---
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

### [Maximize AI Factory Energy Efficiency Through Full-Stack Inference and Training Optimizations](https://yomu.fyi/post/maximize-ai-factory-energy-efficiency-through-full-stack-inference-and.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Sachin Idgunji
- Published: Jun 23, 2026
- Type: Explainer

Power costs comprise up to 40% of AI factory operating expenses, making performance per watt critical within fixed site power limits. Operators maximize throughput by combining architectural enhancements, narrow-precision formats like NVFP4, and mixture-of-experts model designs that activate fewer parameters per token. During distributed LLM training in Megatron-LM, tuning individual GPU speeds along critical execution paths eliminates idle power waste without extending total training time. Additionally, the NVIDIA DSX platform orchestrates dynamic power allocation, 45°C direct-to-chip liquid cooling, and grid integration across racks and facilities. Together, these full-stack optimizations enable performance-optimized AI factories to deliver up to 2.6 times more tokens per second per megawatt.


### [Build Trust in Agentic AI: From POC to Production](https://yomu.fyi/post/build-trust-in-agentic-ai-from-poc-to-production.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Ken Wiebke
- Published: Jun 23, 2026
- Type: Explainer

Transitioning autonomous agentic AI systems from sandbox environments to enterprise production exposes a trust gap driven by hallucination, reasoning errors, and financial liability. To mitigate these risks, organizations can implement a proactive four-step framework covering foundations, verification, governance, and business outcomes. The architecture grounds agents using retrieval-augmented generation, short-term memory, long-term procedural memory, and observability traces consolidated on a unified data layer. Active verification computes an Agent Confidence Score alongside a deterministic Business Risk Score to produce an Agent Decision Score governing autonomy levels. Aggregated execution telemetry feeds business observability dashboards that monitor unit economics and enable analytical agents to recommend operational interventions.


### [Boost Inference Performance up to 15x on NVIDIA Blackwell Using DFlash Speculative Decoding](https://yomu.fyi/post/boost-inference-performance-up-to-15x-on-nvidia-blackwell-using-dflash.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Amr Elmeleegy
- Published: Jun 23, 2026
- Type: Benchmark

Sequential token generation in autoregressive LLMs limits GPU utilization and serving throughput, while conventional speculative decoding remains constrained by autoregressive draft models. DFlash addresses this bottleneck by employing a lightweight block-diffusion drafter that predicts an entire block of masked candidate tokens in a single forward pass. The architecture incorporates target hidden-state conditioning and key-value injection into draft model projections while relying on the target model for parallel verification. Evaluated on NVIDIA Blackwell GPUs across TensorRT-LLM, vLLM, and SGLang, DFlash increases gpt-oss-120b throughput by more than 15x at high interactivity targets and delivers up to 5.8x speedups on Gemma 4 31B compared to autoregressive decoding.


### [Your AI shipped a backend that boots. That is the whole problem.](https://yomu.fyi/post/your-ai-shipped-a-backend-that-boots-that-is-the-whole-problem.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Devlin Duldulao
- Published: Jun 23, 2026
- Type: Problem & solution

Backend security vulnerabilities often emerge from permissive default settings in web frameworks, a risk amplified when AI agents generate code focused solely on passing tests. Common failure points include unmetered request body parsing, unvalidated schemas, prototype pollution, and wildcard cross-origin resource sharing policies. To eliminate these foot-guns, the TypeScript framework DaloyJS enforces secure-by-default behavior across its entire routing and middleware model. It provides built-in request timeouts, streamed body size caps, prototype-stripping JSON revivers, and unified Zod schemas that simultaneously validate payloads and generate OpenAPI contracts. While secure defaults lower the risk of common infrastructure exploits, developers still remain responsible for application-level authorization and business logic correctness.


### [The 2026 Developer Survey is now open (for human developers only)!](https://yomu.fyi/post/the-2026-developer-survey-is-now-open-for-human-developers-only.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Ryan Donovan
- Published: Jun 23, 2026
- Type: Announcement

The 2026 Developer Survey is open to gather data on software builders' tools, working lives, and emerging practices. Marking fifteen years since its initial 2011 release, the annual survey focuses heavily on the role of artificial intelligence and autonomous agents across the software development lifecycle. Past survey results indicated that while developer adoption of AI and agents doubled, user trust declined alongside escalating concerns regarding generated code quality. In addition to modern AI trends and financial return considerations, the questionnaire maintains traditional inquiries regarding day-to-day technologies and developer workflows. The survey is hosted on the Qualtrics platform, which requires users to pause third-party ad blockers and adheres to regional access restrictions across multiple countries.


