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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

App Health Endpoint Design: 3 Probes That Keep Logging and Metrics Useful

Kubernetes probes should be specific: startup checks if initialization is complete, readiness assesses if an instance can handle traffic, and liveness determines if the process is stuck. Startup probes allow extra time for initialization tasks like compiling pricing rules before other probes become active. Readiness probes verify that an instance can serve the active rule and necessary dependency states, removing the pod from service endpoints if failed. Liveness probes check for unrecoverable process issues, triggering container restarts when a problem is detected. This separation prevents unrelated dependency failures from causing unnecessary container restarts, which can amplify problems. For a property-management API, readiness ensures an instance is ready to serve a specific pricing rule, protecting tenants from inconsistent quotes. Startup probes are essential when initialization takes longer than the liveness budget, preventing premature liveness or readiness checks. Readiness probes are chosen for conditions that stop new traffic but can recover without a restart, like lacking a rule snapshot. Liveness probes are reserved for issues a restart can fix, such as an unresponsive event loop, and should be narrow to avoid unnecessary restarts. Health endpoints should use distinct paths for each probe type, avoid network calls in liveness checks, and return minimal status codes.
https://dev.to/evanshepherd5623/app-health-endpoint-design-3-probes-that-keep-logging-and-metrics-useful-50ld dev.to
RSS Hunter RSS Hunter • Today

Why Your AI Agent Fails in Production: Bridging the Memory, Testing, and Tooling Gaps

Agentic workflows that function perfectly locally often fail in production due to critical engineering gaps. These failures stem from memory leakage, evaluation blindness, and tooling fragility, not inherent complexity. LLMs are stateless, making context window limitations and session memory a significant hurdle in production. Naive prompt accumulation overwhelms context windows, leading to increased latency, cost, and degraded reasoning quality.Production agents require a hybrid memory system with short-term buffers, medium-term vector embeddings, and long-term structured data. Traditional unit tests are insufficient for LLMs; production requires LLM-as-a-judge evaluation and golden datasets for regression testing. Tooling fragility arises from unhandled API errors, network issues, and schema changes, necessitating robust retry mechanisms and circuit breakers.Tool orchestration must include exponential backoff for retries, circuit breakers for external dependencies, and schema validation to prevent errors. Effective observability through trace-level logging, cost tracking, and human-in-the-loop escape hatches is crucial. Bridging these gaps shifts focus from prompt engineering to agent systems engineering, demanding discipline in memory management, testing, tooling, and observability. Production readiness involves implementing these patterns and establishing fallback mechanisms and security audits for every tool call.
https://dev.to/tamizuddin/why-your-ai-agent-fails-in-production-bridging-the-memory-testing-and-tooling-gaps-2m5i dev.to
RSS Hunter RSS Hunter • Today

A Simple CI/CD Pipeline That Actually Works

Most CI/CD tutorials oversimplify by focusing on basic deployments, neglecting crucial aspects like secrets management and rollbacks. The author advocates for a straightforward, three-stage pipeline: Test, Build, and Deploy, using GitHub Actions for accessibility. The process begins with automated tests that must pass before any build or deployment occurs. Only pushes to the main branch trigger the build stage, which compiles the application into an artifact. The deploy stage then transfers this artifact to the server using SCP, requiring secure management of server credentials as GitHub secrets. To enable quick rollbacks, the author suggests keeping the last three deployment versions on the server. Dependency consistency is maintained by using lockfiles, ensuring reproducible builds across environments. Before committing, local testing of tests and build steps is recommended to catch issues early. Common problems include incorrect SSH key formats, path mismatches in build output, and insufficient server path permissions for the deploy user. Additional features like linting or database migrations can be added later, prioritizing simplicity and ease of debugging. This minimalist approach is suitable for many projects, emphasizing iterative development and starting with a functional core.
https://dev.to/cloudfrontier/a-simple-cicd-pipeline-that-actually-works-17p2 dev.to
RSS Hunter RSS Hunter • Today

The Upload Succeeded, the Record Did Not

The author developed a YouTube upload system with a sequential process involving session initiation, file transfer, video ID retrieval, verification, and local record creation. A critical flaw emerged because the local record was only written after the verification step. If the verification failed, an exception would halt the process, leaving no record of the uploaded video on disk. Consequently, re-running the upload command would bypass the existing file check, leading to duplicate video uploads.The system's documentation misleadingly stated that retries would not create duplicates, but this only applied to internal low-level retries, not external re-runs of the entire process after a failure. This led to the same video being uploaded twice. A similar bug was discovered in a different part of the repository, where five videos uploaded through an older process also lacked corresponding local records, making them vulnerable to duplication.The author highlights that existing tests passed because they did not account for the system's state on disk after an exception occurred. The fix involved changing the workflow to write the local record immediately after receiving the video ID, even if it was marked as unverified. This record would then allow for either resuming verification or blocking further uploads if the video was already processed. For the five pre-existing videos, a manual backfill of their records was necessary.The core issue generalizes to any operation that creates a remote resource and then verifies it, creating a window where a failure can leave the remote resource created but the local state unrecorded. This ambiguity renders retry logic ineffective. The solution emphasizes recording an identifier the moment it is received, regardless of subsequent verification. It also stresses that all code paths creating a resource must contribute to the same bookkeeping system to prevent invisible inconsistencies.
https://dev.to/hexisteme/the-upload-succeeded-the-record-did-not-44i6 dev.to
RSS Hunter RSS Hunter • Today

52 Days, 2,340 Rows, Every Cost Logged as Zero: The Stop Hook Trap

The author's student side hustle grew into a real business by autonomously running an AI environment. This setup moved beyond issuing instructions to letting the system operate independently, even while the author slept. A key component is a "Stop hook" that triggers automatically upon session completion. This hook is crucial for tracking AI usage costs, as autonomous agents can silently consume resources without direct oversight.The initial cost tracking mechanism failed for 52 days and over 2,300 log entries because the Stop hook's input lacked essential usage and model data. The revised approach focuses on reading the session's transcript file directly, as it is the only reliable data source. This transcript contains detailed logs of assistant responses, including token usage and the model employed.The system utilizes a rate table that defines costs per million tokens for different models like Haiku, Sonnet, and Opus. It also accounts for the cost implications of AI caching, where cache reads can be significantly cheaper than regular input. Robust error handling is implemented to ensure that minor issues, like unreadable files or invalid log lines, do not halt the entire tracking process.The script includes a triple fallback mechanism for determining the session ID, ensuring that even under varied execution contexts, a session identifier is captured. Finally, the cost calculation uses a specific rounding technique to maintain precision at six decimal places, mitigating floating-point arithmetic errors. The cumulative design of the log entries allows for partial data recovery even if a session terminates unexpectedly.
https://dev.to/bokuwalily/52-days-2340-rows-every-cost-logged-as-zero-the-stop-hook-trap-3bn3 dev.to
RSS Hunter RSS Hunter • Today

Why your password manager shouldn't have a "god mode"

A zero-knowledge password manager faces a recovery dilemma: how to regain access without a master backdoor. Having a single "god mode" credential would create a major single point of failure exploitable by attackers. Instead, Passwork designed access recovery using three isolated tiers, each with a specific, limited function.Tier one, the emergency console, requires server-level access and explicit activation to reset an Owner's login credentials when normal recovery fails. This console only restores login access, not vault decryption capabilities. Every action taken by the emergency console is logged for auditability.Tier two is the offline recovery account, which uses pre-shared cryptographic grants for specific vault types. This account's access is limited to what was configured before an incident, preventing retroactive access grants. The master password for this account must be stored securely offline.Tier three addresses service accounts, ensuring they are solely for programmatic access via API tokens, not interactive logins. Leaked tokens only compromise resources explicitly granted to that integration. This prevents automation credentials from becoming universal backdoors.This three-tier approach avoids a single master key by distributing trust across distinct, auditable, and narrowly scoped mechanisms. Recovering access does not rely on one privileged credential that can unlock everything. The design emphasizes recovering access without concentrating security risks. Ultimately, security is achieved by distributing trust, not by consolidating it into one point.
https://dev.to/passwork_team_fd7bbec6480/why-your-password-manager-shouldnt-have-a-god-mode-3ln8 dev.to
RSS Hunter RSS Hunter • Yesterday

'Does ChatGPT Mention My Brand?' Is Three Different Questions

Buyers are increasingly using AI tools like ChatGPT to find information, bypassing traditional search engine results. A critical question for brands is whether they appear when potential customers ask these AI engines questions. This "mention" isn't a single metric but comprises three distinct signals: being named in the answer's text, appearing in the citations, or being cited accurately. A brand can be in the citations without its name appearing in the prose, and vice versa. Both are important as customers might click on the sources. A brand can also be named and cited but with incorrect information, which can be detrimental. Simply being "crawlable" by AI does not guarantee citation. To accurately measure your brand's presence, you must ask real buyer questions to live AI engines and check for all three signals. This measurement needs to be continuous because AI answers and citations are dynamic and can change frequently. A single check offers only a snapshot of your current standing. Being cited with inaccurate information is worse than not being mentioned at all. Therefore, monitoring AI answer presence is an ongoing process, not a one-time audit.
https://dev.to/merlonix/does-chatgpt-mention-my-brand-is-three-different-questions-57kg dev.to
RSS Hunter RSS Hunter • Yesterday

Why Are Your AI Automations Still Running on Your Laptop?

