---
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

### [Log Drains: Now available on Pro](https://yomu.fyi/post/log-drains-now-available-on-pro.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: steven\_eubank
- Published: Mar 5, 2026
- Type: Announcement

Supabase has launched Log Drains for its Pro tier, allowing users to send Supabase logs to their own logging backend and investigate them alongside the rest of their stack. The feature addresses the separation between Supabase logs and external observability tools by exporting events from Postgres, API Gateway, Auth, Storage, Edge Functions, and Realtime. Logs are sent over HTTP in batches of up to 250 entries or every second, whichever comes first, with Gzip compression when supported by the destination. Supported destinations include Sentry, Grafana Loki, Datadog, AWS S3, Axiom, and generic HTTP endpoints; users configure drains in the dashboard and can create multiple drains, with pricing of $60 per drain per project, $0.20 per million events, and $0.09 per GB of egress.


### [Supabase Storage: major performance, security, and reliability updates](https://yomu.fyi/post/supabase-storage-major-performance-security-and-reliability-updates.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: fabrizio
- Published: Mar 5, 2026
- Type: Announcement

Supabase Storage has shipped updates live in all regions for projects using Storage, targeting performance at scale, security vulnerabilities, and reliability issues. As projects reached tens of millions of objects, concurrent uploads exposed write bottlenecks, certain SQL deletes could orphan files, and a crafted path could escape the file backend’s storage root. The object-listing rewrite removes the prefixes table, six triggers, and twelve helper functions, derives folder structure from the objects table with a hybrid skip-scan algorithm, and uses cursor-based pagination; deep pagination on 60 million rows is up to 14.8 times faster without a write penalty. Direct SQL deletes are blocked by default through a statement-level trigger, while a configurable 30-second timeout supports cancellation of disconnected clients via native Postgres protocol. Idempotent migrations, TUS lock-race prevention, orphan-scanner fixes, OpenTelemetry metrics, and adapter and API bug fixes are included, with no code changes required.


### [Introducing Modular Diffusers - Composable Building Blocks for Diffusion Pipelines](https://yomu.fyi/post/introducing-modular-diffusers-composable-building-blocks-for-diffusion.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: YiYi Xu, Alvaro Somoza, Dhruv Nair, Sayak Paul
- Published: Mar 5, 2026
- Type: Announcement

Modular Diffusers introduces a composable alternative to Diffusers’ existing DiffusionPipeline for building diffusion workflows from reusable blocks. A ModularPipeline can load a workflow definition, load model components separately with settings such as bfloat16, and preserve the familiar inference API, while blocks for text encoding, image encoding, denoising, and decoding can be inspected, run independently, or rearranged. Custom Python blocks declare components, inputs, outputs, and computation logic; the example adds a Depth Anything V2 depth processor to a Qwen Image ControlNet workflow, with data flowing between compatible blocks. Blocks and complete pipelines can be published to the Hub, and Mellon can generate node interfaces from their consistent definitions. The post presents the system as an early, flexible direction and says Mellon is not ready for production use.


### [Introducing ClickStack embedded in ClickHouse](https://yomu.fyi/post/introducing-clickstack-embedded-in-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: Mar 5, 2026
- Type: Announcement

ClickHouse 26.2 introduces an embedded distribution of the ClickStack observability UI packaged directly inside the ClickHouse binary. Designed for local exploration and instance inspection, this distribution allows developers to visualize internal query logs, traces, and system metrics without deploying external services. To preserve build integrity and keep the added binary footprint around 4.1 to 4.2 MB, the team avoided a Node.js dependency by embedding a pre-built static Next.js application using CMake-generated C++ structs. Supporting components such as MongoDB, Express, and the Pyodide WebAssembly runtime were omitted, disabling alerting and persistent dashboards in favor of simplicity. Users can access the interface directly over HTTP to monitor local databases and query performance out of the box.


### [Cursor is now available in JetBrains IDEs](https://yomu.fyi/post/cursor-is-now-available-in-jetbrains-ides.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 4, 2026
- Type: Announcement

Cursor is now available in IntelliJ IDEA, PyCharm, WebStorm, and other JetBrains IDEs through the Agent Client Protocol (ACP), extending its agent-driven development experience to developers using those environments. The integration lets users choose frontier models from OpenAI, Anthropic, Google, and Cursor, while Cursor provides a custom-built agent harness for each model. Cursor's secure codebase indexing and semantic search are combined with JetBrains IDE code intelligence and tooling to support work on large enterprise codebases. Users install Cursor ACP from the ACP Registry and authenticate with an existing Cursor account; it is free for users on paid plans, and the release is described as a foundation for deeper JetBrains integrations.


