---
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

### [Replacing the HDB: ClickHouse for historical ticker data](https://yomu.fyi/post/replacing-the-hdb-clickhouse-for-historical-ticker-data.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 15, 2026
- Type: Benchmark

Historical market-data systems separate a durable archive from the real-time trading path, but the archive still must support research, backtesting, transaction-cost analysis, and regulatory reconstruction. The worked example uses Binance’s public archive to load trades and quotes for five USD-M perpetual-futures symbols into ClickHouse, with a MergeTree table ordered by (symbol, ts) and codecs including DoubleDelta and ZSTD. SQL queries calculate VWAP, hourly OHLC candles, and as-of joins, while ClickHouse’s sparse primary index skips most granules for symbol-and-time filters. Across ten months, 16.2 billion rows compress to 76 GiB on disk; the post reports a ClickHouse Cloud cost of $1.88 per month and argues that typical symbol-and-time queries retain low latency at that scale, while broader scans read more data.


### [Building Faster Cryptography with Carryless Multiplication in NVIDIA CUDA 13.3](https://yomu.fyi/post/building-faster-cryptography-with-carryless-multiplication-in-nvidia-c.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jul 15, 2026
- Type: Announcement

NVIDIA CUDA 13.3 introduces clmad, a native PTX carryless multiply-accumulate instruction for Ampere and newer GPU architectures. Prior to this release, GPUs lacked dedicated hardware for carryless multiplication, requiring developers to emulate binary extension field arithmetic with ALU operations or bitsliced circuits. The new primitive performs 64-bit by 64-bit carryless multiplication into 128-bit results with accumulation, allowing full 128-bit field multiplications via Karatsuba in six instructions. Benchmarks demonstrate that clmad achieves up to 6.3 TB/s throughput for GHASH on the NVIDIA B200, representing an 18.8x improvement over bitslicing. Additionally, sum-check protocol latency in zero-knowledge proving workloads decreases by up to 13x on the B200 and 4x on the RTX 5090.


### [AI-Enabled Advisory Services for Higher Education](https://yomu.fyi/post/ai-enabled-advisory-services-for-higher-education.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Chad Ammirati, Zach Langford, Nicole Wong
- Published: Jul 15, 2026
- Type: Problem & solution

Higher-education call centers face costly, limited-coverage monitoring of advisor conversations and brittle, slow methods for identifying student concerns from transcripts. The proposed workflow deploys OpenAI Whisper on Databricks Model Serving, applies AI Functions for sentiment, topics, intent, and rubric scoring, and uses Unity Catalog to govern the resulting data. For advisor quality, an LLM-as-a-judge evaluates every transcript against a reference-table rubric, returns a weighted 1–5 overall score and per-criterion scores, and routes flagged calls for targeted QA review instead of random sampling. For student insights, quarterly transcript enrichment feeds an Agent Bricks Knowledge Assistant for cited reasoning over raw calls and a Genie Space for structured trend queries, while LangGraph orchestrates UC SQL functions as tools. Together, these components let non-technical advisors, mentors, and QA managers query student interactions without reaching out to a data SME.


### [Data-Native AI Agents: Why Agents Must Move to Your Data](https://yomu.fyi/post/data-native-ai-agents-why-agents-must-move-to-your-data.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kaan Kuguoglu, John Karlsson
- Published: Jul 15, 2026
- Type: Explainer

Enterprise AI pilots often move data into separate vector databases, SaaS LLMs, or serving layers, creating governance gaps, compounded latency, fragmented costs and observability, and duplicated lifecycle work. The post advocates data-native agents: models, agents, tools, retrieval, and memory run inside the governed data platform, with policy enforced during query planning and computation rather than after responses are produced. It argues that post-hoc controls cannot undo sensitive information encoded in aggregations and can trigger token-burning retry loops. For state and memory, it presents Lakebase, managed PostgreSQL within Databricks, as transactional storage and a shared source of truth for multi-agent swarms. The described platform pattern combines Unity Catalog, Unity AI Gateway, Model Serving, MLflow 3, AI Search, Lakebase, and business-context services, and recommends inventorying workloads already outside the perimeter before closing seams incrementally.


