---
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

### [Introducing Falcon-H1-Arabic: Pushing the Boundaries of Arabic Language AI with Hybrid Architecture](https://yomu.fyi/post/introducing-falcon-h1-arabic-pushing-the-boundaries-of-arabic-language.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Basma Boussaha, Mohammed Alyafeai, Ahmed Alzubaidi, Leen AlQadi, Shaikha Alsuwaidi, Omar saif alkaabi, Hamza Alobeidli, Hakim Hacid
- Published: Jan 5, 2026
- Type: Announcement

The Technology Innovation Institute introduced Falcon-H1-Arabic, a family of Arabic language models released in 3B, 7B, and 34B parameter sizes. Built on a hybrid architecture, each block runs Mamba State Space Models and Transformer attention in parallel before fusing their representations at the output projection. Pre-training incorporated around 300 billion tokens across Arabic dialects, English, and multilingual content filtered via linguistic analysis. A post-training pipeline combining supervised fine-tuning and direct preference optimization addresses long-context utilization and alignment. Across evaluations on the Open Arabic LLM Leaderboard, the 34B model achieves roughly 75%, exceeding the performance of larger baseline systems like Llama-3.3-70B.


### [NVIDIA brings agents to life with DGX Spark and Reachy Mini](https://yomu.fyi/post/nvidia-brings-agents-to-life-with-dgx-spark-and-reachy-mini.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Jeff Boudier, Nader Khalil, Alec Fong
- Published: Jan 5, 2026
- Type: Tutorial

At CES 2026, NVIDIA presents a desk-based personal assistant that combines DGX Spark processing with a Reachy Mini robot, allowing an agent to converse, perceive camera input, speak, and perform actions. The tutorial assembles NVIDIA Nemotron 3 Nano for reasoning, NVIDIA Nemotron Nano 2 VL for vision, ElevenLabs for text-to-speech, and Reachy Mini hardware or simulation through the NeMo Agent Toolkit and Pipecat. Its configuration routes text, visual, and action requests to different models or a ReAct tool-calling agent, while the robot daemon exposes movement and state capabilities. The guide covers local, cloud, and serverless deployment, then starts the services needed for voice and vision interaction; the completed setup greets users and supports example text and camera prompts.


### [How Merge brought together all it knows using Notion AI](https://yomu.fyi/post/how-merge-brought-together-all-it-knows-using-notion-ai.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Shensi Ding
- Published: Dec 23, 2025
- Type: Problem & solution

Merge found that company knowledge was scattered across Slack, Google Docs, Salesforce, and other tools, causing repeated questions, slow onboarding, and time lost searching for context. It used Notion AI as a shared knowledge layer, connected Salesforce and Slack, and gave teams guidance on what content to add, including detailed project documents, meeting transcripts, and structured databases. Teams populated the workspace with content calendars, sales enablement materials, hiring plans, roadmaps, and an AI Prompt Library, while all-hands and team meetings demonstrated practical workflows. Merge reports that employees now ask Notion AI first, saving each team member several hours per week, helping teams make decisions with less manual searching, and making faster information retrieval one of the most-cited contributors to improved satisfaction in its latest company-wide survey.


### [AprielGuard: A Guardrail for Safety and Adversarial Robustness in Modern LLM Systems](https://yomu.fyi/post/aprielguard-a-guardrail-for-safety-and-adversarial-robustness-in-moder.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Jaykumar Kasundra
- Published: Dec 23, 2025
- Type: Announcement

Modern language model systems face security and safety threats spanning prompt injection, jailbreaks, and tool manipulation across long-context agentic workflows. To address these vulnerabilities, researchers introduced AprielGuard, an 8B parameter causal decoder-only transformer based on a downscaled Apriel-1.5 Thinker Base model. The model detects 16 safety risk categories alongside binary adversarial attacks across standalone prompts, multi-turn dialogues, and agentic workflows. Trained on synthetic datasets created with Mixtral-8x7B, NVIDIA NeMo Curator, and SyGra, AprielGuard operates in both explainable reasoning and fast non-reasoning modes. Evaluation shows high precision and recall on safety and adversarial benchmarks, though trade-offs exist between explainability latency and non-reasoning performance.


