标签：#AI

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . I spent a week deploying a CrewAI agent to AWS Bedrock AgentCore. The SDK wasn't on PyPI. The error messages were 200 OKs. The container crashed without logs. And the naming regex rejected hyphens without telling me why. This is the full debugging trail. Every failure was silent. Every fix required reading source code nobody documented. Table of Contents The Project Failure 1: The SDK That Doesn't Exist on PyPI Failure 2: The 200 OK That Means Failure Failure 3: The Container That Crashed With No Logs Failure 4: The Naming Regex Nobody Documented The Two-Client Split Nobody Mentions What I Learned The project I built a resume-tailoring AI agent with CrewAI and Amazon Bedrock. It takes a job description, analyzes your resume, identifies gaps, and rewrites bullet points to match what the role actually needs. Locally it worked perfectly. CrewAI orchestrates the agents, Bedrock Nova Pro handles the LLM calls, and the output is solid. Deploying it to production was the problem. AWS launched Bedrock AgentCore in June 2026 as a managed runtime for AI agents. You containerize your agent, push the image, and AgentCore handles scaling, memory, and invocation. Sounds simple. It was not simple. Failure 1: The SDK that doesn't exist on PyPI The docs say to install bedrock-agentcore-client . I ran: pip install bedrock-agentcore-client It installed successfully. No errors. That's because there's a placeholder package on PyPI with that name. It installs, imports fail silently, and your container builds successfully with a broken dependency inside. The real SDK lives in AWS's CodeArtifact registry. You need to configure pip to pull from a private index: aws codeartifact login --tool pip \ --domain amazon-agent-runtimes \ --repository agent-runtimes-pypi \ --domain-owner 600427722194 Then install from there. The PyPI package is a trap. Nobody warns you. Hours lost: 3. The error only appears at runtime

Adobe's Project Indigo app will have new features like removing distractions and offering immediate photo feedback.

A judge has paused the Paramount / Warner Bros. deal to review a multi-state lawsuit.

The lawsuit from the states alleges that the deal would harm movie theaters, basic cable distributors, and audiences.

Celebrating $100 million contributed by the community to the people who build and sustain open source every day. The post $100 million for open source: A milestone built by the community appeared first on The GitHub Blog .

Adobe's experimental camera app has taken an unexpected turn. After Project Indigo was launched last year to provide a "more natural (SLR-like) look" for iPhone photography, the Indigo camera app is now being updated with a suite of generative AI tools. And the change doesn't rely upon Adobe's own Firefly AI models. Adobe describes the […]

На прошлой неделе DeepSeek отказался писать мне регулярку. Не «не смог», а вежливо объяснил, что регулярка тут плохая идея, и предложил цикл. Тогда я поймал себя на том, что уже год сравниваю модели «на глаз»: одна затупила — скопировал промпт в соседнюю вкладку, посмотрел, сделал выводы из ничего. Надоело. Я устроил маленький честный замер: три типовые задачи из моих рабочих будней, буквально один и тот же промпт, три модели. Про сетап — один абзац, чтобы дальше не отвлекаться. Хотелось убрать переменную «у одной вкладки отвалился VPN, у другой слетела сессия», поэтому гонял всё в одном окне через агрегатор; ссылка будет внизу, здесь она не важна. Важно одно: модель переключается прямо над полем ввода, так что все три участника получали идентичный текст без копипасты между сервисами. Участники: GPT (флагман, GPT-5-класс), Claude, DeepSeek. Задача 1. Регулярка: split CSV-строки, не ломая кавычки Промпт: «Напиши JS-регулярку, чтобы разбить строку CSV по запятым, но запятые внутри двойных кавычек разделителями не считать». Классическая ловушка: наивный split(',') рвёт поле "Иванов, Иван" пополам. GPT сразу выдал вариант с lookahead: const parts = row . split ( /, (?=(?:[^ " ] *" [^ " ] *" ) * [^ " ] *$ ) / ); Работает. Правда, на моём проверочном кейсе с экранированной кавычкой "" внутри поля — уже нет. Но я такого и не просил, засчитываю. Claude дал ту же регулярку, но с примечанием: lookahead на каждой запятой пробегает хвост строки, на длинных строках это квадратично, для продакшена берите csv-parse . Занудно, но по делу — я однажды именно такую конструкцию ловил на таймауте. DeepSeek — это тот самый отказ, с которого всё началось. Регулярку писать не стал, предложил цикл с флагом «мы внутри кавычек» — мол, так надёжнее. Формально это даже честнее, но задание было «напиши регулярку», и выдал он её только после уточнения. Итог раунда: GPT и Claude ровно, Claude на полбалла впереди за предупреждение. Задача 2. Рефакторинг: функция скидок с вложенными if Скормил всем

