⚡️ OpenAI cuts prices for GPT-5.6 Luna and Terra models
OpenAI has announced price cuts for its GPT-5.6 model family: the fastest and most affordable Luna is now 80% cheaper, while the balanced Terra is 20% cheaper. As of July 30, API pricing is $0.20 per million input and $1.20 per million output tokens for Luna, and $2 / $12 for Terra; the price of the flagship Sol is unchanged. According to the company, Luna delivers the intelligence of last year's frontier-class models at roughly 6% of the cost per task and nearly nine times faster. At the same time, the API introduces Fast mode, replacing Priority Processing: for GPT-5.6 Sol it delivers up to 2.5× higher speed at twice the price, with no change in quality. The lower prices are also reflected in quota usage in Codex and ChatGPT Work, and will arrive on AWS soon.
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OpenAI has announced price cuts for its GPT-5.6 model family: the fastest and most affordable Luna is now 80% cheaper, while the balanced Terra is 20% cheaper. As of July 30, API pricing is $0.20 per million input and $1.20 per million output tokens for Luna, and $2 / $12 for Terra; the price of the flagship Sol is unchanged. According to the company, Luna delivers the intelligence of last year's frontier-class models at roughly 6% of the cost per task and nearly nine times faster. At the same time, the API introduces Fast mode, replacing Priority Processing: for GPT-5.6 Sol it delivers up to 2.5× higher speed at twice the price, with no change in quality. The lower prices are also reflected in quota usage in Codex and ChatGPT Work, and will arrive on AWS soon.
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OpenAI
Advancing the price-performance frontier with GPT-5.6
Explore lower GPT‑5.6 pricing for Luna and Terra—and how OpenAI’s more efficient models help enterprises deploy AI workflows at scale.
⚡️ AKS without a hypervisor: Microsoft runs Kubernetes straight on bare metal
Microsoft has deployed Azure Kubernetes Service directly on physical hardware — with no hypervisor layer. The new option combines the Azure Local Small Form Factor platform with AKS Arc on Linux and connects automatically to Azure Arc for centralized management. Dropping virtualization dedicates all resources to workloads while keeping the familiar Azure experience — portal, Bicep, policies, monitoring, and GitOps. As Thomas Maurer notes, the solution is built for the edge — retail, manufacturing, field sites — as well as sovereign datacenters and scenarios where maximum performance matters. The same Kubernetes and a single interface now run everywhere, from the cloud to the network edge.
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Microsoft has deployed Azure Kubernetes Service directly on physical hardware — with no hypervisor layer. The new option combines the Azure Local Small Form Factor platform with AKS Arc on Linux and connects automatically to Azure Arc for centralized management. Dropping virtualization dedicates all resources to workloads while keeping the familiar Azure experience — portal, Bicep, policies, monitoring, and GitOps. As Thomas Maurer notes, the solution is built for the edge — retail, manufacturing, field sites — as well as sovereign datacenters and scenarios where maximum performance matters. The same Kubernetes and a single interface now run everywhere, from the cloud to the network edge.
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Thomas Maurer
Azure Kubernetes Service (AKS) on Bare Metal
Many organizations need to run Kubernetes on-premises, at the edge, or in sovereign environments, while still benefiting from the simplicity and consistency of Azure. That's exactly where Azure Kubernetes Service (AKS) on bare metal comes in. In this post…
⚡️ Haiqu applies quantum optimization to wireless network design
Haiqu, a Ukrainian startup building quantum software that makes better use of today's noisy quantum processors, has introduced a new algorithm, DCA, for optimizing wireless networks. It solves the connected dominating set problem — finding the smallest number of relay devices that traffic must pass through to keep an entire mesh, IoT, or ad-hoc network connected. Rather than searching for a perfect solution outright, the algorithm gradually improves an existing candidate. The test ran on the 156-qubit IBM Quantum Kingston processor on a 73-node graph: DCA consistently outperformed the standard LR-QAOA and found a more compact network backbone. Tuning it to the processor's specifics cut the number of complex operations and the computation depth by nearly threefold. The algorithm also reuses tuned parameters from smaller problems on larger ones. The results will be presented at the IEEE ICCCN conference.
