DoorDash RAG Architecture, AI Agent Mesh, & Open-Source Supply-Chain Scanner Today's Highlights This week, we explore advanced AI agent orchestration, a detailed production RAG architecture, and an open-source tool for supply-chain security auditing. These stories provide practical insights into deploying and managing AI frameworks in real-world workflows. How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone (InfoQ) Source: https://www.infoq.com/news/2026/07/doordash-ai-ask-assistant/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global This article from InfoQ delves into the intricate architecture behind DoorDash's "Ask DoorDash" AI-powered shopping assistant. Unlike many solutions that solely depend on large language models, DoorDash's approach integrates an LLM with a complex retrieval-augmented generation (RAG) system and a comprehensive intent classification pipeline. This multi-layered framework ensures accuracy and relevance, particularly for tasks like recommending specific items or answering detailed product queries within their extensive catalog. The system also employs sophisticated filtering and ranking mechanisms to refine results, moving beyond simple keyword matching to provide highly personalized and context-aware suggestions. The technical deep-dive covers how DoorDash engineered this system to handle the nuances of user intent and data retrieval efficiently in a production environment. Key aspects include leveraging structured and unstructured data sources, managing latency for real-time interactions, and implementing robust feedback loops for continuous improvement. The article offers valuable insights into building scalable, reliable AI assistants that can augment LLMs with proprietary data and business logic, providing a blueprint for enterprises looking to deploy similar advanced applied AI solutions. Comment: This provides a fantastic real-world case study for augmenting LLMs with custom RAG and

DuckDB Iceberg MERGE, PostgreSQL GUCs, SQLite Optimization Checklist Today's Highlights This week's highlights include powerful new Iceberg data manipulation features in DuckDB v1.5.3 and a deep dive into an obscure PostgreSQL GUC. Plus, the SQLite community discusses a practical optimization checklist for embedded databases. New DuckDB-Iceberg Features in v1.5.3 (DuckDB Blog) Source: https://duckdb.org/2026/05/29/new-iceberg-features.html The latest DuckDB v1.5.3 release significantly enhances its integration with Apache Iceberg, introducing a suite of powerful new features for data engineers and analysts. Key among these are the support for MERGE INTO and ALTER TABLE statements, allowing for more robust data manipulation directly within DuckDB for Iceberg tables. This update enables complex operations like upserting data based on conditions, schema evolution (e.g., adding/dropping columns), and modifying table properties, all achievable through a familiar SQL environment. This capability is crucial for maintaining data integrity and adapting schemas without complex external tooling. Furthermore, DuckDB-Iceberg now supports partition transforms, making it easier to manage and query partitioned Iceberg datasets efficiently by defining how data is distributed across files. The release also brings support for Iceberg V3, ensuring compatibility with the latest features of the Iceberg format, including new manifest list and manifest file layouts which offer performance improvements. These additions position DuckDB as an even stronger tool for building performant data pipelines and performing complex analytics directly on large-scale Iceberg data lakes, fully leveraging DuckDB's in-process analytical capabilities and the flexibility of the Iceberg table format. Comment: This update is a game-changer for working with Iceberg tables directly in DuckDB. MERGE INTO support means simplified ETL for incremental loads, and V3 compatibility ensures we're ready for future Iceberg

We all hear about "Not comparing yourself to others" and that "comparing yourself is the thief of joy". To be honest, I agree and it's strange that I am contradicting myself because I compare myself A LOT. The more I looked into it, the more I realized that we have a natural tendency to compare ourselves. It's a human thing to do. The issue is that we tend to be very excessive over comparing ourselves to others to the point where it takes a toll on us. For example, we are demotivated to see someone's success because we believe we can't reach the goal they are in. We all have jealousy. Big or small. Even where I am at right now, I am still jealous that many people I know that got into big tech companies like Microsoft. To get more context, I want to share a story with you. Story Time Back in the day, I remember it was the year of the ACT. For those who don't know: It's a Standardized test that is needed for the college admissions to determine if you are admitted to their program. I remember I got a national average of 21 as my composite score and I was proud of the score I got since it's the national average during that time. However, I remember the day where my friends talked about the ACT. The most common thing I heard was: "Oh I got a 30" "I got a 32" "Man I got a 35, it was sooo easy" Hearing that makes me feel not only bummed out, but felt left out. I was feeling that I wasn't smart enough to be in the group. What's worse is that they got accepted into colleges and programs that are well known. Then they start boasting about their accomplishments. I felt like I am the odd-one-out because of my scores and their accomplishments I could not match. Why am I Talking about this? Looking back and knowing where they are at now, I am proud of who I become today. It's not that they have fallen downhill (they are still successful), but the route they have taken that I definitely could not follow. For example, on GitHub, many people fill up their contribution graphs to the