Many current AI automation efforts still rely heavily on human intervention to connect different stages, acting as expensive middleware. While individual tasks are automated, the overall workflow remains disconnected, requiring people to manually transfer information between systems. This is inefficient because existing systems like AWS, GitHub, and Jira already have APIs that can share data. The author argues that humans are currently needed to bridge these gaps, but this is a bottleneck that will likely change.The real question is identifying where human involvement is truly necessary, which will vary by team and compliance needs. While AI output isn't always perfect, the author believes humans are often overused in the process. For many tasks, only one or two human decisions are truly required: understanding the initial goal and verifying the final outcome. The author suggests that most of the work between these two points can be handled by machines.A practical example is presented of an automated process for handling a production bug, from error detection to code changes and testing, all without immediate human input. This highlights the potential for significant automation before a human reviewer is needed. The crucial missing piece for more advanced automation is memory, or persistent state, which allows agents to retain and pass on learned information.Currently, this information is often lost in chat windows or terminals, forcing manual summarization. The author proposes that a database or similar mechanism for storing context is essential to enable agents to work together effectively. Building these automated workflows locally on laptops is possible but creates fragile infrastructure that is easily disrupted.These AI-driven automation systems, when performing software development lifecycle tasks, require the same robust infrastructure as any other production service. This includes stability, secure credentials, persistent state, logging, retries, and audit trails. The ultimate goal is for AI systems to autonomously handle tasks like investigating and fixing bugs by morning, eliminating tedious copy-pasting and manual steps for developers.
https://dev.to/elliot_hutchins_13af77186/why-are-your-ai-automations-still-running-on-your-laptop-1c5p dev.to
RSS Hunter RSS Hunter • Yesterday

DaDaScribe API: YouTube Multi-Language Transcript + SRT in One Request

DaDaScribe simplifies transcription by consolidating numerous steps into a single API request, unlike traditional methods. Developers typically need to download videos, extract audio, upload files, and then separately handle translation and speaker labeling. DaDaScribe accepts a YouTube URL or audio/video file directly. It allows specifying source and up to five destination languages, along with custom speaker names. The API returns both .txt transcripts and .srt subtitle files, including translations. A quick start involves submitting a transcription job via curl or Python, polling for status, and then downloading the results. This process eliminates the need for manual data extraction and multiple API calls. DaDaScribe also includes pre-processing steps like noise reduction, improving audio quality before transcription. It offers advantages over services like OpenAI Whisper, Deepgram, and AssemblyAI by providing native YouTube support and built-in translation. The service is ideal for content pipelines, multi-language subtitle generation, and tools requiring named speakers. However, it is not suited for real-time streaming or extremely high-volume, cost-sensitive applications. DaDaScribe aims to reduce the complexity and number of external services developers need to manage.
https://dev.to/infinistrategy/dadascribe-api-youtube-multi-language-transcript-srt-in-one-request-4efk dev.to
RSS Hunter RSS Hunter • Yesterday

Hice mi portafolio interactivo 100% desde Android con Termux - GHOST-OS v5

¡Hola DEV! 👋Soy ghost-0x-coder de Chiapas, México. Hice mi portafolio web sin PC, solo con mi celular usando Termux.🚀 Proyecto: GHOST-OS Web Interface - v5 🔗 Link: https://ghost-0x-coder.github.io/ghost-site/¿Qué tiene?Diseño interactivo con cajitas que se expanden Efectos y animaciones hechas en JS puro 100% responsiveStack: HTML, CSS, JavaScript - todo desde el celular.Es mi primera vez publicando aquí. Acepto todo tipo de feedback para la v6.¿Ustedes han codeado alguna vez desde el teléfono?
https://dev.to/ghost0xcoder/hice-mi-portafolio-interactivo-100-desde-android-con-termux-ghost-os-v5-58ln dev.to
RSS Hunter RSS Hunter • Yesterday

Why I run ceremonies

The author describes twenty attempts to validate a Phebs scale gate, a system designed to build evidence-backed contract intelligence for service fleets. These attempts were not repetitive tests but rigorous exercises of the system against complex data profiles and operational scenarios. The scale gate involves two deterministic repositories: a structural profile with over two million file owners and a semantic profile with hundreds of thousands of unique data blobs. The validation process includes scenarios like cold convergence, resource pressure, and recovery, with fixed rules and outcomes predefined.Four key rules govern these validation ceremonies: freezing inputs to prevent environmental manipulation, closing decisions in advance to ensure auditable outcomes, retaining evidence rather than custody for transparency, and honestly treating all stops as failures that require new attempts and identifiers. The process separates authorization for review from authorization for execution, catching defects early. Failures are classified as unclassified or lacking a receipt, not as probable passes.Previous attempts revealed various protocol failures, including issues with binding executables, mishandling private signing material, and signing against incorrect code versions. Scale also exposed subtle defects, like synchronization bugs causing healthy workers to be cancelled and component interactions leading to unexpected validation errors. A frozen contract contradiction, where input limits were wrongly applied, also highlighted the importance of accurate contract definition. Instrumentation identified performance bottlenecks, such as excessive time spent on source acquisition.Even partial successes, like completing the structural profile but failing the semantic one, were documented as unclassified, reinforcing that only a complete, evidence-backed claim constitutes a pass. Subsequent attempts further exposed gaps in the evidence path, highlighting that corrected code cannot retroactively create missing records. The author argues that ceremonies provide defect discovery and epistemic discipline, making claims defensible through rigorous, costly processes that complement cheaper, constant tests. The incomplete gate status, diligently recorded, holds more meaning than a prematurely declared success.
https://dev.to/benmeddeb/why-i-run-ceremonies-2380 dev.to
RSS Hunter RSS Hunter • Yesterday

Azure Integration Services Interview Prep Part 4: Application Insights, Log Analytics, Distributed Tracing, and Debugging a Broken Pipeline

This article delves into the essential debugging and monitoring toolkit for Azure integration pipelines, building upon previous discussions of messaging services, orchestration, and security. Application Insights acts as a flight data recorder, automatically capturing telemetry like requests, dependencies, exceptions, and custom logs from code-driven components such as Function Apps and APIs. Activation involves installing a NuGet package and configuring a connection string, often leveraging Key Vault for security.Log Analytics Workspace serves as the centralized, queryable data store for telemetry from various Azure services, including Application Insights, Function Apps, Logic Apps, Service Bus, SQL, and APIM. This allows for cross-service correlation of data within a single workspace through Kusto Query Language. Diagnostic settings on individual services are configured to direct their logs to this workspace.Kusto Query Language (KQL) is then employed within the Log Analytics workspace or Application Insights to perform ad-hoc investigations, answering specific questions across multiple services. Example queries demonstrate how to identify failed requests across the entire pipeline or specifically within Service Bus dependencies.Distributed tracing, facilitated by a unique operation_Id, automatically tracks a single request's journey across all touched services. This identifier propagates across HTTP calls, Service Bus messages, and Function App executions, enabling reconstruction of an entire request's path.Logic Apps provide a built-in Run History, offering a deterministic, step-by-step replay of each trigger and action for every execution. Failed runs can be visually inspected for their exact inputs and outputs, with the option to resubmit after addressing the underlying issue.Function App failures are investigated using Live Metrics for real-time monitoring and Application Insights' Failures blade for post-failure analysis, including detailed stack traces. This comprehensive toolkit ensures visibility and diagnostic capabilities across the entire Azure integration pipeline.
https://dev.to/manoharij/azure-integration-services-interview-prep-part-4-application-insights-log-analytics-distributed-46kh dev.to
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RSS Hunter RSS Hunter • Yesterday

I Gave Claude Code a Brain. This Time I Let the Tests Judge It.

This experiment tested Claude Code's ability to perform real repository tasks with a memory layer. Unlike previous tests that focused on answer correctness, this experiment required the agent to modify files and pass deterministic checks. Claude Code with the RE-call memory system achieved a 58.3% success rate, significantly outperforming the bare Claude Code at 50.0% and its own CLAUDE.md baseline at 36.1%. The RE-call system's improvement was most pronounced on tasks sensitive to project-specific memory. Interestingly, the static CLAUDE.md file performed worse than the bare configuration, suggesting static instructions can introduce noise. RE-call demonstrated its utility by only retrieving relevant context when needed, rather than simply adding more tokens. The memory layer was consulted in most eligible sessions and provided useful context. While RE-call increased token usage and cost, it aimed to prevent expensive failures from outdated information. The experiment also encountered operational limitations with GPT-5.3 Codex, preventing a definitive comparison. Ultimately, this validated that production memory layers can improve the success rate of agents on real repository tasks by retrieving crucial project-specific context. Future work will focus on optimizing memory retrieval for efficiency and accuracy.
https://dev.to/gde03/i-gave-claude-code-a-brain-this-time-i-let-the-tests-judge-it-5h0p dev.to
RSS Hunter RSS Hunter • Yesterday

Your codex exec is wasting ~900 ms per turn. I measured where.