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Haiqu, a Ukrainian startup building quantum software that makes better use of today's noisy quantum processors, has introduced a new algorithm, DCA, for optimizing wireless networks. It solves the connected dominating set problem — finding the smallest number of relay devices that traffic must pass through to keep an entire mesh, IoT, or ad-hoc network connected. Rather than searching for a perfect solution outright, the algorithm gradually improves an existing candidate. The test ran on the 156-qubit IBM Quantum Kingston processor on a 73-node graph: DCA consistently outperformed the standard LR-QAOA and found a more compact network backbone. Tuning it to the processor's specifics cut the number of complex operations and the computation depth by nearly threefold. The algorithm also reuses tuned parameters from smaller problems on larger ones. The results will be presented at the IEEE ICCCN conference.
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LinkedIn
#quantumoptimization #wireless #iot #networks #transferlearning | Mykola Maksymenko
The path toward useful quantum computing is not one breakthrough. It is a continuous process of innovation in inventing new algorithms, testing them on real problems, and running them on real quantum hardware.
Our research team at Haiqu is committed to this…
Our research team at Haiqu is committed to this…
⚡️ F# Weekly #31, 2026
A new issue of the F# Weekly digest is out. The spotlight is on Microsoft's announcements for .NET developers: the MCP C# SDK 2.0 release, a new agent for unit-testing polyglot projects, integration of the MSBuild binary log (binlog) analyzer into VS Code, Agent Framework updates for discovering MCP server skills, and the July Visual Studio update with agents powered by the GitHub Copilot SDK. Notable ecosystem projects include MongrelDB-FSharp (a pure-F# client for an embedded database with SQL and vector search), the Axial typed-results library, the reactive Fable.Reactive, and Fable.Giraffe. The issue also covers ten fresh package releases, including Mibo 3.0, plus pieces on Result type design and enterprise AI patterns.
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A new issue of the F# Weekly digest is out. The spotlight is on Microsoft's announcements for .NET developers: the MCP C# SDK 2.0 release, a new agent for unit-testing polyglot projects, integration of the MSBuild binary log (binlog) analyzer into VS Code, Agent Framework updates for discovering MCP server skills, and the July Visual Studio update with agents powered by the GitHub Copilot SDK. Notable ecosystem projects include MongrelDB-FSharp (a pure-F# client for an embedded database with SQL and vector search), the Axial typed-results library, the reactive Fable.Reactive, and Fable.Giraffe. The issue also covers ten fresh package releases, including Mibo 3.0, plus pieces on Result type design and enterprise AI patterns.
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Sergey Tihon's Blog
F# Weekly #31, 2026 — MCP C# SDK v2.0, Unit-Test Agent, and MSBuild Binlog in VS Code
Welcome to F# Weekly, A roundup of F# content from this past week: News What’s new in 2.0 | Elmish Land Microsoft News Announcing v2.0 of the official MCP C# SDK From generated code to truste…
⚡️ Azure now checks your Linux machines' security against the CIS benchmark automatically
Microsoft has added built-in checking of Linux servers against the CIS Benchmark security standards in Azure — and the feature is already generally available. It works in Azure and Azure Arc through Azure Machine Configuration, continuously comparing Linux system settings against the globally recognized CIS requirements. Azure itself finds weak spots in the configuration, flags drift from the desired settings, and produces compliance reports via Azure Policy; rules can be customized and exceptions defined. Previously this required separate tools or manual checks — now it's all built in, making it easier for security teams to tidy things up and demonstrate compliance with regulatory requirements across many servers at once.
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Microsoft has added built-in checking of Linux servers against the CIS Benchmark security standards in Azure — and the feature is already generally available. It works in Azure and Azure Arc through Azure Machine Configuration, continuously comparing Linux system settings against the globally recognized CIS requirements. Azure itself finds weak spots in the configuration, flags drift from the desired settings, and produces compliance reports via Azure Policy; rules can be customized and exceptions defined. Previously this required separate tools or manual checks — now it's all built in, making it easier for security teams to tidy things up and demonstrate compliance with regulatory requirements across many servers at once.
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Petri IT Knowledgebase
Microsoft Brings Native CIS Benchmark Auditing to Linux in Azure
Microsoft adds native CIS Benchmark auditing for Linux in Azure and Azure Arc, helping organizations assess compliance and security posture.
