on STaRK-Amazon, 21% on MAG, and 38% on Prime, while delivering a 23% improvement on FinanceBench and consistent gains across KARLBench; the authors attribute the results to multi-step tool selection, reflection, and precise instructions rather than custom code.


### [Agent Bricks: The governed enterprise agent platform](https://yomu.fyi/post/agent-bricks-the-governed-enterprise-agent-platform.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kasey Uhlenhuth
- Published: Apr 14, 2026
- Type: Announcement

Agent Bricks is presented as Databricks’ enterprise platform for building, deploying, and governing agents that operate on business data under real identities, permissions, and operational constraints. The platform combines multi-model and framework support, execution, routing, fallback, cost optimization, and unified governance through Unity Catalog and AI Gateway, including on-behalf-of token passing and observability across data, models, MCPs, and APIs. Its context layer uses metadata such as schemas, business definitions, lineage, permissions, and data-quality signals, while Genie Spaces, Document Intelligence, Knowledge Assistant, and Agent Mode address structured and unstructured business information. The announcement includes general availability for Document Intelligence, Custom Agents on Apps, and Supervisor Agent, plus AI Gateway guardrails, managed OAuth MCP Connectors, web search, and MLflow’s CLEARS evaluation framework; the post reports 70% higher accuracy than standard RAG and a 30% improvement in multi-step workflows.


### [The hardest percentages](https://yomu.fyi/post/the-hardest-percentages.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Pratik Bothra
- Published: Apr 14, 2026
- Type: Announcement

Complex customer-support queries are relatively infrequent but consume disproportionate handling time and shape customers’ memories of service, especially when they require action across systems. The post distinguishes informational, personalized, and action-led queries, then positions Fin Procedures as a way to automate complex cases through configurable multi-step processes using natural-language SOPs, branching logic, data connectors, and AI-powered simulations. Since managed-availability launch, Procedures has handled more than 1.5 million conversations across hundreds of apps, with volume doubling month over month; a randomized 5% holdout found CSAT 28.93% higher when Procedures ran. The product is available to all 8,000+ Fin customers and adds review, failure reporting, rollback, connector monitoring, email simulation, and Agent in the Loop checkpoints for human approval or missing integrations.


### [Agentic coding with ClickHouse. One person, one data stack, one full-stack application](https://yomu.fyi/post/agentic-coding-with-clickhouse-one-person-one-data-stack-one-full-stac.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Oussama Chakri
- Published: Apr 14, 2026
- Type: Problem & solution

A Solutions Architect at ClickHouse built ClickShop, a full-stack retail analytics platform, within a few days using Cursor to demonstrate production-level capabilities beyond static slides. The platform pairs ClickHouse for sub-second analytical queries across billions of rows with PostgreSQL for transactional workflows, using ClickPipes for real-time change data capture synchronization. LibreChat orchestrates 18 persona-tailored AI agents, while dual observability layers—ClickStack for OpenTelemetry infrastructure metrics and Langfuse for LLM prompt and cost tracking—store their telemetry directly in ClickHouse. Developing with an AI-assisted IDE was accelerated by open-source documentation and community skills, though it required continuous human review to handle unintended code refactoring and token consumption.


### [8 AI and data trends shaping financial services in 2026](https://yomu.fyi/post/8-ai-and-data-trends-shaping-financial-services-in-2026.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kim Hatton, Junta Nakai, Marcela Granados, Antoine Amend, Ashraf Safdar, Jennifer Miller, Andrea DeSosa, Rajaram Suresh
- Published: Apr 14, 2026
- Type: Explainer

The post argues that financial-services AI adoption is widespread, but execution—not model capability or strategy—is determining who captures value in 2026. It attributes stalled pilots to fragmented legacy infrastructure, inconsistent data, weak lineage, and insufficient control for governed, real-time workflows such as fraud detection, pricing, and personalization. Firms advancing further treat data as a managed asset, embed governance in data and model pipelines, and align data, analytics, and AI teams around shared definitions, workflows, and metrics. The proposed remedy is a unified lakehouse environment combining storage, compute, governance, lifecycle management, orchestration, streaming, and AI workflows, with Unity Catalog providing centralized access control, lineage, and auditing. The conclusion is that by the end of 2026, firms that embed AI into operational decisioning at scale will pull ahead of organizations still running pilots.


### [Mintlify boosts NPS 30% and saves 60% with real-time analytics on ClickHouse Cloud](https://yomu.fyi/post/mintlify-boosts-nps-30-and-saves-60-with-real-time-analytics-on-clickh.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 14, 2026
- Type: Problem & solution

Documentation platform Mintlify experienced major dashboard performance degradation and rate limiting under PostHog as multi-tenant analytics traffic grew rapidly. Because PostHog lacked incremental materialized views, queries scanned full raw datasets and took tens of seconds to load. Mintlify migrated its customer-facing analytics infrastructure to ClickHouse Cloud, ingesting events through Amazon MSK and ClickPipes into a single events table backing incremental materialized views. The team executed the transition over two weeks by dual-writing events and replicating the existing PostHog schema to preserve API surface compatibility. The migration reduced dashboard query latency to sub-one-second, decreased related support bug reports to zero, lifted NPS by an estimated 30%, and lowered analytics infrastructure costs by approximately 60%.