### [Demystifying user journeys: Revolutionizing troubleshooting with auto tracking](https://yomu.fyi/post/demystifying-user-journeys-revolutionizing-troubleshooting-with-auto-t.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Alex Prokofiev
- Published: Dec 23, 2025
- Type: Problem & solution

Troubleshooting critical issues in the Grab app was hindered by incomplete user-journey data, because manually instrumenting hundreds of UI components left interaction gaps and delayed diagnosis. AutoTrack is a new SDK that records application state, user interactions, and UI screens in real time, creating an end-to-end session chronicle from launch through navigation. It tracks lifecycle transitions and launch mechanisms such as deeplinks and push notifications, while UIViewController, Activity, and Fragment lifecycle events identify the displayed screen. Screen metadata includes the full hierarchy as JSON; after personal information is redacted for GDPR/PDPA compliance, that data is used to train an AI model that generates an HTML representation of the screen and interactions. AutoTrack supports reproducing elusive bugs, automated issue assignment, UI test generation, and analytics-trigger validation, with future work adding network and performance signals, privacy guardrails, and automated triage.


### [Steam, Steel, and Infinite Minds](https://yomu.fyi/post/steam-steel-and-infinite-minds.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Ivan Zhao
- Published: Dec 22, 2025
- Type: Explainer

The post frames AI as a new “miracle material” that could reshape knowledge work as steel and semiconductors reshaped earlier eras. It argues that coding agents already let one programmer orchestrate several AI coding agents, but broader knowledge work remains constrained by fragmented context across tools and weak verifiability. At the organisational level, it compares AI with steel and steam, suggesting agents could preserve workflow context, surface decisions, and reduce dependence on meetings and hierarchical approvals; Notion is described as using more than 700 agents alongside 1,000 employees for repetitive tasks. At economy scale, it predicts organisations spanning humans and thousands of agents, with continuous workflows and new rhythms, while acknowledging that this transition may be disorienting and requires redesign rather than simply adding chatbots to existing tools.


### [First Block – Tools & craft – Notion](https://yomu.fyi/post/first-block-tools-craft-notion.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Published: Dec 22, 2025
- Type: Announcement

First Block is a set of conversations with founders and company leaders across legal technology, finance, career access, software development, talent, go-to-market, and developer tools. The episodes feature Gabe Pereyra of Harvey, Gerry Giacomán Colyer of Clara, Garrett Lord of Handshake, Jesse Zhang of Decagon, Fabian Hedin of Lovable, Brendan Foody of Mercor, Varun Anand of Clay, and Michael Truell of Cursor. Topics include deploying AI agents in finance operations across Latin America, democratizing software development and career opportunities, intentional customer discovery, and building for current AI capabilities. The collection also covers founder-led selling, product-market fit, specialized go-to-market platforms, commercial thinking, startup pivots, and departures from conventional startup advice.


### [That’s a Wrap: MongoDB’s 2025 in Review & 2026 Predictions](https://yomu.fyi/post/that-s-a-wrap-mongodb-s-2025-in-review-2026-predictions.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Kevin O'Rourke
- Published: Dec 22, 2025
- Type: Announcement

MongoDB recapped its product releases, executive transitions, and enterprise adoption milestones throughout 2025 alongside predictions for 2026. The company acquired embedding and reranking specialist Voyage AI to mitigate large language model hallucinations and launched MongoDB AMP to accelerate legacy application modernization. Vector search and full-text search capabilities were expanded from MongoDB Atlas to Community Edition and Enterprise Server, enabling on-premises and hybrid retrieval-augmented generation workloads. Enterprise implementations included pharmaceutical distributor McKesson scaling tracking infrastructure to handle 1.2 billion containers annually after replacing its monolithic legacy system. Executive predictions for 2026 emphasized human-in-the-loop verification, grounding language models with factual retrieval, and building multi-cloud resilience against outages and data sovereignty constraints.