⚡️ An architectural view of ML.NET: machine learning without leaving the .NET ecosystem
Abdul Rahman breaks down the architecture of ML.NET — Microsoft's open-source, cross-platform machine learning framework that lets you train, evaluate, and deploy ML models in familiar C# without a Python runtime or third-party libraries. He suggests splitting the solution into three projects: a trainer (a console app that loads data, trains, and saves the model), a schema library with shared data types, and a client (Web API, Blazor, or Worker) that runs predictions. At its core are MLContext as the entry point (analogous to EF's DbContext), the lazy tabular IDataView abstraction, and pipelines composed via a chainable Append and executed on Fit(), much like LINQ. Trainers are grouped by task type — classification, regression, clustering, anomaly detection, ranking, forecasting. For ASP.NET Core, PredictionEnginePool provides thread-safe predictions and hot model reload.
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Abdul Rahman breaks down the architecture of ML.NET — Microsoft's open-source, cross-platform machine learning framework that lets you train, evaluate, and deploy ML models in familiar C# without a Python runtime or third-party libraries. He suggests splitting the solution into three projects: a trainer (a console app that loads data, trains, and saves the model), a schema library with shared data types, and a client (Web API, Blazor, or Worker) that runs predictions. At its core are MLContext as the entry point (analogous to EF's DbContext), the lazy tabular IDataView abstraction, and pipelines composed via a chainable Append and executed on Fit(), much like LINQ. Trainers are grouped by task type — classification, regression, clustering, anomaly detection, ranking, forecasting. For ASP.NET Core, PredictionEnginePool provides thread-safe predictions and hot model reload.
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I Love DotNet
An Architectural View of ML.NET
In this post I will teach you the ML.NET architecture, how its learning pipeline works, and how to build machine learning models natively in .NET without Python.
⚡️ Modern authentication in ASP.NET Core: 2FA and passkeys
On the elmah.io blog, Ali Hamza Ansari walks through implementing two modern security mechanisms in ASP.NET Core on .NET 10: two-factor authentication (2FA) and passkey login built on the WebAuthn/FIDO2 standards. For 2FA, he covers registration and login, QR code generation for authenticator apps, TOTP one-time-code verification, recovery codes, and account lockout after failed attempts. The passkey section covers credential registration (attestation) and passwordless login (assertion) with biometric or PIN verification, plus key storage and signature-counter validation. It's built on ASP.NET Core Identity, Entity Framework Core, the Fido2NetLib package, and PostgreSQL. The approach markedly reduces risk compared to plain passwords while making login easier for users.
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On the elmah.io blog, Ali Hamza Ansari walks through implementing two modern security mechanisms in ASP.NET Core on .NET 10: two-factor authentication (2FA) and passkey login built on the WebAuthn/FIDO2 standards. For 2FA, he covers registration and login, QR code generation for authenticator apps, TOTP one-time-code verification, recovery codes, and account lockout after failed attempts. The passkey section covers credential registration (attestation) and passwordless login (assertion) with biometric or PIN verification, plus key storage and signature-counter validation. It's built on ASP.NET Core Identity, Entity Framework Core, the Fido2NetLib package, and PostgreSQL. The approach markedly reduces risk compared to plain passwords while making login easier for users.
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elmah.io Blog - .NET Technical tutorials/guides and new features
Modern authentication in ASP.NET Core with 2FA and passkey
This walkthrough builds a .NET 10 ASP.NET Core Web API (TwoFaNET10) implementing two-factor authentication and WebAuthn/FIDO2 passkey login, using Microsoft.AspNetCore.Identity.EntityFrameworkCore with Npgsql PostgreSQL, JWT bearer tokens, and a custom ApplicationUser…
⚡️ Azure CTO runs DOOM inside Microsoft Paint — 35 FPS and "undo death" via Ctrl+Z
Mark Russinovich, CTO of Microsoft Azure, spent his spare time getting classic DOOM to run inside Microsoft Paint and published the source of the DoomPaint project on GitHub. The project delivers 35 frames per second at 320×200 or 640×400. A standout feature is "undo death": with Ctrl+Z you can rewind frames and effectively turn back time after your character dies. The experiment continues the community's long tradition of running DOOM on anything — from test strips and calculators to cash registers and refrigerators; now plain old Paint joins that list.
