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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.
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RSS Hunter RSS Hunter • Aug 19, 2024

Thread Of Notes

I Wish I Ran the Numbers on Open Source AI APIs Sooner

The author initially believed self-hosting AI models was the most cost-effective solution, but discovered sophisticated API services offer superior economics for freelancers. Calculating the true costs of self-hosting, including GPUs, infrastructure, and DevOps time, reveals it's significantly more expensive than initially perceived. Open-weight models accessed via APIs provide pricing transparency without the burden of managing hardware. Even for low-volume tasks, API usage is drastically cheaper than the fixed overhead of self-hosting. Larger workloads also favor API solutions, as the cost difference remains substantial. Self-hosting only becomes competitive at extremely high token volumes, assuming one has the necessary infrastructure and expertise. Data residency, low latency requirements, and specific client demands for control are the primary justifications for self-hosting. The author now uses a hybrid approach, leveraging APIs for development, production, and scaling, with optional upgrades for quality. This flexibility allows for rapid model switching without redeploying infrastructure. Ultimately, for most freelancers, API-based solutions are more financially viable and preserve valuable time for core business activities.
https://dev.to/truelane/i-wish-i-ran-the-numbers-on-open-source-ai-apis-sooner-i63 dev.to
RSS Hunter RSS Hunter • Today

--- title: Day 1: Starting My Web Dev Journey published: true description: Learning HTML from scratch ---

A tech enthusiast is embarking on a journey to become a web and game developer, building their skills from the ground up. They are currently learning web development basics using mobile apps like Sololearn and Mimo. All coding is done on a tablet with the Acode app. The motivation behind this endeavor is to transform ambitious ideas into tangible projects through programming. The learning process begins with the fundamental language of the web: HTML. Today, the individual successfully created a multi-page website structure directly on their tablet. They learned that HTML code dictates webpage structure and utilizes tags to add various elements. Some tags, like those for buttons and paragraphs, require both opening and closing containers, while others, such as image tags, are empty. The concept of semantic HTML tags was also introduced. The first project undertaken was the development of a personal portfolio website. The next immediate steps involve deepening their understanding of HTML. Following that, they plan to learn CSS for styling and JavaScript to add interactivity to their web pages. The long-term vision includes transitioning into game development, starting with simple projects after mastering JavaScript. The developer intends to document their progress throughout this learning process. They are also seeking advice from the community on effective mobile learning strategies.
https://dev.to/nknexul_tech_45e05d43d440/-title-day-1-starting-my-web-dev-journey-published-true-description-learning-html-from-eg1 dev.to
RSS Hunter RSS Hunter • Today

My MCP Server Kept Crashing. Here's the Error Recovery Pattern That Saved It.

MCP servers can silently fail due to unhandled exceptions. The Model Context Protocol specification does not mandate error handling, and reference implementations are minimal. This can lead to servers becoming unresponsive over time without any visible error messages. Common causes include network issues, malformed tool arguments, or external API timeouts.A robust solution involves wrapping tool handlers in a try-except block. This wrapper catches various exceptions, such as ConnectionError, TimeoutError, and ValueError. For network-level issues, the server should attempt to reconnect the transport layer. Invalid arguments from the client should be clearly communicated back.A general Exception catch-all should log the full traceback and return a descriptive error message. Crucially, the response must set isError: True to signal to the client that an error occurred. Without this flag, the AI might interpret error messages as valid results.This wrapper pattern ensures no silent crashes, provides clear error signals to the client, and keeps the server operational. It's important to distinguish between transient errors that should be caught and fatal errors that should crash the server. For servers with shared state, a health check tool can verify the server's integrity after a reconnect.Partial failures are possible and should be handled explicitly by returning partial data along with an error flag. Effective logging, particularly structured JSON logging, is vital for debugging and identifying patterns in errors. This approach has significantly reduced silent failures in production environments.
https://dev.to/chenyuan20509/my-mcp-server-kept-crashing-heres-the-error-recovery-pattern-that-saved-it-nd5 dev.to
RSS Hunter RSS Hunter • Today

Building an AI-Powered Lead Qualification API with Next.js 15 and Gemini 3.5 Flash

Businesses need to efficiently prioritize incoming leads to maximize sales efforts. Manually reviewing every lead consumes valuable time and resources. This article proposes an AI-powered API using Next.js 15 and Gemini 3.5 Flash to automate lead qualification. The API analyzes company information and prospect messages to assign a priority score. A simple API route is outlined, taking company and message data as input. It constructs a prompt to send to the Gemini API for analysis. The AI response includes a score, priority level, and a reason for the classification. This automated scoring allows CRMs and other systems to instantly identify high-priority leads. Implementing this AI layer reduces manual review, speeds up responses to valuable prospects, and improves overall sales efficiency. Production deployment requires validating requests, securing API keys, and implementing rate limiting. AI should augment sales teams by handling repetitive tasks, allowing them to focus on crucial customer interactions. Building such a lightweight lead qualification API offers a scalable and quick way to enhance business processes. The author shares practical AI implementation ideas for automation and custom workflows.
https://dev.to/shahdinsalman/building-an-ai-powered-lead-qualification-api-with-nextjs-15-and-gemini-35-flash-ag9 dev.to
RSS Hunter RSS Hunter • Today

Business Automation Architect: Turn Your AI Agent Into an Automation Engine

This business-automation-architect skill focuses on empowering AI agents to run workflows independently without third-party platforms. It leverages the agent's existing access to APIs, file systems, and tools for automation. The core philosophy emphasizes automating processes that occur frequently or are time-consuming.The 5x5 Automation Audit is a structured discovery process that scores potential automations based on frequency, time cost, and other factors. Processes scoring high are prioritized for automation. The skill offers a template for designing workflows with defined triggers, inputs, steps, and robust error handling.Error handling includes levels like retries, fallbacks, queuing, alerts, and safe-stops to prevent data corruption. Implementation relies on the agent's native capabilities for fetching, transforming, sending, and storing data. Recurring automations are managed using cron job templates that run as isolated agent sessions.The ROI framework calculates monthly savings by subtracting automation costs from hours saved multiplied by the hourly rate. This skill is ideal for developers and solopreneurs seeking to systematize operations without additional subscriptions. It provides detailed pattern descriptions for various business processes and addresses common edge cases that can break automations. The main advantage is implementing automation using existing AI agent capabilities.
https://dev.to/amrree/business-automation-architect-turn-your-ai-agent-into-an-automation-engine-2faj dev.to
RSS Hunter RSS Hunter • Today

Treat Per-Task Model Switching as a Concurrency Protocol

Changing the AI model for a running task is a distributed operation, not a simple settings update. It involves reading the current task, preparing credentials, requesting a restart, receiving the result, and persisting the active model. When multiple model switch requests overlap, the order of completion can differ from the order of requests, necessitating a rule to determine which intent wins. The MonkeyCode system records model switch attempts with details like model IDs and request IDs. A typical workflow involves creating a switch record, asking the taskflow to restart, and then completing the switch record. However, explicit compare-and-swap generation or per-task serialization contracts around overlapping requests were not established in a source review.The instability of the "last completion wins" approach is demonstrated by scenarios where a later, successful completion can overwrite an earlier one due to network timing. A companion simulator visualizes this order dependence, showing that the caller's latest intent is not inherently considered. To address this, a monotonic generation is proposed, assigning a unique generation number to each request. The system should only update the active model if the completion's generation matches the task's current requested generation. This generation guard ensures that stale operations are not applied, even if they complete later.The generation guard is only one part of a comprehensive protocol that needs to define contracts for duplicate requests, competing requests, late successes, restart failures, process crashes, session loading, and credential binding. Serialization, such as using per-task locks, is an alternative but introduces complexities like lease expiry and fairness. Unit tests should validate this protocol with controlled interleavings of operations at various stages. The invariant is that the active model should always correspond to the successful result for the greatest non-superseded generation. Treating model switching as a protocol ensures consistency across UI, audit records, retries, and persistence.
https://dev.to/robinzzz/treat-per-task-model-switching-as-a-concurrency-protocol-33mp dev.to
RSS Hunter RSS Hunter • Today

LLM Evaluation System Prompts Scored Rubrics Runtime Guardrails: A Practical Guide for Production

Evaluating LLM outputs in production requires more than simple operational checks like HTTP status codes. The Air Canada chatbot incident highlights how a 200 status code can mask harmful hallucinations, like inventing a discount policy. To address this, a separate evaluation layer is needed to assess output quality independently of operational health. This layer uses LLM-as-a-judge with scored rubrics to evaluate dimensions like correctness and relevance. System prompts for these judges must clearly define their role, output format, and embed detailed, consistent scoring rubrics. These rubrics, written in plain language, provide criteria and rating scales for assessing LLM responses. Techniques like G-Eval can improve reliability by breaking down evaluation into sequential steps. Runtime guardrails act as a safety net, intercepting and flagging or blocking unsafe or irrelevant outputs at inference time. These guardrails often employ LLM-as-a-judge with specific rubrics designed for immediate policy enforcement. Implementing a production evaluation workflow involves versioning system prompts and rubrics, running offline evaluations, and using guardrails as a runtime safety net. This creates a continuous feedback loop for prompt tuning and quality maintenance. By integrating system prompts, scored rubrics, and runtime guardrails, LLMs can be managed effectively in production to ensure both quality and safety.
https://dev.to/unfairhq/llm-evaluation-system-prompts-scored-rubrics-runtime-guardrails-a-practical-guide-for-production-53pc dev.to
RSS Hunter RSS Hunter • Today

