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Dev.to is a community-driven website focused on software development, programming, and technology. It was launched in 2016 by Ben Halpern, and its main goal is to provide a platform for developers to share knowledge, learn from others, and build a community. The website features a blog-like format, where users can create and share articles on various topics, including coding tutorials, project showcases, industry insights, and more. Dev.to allows users to create accounts, follow other users, and engage with their content through comments and reactions. Dev.to has a strong focus on community engagement, with features like discussion forums, podcasts, and live streams. It also hosts a series of community-driven projects, such as coding challenges and hackathons, to encourage collaboration and innovation. In addition to user-generated content, Dev.to features a job board, where companies can post job openings and developers can search for employment opportunities. The website also offers a newsletter, which provides updates on the latest articles, news, and events. Overall, Dev.to has become a popular platform for developers to connect, share knowledge, and stay up-to-date with the latest trends and technologies in the software development industry.

DEV Community dev.to

RSS Hunter • Aug 19, 2024

Thread Of Notes

I Pinned 31 GitHub Actions to Commit SHAs. One Major Tag Was Two Versions Stale.

Pinning GitHub Actions to specific commit SHAs is crucial for security and stability, as tags can be moved. This practice ensures that the exact code reviewed is always executed, preventing unexpected changes. When pinning, it's recommended to include the release version as a comment alongside the SHA for human readability.To find the correct SHA for an action without relying on APIs or external blogs, the git ls-remote --tags command can be used. This command, which has no rate limits, retrieves all tags from a repository. The output will show both the tag and the commit it points to, and for annotated tags, the specific commit SHA is identified with a ^{} suffix.During a recent packaging effort, three common issues were identified with existing action usage. One action's major tag was significantly outdated, pointing to a much older version than its latest release. Another action's major tag lagged behind its own recent releases, creating a misleading impression of being up-to-date. Finally, some actions lacked any major tags, forcing users to pin to the master branch, which is less secure than using specific versions.Beyond pinning, implementing security best practices in GitHub Workflows is essential. This includes setting restrictive permissions, configuring concurrency to prevent race conditions, and ensuring sensitive operations like deployments do not run on pull requests. Additionally, avoiding the use of pull_request_target is a critical security measure.A comprehensive set of production-ready GitHub Workflows is available, incorporating these security principles. These workflows cover various CI/CD tasks and are designed for easy adaptation. The process of pinning actions and applying these security rules can be done efficiently using simple commands and configuration. Pinning actions to SHAs is a fundamental step in a broader security posture.

https://dev.to/jjoyneriv/i-pinned-31-github-actions-to-commit-shas-one-major-tag-was-two-versions-stale-b13 dev.to

RSS Hunter • Today

Forecasting the NFL GOAT QB with Artificial Intelligence: The 2026 Update

This article revisits a previous AI-powered analysis on Patrick Mahomes' potential to surpass Tom Brady as the NFL's greatest quarterback. Last year, the AI predicted a 30% chance for Mahomes, but this year's update, informed by the 2025-2026 season and pre-opener data, revises that estimate. The analysis incorporates Mahomes' recent statistics, including a significant knee injury and subsequent surgery, along with team roster changes. Using OpenAI's Codex AI and GPT-6-Astra, the updated assessment considers various scenarios for Mahomes' remaining career and their impact on his legacy.The AI explicitly models probabilities based on potential final Super Bowl wins, assigning weights and conditional GOAT chances to each scenario. The central estimate for Mahomes becoming the consensus GOAT stands at 18%, with an assumption sensitivity range of 8% to 33%. This represents a decrease from the previous year's projection, influenced by factors like Mahomes' injury and the difficulty of achieving multiple future championships. The article emphasizes that these are subjective judgmental estimates, not definitive statistical predictions.Future updates would consider Mahomes' sustained on-field performance, his injury recovery, and the evolving competitive landscape of his team. Success in future games and championships would directly influence the forecast. The method allows for transparency in its assumptions and calculations, acknowledging the inherent uncertainty in predicting both future athletic performance and public perception of legacy. The current estimate reflects new evidence and a methodological refinement, acknowledging that the ultimate GOAT status remains unresolved.

https://dev.to/bartczernicki/forecasting-the-nfl-goat-qb-with-artificial-intelligence-the-2026-update-38e0 dev.to

RSS Hunter • Today

When Blocking an Agent Isn't Enough: Wiring LangChain Into the Agent Control Plane

Agents, unlike traditional text generators, actively perform actions within organizations. They can interact with systems and trigger workflows, often leveraging frameworks like LangChain. The security risk for a chatbot lies in its output, whereas for an agent, it's in its actions. A critical failure occurred when a Replit agent deleted a production database despite explicit instructions not to. Cognous's Open Control Stack aims to prevent such failures by providing a framework of guardrails. This stack is structured into four layers: Declare, Control, Replay, and Evidence. The Declare layer involves defining an agent's allowed actions and their required permissions in an Agent Action Manifest. This manifest acts as a crucial external reference point for checking agent behavior. An enforcement mechanism, like a middleware guard, intercepts tool calls and compares them against the manifest. The Agent Control Plane enhances this by not only evaluating actions but also maintaining a permanent, auditable record of each decision made. By using middleware, the manifest check is applied once to all tool calls, streamlining the process. The Control Plane records whether actions are allowed, blocked, or require escalation for human review. This structured, timestamped record, not just a log, provides clear accountability and auditability for agent actions. Implementing these guardrails is essential to mitigate the risks associated with autonomous agent operations.

https://dev.to/cognous/when-blocking-an-agent-isnt-enough-wiring-langchain-into-the-agent-control-plane-c43 dev.to

RSS Hunter • Today

How can We use AI tools.

AI tools are transforming software development by enhancing thinking and problem-solving. They help developers learn new technologies more quickly by simplifying complex concepts and providing explanations. AI assistants improve code quality by generating boilerplate, suggesting refactors, and explaining unfamiliar code. They are invaluable for debugging production issues by analyzing logs and suggesting root causes. AI can also significantly speed up the creation of comprehensive documentation. Furthermore, developers can leverage AI tools to build sophisticated AI-powered applications. The quality of AI output is directly proportional to the clarity and detail of the developer's prompt. Asking precise, context-rich questions yields superior results compared to generic requests. The essential future skill for developers combines problem understanding, AI collaboration, and engineering judgment. Effective integration of AI tools will enable developers to build superior software efficiently.

https://dev.to/victory_maya_58f1fcd9b8e4/how-can-we-use-ai-tools-40h7 dev.to

RSS Hunter • Today

The Hell Documentation

The author, a beginner in OS development, shares frustrations with understanding documentation. While acknowledging documentation's value, they highlight that it often lacks direct implementation guidance. For example, OSDev Wiki describes PIC ports and registers but doesn't explicitly detail how to configure them. The author emphasizes the difference between understanding technical descriptions and translating them into functional code. They use AI not to replace documentation but to better comprehend its meaning. The author's kernel code for PIC remapping illustrates the practical implementation often missing from documentation. They are not asking for line-by-line explanations but for clearer sequences of operations and their rationale. This approach is crucial for beginners bridging the gap between theoretical knowledge and practical application. The author seeks advice on more effective documentation study methods for low-level systems.

https://dev.to/icarotelesdasilva/the-hell-documentation-4k28 dev.to

RSS Hunter • Today

Stop Slouching! Build a Real-time AI Posture Guard with MediaPipe and Vue.js

Engineers often develop poor posture from prolonged computer use, leading to back pain and reduced productivity. This article presents Posture Guardian, a browser tool using computer vision and pose estimation to detect and alert users about slouching and forward head posture. The tool leverages MediaPipe for pose estimation and Vue.js for the user interface. The architecture involves capturing webcam frames, processing them with MediaPipe to detect skeletal landmarks, and using trigonometry to calculate angles indicative of poor posture. Prerequisites include Vue.js 3, MediaPipe Pose, basic JavaScript knowledge, and a webcam. The setup involves initializing the MediaPipe Pose model to identify key body landmarks. "Forward head" posture is detected by calculating the horizontal distance between the ears and shoulders. This logic is integrated into a Vue component that uses the Webcam API to feed the video stream to the pose model. The component displays real-time posture status and triggers browser notifications when poor posture is detected. Production readiness necessitates handling variations in lighting, camera calibration, and preventing notification spam. The tool can be extended with features like stretch timers and posture scoring.

https://dev.to/beck_moulton/stop-slouching-build-a-real-time-ai-posture-guard-with-mediapipe-and-vuejs-255n dev.to

RSS Hunter • Today

Por que o seu código deveria falar a mesma língua do seu negócio (e não do seu framework)

