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I've been working as Software Developer mainly focussed on Java and builts many application using Eclipse RCP framework or VS Code Application. Almost all the time I had to deal with multiple large files (either read/generate/validate) them which seemed very difficult and some of them almost impossible as most of them would be dependant on each other and would be referencing each other (just like how java files work together). Now assume client1 requires the same content in multiple Json files and client2 needs it in xml files. We couldn't go on writing a different application or go on adding if conditions and blah blah blah !!!! Wouldn't it be easier if as soon as I execute the application it generates the content in whatever format I choose and also taking care of dependencies/ references (like adding import statements). Additionally integrate with features of IDE and provide proposals, perform validations on the fly. Rela World Examples : Try googling Arxml once (Trust me I've dealing with these files for almost 7 years and it's always a nightmare to debug these) Solution: Xtext framework In this tutorial, I will show you how to use Eclipse Xtext and Xtend to build a simple, readable DSL that automatically generates Java boilerplate for you. Fair Warning: There will be no running executions screenshots or anything. You are gonna have to run it yourself and check the results and of course questions are always welcome in the comments section. But if for some reason you are unable to replicate this then let me know I'll try to explain further. I believe the best way to learn is by doing it yourself. The Goal: What are we building? Instead of writing 100 lines of Java with private fields, getters, and setters, we want our developers to write 5 lines of code in our own custom language (basically you can create your own programming language with your own custom syntax), like this: entity User { var name : String var age : Integer } When this file (assume file extension

This is an adapted English version of an article I first wrote in Japanese. I work with AI to shape and review my drafts, but the argument and the field observations are my own. The numbers are cited from public surveys (linked at the end). I built an aggressive prompt-injection block to stop my AI agent from repeating the same mistakes. It worked, so I kept adding rules. By the time I noticed, the file had ballooned to 56,000 characters — and the agent had quietly stopped functioning. Too much context, attention spread too thin to act on any of it. I gutted it back to under 1,200 characters, and here's the part that still stings: it behaved better with fewer rules. That was the day I learned my whole mental model was backwards. This isn't a post about making your AI more accurate. It's about designing so that accuracy stops being the thing you depend on. The mistake I made for months My agent kept skipping the same step in a workflow. So I did what every engineer does on instinct: I added a rule. "Don't skip this step." Then it did something else dumb, so I added another rule. Then another. I was treating the rules file like a conversation with a colleague — as if the agent would remember yesterday's correction and carry it forward. It doesn't. Every run starts cold. "Be careful next time" assumes a next time that shares state with this time. For a stateless model, there is no continuity to appeal to. You are talking to a counterparty with no memory of the conversation you think you're having. So the rules pile up, because each correction feels like progress. And for a while the numbers even improve. But adding rules has a ceiling, and I blew straight through it: at 56,000 characters the agent wasn't reasoning over my guardrails anymore — it was drowning in them. Knowing a rule and stopping at it are different things Here's the distinction that took me far too long to see. Putting a rule in the context window means the model knows the rule. It does not mean the mod

Most agents drag their entire past into every turn. A better default: keep a thin index of what was said hot, and fetch only the few turns you actually need — intact, on demand. Code: github.com/NirajPandey05/jit_context There is a quiet assumption baked into how most agents handle memory: that more context is safer than less. If the model might need something, put it in the window. The conversation grows, every prior turn rides along on every new request, and we trust the model to find the part that matters. That assumption breaks twice. It breaks on cost , because an agent loop re-sends its whole window on every step — a hundred stale turns aren't paid for once, they're paid for on turn 101, 102, and every step after. And it breaks on quality , because models don't read a long window evenly. Relevant facts buried in the middle get underweighted; irrelevant bulk competes for attention with the thing that actually answers the question. Past a point, a bigger context produces a worse answer, not just a costlier one. So the interesting question isn't "how do we fit more in?" It's "how do we keep the window small and dense without losing the one old turn that matters?" This post is the design we built around that question — for the specific case of long conversation history — plus the benchmark we used to keep ourselves honest. 01 · The mechanism: a hot index over a cold store The design borrows directly from how computers have always managed memory that doesn't fit: a small fast tier that's always present, a large slow tier that holds the bulk, and a rule for moving things between them. Virtual memory pages between RAM and disk. We page between the context window and an external store — for attention instead of address space. Concretely, there are two tiers. The cold store holds every turn at full fidelity, keyed by id — nothing is thrown away. The hot index holds one compact entry per turn: a short summary, a little metadata (entities, whether the turn recorded a dec