### [PRX Part 3 — Training a Text-to-Image Model in 24h!](https://yomu.fyi/post/prx-part-3-training-a-text-to-image-model-in-24h.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: David Bertoin, Roman Frigg, Jon Almazán
- Published: Mar 3, 2026
- Type: Benchmark

PRX Part 3 reports a 24-hour speedrun for training a text-to-image diffusion model, combining techniques that performed well in earlier experiments under a budget of about $1,500 on 32 H200 GPUs. The recipe uses x-prediction in pixel space with 32-pixel patches and a 256-dimensional initial bottleneck, followed by LPIPS and DINOv2 perceptual losses, TREAD token routing, REPA alignment with DINOv3, and Muon for matrix parameters while Adam handles the rest. Training ran for 100,000 steps at 512px with batch size 1,024, then 20,000 steps at 1024px with batch size 512 and no REPA, across three synthetic datasets. The resulting model is described as usable, with strong prompt following and consistent aesthetics, though texture glitches, anatomy issues, and difficulty with hard prompts remain. The post attributes these limitations mainly to undertraining and limited data diversity and presents the recipe as a foundation for further scaling.


### [How technical support at Cursor uses Cursor](https://yomu.fyi/post/how-technical-support-at-cursor-uses-cursor.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Kody Fisher & Zach Hudson
- Published: Mar 3, 2026
- Type: Explainer

Cursor’s technical support team treats customer investigations as research problems, using a single session to combine code, logs, team knowledge, and past conversations. Engineers typically start in Ask Mode, use semantic search across a locally indexed codebase and documentation, and group related repositories with multi-root workspaces. MCP servers bring customer data, Datadog logs and traces, Slack and support history, runbooks, account details, and ticketing context into those investigations; Linear MCP turns evidence into escalations, while a Slack-mentioned cloud agent can open documentation pull requests. Slash commands, Rules, Skills, and parallel subagents automate repeated work, including failure analysis, known-issue searches, ticket writing, and customer-response drafting. The team reports that more than 75% of support interactions run through Cursor, with stated throughput gains of 5–10x and estimated productivity improvements of up to an order of magnitude.


### [PlanetScale protects production reliability with Bugbot](https://yomu.fyi/post/planetscale-protects-production-reliability-with-bugbot.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 2, 2026
- Type: Problem & solution

PlanetScale describes code review as the bottleneck created by rapidly increasing agent-generated code, while human review capacity remained fixed. To preserve production reliability, the company adopted Bugbot as a dedicated agentic review layer alongside its existing process. Bugbot surfaces semantic and logical issues that static analyzers, linters, and straightforward frontier-model reviews may miss, including premature completion states, non-converging asynchronous controllers, and database restart edge cases. Across more than 2,000 pull requests reviewed monthly, roughly 80% of Bugbot's comments are addressed before merge. PlanetScale says this prevents potential production downtime, saves the equivalent of two full-time engineers of review effort, and lets engineers focus on complex infrastructure work without sacrificing quality.


### [How we built security into Custom Agents](https://yomu.fyi/post/how-we-built-security-into-custom-agents.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Sean Keenan, Jacob Brackett
- Published: Feb 27, 2026
- Type: Explainer

Custom Agents were designed for collaborative workspaces where useful access must be balanced against dangerous actions. They use a build-from-nothing model with no default read, write, or interaction permissions, then add resource-level controls such as page-level access, Slack “read and reply,” email confirmation, and selected MCP tools. Runtime safeguards combine permission controls with prompt-injection mitigations, monitoring of external data, warnings, owner confirmation for risky actions, and remediation such as deleting an incorrect Slack message. Alpha testing across Notion and customers produced more than 3,000 internal and 25,000 customer agents, and exposed overly broad Slack write access that could reach #general. That finding led to the custom permission and reinforced the case for first-party integrations, while planned work includes admin-approved broader scopes, Workers integrations, and additional permission modes.


### [Inside MongoDB Dublin: The Heart of Our International Growth](https://yomu.fyi/post/inside-mongodb-dublin-the-heart-of-our-international-growth.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: MongoDB
- Published: Feb 27, 2026
- Type: Announcement

Established in 2013 in Ballsbridge, MongoDB Dublin functions as the company's international headquarters and a primary regional engineering hub. The location employs over 300 individuals spanning more than 40 nationalities across Product and Technology, Sales, and Technical Services teams. Engineers at the hub take direct ownership of core database systems, tackling theoretical computer science problems in areas like query optimization and implementing capabilities like native vector search for embeddings. Benefits provided to employees include 27 days of annual leave, 20 weeks of paid parental leave, and access to internal employee resource groups such as MDBWomen, Queer Collective, and Config. The facility continues to expand its engineering teams to own critical product components rather than peripheral tasks.