Keep Rejected Options in Your Agent Decision Log

An activity log shows what an agent did, but a decision log should also record what was considered and rejected. Without rejected options, reviewers see a simplified path that doesn't reflect the actual decision-making process. This omission hinders trust and recovery, as it makes it difficult to challenge a decision without reconstructing the entire session. Execution history, while valuable, differs from decision context.A proposed decision record separates choice from execution, including a "revisit_when" field for rejected options. This indicates that a rejected option might be suitable under different circumstances. The interface should employ progressive disclosure, providing layers of information from a general overview to specific details. When execution fails, the decision and execution results must be kept separate to avoid implying that the chosen action succeeded.A companion validator can ensure the decision log's completeness, checking for context, evidence, rejected options with reasons and revisit conditions, and execution details. This validation highlights omissions, providing a stable surface for review tools. Before making this pattern default, research involving operators reviewing agent work with different log formats is recommended.This research should measure explanation accuracy, recovery time, and the usefulness of detailed information. Rejected options are crucial for understanding decision boundaries, providing the counterfactual context needed for questioning, repairing, and learning from agent traces. These proposals aim to improve the transparency and auditability of AI agent decisions.
https://dev.to/haaaaaley/keep-rejected-options-in-your-agent-decision-log-53g5 dev.to
RSS Hunter RSS Hunter • Today

Compare Cloud and On-Device AI Costs Without Inventing Energy Numbers

Both on-device and cloud AI can seem plausible regarding battery life and efficiency, but these are not measured claims. When deciding AI placement, consider the four distinct budgets: user wait time, network transfer costs, provider expenses, and device energy consumption. Each of these requires its own specific measurement and evidence.The execution path must be clearly identified to make accurate comparisons. For instance, the reviewed MonkeyCode mobile code uses server-supported streaming for tasks and speech-to-text, indicating cloud-based inference, not on-device. A fair study would compare a mobile client using remote services against a separate prototype demonstrating on-device capabilities.A comprehensive measurement envelope should include fields like sample ID, type, placement, device, OS, framework, model, network type, token counts, latency, data transfer in bytes, energy in joules, and cost in USD. These details are crucial for interpreting results and understanding workload size and network behavior. Battery percentage is an insufficient metric for short runs due to numerous external influences.Comparisons must utilize matched user flows, ensuring the same tasks are tested across different placements. This includes short prompts, voice turns, offline scenarios, background/resume behavior, and thermal loops. Warm-up periods should be reported separately, and tests should be randomized, repeated, and failures recorded.An analyzer should prevent false energy conclusions by requiring measured joules for each data point. Synthetic data is useful for testing parsing but does not represent actual performance. In a real pipeline, data provenance should be strong, including profiler exports and raw file preservation.Release decisions should be explicit, based on meeting targets for P95 interaction latency, network bytes, provider spend, energy and thermal behavior, privacy, and quality. On-device AI introduces download size and RAM pressure, while cloud AI relies on network connectivity and service dependency. Using clear units ensures an honest assessment of these tradeoffs.
https://dev.to/roronoa_/compare-cloud-and-on-device-ai-costs-without-inventing-energy-numbers-gdb dev.to
RSS Hunter RSS Hunter • Today

Verify a Self-Hosted Installer Before Running It as Root

Executing an installer directly as root merges artifact selection, integrity checking, and execution approval into a single risky step. Separating these decisions into distinct stages enhances reviewability, reproducibility, and recoverability. A concrete source-review boundary involves checking architecture and performing basic system checks before downloading. The reviewed template, however, disables certificate verification using curl -k and downloads an unversioned file without explicit checks for pinned versions, digests, or signatures. To improve this, a manifest containing immutable metadata like version, architecture, file name, SHA-256 hash, and rollback information should be published separately. This manifest, protected by a secure release process, can be validated using TLS or digital signatures.Verification should occur as an unprivileged staging step. A companion script can check the filename, exact size, digest, version, architecture, and rollback metadata against the manifest. This verification process should never execute the downloaded file. A production flow requires downloading the artifact, verifying it against the manifest, and then, after an explicit maintenance decision, executing it with elevated privileges. Certificate errors should be addressed by fixing trust store or deployment issues, not by bypassing verification with -k.Rollback should be a well-defined executable plan, including details about the prior artifact, its manifest, compatibility, service commands, health checks, reversible migrations, and cleanup procedures. Before production deployment, rollback should be rehearsed in a canary environment, complete with simulated failures. Privileged execution should only be permitted when TLS verification is successful, an immutable version is specified, a trusted manifest matches the downloaded file, signature verification passes if applicable, canary health checks are good, and rollback procedures are verified. This structured approach transforms a blind network-to-root operation into an inspectable and automatable process.
https://dev.to/odd_background_328/verify-a-self-hosted-installer-before-running-it-as-root-1l8e dev.to
RSS Hunter RSS Hunter • Today

Add Arrow-Key Shortcuts to a Confirmation Dialog Without Breaking Accessibility

This text discusses adding keyboard shortcuts to a confirmation dialog, specifically for arrow key navigation between Cancel and Confirm buttons. It emphasizes that this is an enhancement and not a replacement for foundational accessibility features. Essential accessibility requirements for alert-style confirmations are listed, including accessible names, focus management, keyboard constraints, escape dismissal, visible focus, and focus return. The WAI-ARIA Authoring Practices Alert Dialog Pattern is cited as the basis for modal semantics and keyboard interaction.The proposed solution isolates the new arrow key mapping into a pure function and an event handler that only intercepts specific arrow key presses, leaving other keyboard interactions like Tab, Shift+Tab, and Escape untouched. A minimal dialog example using native HTML semantics is provided, demonstrating focus on Cancel by default. The text then outlines a testing procedure, including running pure mapping tests and manually testing the dialog with a keyboard and screen reader. The core design principle is to add shortcuts as an enhancement around semantic controls while ensuring that unhandled keys continue to function as expected. The author notes their contribution to the MonkeyCode project and discloses that the described behavior is based on public issues and code.
https://dev.to/babycat/add-arrow-key-shortcuts-to-a-confirmation-dialog-without-breaking-accessibility-220g dev.to
RSS Hunter RSS Hunter • Today

GPT-5.6 MCP: Testing Servers With Sol, Terra & Luna

OpenAI released GPT-5.6 on July 9, 2026, featuring three tiers: Sol, Terra, and Luna, all optimized for agentic tool calling. Each tier offers a 1 million token context window and a 128K maximum output. The new models introduce Programmatic Tool Calling, enabling the AI to write JavaScript to chain tool calls, significantly reducing token usage and costs. GPT-5.6 also boasts an ultra multi-agent mode, running subagents in parallel to improve performance.Sol is the flagship for complex agentic work, Terra is balanced for high-volume tasks, and Luna is the most affordable for everyday use. Choosing the correct tier is crucial to avoid overpaying, with a recommendation to start with Luna and escalate if needed. Testing these models with MCP servers can be done for free in the browser using tools like MCP Agent Studio. GPT-5.6 excels in tool orchestration and agentic benchmarks, though Claude may still lead in raw code-fixing accuracy.Reliable tool calls depend on clear tool descriptions and schemas, and it is advisable to confirm write actions before execution. Server-side errors are distinct from model errors, and MCP servers should be scanned for security risks. MCP Playground allows for free side-by-side testing of GPT-5.6 tiers and other models without needing an API key. Ultimately, comprehensive testing is the best way to determine the optimal GPT-5.6 tier for specific MCP server workloads.
https://dev.to/rupa_tiwari_dd308948d710f/gpt-56-mcp-testing-servers-with-sol-terra-luna-1524 dev.to
RSS Hunter RSS Hunter • Today

Linux Kernel Modules That Explain How Podman Really Works

This guide demonstrates building and running three Linux kernel modules to illustrate core operating system concepts using a Killercoda Ubuntu playground and Podman. The first step involves preparing the environment by cloning a GitHub repository and installing essential build dependencies for kernel modules. Subsequently, Podman is installed on Ubuntu for managing containerized workloads. Module 1, a basic "Hello World" example, shows how to compile, load, and unload a kernel module, with its debug messages visible via dmesg. Module 2 expands on this by creating a character device, demonstrating how user-space processes interact with kernel functionality through familiar file operations. This module highlights how the kernel acts as an intermediary between user applications and hardware or kernel services. Module 3 explores the relationship between kernel process information and user-space tools like podman ps. It explains that containers are essentially Linux processes running in isolated namespaces. By examining the kernel's internal task list, module 3 reveals the underlying data structures that power both user-space process commands and container management tools like Podman. The guide concludes by emphasizing that container runtimes leverage fundamental kernel primitives, reinforcing the idea that containers are built upon standard Linux processes.
https://dev.to/sergioarmgpl/linux-kernel-modules-that-explain-how-podman-really-works-22c3 dev.to
RSS Hunter RSS Hunter • Yesterday

Logging workouts is solved. I'm building what comes after.

