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ProgrammingDEV Community ·

Why .NET chat loops lose OpenAI encrypted reasoning between turns

A reasoning model can appear to forget the first turn of a conversation, and the cause is often in the application's own chat loop rather than the prompt or model. When stateless requests ask for encrypted reasoning, the response includes an encrypted reasoning item that the service does not store, so it must be sent back on the next turn. In Microsoft.Extensions.AI, this data sits in TextReasoningContent parts, which are separate from the plain text the response exposes. Developers who save only the response text in their history discard this encrypted reasoning, so the follow-up request carries no prior reasoning items. The author demonstrates the problem with an offline .NET sample and tests, and recommends appending the entire response to the history instead.

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ProgrammingDEV Community ·

Crafting and Parsing L2-L7 Packets with Scapy: A Hands-On Lab

The author walks through a networking lab aimed at debugging layer 2, 3, 4, and 7 behavior using Scapy. At layer 2, an ARP request sent to a broadcast address is shown, and a layer 3 test illustrates how a target ignores a broadcast-addressed packet while tcpdump captures traffic. For layers 4 through 7, the author builds a packet with Ether, IP, TCP, and Raw layers, carrying an SSH banner payload to port 22. The packet is verified on a VyOS system using tcpdump, which captured it even though no TCP handshake was performed. The author concludes that Scapy provides full control over payload content within the TCP/IP envelope.

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ProgrammingDEV Community ·

Five Unhappy-Path Tests to Write Before Trusting a .NET 10 Multi-Agent Workflow

A developer building a support-ticket triage workflow on Microsoft Agent Framework for .NET 10 argues that demos only show the happy path, while real failures include malformed model output, unapproved promises and provider outages. They built the workflow with a classifier agent, two specialist agents and plain C# steps, and wrote 58 tests that run without an API key or network access. The core technique is a deterministic fake chat client that can be told to produce specific failures, such as cut-off JSON or an HTTP exception, via trigger strings. The author also describes a reply-guard step that checks specialist drafts for oversized refunds, guarantee language and sensitive data, routing risky replies to human review. The post presents five such tests as a starting point for any agent workflow, along with the bug each one catches.

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ProgrammingDEV Community ·

Kraken CSV Format Change Exposes Risks in Crypto Tax Importers

A CoinTaxman bug report from July 2026 flagged that a newer Kraken Ledgers export adds subclass and wallet columns, which older parsers cannot handle. The same report noted unhandled ledger types such as earn and hybridearnwithdrawal, along with asset naming mismatches like BTC/XBT and DOGE/XDG. The author argues that importers should reject unrecognized layouts and unclassified events rather than guess their tax treatment. The proposed fix recognizes the new twelve-column header as a distinct format and flags unsupported wallet types for manual review. The author stresses that the patch is not a full tax determination or complete Kraken importer.

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ProgrammingDEV Community ·

GSTR-3B Filing History Offers Lenders a Credit Signal for MSMEs

GSTR-3B is a monthly or quarterly GST summary return in which businesses self-declare outward supplies, input tax credit and tax paid, and it cannot be revised once filed. Because each filing is a fixed, dated record, its history can show how regularly a business trades, reports and pays tax. This matters for MSME borrowers in India, who often lack audited financials but do have GSTINs and years of returns. Lenders are urged to read the full period-wise record rather than simply confirming that a GSTIN is active. The article outlines how 2025 changes, including locking of auto-populated liability and a three-year time bar on filing, have strengthened the return as a data source. It also lists signals such as filing regularity, delay trends, nil returns, turnover consistency and cash-versus-credit tax payments, none of which is conclusive alone.

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ProgrammingDEV Community ·

Samsung y Reality Defender llevan la detección de deepfakes al teléfono Galaxy

Samsung Electronics Singapur y la empresa neoyorquina Reality Defender firmaron un memorando de entendimiento durante GovWare 2026 para integrar la detección de contenido falso en dispositivos Galaxy. La herramienta analizará voz, video e imágenes generados con inteligencia artificial directamente en el teléfono, sin enviarlos a la nube. El despliegue arranca con clientes empresariales en Singapur y se apoya en la plataforma de seguridad Knox. Las cifras citadas muestran la magnitud del problema, con víctimas de estafas de suplantación de funcionarios que perdieron al menos 242,9 millones de dólares singapurenses en 2025. Los responsables sostienen que el análisis local protege la privacidad y evita demoras antes de que un empleado actúe sobre un mensaje falso.

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ProgrammingDEV Community ·

Why CSS Variables Can Slow Rendering: Runtime Resolution Costs Explained

A developer found that CSS custom properties, often assumed to be free abstractions, can cause noticeable slowdowns in complex web apps. Unlike fixed values, variables are resolved at runtime because they can be inherited and overridden anywhere in the DOM tree. The browser must therefore climb ancestor elements to find each variable's value during every style recalculation. Changes to variables on parent elements can trigger repeated invalidations and re-resolutions across many descendants, which is costly during animations or theme switches. The article's author traced the problem to frequent style recalculations using Chrome DevTools' Performance tab.