A team lead asks the question in a planning meeting and the room splits three ways: do we build this agent in Copilot Studio, write the orchestration ourselves and host it, or hand our container to Foundry and let it run our code? All three are official Microsoft build paths in 2026, all three end up in the same tenant-wide agent inventory, and the wrong pick costs you a rebuild once the project outgrows it. The answer is not "the most powerful one." It is the one whose service model matches who is building the agent, who owns the runtime, and how much pro-code control over orchestration and protocols you actually need. This article is the decision framework for that choice, grounded in Microsoft Learn and current as of mid-2026. Two of these three paths are public preview, so this is a guide to architectural fit and direction, not a production-reliability scorecard. TL;DR Three build paths, picked by service model, not power. Copilot Studio: low-code managed SaaS for makers. GA. Foundry Hosted agents: managed PaaS runtime for your own container. Public preview. Microsoft 365 Agents SDK: pro-code, self-hosted, widest channel reach. Agent Framework orchestrator in public preview. Monday move: before picking a platform, write down four things for this agent - who builds it (maker or pro-dev), who must own the compute, what channels it has to reach, and whether you need custom protocols or background/async behavior. Those four answers pick the path more reliably than a feature checklist. The three paths in one paragraph each Microsoft's own Cloud Adoption Framework frames the build options as three service tiers, which is the cleanest mental model to start from. The CAF positions them as Copilot Studio (SaaS, no/low-code), Microsoft Foundry (PaaS, pro-code or low-code), and GPUs and Containers (IaaS, code-first frameworks for maximum flexibility). The first two are managed by Microsoft. The third is where the self-hosted SDK path lives when you own the compute end to e

DISCLAIMER: This post was written entirely by me! I used AI for a little research, spelling, grammar, and comprehension checks. This post was originally shared on Hackernoon Entertain me for a moment, let's appreciate where we are today by understanding where we've been… or at least, where I've been. I remember when I first learned to code. I was bad, like really bad. But I was so curious! It all started by writing some VBA in an MS Access Database to create an IT Inventory app in the late 90s. Then I learned JavaScript, ASP (without the .Net), then C#, .Net ( I still have my .Net for Dummies book, see below) , jQuery, Python, Java, Angular, ReactJS, and Python again (yeah, had to relearn that one for some reason), and I'm sure there are others in there I forgot about. Learning to write code was rewarding! There were those days I'd spend hours on a bug, only to realize I didn't initialize the variable or forgot a semicolon. I learned .Net over a weekend thanks to the above book. I never became an artist of the craft, like some of my colleagues have (you know who you are: Josh, Kevin, and many others), but I knew how to build anything. I loved that ability: I could build anything. Pure joy! If you haven't had the joy of learning to code, do your best to learn it, because you can't prompt your way to being a Senior Engineer . As I progressed in my career, I became an architect and senior lead. I started off by leading a single engineering team, and now I support large programs and teams. All the while, I never let go of hands-on-code. I still love coding for work and my myriad of side projects. Then, a few years ago, this GenAI thing showed up. Put me in that group of: oh-no-there-goes-my-joy. Joy, yes, not my job. I love my job because I get to do what I love. I loved the dramatic rollercoasters: architecting a perfect solution, realizing it's wrong, getting to write every line of code, chasing impossible bugs, panicking with deadlines, late nights chasing hot fixes,

Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10 The RAG Reality Check Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small , top-k=5, stuff into context. It works for demos. Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks drown signal in noise Customer tickets: Conversational context needs overlap, not fixed windows Latency: 500ms embedding + 200ms vector search + 300ms LLM = 1s+ per query We rebuilt our retrieval layer from first principles. Here's what actually moves metrics. Chunking: One Size Fits None # rag/chunking.py from abc import ABC , abstractmethod from dataclasses import dataclass @dataclass class Chunk : text : str metadata : dict token_count : int chunk_id : str class ChunkingStrategy ( ABC ): @abstractmethod def chunk ( self , document : str , metadata : dict ) -> list [ Chunk ]: ... class FixedTokenChunker ( ChunkingStrategy ): """ Baseline. Good for homogeneous content. """ def __init__ ( self , chunk_size = 512 , overlap = 50 ): self . chunk_size = chunk_size self . overlap = overlap class RecursiveChunker ( ChunkingStrategy ): """ Respects structure: markdown headers, code blocks, paragraphs. """ def __init__ ( self , separators = [ " \n ## " , " \n ### " , " \n\n " , " \n " , " " ], chunk_size = 512 ): self . separators = separators self . chunk_size = chunk_size class SemanticChunker ( ChunkingStrategy ): """ Uses embedding similarity to find natural boundaries. """ def __init__ ( self , model = " text-embedding-3-small " , threshold = 0.7 ): self . model = model self . threshold = threshold class AgenticChunker ( ChunkingStrategy ): """ LLM decides boundaries. Expensive but highest quality for complex docs. """ def __init__ ( self , model = " gpt-4o-mini " ): self . model = model Our production config by

Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics How we replaced "looks good to me" with automated evaluation catching 92% of hallucinations before deployment The Problem: Why "Vibe Checks" Fail in Production Three months ago, our team shipped a RAG-based customer support assistant. It worked great in testing — we'd ask it questions, read the answers, and say "yeah, that looks right." Then it hit production. A customer asked about their billing cycle. The assistant confidently cited a policy that didn't exist. Another asked about API rate limits and got numbers from a competitor's documentation. By the time we caught it, 500+ users had seen hallucinated responses. The post-mortem was brutal: we had zero automated evaluation . Our test process was literally "ask 5 questions, read answers, thumbs up." What Production Evaluation Actually Needs Academic benchmarks (MMLU, HellaSwag) don't tell you if your system works for your use case. Production evaluation needs: Domain-specific judges — Your criteria, not generic "helpfulness" Speed — Evaluation must run in CI/CD, not overnight Regression detection — Know immediately when a prompt change breaks things CI/CD integration — Block merges that degrade quality Golden dataset management — Versioned, stratified, growing test cases Architecture: The Evaluation Pipeline ┌─────────────┐ ┌──────────────┐ ┌────────────────────┐ ┌──────────────┐ │ Test Cases │────▶│ LLM Under │────▶│ Judge Ensemble │────▶│ Metrics & │ │ (Golden Set)│ │ Test │ │ - Faithfulness │ │ Regression │ └─────────────┘ └──────────────┘ │ - Instruction F. │ │ Detection │ │ - JSON Schema │ └──────┬───────┘ │ - Custom LLM │ ▼ └────────────────────┘ ┌──────────────┐ │ Dashboard/ │ │ PR Comments │ └──────────────┘ Core Abstractions # eval/base.py @dataclass ( frozen = True ) class TestCase : id : str input : dict [ str , Any ] expected : dict [ str , Any ] | None = None tags : list [ str ] = field ( default_factory = list ) # ["edge-case",