Your repo is already your agents' context, whether you designed it on purpose or not That sentence took me a while to understand. In this post I'll save you the trip. It was October 2025, working in Skyward's monorepo with AI agents every day. And every day the same routine: I'd tell the agent in the prompt "don't use this", "don't do it this way", "reuse the component that already exists". I wrote it down. I repeated it. The agent did exactly what I told it not to do. It wasn't that it didn't listen to me. It was that it read the code and saw something else there. The agent believes the code, not your prompt An agent follows the patterns it sees in the repo, not the ones you tell it in the prompt. And subagents are worse, because they start without the conversation's context. The whole fight you put up earlier in the chat, for them it never happened. So this is what kept happening. It created a new component even though one already existed that solved exactly that problem. It didn't respect the design rules or use the design tokens. It followed stale docs because they were still there, alive, with nothing flagging them as outdated. My first instinct was everyone's instinct, cram more context into the prompt. More rules, more "please don't do this", more examples pasted in by hand. It half worked, and for the next task you had to add it all again. Until the next subagent showed up and started from scratch. At some point, tired of repeating myself, I understood the obvious thing. The agent wasn't disobeying me. It was reading the repo and listening to what the repo said about itself. If the good component lives alongside three old versions, it has no way to know which one is the official one. If the docs say one thing and the code does another, it'll believe whichever is closest at hand. It's doing exactly what I asked. The repo itself is the context agents use. If it's badly structured, the answers won't be good. Period. No prompt fixes a repo that contradicts itsel

Tu repo ya es el contexto de tus agentes, lo hayas diseñado a propósito o no Esa frase me costó entenderla. En este post te ahorro el camino. Era Octubre del 2025, trabajando en el monorepo de Skyward con agentes de IA todos los días. Y todos los días la misma rutina: le decía en el prompt al agente "no uses esto", "no hagas esto así", "reutiliza el componente que ya existe". Lo escribía. Lo repetía. El agente hacía exactamente lo que le dije que no hiciera. No era que no me escuchara. Era que leía el código y ahí veía otra cosa. El agente le cree al código, no a tu prompt Un agente sigue los patrones que ve en el repo, no los que tú le dices en el prompt. Y los subagentes son peores, porque arrancan sin el contexto de la conversación. Toda la pelea que tú diste arriba en el chat, para ellos no existió nunca. Entonces pasaba esto. Creaba un componente nuevo aunque ya había uno que resolvía exactamente el problema. No respetaba las normas de diseño ni usaba los design tokens. Seguía documentación obsoleta porque seguía ahí, viva, sin nada que la marcara como obsoleta. Mi primer instinto fue el de todos, meter más contexto en el prompt. Más reglas, más "por favor no hagas esto", más ejemplos pegados a mano. Funcionaba a medias, y para la siguiente tarea había que volver a agregarlo todo. Hasta que llegaba el siguiente subagente y empezaba de cero. En algún momento, cansado de repetirme, entendí lo obvio. El agente no me estaba desobedeciendo. Estaba leyendo el repo y haciendo caso a lo que el repo decía de sí mismo. Si conviven el componente bueno y tres versiones viejas, no tiene cómo saber cuál es el oficial. Si la doc dice una cosa y el código hace otra, le va a creer al que esté más a mano. Está haciendo justo lo que le pedí. El mismo repo es el contexto que usan los agentes. Si está mal estructurado, las respuestas no van a ser buenas. Punto. No hay prompt que arregle un repo que se contradice a sí mismo. Screaming Architecture me llevó hasta media cancha Lo prim