### [Speeding up GPU kernels by 38% with a multi-agent system](https://yomu.fyi/post/speeding-up-gpu-kernels-by-38-with-a-multi-agent-system.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Wilson Lin, Sahil Modi, Yuan Zhang & Edward Lin
- Published: Apr 14, 2026
- Type: Benchmark

The post reports a three-week experiment in which a multi-agent system optimized 235 CUDA kernel problems generated from workloads spanning more than 124 production open-source models. Using SOL-ExecBench on 27 NVIDIA Blackwell 200 GPUs, the system used a planner to distribute and rebalance work, while autonomous workers benchmarked, debugged, and refined kernels without developer intervention. It ran separate CUDA C with inline PTX and CuTe DSL runs, reaching assembly-level optimization and learning a documented higher-level API. Against a single-agent optimized PyTorch baseline, it outperformed 149 of 235 problems, achieving a 1.38x geometric mean ratio, while 45 solutions exceeded 2x. The highlighted cases included an 84% geomean gain over a human FlashInfer baseline for paged-prefill attention, a 39% gain for NVFP4 MoE linear, and a GEMM reaching 86% of a tuned cuBLAS baseline; median SOL was 0.56, leaving room for improvement.


### [Building real-time product search on Databricks](https://yomu.fyi/post/building-real-time-product-search-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Jiayi Wu, Luke Lefebure, Adam Gurary
- Published: Apr 14, 2026
- Type: Explainer

Real-time product search for an online marketplace must retrieve, filter, rank, and respond in milliseconds while balancing latency, relevance, revenue, and click-through rate. The Databricks architecture divides the pipeline into ingestion, retrieval, and refinement, using Lakeflow pipelines and AI Functions for source processing, AI Search for embeddings and hybrid retrieval, and Lakebase for sub-10ms operational signals. It recommends validating p95/p99 latency, throughput, retrieval quality, and engagement metrics, with MLflow supporting systematic evaluation and caching helping meet strict latency requirements. As an example, FOX Sports used AI Search for thousands of QPS and achieved a 2x improvement in query success rate, combining streaming ingestion, two-phase retrieval, model serving, caching, and trending searches.


### [We're Transferring the Stripe Sync Engine to Stripe](https://yomu.fyi/post/we-re-transferring-the-stripe-sync-engine-to-stripe.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: raminder\_singh, matt\_linkous, gregor\_vand, ana\_mogul
- Published: Apr 14, 2026
- Type: Announcement

The Stripe Sync Engine repository is moving from supabase/stripe-sync-engine to stripe/sync-engine after growing from an open-source tool for syncing Stripe data into a Stripe schema in Postgres. The transfer follows collaboration that added cursor-based incremental sync, JSONB storage with generated columns, multi-account support for Stripe Connect platforms, event ordering in a new CLI, and automatic incremental backfilling using Supabase Cron and Queue. The latest release adds coupon synchronization, branching support, immediate syncing on installation, one-click upgrades, SSL enforcement, and tighter installation controls, along with clearer errors and dashboard visibility for Edge Function source code. The project remains open source under Apache 2.0, existing installations continue to work, and the Supabase dashboard integration is unchanged. With Stripe data in Postgres, users can join it with application data for subscription checks, feature gating, MRR, and churn analysis.


### [AI Email Editor](https://yomu.fyi/post/ai-email-editor.md)
- Company: [Resend](https://yomu.fyi/company/resend.md)
- Published: Apr 14, 2026
- Type: Announcement

Resend announces an AI-extended email editor designed to help users create emails that retain their brand voice while catching mistakes and reducing workflow friction. Users can provide a URL, and the AI extracts the brand, voice, and tone to generate a ready-to-send draft from existing content such as a blog post, changelog, or product page. During composition, selected blocks can be rewritten, rephrased, or improved, while pre-send checks cover deliverability, content compatibility, subject and preview, broken links, and typos. The editor also supports agents through the Resend MCP server, with access to the visual editor, real-time collaboration, cursor support, and presence indicators; the announcement says all features are live in the Broadcast and Template editors.


### [Build Better AI Agents: 5 Developer Tips from the Agent Bake-Off](https://yomu.fyi/post/build-better-ai-agents-5-developer-tips-from-the-agent-bake-off.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Frank Guan, Abraham Gomez
- Published: Apr 14, 2026
- Type: Tutorial

The Google Cloud AI Agent Bake-Off is presented as a test of building autonomous agents under severe time constraints for e-commerce, banking modernization, and startup go-to-market problems. Its five patterns recommend decomposing monolithic prompts into specialized sub-agents under a supervisor, designing modularly because model capabilities can quickly replace custom harnesses, and treating multimodality as native. It also advocates open protocols—including MCP, A2A, UCP, AP2, A2UI, and AG-UI—for agent and tool connections instead of bespoke wrappers. Finally, it separates probabilistic reasoning from execution: strict JSON schemas such as Pydantic validate LLM outputs before deterministic Python functions or SQL perform calculations and writes. The stated conclusion is that reliable production agents depend on rigorous software architecture rather than elaborate prompts or flashy single-shot demos.


### [From swarms to product: Turning customer signals into scalable features](https://yomu.fyi/post/from-swarms-to-product-turning-customer-signals-into-scalable-features.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Kevin O'Brien
- Published: Apr 13, 2026
- Type: Explainer

Intercom’s Fin teams use cross-functional swarms to identify customer needs while helping customers increase automation and understand the product. In these high-touch engagements, engineers, data scientists, and a product manager analyze conversations, APIs, Procedures, and workflows, using fast feedback to test configuration changes and estimate achievable automation; one manual taxonomy analysis took more than half a day and predicted a 70% rate that the customer achieved. Cockpit then encodes swarm patterns into repeatable workflows, letting customer success managers and Sales assess automation potential, diagnose issues, and suggest next actions across accounts without repeated data-science involvement. It also tests whether ideas generalize before product development. Patterns that prove broadly useful, robust, and scalable can move into product features such as the automation taxonomy in Insights and CX Score, which measures conversation quality; unlike Cockpit, product features must work for every customer without configuration.


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