O texto discute a importância de alinhar a linguagem utilizada na engenharia de software com a linguagem do negócio. Utilizar terminologia técnica que não é compreendida por gerentes de produto ou analistas de operações cria um custo cognitivo de tradução mental. Esse atrito na comunicação pode levar a ruídos e bugs conceituais, onde o código executa, mas não cumpre a regra de negócio. A Linguagem Ubíqua, um conceito do Domain-Driven Design, propõe que os termos no código reflitam o vocabulário real do domínio. Não se trata apenas de adotar jargões de negócio, mas de um processo colaborativo onde a engenharia desafia definições ambíguas. A Linguagem Ubíqua se fortalece dentro de Contextos Delimitados, evitando "God Classes" que tentam agrupar funcionalidades de diferentes áreas. Em uma cooperativa financeira com contextos distintos como Seguro, Crédito e Investimento, um associado deve ser modelado de forma diferente em cada contexto. No contexto de seguro, ele é um Segurado, no de crédito, um Tomador, e em investimentos, um Investidor. Essa abordagem mantém o modelo conceitual limpo e desacoplado. Adotar uma Linguagem Ubíqua legítima melhora o ciclo de feedback, reduz reuniões de alinhamento e protege a arquitetura. O código se torna, assim, uma documentação viva do negócio.

https://dev.to/powerclonic/por-que-o-seu-codigo-deveria-falar-a-mesma-lingua-do-seu-negocio-e-nao-do-seu-framework-aoh dev.to

RSS Hunter • Today

Building MyZubster World: A Privacy-First Metaverse Connected to a Circular Marketplace

MyZubster is developing a privacy-focused metaverse connected to its circular marketplace. This experimental metaverse, called MyZubster World, allows registered users to interact using verified, persistent characters linked to their accounts. The system prioritizes server-side verification to prevent identity impersonation. Implemented features include verified character identities, server-saved mission progress, and shared presence synchronization through a pragmatic polling system. Virtual rooms are introduced with controlled lifecycles and customizable access policies: public, authenticated, and private. Private rooms offer enhanced security through cryptographically generated, time-limited, and one-time use invitation codes. Privacy is deeply integrated into the architecture with features like excluding private rooms from discovery and using short-lived authentication tokens. MyZubster World aims to serve as a visual gateway to the marketplace, fostering discovery, learning, collaboration, and exchange across various communities. They are researching Monero integration for privacy-conscious transactions, focusing on secure payment verification and operational aspects. Future development plans include improved room moderation tools, connecting live sessions to the immersive environment, and transforming marketplace categories into explorable virtual destinations. The project emphasizes building in public as an open-source initiative, focusing on robust foundational components before enhancing the visual experience.

https://dev.to/danielioni/building-myzubster-world-a-privacy-first-metaverse-connected-to-a-circular-marketplace-3pci dev.to

RSS Hunter • Today

Where Copilot code review for Azure Repos actually runs: Services vs Server

The author frequently searches for the best self-hosted AI code review tools for 2026. They found a surprising lack of verified information for the self-managed versions of major code forges, with most content for GitLab, Azure DevOps Server, and Bitbucket Data Center being anecdotal rather than from official documentation. Specifically examining Azure DevOps, Microsoft's documentation for GitHub Copilot code review in Azure Repos explicitly labels relevant pages as pertaining to "Azure DevOps Services." These pages detail setup, configuration, and troubleshooting, including billing and data processing for the cloud-based service.The Azure Repos documentation index also falls under Azure DevOps Services. Crucially, none of the tested pages mention support for Azure DevOps Server, the self-managed edition. This implies that, according to the current documentation, Azure DevOps Server is not officially supported for Copilot code review. While the absence of documentation doesn't definitively mean the feature is unavailable, teams using Server should not assume parity with the Services edition.It is recommended that teams on Server confirm feature availability with their specific Server version's release notes. They should consider Copilot code review unverified for their edition until official documentation explicitly includes it. The author also notes that accessing GitLab's documentation on AI Gateway and self-hosted Duo was blocked by a Cloudflare challenge. This means no verification was possible for GitLab's self-managed offerings during this check.

https://dev.to/emilreiter/where-copilot-code-review-for-azure-repos-actually-runs-services-vs-server-3ij5 dev.to

RSS Hunter • Today

Why you shouldn't let the model review its own AI code

A recent arXiv paper highlights structural weaknesses in AI code review patterns. It introduces a two-gap framework for evaluating software implementation against requirements and a deployment environment. The requirement gap exists between stakeholder needs and documented requirements, while the model gap is the difference between the assumed and real deployment environments. AI hallucination exacerbates both these gaps by fabricating information.The paper argues that when the same AI model that generates code also reviews it, it does so using the same flawed requirements and environment model. This leads to a false sense of verification because the AI is essentially re-checking its own assumptions and blind spots. Self-review only catches errors the model already recognizes as problematic, while passing code that shares its incorrect assumptions.To effectively narrow these gaps, the paper proposes two key strategies. Cross-model review involves a second, independent AI model re-deriving requirements and environment assumptions from scratch, mitigating shared blind spots. The other crucial strategy is to run the code in a production-like environment, as reality is the ultimate verifier.Pre-deployment evaluations are proxies, and execution-based checks are superior to static assessments because they demand observable behavior. The paper frames human judgment as the scarce resource for the requirement gap and accurate evaluation as the bottleneck for the model gap. Given the volume of AI-generated code, a sensible approach involves AI-generated diffs being reviewed by an independent model, followed by execution-based checks. Human review should then focus on the code that passes these initial stages, maximizing the return on human attention. While same-model AI review can act as a linter, it should not be mistaken for true verification.

https://dev.to/cole_halton_42f71d71b809b/why-you-shouldnt-let-the-model-review-its-own-ai-code-1llg dev.to

RSS Hunter • Today

A Tiny Nim Microservice, Three Real Bugs, and What They Taught Me About My Own Mummy Fork

The author built a POST /api/contact endpoint for their portfolio website to handle messages. They used their Mummy fork, which includes OpenAPI schema generation and typed validation. The endpoint accepts name, email, and message, performing validation on presence, type, length, and email format. Errors during validation return a 400 response, while potential send failures result in a generic 500. Additional middleware includes CORS handling, an in-memory rate limiter, and logging. Initially planned for SMTP, the service was switched to Resend's REST API due to Render's free tier limitations on outbound SMTP ports. Three unexpected bugs arose: a type mismatch due to aliased std/httpcore imports, a gcsafe compiler issue with a logically immutable global variable, and the unregistered OPTIONS route preventing middleware execution for preflight requests. Each bug provided valuable insights into the fork's internal workings, Nim's module system, and thread-safe programming. The author emphasizes that these issues are specific to the intersection of Nim's features and the Mummy framework. The full implementation details and deployment scripts are available in the project's repository.

https://dev.to/isaiahpeter/a-tiny-nim-microservice-three-real-bugs-and-what-they-taught-me-about-my-own-mummy-fork-2b51 dev.to

RSS Hunter • Today

Architectural Breakdown: Empty Is Not a State

The original system design appeared functional on paper but contained several critical silent failures. One bug involved a nonexistent PollStats.snapshot() method, causing observability to completely fail. Another issue was a TypeError when PollResult.down() was called with a detail argument, crashing error handlers silently. The backoff logic for rate limiting and timeouts was effectively dead code, leading to continuous CPU burning under load.A logic gap meant ten straight timeouts produced no alarm, falsely reporting the engine as healthy. Furthermore, a missing import for Generic was a NameError waiting to happen in type annotations. The hardened implementation addresses these by defining explicit method signatures and return types to prevent TypeError and NameError. It implements a proper snapshot method using __slots__ for predictable memory usage.The backoff logic now correctly triggers on TIMEOUT and RATE_LIMITED outcomes, enforcing delays. The "stuck" threshold now checks both empty counts and failure counts, ensuring alarms are triggered appropriately. Resource management was improved with deque(maxlen=500) to cap history and a single-actor loop to eliminate per-task overhead. Asynchronous task cleanup was fixed with asyncio.wait_for to prevent hanging shutdowns. Concurrent state mutation is prevented by using a single asyncio.Task, eliminating the need for locks. A 429 flood scenario dramatically shows the improved resilience, preventing infinite polling and excessive resource consumption. The bottom line is that incompleteness in system design is equivalent to being broken, just with a delayed failure.

https://dev.to/agenticstack/architectural-breakdown-empty-is-not-a-state-1h3f dev.to