Existing workout trackers log performance but offer no guidance on why progress stalls. This gap led to the creation of WhyRep, a workout tracker with an AI coach. The coach's decisions are not arbitrarily generated but derived from a pre-written and approved methodology. The developer, with a background in exercise science, first establishes this methodology before utilizing AI.At its core, WhyRep employs deterministic engines to implement the methodology, which are rigorously tested. The LLM, Claude, serves as a conversational interface, explaining pre-approved coaching decisions and facilitating program adjustments. This approach aims for a nuanced coaching experience grounded in scientific principles.For example, the coach can suggest program modifications to address specific muscle growth goals. It can even identify less obvious training opportunities, like emphasizing the brachialis with shoulder-flexed curls. Every recommendation is traceable to the underlying, validated methodology.The developed features include comprehensive workout tracking, progression detection, autoregulation, deload logic, and plateau diagnosis. Users receive basic alerts or detailed solutions, depending on their subscription. A Kotlin Multiplatform core ensures consistent performance across Android and iOS.The backend coach chat integrates Claude, with methodology documents cached for context. The methodology itself is considered the core product, meticulously crafted and validated. It accounts for fractional muscle contributions from various exercises, providing a more holistic approach to volume calculation.Unlike other AI fitness apps, WhyRep encodes an evidence-based methodology rather than relying on an LLM to invent training science. Marketing efforts focus on educational gym content on social media platforms. The developer is seeking advice on audience building for technical products and communicating correctness and trust effectively.
https://dev.to/arqamwd/logging-workouts-is-solved-im-building-what-comes-after-36cc dev.to
RSS Hunter RSS Hunter • Yesterday

Your AI agent says "done." Who checks that from outside the agent?

A common failure mode in AI agents is the "90% AI Agent" problem, where agents report completion despite not fully executing tasks. This can manifest as empty files, incorrect configurations, or subtle errors propagating through subsequent steps. Studies show a significant percentage of AI agent failures are falsely reported as successes, with simple checks sometimes proving more effective than advanced AI evaluations. AI observability tools acknowledge this issue, yet typically focus on trace depth and cost accounting rather than independent verification of completion claims.The proposed solution is completion verification, an explicit, repeatable layer acting as an external check on an agent's reported status. This layer verifies that an agent's completion claim is grounded in actual changes to the system's state, independent of the agent itself. It's crucial because the agent, as the reporter, is an unreliable narrator, and asking it to narrate more carefully won't solve the underlying issue. Verification must come from an external, independent mechanism.An example illustrates this: a design correction in a recurrence key identification process was caught by an external reviewer before being implemented. This external perspective, distinct from the developer's internal progress, highlighted a flaw in the agent's self-assessment of task completion. The engineering goal is to institutionalize such external audits into a reliable, automated process.This layer is essential for agents that report tasks as done when they are not truly finished. Building completion verification as a deliberate layer acknowledges the inherent unreliability of agent self-reporting. It complements existing observability tools by focusing on the critical step of confirming that an agent's declared outcome matches the real-world state. The core principle is to prefer dumb, independent checks over complex self-judgments.
https://dev.to/nexuslabzen/your-ai-agent-says-done-who-checks-that-from-outside-the-agent-3fe4 dev.to
RSS Hunter RSS Hunter • Yesterday

Why Advanced Industrial Protocols Give Raw Sockets the "Silent Treatment"

The author initially abandoned high-level libraries to understand protocols at a fundamental, socket-based level. This hands-on approach offered direct insight into how protocols communicate on the wire. While successful with simpler protocols like Modbus, applying the same methodology to complex ones like EtherNet/IP and DNP3 proved challenging. Advanced industrial protocols feature intricately nested structures, demanding byte-level precision in manual construction. Errors in these manually crafted binary streams result in system failures and silent timeouts with no clear error feedback. Debugging these issues often necessitates examining container logs to pinpoint the exact point of failure. The author emphasizes that the frustration often begins with incorrect assumptions about how high-level libraries handle protocol formats. When library abstractions prevent necessary packet manipulation, the only recourse is manual payload construction. However, for sophisticated protocols, this manual approach becomes exceedingly difficult due to complex session management and routing headers. Relying solely on automated tools in Operational Technology security is a significant risk. These tools are built on assumptions that rarely hold true in diverse real-world industrial environments. When encountering non-standard setups, devices may fall silent or tools may provide incorrect results. Ultimately, the author concludes that manually interacting with protocols at the wire level, despite its difficulty, is invaluable for deep security research. This direct engagement allows for immediate recognition of network anomalies and a precise understanding of communication failures.
https://dev.to/null_saint/why-advanced-industrial-protocols-give-raw-sockets-the-silent-treatment-33b8 dev.to
RSS Hunter RSS Hunter • Yesterday

Building a Practical Taxonomy for AI World Models

The term "world model" is used broadly in AI, encompassing everything from latent dynamics models to traffic scenario generators. This ambiguity led to the development of "State of World Models 2026: Taxonomy, Benchmarks and Open Challenges," aiming to provide a consistent way to describe these models. The report defines a world model as an AI that learns an environment's representation to predict, simulate, evaluate, or support actions within it. This broad definition includes various AI applications but excludes generative models that lack essential environmental consistency.A universal ranking is deemed misleading because different world models excel in distinct areas, such as visual realism, robot planning, or safety testing. Instead, the report proposes a taxonomy based on practical fields like domain, input/output modalities, action conditioning, representation, temporal horizon, and evaluation type. The domain, such as robotics or video generation, significantly influences a model's purpose and evaluation criteria. Functionality is another key differentiator, with models serving purposes like prediction, simulation, planning, or data generation.Internal representations vary from pixels to latent vectors and symbolic variables, each with its trade-offs. The temporal horizon, from next-state prediction to procedural planning, is crucial as errors can accumulate over time. Action conditioning, distinguishing between passive prediction and "what if I do this" scenarios, is a vital practical distinction. Evaluation is fragmented across perceptual, physical, functional, and planning aspects, highlighting the "perception-functionality gap."The report suggests a structured catalog for models and benchmarks to facilitate filtering and comparison. It emphasizes documenting known information, separating evidence from interpretation, and implementing versioning to manage the rapidly evolving field. Exclusions are necessary to maintain focus, preventing the catalog from becoming an all-encompassing AI directory.
https://dev.to/powehi/building-a-practical-taxonomy-for-ai-world-models-26mk dev.to
RSS Hunter RSS Hunter • Yesterday

Type-safe Elasticsearch queries in TypeScript (and JavaScript) with elasticlink

Elasticsearch queries can silently fail, returning empty results due to mismatches between query types and field mappings or simple typos. For instance, a match query on an un-analyzed keyword field or a typo in a field name like catgory instead of category will pass validation but yield no hits. This occurs because Elasticsearch's DSL is an untyped JSON blob, offering no compile-time checks for field types or query validity.Elasticlink addresses these issues by providing a type-safe, mapping-aware query builder for Elasticsearch. Users define their index mapping once, and elasticlink enforces type constraints on query methods accordingly. For example, match() is restricted to text fields, and term() to exact-value fields, with field names conveniently autocompleting.This approach ensures that potential errors, like using match() on a keyword field or typos, are flagged as red squiggles in the editor rather than becoming runtime production errors. Elasticlink is TypeScript-first but also compatible with plain JavaScript, supporting both ESM and CommonJS. It functions as a builder, generating plain Elasticsearch DSL via a .build() method without runtime overhead, making it suitable for direct use with the official @elastic/elasticsearch client.The tool validates field references against the defined mapping, including within aggregations, offering robust safety for complex queries. Additionally, elasticlink can infer TypeScript types directly from the mapping, eliminating the need for a separate source of truth. For JavaScript users, it provides IDE autocompletion and type constraints through special comments.Elasticlink remains correct across Elasticsearch versions by deferring to the official client's types for options, ensuring compatibility and feature availability. It also offers additional features like type-safe kNN search, conditional query building with .when(), and presets for index management. This comprehensive set of tools aims to prevent silent failures and improve the developer experience when working with Elasticsearch.
https://dev.to/john_rodger_dee953ed28186/type-safe-elasticsearch-queries-in-typescript-and-javascript-with-elasticlink-2716 dev.to
RSS Hunter RSS Hunter • Yesterday

title: "Le problème N+1 en GraphQL — pourquoi votre API Laravel ralentit (et comment le régler avec Lighthouse)"

GraphQL integrates well with Laravel APIs, offering clean queries and developer satisfaction. However, a common performance issue, the N+1 problem, can arise when lists grow in size, drastically increasing response times. This problem occurs when a query for a list of items also triggers a separate database query for each item's related data, such as an author's name.This results in one initial query plus an additional query for every item in the list, hence "N+1." In REST APIs, this is often more apparent in the code, but GraphQL's inherent relationship resolution can mask it until significant data loads occur. The core principle to fix N+1 is to avoid queries within loops; instead, collect necessary keys and perform a single, batched query.For standard Eloquent relationships in Laravel with Lighthouse, this is handled automatically by directives like @belongsTo. These directives batch related data into a single SQL query using WHERE IN clauses, regardless of list size. For computed fields that aren't direct Eloquent relationships, developers must manually implement batching using tools like BatchLoader.This involves creating a loader class that gathers all required IDs and executes a single, grouped query. To detect N+1 problems, developers can use Laravel Debugbar to monitor SQL query counts, DB::listen() in integration tests, or Laravel Telescope for detailed request analysis. The crucial guideline is that GraphQL query performance should not degrade with increased list sizes.While N+1 is a common initial hurdle, other performance considerations for GraphQL APIs include caching strategies, query complexity limiting, and rate limiting. These topics, along with a comprehensive guide to building and consuming GraphQL APIs with Laravel and Angular, are covered in a dedicated training course.
https://dev.to/godwill/title-le-probleme-n1-en-graphql-pourquoi-votre-api-laravel-ralentit-et-comment-le-regler-avec-nmn dev.to
RSS Hunter RSS Hunter • Yesterday

I put a firewall in front of my AI agents. Here is the near-miss that made me build it.

