---
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

### [MongoDB VFS for LangChain Deep Agents: A Searchable Filesystem for Agents](https://yomu.fyi/post/mongodb-vfs-for-langchain-deep-agents-a-searchable-filesystem-for-agen.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 3, 2026
- Type: Announcement

Modern AI agents are expected to complete real work, not just answer questions, which requires reliable filesystem navigation and updates. The MongoDB virtual filesystem (VFS) for LangChain Deep Agents connects this filesystem experience to MongoDB Atlas and an underlying object store like S3. It provides agents with a searchable workspace that persists across sessions, deployments, and sub-agents. Developers can use MongoDB Atlas as the data platform, including MongoDB Search, Vector Search, and hybrid search, without changing agent code. This architecture allows agents to plan, inspect source material, create artifacts, and resume work over hours or days without loading the entire corpus into context.


### [MongoDB Enterprise Advanced for AI-Ready Operational Insights in Manufacturing](https://yomu.fyi/post/mongodb-enterprise-advanced-for-ai-ready-operational-insights-in-manuf.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 3, 2026
- Type: Explainer

Unplanned manufacturing downtime costs $2.3 million per hour in the automotive sector, and a workforce shortage means 26% of existing workers will retire by 2030. Existing predictive maintenance systems operate in isolation, increasing mean equipment recovery times from 49 to 81 minutes per incident. MongoDB Enterprise Advanced provides a unified data platform for multi-agent AI systems that combine sensor telemetry, vector embeddings, agent memory, and operational records in a single database. Its document model, hybrid search, time series collections, and change streams enable agents to perform semantic retrieval, keyword search, and real-time stream processing. A multi-agent workflow includes a monitoring agent that detects anomalies, a diagnostics agent that retrieves historical patterns, a planning agent that drafts work orders with human-in-the-loop review, and a scheduling agent that checks production calendars and inventory, all on a single on-premise deployment with enterprise security and local embedding generation.


### [Welcoming Kiersti Esparza, our new Deliverability Engineer](https://yomu.fyi/post/welcoming-kiersti-esparza-our-new-deliverability-engineer.md)
- Company: [Resend](https://yomu.fyi/company/resend.md)
- Published: Sep 3, 2026
- Type: Announcement

Resend introduces Kiersti Esparza as a Deliverability Engineer on its Trust & Safety Squad, where she will help scale sending infrastructure as the company grows. She brings nearly 20 years in email deliverability, including building teams at Marketo, consulting at Twilio, and deliverability engineering at Klaviyo. Esparza describes the work as a combination of interpreting datasets, understanding email technology, and adapting to evolving authentication in an ongoing contest between bad actors and receivers. She says Resend attracted her with developer-oriented infrastructure, hands-on foundational work, and a culture centered on transparency and partnership, while advising operators to master overlooked infrastructure because its scarcity creates leverage.


### [Build a Video Chat App with ASP.NET Core and Angular using Twilio Video](https://yomu.fyi/post/build-a-video-chat-app-with-asp-net-core-and-angular-using-twilio-vide.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Donal Toomey
- Published: Sep 3, 2026
- Type: Tutorial

This tutorial builds a small group video chat application with an ASP.NET Core Web API and Angular single-page app using Twilio Programmable Video. The API binds typed Twilio credentials, mints short-lived access tokens locally with the API Secret, reads active rooms through the Twilio REST API, and pushes room-list changes over SignalR instead of browser polling. The Angular client uses the twilio-video library to capture camera and microphone media, connect to rooms, render participants, and provide device selectors. Local development runs the API alongside ng serve, while a Release MSBuild target runs npm ci and npm run build so dotnet publish places the SPA in the API's wwwroot. The resulting local test demonstrates a two-participant call and a live active-room count that updates when someone joins or leaves.