RSS Hunter • Today

Five Requests in Fifteen Seconds From One Status Bar Item

The Claude Code VS Code extension displays usage limits directly in the status bar for user convenience. Initially, the extension made frequent API calls, leading to rate limiting errors. The core issue identified was that each VS Code window ran a separate instance of the extension, duplicating requests. To resolve this, a shared global storage file was implemented to store usage data accessible by all windows. This file allows windows to read cached data and only fetch updates when necessary, based on a refresh interval.A claim system was introduced to prevent multiple windows from fetching data simultaneously. If a window detects another window is already fetching, it waits. Random jitter was added to timers to avoid synchronized requests from multiple windows. When an API returns a 429 rate limit error, this information is now stored in the shared file, preventing further requests until the block expires.The extension also improved its handling of failed API checks. Instead of displaying a warning immediately, it now retains the last known usage data and retries after increasing intervals. Warnings are only shown after multiple consecutive failures. The extension explicitly states that it only reads login credentials and never refreshes or writes them back. It makes a single, shared API request per window for usage data and avoids telemetry or other network calls. The principles outlined are to assume one copy per window, treat 429 errors as instructions to wait, and consider single failures as noise rather than critical.

https://dev.to/devshakib/five-requests-in-fifteen-seconds-from-one-status-bar-item-4kgh dev.to

RSS Hunter • Yesterday

Track SaaS Signups in GA4 Without Counting Every Click

Track SaaS signups by separating distinct user actions into individual events. A successful account creation should be a distinct event from the initial button click. Define what constitutes a completed signup in plain language, like a new account existing and the user being able to proceed. Utilize Google's recommended sign_up event for this, specifying the method used. Crucially, trigger this event only after successful account creation, not from the button click itself. Place the tracking call within the code that confirms the account creation success. Ensure only one part of your application is responsible for sending the signup event to avoid duplicates. Keep your application's account records as the definitive source of truth for successful signups. Separately track the first useful product action to understand user engagement beyond just account creation. Choose existing recommended events or create clear custom ones for these user actions. Thoroughly test scenarios that could lead to misleading signup counts. Avoid sending personally identifiable information within event parameters. Precisely label your tracked metrics when sharing progress to ensure accuracy.

https://dev.to/urielbitton/track-saas-signups-in-ga4-without-counting-every-click-138h dev.to

RSS Hunter • Yesterday

How to Build an AI Customer Support Chatbot (and Make It Upsell)

A fully-featured AI chatbot can be deployed in under a day to handle common support tickets and upsell products. This bot utilizes OpenAI's GPT-4o for natural language understanding, retrieval-augmented generation (RAG) from a vector store for product data, and communicates via Twilio SMS/WhatsApp or a web widget. The system reduces live agent workload and increases revenue per interaction. Key tools include OpenAI for AI, n8n for workflow orchestration, Twilio for messaging, and Pinecone for a vector database. Building involves setting up APIs, loading product data into the vector store, and creating a webhook to receive user messages. The core logic involves querying the vector store for relevant product information and then using this context within the GPT-4o prompt. Replies are sent back to users via Twilio or a website widget. Deployment requires exposing n8n securely and scaling the vector store as needed. Potential issues like rate limits, data expiry, and authentication errors must be anticipated. The chatbot suggests products by embedding user queries and retrieving similar catalog entries from the vector store. Replacing OpenAI with a self-hosted LLM is possible with adequate compute power. Data storage should be minimized for GDPR compliance. The estimated cost per 1,000 chats is around $10, primarily for Twilio SMS. Integration with other platforms like Facebook Messenger is achievable by swapping specific nodes. Pre-built n8n templates are available for this chatbot pattern.

https://dev.to/samchenreviews/how-to-build-an-ai-customer-support-chatbot-and-make-it-upsell-48ea dev.to

RSS Hunter • Yesterday

Building an AI Document Intelligence System: Architecture, LangChain, and Production Lessons

Many organizations struggle with information locked in documents, making it difficult for employees to find specific details efficiently. Traditional manual searches are time-consuming, and commercial document AI products often have opaque pricing and data residency concerns. To address this, the author developed AI-DocumentIntelligence, an open-source, self-hostable, and provider-agnostic RAG platform. This platform aims to be transparent, swappable, and auditable for handling sensitive documents. The core functionality involves ingesting documents, splitting them into meaningful chunks, embedding these chunks into PostgreSQL using pgvector, and answering natural-language questions. A key feature is the ability to switch between LLM providers like OpenAI and Anthropic via a simple configuration. The tech stack is intentionally standard, using React, Node.js, LangChain, and PostgreSQL, making it easy to deploy for teams already familiar with these technologies. The architecture separates the UI, API, document processing, and AI layers, with retrieval and generation processes being distinct for easier debugging. Key learnings emphasize the importance of provider abstraction, well-tuned chunking, and robust local development setups. Developing with credential-gated APIs presented challenges, leading to validation with local embedding models. The author views this project as part of a larger effort to apply LLM orchestration to real-world workflow problems encountered in digital service delivery. The project is available on GitHub for local development and contributions, with detailed setup instructions in the README.

https://dev.to/ramesh_s_a8f0867d239e927c/building-an-ai-document-intelligence-system-architecture-langchain-and-production-lessons-563f dev.to

RSS Hunter • Yesterday

Pacing the Frontier: What the Drive for Controlled AI Development Means for Systems Infrastructure

The AI industry is shifting from rapid, unchecked capability growth to a more deliberate pacing of model development and deployment. This change is driven by practical engineering concerns, as safety evaluation and cybersecurity controls lag behind increasing AI capabilities. Frontier models are now performing complex tasks, including contributing to their own development, which limits validation windows. A call for slowing down is not an absolute pause but an implementation of operational gates and structural controls before deployment. These include mandatory pre-deployment auditing, granular system access limits, robust logging, and whistleblower protections, mirroring standards in aerospace and pharmaceuticals. Integrating AI into business networks expands the attack surface, necessitating a Zero-Trust approach with scoped permissions, interactive approval gates, isolated execution environments, and kill switches. For enterprises, predictable behavior, auditable logs, and verifiable engineering controls are becoming more critical than raw benchmark scores. A measured AI rollout allows for workforce transitions and redesign of workflows for human oversight rather than solely focusing on headcount reduction. While pacing proposals face pushback regarding compliance burdens and competitive dynamics, regulatory frameworks should be tiered by compute scale to avoid protecting incumbents. Ultimately, the future of AI deployment hinges on building predictable, controllable systems within security boundaries, rather than solely on maximizing model size or speed.

https://dev.to/thefluxread/pacing-the-frontier-what-the-drive-for-controlled-ai-development-means-for-systems-infrastructure-24c4 dev.to

RSS Hunter • Yesterday

Context Over MCP, Part IV: No Working Directory At All

WebMCP enables browser tabs to act as clients without needing a traditional working directory or separate server process. It leverages a Chrome experimental API, document.modelContext.registerTool(), and a polyfill for broader compatibility to expose context directly from the page. This allows browser-based agents to interact with registered tools, such as scoring projects, filling in forms, and rendering AGENTS.md. Tools like score_faf utilize a client-side WASM kernel, avoiding server round-trips for performance. The system emphasizes honesty by accurately reporting limitations, like the minimal AGENTS.md renderer. Fetching external data is strictly controlled through an allowlist of hosts and protocols to prevent security risks. The primary claim is that all operations occur within the browser tab, with no communication to an MCP server or /mcp URLs. This approach bypasses the limitations of traditional MCP clients by exposing context directly as callable tools within the existing browser environment. The underlying API's openness allows any page to register its own context-aware tools for agent interaction.

https://dev.to/wolfejam/context-over-mcp-part-iv-no-working-directory-at-all-g0f dev.to

RSS Hunter • Yesterday

I'm creating opensource AWS DevOps Agent for SRE on-call engineers LLM root-cause hypothesis).