The author developed Bastion Gateway because AI agents can perform destructive actions at machine speed without oversight. A near-miss incident where an agent attempted to drop a database table highlighted this vulnerability. Existing agent infrastructure lacked layers for identity and governance, creating a critical gap. Bastion Gateway bridges this gap by implementing a default-deny security posture. It meticulously controls agent access through an allowlist of permitted tools and endpoints. The gateway also redacts sensitive information like secrets and PII from outbound data. Destructive actions are paused for human approval via a risk gate mechanism. Crucially, Bastion Gateway generates a signed, immutable audit log of all agent actions. This log serves as verifiable compliance evidence for auditors and stakeholders. The gateway is designed to be self-contained, with no outbound telemetry or external dependencies. It can be easily deployed and integrated by simply redirecting agent traffic to its local address. A self-hosted, open-source version is available, with a hosted version planned for the future.
https://dev.to/vystartasv/i-put-a-firewall-in-front-of-my-ai-agents-here-is-the-near-miss-that-made-me-build-it-13oe dev.to
RSS Hunter RSS Hunter • Yesterday

SRP and the Hero's Journey: Writing Code Like a Jedi Master

The author recounts an experience with a complex legacy codebase where a single User class handled validation, password hashing, email sending, database persistence, and report generation. This overloaded class made even minor changes perilous, akin to defusing a bomb blindfolded. The difficulty in managing such code and onboarding new developers highlighted the need for a better approach, leading to the discovery of the Single Responsibility Principle (SRP). SRP states that a class should have only one reason to change.Applying SRP leads to numerous benefits, including improved clarity, easier testing, greater flexibility, and enhanced safety. The article then contrasts a poorly designed "God" class with a refactored version adhering to SRP. The initial User class demonstrated multiple change motivations, difficult testing, and tight coupling. In contrast, the refactored version breaks down these responsibilities into smaller, focused classes: User, UserValidator, PasswordHasher, UserRepository, EmailService, and ReportGenerator.This separation allows each class to have a single purpose, making them easier to understand, test, and modify independently. For instance, changing the hashing algorithm only requires updating the PasswordHasher class. This adherence to SRP ultimately increases the speed of development, reduces bugs, improves team scalability, and makes systems more future-proof. The author encourages readers to identify and refactor classes with multiple responsibilities in their own projects.
https://dev.to/timevolt/srp-and-the-heros-journey-writing-code-like-a-jedi-master-59mf dev.to
RSS Hunter RSS Hunter • Yesterday

Verifying How IAM and Lake Formation Behave for the Glue REST Catalog and S3 Tables

This article examines the differing authorization behaviors of AWS Glue REST Catalog and S3 Tables endpoints when interacting with Iceberg tables. While the Glue endpoint relies on both IAM policies and Lake Formation grants, the S3 Tables endpoint uses IAM alone for authorization. Tests were conducted by varying IAM permissions and Lake Formation grants to observe the outcomes. The baseline scenario, with both IAM and Lake Formation permissions in place, resulted in successful 200 responses from both endpoints. When Lake Formation grants were removed, the Glue endpoint returned a 403 error, indicating its dependency on Lake Formation, while the S3 Tables endpoint remained accessible with a 200 response. Conversely, removing s3tables IAM actions resulted in 403 errors from both endpoints. CloudTrail logs were used to trace the authorization flow, showing that Glue invokes GetDataAccess for Lake Formation evaluation, even if ultimately denied. The S3 Tables endpoint, however, does not trigger GetDataAccess calls. This confirms that the Glue endpoint orchestrates a two-step authorization process, first checking IAM and then delegating to Lake Formation, whereas the S3 Tables endpoint performs a single IAM authorization check.
https://dev.to/aws-builders/verifying-how-iam-and-lake-formation-behave-for-the-glue-rest-catalog-and-s3-tables-2cgc dev.to
RSS Hunter RSS Hunter • Yesterday

I shipped an LLM efficiency + security kernel — and deleted my own best idea

The author attempted to improve Large Language Models by orchestrating multiple LLMs together, a concept termed "mitosis." This approach involved splitting tasks, having LLMs compete, and then synthesizing the best answer. However, rigorous testing revealed that this method worsened correctness, decreasing passing tests from 95% to 83% while increasing costs significantly. After confirming these negative results across three independent experiments, the author deleted the failing feature. The core lesson learned is that an idea that sounds good in a pitch may not survive actual measurement. Instead, the author developed and shipped BIOMA, a lightweight, provider-agnostic kernel that preprocesses LLM requests. BIOMA employs three key mechanisms: efficiency through context "apoptosis" to reduce token usage, security via a "cognitive firewall" for secret redaction and flood detection, and speed through an efficient signaling system. The efficiency mechanism typically reduces input tokens by 80% and can achieve up to 97% reduction. The security features successfully prevented any secrets from being leaked during red-teaming exercises. BIOMA is designed to work with any LLM provider without vendor lock-in. The code is source-available under a license that allows free use for non-competing purposes and converts to MIT after two years. The author emphasizes the importance of measuring everything and retaining only what is validated by data, even if it means discarding the initial project goal.
https://dev.to/jonathas_cordeiro/i-shipped-an-llm-efficiency-security-kernel-and-deleted-my-own-best-idea-nhi dev.to
RSS Hunter RSS Hunter • Yesterday

From git push to Fine-Tuned Model in Production

The author developed nebius-actions, a set of GitHub Actions to automate model fine-tuning and deployment on Nebius AI Cloud. The goal was to achieve a fully automated pipeline triggered by a single button click in GitHub. This pipeline involves spinning up GPU infrastructure, fine-tuning a model, packaging it, deploying it to an endpoint, testing it, and cleaning up all resources. A demo workflow orchestrates this through five distinct GitHub jobs: submit, wait, deploy, try, and cleanup. State information is passed between these jobs using their outputs.The submit job, which contains most of the logic, creates an Axolotl configuration and a bash script inline. This script handles the fine-tuning process with Axolotl, packages the adapters, and pushes a serving image to the Nebius Container Registry. It also provisions a new S3 bucket for each run and creates a Nebius Job. Authentication is managed securely using short-lived IAM tokens.The wait job streams logs from the Nebius GPU job and polls its status, crucially including logic to cancel the GPU job if the GitHub workflow is cancelled to prevent unexpected costs. The deploy job creates a Nebius Endpoint using the newly built image and then a separate wait job polls for the endpoint to become ready. The try job performs a simple smoke test by checking the endpoint's health and making a sample API call to verify functionality. Finally, the cleanup job, running with an always condition, ensures that the deployed endpoint and provisioned S3 bucket are deleted, preventing leftover resources and cloud bills. The image remains in the registry for potential redeployment. The nebius-actions are designed to be small, composable building blocks, with each action managing a single resource.
https://dev.to/piotrjanik/from-git-push-to-fine-tuned-model-in-production-2ghn dev.to
RSS Hunter RSS Hunter • Yesterday