### [Hooks for security and platform teams](https://yomu.fyi/post/hooks-for-security-and-platform-teams.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Michael Feldstein, Michael Scherr & Travis McPeak
- Published: Dec 22, 2025
- Type: Announcement

Cursor has announced ecosystem partnerships around hooks, which run before or after defined stages of its agent loop to observe, block, or modify behavior. The integrations target MCP governance, code security, dependency scanning, agent safety, and secrets management, connecting Cursor with security tooling, observability platforms, secrets managers, and compliance systems. MintMCP, Oasis Security, and Runlayer provide MCP inventory, least-privilege enforcement, audit trails, and centralized tool-interaction visibility, while Corridor and Semgrep give coding-time feedback and vulnerability scanning. Endor Labs intercepts package installations to scan for malicious dependencies, Snyk’s Evo Agent Guard reviews agent actions for prompt injection and dangerous tool calls, and 1Password validates that all required environment files from 1Password Environments are properly mounted before shell commands execute, enabling just-in-time secrets access without writing credentials to disk.


### [Closing the Books Without the Spreadsheet Shuffle: My Fall Internship at Ramp](https://yomu.fyi/post/closing-the-books-without-the-spreadsheet-shuffle-my-fall-internship-a.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Timothy Kim
- Published: Dec 19, 2025
- Type: Explainer

During a fall internship at Ramp, the author worked on reducing repetitive accounting work around month-end close, focusing on amortization and ERP mapping workflows. One project uses receipt data, merchant category codes, customer history, and embedding similarity to detect likely prepaid expenses and suggest service periods and accounts or templates, with recommendations reviewable before application. A second built Ramp-Native Amortization to create schedules, generate periodic debit and credit journal entries, handle catch-ups and reclassifications, and sync entries to ERPs through a dashboard; transaction and reimbursement support was nearing alpha production release, while bill pay was next. The third generates high-confidence mapping-rule suggestions from customer coding patterns on a nightly schedule. Across the work, backtesting, accuracy metrics, and human workflow research support automation intended to earn trust rather than remove accountant judgment.


### [Token-count-based Batching: Faster, Cheaper Embedding Inference for Queries](https://yomu.fyi/post/token-count-based-batching-faster-cheaper-embedding-inference-for-quer.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Chengcheng Pei, Yuan Lin
- Published: Dec 18, 2025
- Type: Problem & solution

Serving embedding models for short search queries often suffers from poor GPU efficiency because traffic is spiky and memory-bound. Traditional time-window and request-count batching strategies lead to inconsistent GPU utilization, while tensor padding wastes compute on empty tokens. To resolve these bottlenecks, Voyage AI implemented token-count-based batching paired with padding removal in inference engines like vLLM. The architecture uses Redis with Lua scripts to atomically aggregate pending requests until reaching an optimal hardware saturation token threshold. Across production deployments, this approach achieved a 50% reduction in GPU inference latency with 3X fewer GPUs and improved throughput by up to 8×.


### [Make with Notion Showcase Europe 2025: Highlights & Takeaways](https://yomu.fyi/post/make-with-notion-showcase-europe-2025-highlights-takeaways.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Áine Dundas
- Published: Dec 18, 2025
- Type: Announcement

Make with Notion Showcase Europe 2025 brought more than 1,300 attendees across four cities during the second year of the in-person conference series. The event centered on three themes: agentic AI taking action, organized knowledge enabling effective AI, and a community building AI-powered work. Notion presented Notion 3.0, whose Agent can handle multi-step workflows, and previewed Custom Agents that can be shared across teams, scoped to databases or teamspaces, and triggered by schedules or events. With MCP integration, Notion content and connected app context can be accessed from other tools; Custom Agents are in early access and planned for broader availability in early 2026. Research cited losses of up to €16,000 per employee annually in the UK, France, and Germany from searching for scattered information, while customer sessions covered knowledge management, AI workflows, and Notion implementations.


