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Summaries are not the original full articles. ## OpenAI buys tiny software developer to improve Codex DevFeed: [OpenAI buys tiny software developer to improve Codex]() Original publisher: [Read original article]() Author: Maxwell Cooter Published: 2026-11-06T13:46:23Z Content type: news Language: en Sources: [Computerworld]() Topics: [codex](), [AI tools for developers](), [Platform Engineering](), [OpenAI](), [developer-productivity]() Tags: [acquisition](), [agent](), [agents](), [ai](), [ai-developer](), [ai-powered](), [artificial-intelligence](), [artificial-intelligence-careers-developer-roles](), [back-end](), [branch](), [careers](), [code](), [codex](), [company](), [concurrent](), [contribute](), [customers](), [deal](), [developer](), [edition-us](), [language-en](), [news-brief](), [openai](), [roles](), [software](), [software-developer](), [storytype-news](), [tiny]() ### AI overview OpenAI is acquiring Pierre Computer Company to bring its Git infrastructure layer, Code.Storage, into Codex and strengthen support for code review and parallel work by developers and AI agents. Codex already uses Pierre's open-source Trees and Diffs libraries; OpenAI singled out Diffs' Edit mode and Highlights as useful for development teams. The article links the deal to OpenAI's efforts to address Git's limitations, including its Pando backend for concurrent repository work at scale. The acquisition price was not disclosed. Code.Storage customers can continue using the product until its features are brought into Codex, and Pierre's open-source projects will remain open, with OpenAI continuing to contribute. ### Source excerpt OpenAI is buying Pierre Computer Company in order to incorporate the San Francisco-based software developer's Code.Storage infrastructure layer for Git into its Codex AI-powered programming tool. Pierre Computer, whose employees number just barely in the double digits, has got plenty of clout for its small size. It's well-known for Code.Storage offering, as well as for its open-source software libraries, Trees and Diffs, which OpenAI already uses in Codex. OpenAI has experienced some of the limitations of Git itself, and its in-house engineering teams are already working to improve its support for human coders and AI agents working in parallel. Earlier this week the company described how it built a new back end for Git repositories, Pando, to enable concurrent working at scale. The AI developer has been pushing Codex more aggressively this year: offering it to Mac users and expanding it to Windows. It cited Pierre Computer's ability to help developers review code as one of the reasons for the acquisition. OpenAI highlighted the Diffs' Edit mode and the recently released Highlights as components that were particularly helpful to development teams. Announcing the deal on its news page, OpenAI set out how it would integrate the company into its existing offerings. "With the Pierre team, we wish to set the standard for modern software development: from the first branch an agent creates to that magic moment when code is deployed to the world. We want to make Codex the place where developers and agents take on their most ambitious projects." Neither company disclosed details of the acquisition price. OpenAI said that Code.Storage customers will be able to continue using the product until it brings the features into Codex, while Pierre Computer's open-source projects will remain open, and OpenAI will continue to contribute to them. This article first appeared on InfoWorld. ## OpenAI buys tiny software developer to improve Codex DevFeed: [OpenAI buys tiny software developer to improve Codex]() Original publisher: [Read original article]() Author: Maxwell Cooter Published: 2026-11-06T13:42:57Z Content type: news Language: en Sources: [CIO]() Topics: [codex](), [AI tools for developers](), [Platform Engineering](), [OpenAI]() Tags: [acquisition](), [agent](), [agents](), [ai](), [ai-developer](), [ai-powered](), [artificial-intelligence](), [artificial-intelligence-development-tools-open-source-software-development](), [back-end](), [branch](), [code](), [codex](), [company](), [components](), [computer](), [concurrent](), [contribute](), [developer](), [development-tools](), [edition-us](), [language-en](), [news-brief](), [open-source](), [openai](), [software](), [software-developer](), [software-development](), [storytype-news](), [tiny]() ### AI overview OpenAI is acquiring Pierre Computer Company to bring its Code.Storage Git infrastructure into Codex and strengthen support for developers and AI agents working in parallel. OpenAI already uses Pierre's open-source Trees and Diffs libraries in Codex; it singled out Diffs' Edit mode and Highlights as useful for code review. The move complements OpenAI's work on Pando, a Git repository back end designed for concurrent work at scale. Code.Storage customers can continue using the product until its features are incorporated into Codex, and Pierre's open-source projects will remain open, with OpenAI continuing to contribute. The companies did not disclose the purchase price. ### Source excerpt OpenAI is buying Pierre Computer Company in order to incorporate the San Francisco-based software developer's Code.Storage infrastructure layer for Git into its Codex AI-powered programming tool. Pierre Computer, whose employees number just barely in the double digits, has got plenty of clout for its small size. It's well-known for Code.Storage offering, as well as for its open-source software libraries, Trees and Diffs, which OpenAI already uses in Codex. OpenAI has experienced some of the limitations of Git itself, and its in-house engineering teams are already working to improve its support for human coders and AI agents working in parallel. Earlier this week the company described how it built a new back end for Git repositories, Pando, to enable concurrent working at scale. The AI developer has been pushing Codex more aggressively this year: offering it to Mac users and expanding it to Windows. It cited Pierre Computer's ability to help developers review code