Everything Claude Code: How One AI Assistant Becomes a Full Engineering Team Most developers use AI coding assistants in the same way. They open a repository and type: Build this feature. The assistant reads a few files, generates code, and reports that the task is finished. Sometimes the result is impressive. Sometimes it solves the wrong problem with perfectly formatted code. The issue is not always the intelligence of the model. The issue is the workflow around it. A single prompt often expects one AI assistant to behave like: a product planner a software architect a frontend developer a backend developer a security engineer a test engineer a debugger a code reviewer a release manager Real engineering teams do not work that way. They divide responsibility. One person plans. Another implements. Someone reviews security. Someone tests edge cases. Someone challenges the architecture. The final result improves because different people examine the same work from different angles. That is the idea behind Everything Claude Code , often shortened to ECC . ECC is an open-source collection of specialized AI agents, reusable skills, commands, security tooling, hooks, memory systems, and development workflows designed to turn Claude Code into something closer to an AI software engineering team. Instead of asking one general-purpose assistant to do everything in one pass, ECC separates software development into focused roles. That architectural idea is more important than any individual command. This article explains how ECC works, why developers are excited about it, how to install only what you need, and what security boundaries you should establish before giving any AI agent access to a real codebase. What Is Everything Claude Code? Everything Claude Code is not a new language model. It does not replace Claude. It is a workflow and configuration layer built around Claude Code. Think of Claude Code as one highly capable engineer. ECC attempts to turn that engineer into a co

YouTube has updated its monetization policies to more clearly define the kinds of AI-generated and low-quality videos that can’t earn ad revenue.

"Something's coming. Something we may not be able to deter."

After months of teasing us with reminders about how large Avengers: Doomsday's cast is going to be, Marvel has finally released a proper trailer for the film. Though Robert. Downey Jr.'s Doctor Doom definitely appears to be causing a lot of the chaos in Doomsday's first trailer, the movie seems like it's going to feature […]

InfoQ has opened enrollment for three five-week online certification cohorts starting in August, each led by a senior practitioner applying QCon talk frameworks to participants' own work: architecture with Luca Mezzalira, engineering leadership with Michelle Brush, and AI security and privacy with Katharine Jarmul. By Artenisa Chatziou

Why we built Veridexa Many document analysis workflows still rely primarily on OCR OCR is useful for extracting text, but it cannot answer one important question: Does this document show signs of fraud or manipulation? That question led us to build Veridexa. The problem Organizations receive thousands of digital documents every day: Passports National IDs Academic certificates Bank statements Employment documents Invoices Reading the text is only one part of the process. The difficult part is detecting manipulation, inconsistencies, forgery, or suspicious evidence before making a decision. Our approach Instead of relying on OCR alone, Veridexa combines multiple evidence sources into a single fraud assessment. The platform analyzes: OCR extraction Metadata Image forensics Security features Document structure Cross-evidence consistency The result is an explainable decision instead of a simple confidence score. Explainable decisions Every analysis ends with one of three outcomes: ACCEPT MANUAL REVIEW REJECT Each decision is accompanied by supporting evidence so reviewers understand why the system reached that conclusion. Public benchmark We also believe AI systems should be transparent. For that reason Veridexa publishes a public benchmark together with methodology and performance reporting instead of asking users to trust marketing claims. API-first Try the public demo, explore the benchmark, or integrate the API. Feedback from developers and security professionals is always welcome. https://veridexa.io Developers can integrate Veridexa into their own applications using our API while organizations can use the web platform without writing code. We'd love your feedback We're continuing to improve the platform and would genuinely appreciate feedback from the developer community. Website: https://veridexa.io

Your agent isn't hallucinating. It's reasoning correctly over the wrong inputs. Here's a failure pattern every team running agents in production has hit: An alert fires. The agent investigates: pulls metrics, checks recent deploys, scans logs, proposes a fix. The fix is confidently, articulately wrong. Instinct says blame the model (bad reasoning, needs a better prompt, maybe a bigger model). Then someone reconstructs what the agent actually saw. The metrics query returned a 5-minute-old cached aggregate. The deploy list was fetched before the relevant deploy landed. The log window was truncated at 1,000 lines and the line that mattered was #1,014. Given those inputs, the agent's conclusion was reasonable. It just wasn't debugging the incident that happened. It's garbage in, garbage out, with a twist that makes it worse for agents than for any previous software. A dashboard shows you the garbage. A human sees a stale chart and might notice the timestamp. An agent consumes the garbage silently and acts on it, with fluent reasoning layered on top. The output doesn't look like garbage; it looks like a confident, well-argued investigation. That confidence is what makes it dangerous. Why is this surfacing now For chatbots, context was mostly a retrieval problem over documents; mediocre retrieval meant a mediocre answer a human would shrug at. Three things changed with production agents: Agents act. A stale metric doesn't produce an off paragraph; it restarts the wrong service or pages the wrong team at 3AM. The cost went from cosmetic to operational. The input surface exploded. A production agent reads metrics, logs, deploy history, tickets, and chat from separate systems, each with its own latency, rate limits, caching, and clock. The "world" it reasons over is stitched together from partial snapshots, inside the model's reasoning loop, where nobody can inspect it. Errors compound. A single wrong input gives a slightly wrong answer. A 20-step investigation where step 3'