### [How Grab is accelerating growth with real-time personalization using Customer Data Platform scenarios](https://yomu.fyi/post/how-grab-is-accelerating-growth-with-real-time-personalization-using-c.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Saubhagya Awaneesh
- Published: Dec 18, 2025
- Type: Problem & solution

Grab’s Customer Data Platform (CDP) introduced Scenarios to address the limits of daily batch attributes for real-time personalization across marketing and user journeys. The capability combines Scribe event triggers, geo-fencing, optional historical CDP data, and predictive models, with a self-serve GUI for configuring and validating event sequences and APIs for programmatic management. Incoming events are processed through Flink, with historical data evaluated in StarRocks and outputs written to Kafka, Amphawa, or both for downstream consumption; configured Scenarios become available within an hour and are designed for latency under 15 seconds. More than 12 implementations are live, including a Grab Unlimited abandonment intervention that notified users 15 minutes after signup drop-off and produced over a 3% uplift in subscriber conversions versus non-real-time acquisition campaigns. Planned work includes scalable Scenario sharding, multiple output destinations, and delayed triggering.


### [Tokenization in Transformers v5: Simpler, Clearer, and More Modular](https://yomu.fyi/post/tokenization-in-transformers-v5-simpler-clearer-and-more-modular.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Ita Zaporozhets, Aritra Roy Gosthipaty, Arthur Zucker, Sergio Paniego, merve, Pedro Cuenca
- Published: Dec 18, 2025
- Type: Explainer

Transformers v5 overhauls its tokenization framework by separating tokenizer architecture from trained vocabularies. In contrast to v4's dual slow Python and fast Rust files, v5 consolidates each model tokenizer into a single file defaulting to the Rust-backed TokenizersBackend. The pipeline stages—normalizer, pre-tokenizer, model algorithm such as BPE or Unigram, post-processor, and decoder—are now directly exposed and configurable rather than buried in serialized files. Practitioners can instantiate blank tokenizer architectures and train custom vocabularies directly from iterators using native methods like train\_new\_from\_iterator while retaining model-specific formatting rules. The wrapper layer continues to bridge raw tokenization and model requirements by managing chat templates, context limits, and special token insertion.


### [First Block with Garrett Lord, Co-founder and CEO of Handshake](https://yomu.fyi/post/first-block-with-garrett-lord-co-founder-and-ceo-of-handshake.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Anastasia Crew
- Published: Dec 17, 2025
- Type: Announcement

First Block features Garrett Lord, co-founder and CEO of Handshake, discussing the company’s path from his early rejection by hundreds of universities and periods living out of a Ford Focus to building a career platform serving 17 million users and nearly 1 million companies. He describes Handshake’s culture as “Olympic pace,” arguing that extreme clarity about standards is preferable to consensus because it helps the right people self-select and avoids diluting what makes the company special. The conversation also covers scaling through leadership, Handshake AI, and the future workforce. Lord’s central advice is to stack daily effort even when operating at 60% and to go all in on major opportunities, particularly as AI reshapes the economy.


### [The Open Evaluation Standard: Benchmarking NVIDIA Nemotron 3 Nano with NeMo Evaluator](https://yomu.fyi/post/the-open-evaluation-standard-benchmarking-nvidia-nemotron-3-nano-with.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Seph Mard, Isabel Hulseman, Besmira Nushi, Piotr Januszewski, Grzegorz Chlebus, VivienneZhang, Wojciech Prazuch, Pablo Ribalta, Nik Spirin, Ferenc Galko
- Published: Dec 17, 2025
- Type: Tutorial

Assessing whether large language model improvements stem from genuine advances or underspecified evaluation conditions remains a major challenge across the industry. Most published model evaluations omit critical execution parameters, prompt templates, harness versions, and runtime configurations. In response, NVIDIA released Nemotron 3 Nano 30B A3B alongside its complete, reproducible evaluation recipe built with the open-source NeMo Evaluator library. The library serves as an orchestration layer that unifies diverse benchmark harnesses under standard configurations while decoupling evaluation logic from underlying inference backends. Developers can execute the identical evaluation pipeline against local deployments or hosted endpoints using published YAML configurations and structured logging.