### [4 must-ask questions before you delegate to your AI agent](https://yomu.fyi/post/4-must-ask-questions-before-you-delegate-to-your-ai-agent.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Sep 3, 2026
- Type: Problem & solution

Automation roadmaps that prioritize ticket volume and low complexity can improve containment while lowering customer satisfaction when customers did not want a task automated. The post proposes four screening questions: how reversible a mistake is, whether the task touches money, health, or identity, whether the customer needs a transaction or to be heard, and whether the AI can access the necessary context. It cites a Twilio survey of 7,652 consumers, including 80% comfortable with appointment or reservation scheduling and 60% unwilling to have AI handle a medical inquiry. Tasks that fail the test can be split between AI and people: AI gathers information, verifies identity, retrieves history, and drafts a recommendation before a human decides, with Twilio Conversation Orchestrator handing conversation history and profile to Twilio Flex. The recommendation is to start with tasks where errors are relatively cheap and the data is available.


### [Five Reasons Why Your Auth0 Permissions Are Not in your Access Token](https://yomu.fyi/post/five-reasons-why-your-auth0-permissions-are-not-in-your-access-token.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Carla Urrea Stabile
- Published: Sep 3, 2026
- Type: Explainer

Auth0 access tokens can lack a permissions claim despite apparently configured RBAC, and the post identifies five silent causes. The authorization request may omit the API audience, the API’s “Add Permissions in the Access Token” setting may be disabled, or a permission-bearing role may not be assigned to the user, including at the relevant organization level. Developers may also be decoding an ID token rather than the API access token, whose aud identifies the API. Client Credentials tokens represent applications rather than users and typically carry granted scopes instead of a permissions array. For custom Post-Login claims, the post advises using a namespaced claim rather than colliding with Auth0’s reserved permissions claim.


### [Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps](https://yomu.fyi/post/fine-tuning-a-350m-model-for-better-structured-outputs-in-100-grpo-ste.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Leonie Monigatti, ben burtenshaw, Sergio Paniego
- Published: Sep 3, 2026
- Type: Tutorial

This guide presents a low-cost recipe for improving structured-output compliance in Liquid AI’s LFM2.5-350M, a task where parseable, schema-adherent responses determine whether an LLM can feed downstream systems. It fine-tunes the model with Group Relative Policy Optimization (GRPO) through TRL, using about 500 samples, 100 steps, three weighted rewards for format, field count, and JSON Schema validation, and a LoRA adapter training roughly 6M parameters. Prompt augmentation covers fenced-code-block instructions and top-level-array tasks, while evaluation uses IFStruct with llama.cpp on an OpenAI-compatible local server. On 2,000 IFStruct samples, the BF16 base model passes 22.6%, rising to 29.7% after tuning. JSON improves from 18.0% to 31.9%, bare-list compliance from 16.6% to 29.7%, while YAML changes little.


### [Your Agent Speaks MCP. Give It a Computer.](https://yomu.fyi/post/your-agent-speaks-mcp-give-it-a-computer.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Sep 3, 2026
- Type: Announcement

Sprites are presented as disposable cloud computers for running coding agents and as tools agents can create for testing, debugging, benchmarking, and multi-service work. The post describes MCP as the transport and authorization layer, while progressive disclosure comes from plugins and skills that reveal capabilities only when needed; CLI, REST, and machine-readable /.sprite/llm.txt remain available. The hosted MCP server returns file reads as MCP resources and attaches safety annotations, marking read-only and destructive operations and flagging exec and service\_start as crossing the Sprite boundary. Agents connect through sprites.dev/mcp and authenticate to a specific Fly.io organization, with configurable scoping, a default five-Sprite cap, and an mcp- name prefix. The design emphasizes durable filesystems, real networks, and near-zero idle cost instead of stateless sandboxes, while the post argues these properties make disposable computers practical for agent workflows.


