In this post, you will learn a fundamental programming pattern in low-latency, high-performance engineering known as the Single Producer Single Consumer Atomic Ring Buffer.

But first , why do we even need one ? The "obvious" way to share data between two threads in Rust is with a Mutex. 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>use std::sync::{Arc, Mutex};
use std::collections::VecDeque;

let queue = Arc::new(Mutex::new(VecDeque::new()));

</code></pre>

</div>



The problem is contention. Every time the producer wants to send, it has to lock the mutex. If the consumer is currently holding that lock, the producer has to wait. This is a "stop-the-world" event that kills low-latency performance.

We can do better. We can build a "lock-free" queue. To do this, we'll build it from scratch using atomics.

The Blueprint: The SPSC Ring Buffer.

Our data structure looks like this 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>pub struct RingBuffer&lt;T&gt; {
 buffer: Box&lt;[UnsafeCell&lt;MaybeUninit&lt;T&gt;&gt;]&gt;,
 cap: usize,
 head: CachePadded&lt;AtomicUsize&gt;,
 tail: CachePadded&lt;AtomicUsize&gt;,
}

</code></pre>

</div>



The core idea is a circular buffer (a ring) with two "pointers":

head: The "write" pointer. Only the producer can touch this.

tail: The "read" pointer. Only the consumer can touch this.

This SPSC model is the key: by giving head and tail exclusive "owners", we've already eliminated the main source of contention!

But the design creates two new and very subtle problems.

Dragon #1: The "False Sharing" Performance Disaster

You probably noticed the CachePadded type. This is the single most important performance optimisation in the whole struct.

What is "False Sharing" ?

A CPU doesn't read from RAM byte-by-byte. It reads in 64-byte (or 128-byte) chunks called cache lines.

Analogy: Your CPU is a worker at a workbench (L1 Cache). RAM is a giant warehouse. The worker doesn't walk to the warehouse to get one screw (head). It grabs the entire toolbox drawer (the 64-byte cache line) that the screw is in.

The disaster: 
Your head (8 bytes) and tail (8 bytes) are small. Without padding, the OS will put them right next to each other in memory, almost certainly on the same cache line.

Now watch what happens:

Producer (Core 1) wants to write to head. It grabs the whole cache line into its L1 cache.

Consumer (Core 2) wants to write to tail. It grabs the same whole cache line into its L1 cache.

Producer writes to head. This "dirties" its cache line and invalidates the cache line on Core 2.

Consumer now has to stall, wait, and re-fetch the entire cache line from main memory, just to write to tail.

This "cache-line ping-pong" makes your "lock-free" queue slower than a Mutex.

The solution:

CachePadded adds ~56 bytes of empty padding around head and tail, forcing them to live on different cache lines. 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>Cache Line 1: [ head (8b) | ... 56 bytes of padding ... ]
Cache Line 2: [ tail (8b) | ... 56 bytes of padding ... ]

</code></pre>

</div>



Now, the producer and consumer can work on head and tail in parallel with zero hardware-level contention. We've solved the "false sharing" problem.

Dragon #2: The "Lying" CPU (Memory Reordering)

The second, and more dangerous, challenge is memory ordering.

The compiler and the CPU are both allowed to re-order your instructions to make them run faster. This is usually fine, but in concurrent code, it's a nightmare.

Consider our send code. Logically, it must do this: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>buffer[idx] = data; // Write the data

self.head.store(head + 1); // "Publish" that data is ready

</code></pre>

</div>



The Nightmare Reordering:

What if the CPU reorders these steps? 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>self.head.store(head + 1); // Publish!

Consumer (Core 2) wakes up, sees the new head!

Consumer reads from buffer[idx]... and gets garbage data!

buffer[idx] = data; // ...Producer writes the real data, too late.

</code></pre>

</div>



The solution: The Acquire/Release Handshake

We fix this by telling the CPU "NO, you cannot reorder these things." We use std::sync::atomic::Ordering.

Release (A command): "Do not reorder any writes from before this point to after this point."