### [Towards Model-based Verification of a Key-Value Storage Engine](https://yomu.fyi/post/towards-model-based-verification-of-a-key-value-storage-engine.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Will Schultz, Murat Demirbas
- Published: Feb 27, 2026
- Type: Problem & solution

MongoDB developers formalized the interface boundary between their distributed transactions protocol and the underlying WiredTiger key-value storage engine using a compositional TLA+ specification. This modular model established an abstract contract to verify that the storage engine's concrete timestamp-based operations matched the semantics expected by the distributed transaction layer. To check conformance, a modified version of the TLC model checker generates a complete graph of reachable states for finite parameters and computes path coverings. Each identified path is translated into an automated test case consisting of a sequence of storage engine API calls. For a finite model with two keys and two transactions, this workflow generated 87,143 test cases that executed against WiredTiger in approximately 40 minutes.


### [Transformation in action: How AI is evolving support careers](https://yomu.fyi/post/transformation-in-action-how-ai-is-evolving-support-careers.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Feb 27, 2026
- Type: Explainer

AI is changing customer service operations by shifting human agents away from queue-level work such as triage and FAQs toward training systems, complex escalations, and strategic improvement. In research from The 2026 Customer Service Transformation Report, 45% of teams updated job descriptions with AI-related responsibilities, 40% said agents focus more on training AI systems, and 27% primarily handle complex escalations and edge cases. Interviews with 166 customers found nearly 95% reporting meaningful workflow changes, while 83% saw roles become more strategic and supervisory; mature teams also reported less time handling support volume. New positions including AI knowledge managers and conversation designers are emerging, while existing staff can move into AI-focused work such as optimizing knowledge, monitoring outputs, and improving customer journeys. The source recommends transparent communication, agent participation, dedicated time outside inbox, and metrics such as resolution rate as teams build operating models with AI at their core.


### [2,000 robots walk into a shop: Simulated A/B testing (2026) - Shopify](https://yomu.fyi/post/2-000-robots-walk-into-a-shop-simulated-a-b-testing-2026-shopify.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Feb 27, 2026
- Type: Benchmark

Shopify’s SimGym addresses the difficulty of evaluating storefront changes when merchants lack enough traffic for conventional A/B tests, by simulating shopping sessions with LLM-guided robots. Each robot runs in a cloud Chromium browser with a persona, budget, and intent, while a counterpart tests the alternate theme; the system uses real page representations rather than a mocked DOM. To support bursty, sequential workloads with 89K–127K accumulated tokens per session, Shopify self-hosted gpt-oss-120b on 48 NVIDIA B200 GPUs, combining MXFP4 expert quantization, FP8 KV-cache, FlashInfer kernels, async scheduling, and guided decoding. Production measurements report 80K to 88K tokens/second per B200 from async scheduling, a 57% reduction in HTTP/gRPC overhead, and MIG experiments yielding about 20% lower latency and 12% higher daily throughput without quality regression; speculative decoding is validated but queued.


### [The third era of AI software development](https://yomu.fyi/post/the-third-era-of-ai-software-development.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Michael Truell
- Published: Feb 26, 2026
- Type: Explainer

The post describes a third era of AI-assisted software development, in which autonomous agents handle larger tasks over longer timescales while developers define problems and review results. It traces a progression from Tab autocomplete, to synchronous prompt-and-response agents, and then cloud-based agents running on separate virtual machines. These agents can iterate and test for hours, returning logs, video recordings, and live previews that make parallel work and artifact-based review practical. Cursor reports that 35% of its internally merged pull requests are created by agents operating autonomously in cloud VMs, and that agent users now outnumber Tab users two-to-one after usage grew more than 15x in a year. The post says broader adoption still depends on reliable tests, environments, tools, and context, while developers shift toward decomposition, review, and feedback rather than line-by-line guidance.


### [Using LLMs to amplify human labeling and improve Dash search relevance](https://yomu.fyi/post/using-llms-to-amplify-human-labeling-and-improve-dash-search-relevance.md)
- Company: [Dropbox](https://yomu.fyi/company/dropbox.md)
- Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel,Ishan Mishra,Eric Wang,Dmitriy Meyerzon,Dmitriy Meyerzon
- Published: Feb 26, 2026

Dropbox Dash uses large language models (LLMs) to amplify human labeling efforts for training its search relevance and ranking models. By validating and optimizing LLM evaluators against a small set of human-labeled internal data, Dropbox creates massive, high-quality training datasets offline for production rankers like XGBoost without incurring high latency or latency costs at query time.