### [Build an AI Scientist for Life Science Discovery with NVIDIA BioNeMo Agent Toolkit](https://yomu.fyi/post/build-an-ai-scientist-for-life-science-discovery-with-nvidia-bionemo-a.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Kyle Tretina
- Published: Jun 23, 2026
- Type: Tutorial

General-purpose AI agents often struggle with biomolecular discovery because scientific workflows require precise model selection, strict input formatting, and artifact interpretation. The NVIDIA BioNeMo Agent Toolkit bridges this gap by providing BioNeMo Skills and Model Context Protocol wrappers on top of accelerated NVIDIA NIM microservices. These skills instruct agents on tool capabilities, required inputs, expected file formats like CIF and SMILES, and specific failure modes. Developers can route requests through hosted endpoints for testing or deploy local NIM microservices on GPU nodes to lower warm per-call latency during iterative candidate generation loops. Internal benchmarks using Codex CLI demonstrate that equipping agents with BioNeMo Skills improves task completion rates from 57.1% to 100% while doubling token efficiency across passing assertions.


### [Coinbase reduces time from idea to production by 90% with Cursor](https://yomu.fyi/post/coinbase-reduces-time-from-idea-to-production-by-90-with-cursor.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Jun 23, 2026
- Type: Explainer

Coinbase redesigned its engineering model around agent-first infrastructure rather than retrofitting AI into legacy processes, making time from idea to production its north-star metric. More than 2,400 developers use Cursor; engineers define intent, write requirements for agents, plan work with Plan Mode, delegate implementation, and validate results, often running 5–7 asynchronous agents in parallel. The company reports that some teams cut idea-to-production time from 20 days to less than two days, while first PR creation fell from eight days to under 30 minutes; 75% of PRs are agent-created, and average developers save seven hours of coding weekly. Coinbase also uses smaller working groups, internal champions, speedruns, and Superbuilders to spread these workflows and build tooling.


### [What's New in pg\_clickhouse v0.3.2: Postgres 19, TLS, Regex, and Memory](https://yomu.fyi/post/what-s-new-in-pg-clickhouse-v0-3-2-postgres-19-tls-regex-and-memory.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 23, 2026
- Type: Announcement

pg\_clickhouse v0.3.2 updates the Postgres interface for querying ClickHouse, with changes centered on PostgreSQL 19 Beta1 support, TLS configuration, regular-expression pushdown, and memory consumption. The release adds CREATE SERVER options for forcing TLS or plaintext, selecting a minimum TLS version, and enabling ClickHouse native-protocol compression. Revised pushdown logic maps Postgres regular-expression flags to ClickHouse behavior, while regexp\_match() can now be pushed down when its pattern has no capturing groups. Two memory issues were fixed: one involving unbuffered HTTP-driver queries and another involving repeated foreign scans such as parameterized inner scans in nested-loop joins. The minor update requires no reload, restart, or ALTER EXTENSION UPDATE, and also fixes empty-array ANY() errors on ClickHouse versions before 25.


### [Guide to Agentic Systems and AI Agents](https://yomu.fyi/post/guide-to-agentic-systems-and-ai-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 23, 2026
- Type: Explainer

Agentic AI systems are goal-directed software platforms that perceive context, plan and execute multi-step workflows, and adapt based on outcomes with minimal human intervention. The guide distinguishes them from traditional and generative AI, defining agents, broader system architecture, and the role of LLMs as reasoning cores connected to memory, APIs, databases, and other tools. It describes a perceive-reason-act-learn loop, multi-step planning, external tool integration through interfaces such as the Model Context Protocol (MCP), and orchestration patterns for coordinating specialized agents. Production concerns include retries, queues, observability, permissions, privacy, logging, and human escalation, while stated risks include reward-hacking, unintended actions, and explainability gaps. It identifies repetitive, data-rich workflows with clear success criteria and bounded error consequences as the best current enterprise candidates.


### [Top 10 AI Business Solutions Driving Company Growth](https://yomu.fyi/post/top-10-ai-business-solutions-driving-company-growth.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 23, 2026
- Type: Explainer

The article identifies ten AI business solution categories presented as growth drivers, while arguing that value is concentrated in workflows where AI changes the economics of work. It frames successful adoption around three conditions: clean, governed data; process-first use-case selection; and governance designed in from the start. Examples include customer-service agents, forecasting, personalization, intelligent process automation, and supply-chain optimization; the text says customer service accounts for 40% of top use cases, while data quality accounts for roughly 75% of what makes an AI solution work. It also describes productivity, automation, and business reimagination as distinct value paths, including a payments-data forecasting product that became an eight- to nine-figure annual revenue stream. The conclusion favors unified platforms that connect data, analytics, AI, governance, and agentic workflows.