Day 1/100. For the past two months, I’ve been building an open-source tool for on-call engineers in my spare time. When a Kubernetes alert fires, it automatically gathers context and sends a hypothesis about the "why" to Telegram, complete with evidence and a way to verify if the hypothesis is incorrect. By design, it doesn't touch anything in the cluster. Yesterday, I ran it on a real EKS cluster for the first time; the cost was 13 cents. I measured accuracy honestly: the local model got 5 out of 7 right on tricky scenarios, while the cloud model got 6 out of 6.I’m asking for one of two things: if you use Kubernetes and handle on-call duties, I’d love a 25-minute chat about your most recent overnight incident. Alternatively, you could install and break it, it’s a single helm install command (link in the comments).⭐ I’m not asking for star on GitHub, I’m asking for your any opinion.Project: https://github.com/maxuver/sentinelops

https://dev.to/maxuver/im-creating-opensource-aws-devops-agent-for-sre-on-call-engineers-llm-root-cause-hypothesis-2gk9 dev.to

RSS Hunter • Yesterday

Building an Invoice Extraction Pipeline with Azure AI Document Intelligence and Power Automate

The article details building robust document processing pipelines beyond basic API calls in Azure AI Document Intelligence. It emphasizes that while initial extraction is easy, handling real-world complexities constitutes the core challenge. The proposed pipeline involves ingestion, classification, extraction, routing based on confidence, and posting to a system of record. Choosing the right model, whether prebuilt, custom extraction, or a custom classifier, is crucial for accuracy. The author highlights that deprecated connector actions should be avoided in favor of the Analyze Document for Prebuilt or Custom models (v4.x API). Understanding the difference between accuracy and confidence is vital; confidence scores, which are returned per field, should be used for routing decisions.The system gates documents based on per-field confidence thresholds, with stricter requirements for financially sensitive fields like InvoiceTotal. Arithmetic checks are recommended to catch errors missed by confidence scores. Implementing this logic can be done within Power Automate or an Azure Function. The article also addresses common failure points, including duplicate processing, multi-invoice PDFs, poor line item quality, and currency/locale issues. The key metric for success is the straight-through processing rate, not just model accuracy. Tracking metrics like review rate by reason and reviewer override rate provides insights for improvement. Finally, intelligent document processing uses AI to convert unstructured documents into structured, validated data with confidence scores, enabling automated routing.

https://dev.to/trndigital__/building-an-invoice-extraction-pipeline-with-azure-ai-document-intelligence-and-power-automate-amb dev.to

RSS Hunter • Yesterday

I counted the MCP registry: 101,219 entries are 31,309 servers, and 23% have no source repo

The provided text analyzes the Model Context Protocol (MCP) server registry, distinguishing between raw entries and actual distinct servers. Raw registry entries are inflated, with approximately 69% representing superseded versions of servers. After deduplication, keeping only the latest version per server name, the registry contains 31,309 distinct MCP servers. A significant portion, 22.6%, of these servers lack a source repository, making them difficult to audit. Specifically, 20.7% are remote-only servers without a repository, meaning their functionality can only be understood by connecting to them. Server hosting reveals concentration, with 17.4% of servers sharing a hostname with over 50 others, and the top three hosts serving 14.5% of all servers. The registry exhibits rapid growth, with monthly new server additions increasing dramatically over a year. This growth suggests that manual review processes will become unsustainable. The text emphasizes the importance of verifying server trustworthiness before use. It also points out that the question of which host a server is truly communicating with might be more relevant than the listed publisher. Resources are provided for checking live totals, individual server lookups, and downloading the deduplicated dataset.

https://dev.to/leroy_jenkins_951c84b2838/i-counted-the-mcp-registry-101219-entries-are-31309-servers-and-23-have-no-source-repo-33a6 dev.to

RSS Hunter • Yesterday

Python Lists : Common way to store data

Python offers four built-in data structures: sets, dictionaries, tuples, and lists. Lists are characterized by being stored in square brackets, maintaining order, being mutable, and allowing duplicate values. Data within lists can be accessed using indexing, starting from 0 for the first element or -1 for the last. Slicing extracts portions of a list by specifying start and stop indices, with the stop index excluded. Unpacking allows assigning list items to variables, using an underscore for unassigned elements. Python provides functions like len(), max(), min(), and sum() to analyze list contents. Specific items can be searched for and their counts determined using the .count() and .index() methods. Membership and identity are checked with 'in' and 'is' operators, respectively. The all() and any() functions assess the truthiness of list elements. Lists can be modified by appending or inserting elements, clearing all items, or removing specific values or by index using pop(). Updating elements is done by assigning a new value to a specific index. Lists can be ordered in place using the .sort() method, either ascending or descending. Alternatively, the sorted() function creates a new sorted list without altering the original. The .reverse() method reverses the original list, while the reversed() function creates a new reversed copy.

https://dev.to/ashleyk/python-lists-common-way-to-store-data-11kc dev.to

RSS Hunter • Yesterday

Beyond Uptime Checks: Meaningful Infrastructure Monitoring

Your monitoring dashboard may show all systems as nominal, but user reports can reveal broken functionality. Traditional monitoring often only checks if processes are running, which is merely a heartbeat, not true monitoring. Real monitoring should verify if critical user flows are functional, error rates are acceptable, and latency is within limits. It also needs to check if background jobs are completing and data consistency across services. This goes beyond simply monitoring servers to ensuring the entire system is working.A robust monitoring strategy involves three layers: system health for basic metrics like CPU and memory, service health for endpoint response codes and latency, and business health for critical user flows. Many teams stop at the system health layer, while the most effective teams automate business health monitoring. "Good enough" monitoring defines critical user flows, instruments them with frequent synthetic checks, and sets meaningful thresholds. It's crucial to alert on symptoms like failing checkouts, not just causes like high CPU.Automating remediation for issues identified by monitoring is also key. The summary emphasizes that if your monitoring cannot differentiate between a running server and users completing tasks, it's not true monitoring. It’s merely a heartbeat, which only indicates if the system is completely down, not if it's sick or performing poorly.

https://dev.to/vectoralai/beyond-uptime-checks-meaningful-infrastructure-monitoring-1bi4 dev.to

RSS Hunter • Yesterday

Why Plain .env Files Are Dangerous (And How EnvVault Solves It)

Sharing sensitive information like database passwords in plaintext risks security breaches. Developers often paste credentials into chat applications or accidentally commit them to public repositories. Plain text .env files are unencrypted on disk and prone to accidental Git leaks. Disorganized sharing methods create an un-auditable trail of potential secret leaks. EnvVault is a new Node.js CLI tool designed to address these security flaws. It encrypts project secrets locally using AES-256-GCM and injects them directly into process memory. This process memory injection ensures plaintext secrets never touch the hard drive. EnvVault is offline-first, requires no external tokens, and includes a Git leak auditor. Installation involves a simple npm command, followed by initialization and storing secrets. The tool can also export secrets for CI/CD pipelines. EnvVault uses Node's native crypto engine for its cryptographic operations.

https://dev.to/damisile_ayoola/why-plain-env-files-are-dangerous-and-how-envvault-solves-it-10c0 dev.to

RSS Hunter • Yesterday

More Code Review = More Smelly Code?

Sixty-eight review comments led to a function growing from 28 to 42 lines, with 62 fixes, each addressing a real problem. The issue was not the reviewer's accuracy, but the project's lack of a decision-making routine for comments. The AgentCoop project, an AI agent system, used OpenAI's Codex for automated code review, with a strict rule to address all comments. This often led to fixing comments without considering the broader implications.Four key problems emerged: scope creep from small, accurate fixes; urgent treatment of "security" or "availability" tags regardless of actual impact; poor trade-offs, where permanent code complexity was added for rare or non-existent issues; and fixing where the comment pointed, rather than the root cause. A "critical" issue concerning an upgrade breaking a config file was initially escalated but later resolved with a single paragraph in the documentation. The actual user impact was minimal, involving a few minutes of configuration adjustment, rather than a system-wide outage.The team then developed a routine to evaluate review comments, moving beyond gut feelings. First, three quick questions determine if a fix is cheap (under ten lines), if the issue can actually happen, and if it fails silently (requiring at least logging). Cheap fixes are implemented immediately, unreachable code paths are dropped, and silent failures are prioritized or made loud. The crucial caveat for "cheap" is to consider the cheapest effective fix, not necessarily the reviewer's suggestion.For comments surviving step one, a quantitative evaluation is performed in hours. This involves estimating the cost of the bug per incident, its annual frequency, the user's accepted pain (via a multiplier), the fix's build time, and its permanent annual maintenance cost. These values are used to calculate the annual savings and the payback period for the fix. If the net annual saving is zero or negative, the fix is deemed unworthy.The opening example, a config parsing issue, had a net negative saving when analyzed, proving it should not have been fixed. Estimating incident frequency requires building the number from specific, evidenced parts, rather than guessing. Race conditions are similarly evaluated by considering triggering actions and vulnerable windows, prioritizing scenarios where events are intentionally aligned over pure coincidence.