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ProgrammingDEV Community ·

Abbottabad Founder Launches Independent Digital News Platform Buzz Capitol

Sami Ullah, a 23-year-old from Abbottabad, Pakistan, has launched Buzz Capitol in 2026 as a self-funded, independent digital news platform. He serves as its founder, CEO and editor-in-chief, operating without investors or a traditional newsroom. Ullah previously ran more than 15 YouTube channels and built a combined following of over 10 million across social platforms, which he describes as his practical training in online distribution. He says the project aims to cover stories about places outside major cities and to offer young readers independent reporting. He emphasizes that trust and consistent editorial standards, rather than the technology stack, are the platform's core assets.

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ProgrammingDEV Community ·

AWS CDK Adds Deterministic Guardrails to Block Destructive Changes from AI-Written Code

Developers increasingly rely on AI to write AWS CDK code and sometimes to deploy it, raising concerns about accidental deletion of critical resources. Relying on AI to judge diffs is non-deterministic, so the article proposes a mechanical alternative. Deleting or replacing resources is considered destructive, since it can cause downtime or data loss, and some resource properties trigger replacement even when no deletion appears in the template diff. AWS CDK CLI v2.1145.0 introduced the --fail-on destructive option for cdk diff and the --require-approval destructive option for cdk deploy to detect and block such changes. This setup lets AI drive development while requiring human approval only for destructive changes before the CI pipeline proceeds.

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ProgrammingDEV Community ·

LangGraph, CrewAI and AutoGen Compared on Architecture for Production Agents

A comparison of three popular agent frameworks, LangGraph, CrewAI, and AutoGen, focuses on how each executes workflows, manages state, and recovers from failures rather than on feature lists. LangGraph models workflows as cyclical graphs with typed state checkpointed after every step, enabling pause, resume, and replay. CrewAI organizes agents by role and task within crews, with simpler mental models but less explicit state handling, and offers Flows for more control. AutoGen relies on conversational message exchange among agents, which suits discussion-style tasks but can be harder to predict. The article notes that AutoGen has been in maintenance mode since October 2025, with Microsoft Agent Framework as its successor. No single framework is presented as best; the choice depends on whether deterministic, resumable execution or faster prototyping is the priority.

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ProgrammingDEV Community ·

playwright-webext Simplifies Testing Browser Extensions in Playwright

A DEV Community post introduces playwright-webext, a project designed to make testing browser extensions easier than with Playwright alone. Playwright's official guide covers launching a browser with an extension and locating its service worker, but extension-specific setup such as storage, popups, and version upgrades still requires custom fixtures and helpers. The author demonstrates the library using a simple todo-list extension that stores tasks in chrome.storage.local. The tutorial walks through installation and configuration, then shows three example tests covering installation behavior, popup interaction, and migration between extension versions.

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ProgrammingDEV Community ·

Building MOSAIC: Why Reproducible Replay Matters for Multi-Agent AI Experiments

The author describes MOSAIC, a research project for studying populations of language-model agents, and the difficulty of knowing whether observed behaviour changes stem from a model swap or ordinary generation variation. MOSAIC uses an append-only event log and a deterministic reducer so that a run's state can be reconstructed and checked against its recorded history. The system also records model identity via digests, generation parameters, and protocol settings, so differences between experiments can be explained. A record-and-replay mechanism saves model generations so that runs can be re-executed without fresh model calls, supporting debugging and validation. The author stresses that a system can execute exactly as designed yet still fail to yield the observations needed to answer the research question.

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ProgrammingDEV Community ·

tokenprice at $0.001 per call compared with CoinGecko and CoinMarketCap pricing

A benchmark post compares tokenprice, a pay-per-call token price endpoint charging $0.001 per request backed by DexScreener data, against subscription plans from CoinGecko and CoinMarketCap. CoinMarketCap now also offers an x402 pay-per-call option at $0.01 per call, which the author cites as evidence that per-call pricing is gaining traction. Four test queries returned a median latency of about 1.8 seconds, but a ticker search for ETH returned a Solana token instead of Ethereum, exposing the ambiguity of symbol-based lookups. The author's pricing table shows per-call pricing is cheaper below roughly 30,000 calls per month, while subscriptions become more economical at around 100,000 calls per month. The post also notes that the endpoint lacks historical candles, order books, and broad coverage of coins.