The author rebuilt OpenAI's Codex CLI JavaScript harness using Bun, discovering architectural improvements rather than just runtime gains. The Codex engine has a Rust core with a JavaScript layer, which was replaced with Bun. A new client was introduced to sustain a single Codex app-server process, avoiding repeated spawns. Benchmarks revealed that a persistent process significantly reduced execution time compared to spawning a new one for each turn.A surprising finding was that release builds of Codex enable a telemetry exporter, which adds about 900 milliseconds to process shutdown time to upload metrics. This overhead is incurred with every turn if a new process is spawned. A simple configuration change, setting the metrics exporter to "none" in the config file, eliminates this delay without modifying the code.The performance gains from the persistent-process architecture were independent of the JavaScript runtime, as Node.js yielded similar results. Bun's advantages were primarily seen in its toolchain, significantly speeding up test startup and installation times. Initial increases in package size due to generated protocol types were addressed by consolidating declarations into a single file. This effort resulted in a smaller, optimized package. The full article delves deeper into technical details, including diagrams and further benchmarks. The code and a demo are available on GitHub.
https://dev.to/praveenvijayan/your-codex-exec-is-wasting-900-ms-per-turn-i-measured-where-1iip dev.to
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RSS Hunter RSS Hunter • Yesterday

We open-sourced 449 real equipment financing quotes so nobody has to trust our math

Commercial equipment financing sites often obscure how monthly payments are calculated, lacking transparency regarding APRs and amortization schedules. Equipment Capital Index aims to change this by revealing the real per-machine prices and amortization schedules for financing. They have now published their underlying dataset to allow for verification and further development. This dataset comprises aggregate financing benchmarks for real machines across various categories, including construction, agriculture, trucking, power equipment, and material handling. It is meticulously compiled from 449 individually priced machines, not from survey estimates or fabricated averages. The current snapshot shows average APRs ranging from 7.75% to 8.50% across these categories. Overall, the site-wide average APR is 8.17% for the tracked machines. The core principle behind this initiative is that every number traces back to a real machine with a sourced price and a verifiable amortization calculation. The aggregation logic is also reproducible, ensuring a single source of truth. This data is accessible through multiple channels: a live JSON API, a self-updating GitHub repository, and a permanent DOI on Zenodo for reliable access. Developers building tools related to commercial equipment costs are encouraged to use this data directly or cite its source.
https://dev.to/bilal_iftikhar_cf3cfa6901/we-open-sourced-449-real-equipment-financing-quotes-so-nobody-has-to-trust-our-math-5ghn dev.to
RSS Hunter RSS Hunter • Yesterday

Node.js Express vs. Python FastAPI: Which Should You Choose in 2026?

Choosing a backend framework has evolved beyond simple language preferences like JavaScript with Express or Python with Flask/Django. Python FastAPI has gained prominence, especially with the rise of AI and the need for type safety. Node.js Express remains a popular choice for enterprise web development due to its unopinionated nature. Express offers a minimalist approach, providing HTTP tools but requiring developers to build or integrate solutions for data validation and API documentation. In contrast, FastAPI leverages modern Python features like type hints and asynchronous programming for automated data handling and serialization. While FastAPI's opinionated approach to data types can feel restrictive, it streamlines development by automating validation and documentation. Express excels in high concurrency I/O operations, suitable for real-time applications. FastAPI, though fast for Python, incurs slightly more CPU overhead during extensive data serialization. For projects involving AI, machine learning, or data science, FastAPI is the superior choice due to Python's dominant ecosystem. However, if your team is solely composed of JavaScript developers, Express offers the advantage of full-stack JavaScript development with shared type definitions. Ultimately, the decision hinges on project requirements: choose Express for real-time applications and full control, or FastAPI for AI/data science integration and automated features.
https://dev.to/aditya_sorathiya_069252f4/nodejs-express-vs-python-fastapi-which-should-you-choose-in-2026-198k dev.to
RSS Hunter RSS Hunter • Yesterday

100 Writing, Productivity, Coding & Research Lenses for ChatGPT 🧠💻

AI becomes more useful when treated as a collection of specialized working modes rather than a single-purpose chatbot. By using specific commands, users can direct AI to adopt different thinking styles for tasks like debugging, algorithm design, or research planning. This shifts the interaction from a simple question-and-answer format to a structured workflow involving goals, context, specialized lenses, analysis, and iteration. The text details numerous "lenses" or commands categorized by function, including transforming text, controlling tone, structuring writing, compressing information, and managing meetings. Project management lenses help decompose large goals into executable systems with measurable outcomes through OKRs and KPIs. Risk analysis and root-cause analysis lenses provide frameworks for identifying uncertainties and understanding failures. Productivity lenses focus on prioritization and feedback, while learning lenses encourage active retrieval and practice. For developers, coding-specific commands cover generation, explanation, debugging, refactoring, and optimization, each with distinct purposes. Algorithmic and data structure lenses guide problem-solving for technical interviews. The text also highlights lenses for working with data formats like SQL, designing APIs, and conducting research. Crucially, it emphasizes the difference between opinions and testable hypotheses, and between peer review and simple criticism. The /audit lens offers a broader inspection capability, and the meta-lens /framework helps select appropriate problem-solving methodologies. Ultimately, leveraging these specialized modes allows for more precise and effective AI utilization.
https://dev.to/probal_dhali_f7d15eac866a/100-writing-productivity-coding-research-lenses-for-chatgpt-47mm dev.to
RSS Hunter RSS Hunter • Yesterday

Coding Agents Invent Facts When Denied Them. All 4 of My Probes Returned a False Zero.

A new paper reveals that coding agents, when faced with missing information, invent facts rather than admitting ignorance. These agents fabricate files or guess values when crucial data is unavailable. Researchers found that multiple AI models failed identically when their memorized knowledge was obstructed, suggesting shared vulnerabilities. Interestingly, the cost of different system configurations passing tests varied drastically, with more expensive setups offering no truthfulness advantage when facts were absent. The paper highlights that instruments designed to monitor agent reads are blind to these fabrications. These monitoring tools, by observing reads, expect an empty "hole" where information is missing. However, agents fill these holes with plausible inventions, making them appear like normal operations. This phenomenon, where a zero result can indicate either absence or a failed connection, is not unique to coding agents but applies broadly to measurement. The author illustrates this with four toy probes, each returning zero due to assumptions about encoding, schema, type, or vocabulary, despite correct answers existing. The solution proposed is the use of controls, a standard scientific practice, to validate instruments. A positive control confirms an instrument functions, while a negative control ensures it discriminates correctly. For AI systems, this means a procedural approach of running controls for every measurement rather than relying on human perception to detect suspicious zeros. The core issue is that fabricated absences, unlike actual fabrications, are undetectable. Therefore, before trusting a zero-result, one must verify the instrument's integrity through controls.
https://dev.to/vibeagentmaking/coding-agents-invent-facts-when-denied-them-all-4-of-my-probes-returned-a-false-zero-2ape dev.to
RSS Hunter RSS Hunter • Yesterday

Building a Modular C++ Static Library: Clean Architecture, Encapsulation, and Safe Input Handling

Large C++ projects become unmanageable when all code resides in a single file, leading to duplication and slow compilation. A modular architecture separates concerns, improving code organization and testability. This tutorial guides you through building a production-ready C++ utility module. Prerequisites include a C++17 compiler, understanding of headers and translation units, and a code editor. The project structure isolates public headers from implementation files to maintain clean boundaries. Headers act as architectural contracts, declaring available operations without revealing implementation details. Utility functions are placed within the CoreUtils namespace to avoid global namespace pollution. Separating interface declarations from implementation files allows for independent recompilation of modified units. Memory safety is enhanced by defensively handling null pointers and preventing integer overflow in calculations. Functions include boundary guards for null pointers and size zero to avoid crashes. Accumulation guards utilize larger data types to prevent overflow during summation of array elements. Defensive stream handling is implemented to recover from invalid user input, preventing infinite loops. The tutorial outlines a build pipeline to compile translation units into object files and archive them into a static library. Finally, this static library is linked to the main application for execution.
https://dev.to/aaray5/building-a-modular-c-static-library-clean-architecture-encapsulation-and-safe-input-handling-332o dev.to
RSS Hunter RSS Hunter • Yesterday