as one of the reasons for the acquisition. OpenAI highlighted the Diffs' Edit mode and the recently released Highlights as components that were particularly helpful to development teams. Announcing the deal on its news page, OpenAI set out how it would integrate the company into its existing offerings. "With the Pierre team, we wish to set the standard for modern software development: from the first branch an agent creates to that magic moment when code is deployed to the world. We want to make Codex the place where developers and agents take on their most ambitious projects." Neither company disclosed details of the acquisition price. OpenAI said that Code.Storage customers will be able to continue using the product until it brings the features into Codex, while Pierre Computer's open-source projects will remain open, and OpenAI will continue to contribute to them. This article first appeared on InfoWorld. ## OpenAI buys tiny software developer to improve Codex DevFeed: [OpenAI buys tiny software developer to improve Codex]() Original publisher: [Read original article]() Author: Maxwell Cooter Published: 2026-11-06T13:40:48Z Content type: news Language: en Sources: [InfoWorld]() Topics: [codex](), [OpenAI](), [AI tools for developers](), [Platform Engineering](), [engineering-culture]() Tags: [acquisition](), [agent](), [agents](), [ai](), [ai-developer](), [ai-powered](), [artificial-intelligence](), [artificial-intelligence-code-editors-development-tools-open-source-software-development](), [back-end](), [branch](), [code](), [code-editors](), [codex](), [concurrent](), [contribute](), [customers](), [deal](), [developer](), [developers](), [development-tools](), [language-en](), [news-brief](), [open-source](), [openai](), [software](), [software-developer](), [software-development](), [storytype-news](), [tiny]() ### AI overview OpenAI is acquiring Pierre Computer Company to bring its Code.Storage Git infrastructure into Codex. OpenAI says Code.Storage customers can keep using the product until its features are integrated, while Pierre Computer's open-source Trees and Diffs libraries will remain open and receive continued contributions. The article says OpenAI already uses Trees and Diffs in Codex, highlighting Diffs' Edit mode and Highlights for code review. The deal is intended to support developers and AI agents working in parallel; neither company disclosed the price. ### Source excerpt OpenAI is buying Pierre Computer Company in order to incorporate the San Francisco-based software developer's Code.Storage infrastructure layer for Git into its Codex AI-powered programming tool. Pierre Computer, whose employees number just barely in the double digits, has got plenty of clout for its small size. It's well-known for Code.Storage offering, as well as for its open-source software libraries, Trees and Diffs, which OpenAI already uses in Codex. OpenAI has experienced some of the limitations of Git itself, and its in-house engineering teams are already working to improve its support for human coders and AI agents working in parallel. Earlier this week the company described how it built a new back end for Git repositories, Pando, to enable concurrent working at scale. The AI developer has been pushing Codex more aggressively this year: offering it to Mac users and expanding it to Windows. It cited Pierre Computer's ability to help developers review code as one of the reasons for the acquisition. OpenAI highlighted the Diffs' Edit mode and the recently released Highlights as components that were particularly helpful to development teams. Announcing the deal on its news page, OpenAI set out how it would integrate the company into its existing offerings. "With the Pierre team, we wish to set the standard for modern software development: from the first branch an agent creates to that magic moment when code is deployed to the world. We want to make Codex the place where developers and agents take on their most ambitious projects." Neither company disclosed details of the acquisition price. OpenAI said that Code.Storage customers will be able to continue using the product until it brings the features into Codex, while Pierre Computer's open-source projects will remain open, and OpenAI will continue to contribute to them. ## Adding Network Security to AI, HPC and Data Centers DevFeed: [Adding Network Security to AI, HPC and Data Centers]() Original publisher: [Read original article]() Author: Rambus Published: 2026-11-04T08:04:45Z Content type: article Language: en Sources: [Semiconductor Engineering]() Topics: [network security](), [High-Performance Computing](), [Network Segmentation]() Tags: [accelerators](), [ai](), [applications](), [back-end](), [compute](), [data-center-architecture](), [data-center-networking](), [data-center-networking-security](), [data-center-networks](), [data-center-security](), [data-centers](), [front-end](), [gpus](), [high-performance-computing](), [hpc](), [iot-and-security](), [learn](), [management](), [network-security](), [network-security-protocols](), [performance](), [rambus](), [scalability](), [storage](), [white-paper](), [whitepapers]() ### AI overview AI and HPC data centers use distinct network domains: front-end networks connect users, applications, storage, and management systems, while back-end networks provide high-speed links among GPUs, accelerators, and compute nodes. The white paper examines architectural and security considerations for protecting data and communications while meeting distributed workloads' performance demands. The supplied excerpt does not describe specific security measures or compare solutions. ### Source excerpt How data center operators can protect critical data and communications without compromising the performance demanded by distributed AI and HPC workloads. The post Adding Network Security to AI, HPC and Data Centers appeared first on Semiconductor Engineering. ## Can agent standards keep up with the companies building them? DevFeed: [Can agent standards keep up with the companies building them?]