Acquire (A promise): "If I see a new value, I am guaranteed to also see all memory writes that came before the Release that published it."

We create a "synchronises with" relationship.

Producer's send:

<ol>
<li>Write the data
</li>
</ol>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>unsafe { (*slot_ptr).write(item); }

</code></pre>

</div>



<ol>
<li>Publish the write using <code>Release</code>
</li>
</ol>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>self.head.store(head.wrapping_add(1), Ordering::Release);

</code></pre>

</div>



Consumer's recv:

<ol>
<li>Check for new data using <code>Acquire</code>
</li>
</ol>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>let head = self.head.load(Ordering::Acquire);
</code></pre>

</div>



<ol>
<li>If head &gt; tail, we are guaranteed to see the data
</li>
</ol>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>let item = unsafe { (*slot_ptr).assume_init_read() };
</code></pre>

</div>



This Release-Acquire pair creates a "one-way wall," ensuring the consumer never reads data before the producer has written it.

(We do the exact same thing in reverse for the tail pointer to tell the producer that a slot has been freed!)

With the two dragons slain, the rest is just "good engineering."

The &amp; Trick (Power-of-2):

We force the buffer's capacity to be a power of 2 (e.g., 1024). Why?

Slow: head % 1024 (Modulo/division is very slow)

Fast: head &amp; (1024 - 1) (Bitwise-AND is lightning fast)

This cap.next_power_of_two() trick replaces a slow arithmetic operation with a single, fast bitwise operation.

UnsafeCell &amp; MaybeUninit:

UnsafeCell: This is Rust's "interior mutability" escape hatch. It's what lets us write to the buffer from an immutable &amp;self reference.

MaybeUninit: We use this to avoid the overhead of Option. It allows us to treat the buffer as raw, uninitialized memory and safely write Ts into it and assume_init_read() them back out.

The Drop Trait: 
We must implement Drop to manually "drop" any Ts left in the buffer when the RingBuffer goes out of scope. This prevents memory leaks.

The Final Code &amp; Proof

Here is the link to the full, commented source code on Gist/GitHub.

<a href="https://github.com/aodr3w/llt-rs/blob/main/src/ring_buffer" rel="noopener noreferrer">https://github.com/aodr3w/llt-rs/blob/main/src/ring_buffer</a>

And does it work? Yes. We can run cargo test and see our multi-threaded test pass, transferring 1,000,000 items between two threads with zero locks, zero data-races, and maximum performance. 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>$ cargo test
...
running 4 tests
test tests::test_full_and_empty ... ok
test tests::test_single_thread_send_recv ... ok
test tests::test_drop_cleanup ... ok
test tests::test_multi_thread_spsc ... ok

test result: ok. 4 passed; 0 failed; ...

</code></pre>

</div>



What's Next?

We've built a blazing-fast, non-blocking queue. But what if the queue is empty? Our recv just returns None, forcing the consumer to spin in a hot loop.

In the next post, we'll use this RingBuffer primitive to build a higher-level, blocking SPSC Channel that can efficiently put a thread to sleep with Condvar when there's no work to do.

See you then :-)

يتناول هذا المقال حاوية الحلقة الذرية ذات المنتج الواحد والمستهلك الواحد، وهو نمط برمجي يُستخدم في الهندسة ذات الأداء العالي والكمون المنخفض. يسلط الضوء على قيود استخدام القفل (Mutex) لمشاركة البيانات بين الخيوط في لغة Rust، وخاصة التنافس الذي يمكن أن يحدث عندما يحتفظ خيط واحد بالقفل. الحل المقترح هو إنشاء قائمة انتظار بدون قفل باستخدام العمليات الذرية، مما يمكن أن يعزز الأداء من خلال تجنب التأخيرات المرتبطة بقفل القفل.

Este artículo discute el Buffer Circular Atómico de Productor Único y Consumidor Único, un patrón de programación utilizado en ingeniería de alto rendimiento y baja latencia. Destaca las limitaciones del uso de Mutex para compartir datos entre hilos en Rust, especialmente la contención que puede ocurrir cuando un hilo mantiene el bloqueo. La solución propuesta es crear una cola sin bloqueo utilizando operaciones atómicas, lo que puede mejorar el rendimiento al evitar los retrasos asociados con el bloqueo de mutex.

