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ProgrammingDEV Community ·

How to Reliably Send Order-Shipped Email Attachments and SMS Alerts

When a gaming studio's order-shipped event triggers email and SMS notifications, missing report attachments often go undetected because API success metrics do not confirm actual delivery. Developers are advised to validate and generate reports before queuing, assign each notification job a durable database identity, and track distinct states from report generation through delivery confirmation. Alerts should be tied to specific report periods, recipients, and pipeline stages rather than blended success rates, so on-call engineers can pinpoint failures quickly. SMS delivery should never be treated as confirmation that an email attachment was successfully received, as the two channels must be tracked independently. Using a unified REST API for both email and SMS can reduce integration complexity by consolidating credentials and ensuring consistent attachment validation across different runtimes.

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ProgrammingDEV Community ·

Developer Builds Single API to Access Jobs, Tenders, and City Population Data

A developer has created a consolidated Data-as-a-Service platform that combines three commonly needed public datasets behind a single API endpoint. The platform provides access to remote tech job listings with normalized salary benchmarks, government procurement tenders, and demographic data for over 48,000 cities worldwide. It is built using FastAPI running inside Docker, with SQLite databases optimized for fast retrieval through compound indexes. The API is routed through Cloudflare Tunnel to RapidAPI, which handles authentication and rate limiting. The project aims to eliminate the need for developers to rely on multiple fragile scrapers or paid services to access public data.

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ProgrammingDEV Community ·

Systems Admin Ditches Vibe Coding to Learn Software Engineering From Scratch

A systems administrator and data engineer with years of scripting experience in Python, PowerShell, Bash, and SQL is now pursuing a transition into software engineering. Despite experimenting with AI-assisted 'vibe coding' to build a personal website, blog, and note-taking app, he found the approach frustrating due to a lack of understanding of the underlying tools and frameworks. He discovered that building applications from scratch was more effective than relying on AI agents to fix errors he could not diagnose himself. Unlike simple scripts, full applications involve multiple interconnected components — such as database connections, file handling, and server communication — making debugging significantly more complex. He now views learning fundamentals from the ground up as a valuable foundation, with AI tools serving as a complement rather than a replacement for genuine understanding.

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ProgrammingDEV Community ·

84% of Developers Use AI in 2026, But Only 29% Trust Its Output

A 2026 industry analysis highlights a growing disconnect between AI adoption and developer confidence, with usage reaching 84% while trust in AI-generated output has dropped sharply to just 29%, down from 40% in 2024. The dominant frustration among developers is AI code that appears correct but contains subtle logic errors that take longer to debug than to generate. The piece argues that years of chasing new frameworks and AI tools have created a 'maintenance fatigue crisis,' where speed gains were effectively loans taken against future system stability. Drawing on the concept of limited 'Innovation Tokens,' the analysis warns that adopting experimental technology consumes organizational capacity better spent on core business problems. The authors advocate for prioritizing proven, mature technologies whose failure modes are well understood over newer, unproven alternatives.

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ProgrammingHacker News ·

Analysis: Who Holds Power in the Open AI Models Landscape

A recent analysis published on Interconnects.ai examines the current distribution of influence among players in the open AI models space. The piece explores how power dynamics have shifted as more organizations release openly accessible large language models. It considers factors such as model performance, community adoption, and institutional backing in shaping competitive standing. The article has drawn early engagement on Hacker News, where discussion is beginning to form around its findings.

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ProgrammingDEV Community ·

Four Arabic grammar rules that make number-to-words conversion uniquely complex

A developer maintaining an Arabic number-to-words converter (tafqeet), used for invoices and cheques, has detailed four linguistic rules that break naive digit-by-digit translation approaches. Unlike English, Arabic places units before tens, so 25 becomes 'five and twenty,' meaning left-to-right concatenation produces grammatically incorrect output. Arabic also requires numbers three through ten to take the opposite grammatical gender of the noun being counted, making the correct word for a digit dependent on the currency in use. Additionally, the form of a counted noun changes based on whether the last two digits fall in the range 3–10, 11–99, or are an exact hundred, requiring plural, accusative singular, or plain singular forms respectively. A fourth rule governs the dual form, where 'مائتان' must drop its final letter and become 'مائتا' when directly followed by a noun, a single-character difference that proof-readers and bank clerks immediately flag as an error.