The original concept behind this tool won the runner-up award for the Github CLI Challenge earlier this year. As a reminder, ReflectCLI is a tool to promote thoughtful coding through structured reflection. What does it do? Before each commit, answer reflective questions to build your personal developer knowledge dataset. The tool is local-first by design. It stores everything locally inside the current Git repo: .git/git-reflect/log.json No account, no cloud sync, no authentication, and no external AI API call. Why build it? AI-assisted development is powerful, but it can encourage cognitive offloading, letting tools do the thinking instead of developing deeper understanding. git-reflect interrupts this pattern by making reflection part of your workflow. Each answer becomes part of your personal knowledge base, documenting not just what you built, but why you built it that way and what you learned. Over time, this dataset reveals patterns in your thinking and helps you grow as a developer. If you want to read about the original tool, check out my previous post here . Since building the original version, I've spent months researching how AI changes the way developers learn, reason, and make technical decisions. Many of the ideas from those discussions have now been incorporated into ReflectionCLI 2.0. What started as a simple Git pre-commit reflection hook has evolved into a local-first thinking tool for developers working alongside AI assistants. What's New Comprehension debt tracking You can now record artifacts you shipped but don't fully understand yet. reflection debt add "Understand why the cache invalidates on user updates" \ --project api \ --tags cache,ai \ --context "AI generated most of the invalidation logic" These artifacts can easily be retrieved and resolved later. reflection debt list reflection debt resolve debt-001 Explain-back mode This workflow focuses on active recall and asks a series of questions to assess your ability to explain the piece of c

Perplexity added "Skills" to its Agent API, letting developers compose built-in and custom skills for more complex agent outputs. More skills mean more actions, and more actions mean more ways to produce an irreversible change. Before you add a fifth skill to your agent, make sure the first four have an undo path. The undo problem An agent skill that writes, sends, publishes, or deploys is an irreversible action if there is no rollback. The more skills an agent has, the more irreversible actions it can take in a single session. A chain of skills (research → draft → format → publish) can complete before a human notices the first step was wrong. The undo contract Every agent skill should declare: { "skill_name" : "publish_to_blog" , "action_type" : "write" , "reversible" : true , "undo_method" : "set_published_false" , "undo_timeout_seconds" : 3600 , "undo_side_effects" : [ "SEO index will retain the URL for up to 24h" ] } If reversible is false , the skill should require explicit human approval before execution. A skill registry with undo support # skill_registry.py """ Registers agent skills with undo metadata. Blocks irreversible skills from running without explicit approval. """ from dataclasses import dataclass from typing import Optional , Callable import json @dataclass class Skill : name : str action_type : str # "read", "write", "send", "deploy" reversible : bool undo_fn : Optional [ Callable ] = None undo_timeout_seconds : int = 3600 side_effects : str = "" requires_approval : bool = False class SkillRegistry : def __init__ ( self ): self . skills = {} self . execution_log = [] def register ( self , skill : Skill ): # Irreversible write/send/deploy skills require approval if not skill . reversible and skill . action_type in ( " write " , " send " , " deploy " ): skill . requires_approval = True self . skills [ skill . name ] = skill def execute ( self , skill_name : str , inputs : dict , approved : bool = False ): skill = self . skills . get ( skill_name ) i