On June 12, 2026, enterprise developers using the Codex API started seeing an unfamiliar response header: X-Model-Version: kindle-alpha . It appeared on a subset of requests for roughly 18 hours, then vanished. That's the release candidate for GPT-5.6 — OpenAI's next flagship model — leaking through the staging layer. OpenAI's Chief Scientist publicly called the upcoming release "a meaningful leap" the following day. By OpenAI's historically understated communications standards, that's loud. This post covers what the backend traces, developer reports, and Polymarket odds (currently ~80% for a pre-June-30 launch) actually tell you about the model — and what to do before it drops. How the Leak Surfaced Three separate sources converged in the 72 hours after the June 12 header incident. First, developers with ChatGPT Pro OAuth access reported hitting context windows significantly beyond GPT-5.5's supported limit. At least four documented cases logged successful 1.5M-token completions before the backend silently downgraded them to the production model. Second, the Codex enterprise API logs — accessible with full response header exposure enabled — confirmed the kindle-alpha codename across US-east-1 and us-west-2 endpoints. Third, the Polymarket market for "GPT-5.6 public release before July 1, 2026" moved from 61% to 80%+ within 48 hours of the header reports circulating on developer forums. None of this is from OpenAI's press office. No model card, no official benchmark numbers, no pricing. The specifics below are high-confidence inference from multiple corroborating signals — not official spec. Treat it accordingly when making production decisions. The Architecture Shift: Agentic-First, Not Just Smarter GPT-5.5 was trained as a reasoning model with agent capabilities added on top. GPT-5.6 is reportedly designed in the opposite order. The primary optimization target during training was not MMLU or GPQA benchmark scores — it was token efficiency on long-horizon agentic t

A data breach involving government-issued ID documents affects over three million people in Texas.

Uber recently described an internal architecture for propagating identity across multi-agent AI workflows. The design aims to perserve user context, agent provenance, and scoped access as agents delegate work and call internal tools. The case study aligns with Auth0’s view that AI agents need permissions based on delegated authority, scoped credentials, and explicit human approval boundaries. By Eran Stiller

GitHub热门项目 | Introduction to Autonomous Robots | Stars: 2,478 | 276 stars today | 语言: TeX

HashiCorp has announced the general availability of the Terraform MCP Server, an open-source MCP server that enables agents to integrate with Terraform Registry APIs. The company says that it can improve infrastructure teams productivity by relieving engineers of rote tasks. By Sergio De Simone