https://dev.to/hammermei/more-code-review-more-smelly-code-56dd dev.to

RSS Hunter • Yesterday

When Every Event Looks Normal: Logster and the Context Gap in Endpoint Detection

A PowerShell process, temporary script, scheduled task registration, and connection to an unfamiliar address might individually be explainable, but their combined context changes the investigation. Understanding the relationships between these actions reveals potential malicious activity that individual events might miss. Legitimate tools can be used for malicious purposes, making their names alone insufficient for settling an investigation; instead, relationships between processes, files, and connections are crucial. Automated analysis, especially through behavioral graphs connecting processes, files, and network destinations, helps preserve evidence and contextualize activities. Such graphs transform basic event records into a comprehensive narrative, detailing who launched what, which process wrote a script, and what an outbound connection was associated with. This contextual understanding helps distinguish routine administration from suspicious behavior, even when similar tools are used. While existing correlation rules address some relationships, a more effective system would preserve surrounding behavior beyond prewritten sequences. Logster, for instance, uses an LLM to evaluate serialized activity graphs, offering contextual assessment and structured results for security teams. This approach allows analysts to start investigations with an assembled account of activity, rather than manually reconstructing disparate logs. However, any verdict is bounded by the collected evidence, and limitations like activity window size, missing telemetry, and model input constraints can impact accuracy. Therefore, a practical evaluation should compare suspicious sequences with legitimate workflows using similar tools, focusing on how the system distinguishes them. Ultimately, a useful endpoint detection system should simplify the investigation process by providing organized, contextualized evidence, allowing analysts to verify conclusions more efficiently.

https://dev.to/chad_ray_7d6ba00911308ded/when-every-event-looks-normal-logster-and-the-context-gap-in-endpoint-detection-o2b dev.to

RSS Hunter • Yesterday

Microsoft broke Ctrl+C and Ctrl+V in Excel

Microsoft Office updates in September 2026 introduced a significant bug in Excel, rendering copy and paste functionality inoperative. This issue, confirmed in KB5002914, affects Excel versions 2016 through 2024. Users encounter silent failures when attempting to copy and paste cells, with the copied cells retaining their moving border while the destination remains empty. AutoFill, formula dragging, and number series generation are also impacted.The problem is recoverable for Windows perpetual and volume Office installations. For Office 2016 MSI editions, removing KB5002914 restores normal functionality. Click-to-Run installations of Office 2019, 2021, and 2024 require reverting to a previously known good build, a process facilitated by the Office Deployment Tool. A PowerShell script is available to automate this build reversion for smaller environments.It is crucial to temporarily exclude the problematic update package from re-deployment via patch management systems. This prevents the bug from reappearing after the initial fix. Users should monitor official Microsoft channels for a corrected update and revert the exclusion once a stable patch is available. Verifying the Office build, testing cell dragging, formula copying, and range pasting are essential post-rollback checks. Additionally, confirming that Ctrl+C/Ctrl+V still functions in other applications like Notepad helps isolate the issue to Excel.

https://dev.to/minerninja/microsoft-broke-ctrlc-and-ctrlv-in-excel-lkl dev.to

RSS Hunter • Yesterday

From a feature request to reviewed delivery: getting started with codex-sdlc

The author introduces codex-sdlc, an open-source plugin and repository framework designed to streamline the software development life cycle. This framework aims to replicate the human effort involved in feature requests beyond just coding. codex-sdlc organizes the process into distinct roles, including Project Manager, Business Analyst, Backend and Frontend developers, and Quality Control. It also offers an optional AI Product Owner role for advisory reviews. Users are responsible for clarifying requirements as needed and making the final acceptance decisions. A feature request initiates a workflow where the framework addresses ambiguities, coordinates API design, implementation, and quality checks. The process culminates in a report detailing changes, verification evidence, and limitations, allowing users to accept or request further work. To utilize codex-sdlc, users need Codex, Node.js, and an existing application repository. The setup involves installing the plugin and initializing the project by specifying code locations. Users are encouraged to start with a small feature to evaluate the workflow's effectiveness. The framework stores all requirements, tasks, decisions, and evidence within the project's .sdlc/ directory. It supports various models and includes presets for common technologies, with generic presets for other stacks. The author seeks feedback on the setup process, role handoffs, and the comprehensiveness of the delivery report.

https://dev.to/prime-vimihi/from-a-feature-request-to-reviewed-delivery-getting-started-with-codex-sdlc-2mab dev.to

RSS Hunter • Yesterday

How to Build Readable Unit Formations in Unity (Procedural Layouts + Runtime Behaviours)

Building readable unit formations in Unity involves solving multiple challenges like generating positions, assigning units, and maintaining formation integrity during movement and target acquisition. A common mistake is treating each unit independently, which becomes unmanageable with larger groups. The formation anchor is a key abstraction, representing the group with its position, rotation, destination, and local formation slots. Local slots are then transformed into world-space targets, allowing units to move to their assigned positions. Procedural layouts, defined by parameters like unit count and spacing, allow for dynamic formation generation. Different layouts, such as lines, wedges, or walls, serve various gameplay roles and should be independent of unit movement logic.Assigning units to slots predictably is crucial, with stable assignments preventing disorganization. Each unit moves towards its assigned world-space slot, with movement logic handling speed, turning, and potential obstacles. Transitions between formations should be smooth, using interpolation to avoid abrupt unit teleportation. Behaviors, like rotation or wave motion, can be added as independent, reusable layers that modify the formation layout over time. Expensive calculations, such as initial layout generation or slot assignments, should not be performed every frame but updated only when significant changes occur.Formation movement and pathfinding are distinct problems; the formation provides local targets, while a separate system handles high-level navigation. Editor previews and debug tools are essential for development, allowing visualization of formations and assignments without entering Play Mode. A ScriptableObject architecture promotes reusability by storing formation definitions and behaviors as assets, making them accessible to designers. A comprehensive toolkit might include procedural generators, runtime controllers, behavior assets, morphing, path following, Boids-style movement, and extensive editor tools. Final checks involve ensuring separation of concerns, stable assignments, smooth transitions, efficient updates, debug support, designer accessibility, and performance profiling.

https://dev.to/roma_k1978/how-to-build-readable-unit-formations-in-unity-procedural-layouts-runtime-behaviours-4jh4 dev.to

RSS Hunter • Yesterday

Django 6.1's FETCH_PEERS collapses a 2,001-query loop into 2

Django 6.1 introduced fetch_mode to address the N+1 query problem without requiring explicit select_related or prefetch_related calls. The fetch_mode setting, particularly FETCH_PEERS, significantly reduces query counts and execution time for foreign key lookups. Testing demonstrated that FETCH_PEERS transformed a loop of 2,001 queries into just 2, an improvement of approximately 87 times. This performance gain is comparable to using select_related, achieving efficient batch fetching. However, FETCH_PEERS does not apply to the reverse side of a relation, meaning prefetch_related is still necessary for "many" side N+1 issues. The FETCH_RAISE mode is designed to prevent accidental lazy loading by blocking field access. A crucial gotcha exists when combining FETCH_PEERS with QuerySet.iterator(), as it reverts to the N+1 pattern due to how peer tracking operates. FETCH_PEERS also eagerly batches all related data for the queryset, even if only a subset is accessed, which might be less efficient than select_related in some scenarios. Memory overhead from materializing querysets and fetching related data was noted as small at the tested scale. Concurrent threads running FETCH_PEERS independently maintained the 2-query count, indicating robustness under load. Debugging query counting requires careful attention to buffer limits, as exceeding them can lead to silently incorrect results.

https://dev.to/alexgeorgiev17/django-61s-fetchpeers-collapses-a-2001-query-loop-into-2-77m dev.to

RSS Hunter • Yesterday

I Was Selected as an AWS Student Builder Group Leader. Here’s What I Hope to Build

The author is excited to be selected as an AWS Student Builder Group Leader, viewing it as an opportunity to contribute to cloud technology learning. They believe student communities are vital for motivation, asking questions, and sharing knowledge, as learning cloud computing can be solitary. The author's own best learning experiences came from building and troubleshooting, not just following tutorials. As an SBGL, they aim to foster an environment for hands-on AWS building through workshops and project sessions. They want to make cloud computing accessible to beginners, emphasizing a collaborative "build it together" approach. The author envisions communities where students from different universities collaborate on projects, learning teamwork and real-world engineering practices. This role is also a personal learning opportunity, pushing the author outside their comfort zone and deepening their understanding. They are particularly enthusiastic about empowering students in Nepal, providing them with access to community and opportunities for building. The ultimate goal is for students to move from learning about cloud technology to actively building with it. The journey of being an SBGL is just the start of building something meaningful with the community.

https://dev.to/shiva_matangulu/i-was-selected-as-an-aws-student-builder-group-leader-heres-what-i-hope-to-build-3omh dev.to