### [End-to-End RAG Workflow: How Retrieval Augmented Generation Works](https://yomu.fyi/post/end-to-end-rag-workflow-how-retrieval-augmented-generation-works.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 23, 2026
- Type: Tutorial

Retrieval Augmented Generation (RAG) connects a large language model to external knowledge at inference time, addressing outdated training data and access to proprietary or real-time information. The workflow ingests and normalizes sources, splits documents into chunks, embeds them in a vector store, retrieves context, assembles a prompt, and generates an answer. Semantic search can be combined with BM25 keyword search through reciprocal rank fusion, while reranking can improve precision; the same embedding model must be used during ingestion and querying. The guide presents evaluation and deployment considerations, including separate measurement of retrieval precision and generation faithfulness, versioning, monitoring, and containerized components. It identifies poor retrieval as the most common failure mode and explains that RAG reduces, but does not eliminate, hallucinations.


### [What is Vector Search?](https://yomu.fyi/post/what-is-vector-search.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 23, 2026
- Type: Explainer

Vector search retrieves results by comparing embeddings that represent meaning across text, images, audio, and other content rather than matching exact words. A model creates embeddings, an index stores them for fast similarity search, and a query embedding is matched against the index using nearest-neighbor methods. Exhaustive k-nearest neighbor search can become too slow at millions of items, so production systems commonly use approximate nearest neighbor search, trading some precision for speed. The guide positions vector search behind semantic search, RAG, recommendations, and multimodal or cross-language retrieval, while hybrid search combines dense and sparse vectors, keyword results, metadata filtering, and reranking to improve reliability. Quality depends on embeddings, filters, index freshness, and infrastructure, with vector search requiring more memory and compute; Databricks AI Search is presented as a managed service supporting these capabilities and Unity Catalog governance.


### [Data Lake vs. Cloud Data Warehouse: A Practical Guide for Data Scientists](https://yomu.fyi/post/data-lake-vs-cloud-data-warehouse-a-practical-guide-for-data-scientist.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 23, 2026
- Type: Explainer

The guide contrasts data lakes and cloud data warehouses for storing and querying data at scale. Data lakes retain raw structured, semi-structured, and unstructured data in low-cost object storage with schema-on-read, while warehouses enforce schema-on-write for structured analytical workloads. Lakes fit petabyte-scale machine learning, data science, and undefined future use cases; warehouses fit fast, concurrent SQL for dashboards, reporting, and operational analytics. It describes Bronze, Silver, and Gold zones, with Parquet and ORC supporting columnar scans and open-format portability. For teams combining ML and BI, lakehouses use Delta Lake, Apache Iceberg, or Apache Hudi to add ACID transactions, schema enforcement, and quality monitoring to lake storage without duplication; catalogs, staged checks, and access controls help prevent data swamps.


### [How Telcos Build Autonomous Networks with Agentic AI](https://yomu.fyi/post/how-telcos-build-autonomous-networks-with-agentic-ai.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Amogh Dendukuri
- Published: Jun 23, 2026
- Type: Explainer

Telecom operators currently operate mostly within Levels 2 and 3 of the TM Forum autonomous networks taxonomy, relying on predefined scripts. Progressing to Levels 4 and 5 demands autonomous systems capable of intent comprehension, real-time telemetry sensing, and cross-domain action coordination. To address this, an autonomy platform architecture combines domain-specific reasoning models, agent harnesses, and isolated sandboxed runtimes. Workflows separate responsibilities among on-demand agents, long-running execution loops, and deep-research multi-agent systems that evaluate ranked remediation paths or design wireless algorithms. Initial experiments in simulated SR-MPLS routing and wireless physical or MAC layer design show autonomous discovery can deliver measurable gains over standard operational and engineering baselines.


### [Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix](https://yomu.fyi/post/toward-more-controllable-ai-video-editing-an-early-research-exploratio.md)
- Company: [Netflix](https://yomu.fyi/company/netflix.md)
- Author: Netflix Technology Blog
- Published: Jun 23, 2026
- Type: Announcement

Generative video editing tools often regenerate entire video clips, which inadvertently modifies untouched scene details or violates physical continuity during object deletion. To provide artists with precise control, two research prototypes were developed: Vera and VOID. Vera uses a layered video diffusion framework with a Mixture-of-Transformers architecture to jointly generate separate edit and alpha matte layers, compositing them with original footage while leaving unchanged pixels untouched. VOID addresses object deletion by conditioning on interaction-aware quadmasks and using a second-pass appearance refiner to reconstruct scenes with plausible physical continuity. In a user study evaluated across 75 real-world scenarios, reviewers selected VOID 64.8% of the time over six baselines.