Beyond Passing Tests: A 100-Lens Framework for Evaluating Context-Aware AI Coding Agents 🤖

AI coding agents are becoming more adept at generating code, but a significant challenge lies in assessing their engineering judgment. Simply passing tests is insufficient, as it doesn't guarantee an AI's understanding of architectural context, project constraints, or historical decisions. As AI moves from snippets to repository modifications, this contextual correctness becomes crucial. Current benchmarks like SWE-bench are evolving to address real-world software engineering issues, yet they also face challenges with task quality and contamination. The author proposes evaluating AI agents on their ability to adapt to relevant context shifts while maintaining stability when irrelevant context changes. This involves testing for context adaptation and context stability, moving beyond simple pass/fail metrics. The focus should shift from "how much context" to "which context," "when," and "how reliably." Treating repository context as a dynamic entity with a lifecycle is essential for long-lived projects. Future benchmarks should be dynamic, reflecting the evolving nature of software development. The ultimate question is whether AI agents can maintain sound engineering judgment amidst changing software environments, encompassing architecture, requirements, dependencies, and team conventions. This complex problem requires diverse thinking approaches, which can be structured using defined "lenses" for visualization, explanation, and analysis.
https://dev.to/probal_dhali_f7d15eac866a/beyond-passing-tests-a-100-lens-framework-for-evaluating-context-aware-ai-coding-agents-om dev.to
RSS Hunter RSS Hunter • Yesterday

Your Form Is Not Portable If It Contains Callbacks

A truly portable form requires more than just JSON serialization; its validation, conditions, and submission logic must also be transferable. Standard form libraries often embed behavior within callbacks, making them non-portable and unsuitable for backend generation or multi-application use. Attempting to serialize callbacks as source code or invent compact DSLs introduces significant security and maintenance risks. Instead, behavior should be represented declaratively through a serializable expression tree. This approach allows for inspectable, versionable, and independently validatable data structures. While raw expression trees are verbose, a typed authoring API, like TypeScript builders, can abstract this complexity, generating a canonical JSON contract. This contract serves as a universal source of truth, enabling various frontends and backends to interpret and validate forms consistently. Crucially, positional collections need careful handling to avoid silently altering data identity upon submission. Portable form systems must also employ robust validation and security measures to handle untrusted input safely, preventing issues like catastrophic backtracking or invalid configurations. This architectural approach, explored by Modyra, shifts complexity from individual applications to shared infrastructure. It aims to ensure identical meaning across different runtimes, not necessarily identical rendering. The core challenge lies in determining how much behavior can be encoded as portable data without making the contract overly complex.
https://dev.to/lorenzomuschera/your-form-is-not-portable-if-it-contains-callbacks-52d9 dev.to
RSS Hunter RSS Hunter • Yesterday

Static Forms in Astro: Handling Submissions Without a Server

Astro sites, ideal for static content, face a challenge with form submissions. onsubmit.dev offers a solution by allowing forms to POST to an external endpoint, eliminating the need for server-side API routes. This is perfect for static hosting environments like GitHub Pages. The simplest method leverages native HTML form behavior, requiring no client-side JavaScript or hydrated components. This approach ensures the form works even without JavaScript enabled. It avoids the complexity of setting up Astro server endpoints and keeps the static deployment truly static. Astro's philosophy aligns well with this zero-JavaScript pattern, as it avoids shipping unnecessary client-side runtimes for simple forms. Progressive enhancement can then be layered on with JavaScript to improve user experience without being a prerequisite for submission. This includes features like inline loading states and in-place feedback. It is crucial to remember that sensitive information like API keys should never be exposed in browser-side code. Hosted form endpoints handle sensitive server-side processing securely. Client-side validation is for user experience, not security. This pattern is well-suited for forms like contact, feedback, or waitlists. For more complex workflows, an Astro server route or separate backend might be necessary. Ultimately, using an external endpoint keeps the deployment model simple for common static Astro form needs.
https://dev.to/ekwoster/static-forms-in-astro-handling-submissions-without-a-server-7po dev.to
RSS Hunter RSS Hunter • Yesterday

The Validation Rule That Could Never Fail

The author's studio produces animated children's cartoons through an automated pipeline involving language models, text-to-speech, and rendering. A crucial part of this process is validation gates that ensure the content is accurate. One such gate checks if spoken words correctly match the letters being taught, preventing factual errors for young viewers.When this letter-checking gate was tested on all existing episodes, it reported issues in most of them, demonstrating its utility. However, the specific rule for checking letter-word pairings reported no problems. Upon investigation, the author discovered a critical bug in the regular expression used for validation.The regex pattern, intended to check word boundaries, was incorrectly interpreting '\b' as a literal backspace character due to string processing. This made the pattern impossible to match any real-world text, rendering the validation rule completely ineffective. The issue was subtle because the invalid pattern compiled without error and produced no output, appearing to function correctly.This silence masked a significant problem, as the gate would have passed an episode containing a factual error. The author found this bug not by reading the code directly, but by following a rule to test new gates on known-bad input. This practice revealed the unreliability of a gate that has never failed.The author advocates for keeping faulty input as valuable test cases and for asserting positive outcomes, pairing "no problems on clean input" tests with "exactly this problem on dirty input" tests. They also emphasize printing the compiled pattern, not the source, to catch discrepancies. The backspace bug was a classic example of an indicator reporting nothing, which can be mistaken for a healthy state.After the regex was corrected, the gate successfully identified the inaccurate claim about the flower starting with 'P' in two different episodes. This redundancy, stemming from writing rules as statements about the world rather than specific file formats, proved beneficial. The experience highlights the importance of rigorous testing and the deceptive nature of silent failures in automated systems.
https://dev.to/robswierk/the-validation-rule-that-could-never-fail-1505 dev.to
RSS Hunter RSS Hunter • Yesterday

reCAPTCHA: It’s Not Just “I’m Not a Robot”

CAPTCHA has evolved significantly from its original purpose of distinguishing humans from bots by testing the ability to read distorted text. This initial method, while effective against early computers, became obsolete as machine learning advanced. As computers got better at recognizing distorted characters, CAPTCHAs became more difficult, leading to a degraded user experience with challenges like selecting images. This led to a fundamental shift in approach, moving away from direct user challenges to analyzing interaction behavior. Modern systems, like Google's reCAPTCHA v3, now use advanced risk analysis to assign a score indicating the likelihood of an interaction being from a bot.This score is determined by fusing multiple signals, including user behavior, device and network information, and historical interaction patterns. For instance, mouse movements, typing speed, and navigation patterns are analyzed to detect anomalies. Even subtle timing differences between actions can reveal automated activity. While browser history isn't directly read, historical interaction patterns provide crucial context. The device and browser environment also offer signals about the legitimacy of an interaction. Signal fusion, combining these various weak signals, creates a powerful risk assessment. Consequently, the challenge has shifted from solving a puzzle to mimicking human behavioral characteristics convincingly. Although legitimate users might still be challenged if their behavior appears unusual, the system primarily focuses on identifying high-risk interactions rather than definitively proving human identity. This evolution has transformed CAPTCHA from a simple text puzzle into a sophisticated behavioral Turing test.
https://dev.to/utteshkumar/recaptcha-its-not-just-im-not-a-robot-25da dev.to
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RSS Hunter RSS Hunter • Yesterday

17 free, dependency-free JS/PHP components for your next project

This library offers 17 standalone, framework-agnostic components, designed for direct inclusion without npm or bundlers. Each component is a single file encapsulating its own CSS and SVG where needed, simplifying integration. Sixteen are pure JavaScript, while "ac_pdf" is PHP, utilizing FPDF for server-side PDF generation.The collection includes diverse tools like "ac_notif_modale" for notifications, "ac_note_etoiles" for rating widgets, and "ac_slider" for enhanced range sliders. Other notable components are "ac_tags" for tag entry fields, "ac_signature" for signature pads, and "ac_fichier_upload" for drag-and-drop file uploads.For rich data display, there's "ac_graphique" for dependency-free SVG charts, "ac_timeline" for interactive timelines, and "ac_agenda" for a full calendar with various views. Mapping functionality is provided by "ac_carte," which automatically loads Leaflet for interactive maps."Ac_big_select" addresses large dropdowns with AJAX search, while "ac_onglets" transforms HTML into accessible tabs. A unique aspect is the French naming convention for methods and options, reflecting their origin for French-speaking clients.The library emphasizes ease of use, demonstrated by a simple instantiation example for "ac_graphique." Full demos and documentation are available online for all free components.Additionally, premium components like "ac_sidebar" and high-performance datagrids ("ac_datagrid," "ac_datagrid_pro") are offered for more demanding applications. The datagrids, sharing an engine, are tested with millions of rows, showcasing robust performance.
https://dev.to/jnv-33/17-free-dependency-free-jsphp-components-for-your-next-project-31g6 dev.to
RSS Hunter RSS Hunter • Yesterday