### [2026 customer service planning series: Vol. 05](https://yomu.fyi/post/2026-customer-service-planning-series-vol-05.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Dec 16, 2025
- Type: Explainer

AI-first customer service makes capacity planning less predictable because automation changes volume, work mix, human responsibilities, and performance over time. The proposed planning model puts automation rate—AI Agent involvement rate multiplied by AI Agent resolution rate—alongside inbound volume, human output for complex remaining work, and occupancy. It recommends ambitious automation assumptions only when backed by named ownership, content and systems investment, work-type targets, and staged improvement, while modeling lower cases closed per person and more time for off-queue work. Finance should treat the plan as a revisable set of assumptions, review it at least quarterly, and avoid cutting headcount before automation is proven. Surplus capacity should be redirected to AI improvement, product feedback, new channels, or proactive customer experience work rather than automatically reduced.


### [From Python3.8 to Python3.10: Our Journey Through a Memory Leak](https://yomu.fyi/post/from-python3-8-to-python3-10-our-journey-through-a-memory-leak.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Jay Patel
- Published: Dec 15, 2025
- Type: Incident / postmortem

During an initiative to upgrade Python services from version 3.8 to 3.10, Lyft engineers encountered severe latency spikes and timeouts in one service within their test environment. Profiling DynamoDB queries revealed that gevent thread joins were taking up to 30 seconds while pod memory consumption climbed steadily. To isolate the issue, engineers used an internal tracemalloc-based profiler triggered via SIGUSR2 signals, temporarily disabling Gunicorn preload to prevent workers from terminating prematurely on signal receipt. Memory traces pointed to botocore and an incompatibility between weakref.finalize and gevent monkey patching in urllib3 version 1.26.16, which prevented connections from returning to the pool. Downgrading urllib3 to version 1.26.15 immediately resolved both the timeouts and the memory leak before a permanent fix arrived in gevent and urllib3 updates.


### [CUGA on Hugging Face: Democratizing Configurable AI Agents](https://yomu.fyi/post/cuga-on-hugging-face-democratizing-configurable-ai-agents.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Jim Laredo, Avi Yaeli, Sami Marreed, Ayhan Sebin, Merve Unuvar
- Published: Dec 15, 2025
- Type: Announcement

Many existing AI agent frameworks suffer from brittleness, tool misuse, and failures when executing complex workflows. To address these limitations, the open-source Configurable Generalist Agent (CUGA) introduces structured orchestration that decomposes user goals into programmatic subtasks tracked by a dynamic task ledger. The framework integrates agentic patterns like planner-executor and code-act, delegating subtasks to specialized agents that generate pseudo-code before running execution in a secure sandbox. Released under the Apache 2.0 license, CUGA integrates with Langflow for low-code visual workflow assembly and supports multi-tool environments through OpenAPI specs, MCP servers, and LangChain. Testing on inference platforms like Groq with open models such as gpt-oss-120b demonstrates rapid response times during multi-step planning and validation.


### [How we made Notion available offline](https://yomu.fyi/post/how-we-made-notion-available-offline.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Raymond Xu
- Published: Dec 11, 2025
- Type: Explainer

Notion's Offline Mode required turning a best-effort SQLite cache into storage that could guarantee a page and all its dependent records were available without a network connection. The system uses a forest of offline page trees backed by offline\_page and offline\_action tables, allowing independent reasons such as explicit toggles, favorites, automatic downloads, and inheritance to coexist while preserving hierarchy. To keep content current, clients subscribe to push channels emitted by the page version snapshot system, then use lastDownloadedTimestamp and server lastUpdatedTime to fetch only newer pages after reconnecting. When pages or databases change, the client reconciles fresh page and database snapshots with offline\_action, adding or removing rows rather than rebuilding entire trees. This makes offline availability precise and eventually consistent, while supporting conflict resolution through the CRDT data model.


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