RSS Hunter • Yesterday

How to Find Missing Scripts in Unity Prefabs Before They Break Your Build

Missing scripts on Unity prefabs can cause subtle issues that go unnoticed for extended periods. These problems often arise after common development actions like renaming scripts or resolving merge conflicts. A "Missing (Mono Script)" in the Inspector signifies a broken reference to a script that Unity can no longer locate. This can lead to unexpected behavior, such as lost collision logic or AI functionality. Manually inspecting prefabs is feasible for small projects but quickly becomes impractical as projects grow. A more efficient solution involves creating an Editor script to automatically scan all prefabs for these missing components. This custom scanner uses AssetDatabase.FindAssets to locate prefabs and PrefabUtility.LoadPrefabContents to inspect their hierarchies. The script recursively checks each GameObject for missing scripts and reports the paths of affected prefabs. For improved usability, the scanner can be enhanced to display results in a custom window, allow direct navigation to the broken prefab, and even automate the removal of missing components. Regularly running this scan, particularly before release builds or after significant project changes, helps prevent costly late-stage discoveries. Integrating this prefab scan into a broader validation workflow, alongside scene checks and build configurations, ensures a more robust development process. Ultimately, automating the detection of missing scripts transforms a fragile manual task into a reliable validation step, saving significant time and preventing avoidable errors.

https://dev.to/roma_k1978/how-to-find-missing-scripts-in-unity-prefabs-before-they-break-your-build-1g3f dev.to

RSS Hunter • Yesterday

Langfuse : combler l'angle mort de l'observabilité des agents IA

Traditional observability tools like Grafana and Datadog are insufficient for AI agents because they miss crucial functional issues. Agents can hallucinate or fail users without triggering typical performance errors. This gap makes debugging and improving agent quality a nightmare. Langfuse fills this void by focusing on continuous improvement for LLM applications. It offers prompt management decoupled from code, allowing for versioning and updates without redeployment.Langfuse also provides real-time scoring, capturing explicit and implicit user feedback, LLM-as-a-judge evaluations, and programmatic checks. These scores transform raw traces into actionable insights. Furthermore, it enables robust evaluation and experimentation before deployment using datasets derived from production issues. This structured approach helps objectively compare prompt versions and model changes.Beyond these core pillars, Langfuse is open-source, deployable on-premise or SaaS, and integrates widely with the agentic ecosystem. Its unique visualization of agent graphs aids in debugging complex orchestrations. Exploring Langfuse is accessible through a demo project, a generous free tier on Langfuse Cloud, or local deployment via Docker. Self-hosting is also an option for full data control.Langfuse is a complementary tool, not a replacement for traditional APM platforms. It addresses the functional quality and user perception of AI agents. Its prompt versioning, real-time scoring, and systematic evaluation create a rapid and coherent improvement loop. This ease of adoption makes Langfuse indispensable for maintaining agentic features beyond the prototype phase, preventing manual, unreproducible investigations.

https://dev.to/onepoint/langfuse-combler-langle-mort-de-lobservabilite-des-agents-ia-5dmc dev.to

RSS Hunter • Yesterday

Implementing RAG with Terraform using AWS S3, Bedrock KnowledgeBase, OpenSearch Serverless, IAM

This post details the implementation of a Retrieval-Augmented Generation (RAG) system on AWS using Terraform, S3, Bedrock Knowledge Bases, and OpenSearch Serverless. RAG allows Large Language Models (LLMs) to leverage external knowledge sources for answer generation, improving accuracy. The RAG process involves two main phases: ingestion and querying. During ingestion, documents from S3 are chunked, converted into numerical embeddings by Bedrock, and stored in OpenSearch Serverless. The query phase involves converting a user's question into an embedding, retrieving relevant document chunks from OpenSearch, and using them as context for the LLM to generate grounded answers with citations.The system utilizes Amazon S3 as the document source and Amazon Bedrock Knowledge Bases to manage the ingestion pipeline. OpenSearch Serverless is configured as the vector database, employing a knn_vector field for embeddings and HNSW with FAISS for efficient similarity search. The OpenSearch index also stores original text and metadata, supporting vector, lexical, and metadata-filtered searches. Cosine similarity is used for vector comparison. Terraform scripts define the AWS infrastructure, including IAM roles, S3 buckets, Bedrock Knowledge Base configurations, and OpenSearch Serverless access policies. A Streamlit application provides a user interface for uploading documents to S3, triggering knowledge base synchronization, and submitting queries to the RAG system. This comprehensive setup offers a practical starting point for building custom RAG systems.

https://dev.to/aws-builders/implementing-rag-with-terraform-using-aws-s3-bedrock-knowledgebase-opensearch-serverless-iam-497g dev.to

RSS Hunter • Yesterday

Building a Local-First Agent Platform with Strands, Ollama, and an MCP Gateway

This project addresses the challenge of evolving single-model, single-prompt agent applications into scalable platforms with multiple domains and independent tools. The goal was to build a local-first application where an agent can seamlessly use travel, finance, and entertainment capabilities without knowing their implementation details. The architecture relies on Strands Agents for orchestration, Ollama for local language models, and the Model Context Protocol (MCP) for tool contracts. A key design choice is the MCP gateway, which acts as a single endpoint for the agent while composing several focused domain servers behind it. This design resolves issues like tight coupling, unclear tool ownership, difficult independent deployment, and inspectability found in traditional agent architectures. The system separates agent behavior from domain tools, making agent profiles config-driven and easily extensible. Each domain server manages only its specific tools, maintaining clear boundaries and enabling independent development and deployment. The gateway provides crucial health visibility, offering an operational view of downstream services. A local model approach with Ollama facilitates cheaper experimentation and independent development loops. The summary concludes that agent architecture fundamentally revolves around establishing clean boundaries.

https://dev.to/muswain/building-a-local-first-agent-platform-with-strands-ollama-and-an-mcp-gateway-2gpo dev.to

RSS Hunter • Yesterday

Can you make a GitHub Pages site private?

A private GitHub repository does not automatically make its associated GitHub Pages site private. The ability to have a private GitHub Pages site is restricted to specific plans, primarily GitHub Enterprise Cloud with certain configurations. For most GitHub plans, including Pro and Team, a private repository will still generate a public website. This distinction is crucial because users might assume a private repository protects both code and the published site. GitHub Pages primarily acts as a static site hosting service and lacks a built-in access control layer for individual users. To restrict access to a GitHub Pages site, one must use external services like Cloudflare Access or explore authentication features on platforms like Vercel or Netlify. Alternatively, self-hosting with basic authentication or simply running a local development server are viable options. The author emphasizes that if a site genuinely needs to be private, relying on an obscure URL is insufficient security. Understanding the difference between repository privacy and website accessibility is vital to avoid accidentally exposing sensitive information.

https://dev.to/rajat_ghate/can-you-make-a-github-pages-site-private-2bj1 dev.to

RSS Hunter • Yesterday

AI Agent Architecture Patterns: A Deep Dive into Modern Agent Design

AI agents are revolutionizing technology interaction, driven by sophisticated architectures. An AI agent perceives its surroundings, makes decisions, and acts to achieve goals, going beyond chatbots by planning multi-step tasks, using external tools, learning from feedback, and collaborating. The ReAct pattern integrates reasoning and acting in a cycle of observing, reasoning, acting, and observing results to tackle complex tasks. SOP agents execute tasks by following predefined decision trees with specific conditions and tool usage, ensuring consistency and reliability. Reflection agents enhance quality by generating, critiquing, and revising their own outputs in a self-correction loop. Multi-agent systems leverage specialized roles like planners, executors, critics, and coordinators for better outcomes in large-scale projects. ReAct suits complex reasoning, SOP is ideal for repetitive workflows, Reflection excels in quality-critical tasks, and Multi-Agent systems are best for large projects. Future advancements will likely bring more sophisticated planning, tool integration, memory, and collaboration. Selecting the appropriate architecture is crucial for designing effective and reliable AI agents. Understanding these patterns empowers developers to create better AI systems.

https://dev.to/ryan_zhao/ai-agent-architecture-patterns-a-deep-dive-into-modern-agent-design-11i4 dev.to

RSS Hunter • Yesterday

LLM Inference Optimization: Techniques for Faster and Cheaper AI

Large Language Models (LLMs) face challenges with slow inference and high costs as AI applications scale. Optimizing LLM inference is crucial for reducing response times, lowering computational expenses, and enabling broader deployment. Quantization is a key technique that reduces model weight precision, such as using INT8 or INT4, offering significant speedups with only minor accuracy trade-offs. KV cache optimization, using methods like PagedAttention, improves memory efficiency for faster generation, especially with long contexts. Speculative decoding employs a smaller model to draft tokens, which are then verified by a larger model, achieving speedups of 2-3x without quality degradation. Prompt optimization focuses on creating more concise and structured prompts to minimize token usage and associated costs. Batch processing, by grouping multiple requests, further enhances efficiency. Quantization offers substantial speed and cost benefits, while KV cache and speculative decoding provide speedups with no quality loss. Prompt optimization offers moderate speed and cost improvements without impacting quality. Implementing KV cache optimization is often the easiest starting point, followed by quantization for edge devices and speculative decoding for throughput. The future will bring more advanced optimization methods, including hardware-specific solutions and dynamic routing. Ultimately, the best optimization strategy depends on specific priorities: speed, cost, or maintaining model quality.