() Original publisher: [Read original article]() Author: Frederic Lardinois Published: 2026-11-03T22:50:15Z Content type: news Language: en Sources: [The New Stack]() Topics: [Open Source](), [API Governance](), [AI Infrastructure](), [linux foundation](), [developer-relations]() Tags: [agent](), [agent-frameworks](), [agent-skills](), [agentgateway](), [agentic-ai](), [agentic-ai-foundation](), [agents](), [agntcon](), [ai](), [ai-agents](), [ai-models](), [anthropic](), [appliances](), [asia](), [back-end](), [board](), [cncf](), [foundation](), [google](), [linux-foundation](), [podcast](), [sponsor-cncf](), [sponsored-podcast](), [stack](), [standards](), [the-linux-foundation](), [video]() ### AI overview Mazin Gilbert says the Linux Foundation's Agentic AI Foundation must move quickly to keep agent standards relevant while competing companies build products both with and without them. He describes MCP's stateless specification as an example of collaboration: companies contributed to it and updated their stacks; according to Gilbert, downloads grew tenfold in 10 months, fewer than 15% of maintainers work at Anthropic, and more than 1,000 organizations contribute 75% of pull requests. The foundation is also weighing project proposals under neutral governance, but has no consensus yet on whether multiple competing gateway projects should be harmonized. Gilbert says members can begin contributing by joining a working group and listening; the foundation also launched an MCP Associate certification. He argues that missing protocols lead companies to write custom integrations that customers must continue maintaining, even if standards arrive later. ### Source excerpt Mazin Gilbert, executive director of the Linux Foundation's Agentic AI Foundation, left Google earlier this year to lead the new AAIF, The post Can agent standards keep up with the companies building them? appeared first on The New Stack. ## Seven egregious failures of modern web tech DevFeed: [Seven egregious failures of modern web tech]() Original publisher: [Read original article]() Author: Matthew Tyson Published: 2026-11-02T09:00:00Z Content type: opinion Language: en Sources: [InfoWorld]() Topics: [Front end](), [Angular](), [Single-page application (SPA)](), [Tailwind CSS]() Tags: [angular](), [apple](), [back-end](), [browser](), [chrome](), [consensus](), [database](), [deprecated](), [deprecation](), [developers](), [development-approaches](), [development-approaches-libraries-and-frameworks-software-development-web-development](), [execution](), [external](), [framework](), [frameworks](), [front-end](), [google](), [history](), [language-en](), [libraries-and-frameworks](), [modern](), [software-development](), [storytype-feature](), [tech](), [web](), [web-development]() ### AI overview The excerpt's central takeaway is that sound web ideas can fail when implementation ignores standards consensus, migration paths, the wider ecosystem, browser constraints, or end-user costs. Web SQL was deprecated after Mozilla and Microsoft resisted standardizing a SQLite-specific wrapper; Angular 2's incompatible rewrite left large codebases without a clear migration path. Flow struggled outside Facebook's environment, while TypeScript gained ground. Google AMP paired mobile performance goals with rigid proprietary rules and search-ranking pressure. Runtime CSS-in-JS shifted stylesheet work into JavaScript, harming load time and mobile CPU; micro front ends added bundles, duplicate dependencies, and UI complexity in the browser. Client-side-only SPAs concentrated state and business logic in the client, prompting a return toward server rendering through frameworks such as Next.js. The article's lessons are to preserve migration routes, support broad use and open standards, respect native browser capabilities, and judge architecture by its user impact. The supplied text includes the full seven-item discussion. ### Source excerpt The modern web is a miracle of engineering, but its history is littered with brilliant concepts that flamed out in execution. For every innovation that stuck the landing, there is a promising technology that fell on its face. Each of these failures offers an object lesson for us as software developers. New tech is kind of a compulsion. We love to gaze at the shiny new frameworks on the horizon, but true architectural wisdom comes from examining the wreckage. And from catastrophic version migrations and hostile corporate takeovers of HTML to ignoring the realities of web physics and the physical limitations of the front end, we have plenty of wreckage to learn from. Here are seven of the most egregious failures of execution in modern web history--and the architectural lessons we must absorb to ensure our good ideas don't suffer the same fate. Web SQL: A single-vendor dead end The Good Idea: Bring a robust, relational database directly into the local browser. Enable responsive, offline-first web apps. Sync it all with a back-end data store of record. The Fatal Flaw: External standards politics. Apple pushed Web SQL, but it was essentially just a raw wrapper around SQLite. Google baked Web SQL into Chrome, but Mozilla and Microsoft balked because standardizing it meant tying the entire web specification to one specific C library. It was deprecated. The Lesson: Standards require consensus. Tying web infrastructure to a single implementation is a recipe for sudden deprecation. Although developers lost Web SQL and suffered under the clunky IndexedDB, the field has finally progressed into more fertile fields with the Wasm-based PGLite and more recent local-first solutions like PowerSync. Angular 2: Burn the ships The Good Idea: Build on the success of Angular with a completely rewritten, better version. When Hernán Cortéz famously burned his ships, it turned out to be a brilliant strategy. For a mainstream web framework? Not so much. Angular.js was the undisputed king of si ## Universe 2026: What's Next with GitHub Next DevFeed: [Universe 2026: What's Next with GitHub Next]() Original publisher: [Read original article]() Published: 2026-10-29T00:00:00Z Content type: release Language: en Sources: [GitHub Next]() Topics: [GitHub](), [GitHub Copilot](), [Spec Driven