### [Model Routing Is Simple. Until It Isn’t.](https://yomu.fyi/post/model-routing-is-simple-until-it-isn-t.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Yara Rizk, Eyal Shnarch, Jason Tsay, Merve Unuvar
- Published: Jul 15, 2026
- Type: Explainer

Model routing in agentic systems is presented as a systems optimization problem rather than a simple classification task. On 417 AppWorld Test Challenge tasks with the same CodeAct agent, Claude Sonnet 4.6 cost $79 total ($0.19 per task), versus $155 ($0.37 per task) for GPT-4.1, despite GPT-4.1 having lower listed token prices; cache-read pricing and repeated context changed the effective result. The discussion also shows why task difficulty and model speed are incomplete signals: hidden execution work, specialization, governance constraints, routing overhead, cache state, hardware, and endpoint load all affect decisions. The described router optimizes cost, quality, and latency together, producing configurable cost-accuracy operating points; one latency-oriented configuration reached 84% accuracy for $93 and 83 seconds, while adding about 6 ms and 2 kB per task.


### [Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization](https://yomu.fyi/post/exploring-hierarchical-interest-representation-for-meta-ads-deep-funne.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Yuhui Ouyang, Di Wang, Sreedal Menon, Jie Tian
- Published: Jul 15, 2026
- Type: Explainer

Hierarchical Interest Representation is an upstream representation layer for Meta Ads that addresses sparse deep-funnel feedback by connecting users’ inferred interests with advertisers, products, services, and other ads entities. It models a typed, heterogeneous engagement graph with a transformer architecture, sparse attention for long-range relationships, bias-aware attention, self-supervised cross-view distillation, and multimodal content features processed through vision or language models. The system projects the raw graph into learned interest primitives and cascading super-graphs, producing universal embeddings and Bag-of-Meaning tokens at multiple granularities. Temporal cutoffs and chronological splits prevent future-information leakage, while a pipelined online graph engine reportedly achieved a 30x wall-clock speedup over a synchronous baseline during testing. Trained end-to-end on billions of interactions, the representations are designed for personalization, retrieval, supervision, and ranking integrations including GEM, Andromeda, and the Adaptive Ranking Model.


### [Anthropic commits $10 million to Canadian AI research](https://yomu.fyi/post/anthropic-commits-10-million-to-canadian-ai-research.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 15, 2026
- Type: Announcement

Anthropic is committing $10 million CAD to Canadian research institutions to support beneficial and responsible AI applications, building on Canada’s contributions to neural networks, deep learning, and reinforcement learning. The announcement names partnerships with Amii, Mila, Vector, CHEO, CAMH, Université Laval, University of Toronto, and the University of Saskatchewan, with Claude credits supporting work spanning trust and safety, health, scientific discovery, mental health, robotics, sustainability, and public services. Amii, Mila, and Vector will also join Anthropic’s for Startups program, providing affiliated Canadian startups with at least $5,000 USD each in API credits. A Canadian country brief based on the March 2026 Anthropic Economic Index reports that Canada ranks eighth in Claude.ai use, accounts for 2.6% of global consumer use, and ranks second by per-capita adoption; usage is highest in British Columbia and closely follows professional, scientific, and technical employment.


### [Expanding the Uber Transit Innovation Fund: meet our next cohort of recipients](https://yomu.fyi/post/expanding-the-uber-transit-innovation-fund-meet-our-next-cohort-of-rec.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jul 15, 2026
- Type: Announcement

The Uber Transit Innovation Fund responds to reduced transit funding by supporting public agencies and partners pursuing projects that keep communities moving. Launched with a $1 million commitment, the fund initially offered up to $50,000 to each of as many as 20 forward-thinking US transit agencies, municipalities, and regional authorities. Strong interest from US and Canadian agencies led to an expanded investment and eligibility, following an initial cohort of nine winners. The additional 26 recipients bring total awarded funding to $1.7 million, or $1,705,000, with projects spanning microtransit in Oregon, food access in Richmond, Virginia, and transportation gaps across urban, suburban, and semi-rural communities. Additional funding and recipients are scheduled to be announced by the end of the month.