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ProgrammingDEV Community ·

Developer Builds Willow, an Experimental Native Language with GC and Async Runtime

A solo developer has created Willow, an experimental statically typed programming language that compiles to native code using Cranelift. The language combines class-based object-oriented programming, algebraic data types, pattern matching, and a custom stackless async runtime with task scheduling and channels. Unlike Go, Java, or Rust, Willow aims to blend native ahead-of-time compilation with automatic memory management and structured concurrency in a single design. It features a generational garbage collector where major marking runs concurrently and async frames are managed by the GC itself. Willow is not yet production-ready, with a small standard library and an unstable ABI, but it can already run non-trivial programs and produce meaningful benchmarks.

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ProgrammingDEV Community ·

AI hype is masking real software engineering challenges, warns developer

A software engineer with experience in AI since 2018 argues that the current generative AI boom is producing widespread hype and misleading claims about what the technology can replace. He warns that building a working application quickly should not be confused with proper software engineering, which also encompasses security, scalability, testing, and maintainability. The author stresses that technology choices — whether AI, cloud architecture, or programming languages — must be driven by context and trade-offs, not trends. He raises concerns that teams are rushing to add AI without first asking critical questions around data privacy, regulatory compliance, model reliability, and actual business need. His broader point is that the same mistakes made during past technology waves, such as microservices and cloud adoption, are now being repeated with artificial intelligence.

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ProgrammingDEV Community ·

Gas Audit Flags $2.9M Annual Waste in Gauntlet DeFi Protocol Contracts

A gas-efficiency audit of Gauntlet, a DeFi protocol managing roughly $1.64 billion in total value locked across Ethereum and multiple Layer 2 networks, found transaction costs running 15–30% above industry benchmarks. The review, conducted in September 2026, identified seven key inefficiency areas including redundant storage writes, unbounded loops, poor data packing, and repeated external calls inside loops. Beyond cost concerns, auditors flagged security risks such as out-of-gas denial-of-service attacks from unbounded loops, re-entrancy vulnerabilities in rebalancing functions, and potential accounting errors if unchecked arithmetic is later introduced. Implementing the recommended fixes is estimated to save approximately $2.1 million annually on Ethereum mainnet and a further $0.8 million across L2 deployments. The audit includes a threat model with mitigations to address both the gas inefficiencies and their associated security implications.

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ProgrammingDEV Community ·

Anthropic: AI Writes 80% of Code, but Verification Costs Soar 25x

Anthropic revealed that Claude now generates around 80% of its internal code, but the broader impact on its engineering pipeline tells a more complex story. Over six months, the company's test suite grew tenfold while CI jobs increased 25 times, signaling that verification — not code generation — has become the new bottleneck. The author argues that AI compresses the development constraint so rapidly that downstream systems like code review, CI capacity, and deployment controls struggle to keep pace. Anthropic addresses this partly through a deterministic test-impact analysis service that selects relevant tests per change, rather than running full regression suites every time. The piece concludes that as AI agents iterate autonomously, feedback latency becomes a throughput issue, reshaping how quality assurance must operate at scale.

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ProgrammingDEV Community ·

AI Writes the Code, But the Hard Thinking Still Falls on Developers

A developer reflects on how AI-assisted coding tools can rapidly produce functional code, often reducing hours of work to minutes. However, a real-world bug exposed a key limitation: AI handles the expected 'happy path' well but struggles with edge cases that require deliberate product decisions. The author argues that code generated at 85–90% accuracy can be deceptively dangerous, as it looks finished while critical judgment calls remain unresolved. The speed of AI output can also create a false sense of progress, making developers feel behind when they are actually doing the most important work. Ultimately, while AI accelerates the 'how' of coding, the 'why' — understanding intent, context, and consequences — remains a distinctly human responsibility.