The ScyllaDB PHP driver is not a C++ extension anymore. As of 1.4.0 it's pure C23, the ZendCPP template layer we leaned on for the object embed and allocate pattern is deleted, and the build no longer needs a C++ compiler at all. Every hand-written .cpp file is a .c file now (71 of them), the descriptor generator emits .c , and CMake builds with c_std_23 and nothing else. That's the biggest change to how this extension is built since we forked it for PHP 8.0. This is also the release where a plan I opened back in 2023 finally landed. PR #50 laid it out: rewrite the src/Cluster directory to be more maintainable, use Zend Fast Argument Parsing, remove some memory allocations, and add .stub.php files that generate the C headers so nobody has to hand-maintain Zend arginfo by hand. 1.4.0 is that plan finished, and a lot more that grew out of it. The things you'll actually feel: persistent session connect() doesn't allocate a 200-character key string on every call anymore, and the minimum PHP is now 8.3 (8.2 is gone). Nothing in your application code changes, this is almost all under the surface. The .stub.php Build The idea from PR #50 was small. Instead of writing ZEND_BEGIN_ARG_INFO_EX blocks by hand and keeping them in sync with the actual method bodies, write the signature once in a .stub.php file and generate the C arginfo from it. In v1.4.0 that's the whole build. There are 75 .stub.php files now, and each one is just the PHP signature of the class: // src/Keyspace.stub.php interface Keyspace { public function name (): string ; public function replicationClassName (): string ; /** @return array<string, mixed> */ public function replicationOptions (): array ; public function hasDurableWrites (): bool ; /** @return Table|false */ public function table ( string $name ): Table | false ; public function aggregate ( string $name , mixed ... $types ): Aggregate | false ; } At build time CMake runs gen_stub.php (vendored from PHP 8.5's build/gen_stub.php , with two small p

This is a condensed version of my preprint ( DOI: 10.5281/zenodo.21345310 , CC BY 4.0). Reference implementation: askbar.pro . The library problem For thirty years the website has been a library: a visitor arrives with one question and is expected to find the answer themselves, navigating menus, pages, and filters. Visitors read a small fraction of site content. Most leave without doing the thing the site owner hoped for. Chat widgets bolted onto such sites change nothing: the maze remains, the widget just answers questions about the maze. The pattern The Librarian Pattern inverts the relationship. The site does not present itself; it asks what you need and assembles the answer. The bar as the primary interface. One persistent input, text and hold-to-talk voice. It replaces navigation. Scene reassembly (generative UI). The center of the screen is not a page but a scene, composed per recognized intent. Transitions morph rather than reload. A guide with a plan. The conversational layer is a consultant with a goal ladder, asking one next question, never presenting menus of three options. Two button systems. Global suggestion chips above the bar are visually separated from in-scene action cards. This prevents the "six buttons" degeneration of chat UIs. The static shadow. Every live scene has a server-rendered twin page: full text in the DOM, question-shaped headings, FAQ schema, llms.txt, freshness stamps. Humans get the agent; crawlers and AI answer engines get complete, citable pages, generated from the same content source. Structural GEO-readiness. Content already organized as questions and answers matches how generative engines retrieve and cite, by construction. The result that surprised me 24 hours after the discoverability layer went public, Yandex Alice (the largest Russian AI answer engine) began citing the reference implementation as its prime example for the "next-generation website" query, describing the mechanics correctly and distinguishing it from "a chat

Bit by bit, Windows 11 is healing.

Smartphones these days have incredible cameras that are capable of taking smooth, sharp video, but the microphones are often lacking, to say the least. A wireless lavalier microphone can dramatically improve the audio quality of your videos, whether you’re the only one talking, or if you’re getting audio from multiple people. The DJI Mic 3 […]

With residential proxies all the rage, CISA urges router users to be vigilant.

Article URL: https://jobs.ashbyhq.com/SalesPatriot/df223727-5781-433e-bc75-2aa5bf8dc8d7 Comments URL: https://news.ycombinator.com/item?id=48898814 Points: 0 # Comments: 0

Of all the debates raging about the potential downsides of AI, there is one worry causing the most hand-wringing among AI enthusiasts in Silicon Valley — that the giant AI labs that sell proprietary models are somehow acting like Trojan horses.

Noise-canceling earbuds are great for flights and focusing, but they're not always ideal for outdoor workouts. The last-gen Shokz OpenRun Pro's open-ear design lets you enjoy music while staying aware of approaching cars, cyclists, and other potential hazards, and right now they're on sale for $109 ($50 off) at Amazon, Walmart, and B&H Photo. That […]

This year's Google Pixel 11 lineup might come in a bunch of funky colors. A series of now-deleted Amazon listings spotted by 9to5Google show what appear to be placeholders for Google's upcoming Pixel 11 in hot pink Fuchsia (Hibiscus), vibrant green Moss (Pistachio), and Midnight (Obsidian) black. We've seen two sets of names for the […]

Apple has just released public betas for iOS 27 and other major OS updates that are set to publicly launch this fall. The big new feature this year is Siri AI, the delayed AI-powered revamp to Siri. It actually works - which is big praise! - though it keeps things brief. Other betas available now […]

Try the new Siri AI and system-wide performance improvements.

Siri has been on the Apple Watch since day one, though I'm usually hard-pressed to find people who actually make good use of it. It's kind of just… been there - mostly as a way to set timers when my hands are full. But after playing around with the watchOS 27 developer beta, I get […]