Cet article traite du tampon circulaire atomique à producteur unique et consommateur unique, un modèle de programmation utilisé dans l'ingénierie à faible latence et haute performance. Il met en évidence les limites de l'utilisation de Mutex pour le partage de données entre les threads en Rust, en particulier la contention qui peut se produire lorsqu'un thread détient le verrou. La solution proposée consiste à créer une file d'attente sans verrou à l'aide d'opérations atomiques, ce qui peut améliorer les performances en évitant les délais associés au verrouillage de mutex.

This article discusses the Single Producer Single Consumer Atomic Ring Buffer, a programming pattern used in low-latency, high-performance engineering. It highlights the limitations of using Mutex for data sharing between threads in Rust, particularly the contention that can occur when one thread holds the lock. The proposed solution is to create a lock-free queue using atomic operations, which can enhance performance by avoiding the delays associated with mutex locking.

Low Latency Rust : Building a Cache-Friendly, Lock-Free SPSC Ring Buffer in Rust

<A HREF="https://fortune.com/2025/11/19/exclusive-doppel-raises-70-million-series-c-at-more-than-600-million-valuation-to-fight-ai-powered-social-engineering-attacks/"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251119/i39.jpg"></A>
<A HREF="http://www.techmeme.com/251119/p39#a251119p39" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Allie Garfinkle / <A HREF="http://www.fortune.com/">Fortune</A>: 
<A HREF="https://fortune.com/2025/11/19/exclusive-doppel-raises-70-million-series-c-at-more-than-600-million-valuation-to-fight-ai-powered-social-engineering-attacks/">Doppel, which makes an AI social engineering detection service, raised a $70M Series C led by Bessemer at a $600M+ valuation, up from $205M in May</A>&nbsp; &mdash;&nbsp; Senior Finance Reporter And Author Of Term Sheet&nbsp; &mdash;&nbsp; I tried to fool my brother, sort of.&nbsp; &mdash;&nbsp; Next to him and his Pekingese on the couch &hellip;

دوبل، وهي شركة متخصصة في خدمات الكشف عن الهندسة الاجتماعية باستخدام الذكاء الاصطناعي، نجحت في جمع 70 مليون دولار في جولة تمويل من السلسلة C بقيادة بيسمر. وقد زادت هذه الجولة من تقييم الشركة إلى أكثر من 600 مليون دولار، ارتفاعًا من 205 مليون دولار في مايو.

Doppel, una empresa que se especializa en servicios de detección de ingeniería social mediante IA, ha recaudado con éxito 70 millones de dólares en una ronda de financiación Serie C liderada por Bessemer. Esta ronda ha elevado la valoración de la empresa a más de 600 millones de dólares, un aumento significativo desde los 205 millones en mayo.

Doppel, une entreprise spécialisée dans les services de détection d'ingénierie sociale par IA, a levé avec succès 70 millions de dollars lors d'un tour de financement de série C dirigé par Bessemer. Ce tour de financement a porté la valorisation de l'entreprise à plus de 600 millions de dollars, contre 205 millions en mai.

Doppel, a company specializing in AI social engineering detection services, has successfully raised $70 million in a Series C funding round led by Bessemer. This funding round has increased the company's valuation to over $600 million, a significant rise from $205 million in May.

Doppel, which makes an AI social engineering detection service, raised a $70M Series C led by Bessemer at a $600M+ valuation, up from $205M in May (Allie Garfinkle/Fortune)

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The agent offers one-click buying for all your holiday needs and will be free for all US-based users.