Development](), [GitHub Agentic Workflows](), [agentic workflows](), [Test-driven development](), [CI/CD](), [GitHub Actions](), [GitHub Issues](), [GitHub Pages](), [roadmaps]() Tags: [2026](), [ace](), [agentic](), [agentic-development](), [agentic-ops](), [agentic-workflows](), [agents](), [ai](), [alignment](), [automation](), [back-end](), [backend](), [canvas](), [collaborative-planning](), [github](), [github-next](), [multiplayer](), [planning]() ### AI overview GitHub Next presents three experiments for collaborative software work with agents. Ace 2 runs locally and lets invited teammates share sessions, environments, chat context, and agents; because collaborators get shell access to the host machine, the authors say to work only with trusted people and use a VPN rather than expose it publicly. Chopin provides real-time planning with repository-aware agents, shared plans, decision records, diagrams, and interactive prototypes; it can turn plans into pull requests and update code from later specification changes. Its PDD Kit translates natural-language intent into a reviewed plan and Verus formal specification, then checks implementation with proofs, tests for trusted components, and LLM review; it is in limited research preview. Nexthub is a concept for restructuring GitHub around projects, reusable or overlaid code sources, and first-class goals, decisions, tasks, and releases, with multi-repository changes and app deployment. The article describes Nexthub as an exploration, not an established GitHub offering. ### Source excerpt Idan Gazit and Maggie Appleton present multiplayer agents, collaborative planning, and proof-driven development as new foundations for GitHub. ## When GraphQL earns its place in an enterprise application, and when it doesn't DevFeed: [When GraphQL earns its place in an enterprise application, and when it doesn't]() Original publisher: [Read original article]() Author: Vaishnavi Khosla Published: 2026-10-27T09:00:00Z Content type: article Language: en Sources: [InfoWorld]() Topics: [GraphQL](), [REST API](), [Back end](), [business logic]() Tags: [access-control](), [api](), [application](), [authorization](), [back-end](), [backend](), [backend-development](), [connected-data](), [contributor](), [costs](), [data](), [data-management](), [data-management-development-approaches-development-tools-software-development](), [development-approaches](), [development-tools](), [enterprise](), [fields](), [front-end](), [governance](), [graphql](), [it](), [language-en](), [query](), [rest](), [rest-api](), [screen](), [software-development](), [storytype-opinion]() ### AI overview GraphQL is most useful when screens need different slices of highly interconnected data: a client can request related objects in one query, reducing sequential REST calls and the need to build new tailored endpoints as screens change. The author's access-governance example has this pattern, while noting the benefit is fewer endpoints and queries rather than a quantified time saving. The tradeoffs are harder caching, potentially expensive deep or broad queries, and authorization checks at object and field level. The text recommends query depth and cost limits, pagination, and keeping authorization rules in shared business logic rather than resolvers. Adoption depends on varied client needs, data interdependence, schema ownership, and centralized authorization; a read-heavy screen is suggested as a trial when answers are mixed. REST remains the author's choice for command operations such as credential rotation, which benefit from focused authorization, logging, and retries. ### Source excerpt The value of GraphQL emerges for a specific type of enterprise screen, one that brings together highly interdependent data across multiple sources. In all other cases, there is a price to pay, which is bound to come out after you have already made the decision. I build internal access-governance applications, with React front ends that talk to both REST and GraphQL back ends. Using the two side by side has shown me where each earns its keep and where each gets in the way. The article is divided into three sections. The first section covers areas in which GraphQL eliminates round trips and front-end work. The second section highlights the underestimated costs involved. Finally, there are a few questions you can ask yourself before implementing the GraphQL approach. GraphQL helps most on screens built from connected data Imagine the details screen for one client. On this screen, you see the client, his/her accounts, permissions for these accounts, owners of these accounts and some related objects for each. Data is highly interrelated, and your screen requires some portion of each layer at once. There are two scenarios for working with REST API. In the first scenario, the front end makes several requests - first for the client, then for his accounts and then for permissions of each of those accounts. Each request in the chain waits for the preceding request to be finished. Another scenario is when the back end creates a specific endpoint in the desired format. The first one becomes very inefficient whenever the network has some issues. The second one works well as long as the information that is to appear on the next screen appears in the same format as before, but then you get many endpoints nobody cares about maintaining. With the help of GraphQL, it becomes possible to change the decision-maker of the shape. The client asks for the query with the specific fields and receives the response in the specific format. This is how it is described in the GraphQL documentatio ## Self-Heating And Parasitics Challenge Multi-Tier CFET Scaling (TU Munich, IIT Kanpur) DevFeed: [Self-Heating And Parasitics Challenge Multi-Tier CFET Scaling (TU Munich, IIT Kanpur)]() Original publisher: [Read original article]() Author: Technical Paper Link Published: 2026-10-26T07:01:24Z Content type: news Language: en Sources: [Semiconductor Engineering]() Topics: [Chip design](), [Electrical engineering and computer science (EECS)]() Tags: [3d-monolithic-cmos](), [analyze](), [back-end](), [back-end-of-line](), [beol](), [cfet](), [circuits](), [cmos](), [complementary-fet](), [design](), [devices](), [distribution](), [experimental](), [gaa](), [gate-all-around](), [heat](), [indian-institute-of-technology-kanpur](), [mol](), [multi-tier-stacking](), [nanosheet-fet](), [parasitics](), [power-performance](), [scaling](), [self-heating](), [study](), [tcad](), [technical-papers](), [technical-university-of-munich](), [thermal-management](), [transistor-reliability](), [transistors](), [tu-munich]() ### AI overview The study examines how self-heating and interconnect parasitics affect multi-tier CFET design. Using TCAD models calibrated to experimental measurements, the researchers compare four-tier and conventional two-tier CFETs. They also simulate self-heating to analyze heat distribution and temperature profiles, and use 3-D CMOS inverter models with full middle-of-line and back-end-of-line interconnects to extract parasitic RC networks. The supplied excerpt does not state the study's quantitative findings. ### Source excerpt Researchers at the Technical University of Munich and Indian Institute of Technology Kanpur published "Self-Heating and Parasitic Effects in Multi-Tier CFET Design." Abstract Excerpt: "We study the impact of self-heating effects (SHEs) and middle of line (MOL) and back-end of line (BEOL) induced parasitics on multi-tier CFET design, where multiple nanosheet devices are vertically stacked.... " read more The post Self-Heating And Parasitics Challenge Multi-Tier CFET Scaling (TU Munich, IIT Kanpur) appeared first on Semiconductor Engineering. ## Python REST APIs With Flask, Connexion, and SQLAlchemy - Part 2 DevFeed: [Python REST APIs With Flask, Connexion, and SQLAlchemy - Part 2]() Original publisher: [Read original article]() Author: Philipp Acsany Published: 2026-10-25T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [REST API](), [SQLAlchemy](), [Flask](), [Python](), [rdbms](), [Swagger](), [Query (disambiguation)](), [modules]() Tags: [api](), [api-documentation](), [apis](), [array](), [attack](), [back-end](), [boolean](), [class](), [column](), [commit](), [config](), [data](), [database](), [flask](), [python](), [rest](), [rest-api](), [rest-apis](), [sqlalchemy](), [sqlite]() ### AI overview The tutorial replaces a Flask REST API's in-memory `PEOPLE` dictionary with a persistent SQLite database, while keeping the existing endpoints. It demonstrates direct `sqlite3` commands to create and query a `person` table, then explains how interpolating user input into SQL can enable injection attacks. It configures Flask-SQLAlchemy and models the table as a `Person` class with a unique last name, primary key, and UTC timestamps. Flask-Marshmallow schemas validate and convert between JSON and model objects. Updated handlers query, create, update, and delete database records; the home page reads from the database, and Swagger UI can exercise the API. The tutorial notes that SQLite stores the database in a single file and that data now persists across app restarts. The supplied text describes this second installment; adding related notes is reserved for part three. ### Source excerpt Build a Flask REST API backed by a SQLite database. In part two of this series, you'll use SQLAlchemy and Marshmallow to persist and serialize data. ## How AnonAI Scaled Its Private Chatbot Platform on Runpod DevFeed: [How AnonAI Scaled Its Private Chatbot Platform on Runpod]() Original publisher: [Read original article]() Author: Brendan McKeag Published: 2026-10-22T02:42:59Z Content type: article Language: en Sources: [Runpod Blog.]() Topics: [Chat Bot](), [Cryptography]() Tags: [agent](), [agent-economy](), [agents](), [ai](), [ai-apps](), [ai-assistant](), [ai-chatbots](), [ai-models](), [back-end](), [browser](), [build](), [chatbot](), [data](), [data-collection](), [decentralized](), [platform](), [private](), [private-ai](), [scale](), [users]() ### AI overview AnonAI says it serves requests through a decentralized pool of Runpod GPUs, processing them in real time and streaming results to users' browsers; conversations are stored locally, and the company says it does not collect or log identities, prompts, or responses. The excerpt reports 40,000 users, 80,000 text prompts, and 60,000 image prompts after three months. Its backend uses Llama 405b for text and Llama 3.2 Vision and Flux for image handling. The team chose Runpod Pods based on prior familiarity, GPU variety and pricing including A40s, and stable connectivity. It reports Runpod was more affordable than its previous provider and offered fast service creation, a user-friendly interface, and easy payment. These privacy and performance statements are presented as the company's account. ### Source excerpt AnonAI used Runpod to scale its decentralized chatbot platform with 40K+ users and zero data collection. Learn how they power private AI at scale. ## 56 scanned pages to a cited answer in 5 minutes: RAG pipeline for the documents search cannot read DevFeed: [56 scanned pages to a cited answer in 5 minutes: RAG pipeline for the documents search cannot read]() Original publisher: [Read original article]() Author: Ben-Amin York Jr. Published: 2026-10-22T00:00:00Z Content type: tutorial Language: en Sources: [Elasticsearch Labs]() Topics: [Retrieval Augmented Generation (RAG)](), [hybrid-search](), [AI Chat](), [elasticsearch](), [document ai](), [search-engines](), [pdf](), [Elastic Cloud]() Tags: [api](), [api-key](), [back-end](), [base64](), [catalog](), [chat](), [chatbot](), [claude](), [claude-haiku-4-5](), [cloud](), [database](), [elasticsearch](), [hybrid-search](), [jina-ai](), [markdown](), [ocr](), [pdf](), [pipeline](), [rag](), [search](), [structured](), [table](), [vector-database]() ### AI overview The described pipeline turns scanned PDFs into searchable, page-cited answers by sending each page image to jina-ocr-v1, which returns structured Markdown with tables, reading order, and inline handwriting. An Express server runs four pages in parallel through Elastic Inference Service and records page markers; Elasticsearch's semantic_text field chunks and embeds the Markdown at