Trae + SQLazy: A Practical Guide to Writing Complex SQL

AI-assisted data development is transforming SQL writing, utilizing either direct SQL generation or a layered approach with intermediate representations. This article focuses on a combined model of Trae (AI planning) and SQLazy (execution layer). This partnership establishes a "AI planning + human review + deterministic engine execution" framework. The process involves Trae generating step-by-step SQLazy scripts (.nspl) after loading project knowledge, clarifying requirements, and decomposing tasks. SQLazy's IDE facilitates step-by-step execution, real-time debugging, and cross-database compilation from a single script.The workflow begins with environment setup, followed by triggering Trae with detailed business requirements. The most crucial phase is validation and correction in the SQLazy IDE, where test data is used to verify intermediate results. Four real-world case studies illustrate the methodology: statistical aggregation, multi-table merging, cross-subgroup data filling, and amount allocation. While simpler cases were correctly handled in one go, complex scenarios like cross-group data filling and amount allocation required iterative corrections. The cross-group filling case highlighted the importance of using row numbers as a mapping bridge when direct join keys are unsuitable. The amount allocation case, the most complex, required multiple iterations to refine syntax and logic, particularly concerning rounding and remainder distribution to ensure total preservation.
https://dev.to/esproc_spl/trae-sqlazy-a-practical-guide-to-writing-complex-sql-1gi6 dev.to
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RSS Hunter RSS Hunter • Yesterday

Context Engineering for Unity: How to Make AI Useful on Real Projects

AI coding tools perform much better in controlled demos than in complex Unity projects due to a lack of complete project context. Successful AI integration requires providing explicit project information, including Unity versions, package details, architectural boundaries, and platform requirements. Context engineering involves organizing a project to make invisible dependencies visible to the AI, ensuring useful and reviewable changes. Small, focused tasks with clear acceptance criteria are more effective than broad feature requests. AI should never guess crucial details like Unity versions, scene ownership, execution order, or platform-specific behaviors. Automated tests are essential for validating AI-generated code, converting suggestions into verifiable evidence. A well-structured project brief should outline existing decisions, prohibited changes, and a clear definition of "done." Tasks should be broken down into verifiable units, with a plan requested before code for cross-system changes. Developers should measure accepted changes and review costs, not just lines of code. The goal is to make AI an effective assistant rather than a source of unverified experimental code.
https://dev.to/exoa/context-engineering-for-unity-how-to-make-ai-useful-on-real-projects-3moa dev.to
RSS Hunter RSS Hunter • Yesterday

The Scanner Came Back Clean. The Discount Code Still Worked 40 Times.

An automated security scan of a checkout flow revealed no traditional vulnerabilities like SQL injection or cross-site scripting. A junior pentester was prepared to label the engagement as having no significant findings based on the scanner's results. However, a team member noticed an endpoint for applying discount codes did not invalidate the code until after order confirmation. This design flaw meant a single-use code could be redeemed multiple times concurrently.By sending the same request concurrently, a single-use 50% discount code was redeemed forty times within seconds. This type of vulnerability, a race condition, occurs due to a gap between checking code validity and marking it as used. Automated scanners cannot detect race conditions because they test requests sequentially, not concurrently. Vulnerable endpoints often involve a "check-then-act" pattern, such as redeeming coupons or withdrawing funds.To exploit race conditions, requests must be sent simultaneously, not just rapidly, to minimize network jitter. Tools like Burp's race condition tab or Turbo Intruder are designed for this purpose. Proof of a race condition lies in the final state of the system, such as the number of times a code was applied, rather than just successful response codes. This type of business logic flaw is missed by scanners and requires manual investigation into concurrent application behavior. Codelivly's resources and labs focus on these advanced vulnerabilities that go beyond automated scanning capabilities.
https://dev.to/rockyyy/the-scanner-came-back-clean-the-discount-code-still-worked-40-times-p9g dev.to
RSS Hunter RSS Hunter • Yesterday

The CAD-PHP Currency Edge: Why Manila Is a 5x Bargain in 2026

The author highlights a significant economic advantage in hiring engineering talent from the Philippines for US-based SaaS startups. He observed a stark cost difference between US and Filipino development teams, realizing most North American founders overlooked this. The global talent market is tightening, making location-based cost arbitrage harder to ignore, especially with remote work being common. The Philippines offers skilled developers, English proficiency, and a favorable exchange rate, creating substantial financial benefits for companies with strong currencies.The 5x development cost advantage is real, as demonstrated by a project where a Manila team cost significantly less than a comparable US team. High English proficiency among Filipino developers acts as a productivity multiplier, minimizing communication issues and saving time. Time zone differences can be managed and even leveraged into a near 24-hour development cycle through asynchronous communication and structured workflows. The author learned that building a cohesive team with a strong lead is more effective than hiring a single "rockstar" developer.Founders can take concrete steps like researching exchange rates, identifying small pilot projects, and networking with those who have successfully hired in the Philippines. These actions can help startups optimize their runway and access a valuable talent pool. Understanding and utilizing the cost-effectiveness of Filipino developers is a strategic move for efficiency and sustainability. The author advocates for leveraging these advantages to build stronger, more sustainable businesses.
https://dev.to/devwithzach/the-cad-php-currency-edge-why-manila-is-a-5x-bargain-in-2026-1c3l dev.to
RSS Hunter RSS Hunter • Yesterday

🚀 Announcing Pythonaibrain-Warden (v0.1.0): Industrial-Grade, npm-Inspired Package & Environment Manager for Python

The release of warden (pythonaibrain-warden v0.1.0) aims to resolve the conflict between speed and reproducibility in Python package management. Warden introduces an npm-inspired workflow with a deterministic lockfile, virtual environment isolation, and offline build capabilities. Its core feature is the warden.lock file, which enforces exact artifact URLs and SHA-256 hashes, preventing unwanted version substitutions. The thin CLI architecture ensures consistent behavior whether used from the terminal or programmatically. Warden-native environments and absolute paths in subprocess calls eliminate cross-platform resolution issues. Per-file dependency targets allow mutually exclusive package requirements within the same repository. Platform wheel selection correctly resolves PyPI binaries against system tags. The fix command addresses environment drift without altering project requirements. Warden build creates self-contained offline bundles for zero-network deployment. Finally, warden verify provides a CI gate to ensure deep validation and reproducibility.
https://dev.to/divyanshusinha136/announcing-pythonaibrain-warden-v010-industrial-grade-npm-inspired-package-environment-4o2c dev.to
RSS Hunter RSS Hunter • Yesterday

AWS Security Events Through the Lens of the Seven MITRE ATT&CK Tactics

Akira Nishikawa, a security engineer from Yamato Security, presented at HITCON 2026 on AWS threat detection using Suzaku and Senrigan. This article summarizes AWS CloudTrail events aligned with MITRE ATT&CK Enterprise Tactics. The author emphasizes that detecting attacks relies on correlating multiple events, not isolated ones, and organizing detections by ATT&CK provides context for incident response. The MITRE ATT&CK framework, with its 14 tactics, is chosen for its practicality in AWS environments, particularly seven key tactics: Initial Access, Discovery, Credential Access, Persistence, Privilege Escalation, Defense Evasion, and Impact. Initial access often involves compromised access keys, with GetCallerIdentity being a common first call to identify account and user IDs. Discovery involves enumerating IAM permissions and resources, with suspicious patterns like rapid multi-region API calls or an increase in AccessDenied events indicating potential malicious activity. Credential access frequently targets AWS's Instance Metadata Service (IMDS), and prevention through IMDSv2 enforcement is recommended. Persistence techniques include creating new IAM users, abusing federation tokens, or leveraging Lambda functions with API Gateway. Tampering with trust policies and identity providers also serves as a persistence mechanism.
https://dev.to/aws-builders/aws-security-events-through-the-lens-of-the-seven-mitre-attck-tactics-716 dev.to
RSS Hunter RSS Hunter • Yesterday

AI Sprite Generator: Turn 2D Character Art into Game-Ready Animations

Game development often faces a bottleneck between character art creation and usable animation assets. Spritix AI is an AI sprite generator designed to address this by transforming a single 2D character illustration into a set of reusable game animations. The workflow involves uploading a character image, selecting an action like Idle, Walk, Run, Jump, or Attack, and then reviewing and iterating on the AI-generated results. Spritix AI ensures consistency in proportions, palette, and style across different actions by referencing the original character. While not a replacement for art direction or manual cleanup, it significantly reduces repetitive setup tasks. The tool outputs standard file formats such as PNG, GIF, and JSON, making integration into various game engines like Unity, Godot, or web frameworks straightforward. Export settings allow customization of frame count, cell size, and trimming for loops. Solo developers, technical artists, game jam teams, and web developers experimenting with 2D games can benefit from this tool. Spritix AI aims to help characters waiting to be animated come to life with a single reference image. The workflow can be explored on the Spritix AI website.
https://dev.to/chenxer97/ai-sprite-generator-turn-2d-character-art-into-game-ready-animations-3mjm dev.to
RSS Hunter RSS Hunter • Yesterday