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<a href="https://fetch.ai/">Fetch AI</a>, a startup founded and led by former DeepMind founding investor, Humayun Sheikh, <a href="https://www.businesswire.com/news/home/20251119088395/en/Fetch-Combines-Personalized-AI-with-Multi-Agent-Collaboration-to-Handle-Complex-Consumer-Tasks-Launches-Claim-Your-Agent-to-Fight-Brand-Knock-Offs">today announced the release</a> of three interconnected products designed to provide the trust, coordination, and interoperability needed for large-scale AI agent ecosystems. The launch includes <a href="https://asi1.ai/">ASI:One</a>, a personal-AI orchestration platform; <a href="https://business.fetch.ai/">Fetch Business</a>, a verification and discovery portal for brand agents; and <a href="https://agentverse.ai/?sort=relevancy&amp;page=1&amp;recommended=true">Agentverse</a>, an open directory hosting more than two million agents. Together, the system positions Fetch as an infrastructure provider for what it calls the “Agentic Web”—a layer where consumer AIs and brand AIs collaborate to complete tasks instead of merely suggesting them.The company says the tools address a central limitation in current consumer AI: models can provide recommendations but cannot reliably execute multi-step actions that require coordination across businesses. Fetch’s approach centers on enabling agents from different organizations to interoperate securely, using verified identities and shared context to complete end-to-end workflows.“We’re creating the same foundation for agents that Google created for websites,” said Humayun Sheikh, Founder and CEO of Fetch AI, and an early investor in DeepMind, in a press release provided to VentureBeat. “Instead of just finding information, your personal AI coordinates with verified brand agents to get things done.”<h2>Background: Fetch’s Founding and DeepMind Connection </h2>Fetch AI was founded in 2017 by Humayun Sheikh, an entrepreneur whose early investment in DeepMind helped support the company’s commercial development before its acquisition by Google. “I was one of the first five people at DeepMind and its first investor. My check was the first one in,” Sheikh said, reflecting on the period when advanced machine learning research was still largely inaccessible outside major technology companies.His early experience helped shape Fetch’s direction. “Even in 2013, it was clear to me that agentic systems were going to be the ones that worked. That’s where I focused—on the agentic web,” Sheikh noted. Fetch built on this thesis by developing infrastructure for autonomous software agents, focusing on verifiable identity, secure data exchange, and multi-agent coordination. Over the past several years, the company has expanded to a 70-person team across Cambridge and Menlo Park, raised approximately $60 million, and accumulated more than one million users interacting with its model—data that informed the design of the newly launched products.Sheikh added that his decision to bootstrap the company initially came directly from the proceeds of the DeepMind exit, noting in the interview that while the sale to Google was “a good exit,” he believed the team could have held out for a higher valuation. The early self-funding period allowed Fetch to begin work in 2015—well before transformer architectures went mainstream—on the hypothesis that agentic infrastructure would become foundational to applied AI.<h2>ASI:One — A Platform for Multi-Agent Orchestration</h2>At the core of the launch is ASI:One, a language model interface designed specifically for coordinating multiple agents rather than addressing isolated queries. Fetch describes it as an “intelligence layer” that handles context sharing, task routing, and preference modeling.The system stores user-level signals such as favored airlines, dietary constraints, budget ranges, loyalty program identifiers, and calendar availability. When a user requests a complex task—such as planning a trip with flights, hotels, and restaurant reservations—ASI:One retrieves those preferences and delegates work to the appropriate verified agents. The agents then return actionable outputs, including inventory and booking options, rather than generic recommendations.In practice, ASI:One functions as a workflow generator across organizational boundaries. By contrast with conventional LLM applications, which often rely on APIs or RAG techniques to surface information, ASI:One is built to coordinate autonomous agents that can complete transactions. Fetch notes that personalization improves over time as the model accumulates structured preference data.Sheikh emphasized the distinction between orchestrated execution and traditional AI output. “This isn’t searching for options separately and hoping they work together,” he said. “It’s orchestration.” He added that Fetch’s architecture is intentionally modular: “Our architecture is a mix of agentic and expert models. One large model isn’t enough—you need specialists. That’s why we built ASI1, tuned specifically for agentic systems.”The interview also revealed new details about ASI:One’s personalization systems: the platform uses multiple user-owned knowledge graphs to store preferences, travel history, social connections, and contextual constraints. These knowledge graphs are siloed per user and not co-mingled with any Fetch-operated data. Sheikh described this as a “deterministic backbone” that gives the personal AI a stable memory layer beyond the probabilistic output of a single large model.ASI:One launches in Beta today, with a broader release planned for early 2026. Fetch also offers ASI:One Mobile, released earlier this year, giving users access to the same agent-orchestration capabilities on iOS and Android. The mobile app connects directly to Agentverse and the user’s knowledge graphs, enabling on-the-go task execution and real-time interaction with registered agents.