Indian villages with live market prices, government schemes, and soil data

The Village Finder is a fully open-source and interactive map that tracks over 78,000 villages across 130 districts in India, providing live market prices, government schemes, and soil data. The map is built on top of the GitHub ecosystem with zero server costs, using a daily GitHub Action to update the data. The project uses a unique approach to render millions of land parcel polygons without incurring expensive database and tile server costs. The data is sourced from various government portals, including the Local Government Directory, and is processed and published under the open GODL-India framework. The Village Finder map is available in six languages and provides an interactive choropleth map that drills down from district to village level, with instantaneous client-side fuzzy search. The map also streams real-time APMC market quotes and provides dynamic agriculture profiles, including 7-day agromet forecasts and organic soil profiles. The project's architecture operates natively on top of the GitHub ecosystem, using Git branches as a free CDN and CI/CD as a data audit trail. The code is under the MIT License, and the processed datasets are published under the open GODL-India framework, making it available for other engineers to use. The Village Finder is a valuable resource for those working in civic tech, agritech, logistics, or geospatial architectures, and contributions to the project are welcome. The live app and source code are available on GitHub, and the data can be downloaded directly from the repository's Releases tab.
https://dev.to/mchittineni/indian-villages-with-live-market-prices-government-schemes-and-soil-data-307f dev.to
RSS Hunter RSS Hunter • Yesterday

cordless v0.8: Tab Groups, Custom Launchers & Bring-Your-Own Copilot

Cordless v0.8 is a CLI-first tool for managing remote terminal and coding agent sessions, focusing on organizing a swarm of sessions on your phone. The main feature is tab groups, allowing users to categorize sessions by name and color, similar to Chrome mobile, with collapsible headers and real-time counts in the terminal dashboard. Users can manage groups and filter sessions by criteria like "Attention" or "Copilot." Custom launchers are introduced, enabling users to define their own execution profiles with specific commands and arguments, alongside a new built-in GitHub Copilot CLI profile. Session tabs can now be renamed for improved clarity and convenience.A significant engineering effort went into implementing persisted scrollback history, ensuring sessions retain their history even after a system reboot. This involved storing logical lines from the terminal buffer, rendered above the live session, with periodic saving to prevent data loss. A critical bug related to history persistence on Linux and macOS, caused by an unexpected interaction between graceful shutdown and PTY exit handlers, was identified and fixed through a _shuttingDown flag. The development process emphasized a "feature-per-branch" methodology, with extensive testing across Windows, Linux, and macOS. The overall goal of v0.8 is to provide a manageable and intuitive experience for users operating numerous remote sessions, delivering on the "browser-tabs promise" for terminals.
https://dev.to/naveenneog/cordless-v08-tab-groups-custom-launchers-bring-your-own-copilot-2i0b dev.to
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RSS Hunter RSS Hunter • Yesterday

How I Made a Product Demo with Claude Code, Playwright, and FFmpeg

The author created a 56-second product demo for Agent OS, an open-source project, in about 30 minutes using Claude Code, Playwright, and FFmpeg. The goal was a clear, unpolished walkthrough of the real product, avoiding manual recording and complex video editing software. Claude Code inspected the application and identified key product states for the narrative, then used Playwright to navigate and record these states. The demo featured existing run records from the real Pulseboard build, ensuring authenticity. Instead of one long recording, several short clips were recorded, each with its own browser context, to improve stability. FFmpeg was then used to trim clips, add labels, create opening and closing cards, and concatenate everything into a final MP4. This workflow is ideal for open-source demos and internal walkthroughs where authenticity and reproducibility are prioritized over elaborate graphics. The author notes that the browser flow is code, product state is deterministic, and labels/timings are configuration, allowing for easy regeneration of the video. Agent OS is described as a local-first AI Project Operating System that adds memory, execution control, verification, recovery, and delivery to coding models. The demo serves to quickly show users what the system actually does.
https://dev.to/_2e39841ea0f3747512e67/how-i-made-a-product-demo-with-claude-code-playwright-and-ffmpeg-2ach dev.to
RSS Hunter RSS Hunter • Yesterday

AI, the Future of IT, and Why I Believe the Industry Is Heading Toward Another Cycle

Artificial intelligence has become the dominant topic in the IT industry, leading to widespread discussion about its impact on software engineering jobs and the overall market. The software engineering job market itself has experienced a downturn, with fewer open positions and increased difficulty for developers to secure roles. This raises questions about whether AI is the direct cause or if concurrent trends are at play. Extensive research into industry reports, executive interviews, and academic papers reveals a nuanced reality far removed from sensational headlines.This article aims to consolidate these findings, examining how major tech companies integrate AI, the reported benefits, unexpected challenges, and the emerging debate about an "AI bubble." It is presented as an interpretation of current trends, acknowledging that predictions may evolve. Just a few years ago, AI was viewed as novel; now, with advanced models like GPT-4 and AI coding agents, it has become a practical tool. These agents can analyze code, create files, execute commands, and even open pull requests, shifting developers' roles towards defining requirements and validating quality.Major tech companies, including Microsoft, Google, and Amazon, are heavily investing in AI, integrating it into their core engineering strategies and reporting significant gains in pull request volume, delivery speed, and developer productivity. Shopify and Duolingo, for instance, are adopting "AI-first" strategies, making AI proficiency a core employee competency. Microsoft views GitHub Copilot as an essential tool for efficiency, while Amazon sees AI as a means to achieve more with smaller teams.Meta focuses on automating internal workflows, and Spotify's internal AI platform, Honk, has dramatically increased pull request volume and automated code changes. Other companies like Google, Anthropic, and monday.com also report substantial productivity improvements. The common goal is to enhance team productivity, automate repetitive tasks, and reduce costs, rather than outright replacing engineers. However, this increased development velocity has introduced new challenges, including growing technical debt, complex codebases, and heavier code review workloads.The rapid ascendancy of AI has sparked debate about whether the industry is in an "AI bubble." Opinions are divided between those who see AI as a revolutionary breakthrough and those who question the high valuations of AI companies lacking sustainable business models. These concerns underscore the complexity of AI's current impact and future trajectory.
https://dev.to/itsisyphus/ai-the-future-of-it-and-why-i-believe-the-industry-is-heading-toward-another-cycle-3c8e dev.to
RSS Hunter RSS Hunter • Yesterday

Village Finder!

Finding accurate rural data in India shouldn't mean wrestling with clunky legacy portals.Meet Village Finder—an open-source project mapping 78,000+ villages across South India in a unified District ➡️ Mandal/Taluk ➡️ Village hierarchy.What's inside? 🔹 Instant fuzzy search by village name or PIN code 🔹 On-demand lookups for nearby hospitals, police stations & post offices 🔹 Real-time district mandi prices & Open-Meteo forecasts 🔹 Interactive cadastral layers showing survey plot linesBest part? Hosting costs are exactly $0. The entire pipeline runs via GitHub Actions, compiling vector layers into cloud-optimized PMTiles for serverless streaming.100% free and open-source. Explore the map or download the flat data splits: 👉 https://mchittineni.github.io/india-village-finder/OpenData #GIS #BuildInPublic #Serverless #CivicTech
https://dev.to/mchittineni/village-finder-1f7e dev.to
RSS Hunter RSS Hunter • Yesterday

I created a platform with a list to help you share your projects 🔥

The author introduces a new commercial open-source platform called "List a project and promote it!", designed to offer sustained promotion for projects. Unlike typical project listing services, this platform promises long-term, periodic visibility for included projects. The name, though lengthy, is deemed fitting for its purpose. The core concept of "promotion" here involves consistent efforts to increase a project's visibility across various external platforms, rather than solely building an audience on the platform itself. The platform limits the number of projects accepted monthly, enabling the team to continuously work on promoting both new and previously listed projects through content creation and integration into relevant articles. The initial version of the platform was built using Next.js v16, Tailwind v4, and ShadCN, with future plans to integrate an API, database, and user registration. The author welcomes feedback and community contributions, as the project is open source. A GitHub repository and a website are provided for further engagement. The project emphasizes efficient, ongoing visibility efforts for listed projects.
https://dev.to/anthonymax/i-created-a-platform-with-a-list-to-help-you-share-your-projects-567j dev.to
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RSS Hunter RSS Hunter • Yesterday

🗺️ Empowering Rural Analytics: Announcing India Village Finder

Village Finder is an open-source interactive mapping application designed to provide accurate, localized information for rural India. It organizes and visualizes geographical data for Andhra Pradesh, Telangana, Karnataka, and Tamil Nadu using official administrative hierarchies. The application offers instant fuzzy search capabilities, allowing users to locate over 78,000 villages by name, region, or PIN code. Users can access live APMC market prices, showing real-time commodity quotes aggregated at the district level. It also provides agricultural and soil insights, including 7-day weather forecasts, groundwater prospects, and WRB soil classification profiles. Village Finder enables civic infrastructure mapping, allowing on-demand lookups for nearby critical services like hospitals and police stations using OpenStreetMap. Additionally, it offers cadastral and land record linkage, displaying high-resolution survey plot layers and copying unique boundaries for official state portals. The platform aims to transform local governance, agricultural planning, and business operations through accessible open data. Its application logic is under the MIT License, and data assets are under the Government Open Data License (GODL-India). Users can explore the live map, download raw data, or contribute to expanding coverage to other states.
https://dev.to/mchittineni/empowering-rural-analytics-announcing-india-village-finder-3g31 dev.to
RSS Hunter RSS Hunter • Yesterday

What If Every Stadium Had an AI Copilot?