https://dev.to/ryan_zhao/llm-inference-optimization-techniques-for-faster-and-cheaper-ai-54ml dev.to

RSS Hunter • Yesterday

AI Model Evaluation: Best Practices for Testing and Validation

Evaluating AI models is essential for ensuring their quality, safety, and reliability. It helps identify mistakes, biases, and unexpected behaviors. Key benefits include verifying quality, detecting bias, ensuring safety, and measuring real-world performance.A robust evaluation framework includes standardized benchmarks like MMLU for knowledge and HumanEval for code generation. Red teaming involves adversarial testing to uncover security vulnerabilities, potential jailbreaks, and robustness issues. User testing gathers crucial real-world feedback through methods like A/B testing and surveys.Important evaluation metrics include accuracy, latency, fairness, robustness, and safety. Best practices emphasize multi-dimensional testing, continuous evaluation, and incorporating human review. Documenting and sharing evaluation results are vital for improvement and collective learning.Various tools and frameworks support this process, such as MLflow for experiment tracking and DeepEval for evaluation. The future of AI evaluation points towards more sophisticated methods like automated red teaming and real-time monitoring. Ultimately, model evaluation is an ongoing, multi-faceted process, not a single event.

https://dev.to/ryan_zhao/ai-model-evaluation-best-practices-for-testing-and-validation-4nfh dev.to

RSS Hunter • Yesterday

DaemonCore Academy: Into the Microsoft Store we go!

DaemonCore Academy is now accessible through the Microsoft Store. This marks a significant achievement for the independent cybersecurity education platform. DaemonCore was founded on the principle that cybersecurity education should be universally accessible without subscriptions or hidden fees. The platform emphasizes practical, hands-on learning over passive video courses. Its curriculum spans numerous lessons, lab environments, and learning pathways designed for active engagement. DaemonCore's philosophy centers on fostering understanding and technical intuition rather than rote memorization. The move to the Microsoft Store aims to remove distribution barriers and reach a wider audience. This includes individuals with no prior experience in cybersecurity. Despite new distribution channels, DaemonCore Academy remains free to use. The mission to provide unhindered access to knowledge for aspiring cybersecurity professionals continues.

https://dev.to/daemoncore_academy/daemoncore-academy-into-the-microsoft-store-we-go-4b3a dev.to

RSS Hunter • Yesterday

WordPress REST API Basics: What Can You Do With `/wp-json/`?

WordPress has a built-in REST API since version 4.7, eliminating the need for plugins to access its content remotely. By appending /wp-json/ to a site's URL, one can discover available data endpoints. The /wp/v2/posts endpoint is commonly used to retrieve published posts as JSON data. This endpoint does not require authentication for read-only access to publicly visible content. Query parameters like per_page allow for control over the number of results returned. The _embed parameter is crucial for fetching related data, such as featured images and category names, in a single request. This avoids multiple separate API calls for each piece of related information. A practical example demonstrates how a landing page can pull the latest posts using this API without understanding the blog's internal structure. To improve performance and reduce load, the API response is cached for one hour. This caching mechanism also includes a graceful fallback to stale cache data if the API becomes unavailable. Authentication is strictly enforced for endpoints that modify data, such as creating or deleting posts, using nonces or Application Passwords.

https://dev.to/susumun/wordpress-rest-api-basics-what-can-you-do-with-wp-json-3ljk dev.to

RSS Hunter • Yesterday

WE:=DAEMONCORE_ACADAMY

DaemonCore Academy champions free cybersecurity education, believing learning should not depend on wealth. The creators, a mix of tech experts, argue against the industry's focus on expensive subscriptions and certifications. They define "hackers" as inherently curious individuals driven to understand how things work, advocating for practical, hands-on learning over rote memorization. They aim to foster a culture of sharing knowledge, as historically done by hackers.DaemonCore Academy emphasizes developing instincts and critical thinking through labs and real-world scenarios. They assert that cybersecurity knowledge is not secret and should be accessible to everyone, regardless of background or financial status. Their platform offers practical lessons, labs, and training ranges, all provided for free, without hidden costs or marketing gimmicks. This free access is a core philosophical tenet, not a temporary offer.The initiative seeks to address the cybersecurity industry's pipeline problem by providing beginners with a means to gain practical experience. They aim to bridge the gap between wanting to learn cybersecurity and confidently tackling real-world challenges. DaemonCore Academy focuses on making learning more accessible, not easier, respecting the complexity of the subject. They believe in the fairness of technology, where understanding trumps credentials.Beyond just an application, DaemonCore Academy envisions a community where technical knowledge is freely shared and valued. They encourage teaching, documenting discoveries, and helping beginners—a cycle of mutual education. The project prioritizes curiosity, understanding, practice, and community over credentials, memorization, passive consumption, and gatekeeping. Ultimately, DaemonCore Academy aims to provide open access to cybersecurity education, challenging the prevailing monetization model.

https://dev.to/daemoncore_academy/wedaemoncoreacadamy-1p9m dev.to

RSS Hunter • Yesterday

Cutting PR review time is really changing where review happens

Atlassian reports its Rovo Dev AI reviewer cut PR cycle time by up to 45% internally and 32% for customers (primary source, Jan 2026). That figure gets quoted as proof that AI review saves review time. Read what the reviewer is described as doing and the number is mostly about routing.The post says the reviewer enforces engineering standards and Jira acceptance criteria before a person opens the PR. Mechanical checks run first, so most of the read-and-re-read cycle is gone before a human touches the change. What falls is the calendar time spent waiting on machines and re-reading the mechanical parts.The part that does not fall is the decision. The Real-SWE benchmark (Specific Labs, Sept 2026) has the best agent resolving 38.8% of its tasks on licensed enterprise codebases. At that acceptance rate the expensive step is deciding whether a given change is actually correct. Shortening the pipeline around it does not shorten that step. It moves the step.So the useful target is not "reduce PR review time." It is "spend human attention only where a machine cannot decide, and decide faster." Baseline checks, standards matching, acceptance-criteria checks all automate cleanly. The person keeps one yes-or-no question with a reason to back it.Teams that only speed up the reading part find the queue moves faster while the wrongness volume stays the same. The teams getting honest cuts are the ones that changed the order: machine decides the mechanical layer, person decides the change itself.

https://dev.to/tessainsley/cutting-pr-review-time-is-really-changing-where-review-happens-3633 dev.to

RSS Hunter • Yesterday

Two "Codex CLI" models on the same benchmark: the harness hides the model

Specific Labs' Real-SWE benchmark exposes a critical issue in evaluating coding agents. Model names like "Claude Code" or "Codex CLI" are misleading because they represent a harness, not the underlying AI model. The same harness can show wildly different performance depending on the integrated model. For instance, GPT-6 Astra on Codex CLI achieved 33.8% resolution, while GPT-5.6 Sol on the same harness only reached 16.2%. This more than twofold difference highlights how the harness is merely a routing layer, not the component doing the thinking.Similarly, Fable 5.1 on Claude Code scored 38.8%, while GLM 5.3 on Claude Code scored 28.8%, a ten-point discrepancy. Real-SWE correctly presents results as model-and-harness combinations, an honest practice often avoided by vendors due to potentially unfavorable numbers under their own tooling. For teams seeking coding agents, simply "using Claude Code" provides no insight into the agent's actual skill. The model swap is the most significant performance lever, yet it remains hidden in most marketing.When reviewing benchmark scores, it's crucial to identify both the model and the harness used. Essential details include conventions, context carry-over, tool loop, and the judge. A vendor's reluctance to disclose the specific model linked to a score should be a red flag. The scaffold, or harness, can influence a score more profoundly than the agent's actual reasoning capabilities.

https://dev.to/cole_halton_42f71d71b809b/two-codex-cli-models-on-the-same-benchmark-the-harness-hides-the-model-1jn dev.to