<h2>Fetch Business — Verified Identity and Brand Control</h2>To enable reliable coordination between consumers and companies, Fetch is introducing a verification and discovery portal called Fetch Business. The platform allows organizations to verify their identity and claim an official Brand Agent handle—for example, @Hilton or @Nike—regardless of which tools they use to build the underlying agent.Fetch positions the product as an analogue to ICANN domain registration and SSL certificate systems for websites. Verified status is intended to protect consumers from interacting with counterfeit or untrusted agents, a problem the company describes as a major barrier to widespread agent adoption.The system includes low-code tools for small businesses to create agents in a few steps and connect real-time APIs such as inventory, booking systems, or CRM platforms. “With Fetch, you can create an agent in one minute. It gets a handle, like a Twitter username, and you can personalize it completely—even give it your social media permissions to post on your behalf,” Sheikh said. Once a brand claims its namespace, its agent becomes discoverable to consumer AIs and other agents inside Agentverse.The company has pre-reserved thousands of brand namespaces in anticipation of demand. Verification status persists across any platform that integrates with Agentverse, creating a portable identity layer for business agents.The interview highlighted that Fetch Business inherits web-trust primitives directly: domain owners verify their identity by inserting a short code snippet into their existing website backend, allowing the system to pass a cryptographic challenge and grant the agent an authenticity badge similar to a “blue check” for agent identities. Sheikh framed this as “reusing the trust layer the web already spent decades building.”Companies can begin claiming agents now at <a href="https://business.fetch.ai/">business.fetch.ai</a>.<h2>Agentverse — An Open Directory of More Than Two Million Agents</h2>The final component of the release is <a href="https://agentverse.ai/">Agentverse</a>, an open directory and cloud platform that hosts agents and enables cross-ecosystem discoverability. Fetch states that millions of agents have already registered, spanning travel, retail, entertainment, food service, and enterprise categories.Agentverse provides metadata, capability descriptions, and routing logic that ASI:One uses to identify appropriate agents for specific tasks. It also supports secure communication and data exchange between agents. The company notes that the directory is platform-agnostic: agents built with any framework can join and interoperate.According to Sheikh, the lack of a discovery layer is one reason most AI agents see little or no usage. “Ninety percent of AI agents never get used because there’s no discovery layer,” he said. He framed the role of Agentverse in more technical terms: “Right now, if you build an agent, there’s no universal way for others to discover it. That’s what AgentVerse solves—it’s like DNS for agents.” He also described the system as an essential component of the emerging agent economy: “Fetch is building the Google of agents. Just like websites needed search, agents need discovery, trust, and interaction—Fetch provides all of that.”The interview further underscored that Agentverse is cloud-agnostic by design. Sheikh contrasted this with competing agent ecosystems tied to specific cloud providers, arguing that a universal registry is only viable if independent of proprietary cloud environments. He said the open architecture enables an LLM to query any agent “within one minute of deployment,” turning agent publication into a near-instantaneous process similar to registering a domain.Agentverse also integrates payment pathways, enabling agents to execute purchases using partners such as Visa, Skyfire, and supported stablecoins. Consumers can configure spending limits or require explicit approval for transactions.<h2>Industry Context and Implications</h2>Fetch’s launch comes at a time when consumer AI platforms are exploring the shift from static chat interfaces toward autonomous agents capable of completing actions. However, most agent systems remain limited by siloed architectures, limited interoperability, and weak verification standards.Fetch positions its infrastructure as a response to these limitations by providing a cross-platform coordination layer, identity system, and directory service. The company argues that an agent ecosystem requires consistent verification mechanisms to ensure that consumers interact with authentic brand representatives rather than imitations. By establishing namespace control and portable trust indicators, Fetch Business aims to fill a gap similar to early web domain verification.At the same time, ASI:One attempts to centralize user preference data in a way that enables more efficient personalization and multi-agent coordination. This approach differs from generalist LLM applications, which often lack persistent preference architectures or direct access to brand-controlled agents.The interview also made clear that micropayments and digital transaction infrastructure are central to Fetch’s long-term vision. Sheikh referenced integrations with protocols such as Coinbase’s 402 and AP2, positioning these capabilities as essential for autonomous agents to complete end-to-end tasks that include financial execution.<h2>Takeaway</h2>Fetch’s combined release of ASI:One, Fetch Business, and Agentverse introduces an interconnected stack designed to support large-scale deployment and usage of AI agents. The company frames the system as foundational infrastructure for an agentic ecosystem, where consumer AIs can coordinate with verified brand agents to complete tasks reliably and securely. The additions to its identity, discovery, and orchestration layers reflect Fetch’s long-standing thesis—rooted partly in lessons from DeepMind’s early development—that intelligence becomes meaningful only when paired with the capacity to act.