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Mark Russinovich, CTO of Microsoft Azure, spent his spare time getting classic DOOM to run inside Microsoft Paint and published the source of the DoomPaint project on GitHub. The project delivers 35 frames per second at 320×200 or 640×400. A standout feature is "undo death": with Ctrl+Z you can rewind frames and effectively turn back time after your character dies. The experiment continues the community's long tradition of running DOOM on anything — from test strips and calculators to cash registers and refrigerators; now plain old Paint joins that list.
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IXBT.games
DOOM launched in Paint - at 35 FPS and with the ability to undo death
From a technical standpoint, Paint merely acts as a display.
⚡️ F# Weekly #32, 2026
A new issue of the F# Weekly digest is out. The week's main releases are the FSharp.Data 8.2.0 data-access library and the Mibo 4.0 game framework, whose notably faster PBR pipeline for 3D rendering reaches GPU-bound performance across the raylib and MonoGame backends. The issue also covers fresh updates to FSharp.TypeProviders.Templates 8.11, SQLProvider 1.5.27, SwaggerProvider, linq2db.FSharp, and Polars.FSharp. Notable projects include Plinth (a local-first markdown notebook built with F# and Tauri), Thuja (a minimalist library for terminal UIs), and FsLangMCP (an MCP server that exposes F# compiler services to AI agents). A key theme of the issue is the growth of the F# MCP ecosystem for AI workflows. From Microsoft: NuGet security enhancements, testing-platform improvements, and Agent Framework updates.
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A new issue of the F# Weekly digest is out. The week's main releases are the FSharp.Data 8.2.0 data-access library and the Mibo 4.0 game framework, whose notably faster PBR pipeline for 3D rendering reaches GPU-bound performance across the raylib and MonoGame backends. The issue also covers fresh updates to FSharp.TypeProviders.Templates 8.11, SQLProvider 1.5.27, SwaggerProvider, linq2db.FSharp, and Polars.FSharp. Notable projects include Plinth (a local-first markdown notebook built with F# and Tauri), Thuja (a minimalist library for terminal UIs), and FsLangMCP (an MCP server that exposes F# compiler services to AI agents). A key theme of the issue is the growth of the F# MCP ecosystem for AI workflows. From Microsoft: NuGet security enhancements, testing-platform improvements, and Agent Framework updates.
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Sergey Tihon's Blog
F# Weekly #32, 2026 — FSharp.Data 8.2.0, Mibo 4.0, and F# MCP Ecosystem Grows
Welcome to F# Weekly, A roundup of F# content from this past week: Microsoft News Strengthening NuGet Supply Chain Security: Reducing API Key Lifetime Test reporting in Microsoft.Testing.Platform: …
⚡️ Six performance improvements in .NET 11
Steven Giesel breaks down six notable performance improvements in .NET 11, with benchmarks of .NET 10 vs .NET 11 Preview 6 on an Apple M2 Pro. They include: eliminating boxing in generic enum equality checks — up to 86% faster with zero allocations; removing redundant range and null checks in vector operations (~10%); timezone-rule caching that speeds up DateTime.Now by 48%; Guid parsing optimized by 20–25%; faster LINQ Min/Max on small types via vector shuffling (up to 70–75% on bytes and shorts); and overall reductions in generated code size. The key takeaway: the gains come automatically — just upgrade, with no changes to your own code.
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Steven Giesel breaks down six notable performance improvements in .NET 11, with benchmarks of .NET 10 vs .NET 11 Preview 6 on an Apple M2 Pro. They include: eliminating boxing in generic enum equality checks — up to 86% faster with zero allocations; removing redundant range and null checks in vector operations (~10%); timezone-rule caching that speeds up DateTime.Now by 48%; Guid parsing optimized by 20–25%; faster LINQ Min/Max on small types via vector shuffling (up to 70–75% on bytes and shorts); and overall reductions in generated code size. The key takeaway: the gains come automatically — just upgrade, with no changes to your own code.