### [Give Your Coding Agents a Memory You Own](https://yomu.fyi/post/give-your-coding-agents-a-memory-you-own.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: David Corvoysier
- Published: Sep 3, 2026
- Type: Announcement

funes is presented as a durable memory layer for coding agents that turns existing session traces into searchable, provenance-backed working memory across machines and agent types. A single binary adds recall and get tools to Claude Code, Codex, pi, or Hermes, builds an incremental local index, and automatically indexes completed turns. Its deterministic pipeline normalizes traces, chunks them, embeds them with a pinned local model, stores them in Lance, and combines vector and BM25 search with rank fusion, cross-encoder reranking, recency weighting, and neighboring chunks. Memories can remain local or be bound to a private Hugging Face dataset owned by the user, with credential redaction, secret scanning, local caching, and Hub-provided access control and versioning. In the handoff-vs-recall benchmark, recall was the cheapest option on both tasks, costing 8x less than a written handoff on one and 4x less on the other, while compaction succeeded on only one task.


### [Training a coding model to paint watercolours with TRL and OpenEnv](https://yomu.fyi/post/training-a-coding-model-to-paint-watercolours-with-trl-and-openenv.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Sergio Paniego
- Published: Sep 3, 2026
- Type: Explainer

The project reproduces an earlier idea: training a language model to write JavaScript that paints watercolours with p5.brush, using TRL and OpenEnv. Its open pipeline includes a hand-rated reference pool, an RL environment, training scripts, trained models, and Hugging Face Jobs, Spaces, Inference Providers, and Hub artifacts. The environment renders sketches in headless Chromium, restricts the model to ten library methods, rejects cheats through a gate, and combines gate, length, pairwise-judge, and HPSv3 rewards. Three reward mixes were trained: judge-led (0.60/0.30), hps-led (0.30/0.60), and hps-only (0.00/0.90); the first two ran to step 110, while validation stopped at step 60. The post reports that both pairwise-judge runs learned and identifies the hand-rated pool as the bottleneck.


### [Southern Company’s SCOUT: Completing the Storm Intelligence Story](https://yomu.fyi/post/southern-company-s-scout-completing-the-storm-intelligence-story.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Boyd, Blake Moore, Salome Abekhe
- Published: Sep 2, 2026
- Type: Explainer

Southern Company’s SCOUT is a real-time storm operations application designed to make outage intelligence accessible beyond control-center desks during active events. Built on the Databricks lakehouse, it combines Delta Lake storage, Databricks Lakehouse warehouses, Unity Catalog governance, and collaborative notebooks, querying a dedicated warehouse every minute for current operational data. The mobile-friendly application unifies outage, customer, GIS, weather, terrain, crew, and reliability information, helping teams assess restoration times, assign appropriately equipped crews, coordinate mutual assistance, and communicate with customers. SCOUT is used across Southern Company’s operating businesses and had 1,139 adopted employees, including more than 250 users on a peak storm day; the source also describes exploration of AI crew recommendations and autonomous-drone integration, not a completed deployment.


### [Register Now for JetBrains GameDev Day 2026](https://yomu.fyi/post/register-now-for-jetbrains-gamedev-day-2026.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Anna Ruban
- Published: Sep 2, 2026
- Type: Announcement

JetBrains GameDev Day 2026 is scheduled for September 28 as a free online event for people building games, engines, tools, and production pipelines. The program covers Unity, Unreal Engine, Godot, proprietary engines, and engine-neutral infrastructure, with sessions on AI-assisted Figma-to-Unity integration, procedural content generation, ECS migration, cross-runtime C# and C++ debugging, Unreal performance profiling, and CI troubleshooting. The livestream starts at 10:00 am UTC, and recordings will be available on demand afterward; the full schedule and speaker details are still forthcoming. Watch parties are planned in Belgrade, alongside DevGAMM x FTW! attendee check-in, and at the JetBrains office in Limassol, with separate local timings and registration requirements.