أطلقت شركة Fetch AI، وهي شركة ناشئة يقودها هميون شيخ، ثلاثة منتجات مترابطة تهدف إلى تعزيز نظام وكلاء الذكاء الاصطناعي. تشمل العروض الجديدة ASI:One، وهي منصة تنسيق ذكاء اصطناعي شخصي، وFetch Business، وهو بوابة للتحقق والاكتشاف لوكلاء العلامات التجارية، وAgentverse، وهو دليل مفتوح يضم أكثر من مليوني وكيل. تسعى هذه المبادرة إلى إنشاء بنية تحتية قوية لما تصفه Fetch بـ 'الويب الوكالي.'

Fetch AI, una startup dirigida por Humayun Sheikh, ha lanzado tres productos interconectados destinados a mejorar el ecosistema de agentes de IA. Las nuevas ofertas incluyen ASI:One, una plataforma de orquestación de IA personal, Fetch Business, un portal de verificación y descubrimiento para agentes de marca, y Agentverse, un directorio abierto con más de dos millones de agentes. Esta iniciativa busca establecer una infraestructura sólida para lo que Fetch describe como la 'Web Agente.'

Fetch AI, une startup dirigée par Humayun Sheikh, a lancé trois produits interconnectés visant à améliorer l'écosystème des agents IA. Les nouvelles offres comprennent ASI:One, une plateforme d'orchestration d'IA personnelle, Fetch Business, un portail de vérification et de découverte pour les agents de marque, et Agentverse, un annuaire ouvert avec plus de deux millions d'agents. Cette initiative vise à établir une infrastructure robuste pour ce que Fetch décrit comme le 'Web Agentique.'

Fetch AI, a startup led by Humayun Sheikh, has launched three interconnected products aimed at enhancing the ecosystem of AI agents. The new offerings include ASI:One, a personal-AI orchestration platform, Fetch Business, a verification and discovery portal for brand agents, and Agentverse, an open directory with over two million agents. This initiative seeks to establish a robust infrastructure for what Fetch describes as the 'Agentic Web.'

The Google Search of AI agents? Fetch launches ASI:One and Business tier for new era of non-human web

If aesthetics and efficiency top your list of needs, there are several Linux distributions that are right up your alley. Both Ubuntu Budgie and Pop!_OS should top that list.

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SQL Server 2025 has been <a href="https://techcommunity.microsoft.com/blog/sqlserver/sql-server-2025-is-now-generally-available/4470570" rel="noopener noreferrer">officially announced</a> at Microsoft Ignite 2025 and is now generally available. It had been in public preview since May of 2025 and consumers have been eagerly anticipating the official release of one of Microsoft's most popular software products. I had previous written a fun article where I used AI and historical date like previews, events, and past release dates to try and guess the <a href="https://www.trustedtechteam.com/blogs/sql-server/sql-server-2025-release-date" rel="noopener noreferrer">release date of SQL Server 2025</a>. 