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ProgrammingDEV Community ·

Building an OpenTelemetry Logs Bridge: Why API Contracts Were the Real Challenge

Engineers developing HaloLog, a high-performance logging library, built an OpenTelemetry Logs bridge adapter that maps log entries into OpenTelemetry LogRecords for pipeline-compatible telemetry. The straightforward part was field mapping — converting message, level, and attributes — but the real complexity lay in preserving API contracts around context ownership, memory safety, masking precedence, and provider lifecycle. The bridge originated from community feedback requesting true OpenTelemetry log pipeline integration, not just trace metadata printed alongside messages, and was initiated via the project's first external pull request by contributor Daniel Loader. The adapter is packaged as a separate Go module so applications that do not need it avoid pulling OpenTelemetry into HaloLog's core dependency graph. Benchmarks cited are adapter-level microbenchmarks excluding the SDK, exporters, and network, and are not intended as production throughput comparisons.

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ProgrammingDEV Community ·

Elanat's WebForms Core 2.1 Adds Rust WebAssembly Support via Two Execution Models

Elanat has released WebForms Core 2.1, a server-oriented web framework that now supports Rust-compiled WebAssembly as a client-side execution layer. The update enables developers to run Rust code in the browser through two approaches: using wasm-bindgen for JavaScript interoperability, or exporting raw functions via extern "C" without any JavaScript glue code. In both models, the server-side WebForms class controls when and how WebAssembly methods are invoked, while WebFormsJS handles resulting commands in the browser DOM. Rust projects can target the wasm32-unknown-unknown architecture and integrate with the framework using the webformscore crate available on crates.io. This pipeline allows Rust logic compiled to WebAssembly to generate and execute WebForms Core UI commands directly within the browser.

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ProgrammingDEV Community ·

Convertum processes PDFs, images, JWTs and hashes entirely in your browser

A developer has launched Convertum, a free browser-based toolkit that handles common developer tasks such as JWT inspection, PDF manipulation, image conversion, and cryptographic hashing without uploading any data to external servers. All processing runs locally in browser memory using WebAssembly, the Web Crypto API, and HTML5 Canvas, meaning sensitive files and tokens never leave the user's machine. The tool addresses a common privacy concern with popular online utilities that route confidential data through third-party cloud backends, often behind paywalls or usage limits. Convertum is available as both a web app and a Chrome extension, with the extension supporting a Side Panel mode for use alongside active browser tabs. Built with React, TypeScript, and Vite, it also supports specialized formats including Apple HEIC images, Korean HWP documents, and European e-invoice standards like XRechnung and Factur-X.

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ProgrammingDEV Community ·

CERT-In Flags 14 High-Severity ISC BIND Vulnerabilities Across Multiple Releases

India's CERT-In issued advisory CIVN-2026-0467 on 21 September 2026, identifying 14 high-severity vulnerabilities in ISC BIND across a wide range of versions from 9.11.0 through 9.21.25. The flaws span three weakness categories: memory-safety issues that can crash resolvers, resource-consumption bugs that degrade performance, and trust-boundary flaws that may cause DNS servers to return incorrect answers. No active exploitation of any of the 14 CVEs has been reported by CERT-In at this time. Affected operators are advised to update to fixed builds listed in ISC's official advisory index and to restrict resolver exposure by limiting recursion, requiring TSIG for transfers, and rate-limiting DNS-over-HTTPS. With over 19 million internet-facing assets fingerprinted as ISC BIND by ZoomEye, the potential attack surface is considered significant.