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Steven-Giesel
.NET 11 Performance Edition
As always my yearly take on the latest .NET release. In a few months dotnet 11 will be released and I wanted to have a look into some performance metrics.
⚡️ F# Weekly #33, 2026
A new issue of the F# Weekly digest is out. The week's main event is the release of .NET 11 Preview 7 with F# language updates. Also in focus is PaketaBot — a dependency-management bot for Paket projects: written in F#, compiled with Fable, and running directly in GitHub Actions with no hosting required, filling the gap where Dependabot lacks Paket support. Other notable projects include fsdb (a MySQL-compatible database server with wire-protocol support and an FParsec SQL grammar), toro (a deep-learning framework bringing PyTorch semantics to F# via TorchSharp), Farse (JSON parsing on System.Text.Json), and Falco.UnionRoutes (type-safe routing for the Falco web framework). The issue also covers fresh library releases: Ionide.Analyzers 0.16, FSharpLint.Core 0.27.1, and Fable.Logging 1.0.
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A new issue of the F# Weekly digest is out. The week's main event is the release of .NET 11 Preview 7 with F# language updates. Also in focus is PaketaBot — a dependency-management bot for Paket projects: written in F#, compiled with Fable, and running directly in GitHub Actions with no hosting required, filling the gap where Dependabot lacks Paket support. Other notable projects include fsdb (a MySQL-compatible database server with wire-protocol support and an FParsec SQL grammar), toro (a deep-learning framework bringing PyTorch semantics to F# via TorchSharp), Farse (JSON parsing on System.Text.Json), and Falco.UnionRoutes (type-safe routing for the Falco web framework). The issue also covers fresh library releases: Ionide.Analyzers 0.16, FSharpLint.Core 0.27.1, and Fable.Logging 1.0.
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Sergey Tihon's Blog
F# Weekly #33, 2026 — .NET 11 Preview 7 Ships with F# Updates and PaketaBot
Welcome to F# Weekly, A roundup of F# content from this past week: News F# in .NET 11 Preview 7 — Release Notes Core Framework v3 4.0.0 [2026 August 14] | xUnit.net Microsoft News .NET 11 Preview 7…
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⚡️ How agent memory works in the Microsoft Agent Framework
Jesse Liberty explains how AI agents in .NET remember what happens to them. Memory is handled by Agent Memory, built on the Neo4j graph database, and it works at three levels: short-term memory holds the current conversation, long-term memory stores accumulated knowledge and the relationships between facts, and "reasoning memory" keeps the agent's past actions and decisions. The author highlights bitemporality in particular: the system records not only when a fact occurred, but also when it recorded that fact. The article also walks through code: connecting to Neo4j, creating a MemoryStore, and the StoreMemory() and RetrieveMemory() methods. This lets an agent keep context across sessions.
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Jesse Liberty explains how AI agents in .NET remember what happens to them. Memory is handled by Agent Memory, built on the Neo4j graph database, and it works at three levels: short-term memory holds the current conversation, long-term memory stores accumulated knowledge and the relationships between facts, and "reasoning memory" keeps the agent's past actions and decisions. The author highlights bitemporality in particular: the system records not only when a fact occurred, but also when it recorded that fact. The article also walks through code: connecting to Neo4j, creating a MemoryStore, and the StoreMemory() and RetrieveMemory() methods. This lets an agent keep context across sessions.
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⚡️ An overview of quantum computing programming languages and frameworks in 2026
The Quantum Insider has published an overview of the quantum programming ecosystem in 2026. The author explains how quantum development fundamentally differs from classical: algorithms are probabilistic, measurement collapses the state and prevents free debugging, and today's processors are limited to 50–300 qubits. The overview covers the major frameworks — IBM's Qiskit as the most widely used, Google's Cirq, PennyLane for quantum machine learning, Microsoft's Q# for Azure Quantum, NVIDIA's CUDA-Q, the Amazon Braket SDK, and others. Key trends include the dominance of Python as the common language, senior developers working across several frameworks at once, hardware abstraction layers, and GPU acceleration. The overall takeaway: most real-world applications today are hybrid, combining classical and quantum computing, while the software stack itself is maturing from experiments to practice.