ingest. Hybrid retrieval combines lexical matching for exact terms with semantic matching for meaning, then Claude Haiku 4.5 generates answers grounded in retrieved sources and recommends only catalog products. The example uses a 56-page plant guide and a 48-plant catalog. Unlike the described legacy OCR chain of separate layout, recognition, table, and reading-order models, jina-ocr-v1 handles these in one pass. Each page must still be looped through individually; the article says legibility holds up to about 2048 x 2048 pixels. EIS is metered per token and billed as a Serverless add-on. ### Source excerpt Ask a scanned, table-heavy PDF a question in plain language and get an answer stamped with the page it came from, because jina-ocr-v1 turns every page into structured Markdown that Elasticsearch embeds at ingest. ## How to Test a Compiler Against Itself: Thirteen Generators and 7,800 Programs DevFeed: [How to Test a Compiler Against Itself: Thirteen Generators and 7,800 Programs]() Original publisher: [Read original article]() Author: Tech\_World Published: 2026-10-21T20:01:48Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Software Testing](), [Test automation](), [Fuzzing/Fuzz testing](), [Compilers and Linkers]() Tags: [back-end](), [backends](), [bytecode](), [candidate](), [chuks-programming-language](), [chukslang](), [compiler](), [errors](), [free-software](), [generators](), [http](), [native](), [nightly](), [programming](), [release-notes](), [self-testing](), [software-engineering](), [test](), [tests]() ### AI overview Comparing Chuks's bytecode VM with its native compiler can miss bugs shared by both, so v0.2.0-rc.3 adds tests with answers independent of that comparison: 450 translated compiler regression programs, about 7,800 programs from 13 feature-combination generators, and native builds of Valet and CodeStudio. The tests exposed issues including cancelled tasks continuing to run, locks remaining held after errors, different `a || b` results across backends, and an HTTP parser accepting forbidden framing; each was fixed and made a permanent check. The generators pass on the full tree, and a higher-strength nightly run has caught a regression. Based on this evidence, the project says rc.3 will be its last candidate and plans to publish v0.2.0 from this build, while acknowledging that tests cannot prove bugs are absent. ### Source excerpt Comparing two backends only catches disagreement. How Chuks tests against known answers with 13 generators and about 7,800 programs. ## Companies route AI tasks across models to control costs, but switching requires evaluation DevFeed: [Companies route AI tasks across models to control costs, but switching requires evaluation]() Original publisher: [Read original article]() Author: Matthew Burns Published: 2026-10-10T10:00:00Z Content type: news Language: en Sources: [The New Stack]() Topics: [Model Routing](), [Frontier Model](), [Claude](), [Prompt Engineering]() Tags: [agent](), [agents](), [ai](), [ai-models](), [anthropic](), [back-end](), [benchmarks](), [claude](), [coding](), [large-language-models](), [software-testing]() ### AI overview Companies are increasingly choosing AI models by task, routing high-volume work to cheaper models and reserving frontier models for tasks that need them. Interviews with leaders describe funnels that use rules engines and smaller models before escalating requests. The article also warns that model changes can break workflows, making evaluation important before switching. OpenRouter usage data shows cheap models handling substantial volume while Claude Opus 5.5 usage grew quickly, though token counts do not measure users or spending. ### Source excerpt I'm Matt Burns, Chief Content Officer at Insight Media Group. Each week, I round up the most important AI developments, The post Elon Musk's Grok Bot will pick Claude over Grok when it's better. One-model loyalty is dead. appeared first on The New Stack. ## Case Study: Scanning for quality: How Yuka fuels healthy choices at global scale with Symfony DevFeed: [Case Study: Scanning for quality: How Yuka fuels healthy choices at global scale with Symfony]() Original publisher: [Read original article]() Author: Eloïse Charrier Published: 2026-09-23T12:30:00Z Content type: article Language: en Sources: [Symfony]() Topics: [Symfony](), [Back end](), [Cache](), [PHP](), [Algolia](), [Database](), [API](), [upgrade](), [Android](), [iOS](), [Security]() Tags: [7-4](), [algolia](), [android](), [back-end](), [caching](), [case-study](), [database](), [end-of-life](), [infrastructure](), [ios](), [mobile](), [modernization](), [symfony]() ### AI overview A case study of how Yuka uses Symfony as the back-end foundation for its global mobile application. It describes caching API data, reducing repeated Algolia queries, and upgrading a legacy Symfony 4 and PHP 7.4 stack toward Symfony 7. ### Source excerpt Yuka is a global leader in health and environmental transparency. Available across iOS and Android, its mobile application helps over 68 million consumers worldwide decode food and cosmetic labels instantly. With a lean technical team of just five... ## How we built PromQL into Elasticsearch DevFeed: [How we built PromQL into Elasticsearch]() Original publisher: [Read original article]() Author: Sergey Sidorov,Felix Barnsteiner Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Elasticsearch Labs]() Topics: [elasticsearch](), [Prometheus alert rules](), [Data serialization](), [Continuous integration]() Tags: [aggregation](), [alerting](), [alerts](), [architecture](), [back-end](), [capacity](), [cardinality](), [cloud](), [cluster](), [column](), [columnar](), [cost](), [cpu](), [elasticsearch](), [es-ql](), [inside-elastic](), [plugin](), [process](), [query-languages]() ### AI overview The article explains how Elasticsearch 9.5 integrates PromQL into its existing compute engine and Prometheus-compatible APIs. It describes engine changes for time-window semantics, dynamic label grouping, compact aggregation keys, response shaping, and testing against more than 2,000 public PromQL queries. The release supports over 80% of