### [Inkling by Thinking Machines now available on Modal](https://yomu.fyi/post/inkling-by-thinking-machines-now-available-on-modal.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Jul 15, 2026
- Type: Announcement

Thinking Machines released Inkling, a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. The 975B-parameter mixture-of-experts transformer activates 41B parameters per token, uses a 1M-token context window, and employs local attention: five sliding window layers for every global layer. Modal provided day-zero support with a custom DFlash block-diffusion speculator, achieving 250 tokens per second per user on 8x B200s with 67% faster interactivity than the model’s built-in multi-token prediction path. The DFlash drafter uses all local attention and causal layers, keeping cost flat as block size grows. Inkling is available today as an OpenAI-compatible shared endpoint through Modal Auto Endpoints with token-based pricing, allowing users to try the model interactively.


### [Together AI brings Thinking Machines Lab’s new model Inkling on day 0](https://yomu.fyi/post/together-ai-brings-thinking-machines-lab-s-new-model-inkling-on-day-0.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Jul 15, 2026
- Type: Announcement

Thinking Machines Lab released Inkling, and Together AI announced day-zero access through its production inference platform. The multimodal mixture-of-experts model accepts text, image, and audio inputs and generates text through a unified decoder architecture, with controllable inference effort for trading reasoning depth, token usage, and latency. Inkling has 975B total parameters, 40B active parameters per token, a 1M-token context window, query-conditioned relative attention, short causal convolutions, and shared-sink MoE routing; Together serves it with a FlashAttention-4-based kernel. Preliminary evaluations report strong results across scientific reasoning, mathematics, coding, agentic, vision, and audio benchmarks, while the model is also post-trained for forecasting and calibrated prediction. It is available on Together Serverless through OpenAI-compatible APIs without infrastructure provisioning.


### [New in Together GPU Clusters: Reliability and control for production GPU clusters](https://yomu.fyi/post/new-in-together-gpu-clusters-reliability-and-control-for-production-gp.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Jul 15, 2026
- Type: Announcement

Together GPU Clusters introduces changes for production training and inference workloads, addressing hardware failures, scheduler problems, and the limits of single-admin access. Platform health improvements include continuously running passive health checks, human-approved node repair recommendations, and Slurm-on-Kubernetes 2.0, which adds self-healing daemons, durable job accounting, descendant-process cleanup, and refreshed GPU state after reschedules. Operational controls include a cluster details view with health, utilization, events, configuration, and node actions, plus external OIDC, startup scripts, and an acceptance-test opt-out. Newly provisioned Slurm clusters use the latest stack by default, while existing managed clusters can be migrated during a maintenance window at no cost; the stated result is faster recovery, fewer support tickets, and greater visibility and control for growing teams running large clusters.


### [Welcome Inkling by Thinking Machines](https://yomu.fyi/post/welcome-inkling-by-thinking-machines.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw, merve, Pedro Cuenca, Aritra Roy Gosthipaty, Andres Marafioti
- Published: Jul 15, 2026
- Type: Announcement

Thinking Machines Lab has released Inkling and the smaller Inkling-Small family as open multimodal models that accept image, text, and audio inputs. Inkling is described as a roughly 1T-parameter decoder-only Mixture-of-Experts model, with 975B total and 41B active parameters, a 1M-token context window, and training on 45 trillion multimodal tokens. Its architecture combines relative attention, alternating global and sliding-window attention, short convolution, shared experts, hierarchical image patchification, and discretized mel-spectrogram audio processing. The release includes BF16 and NVFP4 checkpoints, speculative MTP layers, day-zero support in Transformers, SGLang, vLLM, and llama.cpp, plus deployment configurations and interactive voice and image demos. Reported benchmarks show Inkling and Inkling-Small achieving strong but varied results across reasoning, coding, vision, audio, factuality, chat, and safety evaluations.