### [Closing the code review loop with Bugbot Autofix](https://yomu.fyi/post/closing-the-code-review-loop-with-bugbot-autofix.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jon Kaplan
- Published: Feb 26, 2026
- Type: Announcement

Bugbot Autofix extends the code review agent so it can find issues in pull requests and automatically propose fixes. It spawns cloud agents in independent virtual machines that test software, and more than 35% of its changes are merged into the base PR. Now out of beta and available to all Bugbot users, the feature adds proposed fixes to the pull requests Bugbot reviews once enabled. Bugbot’s average issues identified per run has nearly doubled over six months, while the user resolution rate rose from 52% to 76%, indicating more bugs are caught and fewer false positives are flagged. Future work includes configurable automations, self-verification, deeper research, and continuous codebase scanning.


### [New book: Intercom on Jobs](https://yomu.fyi/post/new-book-intercom-on-jobs.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Des Traynor
- Published: Feb 26, 2026
- Type: Announcement

Intercom launched its fourth book, Intercom on Jobs-to-be-Done, to document lessons from applying the Jobs-to-be-Done framework to product, design, and marketing for software. The book addresses a gap: although the framework is nearly 30 years old and often discussed through physical products, few materials cover its application to software companies. Intercom says the framework helped it understand the needs customers addressed with its product and improve their experience. Rather than offering a prescriptive, step-by-step method, the book presents hard-learned lessons intended to be timeless and universal, including customer switching, real competitors, product scope, exact jobs, Job Stories versus personas, and software research.


### [Dario Amodei on the Department of War discussions](https://yomu.fyi/post/dario-amodei-on-the-department-of-war-discussions.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Feb 26, 2026
- Type: Announcement

Dario Amodei says Anthropic considers AI deployment for US and allied democratic defense existentially important and has deployed Claude across the Department of War and other national security agencies, including classified networks and National Laboratories. The company says it supports intelligence analysis, modeling and simulation, operational planning, and cyber operations, while restricting Claude’s use by firms linked to the Chinese Communist Party and advocating chip export controls. Anthropic maintains two safeguards: mass domestic surveillance is incompatible with democratic values, and current frontier systems are not reliable enough for fully autonomous weapons without proper oversight. The Department has stated it demands “any lawful use” and threatens offboarding, a “supply chain risk” designation, and Defense Production Act action; Anthropic says it will not remove the safeguards, while offering continued service or transition support.


### [Mixture of Experts (MoEs) in Transformers](https://yomu.fyi/post/mixture-of-experts-moes-in-transformers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Aritra Roy Gosthipaty, Pedro Cuenca, merve, Ilyas Moutawwakil, Arthur Zucker, Sergio Paniego, Pablo Montalvo
- Published: Feb 26, 2026
- Type: Explainer

Mixture of Experts (MoEs) in Transformers let a Transformer retain its backbone while replacing selected feed-forward layers with routed expert sub-networks, allowing total model capacity to exceed the active parameters used per token. The post explains why sparse models can reduce inference cost and memory pressure, then focuses on making them first-class in Transformers. Its weight-loading refactor converts independently serialized expert tensors into packed runtime layouts through composable WeightConverter operations and lazy materialization. The Experts Backend supplies eager, batched\_mm, and grouped\_mm execution strategies, while expert parallelism shards experts across devices and aggregates partial outputs. Training support uses grouped GEMM and custom Triton plus LoRA kernels, with reported improvements of approximately 12× speed, over 35% VRAM reduction, and roughly 6× longer context.


### [Announcing major updates to Procedures and Simulations: Enabling Fin to handle complex queries](https://yomu.fyi/post/announcing-major-updates-to-procedures-and-simulations-enabling-fin-to.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Chris Dalley
- Published: Feb 25, 2026
- Type: Announcement

Intercom announces 12 updates to Fin’s Procedures and Simulations, the mechanisms it presents for handling complex customer queries while keeping teams in control. Procedures combine natural-language instructions, deterministic controls such as conditional steps, data connectors, code snippets, checkpoints, and fully agentic behavior; new features include AI drafting, reusable Sub-procedures, explicit switching rules, targeted knowledge-hub references, internal handoff notes, automatic switching, and structured extraction from PDFs and images. The Procedures UI also adds improved authoring and reporting on triggers, resolutions, and handoffs. Simulations support full multi-turn conversations, image uploads, clearer visibility into Fin’s reasoning, and AI-generated tests stored in a central library that can be rerun after changes. Procedures and Simulations are available now, with the stated goal of helping teams train, test, deploy, analyze, and refine complex workflows before and after launch.


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