PromQL workflows in the authors' corpus, while several advanced language features and API endpoints remain unsupported. ### Source excerpt PromQL runs on the same Elasticsearch compute engine as ES|QL, with no plugin and no separate process to operate. Getting there meant changing how the engine evaluates time windows and builds grouping keys. ## SvelteKit 3 puts heat on Next.js with radical approach to RPCs DevFeed: [SvelteKit 3 puts heat on Next.js with radical approach to RPCs]() Original publisher: [Read original article]() Author: Joab Jackson Published: 2026-08-20T21:17:37Z Content type: news Language: en Sources: [DevClass.com]() Topics: [Svelte](), [JavaScript](), [Next.js](), [Web Development](), [Back end](), [Server-side rendering](), [Routing (disambiguation)]() Tags: [back-end](), [devops](), [front-end](), [javascript](), [next-js](), [performance](), [react](), [remote-procedure-calls](), [rendering](), [routing](), [svelte](), [vercel](), [web-developers]() ### AI overview The article covers the SvelteKit 3.0 release candidate and its experimental remote functions, which allow components to fetch and update server data without a full page refresh while preserving type safety. It compares SvelteKit with Next.js and discusses its backend features, server-side rendering, performance, and RPC approach. ### Source excerpt In today's sprawling JavaScript ecosystem, Svelte was designed to be a simpler alternative to the Re ... ## Website replatforming: How to migrate your site smoothly DevFeed: [Website replatforming: How to migrate your site smoothly]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-06-27T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [migration](), [Website](), [Content Management System](), [Scalability](), [hosting](), [Back end](), [Front end]() Tags: [back-end](), [cms](), [front-end](), [guide](), [hosting](), [how-to](), [migration](), [scalability](), [strategy]() ### AI overview A guide to website replatforming: migrating from an older CMS or custom setup to a new platform while preserving much of the existing content and design. It explains common reasons for replatforming, including outdated technology, poor performance, limited integrations, scalability limits, and rigid workflows. ### Source excerpt Explore how website replatforming preserves the best of your existing site, while making it more scalable and manageable for long-term performance. ## GitHub Copilot and me: The evolution of my partnership with a coding assistant DevFeed: [GitHub Copilot and me: The evolution of my partnership with a coding assistant]() Original publisher: [Read original article]() Author: Bruno Belarte Published: 2026-05-07T00:00:00Z Content type: opinion Language: en Sources: [Thoughtworks Insights]() Topics: [coding assistant](), [GitHub Copilot](), [Artificial Intelligence](), [Code](), [Front end](), [Java](), [JavaScript](), [Microservices](), [React](), [Spring Boot](), [TypeScript](), [IntelliJ IDEA]() Tags: [ai-coding](), [ai-coding-assistant](), [back-end](), [coding](), [coding-assistant](), [developers](), [front-end](), [github-copilot](), [intellij](), [java](), [javascript](), [learning](), [microservices](), [react](), [repository](), [software]() ### AI overview A first-person account of using GitHub Copilot as a coding assistant on a real-world project. It describes how Copilot evolved from inline suggestions to chat and agent features, and how the author adapted its use to improve team delivery while maintaining coding standards. ### Source excerpt GitHub Copilot and me: The evolution of my partnership with a coding assistant ## A Day in the Life as a Tech Grad (Office Edition) DevFeed: [A Day in the Life as a Tech Grad (Office Edition)]() Original publisher: [Read original article]() Author: Hong Ng Published: 2026-03-16T05:11:14Z Content type: article Language: en Sources: [REA Group]() Topics: [Back end](), [Full Stack Development](), [jira](), [pair\_programming](), [Learning]() Tags: [back-end](), [collaboration](), [entry-to-rea](), [full-stack](), [jira](), [learning](), [pair-programming](), [team](), [tech](), [work]() ### AI overview A graduate engineer describes a typical office day at REA Group, including commuting, collaboration with other technology graduates, Jira planning, stand-up meetings, sprint kick-offs, and pair programming. The article also discusses learning through backend and full-stack engineering rotations. ### Source excerpt A Day in the Life as a Tech Grad (Office Edition) Hello! I'm Hong, a graduate engineer currently working at REA Group. Before joining REA, I completed my Bachelor of Information Technology at Swinburne University of Technology. Since starting at REA, I've had the chance to work as both a back end and full-stack engineer, to contribute to real products, and to learn from engineers across two different rotations. In this blog, I'll walk through a typical day in the office as a tech grad. ## Screaming Architecture & Colocation: Let Your Project Structure Tell the Story DevFeed: [Screaming Architecture & Colocation: Let Your Project Structure Tell the Story]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-03-14T07:19:34Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [Code](), [Back end](), [Node.js](), [Express](), [Front end](), [React]() Tags: [app](), [architecture](), [back-end](), [code](), [express](), [front-end](), [node-js](), [project](), [structure](), [technical](), [tests]() ### AI overview This tutorial explains how to organize Node.js, TypeScript, and React projects around business capabilities rather than technical layers. It presents Screaming Architecture and colocation as ways to improve code discoverability, team ownership, change isolation, feature removal, and maintainability. ### Source excerpt Learn why you should organize your code by what your app actually does, not by technical roles. (5 min) ## Zero to Deploy: Launching Your Career at DigitalOcean DevFeed: [Zero to Deploy: Launching Your Career at DigitalOcean]() Original publisher: [Read original article]() Author: Sujatha R Published: 2026-02-25T08:14:05Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Tech