How I Built 5 Developer Tool APIs on a Raspberry Pi (And Listed Them on RapidAPI)

The author built a small API hub to address the need for reliable API endpoints for common developer tasks. These include AI prompt optimization, JSON validation, JSON formatting and conversion, and text readability/SEO analysis. The tech stack utilized Python's FastAPI within a Docker container, hosted on a Raspberry Pi 4. Cloudflare Tunnel provided secure remote access without opening router ports, and the domain was managed via GitHub Pages. The API hub was launched on the RapidAPI marketplace.A crucial element of success was offering a generous free tier, allowing users to test the APIs before committing to a paid plan. Search engine optimization, particularly by including FAQ schema in blog posts, proved effective in driving organic traffic and securing Google answer snippets. Cloudflare Tunnel was deemed an ideal solution for homelab hosting due to its security features and ease of use.However, promotional efforts on platforms like Reddit's r/programming and Stack Overflow were unsuccessful due to strict self-promotion policies. Direct cold outreach to developers also yielded a low response rate. The author concludes that inbound marketing strategies, such as SEO and leveraging API marketplaces, are more effective for driving adoption. Users can explore the API hub through its free tier on RapidAPI or access full documentation on the author's website.
https://dev.to/branga_breow/how-i-built-5-developer-tool-apis-on-a-raspberry-pi-and-listed-them-on-rapidapi-3k63 dev.to
RSS Hunter RSS Hunter • Yesterday

I built a dependency-free sidebar with drag-and-drop and magnetic snapping , in vanilla JS

ac_sidebar is a unique JavaScript class designed for sidebar components, notably operating without frameworks, build steps, or external CSS libraries. It integrates its own CSS and SVG icon set directly, making it entirely self-contained. The component offers two menu levels with a recursive structure and role-based filtering, allowing menus to auto-hide if empty. It supports five positions: left, right, top, bottom, or a draggable "free" mode with magnetic snapping.The sidebar can collapse into an icon rail or be hidden behind a burger button. It also features counter badges per entry, supports multiple instances on a single page, and can persist state using localStorage. Developers can instantiate ac_sidebar directly with a configuration object or load it from an external JSON file.A notable characteristic is its configuration keys and values, which are in French, reflecting its original development for French-speaking clients. For example, "gauche" is used for "left" and "clair" for "light." This class is part of a collection of standalone, dependency-free JS/PHP classes maintained by artisan-code.fr. It is specifically built for projects where using a full framework would be unnecessary or excessive.
https://dev.to/jnv-33/i-built-a-dependency-free-sidebar-with-drag-and-drop-and-magnetic-snapping-in-vanilla-js-17fb dev.to
RSS Hunter RSS Hunter • Yesterday

What MCP Doesn't Solve

The article discusses the limitations of the Multi-Capability Protocol (MCP) in complex production workflows, using an employee offboarding example. While MCP standardizes tool invocation and security controls, it doesn't inherent address business logic or authority. The system can confirm tool existence and valid arguments, but not whether a manager can actually change a termination time. The article emphasizes that transport authorization is distinct from business authorization, as it only verifies client access to the server, not the validity of the business action.Production workflows involve multiple identities: requester, actor, subject, and approver, which if collapsed, create misleading audit trails. A robust runtime environment needs a compact record, an "execution envelope," to explain why an action is permitted before a consequential tool call. This envelope includes details like authoritative event, policy version, and execute-after timestamps, tying the action to official records and preventing premature or unauthorized actions.Approvals can expire if material facts change, necessitating revalidation of critical information like employment status and legal holds close to execution time. A timeout during a write operation doesn't confirm failure, highlighting the need for tools to expose execution details like idempotency keys and status lookups to guide retry or reconciliation strategies.Ultimately, responsibility for workflow integrity remains distributed. The host manages tool exposure, the runtime evaluates policy and handles recovery, the MCP server validates requests, and the target system is authoritative for its own records. Implementing these controls has a cost, but it is necessary for high-consequence actions like account disabling, where precise authority, current evidence, and failure semantics are paramount.
https://dev.to/coryntas/what-mcp-doesnt-solve-1ahe dev.to
RSS Hunter RSS Hunter • Yesterday

What Spring Data Tests Can Miss: Testing Beyond Hibernate’s Persistence Context (Chapter 7)

Every EntityManager maintains a first-level cache, an identity map of entities in the current persistence context. When find() is called for an already managed entity, Hibernate returns the existing object without querying the database, which is standard L1 cache behavior. However, standard Spring Data tests using @DataJpaTest can mistakenly validate this L1 cache state instead of actual database persistence. This can hide critical issues like missing constraint violations or column mapping bugs until production. The text introduces a testing architecture designed to force real database interactions, thereby ensuring tests validate true persistence.This architecture employs an explicit EntityManager clearing strategy, generic test fixtures, and in-memory isolation. A test-only proxy wraps DAO operations like create(), update(), and delete(), automatically invoking EntityManager.flush() and EntityManager.clear() after each write. This forces Hibernate to write changes to the database and then clear the L1 cache, ensuring subsequent reads retrieve data directly from the database, not a cached object. This proxy layer is exclusive to the test scope, preventing any performance impact on production code.For read operations like loadById() or custom @Query methods, the proxy does not intervene as these either retrieve fresh data after a clear or inherently issue real SQL. Additionally, a TablesEraser utility is used to empty all tables before each test, ensuring a clean schema and preventing issues like second-level cache pollution or batching side effects across tests. This process uses DELETE FROM statements, respecting transactions and providing safe rollback.Reusable abstract test classes like AbstractCrudTestCase and AbstractSearchableTestCase provide shared assertions for common CRUD and search functionalities. These abstract classes handle structurally identical test cases, reducing boilerplate, while concrete test classes implement domain-specific payload generation and add tests for unique query methods. This layered approach ensures that the base classes manage common behavior, and specific DAOs can extend and customize as needed.A key example of this system's effectiveness is a test case, testSearchNullParams, which specifically checks boundary conditions for search operations. This test exposed a NullPointerException in an early version of the search() implementation when a null Params object was passed. This validated the design's ability to catch real-world issues before deployment, ensuring robustness.
https://dev.to/kamenivanov/what-spring-data-tests-can-miss-testing-beyond-hibernates-persistence-context-chapter-7-5d7k dev.to
RSS Hunter RSS Hunter • Yesterday

Construyendo un recomendador de emparejamiento de expertos

This text describes a complex recommendation system designed to connect people needing expertise with qualified experts. Unlike typical recommendation systems, this one faces unique challenges like the limited capacity of experts and the high cost of bad recommendations. It employs a hybrid retrieval method using Reciprocal Rank Fusion to combine results from multiple independent expert finders. The scoring mechanism is a weighted combination of various factors, including explicit directional fit, semantic similarity, skill overlap, capacity, expert quality, specialty match, and fairness.Expert quality uses a saturating exponential function to account for experience, preventing veterans from completely dominating. Fairness incorporates a logarithmic decay for exposure, ensuring less frequently recommended experts still get opportunities. Importantly, the system operates as a global assignment problem rather than individual top-N recommendations to prevent the most popular experts from being overwhelmed. A greedy allocation algorithm is used, prioritizing higher-scoring pairings while respecting expert capacity limits.The data layer focuses on defensible statistics to determine what skills are valuable. This involves source weighting with exponential decay for recency, weighted medians to handle skewed data distributions, and stratification by role, seniority, and experience to avoid conflating skill value with seniority. Bootstrap confidence intervals are used instead of point estimates for skill value, providing a more robust measure.A critical dry run revealed significant flaws, including an overzealous exclusion rule that severely limited expert availability and a tendency for recommendations to lack clear justifications. The trending badge also exhibited inflated values due to a lack of a meaningful baseline. These findings highlight the importance of careful implementation and continuous evaluation in complex recommendation systems.
https://dev.to/aws-builders/construyendo-un-recomendador-de-emparejamiento-de-expertos-57h2 dev.to
RSS Hunter RSS Hunter • Yesterday

Building PickTool with Next.js and Laravel: Lessons from Creating a Software Discovery Platform