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The Quantum Insider has published an overview of the quantum programming ecosystem in 2026. The author explains how quantum development fundamentally differs from classical: algorithms are probabilistic, measurement collapses the state and prevents free debugging, and today's processors are limited to 50–300 qubits. The overview covers the major frameworks — IBM's Qiskit as the most widely used, Google's Cirq, PennyLane for quantum machine learning, Microsoft's Q# for Azure Quantum, NVIDIA's CUDA-Q, the Amazon Braket SDK, and others. Key trends include the dominance of Python as the common language, senior developers working across several frameworks at once, hardware abstraction layers, and GPU acceleration. The overall takeaway: most real-world applications today are hybrid, combining classical and quantum computing, while the software stack itself is maturing from experiments to practice.
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The Quantum Insider
Top Quantum Programming Languages and Frameworks in 2026
A look at the quantum software tools developers use in 2026, from quantum SDKs and compilers to hardware-specific frameworks.
⚡️ Building a distributed job scheduler with Akka.NET
Aaron Stannard of Petabridge describes how to build a distributed scheduler for long-running jobs with Akka.NET — for cases where jobs run from minutes to hours and the cluster must scale without losing work already done. He explains why plain Akka.Cluster.Sharding doesn't fit here: when the cluster resizes, it moves jobs to new nodes and throws away work already completed. Instead, the system is built on several actors, where the main one, JobTracker, keeps the schedule and survives crashes. The key idea is to distribute jobs by nodes' actual load: the scheduler estimates a job's "size" and sends it to the least busy node, avoiding overloading individual machines. This approach wastes no computation and fits well for transcription, transcoding, ETL, and financial reporting.
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Aaron Stannard of Petabridge describes how to build a distributed scheduler for long-running jobs with Akka.NET — for cases where jobs run from minutes to hours and the cluster must scale without losing work already done. He explains why plain Akka.Cluster.Sharding doesn't fit here: when the cluster resizes, it moves jobs to new nodes and throws away work already completed. Instead, the system is built on several actors, where the main one, JobTracker, keeps the schedule and survives crashes. The key idea is to distribute jobs by nodes' actual load: the scheduler estimates a job's "size" and sends it to the least busy node, avoiding overloading individual machines. This approach wastes no computation and fits well for transcription, transcoding, ETL, and financial reporting.
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Petabridge
Building a Distributed Job Scheduler with Akka.NET
⚡️ AWS Control Tower vs Azure Landing Zone vs Google Cloud: comparing cloud governance approaches in 2026
Tech Insider compares the three reference architectures for governing multi-account cloud environments in 2026 — AWS Control Tower, Azure Landing Zone built on the Enterprise-Scale accelerator, and Google Cloud's approach based on Organization Policies and Terraform. None of them charges a license fee, yet the real bills differ dramatically. The key takeaway: "free" governance hides the cost of infrastructure — a minimal Azure network hub with Azure Firewall Standard runs about $913 a month before any workloads, while a comparable 10-account setup costs around $150–400 on AWS and $100–300 on Google Cloud. The author also breaks down the guardrail differences: AWS has 750+ prebuilt controls, Azure bets on composable policies via Azure Policy, and Google relies on strict Organization Policies plus detection through Security Command Center. Deployment time, IaC integration, and account structure are compared as well.
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Tech Insider compares the three reference architectures for governing multi-account cloud environments in 2026 — AWS Control Tower, Azure Landing Zone built on the Enterprise-Scale accelerator, and Google Cloud's approach based on Organization Policies and Terraform. None of them charges a license fee, yet the real bills differ dramatically. The key takeaway: "free" governance hides the cost of infrastructure — a minimal Azure network hub with Azure Firewall Standard runs about $913 a month before any workloads, while a comparable 10-account setup costs around $150–400 on AWS and $100–300 on Google Cloud. The author also breaks down the guardrail differences: AWS has 750+ prebuilt controls, Azure bets on composable policies via Azure Policy, and Google relies on strict Organization Policies plus detection through Security Command Center. Deployment time, IaC integration, and account structure are compared as well.
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Tech Insider
AWS Control Tower vs Azure Landing Zone vs GCP 2026
AWS Control Tower vs Azure Landing Zone vs Google Cloud landing zones compared: pricing, guardrails, deployment time, and IaC support for 2026.