Careers](), [Machine learning](), [User interface design]() Tags: [3](), [about](), [agentic](), [agentic-workflows](), [ai](), [ai-application](), [ai-infrastructure](), [ai-native](), [application](), [back-end](), [best](), [build](), [builder](), [capabilities](), [career](), [challenges](), [class](), [culture](), [deploy](), [digitalocean](), [zero]() ### AI overview DigitalOcean's entry-level employees can contribute to production work early, with examples spanning AI infrastructure, platform reliability and product design. Software Engineer Mitali Soni helped build a live tool that maps uploaded invoices to DigitalOcean offerings. Software Engineer Yeshvanth Raju Kurapati built live machine-learning remediation methods intended to predict hypervisor crashes and prevent infrastructure failures. Junior Product Designer Isabel Shic began improving the onboarding flow in her first week and saw changes appear in the product a few weeks later. The excerpt also cites ownership and mentorship as features of these roles, but gives no further detail on the mentorship. ### Source excerpt Diving into the professional world is a big moment for recent graduates. At DigitalOcean, we believe the best way to become a world-class builder is to build. Our entry-level roles are designed to treat you as a core member of the team from Day One. We sat down with 3 first-year builders, Isabel Shic, Junior Product Designer, Mitali Soni, Software Engineer, and Yeshvanth Raju Kurapati, Software Engineer, to hear about how they transitioned from graduation to making a production-grade impact at DigitalOcean. Their journeys show what it really means to start your career at DigitalOcean: ownership from day one, meaningful mentorship, and the opportunity to work on AI infrastructure. Shipping real code At DigitalOcean, employees solve real challenges from day one on the job. Cloud and AI-native disrupters are moving at lightning speed. We ship continuously so that new capabilities show up where customers need them - in core cloud, AI infrastructure, platforms, and agentic workflows. For Mitali Soni, this meant working directly on AI infrastructure to help customers save costs. "We were trying to build out an AI application where potential customers could upload their invoice, and on the back end, we infer the current usage and map that out with DigitalOcean's offerings," Mitali explains. "The project did end up going live. It's out there on the internet for people to use, and it was a really nice feeling." View YouTube video Yeshvanth Raju Kurapati worked on critical stability features for the core platform, using Machine Learning to predict crashes. "My first project was to build proactive remediation methods using ML for hypervisor crashes," he says. "The system is live, and it helps prevent infrastructure failures before they impact customers." During Isabel Shic's first week, she was already improving the onboarding flow. She started shaping the very first interaction users have with the experience. "I immediately saw it in the product a couple of weeks later, which ## Agentic AI is coming for your database DevFeed: [Agentic AI is coming for your database]() Original publisher: [Read original article]() Author: Becca Weng Published: 2026-02-13T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Agentic AI](), [Database](), [consistency](), [Back end](), [Cloud Architecture](), [data-engineering](), [Transactions](), [AI Integration Strategies](), [AI-assisted coding](), [Vectors & LLMs]() Tags: [adoption](), [agent](), [agentic](), [agentic-ai](), [agents](), [ai](), [ai-agents](), [apis](), [architecture](), [architectures](), [back-end](), [code](), [data](), [database](), [machine](), [speed](), [store](), [systems](), [work](), [writing-code]() ### AI overview The webinar argues that autonomous agents could drive far more frequent, bursty database traffic than human users, making stale reads and scaling costs more consequential. It recommends data systems that provide serializable consistency, predictable behavior under concurrency, live elastic scaling, continuous availability, burst handling, and cost efficiency at high volume. Spencer Kimball identifies total cost of ownership at massive scale as a key competitive factor. The excerpt presents CockroachDB as a distributed, PostgreSQL-compatible system with strong consistency, horizontal scaling, and resilience across nodes, regions, and clouds; it explicitly says CockroachDB is not an AI or vector database. ### Source excerpt AI agents are no longer a thought experiment. They're already writing code, calling APIs, retrying failed requests, and coordinating work at machine speed. And they're about to put unprecedented pressure on the systems we use to store and move data. ## How to Integrate OAuth2 Into a Django/DRF Back End Without Losing Control DevFeed: [How to Integrate OAuth2 Into a Django/DRF Back End Without Losing Control]() Original publisher: [Read original article]() Author: Prachi Kothiyal Published: 2026-02-06T10:58:21Z Content type: tutorial Language: en Sources: [Talent500]() Topics: [Django](), [OAuth 2.0](), [Back end](), [Authentication](), [API](), [Framework]() Tags: [a-minimal-view-for-token-exchange](), [api](), [authentication](), [back-end](), [backend](), [configuring-back-end-authentication](), [django](), [newsletters](), [oauth2](), [rest](), [server-side-vs-client-side-oauth2-flows](), [testing-and-reliability-considerations](), [what-the-back-end-needs-to-do](), [why-rely-on-a-social-auth-library]() ### AI overview A practical guide to integrating OAuth2 social login into a Django or Django REST Framework back end. It recommends letting the front end handle the provider flow while the back end validates third-party tokens, maps social identities to local accounts, and issues API credentials. ### Source excerpt When a Django or Django REST Framework (DRF) back end is already handling authentication, adding "Log in with Google" or [...] The post How to Integrate OAuth2 Into a Django/DRF Back End Without Losing Control appeared first on Talent500 blog. [Next page]()