### [Introducing Real World VoiceEQ: Measuring the human quality of voice AI](https://yomu.fyi/post/introducing-real-world-voiceeq-measuring-the-human-quality-of-voice-ai.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: David Ayllon, Alice, Jeff Brooks, Franc Camps Febrer, Jakub Piotr Cłapa, Theo Lebryk, Jens Madsen, Olya Ossipova, Sharath Rao, Hoon Shin, Tigran, Rashish Tandon, Panagiotis Tzirakis
- Published: Jul 15, 2026
- Type: Announcement

Existing voice-AI benchmarks increasingly suggest near-human performance, while real-world conversations still expose failures involving speaker consistency, hesitation, accents, noise, emotion, and natural responses. Real World VoiceEQ measures human quality in voice interaction by testing whether systems recognize, produce, and respond to acoustic information omitted by transcripts, including tone, emotion, speaker identity, and background context. It evaluates more than 40 proprietary and open-source models across 15+ dimensions and 60+ metrics covering ASR, TTS, S2S, and Speech Understanding, using more than 1 million human ratings, including 785,000 TTS and 48,000 STS ratings. Findings indicate that capabilities are specialized, speech-to-speech systems often underuse paralinguistic cues, and traditional benchmarks can overestimate performance in noise and other conversational conditions. The research also reports that human agreement remains essential for subjective acoustic and social judgments because SLM evaluators were less reliable outside clear, verifiable tasks.


### [How Bullet uses ClickHouse Cloud to give DeFi's fastest exchange real-time analytics](https://yomu.fyi/post/how-bullet-uses-clickhouse-cloud-to-give-defi-s-fastest-exchange-real.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 15, 2026
- Type: Problem & solution

Bullet operates a DeFi exchange for perpetuals, spot trading, and lending, with trades settled on a blockchain while users retain custody of their assets. Its sub-millisecond trading system produces more than 150 million event rows per hour, creating a need for historical indexing, real-time analytics, and operational monitoring beyond the blockchain’s state-machine model. ClickHouse Cloud consumes events from Kafka, stores them in dedicated tables, and supports user features, leaderboard and reward calculations, P&L charts, audit logs, and Grafana-backed alerts. Replacing Databricks reduced query latency from 10–15 seconds to milliseconds, improved data freshness from 1–2 hours to under 5 seconds, and removed a separate DynamoDB serving layer at comparable cost while handling 1,000 times more data across staging, testnet, and mainnet.


### [Take insights anywhere with Genie One on mobile](https://yomu.fyi/post/take-insights-anywhere-with-genie-one-on-mobile.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mohit Hingorani, Christine Li, Rhetta Nadas, Sydney Sundell
- Published: Jul 14, 2026
- Type: Announcement

Genie One mobile apps for iOS and Android let business users ask questions of company data, view dashboards, access Databricks Apps, and use conversational agent capabilities away from a desk. Answers draw on Genie Ontology, Genie Agents, enterprise governance, and connectors to Google Drive, Microsoft 365, and Atlassian, while respecting source permissions. AI/BI Dashboards reflow widgets into a single column in portrait mode and preserve their designed layout in landscape. Authentication uses the existing OAuth flow and identity provider, with MFA, conditional access, device posture, network controls, regional data handling, and workspace permissions carried over from the browser; there is no separate mobile backend or mobile-only endpoint. The app is available in Public Preview, with dark mode, push notifications, voice mode, and account-level access listed as planned capabilities.


### [@clickhouse/rowbinary: when your library is also a parser compiler](https://yomu.fyi/post/clickhouse-rowbinary-when-your-library-is-also-a-parser-compiler.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 14, 2026
- Type: Explainer

ClickHouse's @clickhouse/rowbinary is a Node.js reader and writer for the RowBinary, RowBinaryWithNames, and RowBinaryWithNamesAndTypes formats, offering both a generic parser and an accompanying Agent Skill. RowBinary avoids per-row overhead but is difficult to consume in JavaScript because nested types, LEB128 varints, precision-sensitive DateTime64 values, and recursive self-describing types require extensive runtime dispatch; JSON alternatives can also round large UInt64 values. The package provides small, composable, monomorphizable reading primitives, while SKILL.md instructs a coding agent to assemble query-specific parsers with inlined operations and selected policies such as BigInt handling and array materialization. Generated parsers run 1.5–3.4x faster than composing library functions in loops, with a fixed-width benchmark parser reaching 3.41x faster, and produce reviewable source that can be tested and benchmarked like ordinary application code.