The project ArenaMind was built for the Google GenAI Hackathon, with the goal of transforming the experience of hosting the FIFA World Cup 2026 using Generative AI. The challenge was to imagine how AI could improve the event, and the solution went beyond creating a simple chatbot. ArenaMind is an AI-powered platform designed to assist both fans and stadium operations teams in real time, with a focus on reliability during high-footfall events. The platform combines AI-powered decision making with structured backend logic using Google Gemini function calling. For fans, ArenaMind offers a range of features, including a multilingual voice and chat companion, QR e-ticket awareness, and real-time food stall and restroom queue monitoring. The platform also provides dedicated wheelchair and step-free routing, making it more accessible for all attendees. For organizers and venue staff, ArenaMind offers interactive crowd congestion heatmaps, predictive crowd overload forecasting, and a natural-language operations assistant. The tech stack used to build ArenaMind includes Google Gemini, TypeScript, React, Node.js, PostgreSQL, Docker, and SOLID Architecture. The project demonstrates how Generative AI can create meaningful impact beyond conversations by improving accessibility, navigation, crowd management, and operational efficiency for large-scale live events. The success of ArenaMind highlights the potential of AI to enhance the experience of attending major sporting events, and its features could be useful for attendees of the FIFA World Cup 2026.
https://dev.to/drpratik/what-if-every-stadium-had-an-ai-copilot-33dn dev.to
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RSS Hunter RSS Hunter • Yesterday

From Prototype to Production: how we took an AI app from PartyRock to the real world in one night

The AWS User Group Campinas leaders often encounter the problem of clients not knowing how to start using AI, a process usually involving weeks of setup. They presented a faster path, beginning with AWS PartyRock, an accessible tool for rapid AI prototyping. PartyRock allows users to build functional generative AI apps in minutes, without infrastructure, code, or a credit card. It's ideal for validating ideas and demonstrating concepts to stakeholders, though it has limitations for production use. The second part of the presentation demonstrated evolving a PartyRock prototype into a real production application, capable of processing user data and integrating into workflows. This involved architectural decisions and selecting AWS AI services for tasks like transcription and summarization. This "PartyRock to Production" journey helps companies overcome paralysis while avoiding the illusion of simple ChatGPT solutions. Key lessons included the value of live demos, co-presentations, and that the gap between prototype and production is smaller than perceived. To replicate this journey, one should start with a real problem, prototype quickly in PartyRock, validate the concept, and evolve to a serverless production architecture.
https://dev.to/pimenta/from-prototype-to-production-how-we-took-an-ai-app-from-partyrock-to-the-real-world-in-one-night-4kg2 dev.to
RSS Hunter RSS Hunter • Yesterday

FutureX · Physical AI Daily — Issue 57 (07/14)

US NHTSA has issued an ultimatum to robotaxi operators, demanding remediation plans for emergency-response obstructions by the end of the month. China Mobile is procuring 400 humanoid robots from Zhiyuan and Unitree for 124 million yuan. Galbot secured a substantial 236 million yuan bid for 500 robots at Yibin high-speed rail station, setting a new single-procurement record. ByteDance is reportedly exploring autonomous driving technology, though the company has officially denied any business plans in this area. South Korea's Holiday Robotics has raised a record 155 billion won in Series A funding.Research highlights include B-spline Policy, which parameterizes actions as continuous curves to speed up manipulation policy inference. A new technique improves VLA post-training sample efficiency by five times through hindsight relabeling, reusing failed rollouts. BeyondSight aims to restore "object permanence" to end-to-end autonomous driving systems, maintaining object hypotheses even when occluded. PanoWorld addresses long-horizon memory issues in video world models by exploiting panoramic rotational equivariance.CD-LAM debiases world models, improving action controllability and reducing real-robot adaptation updates. TactiDex is a new benchmark for evaluating dexterous manipulation based on contact, not just motion imitation. Energy profiling of on-device VLMs reveals that model output, not visual input, is the primary energy bottleneck. VLANeXt provides actionable engineering findings for building strong VLA models, with soft connections between VLM and policy modules outperforming other configurations.Open-source developments include Amap's ABot-World Studio for generating walkable 3D worlds locally on a single GPU. DexJoco offers a MuJoCo-based benchmark for dexterous hand manipulation using low-cost motion capture data. The Zhiyuan LinkSoul Community has launched as a visual platform for building robot interaction agents. Ling Cha Yun Kong and Qingyan Precision have secured significant funding rounds to expand their hardware manufacturing capabilities, particularly for humanoid robot components.
https://dev.to/future_x/futurex-physical-ai-daily-issue-57-0714-3g87 dev.to
RSS Hunter RSS Hunter • Yesterday

🛠️ How We Built a Serverless Interactive Map for 68,000+ Indian Villages with Zero Hosting Costs

The Village Finder is a fully open-source, interactive geospatial platform that provides administrative boundaries and village-level coordinates for Andhra Pradesh, Telangana, Karnataka, and Tamil Nadu. The platform manages structural data for over 68,000 villages and hosts fluid visual map layers, streams individual cadastral land parcels, and handles multi-language transliteration. The project's architecture is unique in that it operates with zero server or infrastructure costs, making it a highly scalable and serverless civic-tech application. The data pipeline is orchestrated by GitHub Actions, which queries the official Local Government Directory via the data.gov.in open API and cross-checks metrics against the live portal to catch stale data. The validated datasets are compiled into normalized JSON and flat CSV assets, which are then automatically committed back into the repository as version-controlled data releases. The platform uses PMTiles to handle cadastral data, which allows for fast and fluid vector tile maps to be served directly to the user with no database queries or active server computing required. The platform also uses an offline neural model for native script translation, which eliminates the need for runtime machine translation APIs and reduces latency and operational expenses. The Village Finder project demonstrates that building impactful public utility platforms does not require a massive cloud infrastructure budget, and that static site architecture, edge-hosted assets, and cloud-optimized geospatial files can be used to build fast, robust, and free community applications. The project is open-source and available for exploration, auditing, and contribution, with the goal of adding support for the remaining states in India. Overall, the Village Finder project showcases a innovative approach to building civic-tech applications, and its architecture and design can serve as a model for other similar projects.
https://dev.to/mchittineni/building-a-serverless-interactive-map-for-68000-indian-villages-with-leaflet-pmtiles-and-github-4k42 dev.to
RSS Hunter RSS Hunter • Yesterday

The Hybrid Agent: A Retrospective

The author began by questioning whether MCP or CLI was cheaper, but discovered it was the wrong question. The true question became what architectural elements would survive in practice. Initial analysis revealed MCP had significantly lower token costs per call than raw CLI, but schema overhead for a large monolith was astronomical. The key insight was wasteful schema injection, which could be solved by a gateway filtering schemas by actual usage.The author learned that MCP servers, unlike plugins, possess lifecycle independence and can recover their own state. This realization led to the understanding that a server survives client termination, whereas a plugin inherits its parent's mortality. Containerization emerged as a cost-effective solution for environment-specific setups, offering a single image deployable across various clients without per-target configuration.A lengthy pull request lifecycle highlighted the importance of a decision tree for architectural choices, leading to the realization that an MCP server's survival is independent of its client. The original 93-tool monolith, per-WSL install scripts, and a specific git-push MCP tool were discarded. The author concluded that architectural decisions must be made early, and failures should be learned from quickly.The revised strategy prioritizes MCP for structure with typed schemas and CLI for low-overhead execution. Focused servers with a limited number of tools are preferred for independent deployability. Containers are now the default deployment method, providing a consistent stack across clients. The surviving architecture includes an MCP gateway for lifecycle management, a CLI bridge for execution, and multiple focused MCP servers.
https://dev.to/ev3lynx727/the-hybrid-agent-a-retrospective-a8e dev.to
RSS Hunter RSS Hunter • Yesterday

RivalRy — Log Every Rivalry, Feel Every Match, Hear the Hype

RivalRy is a passion tracker designed for football rivalry enthusiasts. Users can meticulously log every match outcome against their primary rivals, including wins, losses, and draws. Beyond just recording results, users can also rate the emotional intensity of each game and provide a personal narrative. Over time, these entries accumulate to form a comprehensive Timeline of all rivalry encounters. A key feature is the "Passion Card," a shareable stat card summarizing total battles, win/loss/draw records, and an overall Passion Rating. This card also highlights the user's single most intense rivalry moment, which is narrated aloud by an AI hype announcer. The app emphasizes that passion extends beyond major derbies to every significant match that leaves a lasting impression. RivalRy is built using React, Vite, and Tailwind CSS, with data stored locally in the browser's localStorage for a backend-free experience. The standout "Narrate My Rivalry" feature leverages the ElevenLabs Text to Speech API to generate an AI-narrated hype summary. In case ElevenLabs credits are exhausted, the app gracefully falls back to the browser's native speech synthesis.
https://dev.to/arjun_pratapdas_ee6c8089/rivalry-log-every-rivalry-feel-every-match-hear-the-hype-47j4 dev.to
RSS Hunter RSS Hunter • Yesterday