### [Introducing Modal Auto Endpoints: Optimized inference you actually own](https://yomu.fyi/post/introducing-modal-auto-endpoints-optimized-inference-you-actually-own.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Jun 23, 2026
- Type: Announcement

Modal Auto Endpoints is a self-serve service for production-grade LLM inference that gives teams ownership of the entire serving stack, not just an API. Unlike proprietary providers, it exposes the inference code, engine flags, and GPU selection, and provides detailed metrics like speculative decoding acceptance length and per-replica token latency quantiles in a dashboard. Built on Modal’s infrastructure, it uses autoscaling to handle traffic spikes by spinning up additional replicas automatically, global GPU capacity without capacity management, and Modal Servers for ultra-low-latency routing with only 5ms overhead. For each supported model, it offers a pre-configured deployment using speculative decoding with the DFlash block-diffusion drafter from Z Lab, and teams can benchmark latency and throughput tradeoffs with a click after deployment. The platform is designed with a declarative interface based on workloads and SLOs, with future automation levels including autoinference, autospec, autodistill, and autoresearch for continuous improvement of inference performance.


### [ParallelKernelBench: Frontier LLMs can't write fast multi-GPU kernels (yet)](https://yomu.fyi/post/parallelkernelbench-frontier-llms-can-t-write-fast-multi-gpu-kernels-y.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Jun 23, 2026
- Type: Benchmark

ParallelKernelBench (PKB) measures whether language models can replace PyTorch + NCCL implementations with fast CUDA kernels for multi-GPU workloads, where communication can account for over 20% of inference latency. Its 87 problems come from real codebases and span tensor, context, data, expert, sequence, and FSDP/ZeRO parallelism, with hardware topology supplied and symmetric memory used for direct GPU communication over NVLink. In zero-shot evaluation, GPT-5.5 solved 28 problems and produced 22 faster-than-baseline solutions; sampling three attempts raised those figures to 36 and 27, but the best fast 1@3 result was only 31%. Models performed best on familiar collective primitives, tensor-parallel GEMMs, and Ulysses-style context parallelism, while failures often involved rank coordination, data partitioning, collective ordering, and underuse of TMA or NVLS.


### [Shipping huggingface\_hub every week with AI, open tools, and a human in the loop](https://yomu.fyi/post/shipping-huggingface-hub-every-week-with-ai-open-tools-and-a-human-in.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Lucain Pouget, Célina Hanouti
- Published: Jun 23, 2026
- Type: Problem & solution

huggingface\_hub, the Python client underlying libraries such as transformers, datasets, diffusers, and sentence-transformers, moved from releases every four to six weeks to a weekly cadence. A single manually triggered GitHub Actions workflow now computes versions, manages branches, tags and publishes both huggingface\_hub and the hf CLI, creates downstream RC test branches, and handles post-release tasks. An open-weights model drafts structured release notes and Slack announcements using PR metadata and documentation diffs, while OpenCode, HF Inference Providers, PyPI Trusted Publishing, and pinned, checksum-verified tooling provide the stack. Deterministic manifest validation checks missing or extra PR references, then a human reviews and publishes the drafts. The reported result is weekly shipping at about $0.25 per full release, with more consistent notes, earlier downstream breakage detection, and shorter contributor feedback loops.


### [Experimenting with the proposed Cross-Origin Storage API in Transformers.js](https://yomu.fyi/post/experimenting-with-the-proposed-cross-origin-storage-api-in-transforme.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Thomas Steiner
- Published: Jun 23, 2026
- Type: Tutorial

Transformers.js browser apps currently redownload and recache identical AI model and WebAssembly resources when they are requested from different origins. Chrome's cache uses a Network Isolation Key composed of the top-level site and current-frame site, so matching CDN URLs do not produce cache hits; the toy example incurs 177 MB of duplicate model storage, while a shared ONNX Runtime Wasm file is 4,733 kB. The proposed Cross-Origin Storage (COS) API addresses this by identifying large files with cryptographic hashes and sharing them across opted-in origins, with a polyfill extension enabling experimentation before native browser support. In the extended Transformers.js flow, a COS lookup can serve resources in milliseconds, while a miss downloads and stores them, and ModelRegistry.is\_pipeline\_cached() can help select among models already cached.


### [What's new in ClickStack - May 2026](https://yomu.fyi/post/what-s-new-in-clickstack-may-2026.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 23, 2026
- Type: Announcement

ClickStack’s May and early-June release focused on making observability dashboards more useful during investigations and expanding access to related workflows. Dashboard table actions can open searches against selected sources with templated WHERE clauses or link to other dashboards, while scoped filters prevent source-specific conditions from affecting unrelated tiles. Number visualizations gained fixed or threshold-based colors and automatic text scaling, and the service map added latency percentiles, throughput metrics, server-side filtering, and focus mode. The release also includes a Browser RUM dashboard template, experimental PromQL support through the ClickHouse TimeSeries Engine, expanded MCP tools, AI Notebooks beta for Managed ClickStack, and ClickStack Cloud in private preview.


[Newer posts](https://yomu.fyi/posts/page/106.md) · [Older posts](https://yomu.fyi/posts/page/108.md)