Finding the right software is challenging due to the overwhelming number of options and generic comparison articles. PickTool aims to solve this by providing structured information on AI and SaaS tools, including features, pricing, use cases, and comparisons. The platform utilizes a decoupled architecture with Next.js for the frontend and Laravel for the backend, allowing for independent evolution of these components. Content is modeled relationally, with interconnected data between categories, tools, guides, and comparisons to facilitate intentional internal linking.The creator learned that early scaling with incomplete content can be detrimental, leading to thin pages and inconsistent information. Instead, a focused approach is taken, strengthening one topic cluster, like email marketing, at a time. Search engine optimization is built into the application architecture from the start, with every indexable page requiring specific SEO elements. Performance is also considered a content problem, with efforts to keep initial page loads efficient and avoid unnecessary data.Maintaining consistency between the decoupled backend and frontend is crucial, ensuring predictable data for public pages. Transparency is vital for comparison platforms, and PickTool is working on explaining how tools are evaluated. If starting again, the creator would focus on one narrow category first, define minimum publishing requirements, and design internal linking with the data model. They would also separate discovery from editorial content and build auditing into the workflow. Future priorities include strengthening the email marketing cluster, improving tool pages and comparisons, and refining the rating methodology. PickTool is an ongoing project focused on integrating product design, data modeling, performance, and editorial standards.
https://dev.to/doaaabed94/building-picktool-with-nextjs-and-laravel-lessons-from-creating-a-software-discovery-platform-d72 dev.to
RSS Hunter RSS Hunter • Yesterday

Planning Over Execution: Lessons from 157 Agent Runs and the Rise of Orca-Style Agent Fleets

A significant study of 157 AI agent deployments revealed that planning quality, not execution speed or model size, is the primary determinant of success. This observation has led to the development of "Orca-style" agents, which are hierarchical and prioritize planning. The research involved varying agent architecture, planning depth, and execution models across diverse use cases. Agents dedicating more tokens to planning achieved significantly higher task completion rates and fewer rollbacks.The economic principle behind this is that planning is inexpensive compared to the high cost of correcting mistakes made during execution. Orca-style agents separate planning from execution, with a strategic planner handling complex reasoning and specialized executors performing defined tasks. Specialists have "skill cards" describing their capabilities, and a shared memory layer maintains state.Key effective implementation patterns include recursive decomposition with validation gates, specialist routing, and stateful context propagation. Pitfalls to avoid are over-planning, excessive specialist fragmentation, silent replanning, and context window hoarding. Orca-style fleets are recommended for multi-step workflows and high-stakes operations.The cost model favors Orca-style architectures for complex tasks, showing reduced total token costs due to fewer retries and escalations. The future of AI agents will likely focus on enhancing planning capabilities with dedicated tools and libraries. The core insight is that thoughtful planning is the most crucial aspect of agentic work. Implementing Orca-style planning can begin with a single planner function that decomposes goals and validates each step.
https://dev.to/tamizuddin/planning-over-execution-lessons-from-157-agent-runs-and-the-rise-of-orca-style-agent-fleets-3cb8 dev.to
RSS Hunter RSS Hunter • Yesterday

Four Alarm Slots, Three Failure Modes: Building a Nightly Drain That Survives Sleep, Races, and Timeouts

Claude Code conversations, by design, lack persistent memory, forcing users to re-explain context repeatedly. This absence of a long-term memory hinders productivity, especially when managing multiple projects. To combat this, the author developed a nightly script that exports Claude Code logs into an Obsidian vault. This automated process eliminates the need for manual summaries, ensuring knowledge is preserved regardless of user willpower. Obsidian was chosen for its local Markdown files, Git version control, and linking capabilities, creating an effective external brain.The script's development encountered three critical failures that necessitated a robust, triple-layered design. A sleep freeze issue occurred when the Mac slept mid-script execution, leaving processing incomplete. A double-execution race happened when a scheduled script run overlapped with a manual one, causing Git conflicts. Finally, a timeout failure occurred when processing extensive logs exceeded the script's time limit.These failures led to the implementation of a multi-slot re-firing system, caffeinate for sleep prevention, and idempotent retries using step markers. This design ensures that processing resumes from where it left off, even if interrupted. The system now features a four-layer architecture, starting with Claude session logs and progressing through raw conversation files, an Obsidian vault, and finally a private Git repository.Multiple scheduled slots for the script provide redundancy, allowing subsequent runs to pick up any unfinished tasks. Caffeinate is used to prevent system sleep, and a lock directory prevents concurrent script executions. Idempotent retries are achieved by marking completed steps, allowing the script to skip already processed segments. This ensures that the daily ingestion process, even with failures, will eventually complete.The launchd plist configuration and PATH adjustments, including nvm node discovery, ensure the script runs reliably in its minimal execution environment. Full Disk Access for /bin/bash is checked early to prevent silent failures due to macOS privacy protections. The script utilizes a run_to function with GNU coreutils' timeout for guaranteed process termination, even if initial signals are ignored. This comprehensive setup creates a resilient external memory for Claude Code conversations.
https://dev.to/bokuwalily/four-alarm-slots-three-failure-modes-building-a-nightly-drain-that-survives-sleep-races-and-5663 dev.to
RSS Hunter RSS Hunter • Yesterday

My Caption Width Guard Passed Every Test. It Was Measuring Text the Renderer Never Drew.

A user complaint about poorly segmented subtitles led to a bug fix that introduced a new, subtle issue. The original problem was that subtitles were split into fixed word chunks, disregarding sentence structure. This led to nonsensical breaks, such as cutting off mid-sentence.The fix involved implementing rules for better chunking, including respecting punctuation and aiming for a specific word count per chunk. A crucial addition was a pixel-width cap for rendered chunks to ensure they fit on screen. This width cap was intended to measure the actual rendered text's width.However, the code measuring the text width incorrectly uppercased the text before measurement. This was due to borrowing a step from a different part of the video pipeline that deliberately used all caps for visual style. Uppercased text in the chosen font is wider than mixed-case text.The measurement was accurate for the uppercased input, but the actual subtitles were rendered in their original mixed case. This discrepancy meant the width guard, believing the text was much wider, forced unnecessary splits. This created one-word chunks, a worse user experience than the initial problem.Crucially, all automated tests passed because the width-measurement function operated correctly on the input it received, which was the uppercased text. The tests did not verify that the measured text matched the text actually rendered on screen. The bug was only discovered by a human watching the rendered video.This situation highlights a common pitfall: separate code paths for production and verification that are supposed to agree on assumptions like text transformations. When these assumptions drift, subtle bugs emerge that automated tests miss. The author suggests sharing transformation functions between paths or regularly sampling and inspecting the actual rendered output as mitigation strategies. Trusting solely in green test suites without human oversight can allow such subtle divergences to persist unnoticed.
https://dev.to/hexisteme/my-caption-width-guard-passed-every-test-it-was-measuring-text-the-renderer-never-drew-160d dev.to
RSS Hunter RSS Hunter • Yesterday

ESP32 + Python: From Microcontroller to IoT

This article introduces a series about using ESP32 with Python for IoT projects. The ESP32 is a powerful microcontroller with built-in Wi-Fi and Bluetooth capabilities. It can interact with sensors and control devices, essentially a more advanced version of Arduino. Python, a high-level programming language with a readable syntax, was first released in 1991. It is versatile and used in various fields, including hardware communication. The Internet of Things, or IoT, refers to physical devices connected to a network, usually the internet, to collect and exchange data. These devices can often perform tasks automatically. The combination of ESP32 and Python bridges hardware and software, enabling interactive and connected applications. For example, an ESP32 can read sensor data and send it over Wi-Fi for processing and action. This synergy offers a strong basis for building IoT and other hardware-software projects. This introductory article sets the stage for future articles that will delve into practical examples and theory. The goal is to teach how to make ESP32 and Python communicate effectively.
https://dev.to/janadapelahara/esp32-python-from-microcontroller-to-iot-2498 dev.to
RSS Hunter RSS Hunter • Aug 23

99% token accuracy, zero learning. Field notes from fine-tuning vision models with RL.

The author recounts experiences fine-tuning vision-language models, highlighting that many failures stemmed from operational issues rather than algorithmic flaws. One 18-hour supervised fine-tune showed 99% token accuracy, but the actual evaluation metric, multiple-choice accuracy, remained unchanged. This was due to supervising free-text reasoning while evaluating a single extracted answer, demonstrating a proxy metric drift. A GRPO trainer crashed because two libraries disagreed on sequence length, with image-pad tokens being counted twice, pointing to integration bugs at component boundaries. The author learned that such monkeypatches require cheap regression tests to prevent silent reintroduction of bugs. A significant RL loop exhibited flat learning for an extended period, which was eventually traced to label noise in the reward pipeline and an inverted advantage signal. This "quiet" failure showed no crashes, only an absence of learning, emphasizing the need for thorough auditing of reward computation. These experiences led to crucial "harness rules" for all training runs. These include performing smoke tests before committing compute, ensuring runs are gated fail-closed so no unscored run can be mistaken for a scored one, and strictly separating infrastructure failures from poor model performance. Critically, only the held-out evaluation metric is considered the true decider, with all other metrics serving as mere telemetry. The author emphasizes that these "boring" rules are essential for obtaining trustworthy results.
https://dev.to/rickeshtn/99-token-accuracy-zero-learning-field-notes-from-fine-tuning-vision-models-with-rl-306l dev.to
RSS Hunter RSS Hunter • Aug 23