### [Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning](https://yomu.fyi/post/lessons-from-the-leaderboard-what-5-000-kagglers-taught-us-about-impro.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Jul 14, 2026
- Type: Explainer

The NVIDIA Nemotron Model Reasoning Challenge on Kaggle tasked over 5,000 participants with improving reasoning accuracy on Nemotron-3-Nano-30B using restricted LoRA adapters on Google Cloud G4 VMs. Top-performing competitors approached reasoning as an end-to-end engineering workflow by auditing synthetic chain-of-thought traces with programmatic solvers before training. Successful teams also compressed reasoning representations into compact signatures and bit-manipulation formats to prevent long chains from exceeding runtime token generation limits. Additionally, leading approaches separated precomputed reusable structures from live computation and conducted granular per-category validation to monitor regression and non-determinism. These results show that reasoning improvements stem from verifiable training data, compact context encoding, and structured validation rather than unconstrained data scaling.


### [From weeks to a day: how we made LLM evaluation fast enough to iterate on](https://yomu.fyi/post/from-weeks-to-a-day-how-we-made-llm-evaluation-fast-enough-to-iterate.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: Baharak Saberidokht
- Published: Jul 14, 2026

Airbnb built a four-layer LLM infrastructure framework to reduce iteration and evaluation turnaround from weeks to a single day. By using per-sample caching for generated references and judge scores, they established a deterministic evaluation foundation that separates genuine model drift from measurement noise. This setup enables rapid, bounded model hotfixes using micro LoRA adapters and comprehensive end-to-end validation across component boundaries.


### [Shipyard: How We Built Slack’s Next-Generation EC2 Platform](https://yomu.fyi/post/shipyard-how-we-built-slack-s-next-generation-ec2-platform.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Archie Gunasekara
- Published: Jul 14, 2026
- Type: Problem & solution

Slack developed Shipyard to modernize its EC2 infrastructure by replacing continuously updated, mutable instances with immutable deployable artifacts. The platform adopts a layered image architecture starting with a standardized base image called slack-zero, which is built using AWS Image Builder and consumed by downstream service-specific machine images. Rather than running periodic Chef jobs in the background, configuration management is restricted to image baking and initial instance provisioning phases. Fleet management is handled through the Peekaboo inventory system, metric-driven progressive rollouts via Gondola, and automated instance rotation managed by a lifecycle component known as the Reaper. Developers validate cookbook updates prior to merging using Ship Quick, which provisions real infrastructure across dedicated worker fleets to verify provisioning workflows.


### [How to Run an Autoresearch Workflow with RL Agent Skills and NVIDIA NeMo](https://yomu.fyi/post/how-to-run-an-autoresearch-workflow-with-rl-agent-skills-and-nvidia-ne.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Jul 14, 2026
- Type: Tutorial

Setting up reinforcement learning workflows and running repetitive experimentation often consumes significant engineering time before meaningful metrics emerge. To address this, an autonomous autoresearch workflow uses a frontier coding agent running Codex with GPT 5.5 to manage NVIDIA NeMo RL and NeMo Gym on an NVIDIA Brev GPU instance. The agent executes full-stack setup, creates a novel NeMo Gym visual counting environment, tunes Qwen3-VL-2B-Instruct accuracy from 25.0% to 96.9%, and implements an off-policy reinforcement learning algorithm directly from a research paper. Reusable skills, including brev-etiquette, session-memory, and autoresearch, enforce local conventions and prevent context drift during long executions. This setup offloads environment configuration, dependency resolution, and experiment tracking while preserving human oversight for goals and strategy.


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