How I use Claude Code and Comet to build and test AI voice agents in a day

Building production AI voice agents is a time-consuming process, largely due to wiring and testing, not prompt engineering. The complexity arises from integrating custom functions, calendar and CRM systems, and handling numerous edge cases. Manually testing these agents through countless call scenarios is inefficient and slow.To address this, a pipeline was developed using AI coding tools to automate these tasks. Claude Code generates the agent's structure and wiring from a simple specification. This includes defining custom functions and setting up the underlying workflow. The specification details the agent's purpose, capabilities, data collection needs, and desired tone.Subsequently, Comet, an AI browser automation tool, tests the generated agent. It simulates dozens of challenging call scenarios, mimicking real user interactions. These scenarios include interruptions, silence, off-script questions, and aggressive behavior. Comet analyzes transcripts and post-call data to identify where the agent fails.This automated loop replaces manual testing, allowing for rapid iteration. If an agent fails a test, the spec or flow is adjusted, and the relevant part is regenerated or edited. The pipeline significantly speeds up the process from initial concept to a robust, testable draft.However, human oversight remains crucial for critical decisions. Judgement regarding escalation boundaries, safety protocols, and compliance relies on human expertise. Automated analysis cannot fully capture nuances like robotic tone or an agent's responsiveness. Additionally, real-world processes like compliance registration and phone number provisioning are not affected by code generation.The pipeline's primary benefit is accelerating the non-core aspects of AI agent development. It frees up human time for high-value judgment calls that ensure trustworthiness. This automation explains why some AI voice builds are completed in days while others take months. The key differentiator is the automation of the development and testing loop.
https://dev.to/nabeelbaghoor/how-i-use-claude-code-and-comet-to-build-and-test-ai-voice-agents-in-a-day-3ikl dev.to
RSS Hunter RSS Hunter • Yesterday

GeekNews AI Weekly Deep Dive - 2026-07-13

An AI coding agent nearly deleted a user's home directory due to a misunderstanding of PowerShell's case-insensitive variables. This highlights the critical need for sandboxing, containerization, and safeguards against destructive commands for CLI agents. Another AI project integrated fact-checking into a political community, focusing on separating opinions from verifiable facts and transparently showing sources. This system also incorporated asynchronous processing and fallback models to handle hallucinations and costs.Anthropic mistakenly sent $16.6 million "ghost bills" to free users in Korea, raising concerns about the billing reliability of AI API services. Developers are reminded that usage tracking and billing verification are as important as model performance. The rise of AI agents is shifting SaaS defenses from UI and features to unique data, operational permissions, and distribution channels. Performance-based pricing is becoming more important, requiring providers to manage failure risks and inference costs.A new platform allows AI bots and humans to publicly predict stock and crypto movements, with automated scoring to verify their accuracy. This system also archives prediction records to prevent modifications, acting as an interesting AI evaluation platform. Short-form videos are increasingly being cited in B2B search results and AI answers, making it crucial to repurpose content like product demos into short, search-optimized formats. This trend indicates a growing role for video content in generative search optimization.The intricate journey of an AI token through data centers involves tokenization, routing, scheduling, and memory management. Optimizations like batching and quantization are essential for managing token costs and latency. The article also discusses a cautionary tale where an AI build CLI uploaded repository data, including Git history and test secrets, to its developers. This incident underscores the importance of verifying the data collection scope and default settings of AI coding tools.
https://dev.to/ageofclick/geeknews-ai-weekly-deep-dive-2026-07-13-20ko dev.to
RSS Hunter RSS Hunter • Yesterday

I Built an AI That Turns Your Passion Into a Cinematic Origin Story

Origin is a web application that transforms personal passions into cinematic microsites. Users answer seven short questions about a passion, and the app generates an origin story, timeline, character card, AI-generated movie poster, and voice narration. The goal is to create an engaging experience rather than a simple text output. Origin prioritizes a guest-first approach, eliminating sign-up or login requirements to reduce friction. The project uses Next.js 16, React 19, TypeScript, and Tailwind CSS for its frontend. Three.js and React Three Fiber provide 3D hero scenes with 2D fallbacks. Google Gemini 2.5 Flash powers the storytelling, structuring answers into a movie narrative and designing the movie poster. The application validates AI responses and automatically corrects errors before display. ElevenLabs handles voice narration, offering male and female voice options with a fallback to the browser's speech engine. A framework-agnostic core logic ensures reliability and testability, operating without API keys for Gemini or ElevenLabs. This design allows for end-to-end testing and a seamless user experience even without authentication. Origin demonstrates exceptional use of Google AI for story generation and ElevenLabs for voice narration with robust fallback mechanisms.
https://dev.to/divyesh5981/i-built-an-ai-that-turns-your-passion-into-a-cinematic-origin-story-4j8l dev.to
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RSS Hunter RSS Hunter • Yesterday

Improve Performance by Loading Videos Only When They're Needed

Videos are significant assets that can slow down web page performance. Modern browsers now support lazy loading for video elements, deferring downloads until a user is likely to watch them. However, this feature is not yet universally supported across all browsers. Currently, Chromium-based browsers like Chrome, Edge, and Opera support native video lazy loading. Firefox and Safari do not yet offer this functionality. Lazy loading delays video downloads until they are close to entering the user's viewport. This reduces initial network requests, bandwidth usage, page load time, and memory consumption. Using a poster image provides a preview, improving perceived performance and reducing initial network demands. For videos below the fold, further optimization can be achieved by loading them only after user interaction or using an Intersection Observer. This provides a fallback for browsers lacking native support. Best practices include lazy loading off-screen videos, using poster images, compressing videos, and considering alternative loading strategies for wider compatibility. Implementing lazy loading, especially with poster images and fallback methods, significantly enhances the loading experience for media-heavy websites.
https://dev.to/jacobandrewsky/improve-performance-by-loading-videos-only-when-theyre-needed-245o dev.to
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RSS Hunter RSS Hunter • Yesterday

Can an AI tell a rivalry's story without inventing the score?

The Rivalry Engine is a Snowflake-powered application designed to analyze and narrate the history and intensity of football team rivalries. It processes 150 years of match data using SQL to calculate a rivalry's "heat" and assigns a one-word description of its shape. The system then predicts the outcome of the next match using a Snowpark Elo model. Cortex is utilized to narrate the story of the rivalry, strictly using facts computed in SQL. A key design principle is that the AI never invents facts. If two teams have never met, the application honestly indicates an "unwritten first chapter" and does not engage the AI. The entire application, including data, analytics, and AI, runs within Snowflake, with nothing leaving the data warehouse. This ensures data security and deterministic results grounded in reality. The user interface is built with Streamlit within Snowflake, meaning no external hosting or API keys are required. The architecture emphasizes separating factual data computation in SQL from the creative narration provided by Cortex. This project aims to move beyond simple scoreboards to capture the emotional essence of sporting rivalries.
https://dev.to/itsashita/can-an-ai-tell-a-rivalrys-story-without-inventing-the-score-j5b dev.to
RSS Hunter RSS Hunter • Yesterday

Evolution of Accuracy and Visual-Cognitive Errors in a Decade of Vision-Language AI Models

Vision-language models have claimed human-level performance in scene description, primarily using easy benchmarks like MS-COCO. These benchmarks feature simple scenes and are not representative of complex real-world interactions. Previous evaluations often relied on metrics that inflated perceived progress by rewarding superficial word overlap. A significant gap existed in understanding which specific visual-cognitive errors models still commit.To address this, researchers created a new dataset, Complex Social Behavior (CSB), comprising 100 challenging movie frames requiring social reasoning. They also developed a more reliable semantic similarity metric, correlating better with human judgment than existing scores. Nine models, from older captioners to modern multimodal large language models (MLLMs), were evaluated on both MS-COCO and CSB. A five-way error taxonomy—detection, recognition, hallucination, scene understanding, and spatial dependence—was used to analyze model failures.The results showed that while pre-MLLMs performed poorly on CSB, MLLMs achieved human-level performance on this complex dataset. MLLMs largely eliminated detection, recognition, hallucination, and scene understanding errors on both datasets. The primary remaining systematic failure for MLLMs is spatial dependence, where models focus on different image regions than humans. This error is less detrimental to overall description quality than the others.This study suggests that the field has moved beyond basic object recognition challenges to more nuanced understanding of relational reasoning. The methodology, including ranked human descriptions and semantic similarity metrics, provides a more robust evaluation framework. The findings are crucial for applications requiring interpretation of human behavior, offering quantitative evidence of MLLM capabilities and a diagnostic language for future model development. However, limitations include a small sample size and potential biases from cinematic content. Future work may focus on embodied and 3-D-aware architectures to further improve spatial understanding.
https://dev.to/s1infeb/evolution-of-accuracy-and-visual-cognitive-errors-in-a-decade-of-vision-language-ai-models-4gp3 dev.to
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RSS Hunter RSS Hunter • Yesterday

Casting your friend group as a K-Pop group without making a database the product

K-Saju Crew is an entertainment project playfully interpreting saju-inspired signals for K-Pop roles. It allows friend groups to create a virtual K-Pop group by entering birth dates. A key design choice was to handle temporary group creation without persistent databases. Initially, the project maintained statelessness, but the group feature required temporary shared state. They opted for a hybrid model where a temporary lobby stores group data for 30 days. Once completed, a group generates a stateless, permanent permalink containing all input data. This permanent result URL allows for reproducible results and social sharing even after the lobby expires. The K-Pop casting and role assignments are determined by a deterministic engine for consistent results. Concurrency and abuse were managed through in-memory serialization and IP-based rate limits for the file system store. A stateless design also enabled the generation of durable social preview images using Satori. The separation of the temporary lobby from the permanent result was crucial for managing data retention and sharing.
https://dev.to/piyaklabs/casting-your-friend-group-as-a-k-pop-group-without-making-a-database-the-product-543l dev.to
RSS Hunter RSS Hunter • Yesterday