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ProgrammingDEV Community ·

XRPL Confidential MPT Feature Passes QA Testing with Full Cryptographic Coverage

A QA test report dated September 22, 2026, documents results for Confidential Transfer (XLS-0096), a new feature built on top of XRPL's Multi-Purpose Tokens. The feature addresses privacy shortcomings of standard MPTs by storing token balances as ElGamal ciphertexts and validating every state change using zero-knowledge proofs, keeping amounts confidential yet verifiable on-ledger. Testing was conducted by engineer sgramkumar on a GitLab CI runner connected to the xrpld devnet, covering all five new transactors: Convert, Send, MergeInbox, ConvertBack, and Clawback. The test suite verified cryptographic correctness, balance conservation, issuance control flags, auditor mirror integrity, and resistance to adversarial inputs such as bad proofs and stale-proof replay attacks. Confidential MPT's compatibility with related XRPL features including Batch, Permission Delegation, and Sponsored Fees was also confirmed during the evaluation.

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ProgrammingDEV Community ·

Machine Learning Explained: How Computers Learn Patterns From Data

Machine learning (ML) is a branch of artificial intelligence that enables computers to learn patterns from data and make predictions without being explicitly programmed for every scenario. Unlike traditional programming, which relies on manually written rules, ML systems are trained on labeled examples to build models that generalize to new data. A typical ML workflow covers stages including data collection, cleaning, exploratory analysis, feature engineering, model training, evaluation, and ongoing monitoring. Real-world applications range from spam filters and fraud detection to voice assistants and personalized recommendations. As organizations accumulate larger datasets, ML has become an essential tool for extracting actionable insights and powering modern data-driven systems.

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ProgrammingDEV Community ·

XLS-0096 Confidential Transfer Feature Gets 561-Test Automated Coverage Suite

A comprehensive automated test suite has been developed for the Confidential Transfer feature defined under XLS-0096, covering 561 individual test cases. The tests are organized into 14 categories, including transactor operations such as Convert, Send, MergeInbox, ConvertBack, and Clawback. Additional categories address cryptographic correctness, security hardening, adversarial scenarios, invariants, and cross-feature interactions. The largest single category is Cross-Feature with 63 tests, while transactor Convert tests lead among individual operations with 72 cases. The suite is designed to validate correctness, edge cases, and security properties across the full lifecycle of confidential MPT token transfers.

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ProgrammingDEV Community ·

Why Multi-Agent AI Supervisors Break Down After Step 7 and How to Fix It

A software developer discovered that a seven-specialist, one-supervisor AI workflow that performed well in demos began failing consistently around the fourth or fifth task handoff in production. The root cause was not prompt quality or model size, but a structural limitation: after roughly five worker interactions, the supervisor's context window saturates, causing it to repeat outputs, lose earlier decisions, and make inconsistent routing choices. Research literature from 2026 identifies three compounding failure modes at this stage — task assignment errors, output misinterpretation across handoffs, and consensus loops between disagreeing specialists. The developer concluded that the standard supervisor pattern, used by frameworks like LangGraph, CrewAI, and AutoGen, is inherently limited to three-to-seven agents and acts as a single point of failure. A senior architect reframed the issue by describing the supervisor not as a true manager but as a stateless router, pointing toward hierarchical architectural changes rather than prompt tuning as the real solution.

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ProgrammingDEV Community ·

Solo Developer Trains 1.18B-Parameter LLM on Single Consumer GPU, Hits 4.5B Tokens

Independent developer Mert Çetin of Me Force Technology has reached the 4.50 billion processed token milestone while training CetinLM Base-v1, a 1.18-billion-parameter language model, entirely on a single Nvidia RTX 4070 Ti SUPER graphics card in a residential setting. The model has shown a consistent decline in validation loss, dropping to 2.555976 with a perplexity of 12.884 at the 4.10 billion token mark. A 1,000-sample generation health test conducted at the 4.00 billion token stage recorded zero looping incidents and zero severe repetitions, with nearly half of outputs ending naturally via end-of-sequence tokens. The project publishes raw training metrics and unedited model outputs, positioning itself as a contrast to what Çetin describes as opaque evaluation practices at large AI labs. The work is being cited as a case study in whether high-quality foundational language models can be built without large-scale corporate compute infrastructure.

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