Now it's finally here, and with it comes a whole host of highly anticipated features, most notably AI integration. 

<h2>
 
 
 Major Themes
</h2>

<a href="https://www.trustedtechteam.com/blogs/sql-server/sql-server-2025-new-ai-features" rel="noopener noreferrer">Built-in AI</a> &amp; vector search

<ul>
<li>SQL Server 2025 adds native support for vector-stores/indexing (via DiskANN) enabling semantic search over large datasets. </li>
<li>You can combine traditional keyword SQL queries with vector‐based search in one engine. </li>
<li>Support for retrieval-augmented generation (RAG) patterns, connecting to AI models via REST endpoints, etc. </li>
</ul>

Modern developer productivity enhancements

<ul>
<li>Native JSON support, regular expression support, REST APIs, GraphQL via Data API Builder, etc. </li>
<li>The idea is to let developers build AI-driven/modern applications whether on-premises or cloud with more extensibility. </li>
</ul>

Security, performance &amp; scalability

<ul>
<li>New authentication support via Microsoft Entra ID managed identities and outbound authentication via Azure Arc for hybrid/edge. </li>
<li>Improved query optimization: e.g., persisted statistics on replicas, improved locking mechanisms (TID Locking, LAQ) to reduce contention, improved batch processing and columnstore support for analytics. </li>
</ul>

Hybrid and cloud‐centric analytics &amp; data integration

<ul>
<li>Integration with Microsoft Fabric: near real-time replication of SQL Server data (mirrored database) into OneLake for analytics. </li>
<li>Better support for hybrid cloud, on-premises, and edge scenarios — managing, governing, monitoring across environments with Azure Arc, centralized management etc. </li>
</ul>

<h2>
 
 
 Why this matters
</h2>

<ul>
<li>The database is being positioned as a first‐class AI platform, not just a relational engine.</li>
<li>For companies with large datasets, or those looking to embed AI/semantic search capabilities, this version could significantly change how they build data and app-architectures.</li>
<li>For hybrid/edge scenarios (on-premises + cloud + edge), the enhancements around Azure Arc, Fabric integration, and real-time replication make it easier to maintain consistency, governance, and analytics across environments.</li>
<li>Developers get more flexible tooling (JSON, REST, GraphQL etc) so the barrier to building modern apps on top of the database is lower.</li>
<li>Performance/scalability/security updates mean this is not just “shiny new features” but also foundation</li>
</ul>

تم الإعلان رسميًا عن SQL Server 2025 في مؤتمر Microsoft Ignite 2025 وهو متاح الآن بشكل عام. بعد معاينة عامة منذ مايو 2025، يتضمن الإصدار ميزات متوقعة بشدة، لا سيما دمج الذكاء الاصطناعي، مما يعزز قدراته كواحد من أكثر منتجات البرمجيات شعبية من Microsoft.

SQL Server 2025 ha sido anunciado oficialmente en Microsoft Ignite 2025 y ya está disponible. Después de una vista previa pública desde mayo de 2025, el lanzamiento incluye características muy esperadas, especialmente la integración de IA, mejorando sus capacidades como uno de los productos de software más populares de Microsoft.

SQL Server 2025 a été officiellement annoncé lors de Microsoft Ignite 2025 et est désormais disponible. Après une période de prévisualisation publique depuis mai 2025, cette version comprend des fonctionnalités très attendues, notamment l'intégration de l'IA, renforçant ses capacités en tant que l'un des produits logiciels les plus populaires de Microsoft.

SQL Server 2025 has been officially announced at Microsoft Ignite 2025 and is now generally available. After a public preview since May 2025, the release includes highly anticipated features, particularly AI integration, enhancing its capabilities as one of Microsoft's most popular software products.

SQL Server 2025 is Here!

<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

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