How to Start Learning Web Development Without Getting Overwhelmed by the Setup

Learning web development often begins with daunting setup processes involving code editors, Node.js, and terminals. This initial complexity, requiring hours before writing code, acts as a significant barrier for beginners. Many aspiring developers get stuck on concepts like IDEs or npm, leading them to abandon their learning journey. However, understanding HTML, CSS, and JavaScript fundamentals does not necessitate any installations, as they run directly in the browser. The author experienced this firsthand, wasting time configuring complex environments instead of coding. To address this, tools like Deoit, a browser-based editor, offer an immediate start without setup. Other examples include CodePen and JSFiddle, emphasizing immediate coding over tool configuration. For absolute beginners, the recommended approach is to start with a browser-based editor for two weeks, focusing on HTML, then CSS, and finally JavaScript. This hands-on experimentation fosters understanding of each element's function. Once these fundamentals are grasped, transitioning to local editors like VS Code becomes more logical. Beginners are advised against trying to learn everything at once, advocating for a sequential approach starting with HTML, then CSS, and later JavaScript. The author stresses the importance of writing code rather than just watching tutorials, urging immediate application of new concepts. The core message is to prioritize starting to code immediately, as the tools and setup are secondary to the act of writing code. Trying a browser-based editor can help those struggling with setup refocus on learning.
https://dev.to/deoit_platform/how-to-start-learning-web-development-without-getting-overwhelmed-by-the-setup-37ob dev.to
RSS Hunter RSS Hunter • Yesterday

The Silent Killer of Context Windows: Why Token Estimation is Failing Your Agents

Developers building LLM-powered agents often encounter the context wall, where the model starts hallucinating or throwing errors due to excessive input. To address this, most developers use naive character counts, but this approach is flawed because tokens don't work like characters. The LLM Token Counter MCP is a tool that helps developers measure token counts precisely, taking into account the specific encoding used by the model provider. The encoding used by the model provider can significantly impact token counts, and using an outdated encoding can lead to undercounting. The LLM Token Counter MCP allows for precise counts across different encodings, including cl100k_base and o200k_base. When building multi-model pipelines, it's critical to consider the token density across different architectures. The tool also accounts for hidden structural delimiters in API templates, which can consume a significant portion of the context window. Proactive truncation and complexity analysis are essential for managing context windows effectively, and the LLM Token Counter MCP provides tools like find_truncation_point and analyze_complexity to help with this. By using the LLM Token Counter MCP, developers can reduce friction and implement token management as a first-class engineering constraint. The tool is available through the Vinkius MCP Catalog and can be easily integrated into agentic workflows, providing a secure and governed way to manage token counts and complexity analysis.
https://dev.to/renato_marinho/the-silent-killer-of-context-windows-why-token-estimation-is-failing-your-agents-309j dev.to
RSS Hunter RSS Hunter • Yesterday

log4cplus user manual

log4cplus is a C++ logging library inspired by log4j, offering thread-safe and configurable logging. It supports levels, appenders for output destinations, and layouts for formatting. Installation can be done via package managers like apt-get or by building from source. The library requires C++11 for version 2.x and C++23 for the latest 3.x series. Integration with CMake projects is facilitated by package managers like Conan. A minimal example demonstrates basic initialization, logger creation, and logging a message. BasicConfigurator provides a simple default setup for console logging. For real projects, PropertyConfigurator is recommended for configuring loggers via a properties file. Log levels range from TRACE to FATAL, with ALL and OFF as pseudo-levels for filtering. Common appenders include ConsoleAppender, FileAppender, and RollingFileAppender. CallbackAppender allows routing log events to custom C-style functions. Appenders run synchronously by default, but asynchronous operation can be enabled for performance. Clean shutdown is achieved by calling log4cplus::Logger::shutdown() at program exit.
https://dev.to/pravinc/log4cplus-user-manual-hde dev.to
RSS Hunter RSS Hunter • Yesterday

Your AWS Cost Explorer Shows the Total. It Doesn't Show You Whose Dev Environments Are Burning It.

The AWS bill can be a source of surprise and frustration for engineering teams, with unexpected costs often going unnoticed until it's too late. A common scenario is when an engineer spins up an instance for a proof of concept and forgets to turn it off, resulting in continuous billing. Cost Explorer, a tool provided by AWS, can help identify trends and anomalies in billing data, but it has limitations, such as not being able to show which specific instance belongs to which engineer or team. The tool operates on billing records, allowing for aggregation by service, region, account, and tag, but it cannot distinguish between running and idle instances. Tagging resources can improve attribution, but it has gaps, such as not being able to tell if a resource is actively being used or if it's sitting idle. The idle-resource problem is particularly prevalent in dev and staging environments, where resources are often left running continuously, resulting in significant idle time. To solve this problem, instrumentation at two layers is required: activity signal and per-resource attribution with idle cost visibility. The activity signal involves determining whether a resource is actually being used, while per-resource attribution involves surfacing the cost consequence of idle resources. Different resource types, such as EC2 dev boxes, RDS staging databases, and ECS services, have varying idle-cost patterns. The consequence of not having visibility into idle cost is that cost optimization becomes a blunt instrument, relying on assumptions about when work happens. A more precise approach is to make idle cost visible at the resource level, with ownership attribution, to inform optimization decisions. To get a clearer picture of where idle cost is accumulating, teams can start by pulling instance uptime vs. CloudWatch activity, checking RDS connection counts over the weekend, reviewing ECS minimum task counts, and running a tag compliance audit on running instances. Ultimately, addressing the idle-cost problem requires a systematic approach, such as using a tool like Trigops, which is built around attribution of idle cost to specific engineers, teams, or environments, with activity-aware automation.
https://dev.to/trigops/your-aws-cost-explorer-shows-the-total-it-doesnt-show-you-whose-dev-environments-are-burning-it-2g9l dev.to
RSS Hunter RSS Hunter • Yesterday

Introduction to media streaming: Part 2

Bitrate and resolution both relate to media file data, but resolution measures pixel detail while bitrate is the data used per second. A high resolution doesn't guarantee good quality if the bitrate is low, and a lower resolution with a good bitrate can look better than a poorly compressed high resolution video. The codec used also significantly impacts visual efficiency, with newer codecs like H.265 requiring less bandwidth for the same quality. High bitrates increase file size and bandwidth demands, potentially causing buffering for viewers. Adaptive Bitrate Streaming (ABR) addresses this by creating multiple video renditions at different resolutions and bitrates. A manifest file lists these renditions, and the player dynamically selects the best one based on the viewer's real-time network conditions and buffer status. This allows the video to adjust quality seamlessly, preventing interruptions. ABR algorithms primarily use throughput-based and buffer-based methods, often combined in hybrid approaches, to balance quality, stability, and rebuffer risk. Digital Rights Management (DRM) is employed to protect content from unauthorized copying and distribution. DRM encrypts media, requiring a decryption key from a license server after verifying user legitimacy and device authorization. Major DRM systems include Widevine, FairPlay, and PlayReady, with content often encrypted with multiple systems for broader platform compatibility. When a license is requested, the player sends a device-specific request to the license server, which verifies the request and returns an encrypted license containing the decryption key. This key is then decrypted locally by the device's Content Decryption Module (CDM) within a secure environment, preventing access to the raw key or video data. Different security levels exist within DRM systems, with premium content often requiring higher security tiers for playback.
https://dev.to/xheghun/introduction-to-media-streaming-part-2-473p dev.to
RSS Hunter RSS Hunter • Jul 12

Meta-Analysis: Between-Study Heterogeneity

Between-study heterogeneity refers to the variation in true effect sizes across studies in a meta-analysis. The random-effects model accounts for this by estimating tau-squared, which quantifies the variance in true effects. High heterogeneity might indicate distinct subgroups of studies or that pooling results is meaningless. Quantifying and analyzing heterogeneity is crucial for assessing the trustworthiness of an overall effect estimate. Cochran's Q statistic, a weighted sum of squares, is traditionally used to distinguish between sampling error and true heterogeneity. It measures deviations of individual study effects from the summary effect, weighted by study precision. An approximately chi-squared distribution is assumed for Q, allowing for hypothesis testing of heterogeneity. However, Q is influenced by the number of studies and their precision, limiting its reliability as the sole indicator. The I-squared statistic, derived from Q, represents the percentage of variability not due to sampling error. It provides a more interpretable measure of heterogeneity, with common benchmarks for low, moderate, and substantial levels. The H-squared statistic is another measure based on Q, indicating the ratio of observed to expected variance due to sampling error. Tau-squared and its square root, tau, quantify the variance and standard deviation of true effect sizes, respectively. While useful, tau-squared can be difficult to interpret practically. Prediction intervals, which consider both heterogeneity variance and pooled effect standard error, offer a more informative way to represent the range of future study effects. Therefore, reporting I-squared with confidence intervals and prediction intervals is recommended for assessing heterogeneity.
https://dev.to/davisethan/meta-analysis-between-study-heterogeneity-28p2 dev.to
RSS Hunter RSS Hunter • Jul 12