### [Expanding Genie Agents: Deep analysis, file reasoning, and more](https://yomu.fyi/post/expanding-genie-agents-deep-analysis-file-reasoning-and-more.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hanlin Sun, Sydney Sundell
- Published: Sep 2, 2026
- Type: Announcement

Genie Agents updates expand expert-curated business agents beyond quick factual lookups to deeper analysis across structured and unstructured data. Agent mode can create and refine research plans, issue iterative queries, and return reports with findings, visualizations, and citations; Agent mode APIs extend this workflow to custom applications, chatbots, scheduled reports, and internal tools through streaming Server-Sent Events. Agents can analyze PDFs, documents, slide decks, and images in Unity Catalog volumes alongside governed tables, with permissions and content search indexing applied to retrieval. Genie Code helps authors create baseline agents, diagnose conversation or benchmark failures, and identify knowledge gaps from user feedback, while authors retain review control over proposed changes. Shared chats keep follow-up messages and edited visualizations visible to authorized users, supporting collaboration and agent-quality review.


### [Developing Enterprise Frontier Safeguards with our customers](https://yomu.fyi/post/developing-enterprise-frontier-safeguards-with-our-customers.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 2, 2026
- Type: Announcement

Anthropic is announcing Enterprise Frontier Safeguards (EFS), designed to combine zero data retention (ZDR) with monitoring for serious misuse of frontier models. The service addresses enterprise concerns that detecting activity such as fraud, cyberattacks, or biological capability development, or stolen credentials, may require retaining and correlating traffic across sessions and accounts, while regulated organizations may not accept provider-held data or human review. EFS stores activity data in customer-controlled cloud infrastructure, under customer-managed encryption keys, access policies, and audit logging, while automated systems analyze a rolling traffic window and send flags directly to the customer for review. It will roll out in phases later this fall, support Anthropic and partner cloud offerings, and let customers opt into storage, encryption keys, and automated review without changing model behavior, pricing, or rate limits.


### [How we make AI coding more cost efficient without sacrificing task quality](https://yomu.fyi/post/how-we-make-ai-coding-more-cost-efficient-without-sacrificing-task-qua.md)
- Company: [Github](https://yomu.fyi/company/github.md)
- Author: Erik Kristensen
- Published: Sep 2, 2026
- Type: Explainer

GitHub Copilot’s AI coding efficiency work focuses on completed task cost rather than tokens in individual tool calls, because shorter responses can trigger recovery work. Using agentic coding benchmarks and controlled online experiments, the team evaluated selective output compression, removal of unused line-number prefixes, prompt shortening, and direct delivery of completed background results. The shipped compressor preserves source-like and arbitrary output, reorganizes search results without dropping content, and compresses repetitive install, build, test, and progress output while retaining a recovery path to originals. Removing line-number prefixes cut offline model-inference cost roughly 5% and online daily cost about 3%; direct background-result delivery reduced AI Credit usage about 2.3%, while prompt changes required workload-specific validation.


### [The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough](https://yomu.fyi/post/the-modern-cuda-toolbox-in-practice-a-step-by-step-optimization-walkth.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Sep 2, 2026
- Type: Tutorial

An image-processing pipeline serves as the test case for a modern CUDA toolbox, starting with RGB-to-grayscale conversion, 32-by-32 tile median computation, and host/device transfers. It advances through six incremental changes: adopting CCCL and Compute Sanitizer for indexing bugs, adding NVTX for Nsight Systems benchmarks, replacing hand-rolled work with CUB algorithms, using pooled device containers and pinned host containers, and assigning each image its own stream with asynchronous copies. The implementation uses cuda::stream, cuda::copy\_bytes, cuda::device\_buffer, and CUB calls on per-thread streams, allowing kernels and transfers to overlap instead of executing sequentially on the default stream. The reported total duration falls from 6.8 seconds to about 23 milliseconds for three images, a roughly 300x improvement, while the post characterizes the code as safer and easier to maintain without low-level optimizations.