Log Level Design and Rotation: Why Apps Use DEBUG/INFO/WARNING/ERROR

Application logs use varying labels like DEBUG, INFO, WARNING, and ERROR, which primarily function as filtering thresholds. Python's logging module defines five levels, from DEBUG (10), representing fine-grained detail, to CRITICAL (50), indicating application failure. Setting a logger's level to INFO means only messages with a numeric value of 20 or higher will be recorded. This allows developers to embed detailed diagnostic statements without cluttering normal operation logs, activating them only when needed for investigation. The example application, maintenance_agent.py, routes log output to a RotatingFileHandler and a StreamHandler, initially set to INFO, so DEBUG messages are typically suppressed. The distribution of log calls in the application reflects its design assumptions, with INFO for progress, WARNING for recoverable issues, and ERROR for genuine failures. CRITICAL is unused as the application is designed to handle site-specific failures independently. Log rotation, implemented by RotatingFileHandler, prevents log files from growing indefinitely by setting a maximum size and backup count. This mechanism ensures log history is preserved without consuming excessive disk space. A separate handler, _SiteLogCapture, collects ephemeral logs for individual site maintenance runs to generate reports or emails, then discards them. This demonstrates how multiple handlers can process the same log stream for different purposes: long-term history versus temporary, specific use. Log levels act as a vertical filter for detail, while handler choices serve as a horizontal filter for audience and purpose.
https://dev.to/susumun/log-level-design-and-rotation-why-apps-use-debuginfowarningerror-4bdi dev.to
RSS Hunter RSS Hunter • Aug 23

Same tokens. Different DNA. Why a Texas notebook must not become a parlor.

Most agent redesigns are merely superficial, lacking genuine transformation. The "/reimagine-it" contract demands extraction of all nouns, dates, and colors from a source. It then requires the creation of a distinct artifact like a webpage or infographic poster. Crucially, two sources with the same command tokens must not share layout chrome. The primary failure case is when a redesign merely swaps nouns on a gold layout. For instance, a "Jules Ice Cream" output must not resemble a "Texas notebook" layout. The skill must intentionally avoid this superficial similarity. A "Texas notebook" should result in an SVG of the actual Texas flag, not a generic gold-star logo. Similarly, "Jules Ice Cream" should evoke a parlor environment, not a Texas map with scoops. Infographics here mean paper posters with common-scale encodings and lossless tables, not dashboards or resume charts. AntV Infographic is a structural guide, not a template for direct import. The project is accessible via a gallery, GitHub repo, and various plugin/CLI commands. A false output would be a Jules Ice Cream design indistinguishable from Texas gold or one that invents nouns not present in the source.
https://dev.to/kayforkind/same-tokens-different-dna-why-a-texas-notebook-must-not-become-a-parlor-2n82 dev.to
RSS Hunter RSS Hunter • Aug 23

Running the AWS CLI Against Your Own Hardware

Developers often use the AWS CLI, but it assumes connectivity to amazonaws.com, which breaks in disconnected environments. This forces teams to maintain a separate, costly toolchain for on-premise deployments, leading to divergent operational models. Spinifex solves this by providing an identical AWS API surface locally, running on your hardware. This means existing AWS CLI commands, SDKs, and tools like Terraform continue to work seamlessly. Users simply change the endpoint URL in their AWS profile or use the --endpoint-url flag for individual commands. Spinifex delivers real EC2 compute, EBS volumes, S3 storage, and EKS clusters, not just mocks. This enables air-gapped deployments to use the same automation, Terraform configs, and IAM policies as cloud environments. It satisfies requirements for disconnected field deployments, secure facilities, data sovereignty, and cost control for steady-state workloads. The only change needed for existing scripts is the endpoint override. For those interested, a full setup guide and a free sandbox are available.
https://dev.to/toddyholiday/running-the-aws-cli-against-your-own-hardware-11i2 dev.to
RSS Hunter RSS Hunter • Aug 23

How we run five coding agents side by side in one window

NestMux is a desktop application that allows users to run multiple AI command-line interfaces (CLIs) and terminals in a grid of independent panes. Each pane operates as a separate environment, isolated by its own account and Git worktree. The core functionality relies on spawning a shell via node-pty, redirecting the HOME directory, setting the current working directory, and typing user commands.A key feature is the "broadcast" mode, which sends a single keystroke input to all active panes simultaneously, enabling a uniform prompt to multiple AI agents. This simple implementation avoids complex protocols but means all inputs, including control commands, are broadcast indiscriminately. Resource attribution for each pane is a significant challenge, as AI agents often run as child or reparented processes, requiring file system-based heuristics to identify processes belonging to a specific worktree.For reviewing, NestMux generates and parses Git diffs for each worktree, with a deliberate decision to limit rendering for very large files to prevent performance issues. The application's state, including pane configuration and layout, is saved atomically to session.json, ensuring recovery from crashes. However, terminal scrollback and session history are not preserved across restarts; panes respawn, and CLIs rerun.This design choice, while simplifying new agent integration by treating them as opaque processes, leads to a significant limitation: the lack of a unified log or timeline across panes, making it difficult to track interactions and changes. This absence of cross-pane insight is the trade-off for not parsing individual agent outputs. NestMux is cross-platform, local-first, and currently free during its launch phase.
https://dev.to/eliseomdq/how-we-run-five-coding-agents-side-by-side-in-one-window-32gf dev.to
RSS Hunter RSS Hunter • Aug 23

I pushed the badBANANA Threat Observatory public

The project originated from a long-standing desire to create a threat visualization tool that prioritized evidence over impressive presentation. Many existing threat maps are visually engaging but often lack analytical depth, serving different purposes like marketing or education. The author aimed to build something that maintained a sense of observation and movement, but where the underlying data rigorously justified the interface's display. This led to the creation of the badBANANA Threat Observatory, which monitors sources like CISA KEV and ThreatFox. Crucially, the focus was on robust evidence handling rather than just the dashboard itself. The author meticulously audited the system to prevent overstating what it knew, addressing issues like missing data becoming zeroes or invalid indicators being reclassified. This involved stricter validation for IOCs and hashes, safer exports, and careful deployment gates. The current version maintains a clear distinction between what a source reports, what changed, what was retained, and what was rejected. It ensures that bounded APIs look bounded, failed sources appear failed, and missing confidence remains missing. The Observatory is not intended to replace analysts or imply a live global cyberwar but rather to facilitate the inspection of evidence without silently filling in gaps. The public release underwent multiple audit and remediation passes to ensure its integrity.
https://dev.to/gnomeman4201/i-pushed-the-badbanana-threat-observatory-public-49e8 dev.to
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RSS Hunter RSS Hunter • Aug 23

I spent one day smashing three real open source bugs

The author participated in the DEV Summer Bug Smash challenge, focusing on making real fixes accompanied by tests that failed before and passed after implementation. Their efforts spanned three open-source projects over a single day. The first bug addressed was in OpenClaw, an AI assistant, where canceling a setting change incorrectly triggered a gateway restart. The fix ensured that cancel operations would revert to the actual running configuration, preventing unnecessary restarts. The second bug involved Rocket.Chat's design system, Fuselage, where a Slider component's track fill incorrectly rendered in right-to-left locales and with non-zero minimum values. This was resolved by having the fill calculate its position based on component state and correctly adapt to locale direction. The third bug was found in npmx.dev, which failed to render many complex TypeScript types, showing "unknown[unknown]" instead. The author implemented eleven new formatters for various TypeScript type kinds, significantly improving the display of API documentation for npm packages. Each fix was submitted as a pull request with a descriptive commit message and accompanying tests. The author learned that creating small, reproducible bug cases is crucial, tests that fail first are invaluable, and understanding a project's conventions saves time. The day resulted in three working fixes across three projects, each with a passing test suite.
https://dev.to/aniruddhaadak/i-spent-one-day-smashing-three-real-open-source-bugs-4b74 dev.to
RSS Hunter RSS Hunter • Aug 23

Ryker: In-Browser Visual Editor for HTML and Markdown with agent-ready change exports

HTML's utility for large language models is growing, though manual editing of generated HTML documents can be cumbersome. Traditionally, HTML is edited via source code, which is effective for development but less so for proofreading or minor content adjustments. To address this, Ryker, a Chrome extension, has been developed for visual editing of HTML and Markdown. Ryker records these visual edits as prompt-ready change requests. The intention is to facilitate a more intuitive document review process. Edits are made directly on the document itself. Ryker then captures these modifications as machine-readable text. These change requests can be returned to a language agent for implementation. The system aims to complement, not replace, the agent's role entirely, allowing for natural review while the agent manages final execution. Ryker enables direct editing of live HTML pages in the browser and editing of local HTML and Markdown files. All recorded edits are exportable as prompt-ready change requests. Further information and installation are available at the provided link.
https://dev.to/gbti/ryker-turn-html-and-markdown-edits-into-agent-ready-change-requests-3o6c dev.to
RSS Hunter RSS Hunter • Aug 23