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ProgrammingDEV Community ·

FSx for ONTAP Allows QoS Burst Default Changes in ONTAP 9.19.1, But Only via ONTAP Interfaces

A tester checked whether the QoS burst defaults introduced in ONTAP 9.19.1 can be modified on Amazon FSx for NetApp ONTAP, as on-premises systems allow. On a live file system running ONTAP 9.19.1P2 in the ap-northeast-1 region, the settings were accepted and a brief throughput overshoot above the ceiling occurred as configured. However, the burst settings cannot be changed through the AWS Management Console or AWS CLI, and must instead be set directly through ONTAP's own REST API or CLI. The author notes that QoS ceilings help prevent noisy-neighbor problems on shared file systems, and that burst lets workloads briefly exceed those limits during events like startup. The post does not verify whether burst settings can be reverted or how they affect cost and features like SnapMirror.

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ProgrammingDEV Community ·

Browser-Based Word-to-Markdown Converter Runs Pandoc via WebAssembly Without Uploads

A developer built a Word-to-Markdown converter that runs the Pandoc engine compiled to WebAssembly entirely inside a browser tab, so documents never leave the user's device. The author argues that converting through an intermediate HTML step loses document semantics like footnotes and citations, whereas Pandoc parses formats into a typed syntax tree that preserves them. Because the roughly 58 MB module is too heavy to load on page load, it is fetched lazily on user intent and run in a Web Worker to keep the interface responsive. Initially assuming download speed was the bottleneck, the developer found compilation was slower, so compiled modules are cached in IndexedDB, with fallbacks to the HTTP cache or streaming. Safari does not allow persisting compiled modules, so there the caching step degrades to re-downloading and recompiling.

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ProgrammingDEV Community ·

CrescoDB Plans Post-Deployment Monitoring for Application and Server Health

CrescoDB is preparing monitoring features aimed at helping developers keep track of their backends once applications go live. The planned tools would report request and error rates by route, latency including p95 figures, slow or failing database queries, and runtime indicators such as memory use and event-loop delay. Server-level visibility is also planned, covering CPU load, memory, disk usage, running containers, and availability. The company additionally intends to add external uptime checks that test public URLs for reachability, DNS, TLS, and response times from outside the server.

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ProgrammingDEV Community ·

Why Windows Battery Life Estimates Often Differ From Real-World Runtime

A Windows battery report may show around five hours of runtime while a coding session lasts only about two, and this gap alone does not indicate a faulty battery. The key distinction is between stored energy, which reflects battery capacity, and power demand, which depends on the workload. The article explains how to generate a battery report with powercfg and read the Battery life estimates section, which separates projections at full charge from comparisons using design capacity. Using simplified examples, it shows that doubling power draw roughly halves idealized runtime even when capacity stays the same. It recommends logging controlled unplugged sessions under consistent conditions, since the estimates are based on recorded history that may not reflect heavy tasks.

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ProgrammingDEV Community ·

Doze Sort uses a local model to file notes into copied folders, keeping originals

Doze Sort is a small open-source tool built for a Hacktoberfest AI challenge that uses an open-weight model to sort a pile of text or Markdown notes by comparing their contents to topic descriptions. Users review a plan before one batch action creates organized copies in proposed folders, while uncertain or low-scoring notes go to a review folder. Original files are never removed, and byte-identical duplicates point to a single canonical copy. The classifier uses the all-MiniLM-L6-v2 model, with heuristic thresholds that can still miss mixed-topic notes, and undo moves a completed run into recovery without deleting files. The author notes the system assumes a stable local filesystem and is not designed for hostile concurrent writes.

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ProgrammingDEV Community ·

Explainer Distinguishes LangChain, Generative AI, AI Agents, and Ollama

A DEV Community glossary post clarifies how several AI terms relate to one another. It explains that LangChain is an open-source framework for connecting large language models to external data and tools, rather than an agent itself. Generative AI is described as producing text or code from prompts without taking actions, while AI agents are given goals and can loop through tools to act and correct course. The piece also distinguishes Ollama, which runs models like LLaMA and Qwen locally, as operating at a different layer of the software stack. It closes with an example project using retrieval-augmented generation to answer queries from regulatory circular documents.

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ProgrammingDEV Community ·

Rogue Commit Injected Destructive Prompt into Amazon Q VS Code Extension v1.84.0

A malicious commit pushed on July 13, 2025 by a GitHub account called lkmanka58 inserted a prompt into Amazon Q Developer for VS Code, and the code shipped in version 1.84.0 released July 17. The payload instructed the AI agent to wipe local home directories and delete AWS resources, and it ran only in production builds. AWS attributed the breach to an overly scoped GitHub token in the extension's CodeBuild configuration, tracked as CVE-2025-8217. The attacker claimed to have gained access through a pull request and said they aimed to expose weaknesses in Amazon's AI security, though that claim remains unverified. Version 1.85.0, released July 19, removed the malicious downloader, and AWS issued its bulletin after the story was reported by 404 Media.

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