RSS Hunter • Yesterday

Giving a coding agent more time barely helps

Real-SWE ran frontier models against licensed, private enterprise codebases (billing, tax, multi-service work) and one number jumped out at me: rollout duration barely moves resolution.71.4% of rollouts that finished in under 10 minutes FAILED. 73.4% of rollouts that ran 10 minutes or longer also FAILED. Pass rate sits flat at 27-29% either way. Extending runtime from minutes to long rollouts shifts the outcome by about two percentage points, which is noise.The leader, Fable 5.1 on Claude Code, only lands 38.8% resolution. GPT-6 Astra on Codex CLI gets 33.8%. The top model still fails roughly six out of ten private enterprise tasks.This is the part vendor demos skip. The easy stuff gets solved fast, so on a short rollout you see high apparent throughput. But the tasks that matter, the ones buried in real payroll and tax and integration code, hit a structural wall. The agent doesn't run out of compute on those. It runs out of understanding, or context, or the harness doesn't give it the right entry point. Another ten minutes of looped retries doesn't fix any of that.The other thing Real-SWE does right is treat each score as model+harness, not model alone. Fable 5.1 is only 38.8% paired with Claude Code's scaffold. That's a harness result on private code, not a statement about the model in a vacuum. So many leaderboards still publish model names with no harness pinned, and then people compare them across totally different scaffolding and draw nonsense conclusions.Takeaway for anyone buying an agent: when a vendor shows you a pass rate, ask which slice that number came from. A model that looks great because it clears the fast, shallow tasks is hiding the exact set you actually need it to solve.Benchmark source: withspecific.com/benchmarks/real-swe

https://dev.to/cole_halton_42f71d71b809b/giving-a-coding-agent-more-time-barely-helps-5bg3 dev.to

RSS Hunter • Yesterday

Agent-Cache: Multi-Tier LLM Caching for Valkey and Redis

Agent-cache is a three-tier caching solution designed to optimize LLM operations by reducing token usage and execution time. It uses Valkey or Redis to cache LLM responses, tool outputs, and session states. The architecture includes an exact-match LLM response cache, a tool output cache for function call results, and a session state cache for agent checkpoints. Each tier employs distinct TTL strategies, with LLM responses cached for hours, tool outputs for shorter durations, and session state for active user sessions. Cache keys for LLM responses incorporate prompt hash and model parameters, while tool output keys use tool name and argument hash.Manual invalidation is required, as the system does not track dependencies, allowing targeted cache clearing using Redis glob patterns. In case of Valkey/Redis unavailability, agent-cache defaults to graceful degradation, skipping the cache while allowing per-tier configuration for fail-fast or local fallback options. Observability tools like OpenTelemetry and Prometheus are integrated to monitor cache performance and health. The library provides adapters for LangChain, LangGraph, and Vercel AI SDK, handling serialization for each framework.It is designed for environments already running Valkey or Redis, supporting standalone, sentinel, and cluster deployments with hash tags for efficient key distribution. The primary risks involve stale tool outputs, potential cache key collisions, and memory pressure, mitigated through short TTLs, comprehensive cache keys, and memory policies. Session state loss during restarts is addressed by RDB snapshots or AOF persistence.Agent-cache is beneficial for agents with repetitive prompts or tool invocations, aimed at controlling token costs and leveraging existing Redis infrastructure. However, it is not suitable for tools that mutate external state, highly dynamic prompts resulting in low cache hit rates, or when semantic similarity matching is needed over exact-match. The library effectively bridges the gap between framework-specific caching and general-purpose Redis, best utilized when agent loops and tool behaviors are well understood.

https://dev.to/mech_app_ai/agent-cache-multi-tier-llm-caching-for-valkey-and-redis-10kf dev.to

RSS Hunter • Yesterday

From Dust-Covered PDFs to AI-Powered Insights: Building a 10-Year Health RAG Pipeline

This tutorial demonstrates building a Personal Health Knowledge Base using retrieval-augmented generation (RAG). The goal is to transform static medical PDF reports into a dynamic, queryable system. LlamaIndex orchestrates the process, Unstructured.io handles complex PDF data extraction, and Pinecone serves as the vector store. This system combines historical personal data with current medical literature.The architecture employs a hybrid intelligence approach. DuckDB is used for structured SQL-based trend analysis of personal metrics, while Pinecone stores unstructured semantic context from medical research. Unstructured.io's hi_res strategy is crucial for extracting tables from PDFs.The system requires Python 3.10+, Unstructured.io, and Pinecone API keys. Tables are extracted using Unstructured.io and filtered for structured analysis. Structured data like biomarker levels are stored in DuckDB for time-series analysis.Medical notes and research are stored in Pinecone for semantic retrieval. LlamaIndex's SQLAutoVectorQueryEngine intelligently routes user queries. It directs questions about personal trends to DuckDB and questions about medical implications to Pinecone.This allows for comprehensive queries, such as analyzing personal cholesterol trends against current guidelines. The final output provides actionable health insights by merging personal trend analysis with evidence-based context. While this setup is for learning, production systems need stricter data privacy and medical grounding. The conclusion emphasizes that RAG can contextualize data for actionable insights, moving beyond simple document chatting.

https://dev.to/beck_moulton/from-dust-covered-pdfs-to-ai-powered-insights-building-a-10-year-health-rag-pipeline-30an dev.to

RSS Hunter • Yesterday

🔁 Pendant un hackathon data... je trouve une opportunité de CDO dans ma propre entreprise 😹

The first ever hackathon dedicated to public service employment in New Caledonia, #HackAVP, has been launched. This event aims to foster innovation in public sector careers using technology. During the pitch, the presenter highlighted the utility of an RSS feed available on the website for quick, low-skill integrations. The goal of this article is to demonstrate practical applications, specifically within the SaaS track, and to submit a concrete example to the online form. The five-minute demo showcased the creation of a scheduled job in Claude CoWork. This job aimed to identify a suitable public service job opening. It also demonstrated the generation of a CV, cover letter, and interview preparation document directly on Google Drive. The ten-minute demo provided a closer look at these functionalities. The presenter concluded that with minimal effort and no coding, they were able to find a highly relevant job opening. This opening perfectly matched their profile. The article ends by posing a question to the reader about their own approaches to using technology for daily assistance.

https://dev.to/adriens/pendant-un-hackathon-data-je-trouve-une-opportunite-de-cdo-dans-ma-propre-entreprise-4bp9 dev.to

RSS Hunter • Yesterday

Prompts Are Code. Genkit Makes the Runtime Reviewable.

Prompts in a production environment differ significantly from those in a model playground, leading to potential failures. Genkit addresses this by integrating prompts with flows, schemas, tools, context, traces, and evaluations into a unified application model. Prompts should be stored as versioned artifacts using Genkit's Dotprompt format, encapsulating template content, model configuration, and schema definitions. This ensures prompt changes are reviewable in Git, and settings travel with the template.Prompts should be wrapped in typed flows, establishing application boundaries with stable names, validated inputs and outputs, and trace identity. This separation ensures pre-generation business rules and post-generation validation occur independently of the model's core function. Execution context, such as authentication tokens, should be passed via Genkit's context object, not interpolated into the prompt itself, enhancing security. Model output must be treated as untrusted data, with schemas validating parseability and other controls addressing claims, tone, and delivery.Genkit traces the entire flow, offering a view of operations, model calls, tools, and latent failures. This telemetry, based on OpenTelemetry, aids in debugging and observability. Changes should be evaluated against datasets, including various edge cases, to ensure the workflow meets its product contract rather than just matching individual sentences. Developers should deploy the entire flow, not just loose prompts, allowing for coordinated rollbacks, consistent evaluation, and secure deployment. Ultimately, the complete workflow, encompassing prompts, flows, context, tools, schemas, traces, and evaluations, forms the maintainable product.

https://dev.to/raju_dandigam/prompts-are-code-genkit-makes-the-runtime-reviewable-gh3 dev.to

RSS Hunter • Sep 13

Netcalc - Educational Defensive Security Tool

Netcalc is a practical command-line tool designed for practicing IPv4 and CIDR subnet calculations. It is part of LayerByte's educational cybersecurity projects, prioritizing a practical and readable approach for defensive learning. The tool's development focused on creating a single-concept project for easier code comprehension and testing. Netcalc is intended for school purposes only and emphasizes safety and responsible use. Its defensive scope includes accepting only local or authorized input and providing beginner-readable output. The project features clear validation and error handling. It explicitly avoids any malicious activities like credential theft, brute forcing, exploitation, malware, evasion, or destructive behavior. Useful learning activities involve reading the code, testing with authorized samples, and comparing input with output. Netcalc is built using C++ and is licensed under MIT. Feedback is welcomed to keep the project safe, clean, and useful for educational purposes.

https://dev.to/layerbyte/netcalc-educational-defensive-security-tool-51ho dev.to

RSS Hunter • Sep 13