Model Context Protocol (MCP): Giao Thức Kết Nối Thế Giới Cho Trí Tuệ Nhân Tạo Trong thế giới AI đang phát triển với tốc độ chóng mặt, việc xây dựng các ứng dụng thông minh, có khả năng tương tác linh hoạt với dữ liệu và công cụ bên ngoài là một thách thức lớn. Các mô hình ngôn ngữ lớn (LLM) như GPT, Claude, hay Gemini dù mạnh mẽ nhưng thường hoạt động trong "vùng cô lập", thiếu khả năng truy cập trực tiếp vào các hệ thống bên ngoài theo thời gian thực. Đây chính là lúc Model Context Protocol (MCP) xuất hiện như một giải pháp cách mạng. MCP là một giao thức mở, được thiết kế để tiêu chuẩn hóa cách thức các ứng dụng cung cấp ngữ cảnh (context) cho LLM, giúp phá vỡ rào cản giữa trí tuệ nhân tạo và thế giới thực. Bài viết này sẽ đi sâu vào phân tích Model Context Protocol , từ định nghĩa, kiến trúc, đến các lợi ích và ứng dụng thực tế, giúp bạn hiểu tại sao nó được coi là "ngôn ngữ chung" của tương lai AI. Model Context Protocol (MCP) Là Gì? Model Context Protocol (MCP) là một giao thức mở, được phát triển để tạo ra một chuẩn giao tiếp thống nhất giữa các LLM và các nguồn dữ liệu, công cụ bên ngoài. Hãy tưởng tượng MCP như một "cổng USB" dành cho AI. Thay vì mỗi ứng dụng AI phải viết mã tích hợp riêng lẻ với từng loại cơ sở dữ liệu, API, hay hệ thống tệp tin (mỗi loại một kiểu "phích cắm" khác nhau), MCP cung cấp một giao diện chuẩn. Bất kỳ ứng dụng nào hỗ trợ MCP đều có thể kết nối với bất kỳ nguồn tài nguyên nào cũng hỗ trợ MCP một cách liền mạch. Mục Đích Cốt Lõi Của MCP Mục tiêu chính của Model Context Protocol là giải quyết vấn đề "fragmentation" (phân mảnh) trong hệ sinh thái AI. Trước MCP, việc tích hợp thường diễn ra rời rạc: Mỗi nhà phát triển ứng dụng phải tự xây dựng các "kết nối" tùy chỉnh. Mỗi lần cập nhật mô hình hoặc công cụ có thể làm hỏng các tích hợp cũ. Khó khăn trong việc chia sẻ và tái sử dụng các công cụ AI giữa các dự án. MCP giải quyết những vấn đề này bằng cách cung cấp một lớp trừu tượng chuẩn hóa. Kiến Trúc Và Cách Thức Hoạt Động Của MCP Kiến

One of those nuances to keep in your back pocket when writing for screen readers. There’s no need to include ‘navigation’ in your navigation labels originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

The funding round was led by Norwest, with participation S Capital VC, Cerca Partners, and Oceans Ventures. Snowflake Ventures also participated as a strategic investor.

Birgitta Böckeler, Distinguished Engineer at Thoughtworks, returns to discuss the rapid evolution of AI in software delivery. She touches on the evolution from vibe coding, the changing tools landscape and the more autonomous agents that, besides higher velocity, introduce higher risk. By Birgitta Böckeler

Releasing sterilized flies can crash a local population of flesh-eating screwworms. But the US currently has limited capacity to produce them.

Nvidia's announcement that it's getting into the consumer laptop chip space with RTX Spark is huge. Apple has proved for years that Arm-based chips can perform incredibly well while also delivering great battery life - at least on the Mac. In the Windows world, performance hasn't fully matched up under Qualcomm chips, mostly in the […]

Computex 2026 is kicking off in Taipei, Taiwan this week, where Nvidia, AMD, Qualcomm, Intel, and other tech brands are announcing new laptops, handhelds, chips, and more. Nvidia unveiled RTX Spark, its first family of consumer PC chips, arriving in laptops and mini PCs starting this fall. Intel is launching two new custom chips made […]

Nvidia has officially entered the world of consumer laptop chips with the RTX Spark, and several device makers already have hardware lined up for it. Microsoft, Asus, HP, MSI, Lenovo, and Dell are expected to launch RTX Spark laptops sometime this fall, and some of those partner companies have shared details about what we can […]

If you asked me what I'd change about the Xbox Ally X handheld - aside from fixing Windows, I mean - I'd tell you two key things. First, give me a bigger, better screen. Even a little bit bigger, so games feel less claustrophobic and with less ugly bezel. Second, get rid of the "Library" […]

Once upon a time, Microsoft had to write off $900 million betting an Arm-based Nvidia chip could power its first flagship Windows portable, the original Microsoft Surface. But today, it's trying again. Microsoft and Nvidia have just announced the Surface Laptop Ultra, a computer with a new Arm-based Nvidia chip at its core. There's a […]