### [Uber Pro: rewards that go the extra mile, like you](https://yomu.fyi/post/uber-pro-rewards-that-go-the-extra-mile-like-you.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Sep 2, 2026
- Type: Announcement

Uber is rolling out a redesigned Uber Pro driver rewards program that combines Uber Pro with Advantage Mode in markets where Advantage Mode operates. Drivers earn points by accepting trips, cancelling less, completing safe rides, and advancing through statuses, while lower Gold and Platinum thresholds and reduced Gold requirements add flexibility. Gold includes a 5% Pro Perk on eligible non-premium rides, Exclusive trip requests, and Trip Radar Advantage; Platinum adds Priority Reserve and a third Destination Mode session, and Diamond offers a renewable free three-month Uber One membership. Existing rewards, including Costco Gold Star membership, ASU tuition coverage, and Upside gas discounts, remain available, while eligibility and exclusions vary by ride type, status, market, and program terms.


### [Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference](https://yomu.fyi/post/co-designing-ai-models-using-speculative-decoding-for-faster-llm-infer.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Published: Sep 2, 2026
- Type: Explainer

Speculative decoding accelerates large language model inference by having a smaller draft model propose multiple tokens and a target model verify them in parallel, preserving the standard output sequence under its acceptance criteria. It reduces decoding iterations and increases target-model arithmetic intensity, but speedup depends on acceptance length, verification time, draft latency, batch size, and hardware behavior. For attention-heavy workloads, the recommended starting point is D = 128/G - 1, where G is query heads per KV head; larger D values should align G × (1 + D) with 128-token kernel tiles. The remaining guidance covers compute-bound GEMMs, low-latency draft-cost tradeoffs, and choosing among external models, EAGLE-3, MTP, DFlash, DSpark, and suffix or n-gram methods; SPEED-Bench, TensorRT LLM, and NVIDIA/Model-Optimizer support measurement, training, and quantization workflows.


### [TeamCity 2026.2: Pipelines General Availability, BYOK for AI Assistant, and More](https://yomu.fyi/post/teamcity-2026-2-pipelines-general-availability-byok-for-ai-assistant-a.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Dmitrii Korovin
- Published: Sep 2, 2026
- Type: Announcement

TeamCity 2026.2 makes pipelines generally available on TeamCity Cloud and On-Premises, moving them beyond the Early Access Program and adding branch-aware editing, failure-tolerant job execution, promotion, job debugging, and VCS-unbound workflows. The release also expands AI capabilities: AI Assistant supports Bring Your Own Key for Anthropic, OpenAI, Google Gemini, other supported models, or a local LLM, while MCP adds pipeline management tools and OAuth/PKCE authorization. Additional changes include configurable retries for failed dependencies, IDEA plugin improvements, GitHub pull-request matching by source branch, and a choice of server- or agent-side Kotlin DSL compilation. The pipeline MCP post and delete tools require manually enabled Brave mode, and DSL compilation on agents is described as less restricting and more secure but subject to trade-offs. Bug-fix updates 2025.11.8 and 2026.1.4 also ship.


### [The MPS 2026.2 Early Access Program Has Started](https://yomu.fyi/post/the-mps-2026-2-early-access-program-has-started.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Vaclav Pech
- Published: Sep 2, 2026
- Type: Announcement

MPS 2026.2 EAP1 introduces improvements to editor test reporting, usage-result grouping, node selection, and startup configuration, alongside bug fixes. Editor tests can now use node-matching rules or per-node markers to ignore irrelevant containment roles, properties, references, or other fixture differences, while failures identify the responsible node and cell with presentation, concept name, and node ID. Find Usages can group results by virtual package, including one tree level per segment of dotted names, and this option is enabled by default. Select Up now treats lists of sibling nodes as an intermediate editing unit, supporting more predictable repeated selection and group copy-paste. Build tooling separates the IntelliJ Platform-provided boot classpath from MPS-specific extensions, exposes additional JVM arguments, and applies shared and operating-system-specific --add-opens options more precisely, reducing launcher maintenance and stale configuration risks.


[Newer posts](https://yomu.fyi/posts/page/57.md) · [Older posts](https://yomu.fyi/posts/page/59.md)